Silviase/jgov_v1.4_full_ann huggingface dataset:
### 1. Introduction

There is a now a wide ranging literature examining the hypothesis that ownership modes affect firm productivity. More specifically, economists often assert that multinational corporations (MNCs) tend to be relatively efficient and that state-owned enterprises (SOEs) are generally relatively inefficient. MNCs are usually thought to possess relatively large amounts of intangible assets that facilitate efficiency (e.g., patents and other fruits of R&D, management know-how, marketing resources). Meanwhile, motivation to pursue profits and efficiency is generally believed to be relatively weak in SOEs. In addition, MNCs are often thought to generate spillovers that affect the efficiency of non-MNCs through linkages, labor mobility, and competition effects, for example. Similar spillovers can also be imagined for SOEs in transition economies like Vietnam, where SOEs are designated to play leading roles in industry. The degree of competition is another element thought to affect firm productivity and the nature of productivity spillovers.

Vietnam provides an interesting case to study these relationships. The process of Doi Moi (reform) which began in 1986 has gradually transformed a command economy into a more market-oriented one. Although the reform process has been uneven, there is now substantial evidence of marked changes in ownership patterns and market structure, especially after the implementation of Enterprise Law in 2000 and subsequent, related reforms. The rapid growth of the local private sector and the marked decrease of producer concentration have been particularly conspicuous.

Firm-level data from Vietnam’s enterprise surveys are available annually from 2000 and used to analyze the following three questions:
(1) Is there a meaningful relationship between ownership modes and firm productivity levels?

(2) Does the presence of MNCs or SOEs affect the productivity levels in private, local firms (hereafter referred to as private firms)?

(3) Has variation in producer concentration had an effect on firm productivity?

This paper begins with a brief review of the literature (Section 2), followed by a description of the data and key patterns observed in average productivity differentials and related variables (Section 3). Productivity differentials (Section 4) and productivity spillovers (Section 5) are then analyzed after accounting for (1) firm-level variation in factor intensities and scale, (2) industry-level variation in producer concentration, and (3) generic effects related to a firm’s industry affiliation, location, and year of operation. Finally, some concluding remarks are offered (Section 6).

### 2. Productivity, Spillovers, and Market Structure

There are two major strands of the literature that examines how ownership relates to productivity levels and/or spillovers. One focuses on the determinants and effects of MNC behavior, while the second focuses on analysis of SOE behavior and the effects of privatization, and related issues. Analyses of productivity levels and differentials also differ substantially from spillover analyses.

### 2 a. Productivity Levels and Differentials

The theoretical literature on MNCs often asserts that for a firm to overcome the extra costs of doing business in more than one economy, it must have offsetting cost advantages generated by the
possession of firm-specific assets. These are often intangible assets such as patents or other fruits of research and development (e.g., production techniques and processes), marketing networks, and/or management abilities. $ ^{1} $ In turn, the possession of these assets in relatively large amounts should make MNCs more efficient than non-MNCs. Because MNCs possess firm-specific assets that distinguish them from non-MNCs, they are by definition heterogeneous and must therefore operate in imperfectly competitive industries. Moran (2001) also asserts that affiliates which are closely integrated with the parent company and its network, usually through high parent ownership shares and/or other means of control, are more efficient than more loosely related affiliates. On the other hand, MNC parents are generally thought to be more reluctant to share their firm-specific assets with minority-owned affiliates than with majority-owned or wholly-owned affiliates. $ ^{2} $

Many economists believe that SOE managers have weaker motives to pursue profit and efficiency than those in privately owned firms, including MNCs. $ ^{3} $ Hence SOEs are often expected to be relatively inefficient compared to other firms. Moreover, governments have often established SOEs in imperfectly competitive or highly regulated industries, where the lack of competition further weakens the pressure to instill efficiency.

Evidence from firm- or plant-level investigations of productivity differentials among ownership
modes is more ambiguous than theory suggests. For example, evidence for manufacturing plants in Malaysia (Oguchi et al. 2002; Haji Ahmad 2010) and Thailand (Ramstetter 2004, 2006) suggests that productivity differentials between MNCs and non-MNCs were relatively small and were often statistically insignificant. $ ^{4} $ Indonesian evidence suggests that productivity differentials were somewhat larger and statistically significant in samples of all manufacturing plants combined (with intercept dummies to capture industry effects). However, differentials often become statistically insignificant when plants are disaggregated by industry (allowing for differences in production function slopes, Takii 2006). Evidence for China suggests significant differences in both capital- and labor-productivity when all manufacturing firms are combined (Jefferson and Su 2006), but we know of no firm-level estimates for disaggregated industries.

