Employee performance in the Indian textile industry.

Punnoose, Eldos Mathew ; Modekurti, Madhuri

Introduction

Over View of the Indian Textile Industry

The textile industry occupies a unique place in the country. One of the earliest to come into existence in India, it accounts for fourteen percent of the total Industrial production, contributes to nearly thirty percent of the total exports and is the second largest employment generator after agriculture. It provides direct employment to about thirty five million people and to another fifty million in allied areas. It means that one out of every six Indians is linked to the textile sector.

Textile Industry is providing one of the most basic needs of people and holds importance; maintaining sustained growth for improving quality of life. It has a unique position as a self-reliant industry, from the production of raw materials to the delivery of finished products, with substantial value-addition at each stage of processing; it is a major contributor to the Indian economy. Its vast potential for creation of employment opportunities in the agricultural, industrial, organized and decentralized sectors and rural and urban areas, particularly for women and the disadvantaged is noteworthy.

Textile Policies In India

Although the development of textile sector was earlier taking place in terms of general policies, in recognition of the importance of this sector, for the first time a separate Policy Statement was made in 1985 in regard to development of textile sector. The Industry was de-licensed in 1991-1992 and from then the per capita cloth availability increased from 22.87 Sq. Meters (1990) to 33.51 Sq. Meters (2005).

The textile policy of 2000 aims at achieving the target of textile and apparel exports of US $ 50 billion by 2010 of which the share of garments will be US $ 25 billion. The main markets for Indian textiles and apparels are USA, UAE, UK, Germany, France, Italy, Russia, Canada, Bangladesh and Japan. The main objective of the textile policy 2000 is to provide cloth of acceptable quality at reasonable prices for the vast majority of the population of the country, to increasingly contribute to the provision of sustainable employment and the economic growth of the nation; and to compete with confidence for an increasing share of the global market.

Current Scenario of the Industry

India with both textile and clothing capacity may be able to prosper in the new competitive environment after the textile quota regime of quantitative import restrictions under the multi-fiber arrangement (MFA) came to an end on January, I 2005 under the World Trade Organization (WTO) Agreement on Textiles and Clothing. As a result, the textile industry in India will face intensified competition in both their export and domestic markets. However, the migration of textile capacity will be influenced by objective competitive factors and will be hampered by the presence of distorting domestic measures and weak domestic infrastructure in India. The elimination of quota restriction will open the way for India to develop stronger clusters of textile expertise, enabling them to handle all stages of the production chain from growing natural fibers to producing finished clothing, The OECD reports says that low wages can still give India a competitive edge in world markets. Another reason is the structural similarities between India and US textile industries (Parikh, 1975).

Twelve percent of the invested capital and thirteen percent of the factories in the country is in this sector. In 2005-06 it contributed seventeen percent to gross export earnings and added less than 1.27 percent to the import bill, having International Trade Accounts 4.5 percent of the World Market. The period from 1994-2004 marked a CAGR of 5.91 percent projected CAGR is 19.85 percent over next five years

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Facts, Figures and Aspiration (Vision 2010)

The mood in the Indian textile industry given the phase-out of the quota regime of the multi-fiber arrangement (MFA) is upbeat with new investment flowing in and increased orders for the industry as a result of which capacities are fully booked up to April 2007. As a result of various initiatives taken by the government, there has been new investment of Rs.50,000 crore in the textile industry in the last five years. Nine textile majors invested Rs.2,600 crore and plan to invest another Rs.6,400 crore. Further, India's cotton production increased by fifty seven percent over the last five years; and three million additional spindles and 30,000 shuttle-less looms were installed. The industry expects additional investment of Rs.1,40,000 crore in this sector in the recent future. The low wage structure in India also causes for the shift in production of textiles from developed countries to India (Bheda, Narag, Singla, 2003).

The thrust areas of vision 2010 are given below

* Increase India's share in world's textile trade from the current four percent to eight percent by 2010

* Achieve export value of US $ 50 billion by 2010

* Growth in Indian textile economy from the current US $ 37 billion to $ 85 billion by 2010

* Creation of twelve million new jobs in the textile sector

* Modernization and consolidation for creating a globally competitive textile industry.

