4.3.1. Labor Heterogeneity
Table 5 presents the estimation results of the 21 manufacturing sectors across heterogeneous groups in terms of employment size (full results are in
Supplementary Materials, Table S2). Relevant tests in the bottom line of
Table 5 include tests of high-level autocorrelation and the validity of instrument variables. Specifically, high levels of autocorrelation are not held in most manufacturing sectors as suggested by the AR (2) test statistics, which are not significant at the 10% level, except for food products (code 10—number of workers between 10–50, and 1000–5000), and other non-metallic mineral products (code 23—number of workers between 50–200).
On top of that, the validity of instrument variables based on Hansen J statistics is observed in most manufacturing sectors, except for wearing apparel (code 14—number of workers between 10–200, 300–500, and 1000–5000), wood and products of wood/cork (code 16—number of workers between 10–200), paper and paper products (code 17—laborers between 10–200). The excluding list is continued with chemicals and chemical products (code 20—number of workers between 200–300, and 500–1000), rubber and plastics products (code 22—number of workers between 10–50, and 200–300), other non-metallic mineral products (code 23—number of workers between 10–200), fabricated metal products (code 25—number of workers between 10–50, and between 200–300), furniture (code 31—number of workers between 10–50, and 1000–5000), and other manufacturing sectors (code 34—number of workers between 10–50).
In the following part, we discuss the significant findings.
Table 5 shows that firms’ size is statistically significantly positive in food products (code 10—number of workers between 300–500), wearing apparel (code 14—number of workers between 200–1000), leather and related products (code 15—number of workers between 10–50 and between 1000–5000), wood and products of wood/cork (code 16—number of workers between 200–300), paper and paper products (code 17—laborers between 200–300), and furniture (code 31—number of workers between 500–1000). Besides, two sectors belonged to medium low-tech manufacturing, showing statistically significant positive effects in (1) other non-metallic mineral products (code 23—number of workers between 300–500 and between 500–5000); and (2) fabricated metal products (code 25—number of workers between 500–1000). Moreover, the sector of electrical equipment (code 27) that belonged to medium high-tech manufacturing has evidence of a statistically significant positive effect for firms with number of workers between 200–300. Similar results can also be found in
Kreuser and Newman (
2018), although these authors conducted their research for the aggregated manufacturing sector in South Africa and the classification of a firm’s size by number workers is not the same with us. Productivity heterogeneity in labor was discovered in
Kim (
2018), who found that large firms are associated with higher productivity in low-technology, medium-low-technology, and medium-high-technology manufacturing sectors, but with lower productivity in the high-technology manufacturing sector.
A statistically negative effect of a firm’s size is observed in printing and reproduction of recorded media (code 18—number of workers between 50–200), which belongs to the low-tech manufacturing sector, and in electrical equipment (code 27—number of workers between 10–50 and 50–200), which belongs to the medium high-tech manufacturing sector.
Giang et al. (
2018) found a negative effect in the sector of metal and machinery products for Vietnamese manufacturing SMEs in 2011–2015.
Regarding labor quality measured by average wage,
Table 5 shows that firms’ average wage is statistically significantly positive in food products (code 10—number of workers between 50–300), beverages (code 11—number of workers between 50–200), textiles (code 13—number of workers between 10–200 and 300–500), wearing apparel (code 14—number of workers more than 5000), leather and related products (code 15—number of workers between 10–50), printing and reproduction of recorded media (code 18—number of workers between 50–200), and furniture (code 31—number of workers between 200–500). All of the sectors above belong to the low-tech manufacturing sector. Besides, regarding the medium low-tech manufacturing sectors, the firms’ size is statistically significantly positive in other non-metallic mineral products (code 23—number of workers between 200–300), and fabricated metal products (code 25—number of workers between 50–200). Besides, concerning the medium high-tech manufacturing sector, the firms’ size is statistically significantly positive in electrical equipment (code 27—number of workers between 10–200), not-yet-classified machinery and equipment (code 28—number of workers between 10–50). In relation to the high-tech manufacturing sector, the firms’ size is statistically significantly positive in the computer, electronic and optical products (code 26—number of workers between 200–300). Productivity heterogeneity in labor quality has been discovered in
Kim (
2018), who has found that lower-wage level is associated with lower productivity in low-technology, medium-low-technology, medium-high-technology, and high-technology manufacturing sectors in Japan.
A statistically negative effect of labor quality is observed in low-tech manufacturing sectors such as leather and related products (code 15—number of workers between 300–500), and paper and paper products (code 17—number of workers between 300–500).