For Vietnam, Nguyen, T.T.A. et al. (2006) show that MNCs had relatively high sales per employee in three manufacturing groups (mechanics and electronics, textiles, garments, and footwear, and food processing) in 2001-2003. Athukorala and Tran (2010) indicate that MNCs had relatively high productivity in a sample of all manufacturers for 2000-2005, but industry-level estimates again suggest that MNC- or SOE-private differentials were often insignificant (Ramstetter and Phan 2008). Alternative evidence shows that MNC takeovers of SOEs have generated larger productivity gains than takeovers by local, private companies in Eastern Europe (Brown et al. 2004, 2005).

Direct evidence regarding differences between SOEs and non-SOEs is rather limited and focused on transition economies. For China, Jefferson and Su’s (2006) results indicate that capital- and
labor-productivity were significantly lower in SOEs than private firms or MNCs, who had the highest productivity by both measures. Their results also indicate that conversion of SOEs to shareholding corporations contributed to increases in productivity. Results from Brown et al. (2004, 2005) suggest that privatization resulted in relatively large productivity gains in manufacturing firms in Hungary and Romania, but relatively small gains in Ukraine, and declines in Russia. Evidence from firms in 25 transition economies located Eastern Europe, the Commonwealth of Independent States (CIS), and Central Asia also suggests that the degree of competition had a key impact on privatization outcomes (Carlin et al. 2001). The survey by Djankov and Murrell (2002) reinforces this finding in the case of Eastern Europe, but not in the case of the CIS. The one known study of privatization in Vietnam (Truong et al. 2006) is also consistent with the proposition that privatization improves firm performance. On the other hand, Nguyen’s (no date; 2004) study of textiles and apparel firms found that SOEs are more efficient than locally owned private firms, but less efficient than MNCs in this industry. $ ^{5} $

Most of this evidence seems consistent with the conclusion of Megginson and Netter's (2001, p.380) survey that "Research now supports the proposition that privately owned firms are more efficient and more profitable than otherwise-comparable state-owned firms". However, the studies reviewed above, as well as earlier surveys by Aharoni (2000), and Stretton and Orchard (1994), also
highlight a number of cases in which SOE do not appear to be less profitable and/or less efficient than private firms. Thus, the size and direction of productivity differentials between SOEs and non-SOEs, and between MNCs and non-MNCs, remains an empirical matter.

### 2 b. Productivity Spillovers

The spillover literature focuses on how MNCs’ possession of ownership advantages and related firm specific assets can result in MNC presence affecting the performance of non-MNCs. For example, MNCs purchase inputs from local suppliers or subcontract certain production lines to local firms. Especially in developing countries such as Vietnam, the local supplier base is relatively weak and MNCs often teach local suppliers how to guarantee quality control commensurate with MNC requirements. Labor mobility, which is rather high among relatively skilled workers in Southeast Asia’s developing economies, is a second avenue of spillovers from MNCs. Local firms headhunt talent from MNCs, who have often learned relatively sophisticated business practices in the MNC. There are also examples of local workers quitting an MNC to start firms which produce goods and/or services that compete with and/or serve as inputs for their former MNC employers. The entry of MNCs can also increase the level of competition in a host market, forcing local firms to increase their efforts to become more efficient, and the extent of this kind of spillover may have a close relationship to market structure or producer concentration.

Several reviews emphasize that empirical evidence regarding productivity spillovers is mixed (Görg and Greenaway 2003; Lipsey and 2005). In Asia, there is growing evidence consistent with
the existence of positive productivity spillovers in China (Buckley et al. 2006, 2007; Hale and Long 2006; Tong and Hu 2003; Wei and Lu 2006), Indonesia (Takii 2006), and Thailand (Kohpaiboon 2006; Ramstetter 2006). Kohpaiboon's cross section study of Thai manufacturing highlights how spillovers tend to be higher in industries where import protection is relatively weak, while Kokko (1996) emphasizes how competition between MNCs and local firms appears to have fostered relatively large productivity spillovers in Mexico. However, recent evidence for Malaysia (Khalifah and Adam 2009; Haji Ahmad 2010) suggests spillovers are sensitive to how foreign presence is measured (e.g., employment, production, fixed assets) and the degree of industry disaggregation.

For Vietnam, cross section results tend to suggest some degree of positive spillovers, but evidence from panel analysis is relatively weak. Nguyen, T.T.A. et al. (2006) conclude that “there is little evidence of positive spillover effects at the firm level”, though there are also “no signs of negative spillover effect either” (p. 56). In contrast, Pham’s (2008) cross section, Cobb-Douglas estimates generally suggested positive spillovers that were largest in Hanoi and Ho Chi Minh City, and from MNCs that were not wholly-foreign. Combining firm-level data for 2000-2005 with the 2000 input-output table, Nguyen, P.L. (2008) estimates cross section Cobb Douglas functions finding that both horizontal and vertical spillovers were generally positive, and largest in more advanced regions and in more sophisticated local firms. Analysis of an unbalanced panel of the same data, Nguyen, N.A. et al. (2008) finds that backward, vertical spillovers were positive in manufacturing, while horizontal spillovers were positive in services. Using a similar approach, Le and Pomfret (2008) find positive backward spillovers in manufacturing but negative horizontal spillovers during 2000-2004,
which were relatively strong on private firms, domestic-oriented firms, firms without R&D, and firms in low technology industries.