Strengths and Weakness

The strengths of the industry in India

* Strong raw material base--cotton, man-made fibers, jute, silk

* Large production capacity (spinning--twenty one percent of world capacity and weaving--thirty three percent of world capacity)

* Vast pool of skilled manpower

* Entrepreneurship

* Flexibility in production process

* Long experience with US and European Union.

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The constraints for smooth growth are

* Fragmented industry

* Difficulties in processing

* Quality of cotton

* Concerns over power cost, labor reforms and other infrastructural constraints and bottlenecks. E.g., cost of power was Rs. 8 per garment in India whereas in China it was only Rs. 2 per garment.

HR in the Industry

Human resources are gaining importance in all organizations and industries and Indian textile industry cannot escape the tides of the same. Many of the manufacturing organizations and industries have realized the importance of HR. Importance of HR and the vital role it could play in the food and canning industry (Chomka, 2002). Many of the giant corporations like Toyota is also giving much more focus on HR in the recent years (McKenna, 2002). Thus even in the Indian textile industry there has to be a rising focus towards HR and people management. However the present condition of the industry is in stark aberration from the desired state. A study puts forward a framework on how a manufacturing enterprise can emphasize the use of human intelligence and human resources to the full in the process of adopting advanced manufacturing and computer technologies (Zhou, Chuah, 2000). The unique features of the industry with relation to the human resources in the Indian textile industry are

* High employment potential

* Low wages

* Rigid labor laws

* Insignificance attached to HRD in general

These features raise a general hue and cry for the overall improvement of the labor force in the industry. Even HRD features in the bottom of the nine-point strategy list by the Government, intended to improve the industry standards. This really points to the fact that we cannot overlook this aspect anymore. The delay in realizing the facts results in growth inhibition of the industry. Human concerns are the real need of the day and future not come if these concerns are not dealt with.

Rationale of the Study

Textile Industry is viewed as one of the oldest industries in India which caters to a large employee base. This industry has always remained a labor-intensive abode and continues to be so. But with the aggrandizement of intensified competitions within this industry, caused by the death of the quota regime, highly skilled labor alone can contribute to the survival of firms. Hence re-scrutinizing the levels of labor productivity of this industry becomes essential.

Wages are widely believed to be the essential motivating forces behind the contributions made by any employee to a firm. Hence, this study takes into account the likely impact of wages on labor productivity. But again, wages cannot be viewed in isolation. They are under the influence of firm size and ownership structure. Hence, this study seeks to analyze all the three variables (labor productivity, wages & firm size) in the context of different types of ownership structures.

Review of Literature

Firm Size and Labor Productivity

* Gunther and Gebhardt (2005)

This paper looks at foreign direct investment (FDI) as a means to support economic transformation in the context of East Germany. The sudden and unexpected political changes of the 1989-90 created problems for the former socialist countries of Europe, especially for East Germany. Some of the problems these countries were faced with dealt mostly with the challenge involved in rebuilding their societies and economies. Foreign Direct Investment (FDI) was seen as a major source and relied upon heavily by all the Central and Eastern European countries (CEECs) as well as East Germany not only as sources of investment but also as transmitters of technology and management know-how. The study was conducted on 1780 firms of which foreign (628), East German firms (1080), others (72). The study found out that foreign firms have higher productivity than East German establishments. The variations among these two types of firms were on the sales productivity (sales per employee) front as well as the value added productivity (value added per employee) front. According to the IAB establishment panel (consists of all German firms employing at least one employee and hence are subject to the compulsory social security scheme), foreign establishments in 2001 exhibit a sales productivity that is 3.5 times that of East German establishments (and twice that of West German establishments). Value-added productivity of foreign and West German investors is about two times higher than that of East German establishments. Though these differences in productivity were attributed to the presence of these foreign establishments in high productivity industries, the authors say that differences in productivity are largely due to the differences in firm-size structure. Not only with regard to productivity, all other parameters which were adopted for comparison like technological capability, product innovations, R&D, Organizational changes.

