Silviase/jgov_v1.4_full_ann huggingface dataset:
$$ \begin{aligned}(2)LV_{ijt}&=b0+b1(LE_{ijt})+b2(LK_{ijt})+b3(LE_{ijt}^{~2})+b4(LK_{ijt}^{~2})+b5(LELK_{ijt})+b6(DS_{ijt})+b7(DM_{ijt})\\&\quad+b8(DJ_{ijt})+b9(H_{jt})\end{aligned} $$

where

$ C_{jt}=4- $ firm concentration ratio of industry j in year t

$ DJ_{ijt}= $ dummy variable for MNC joint venture firm i of industry j in year t

$ DM_{ijt}= $ dummy variable for wholly-foreign MNC firm i of industry j in year t

$ DS_{ijt}= $ dummy variable for SOE firm i of industry j in year t

$ H_{ji}= $ Herfindahl index (ratio) of industry j in year t

$ LE_{ijt}= $ natural log of the number of employees in firm i of industry j in year t

$ LELK_{ijt}= $ the product of $ LE_{ijt} $ and $ LK_{ijt} $

$ LK_{ijt}= $ natural log of fixed assets in firm i of industry j in year t (million dong, 1994 prices)

$ LV_{ijt}= $ natural log of value added in firm i of industry j in year t (million dong, 1994 prices). $ ^{20} $

Estimates are performed in a highly unbalanced panel, reflecting high growth and substantial entry and exit during 2000-2006. Because ownership does not change for the vast majority of firms in the sample, ownership is itself a fixed effect for most firms. Thus, a fixed effects estimator would eliminate most ownership-related productivity differentials and a random effects estimator is used to analyze the question of whether TFP levels differ among ownership groups. $ ^{21} $

Partially because labor and capital are measured at year end and affected by the level of production during the year, estimates of the contemporaneous equations (1) and (2) are likely to be affected by simultaneity bias. Concentration is also clearly affected by the scope of firm production and another source of potential simultaneity. Results from estimating contemporaneous equations are thus compared to results where labor, capital, and concentration variables are lagged one period. Ownership dummies are not lagged because they are predetermined for most firms in each year. This
approach is rather unsatisfactory because it does not account for the exact nature of potential simultaneity and because lagging these independent variables results in substantially smaller samples. However, it is the only practical alternative given the lack of appropriate instruments in this data set.

As is common practice, vectors of industry, time, and region dummies are added to account for industry-, year-, and region-specific differences in intercepts. $ ^{22} $ In addition, separate production functions are estimated for the seven major industry groups discussed above and two subperiods (2001-2003, 2004-2006). These estimates suggest differences in many slopes across industries and over time. This finding makes sense in Vietnam where there are large differences among industries and the economy has been changing rapidly.

Although all details are not presented here to save space, production function estimates were generally in line with expectations. $ ^{23} $ Measures of fit were satisfactory (overall R-squared was a minimum of 0.60) and coefficients on labor and capital were always positive and statistically significant at standard levels (5 percent or better). Coefficients on the square of capital were also positive and significant in the vast majority of estimates, but signs on labor squared and the interaction terms were most often insignificant. Tests of Cobb-Douglas restrictions were rejected in most cases, indicating that the translog form is the most appropriate for this analysis.

Samples were relatively large, the smallest being the lagged samples in machinery and
transportation equipment in 2001-2003 (1,105 and 802 firms, respectively). As indicated above, samples were substantially smaller in the lagged specifications. For example, the entire sample was 55,306 firms for 2001-2006 in the contemporaneous specification but 38,059 in the lagged one.

Table 5 summarizes results for coefficients measuring ownership-related productivity differentials and the productivity effects of concentration. Estimates of SOE-private differentials were consistently positive and significant at standard levels (5 percent or better) in samples of all manufacturing firms combined. However, at the industry level, this was true in only two industries, wood and metals. If differentials that were weakly significant (10 percent level) are also included, consistently positive coefficients are observed in food and beverages and textiles, and in the contemporaneous estimates for chemicals. These estimates suggest that SOE-private differentials declined in food and beverages, textiles, and wood, rose in chemicals, changed very little in metals, and rose slightly when all industries were combined. In contrast, SOE-local differentials were not consistently significant in machinery and transportation equipment, but there was weak evidence that these differentials became positive and significant in 2004-2006.

## Table 5 about here

Results in Table 5 are also consistent with previous descriptive analysis in suggesting that MNC joint ventures tended to have the highest productivity levels among ownership groups. $ ^{24} $ These differentials were always highly significant statistically (at the 1 percent level or better) in samples of
all manufacturing industries combined and at standard levels in textiles, metals, machinery, and transportation equipment. In 2004-2006, these differentials were also consistently positive and significant in wood and chemicals, and weakly significant in food and beverages. Unlike SOE-private differentials, these differentials tended to increase over time, the decline in textiles and the small change in machinery being the only exceptions. The variation of these differentials among industries (e.g., from 0.28-0.46 in textiles to 0.85-0.91 in transportation equipment in 2004-2006) was also relatively large compared to SOE-local differentials, for example.