Few studies analyze productivity spillovers from SOEs, probably because SOEs are not thought to possess the firm-specific assets and competitive advantages of MNCs. $ ^{6} $ On the other hand, Vietnam’s policy makers often emphasize how SOEs should play leading roles in industry and that private firms should seek to cooperate with SOEs (Vu 2005, pp. 304-306). Correspondingly, the government has regulated MNCs, and particularly private firms, rather strictly in industries where SOEs dominate. Because these regulations can encourage inefficiency, it is also interesting to see if large SOE presence is correlated with productivity in Vietnam’s private firms.

### 3. Ownership Patterns, Productivity Differentials, and Related Indicators

In 2000, Vietnam implemented a new Enterprise Law, which removed many of the legal and regulatory barriers previously faced by private businesses (Van Arkadie and Mallon 2003, pp.164-169). This law and subsequent reforms removed many ownership-related biases in Vietnam's corporate sector. $ ^{7} $ Implementation has been uneven, but these policy changes have contributed to rapid increases in the number of manufacturing firms with positive employment and sales, from 10,366 in 2000 to 25,968 in 2006 and 38,249 in 2008. $ ^{8} $ Manufacturing employment and sales also
grew very quickly, from 1.6 to 3.3 and 3.9 million and from 246 to 857 and 1,507 trillion dong, respectively (Table 1). Growth was concentrated in private firms, who benefited most from the changes. SOE shares of manufacturing firm sales fell from 38 to 19 and 13 percent, respectively, while MNC shares rose from 41 to 47 percent before falling to 45 percent, respectively. $ ^{9} $

Table 1 about here

The enterprise data imply relatively rapid decreases in SOE shares of the corporate sector, which contrast sharply with corresponding trends in nation-wide estimates for GDP and employment (Table 1). SOE shares of non-household GDP (value added) and SOE shares of all firm sales, including non-manufacturers, were similar in 2000 (57 and 55 percent, respectively). The SOE share of firm sales fell rapidly to 25 percent in 2008, but the SOE share of non-household value added remained much higher (51 percent). $ ^{10} $ On the other hand, trends in MNC shares are more consistent in nation-wide and enterprise survey estimates.

Because estimates of value added (or intermediate consumption) and fixed assets are crucial for productivity analysis, this study focuses on subsamples of enterprises reporting positive values for these variables as well as sales and employment. For 2000 (10,100 firms) to 2006 (24,217 firms),
value added samples were similar to the overall samples, but value added samples were much smaller than overall samples in 2007 and 2008. $ ^{11} $ The ratio of sales by firms reporting positive sales, employment, value added, and fixed assets to sales of all firms were only 85 percent in 2007 and 44 percent in 2008, compared to 95 percent in 2003, and 98-100 percent in 2000-2002, 2004-2006 (Table 1). If calculated using employment instead of sales, corresponding ratios were similar, but slightly lower in most years. Because the coverage of the value added data is poor for 2007 and 2008, the following analyses focus on 2000-2006.

Small firms with 19 or fewer employees differ systematically from larger firms and are predominantly private firms. In the value added samples, small firms were 49-56 percent of private firms in 2000-2006, but only 1-2 percent of SOEs and 7-9 percent of MNCs (authors’ calculations). Small firms accounted for 18 percent of private firm sales in 2000 and 9-12 percent in 2001-2006, and 6-8 percent of private firm employment in 2000-2006, but no more than 1 percent of SOE or MNC employment or sales in any of these years. In the following analyses, we focus on comparisons among medium-large firms with 20 or more employees because comparisons among ownership groups are likely to be distorted if smaller, predominantly private firms are included. $ ^{12} $

Ownership shares differed greatly depending on the industry. SOEs have always had a relatively
large presence in heavy manufacturing industries such as the chemicals and metals groups in Table 2. On the other hand, SOE shares were generally relatively small in machinery and transport equipment, and fell from relatively high to relatively low levels in the food, textiles, and wood groups. SOE shares were also very high in the small, heterogeneous group of other manufacturing, primarily because of large SOE presence in tobacco and printing/publishing.