Value Added and Wages

* Wanik (1984)

This study considered one hundred fifty one German industrial corporations, registered on the German stock exchange, for a period of eighteen years from 1960 to 1978. The study derived its data from the published financial data of the sample firms like balance sheet, profit and loss account etc. The 151 firms were drawn from 12 different industries namely Textiles, Building Materials, Electrical Engineering, Iron Tin and Metal Working, Machinery, Vehicles, Iron and Steel, Constructional Steel, Brewing and Malting, Chemicals, Cement and Electrical industries. Published data was opted for the assessment of Inter-industry wage structure and Average annual rate of change of wages of each industry because data was firm specific and was in accordance to the German Corporation Law.

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The paper defined wages as the total expenditure/cost for labor per employee which can be termed as the total sum of wages and salaries, social expenditures, pension expenditures and voluntary payments divided by the number of employees. With Average wages rising from DM 8693 to DM 14043 over the analysis period of eighteen years, this rise in average wages accounted for an average annual rate of change of wages of 9.01percent for each industry. Keeping in mind, the average wage rise, a further cross sectional analysis revealed that average industry wages and average productivity (value added per employee) were significantly positively correlated.

The firms were also grouped into seven special groups of eighteen firms each with respect to growth, factor intensity and size. Further regression was run to consider the effect of Productivity, Degree of Unionization and Rate of return on Total Capital on Wages. In cases of Productivity and Degree of Unionization, it was found that they had a significant positive relationship with wages while the Rate of return was significantly negative. Results also indicated that in comparison to capital/investment intensity, labor productivity explained wages better than other variables. To consolidate this study's findings, we can say that wages have a positive relationship with excess demand for labor, labor productivity and business cycle changes of the firm/industry.

Noticeable improvements on the labor productivity front will help an industry to improve its relative position within the inter-industry wage structure.

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Ownership Structure and Wages

* Tosi and Werner (1995)

This paper tries to capture the pay mix differences, through an analysis of the influencing role played by managerial discretion in determining the compensation strategy of their firms. Basically wages in any type of firms, according to the classical economic approach, are determined by considering the demand and supply of labor in the market. The conventional economic and industrial determinants of pay were the individual, job and environmental factors. Though the traditional approaches have put forth the major wage determining factors, they are largely incomplete as they could not explain the pay mix differences among firms. The study considered a sample of 351 firms (116-owner-controlled, 149-manager-controlled and 86-owner-manager) for the period 1989-1992. The results revealed that though ownership structures bear no relationship to average pay per employee, changes in wage levels were linked to performance in owner-controlled firms while it was linked to firm-size changes in manager-controlled firms.

* Meng and Perkins (1998)

Different types of Chinese firms (State, Urban-collective, Rural township/Village owned & Private enterprises) differ in their earnings determination largely due to the differences in their ownership structures. This study, from the earnings determination perspective, looks into the impact of economic reform on the firm behavior by considering 288 firms for the years 1980, 1985, 1990-1992. Findings indicate that state owned and urban collective firms pursue income maximization while private enterprises seek to maximize on the profits front. State owned firms focus more on the short run welfare of their employees as they do not face problems on the financial fronts owing to their ownership structures. Unlike State owned firms, Collective firms give more weightage to productivity growth whilst distributing their profits. Hence, State owned firms and Collective firms are more labor-managed due to their pursuance of maximization of income per employee while private owned firms totally bank on profit maximization.

Objectives and Hypotheses

From the thoughts in the extant literature the study was carried out to empirically prove the authenticity of the arguments outlined in the body of literature related to this area. They are translated into the following objectives in the Indian context.

* To verify the age old proposition that employee compensation determines the output generated by the firm.

* To test the impact of size of a firm on labor productivity.

* To identify whether the ownership structure significantly impacts the relationship between output and input measures.

The aforesaid objectives are converted into the following hypotheses and their veracity tested using regression analysis. All these hypotheses were tested in high, medium and low category firms. Additionally the overall impact is also tested.

H1: There is a relationship between the net value added by the firm and the wages and salaries paid to its employees.

