Local Firms versus MNCs in India: A Study of Competitive Performance
Abstract:
We studied the performance of 187 firms drawn from MNC subsidiaries (55), domestic privateowned (76), and domestic stateowned (56) firms operating in India. The underlying objective was to assess which group of firm demonstrated superior economic performance and competitiveness. We analyzed data for two periods of time 200203 and 201112 using four measures of economic performance namely operating profit margin (OPM), net profit margin (NPM), return on net worth (RONW) and asset turnover ratio (ATR). As the data set did not lend itself to parametric analysis, we adopted the nonparametric method. We employed KruskalWallis H Test, MannWhitney U Test, TwoStep Cluster Analysis, and ChiSquare Test. We found that domestic privateowned firms performed better and were more competitive than the other two groups of firms.
1. Introduction
During the past two decades foreign direct investment (FDI) flow into emerging markets has increased considerably, particularly to the BRICS (Brazil, Russia, India, China, and South Africa) countries. These countries have experienced relatively higher levels of economic growth rate compared to the developed economies and hence have attracted substantial amounts of FDI. While on the one hand such investment can lead to positive implications in terms of stimulating economic activity, generating employment, raising the standard of living, increasing competition, bringing in new technology and international brands, it can also have certain negative consequences. A prominent factor is the fear that international competition is primarily inflicted by multinational firms (MNCs) which are more efficient visàvis domestic firms in emerging markets; that the former will eventually dominate the latter and may even eliminate them from the competitive arena. This is based on the assumption that MNCs are better resourceendowed in terms of technology, capital, brands and management practices. With such assets multinational firms will be able to win over the loyalty of customers, attract the best talent and be able to quickly gain confidence of supply chain partners.
At a policy level emerging market governments enact laws to protect and nurture domestic firms. Protectionism has its consequences in terms of inefficiencies and below par quality besides creating unequal playing field for all players. We felt it necessary to study the performance of local and MNC firms to determine the level of competitiveness of both these sets of firms in India. Should the local firms panic and the government in turn legislate to give preferential treatment to Indian firms over MNCs?
There is yet another dimension of competition that happens between domestic firms. This is the competition between the private and the public sector firms. The general perception is that the private sector is more efficient and proactive and therefore more competitive. Consequently products and services offered by the private sector are of superior quality in comparison to the public sector counterparts. Our study intends to analyze this aspect as well.
2. Literature Review
Extent literature on corporate performance and firm ownership is divided when it comes to performance between foreign firms and domestically owned firms. Some of the studies find that foreign firms are more efficient compared to domestically owned firms while other studies have found that both these types of firms have performed equally well. Very similar is the debate between domestic governmentowned firms visàvis privatelyowned firms.
A study conducted by (Asheghian, 1982) examined the comparative efficiencies of foreign firms, which consisted of IranianAmerican joint venture firms (IAJV) and local firms in Iran during the prerevolutionary 197176 period. This study of interfirm efficiency comparison of eleven matched firms was based on three indexes of efficiency namely, labour productivity, capital productivity and total factor productivity. The study concluded that with minor exceptions the IAJV firms were more efficient that their Iranian firms' counterparts. (Willmore, 1986) analyzed data of 282 pairs of foreignowned and Brazilian firms in the manufacturing industry. The study found that differences between the two types of firms were large and highly significant. Compared to their local counterparts, foreign firms operated fewer plants, had higher ratios of valueadded to output, higher levels of advertisement and royalty payments, higher labour productivity, greater exports, higher wages and greater capital intensity.
(Voicu, 2004) examined whether foreign firms in Romania were technologically superior to domestic firms by separately estimating the technologyrelated productivity differentials between domestic firms and international joint ventures, and between domestic firms and foreign wholly owned enterprises. The study revealed that both types of foreign firms exhibited a technological advantage in virtually all manufacturing sectors compared to domestic Romanian firms. (Kimura and Kiyota, 2004) utilized micropanel data for firms located in Japan to examine differences in static and dynamic corporate performance between foreignowned and domesticallyowned firms in the 1990s. The authors found that foreignowned firms not only reflected superior static characteristics but also achieved faster growth. Further, foreign investors invested in firms that may not be immediately profitable at the time of investment but those that had profit potential.