With respect to firms’ age,
Table 5 shows that the firms’ age is statistically significantly positive in food products (code 10—number of workers between 500–1000), wearing apparel (code 14—number of workers between 200–300, and 500–1000), and paper and paper products (code 17—number of workers between 300–500). Only one sector belonged to medium low-tech manufacturing sectors, namely other non-metallic mineral products (code 23—number of workers between 50–200), and basic metals (code 24—number of workers between 50–200), the signals are statistically significantly positive effects. That is in line with
Xu et al. (
2019) for the case of the Chinese furniture sector. However, the statistically negative effect is observed in textiles (code 13—with workers between 10–50), leather and related products (code 15—number of workers between 50–300), paper and paper products (code 17—number of workers between 50–300). That being said, all have belonged to the low-tech manufacturing sector. In addition, the medium high-tech manufacturing sectors such as electrical equipment (code 27—number of workers between 10–50) also accompanies a statistically negative effect. Besides, the high-tech manufacturing sector, namely pharmaceuticals, medicinal chemicals (code 21—number of workers between 10–50) shows a statistically negative effect.
With reference to the firms’ lagged total factor productivity,
Table 5 shows that the firms’ lagged total factor productivity is statistically significantly positive in food products (code 10—number of workers between 300–500), textiles (code 13—number of workers between 200–300), leather and related products (code 15—number of workers between 10–50), paper and paper products (code 17—number of workers between 200–500), furniture (code 31—number of workers between 500–1000). Besides, about medium low-tech manufacturing sectors, namely rubber and plastics products (code 22—number of workers between 300–500), other non-metallic mineral products (code 23—number of workers between 50–200, and 300–500), and fabricated metal products (code 25—number of workers between 50–200), there exist a statistically significantly positive effect. On top of that, medium high-tech manufacturing sectors such as chemicals and chemical products (code 20—number of workers between 10–50), electrical equipment (code 27—number of workers between 10–300), and not-yet-classified machinery and equipment (code 28—number of workers between 50–200) also go in line with a statistically significantly positive effect. Besides, the high-tech manufacturing sector, namely computer, electronic, and optical products (code 26—number of workers between 10–200) obtains a statistically positive effect.
In relation to firms’ capital-to-labor ratio,
Table 5 shows that the firms’ capital intensity is statistically significantly positive in food products (code 10—number of workers between 300–500, and 1000–5000), wearing apparel (code 14—number of workers 50–200, and 300–500), leather and related products (code 15—number of workers between 200–300, and 500–1000), wood and products of wood/cork (code 16—number of workers between 50–200), paper and paper products (code 17—number of workers between 200–500); and furniture (code 31—number of workers between 300–500). Two sectors belonged to medium low-tech manufacturing sectors, namely other non-metallic mineral products (code 23—number of workers between 500–1000), and fabricated metal products (code 25—number of workers between 10–50) show significantly positive effects. However, a statistically negative effect of the firms’ capital intensity is observed in beverages (code 11—number of workers between 10–50), and rubber and plastics products (code 22—number of workers between 10–50). Medium high-tech manufacturing sectors, including chemicals and chemical products (code 20—number of workers between 500–1000), and electrical equipment (code 27—number of workers between 300–500) are found with statistically negative effects.
The level of real value-added per worker of the firms is shown to be passively correlated with TFP only in beverages (code 11—number of workers between 10–50); in wearing apparel (code 14—number of workers between 500–1000); in leather and related products (code 15—number of workers between 1000–5000); and in rubber and plastics products (code 22—number of workers between 50–200). With respect to medium high-tech manufacturing sectors, three sectors, namely chemicals and chemical products (code 20—number of workers between 50–200), electrical equipment (code 27—number of workers between 300–500), and other transport equipment (code 30—number of workers between 50–200) are also accompanied by a negative relationship.
4.3.2. Capital Stock Heterogeneity
Table 6 presents the estimation results of the 21 manufacturing sectors across groups of fixed capital (full results are in the
Supplementary Materials, Table S3). Similar tests in were conducted
Section 4.2 and
Section 4.3.1 to examine the existence of high levels of autocorrelation and the validity of instrument variables. The results in
Table 6 indicate that the first problem does not hold in most manufacturing sectors, except for food products (code 10—fixed capital between 200–500 Vietnamese Dong (VND) billion), leather and related products (code 15—fixed capital between 10–50 VND billion), wood and products of wood/cork (code 16—fixed capital less than 10 VND billion), and other non-metallic mineral products (code 23—fixed capital less than 10 VND billion).