H2: There is a relationship between size of the firm and labor productivity of the firm.

H3: Ownership structure affects the relationship between net value added and wages and salaries paid the employees.

H4: Ownership structure affects the relationship between size of the firm and labor productivity of the firm.

Methodology

Data Collection and Sample selection: The initial sample included all the five hundred twenty listed companies in Indian textile industry (NIC code-17), provided by the CMIE database, Prowess. The study period was three years (2004-2006). Data was collected for total assets, wages and salaries and net value added for the study period. In addition to these measures the ownership information was also retrieved. The database returned the name of the group if a company belonged to group. All the other classifications were classified as individual firms. However companies were filtered out on the basis of the availability of data and the final sample size was trimmed down to three hundred forty.

Industry Classification: From the extant literature, assets were used as the basis of industry classification. Adopting assets as a measure of size has been practiced in previous researches also (Haveman, 1999). For the purpose of classification, assets were averaged out for the three year period (Haveman, 1999). These average figures were arranged in the descending order and thirty percent (102) of the companies falling in the top were classified as the high category firms. The next forty percent (136) formed the medium category and the rest 30percent (102) belonged to the low category. This is similar to the classification adopted by (Connor and Sehgal, 2001). Thus firms with average asset size of above one hundred twenty seven crores fell into high category, between 24.48 and 127 crores into medium category and less than 24.48 into the low category. The final classification based on the ownership status and categorization on size is given in Table I.

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Adopted Measures: The measures that were adopted for the study and their rationalization follows.

Size: The log of assets was considered. This was done not only to reduce the skewness in using the absolute figures but is also similar to the technique adopted by (Herrerea & Lora, 2005).

Output: Output of the firm is measured in terms of the net value added by the firm (Eilon, 1985).

Input: The input is measured in terms of the wages and salaries paid to the employees (Eilon, 1985).

Labor Productivity: It is measured as the ratio of output produced by the employees to the input put in by the employees (Eilon, 1985).

Statistical Tools: Regression analysis was the primary tool that was used for testing the hypotheses. Significance for the F-statistics was checked. The regression was deemed meaningful if the significance of the F statistic was less than .05. Whenever the F-statistic was greater than .05 regressions were deemed meaningless at 5percent level of significance. For testing the first two hypotheses the significance of partial regression coefficient was looked into. Only if they were significant at 5percent level of significance a relationship was established. For testing for the third and fourth hypotheses the partial regression coefficients were compared using simple t-tests. While running the regression for the first hypotheses NVA was taken as the dependent variable and wages as the independent variable. In the second hypotheses labor productivity was the dependent variable and size was the independent variable.

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Analysis and Interpretation

Output As a Function of Salaries: Regression was run on net value added on wages and salaries. As the idea was to find out the unit change in NVA with unit change in salaries absolute values in both the cases were considered. This was run separately on all the three categories (high, medium and low). The results of the regression analysis are summarized as follows.

The results show that wages and salaries have got significant impact on the net value added, in the industry as a whole and in each of the category separately. Almost forty percent of the variation in NVA is explained by the wages and salaries in the industry as a whole. In the Indian textile Industry the table clearly shows that with every rupee increase in wages and salaries, the NVA is increased by 1.7 rupee. The impact of wages and salaries on NVA is the highest when the industry is taken as a whole than taking each category separately. In the category wise analysis thirty percent of variation in NVA is explained in the high category, seventeen percent in medium and twenty three percent in the low category by the wages and salaries. The expected rise in NVA also differs with each category. Only in the high category a more than proportionate increase in NVA is shown. It is expected that in this category for every rupee rise in salaries and wages the NVA goes up by 1.6 rupee. In the other two categories for every rupee rise NVA goes up only by 0.4 rupee and 0.53 rupee respectively. However none of these results are counter intuitive. All assert the fact that increase in the wages and salaries increase the output (NVA) of the firm. The result could also imply that the companies in the high category are still in the increasing returns of scale side of the productivity graph. In contrast medium and low category firms are on the diminishing returns

side which means that with increase in input there is not a proportionate increase in the output. The industry as a whole, like the high category firms is experiencing an increasing returns to the scale trend. For the entire regression the problem of hetroskedacity was checked for and it was verified that errors do not follow any particular pattern.