(Ayudin et al., 2007) in a study investigated whether foreignowned firms performed significantly better than domesticallyowned Turkish corporations listed on Istanbul Stock Exchange. The ttest statistic was applied to examine if there was significant differences in operating profit margin, return on assets and return on equity between the two groups of firms. The results revealed that firms with foreign ownership performed better than domesticallyowned ones in respect of return on assets. (Kesari, 2010) empirically examined the differences in the relative characteristics, conduct and performance of two different ownership groups of firms, namely, foreign affiliates of multinational enterprises and domestic firms. The study was restricted to nonelectrical machinery industry in India for the period 2001 to 2007. Three alternative techniques were employed, univariate statistical method based on Welch’s ttest, the multivariate linear discriminant analysis and the dichotomous logit and probit models. The findings suggest that foreign affiliates had greater technological efficiency, firm size, export intensity, intensity of import of intermediate goods and intensity of import of disembodied technology along with lower advertisement and marketing intensity and financial leverage.
In a study which explored the differences between domestic and foreignowned firms operating in Greece, (Valsamis et al., 2011) in particular focused on financial management characteristics of the firms under investigation for the year 2008. The firms were grouped into two categories based on the origin of their capital share. Using a nonlinear model the study found that foreign enterprises made higher use of capital, managed more financial elements, had more access to longterm capital, while they fell short against domestic firms in short term financing. Overall, foreign firms had higher sales and presented greater profitability.
In their study (Barbosa and Louri, 2003) investigated whether multinational corporations operating in Portugal and Greece performed differently than domestic firms. They used two sets of sample firms one set operating in Greece in 1997 and another set operating in Portugal in 1992. Results suggested that ownership ties did not make a significant difference with respect to performance of firms operating in both the countries. However, it was also found that when firms in the upper quartiles of gross profits were compared, MNCs were found to significantly perform better than domestic firms.
A study undertaken by (Basti and Akin, 2008) compared the relative productivities of foreignowned and domesticallyowned companies operating in Turkey. Nonfinancial sector companies listed in Istanbul Stock Exchange from the period 20032007 were included in the analysis. Malmquist index, which is a data envelopment analysis type nonparametric technique, was utilized as the productivity measurement tool. Study results indicated that there was no difference between productivity of foreignowned and domesticallyowned firms operating in Turkey. (Basti et al., 2011) analyzed the performance of foreignowned firms in contrast to domesticallyowned firms in the manufacturing sector in Turkey. The impact of several firm indicators like age, size, assets, firm risks on different corporate performance measures such as ROE, ROA, Basic Earning Power and Total Factor Productivity were investigated by a panel data regression model. Contrary to findings of former studies in Turkey, the results of this study revealed that there was no significant difference between the performances of foreignowned and domesticallyowned firms.
(Caves and Douglas, 1980) compared the postwar productivity performance of a public firm (Canadian National Railroads) with a private firm (Canadian Pacific Railroad) through a case study approach. In their study they found no evidence of inferior performance by the governmentowned railroad. Their study concluded that any tendency towards inefficiency resulting from public ownership was overcome by the benefits of competition.
(Xu et al., 2006) examined the performance of domestic Chinese firms in various ownership categories versus foreigninvested enterprises based on two nationwide surveys conducted by the National Bureau of Statistics in 1998 and 2002. The study found that both domestic nonstateowned firms and foreigninvested enterprises performed better than stateowned enterprises. Meanwhile, three categories of Chinese firms  privately owned, collectively owned, and shareholding  had higher performance levels than the foreigninvested enterprises.
(Erdogan, 2010) analyzed the major aspects of conduct and performance that distinguishes foreignowned and domesticallyowned firms that operated in Turkey. Repeated measures logistic regression technique was used on 77 foreignowned and 215 domesticallyowned firms for the period 20042008. The results showed that domesticallyowned firms had higher capital productivity visàvis foreignowned firms. In terms of the other performance variables studied such as pretax profit margin, return on equity and labour productivity there was no difference between foreignowned and domesticallyowned firms. The two groups of firms also do not differ in terms of size, capital intensity, export intensity, patent intensity and trademark intensity.