In contrast to SOE-private and joint venture-private differentials, differentials between wholly-foreign and private firms differed between the contemporaneous and lagged specifications (Table 5). In the contemporaneous specification they were generally insignificant. However, they were consistently negative and significant or weakly significant in metals and machinery, and in all industries combined, wood, and chemicals for 2001-2003. In 2004-2006, there was only one positive and weakly significant positive differential in the contemporaneous specification (wood).

In contrast, there was only one weakly significant, negative differential in the lagged specification (metals in the early period; Table 5). Differentials were always positive and significant when all industries were combined and in textiles. In 2004-2006, they were positive and significant at standard levels in food, wood, chemicals, metals, and machinery, and weakly significant in transportation equipment. Unfortunately, it is impossible to determine whether differences in contemporaneous and lagged results are related to simultaneity bias in the contemporaneous specification or the omission of many firms in the lagged samples, or both. However, the lagged results reflect patterns observed in
the descriptive data (c.f., Tables 3-4) more than the contemporaneous results.

The significance and signs of the productivity effects of concentration were never consistent across periods and estimation techniques in any of the industry groups or in manufacturing industries combined. On the other hand, when significant, signs on the two concentration measures (C4 and HF) were usually the same, which reflects the relatively high correlation of these measures. $ ^{25} $ There are important differences between the contemporaneous specification, which suggests concentration’s effects were never consistently significant in any of the samples examined, and the lagged specification, which indicates concentration was negatively correlated with productivity in wood, and positively correlated in metals, and that these correlations were at least weakly significant. In the earlier period, there was a significantly negative correlation in food and a positive one in all industries combined. However, the most reasonable conclusion seems to be that concentration’s relationship to productivity was haphazard during this period in Vietnam’s manufacturing industries.

### 5. Productivity Spillovers to Private Firms

The extent of productivity spillovers to private firms is examined by estimating equations similar to (1) to (2) in samples of private firms, where SOE and MNC shares of industry employment are added as explanatory variables.

$$ \begin{aligned})LV_{ijt}&=c0+c1(LE_{ijt})+c2(LK_{ijt})+c3(LE_{ijt}^{~2})+c4(LK_{ijt}^{~2})+c5(LELK_{ijt})+c6(SS_{ijt})+c7(SM_{ijt})\\&\quad+c8(SJ_{ijt})+c9(C_{jt})\end{aligned} $$
$$ \begin{aligned}4)LV_{ijt}=&d0+d1(LE_{ijt})+d2(LK_{ijt})+d3(LE_{ijt}{}^{2})+d4(LK_{ijt}{}^{2})+d5(LELK_{ijt})+d6(SS_{ijt})+d7(SM_{ijt})\\&+d8(SJ_{ijt})+d9(H_{jt})\end{aligned} $$

where

$ SJ_{jt}= $ the MNC joint venture share in the employment of industry j in year t (ratio)

$ SM_{jt}= $ the wholly-foreign MNC share in the employment of industry j in year t (ratio)

$ SS_{jt}= $ the SOE share in the employment of industry j in year t (ratio)

all other variables as defined in equations (1) to (2) above

Like concentration, SOE and MNC shares are calculated from larger samples of all firms reporting positive sales and employment in order to reflect overall presence of these ownership groups. Coefficients on ownership shares reflect how private firm production responds to SOE or MNC presence and are generally interpreted to indicate the degree of productivity spillovers from the ownership group in question to private firms.

Following the usual practice, results of estimating these equations are first analyzed for samples of all 37 narrowly defined manufacturing industries. In addition, spillovers were compared between samples of 14 industries that were highly concentrated in both 2001-2003 and 2004-2006 and another 14 industries that were lowly concentrated in both periods. In other words, these spillover analyses are similar to most of those in the literature in that they fail to account for inter-industry differences in slope coefficients to the extent that the previous analysis of productivity differentials did, for example. In addition, because we remain concerned about simultaneity, contemporaneous estimates (equations 3, 4) are compared with alternative lagged specifications. In this case, we lag all independent variables including ownership shares, because like concentration measures, ownership shares can be influenced by the scope of large firm production.

Fixed effects panel estimates are common in the spillover literature because they measure how
private firm productivity changes over time after controlling for unobserved firm-specific characteristics, in addition to the observable characteristics specified in equations (3) and (4). In many ways, fixed effects estimates are more appropriate for examining spillovers, because they focus more on the question of whether larger MNC or SOE presence leads to increases or decreases of productivity in private firms over time, rather than on the question of whether productivity in private firms at a given point in time is related to the size of MNC or SOE presence. By focusing on changes in productivity rather than productivity levels, fixed effects estimates are also less likely to be affected by simultaneity problems. Thus, we follow the literature and use a fixed effects estimator. Because industry affiliation and location are fixed effects for most firms, corresponding dummies are excluded from the spillover analysis, though year dummies are included.