In addition, the second problem is solved in most manufacturing industries, except for wearing apparel (code 14—fixed capital less than 50 VND billion), leather and related products (code 15—fixed capital between 10–50 VND billion), wood and products of wood/cork (code 16—fixed capital less than 50 VND billion and between 200–500 VND billion), and printing and reproduction of recorded media (code 18—fixed capital between 50–200 VND billion). The list also includes some more sectors, such as chemicals and chemical products (code 20—fixed capital less than 10 VND billion), pharmaceuticals and medicinal chemicals (code 21—fixed capital less than 10 VND billion), rubber and plastics products (code 22—fixed capital between 50–500 VND billion), other non-metallic mineral products (code 23—fixed capital less than 10 VND billion), fabricated metal products (code 25—fixed capital less than 10 VND billion), and computer, electronic and optical products (code 26—fixed capital between 50–200 VND billion).
In the following part, we discuss the significant findings.
Table 6 shows that firms’ size is statistically significantly positive in wearing apparel (code 14—fixed capital between 200–500 VND billion), leather and related products (code 15—fixed capital between 50–200 VND billion), and fabricated metal products (code 25—fixed capital between 10–200 VND billion). However, the statistically negative effect is observed in beverages (code 11—fixed capital between 10–50 VND billion), textiles (code 13—fixed capital less than 10 VND billion), leather and related (code 15—fixed capital larger than 200 VND billion), paper and paper products (code 17—fixed capital between 10–50 VND billion), printing and reproduction of recorded media (code 18—fixed capital up to 50 VND billion), and rubber and plastics products (code 22—fixed capital between 50–200 VND billion and larger than 500 VND billion). In terms of the medium low-tech manufacturing sector, other non-metallic mineral products (code 23—fixed capital between 10–50 VND billion) also show a significant negative effect. Besides, the medium high-tech manufacturing sectors chemicals and chemical products (code 20—fixed capital larger than 500 VND billion), electrical equipment (code 27—fixed capital less than 10 VND billion and in the range 50–200 VND billion), not-yet-classified machinery and equipment (code 28—fixed capital less than 10 VND billion), and motor vehicles, trailers and semi-trailers (code 29—fixed capital between 200–500 VND billion) are in line with a significantly negative correlation. Moreover, the high-tech manufacturing sector, namely pharmaceuticals and medicinal chemicals (code 21—fixed capital between 50–200 VND billion), obtains a statistically negative effect.
As for labor quality measured by average wage,
Table 6 shows that firms’ size is statistically significantly positive in food products (code 10—fixed capital up to 50 VND billion), beverages (code 11—fixed capital between 10–50 VND billion), textiles (code 13—fixed capital up to 10 VND billion), printing and reproduction of recorded media (code 18—fixed capital between 10–50 VND billion), and rubber and plastics products (code 22—fixed capital between 10–50 VND billion and larger than 500 VND billion). In terms of medium high-tech manufacturing sectors, electrical equipment (code 27—fixed capital less than 10 VND billion and between 50–200 VND billion), not-yet-classified machinery and equipment (code 28—fixed capital less than 50 VND billion and between 200–500 VND billion), and motor vehicles, trailers and semi-trailers (code 29—fixed capital between 200–500 VND billion) also have a significantly positive effect. In addition, the high-tech manufacturing sector, namely pharmaceuticals and medicinal chemicals (code 21—fixed capital between 50–200 VND billion) obtains a significantly positive effect. However, a statistically negative effect is observed in wearing apparel (code 14—fixed capital between 200–500 VND billion) and leather and related products (code 15—fixed capital between 10–50 VND billion). Moreover, in terms of the medium low-tech manufacturing sector, other non-metallic mineral products (code 23—fixed capital between 10–50 VND billion), and in terms of medium high-tech manufacturing sector, other transport equipment (code 30—fixed capital between 10–50 VND billion), are associated with statistically negative effects.
With regard to firms’ age,
Table 6 shows that firms’ age is statistically significantly positive in wearing apparel (code 14—fixed capital larger than 200 VND billion) and printing and reproduction of recorded media (code 18—fixed capital between 10–50 VND billion). In addition, other non-metallic mineral products (code 23—fixed capital between 10–50 VND billion), which belong to the medium low-tech manufacturing sector, and not-yet-classified machinery and equipment (code 28—fixed capital less than 10 VND billion), which belong to the high-tech manufacturing sector, are also affiliated with significantly positive effects. However, a statistically negative effect is observed in leather and related products (code 15—fixed capital between 50–200 VND billion) and rubber and plastics products (code 22—fixed capital between 10–50 VND billion). The negative signs are also found in the medium low-tech manufacturing sectors, such as other non-metallic mineral products (code 23—fixed capital more than 500 VND billion), basic metals (code 24—fixed capital between 10–50 VND billion), and fabricated metal products (code 25—fixed capital between 10–50 VND billion). Only one high-tech manufacturing sector, namely pharmaceuticals and medicinal chemicals (code 21—fixed capital between 50–200 VND billion) observes a statistically negative effect.