A study undertaken by (Majumdar, 1998) evaluated performance difference between public sector, joint sector (joint venture between private and public sector firms) and private sector enterprises in India for the period 197374 to 198889. The study results established that enterprises owned by the central and state governments were less efficient than joint sector or private sector enterprises. Further, it found that joint sector enterprises were less efficient than those in private sector. (Boitani et al., 2013) focused on how the ownership and selection procedure of firms operating in the Local Public Transport sector affected their productivity. A comparative analysis of 77 firms operating in large European cities over the period 1997 to 2006 was conducted using the measure of Total Factor Productivity. The authors found that totally and partially public firms displayed lower productivity than privately owned firms.
3. Research Methodology
We intended to attempt a study on the competitive performance of multinational firms versus domestic firms as well as between domestic firms. We categorised domestic firms into privateowned and stateowned. In all, there are three groups of firms: MNCs, domestic privateowned and domestic stateowned. The study is for two different time periods; the base year 200203 and the recent year 201112.
There was a reason why these two time periods were considered for the study. The government of India ushered in reforms in periodic dosages from 1991 to liberalize the economy from a controlsdriven to a marketdriven one. It was assumed that over a tenyear period the economy would have changed significantly. Therefore, the base year of 200202 was chosen to determine how different groups of companies had performed in the post reform competitive era. Further, the year 2008 saw major changes in the global economy with recession raising its ugly head. By 201112, three years post the commencement of global downturn, the idea was to assess how well the firms had done given the hostile nature of the environment. The idea was also to observe the change in performance of the three groups of firms over the nineyear period.
Four financial measures are considered namely, Operating Profit Margin (OPM), Net Profit Margin (NPM), Return on Net Worth (RONW), and Asset Turnover Ratio (ATR). We found very little published research work on competitive performance of local firms and multinational firms in India. Our research, it is hoped, will fill this gap to an extent. We had done an earlier study taking a sample size of 45 firms (15 each from the three groups of firms mentioned above). The limitation of the study was the small sample size used which did not satisfy the requirements of some of the statistical tests employed. Therefore we undertook this exercise using a larger sample size of 187 firms using SPSS (16).
The hypothesis proposed to be tested is:
H0: There is no difference in the performance of foreign companies in India compared to domestic privateowned and domestic stateowned companies.
The data for this study was extracted from secondary sources. The main source is the Ace Analyzer data base, besides the websites of the firms listed in the BSC (Bombay Stock Exchange) and NSE (National Stock Exchange) in India. 187 firms operating in India have been included in the study, of which 55 are foreign firms, 76 are domestic privateowned firms and 56 are domestic stateowned firms.
Our research design has two stages. First stage involved classifying the firms into three categories namely, low (7%), medium (7% to 15%) and high (>15%) performing ones using the financial measure ‘Return on Capital Employed’ (ROCE). To validate this classification we used four variables mentioned above namely OPM, NPM, RONW, and ATR. We wanted to use parametric tests for analyses and began with the oneway ANOVA test. The idea was to carry out this test for each of the four independent variables to determine if there is significant variation in the performance of the three groups of firms. However, the data set did not satisfy the basic tests of normality of population distribution and homogeneity of variance. If these two tests would have been satisfied and the ANOVA results were to be significant we intended to use a post hoc test and subsequently the discriminant analysis test to validate our initial classification of the three sets of firms. This would have enabled us to comment on the competitiveness of the three groups of firms.
Since this was not possible owing to the limitations of the dataset we decided to adopt the nonparametric approach to pursue our study. We chose the KruskalWallis H test (the nonparametric version of the onefactor independent measures ANOVA) for comparing two or more independent samples. We then wanted to performed the MannWhitney U test (the nonparametric version of the independent samples t test) to determine which group median score(s) is/are responsible for the variation. Next, to cross validate our initial classification we used the TwoStep Cluster Analysis, which is somewhat similar to the discriminant analysis used in parametric analysis. Finally, we employed the ChiSquare test to find out if there exists an association between groups of firms and their performance during the two periods of time considered for the study. This was done to compare and determine how foreign, domestic privateowned and domestic stateowned firms performed over the tenyear period and to comment on their competitiveness.