In general, estimates for private firms performed somewhat more poorly than estimates for all firms presented in the previous section. Goodness of fit measures were lower, especially in the lagged specifications (overall R-squared as low as 0.31 for all industries in 2001-2003 and 0.08 for highly concentrated industries in 2004-2006). Coefficients on labor and capital variables continued to be significant in all cases but signs on squares or cross products were often insignificant. Correspondingly, tests of the null hypothesis that Cobb-Douglas restrictions were appropriate were not rejected for highly concentrated industries and in about half of the cases for lowly concentrated industries or all industries. Sample size remained large enough that translog specifications were judged preferable for comparisons across industries and samples, however.

Coefficients on MNC or SOE shares and concentration are the main concern here and summarized
in Table 6. The lack of consistent results between contemporaneous and lagged specifications and between equations (3) and (4) is conspicuous in estimates for all manufacturing combined. SOE shares were positively correlated with local firm productivity in 2004-2006 in both contemporaneous and lagged specifications, but this result is significant (at standard levels) only when the concentration ratio (equation 3) is used to measure concentration. In 2001-2003, there was a negative correlation to the SOE share, but it was only significant in the lagged specification. Correlations to shares of wholly-foreign firms were negative and significant in 2001-2003, but insignificant in 2004-2006. Coefficients on joint venture shares were negative and significant in the lagged specification in 2001-2003, but insignificant in the contemporaneous specification. These coefficients were also negative in 2004-2006, but only weakly significant or insignificant. Both contemporaneous and lagged specifications suggested a positive correlation to concentration in 2001-2003, but this correlation became negative and significant or weakly significant in 2004-2006.

Table 6 about here

Results for highly concentrated and lowly concentrated industries were similarly inconsistent depending on the specification used with one notable exception. Namely, the positive correlation of private firm productivity to concentration observed in all manufacturing in 2001-2003 was also observed in lowly concentrated industries (Table 6). This suggests that private firms in lowly concentrated industries tended to be more productive the higher the degree of concentration. Nonetheless, these results do not support any clear, general conclusion about the nature of concentration’s effect on productivity in private firms or all firms (c.f., Table 5). Similarly, there are
no consistent indications of productivity spillovers from MNCs or SOEs to private firms.

### 6. Conclusion

This paper has examined relationships between producer concentration, firm ownership, and productivity in Vietnam’s manufacturing enterprises in 2000-2006. Simple calculations indicate that wholly-foreign MNCs, MNC joint ventures and SOEs often had substantially higher labor productivity and lower capital productivity than local, private firms. After controlling for firm-level variation in factor intensities and scale, and industry-level variation in producer concentration, as well as generic effects related to industry, region, and year of operation, TFP differentials between MNC joint ventures or SOEs on the one hand, and private firms on the other, were generally positive and often significant statistically. On the other hand, differentials between wholly-foreign MNCs and private firms were generally insignificant or negative in 2001-2003 and in the contemporaneous specification for 2004-2006, but more often positive and significant, when a lagged specification is used to account for potential simultaneity in 2004-2006. There was large variation in the size of all differentials among industries and between periods (2001-2003 and 2004-2006), but estimates of differentials were not very sensitive to how concentration was measured and concentration was usually an insignificant determinant of firm-level productivity. Estimates of productivity spillovers from SOEs and MNCs to private firms also tended to be insignificant statistically and were particularly inconsistent among the samples (all, highly-concentrated, or lowly-concentrated industries), periods, and specifications examined.

As the literature review emphasizes, large variation of MNC-local productivity differentials
among MNC ownership groups, industries, and/or periods has also been observed in previous industry-level studies for Indonesia, Malaysia, and Thailand. Significant industry-level differentials between MNC joint ventures and local firms were somewhat more common in Vietnam than in other Southeast Asian economies, but wholly-foreign-local differentials have a similar tendency to be insignificant and all differentials varied substantially over this relatively short time period. In other words, allowing all production function coefficients to vary among industries and over time influences estimates of productivity differentials substantially. Correspondingly, combining manufacturing industries into a single sample or two samples may lead to unreliable estimates of productivity differentials or spillovers.

Rapid economic growth and important institutional changes (e.g., the implementation of the Corporate Law in 2000) encouraged entry and turnover, and explain much of the variation in data samples and results over time. There were also potentially problematic changes in data coverage, but samples have been carefully chosen to minimize related problems. In short, the results are probably realistic and largely consistent with the view that Vietnam's manufacturing firms, especially wholly-foreign MNCs and private firms, are often engaged in assembly operations using relatively simple technologies, and that private firms are often quick to imitate MNC and SOE competitors.

However, these rather standard estimates are only a first step toward understanding related issues, and there are relatively long lists of tasks for future research. First, although we have done a lot of work cleaning the data, further efforts to remove errors and resolve inconsistencies with nation-wide estimates are warranted. Second, variation among industries is apparently large, but industry
definitions are always rather arbitrary. Experimentation with alternative definitions and alternative industry groupings (for spillover analysis) should also be pursued. Third, alternative measures of foreign presence (i.e., shares of production or fixed assets) have also been shown to influence spillover estimates and should be investigated. Fourth, further efforts to deal with potential simultaneity problems are warranted, but complicated by the lack of good instruments in the data. Fifth, although this period of rapid change is interesting, large changes in samples over time make it difficult to interpret the results. Because the pace of change inevitably has to slow some, it will be very important to revisit these issues as time passes.

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