## Table 2 about here

Table 2 indicates that shares of wholly-foreign MNCs tended to rise, while shares of MNC joint ventures tended to fall. Shares of wholly-foreign MNCs rose in all of the industries in Table 2 and were particularly large in textiles (much of which is footwear) and machinery, where shares rose conspicuously from 2002 to 2006. Wholly-foreign shares were smallest but tended to rise in the metals, food, and transportation equipment. They were close to the manufacturing average in chemicals for most years and relatively small in food. Shares of MNC joint ventures were by far the highest in transportation equipment but tended to decline in this industry and in metals, the other group with a relatively large initial share. Shares were close to the manufacturing average in chemicals and machinery. Joint ventures with SOEs were rather common in these four industry groups. On the other hand, shares of joint ventures were relatively small in the remaining industries.

Among medium-large firms in the value added samples, there was large variation of mean, real value added per worker across years, industries, and ownership groups (Table 3). $ ^{13} $ In marked contrast to nationwide estimates, which suggest rather steady increases in manufacturing value added
per worker during 2000-2006, the enterprise data suggest unusual spikes in 2000 and 2003 in both nominal and real series. On the other hand, nationwide estimates and enterprise data suggest similar increases in average labor productivity in 2001-2004 and 2004-2006. Moreover, the unusual spikes are observed in most owner-industry combinations in 2000 and 2003, which suggests the estimates for these years differ systematically from those for other years.

## Table 3 about here

According to the data in Table 3, both wholly-foreign MNCs and joint ventures generally had higher labor productivity than SOEs or private firms. Relative to private firms, average labor productivity differentials were often very large (2-fold or more) for joint ventures in all industries except textiles. Corresponding differentials were smaller for wholly-foreign MNCs, but average labor productivity was 1.5-fold or more than in private firms for at least five of the seven years in all industries except wood. Differentials between SOEs and private firms tended to be even smaller, exceeding 1.2-fold for all seven years in wood, five years in chemicals, and four years each in metals and transportation equipment, but only two years in the remaining industries. Average labor productivity was also lower in SOEs than in private firms for four years in machinery, three years in transportation equipment, and less frequently in four other industries.

In contrast, calculations of mean value added-fixed asset ratios in Table 4 indicate average capital productivity was generally lower in MNCs than in private firms, and often lower than in SOEs. $ ^{14} $ Average capital productivity was only half or less of private firm levels in six or seven years for
wholly-foreign MNCs in all industries except wood. The same was true for MNC joint ventures in all industries except wood and machinery. Similarly large, negative differentials were also common for SOEs in textiles (all years), food, wood, and metals (five years each), and machinery (four years). The fact that MNCs and SOEs tended to have relatively high average labor productivity but relatively low average capital productivity reflects a tendency for MNCs and SOEs to use more fixed assets per worker than private firms. $ ^{15} $ MNCs and SOEs also tend to be much larger than private firms, which may also contribute to relatively high labor productivity levels. $ ^{16} $ The following section thus attempts to measure productivity differentials and spillovers after accounting for differences in size and factor intensity.

Table 4 about here

### 4. Productivity Differentials after Accounting for Firm and Industry Characteristics

In order account for differences in factor intensities and scale, and to allow for the most flexible assumptions about technology, translogarithmic (translog) production functions are estimated. The constant in these equations reflects productivity not explained by variation in labor and capital, and is commonly called total factor productivity (TFP). This interpretation can be problematic, however, because the constant not only reflects productivity after accounting for the use of capital and labor,
but also errors in measurement and specification, which can be substantial. Nonetheless, this approach is standard in the literature and has been adopted by most of the studies reviewed above. $ ^{17} $

The effects of producer concentration on productivity are investigated by adding either the 4-firm concentration ratio or the Herfindahl index for 37 industries, which were generally defined at the 3-digit level, to the production function. $ ^{18} $ Concentration measures come from larger samples of all firms reporting positive sales and employment (c.f. Table 2), because they are designed to include the effect of competition from smaller firms and other firms excluded from the estimation sample (e.g., firms with missing variables or negative value added). Producer concentration tended to fall over this period in Vietnam with the mean 4-firm concentration ratio in these 37 industries declining from 45 percent in 2000 to 36 percent in 2006. Relatively large declines of 10 percentage points or more were relatively common, being observed in 15 of the 37 industries. $ ^{19} $

Dummy variables for wholly-foreign MNCs, MNC joint ventures, and SOEs are also added, their coefficients revealing whether ownership-related TFP differentials between these groups and private firms remain after accounting for firm-level inputs and industry-level characteristics:

$$ \begin{aligned}(1)LV_{ijt}&=a0+a1(LE_{ijt})+a2(LK_{ijt})+a3(LE_{ijt}^{~2})+a4(LK_{ijt}^{~2})+a5(LELK_{ijt})+a6(DS_{ijt})+a7(DM_{ijt})\\&\quad+a8(DJ_{ijt})+a9(C_{jt})\end{aligned} $$