4. Discussion
We intended to formally test the data visàvis the two main conditions –normality of population and homogeneity of variance – for reliable results for the oneway ANOVA. To test for normality we used the KolmogoroveSmirnov Test (since our n is >50), as it assesses whether there is a significant departure from normality in the population distribution of the four variables being studied.
The test statistic is:
$W=\frac{\left(\sum_{i=1}^n a_i x_i\right)^2}{\sum_{i=1}^n\left(x_i\bar{x}\right)^2}$
Where:
$x_i$ (With parentheses enclosing the subscript index $i$ ) is the $i^{\text {th }}$ order statistic, i.e., the $i^{\text {th }}$ smallest number in the sample;
$\bar{x}=\frac{\sum_{i=1}^n\left(x_i\right)}{n}$ is the sample mean; the constants $a_i$ are given by
$\left(a_1, \ldots \ldots, a_n\right)=\frac{m^T V^{1}}{\left(m^T V^{1} V^{1} m\right)^{1 / 2}}$
When we look at the test statistic and significance column (see table 1) for each of the variables for both 200203 and 201112, we find that the Pvalues are less than the chosen α (.05), so we reject the null hypothesis and conclude that the data violates normality assumption.
 KolmogorovSmirnov^{a}  ShapiroWilk  
Statistic  df  Sig.  Statistic  df  Sig.  
NPM 2003  .346  187  .000  .190  187  .000 
OPM 2003  .301  187  .000  .340  187  .000 
RONW 2003  .286  187  .000  .641  187  .000 
ATR 2003  .224  187  .000  .737  187  .000 
 KolmogorovSmirnov^{a}  ShapiroWilk  
Statistic  df  Sig.  Statistic  df  Sig.  
NPM 2012  .436  187  .000  .129  187  .000 
OPM 2012  .411  187  .000  .194  187  .000 
RONW 2012  .155  187  .000  .862  187  .000 
ATR 2012  .208  187  .000  .739  187  .000 
To test homogeneity (equality) of variance assumption we used Levene’s Test, which assesses whether the population variances for the variables are significantly different from each other.
The Levene’s test statistic, W, is defined as follows:
$W=\frac{(Nk)}{(k1)} \frac{\sum_{i=1}^k N_i\left(Z_{i .}Z_{. .}\right)^2}{\sum_{i=1}^k N_i\left(Z_{i j}Z_{i .}\right)^2}$
Where:
$\mathrm{W}$ is the result of the test,
$\mathrm{k}$ is the number of different groups to which the samples belong,
$\mathrm{N}$ is the total number of samples,
$\mathrm{N}_{\mathrm{i}}$ is the number of samples in the $i^{\text {th }}$ group,
$\mathrm{Y}_{\mathrm{ij}}$ is the value of the $j^{\text {th }}$ sample from the $i^{\text {th }}$ group,
$Z_{i j}= \begin{cases}\leftY_{i j}\bar{Y}_{L_l}\right, & \bar{Y}_{\text {i. }} \text { is a mean of } i\text { th group } \\ \leftY_{i j}\widetilde{Y}_{\mathrm{L}}\right, & \widetilde{Y_{i .}} \text { is a median of } i\text { th group }\end{cases}$
When we look at table 3 we see that the Pvalues for three variables in 200203 are <.05, which is less than our chosen α (.05), we reject the null hypothesis and conclude that the data violate the homogeneity assumption. Only for OPM the Pvalue is >.05 and it alone satisfies the homogeneity assumption. For the year 201213 again the Pvalues for three variables are <.05 (see table 4). Here again we reject the null hypothesis and conclude that the data violate the homogeneity assumption. However, in case of the OPM variable we accept the null hypothesis as the Pvalue is >.05.