Regarding firms’ lagged total factor productivity,
Table 6 shows that the firms’ lagged total factor productivity is statistically significantly positive in food products (code 10—fixed capital between 50–200 VND billion), beverages (code 11—fixed capital less than 10 VND billion and in the range 50–200 VND billion), textiles (code 13—fixed capital between 50–200 VND billion and larger than 500 VND billion), and paper and paper products (code 17—fixed capital between 50–200 VND billion). The list further includes printing and reproduction of recorded media (code 18—fixed capital between 10–50 VND billion), rubber and plastics products (code 22—fixed capital larger than 500 VND billion), and fabricated metal products (code 25—fixed capital between 50–200 VND billion and more than 500 VND billion). In addition, two medium low-tech manufacturing sectors, including other non-metallic mineral products (code 23—fixed capital larger than 500 VND billion) and basic metals (code 24—fixed capital between 10–50 VND billion) have significantly positive effects. Moreover, several medium high-tech manufacturing sectors, such as chemicals and chemical products (code 20—fixed capital between 10–50 VND billion and between 200–500 VND billion), electrical equipment (code 27—fixed capital between 10–500 VND billion), not-yet-classified machinery and equipment (code 28—fixed capital between 50–200 VND billion), motor vehicles, trailers and semi-trailers (code 29—fixed capital up to 200 VND billion), and other transport equipment (code 30—fixed capital between 200–500 VND billion) are also observed with significantly positive effects. Besides, two high-tech manufacturing sectors, namely pharmaceuticals and medicinal chemicals (code 21—fixed capital between 10–50 VND billion), and computer, electronic and optical products (code 26—fixed capital between 200–500 VND billion) indicate the existence of significantly positive effects.
With regards to firms’ capital-to-labor ratio,
Table 6 shows that the firms’ capital intensity is statistically significantly positive in food products (code 10—fixed capital larger than 500 VND billion), textiles (code 13—fixed capital larger than 500 VND billion), and wearing apparel (code 14—fixed capital between 50–200 VND billion and larger than 500 VND billion). Others include leather and related products (code 15—fixed capital larger than 500 VND billion), paper and paper products (code 17—fixed capital between 50–200 VND billion and larger than 500 VND billion), and fabricated metal products (code 25—fixed capital between 50–200 VND billion and larger than 500 VND billion). Moreover, two medium low-tech manufacturing sectors, including rubber and plastics products (code 22—fixed capital larger than 500 VND billion) and other non-metallic mineral products (code 23—fixed capital between 50–200 VND billion and for larger than 500 VND billion), show significantly positive effects. Besides, two high-tech manufacturing sectors, namely pharmaceuticals and medicinal chemicals (code 21—fixed capital between 50–200 VND billion) and computer, electronic and optical products (code 26—fixed capital between 200–500 VND billion) show significantly positive impacts. Last but not least, one medium high-tech manufacturing sector, namely other transport equipment (code 30—fixed capital between 200–500 VND billion) also shows a significantly positive impact. However, the statistically negative effect of the firms’ capital intensity is observed in low-tech manufacturing sectors, such as textiles (code 13—fixed capital less than 10 VND billion) and rubber and plastics products (code 22—fixed capital up to 50 VND billion); and in the medium low-tech manufacturing sectors, such as other non-metallic mineral products (code 23—fixed capital between 10–50 VND billion). The list includes the medium high-tech manufacturing sectors, namely not-yet-classified machinery and equipment (code 28—fixed capital in the range 50–200 VND billion) and motor vehicles, trailers and semi-trailers (code 29—fixed capital in the range 50–200 VND billion). Productivity heterogeneity in the capital has also been discovered in
Kim (
2018), who found that higher capital intensity is associated with lower productivity in the medium-low-technology and high-technology manufacturing sectors in Japan.
The level of real value-added per worker of firms is shown to be positively correlated with TFP only in wearing apparel (code 14—fixed capital between 50–500 VND billion) and fabricated metal products (code 25—fixed capital between 10–50 VND billion), and in two medium high-tech manufacturing sectors, including chemicals and chemical products (code 20—fixed capital between 50–200 VND billion), and other transport equipment (code 30—fixed capital between 10–50 VND billion). However, a statistically negative effect of the firms’ capital intensity is observed in pharmaceuticals and medicinal chemicals (code 21—fixed capital between 50–200 VND billion).