 Levene Statistic  df1  df2  Sig. 
NPM 2003  3.605  2  184  .029 
OPM 2003  1.620  2  184  .201 
RONW 2003  10.020  2  184  .000 
ATR 2003  10.698  2  184  .000 
 Levene Statistic  df1  df2  Sig. 
NPM 2012  4.977  2  184  .008 
OPM 2012  2.029  2  184  .134 
RONW 2012  4.947  2  184  .008 
ATR 2012  9.851  2  184  .000 
Since the data did not satisfy the assumptions of oneway ANOVA, we decided not to proceed using parametric tests but shifted to the nonparametric method. Since KruskalWallis H test enjoys the same power properties relative to the oneway ANOVA F test, we decided to employ this test.
The KW test statistic is given by:
Where:
 $\mathrm{n}_{\mathrm{i}}$ is the number of observations in group $i$
 $\mathrm{r}_{\mathrm{ij}}$ is the rank (among all observations) of observation $j$ from group $i$
 $\mathrm{N}$ is the total number of observations across all groups
$\bar{r}_{i .}=\frac{\sum_{j=1}^{n_i} r_{i j}}{n_i},
$\bar{r}=\frac{1}{2}(N+1)$, is the average of all the $r_{i j}$
When we look at table 5, we see that the Pvalues for 200203, where three of the variables have significance level of <.05, which is less than our chosen α (.05). We reject the null hypothesis and conclude that there are differences among the groups of firms and therefore their rank score cluster systematically. Only for ATR the Pvalue is >.05 and so we do not reject the null hypothesis. For the year 201112 (see table 6) for all the four variables Pvalues have significance level of <.05 and therefore we reject the null hypothesis. Thus considering the data for both the base year as well as the recent year, it is clear that there are significant differences in the performance of the three groups of firms.
 NPM 2003  OPM 2003  RONW 2003  ATR 2003 
ChiSquare  22.424  16.037  37.047  3.447 
df  1  1  1  1 
Asymp. Sig.  .000  .000  .000  .063 
 NPM 2012  OPM 2012  RONW 2012  ATR 2012 
ChiSquare  51.654  34.561  93.773  32.842 
df  2  2  2  2 
Asymp. Sig.  .000  .000  .000  .000 
Since KurskalWallis test revealed significant differences in Pvalues for the variables being studied, we attempted the MannWhitney U test, a nonparametric test that can be used when there are two independent samples with the assumption that they are drawn from population with the same shape, although not necessarily normal. This test is used in lieu of parametric posthoc tests. The null hypothesis is that the scores from the two groups are not systematically clustered and thus there is no difference between the groups.
The MannWhitney U test statistic is given by:
$z=\frac{Um_u}{\sigma_U}$
Where, where $m_U$ and $\sigma_U$ are the mean and standard deviation of $U$
$m_{U=} \frac{n_1 n_2}{2}$
$\sigma_U=\sqrt{\frac{n_1 n_2\left(n_1+n_2+1\right)}{12}}$
When we examine table 7, it is observable that for all variables the significance level is lower than the chosen α (.05). This clearly indicates that there are significant differences in the values or median scores amongst the three groups of firms in the year 200203. The only exception is for the variable ATR 2003 and that too for one pair of ‘lowmedium’ performing firms.
LowMedium Test Statistics  
 NPM2003  OPM2003  RONW2003  ATR2003 
MannWhitney U  280.000  354.000  144.500  571.500 
Wilcoxon W  776.000  850.000  640.500  1067.500 
Z  4.735  4.005  6.087  1.857 
Asymp. Sig. (2tailed)  .000  .000  .000  .063 
LowHigh Test Statistics  
 NPM2003  OPM2003  RONW2003  ATR2003 
MannWhitney U  323.000  550.000  67.000  831.000 
Wilcoxon W  819.000  1046.000  563.000  1327.000 
Z  6.813  5.655  8.122  4.222 
Asymp. Sig. (2tailed)  .000  .000  .000  .000 
HighMedium Test Statistics  
 NPM2003  OPM2003  RONW2003  ATR2003 
MannWhitney U  1443.500  1618.500  646.000  1964.500 
Wilcoxon W  2668.500  2843.500  1871.000  3189.500 
Z  4.498  3.830  7.543  2.509 
Asymp. Sig. (2tailed)  .000  .000  .000  .012 
The results are similar when we examine the table 8, which shows SPSS output for the year 201112. We again arrive at the same conclusion as did for the year 200203. However, there are three variables which have Pvalues that are >.05, these are, ATR ‘lowmedium’ firms and NPM and OPM ‘mediumhigh firms. We decided to ignore these as aberration, since KW test too showed significant differences in the performance of the three groups of firms and proceeded with further analyses.
Low Medium Test Statistics  
 NPM2012  OPM2012  RONW2012  ATR2012 
MannWhitney U  165.000  210.000  118.000  535.500 
Wilcoxon W  795.000  840.000  748.000  1063.500 
Z  4.958  4.393  5.551  .308 
Asymp. Sig. (2tailed)  .000  .000  .000  .758 
LowHigh Test Statistics  
 NPM2012  OPM2012  RONW2012  ATR2012 
MannWhitney U  442.000  753.000  281.500  1187.500 
Wilcoxon W  1072.000  1383.000  911.500  1817.500 
Z  7.096  5.765  7.783  3.905 
Asymp. Sig. (2tailed)  .000  .000  .000  .000 
MediumHigh Test Statistics  
 NPM2012  OPM2012  RONW2012  ATR2012 
MannWhitney U  1674.000  1887.000  404.000  816.000 
Wilcoxon W  2202.000  9147.000  932.000  1344.000 
Z  1.112  .149  6.851  4.990 
Asymp. Sig. (2tailed)  .266  .881  .000  .000 
As we found all four variables indicating significant differences in the performance of the groups of firms, hence we decided to use all the four variables to cross validate our initial classification of the three groups of firms (which was done using ROCE). We chose the TwoStep Cluster Analysis for this purpose. The TwoStep Cluster is an algorithm primarily designed to analyze large datasets. The algorithm groups the observations in clusters, using the approach criterion. The procedure uses an agglomerative hierarchical clustering method. Compared to classical methods of cluster analysis, the TwoStep enables both continuous and categorical attributes. Moreover, the method can automatically determine the optimal number of clusters.
 TwoStep Cluster Number_PERM2003  Total  
Low  Medium  High  
PERM 200203  Low  Count  31  0  0  31 
% of Total  16.6%  0.0%  0.0%  16.6%  
Medium  Count  48  1  0  49  
% of Total  25.7%  0.5%  0.0%  26.2%  
High  Count  0  4  103  107  
% of Total  0.0%  2.1%  55.1%  57.2%  
otal  Count  79  5  103  187  
% of Total  42.2%  2.7%  55.1%  100.0% 
 TwoStep Cluster Number_PERM2012  Total  
Low  Medium  High  
PERM 2011 12  Low  Count  34  1  0  35 
% of Total  18.2%  0.5%  0.0%  18.7%  
Medium  Count  32  0  0  32  
% of Total  17.1%  0.0%  0.0%  17.1%  
High  Count  0  1  119  120  
% of Total  0.0%  0.5%  63.6%  64.2%  
Total  Count  66  2  119  187  
% of Total  35.3%  1.1%  63.6%  100.0% 
Table 9 shows the TwoStep Cluster Analysis results for 200203 data. Prominent is the fact that 48 or 25.7% of firms are classified as Low (instead of Medium) and 4 or 2.1% firms are classified as Medium (instead of High). In all 71.13% of the 187 firms are classified in the same way as we had done earlier. Table 10 shows the outcome for 201112 data. 81.87% of the 187 firms are classified as per our earlier classification. Here again the major difference in classification, like the base year, is with Medium performing firms with 17.15% classified as Low performing ones. We considered the overall classification which emerged from the TwoStep Cluster Analysis as a validation of our initial classification which was done using ROCE. Therefore, we decided to follow the same to study the relationship between the groups of firms’ and their performances as well as competitiveness.
Superior firm performance can be inferred from the movement of firms from lowperformer to medium or highperformer and medium to high. Maintaining high performance even after the lapse of a decade in a growing and competitive market is also an indicator of superior performance. To determine this, we undertook cross tabulation of the firms being studied to check the movements of low, medium and high performing firms from the base year (200203) to the recent year (201112). The result of this cross tabulating exercise can be seen in table 11. It is obvious from the table that domestic privateowned firms have shown greatest level of competitiveness as the number of high performing firms increased by 15 during the period of study. MNC as well as domestic stateowned firms have maintained status co.
Groups of firms  Classification  200203  201112  Difference 
MNC  Low  8  11  +3 
Medium  9  6  3  
High  38  38  0  
Domestic Private  Low  11  8  3 
Medium  27  15  12  
High  38  53  +15  
Domestic Public Sector  Low  12  16  +4 
Medium  12  9  3  
High  30  29  1 
After observing the results of cross tabulation, we next wanted to use the Chisquare test to statistically arrive at a conclusion about the performance and competitiveness of the three groups of firms being studied. The test results revealed that the calculated Pvalues for MNCs and stateowned firms were .006 [significant at α (.01)] and .061 [significant at α (.05)] respectively. Thus the null hypothesis that there is no significant association between the performances of MNC firms and state owned firms was not rejected. However, the calculated Pvalue for domestic privateowned firms was.250, which was higher than α (.05). Thus, in this case we reject the null hypothesis and conclude that there is significant difference in the performance of these firms during the two periods of time studied. In other words, the domestic privateowned firms have performed significantly differently in 201112, visàvis 200203, which in fact is better performance. This finding matches our previous finding based on the cross tabulation. Thus, despite passage of time and increase in competition owing to liberalization of the economy and arrival of foreign competition, the domestic privateowned firms have managed to perform better than MNC and stateowned firms.
Sector  Value  df  Asymp. Sig. (2 sided)  
MNC  Pearson ChiSquare  14.542b  4  .006 
Likelihood Ratio  13.945  4  .007  
LinearbyLinear Association  11.890  1  .001  
N of Valid Cases  55 

 
PVT  Pearson ChiSquare  5.380c  4  .250 
Likelihood Ratio  6.659  4  .155  
LinearbyLinear Association  .000  1  .998  
N of Valid Cases  76 

 
PSU  Pearson ChiSquare  8.992d  4  .061 
Likelihood Ratio  9.325  4  .053  
LinearbyLinear Association  4.786  1  .029  
N of Valid Cases  54 

 
Total  Pearson ChiSquare  18.552a  4  .001 
Likelihood Ratio  18.593  4  .001  
LinearbyLinear Association  11.292  1  .001  
N of Valid Cases  185 


5. Conclusion
In this study, we analyzed the performance of 187 firms operating in India drawn from MNC (55), domestic privateowned (76), and domestic stateowned (56). The null hypothesis tested is that there is no difference in performance of MNC, domestic privateowned, and domestic stateowned firms. This hypothesis was arrived at based on the review of several research studies conducted in different countries, which indicated that subsidiaries of MNC firms perform better than domestic firms. A limitation of our study is that we could not undertake parametric analysis as the data did not satisfy the assumptions of oneway ANOVA model. This gives scope for future research using larger or different data set which may permit the use of parametric as well as nonparametric analysis and thus increase the robustness of the study.
At a managerial level, it indicates that executives of privateowned firms have demonstrated superior competitiveness visavis MNC firms despite increase in competition (both domestic and foreign) owing to liberalization of the economy and the global recession. This finding goes against many earlier research findings as well as general belief that MNCs are more competitive than local firms. However, in a dynamic environment there is no room for complacency for local privateowned firms. They have to further strengthen their competitiveness to take on the better endowed MNCs in future. As far as stateowned firms are concerned, there is need for introspection and selfanalysis to determine reasons for lessthandesired performance. Corrective measures will enable them to improve performance and competitiveness. Same holds for MNC firms as well.