Output As a Function of Salaries: The log model: In the earlier model the absolute values of the output (NVA) and the absolute values of the input (wages and salaries) were considered. There were drastic fluctuations among them and in order to accommodate for these fluctuations and log transformations were done on both NVA and wages. This not only reduced the fluctuations but also reduced the skewness. The fluctuation problem was more prominent when the industry was taken as whole when compared to the fluctuation within each of the categories. This is logical and the regression coefficient in this case should be interpreted with caution. As log was taken on both sides the regression coefficient actually returns the expected percentage change in the NVA with a unit percentage change in wages and salaries. The regression result is summarized below.

The results show that there is not a proportionate percentage increase in NVA with percentage increase in wages and salaries. The increase is less than proportionate. However the increase is highest in the low category and the least in the medium category. When the industry is taken on the whole for every percentage change in wages and salaries there is 0.82percent change in NVA. The results are however slightly deviant from the earlier observations. This discrepancy can be attributed to the companies that were neglected in this regression. The companies which had negative NVA were not considered in this regression as their log transformations are not meaningful. This attribution to the neglected companies is further corroborated by regression which was run on the absolute values after neglecting the same set of discarded companies. The regression coefficients obtained corresponded to the regression coefficient obtained in the log model. Here also results are totally intuitive establishing a positive relationship between salaries and NVA. This concretize the old notion that higher the salary higher the output. Hetroskedacity was checked for and its absence verified.

Relation Between Size and Labor productivity: From the literature it follows that there is a relation between the size and labor productivity. As mentioned earlier, labor productivity in this case is measured as the ratio between NVA and wages and salaries. This gives the rupee output produced for every rupee input. The idea was to compare between the regression coefficients obtained on running the regression of labor productivity on size. However log of size was taken in order to accommodate for the variation in size. The results of the regression are provided in the table below.

The regression result in this case was not as encouraging as the other regressions run. Though there is a significant relationship between size and labor productivity in the high category, it is absent in the other two categories. The regression as such is not significant in the medium and low categories. But the relationship is also significant when the industry is taken on the whole. In the high category for every percentage increase in size labor productivity climbs up by almost 6 units. For the industry on the whole for every percentage rise in size the labor productivity climbs up by 1.8 units. This shows a strong association between the two and establish that bigger the company higher the labor productivity. Also the relationship between the two increases while moving up from the low category to the high category. In the high category sixteen percent of the variance in labor productivity can be accounted to the size factor. Further, in the industry it explains three percent of the variance. This is however significant and can be deemed pretty high on considering the sample size of three hundred forty. The reason could be that firms grow in size with time and the learning curve and experience curve comes into picture, thus increasing the labor productivity. As with other regression, the problem of hetroskedacity was not present in this case either.

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The Link With Ownership Structure: The idea of third and fourth hypotheses were to check for differences in the relationships between NVA and wages and salaries and labor productivity and size between the group and stand alone companies. Results are summarized as follows. Both of them were tested for hetroskedacity and were found absent.

There is a strong association between NVA and wages in the group companies. The association is pronounced in all the three categories and when taken as a whole. The coefficients are also positive. This just reinforces our earlier findings, established through the earlier regressions. In the high category group companies NVA increases by 1.85 units when there is a unit increase in salaries. However in the other two categories NVA increase only by less than a unit for unit increase in the wages. Overall among the group companies NVA grows by 1.8 units with unit rise in the wages. In the individual companies the relationship is not strong in the high category. In the low category NVA shoots up by 0.5 units and in medium category it increase by .19 units for unit rise in wages. When the individual companies are taken on the whole, however there is an increase of 1.6 units. The variation in NVA is explained to a very high extent by the wages and salaries in the group companies. On the whole it explains 76percent of the variation in NVA among the group companies. Maximum variation is explained in the medium group companies, where 81percent of the variance can be attributed to the wages and salary. But wages and salaries can explain only slightly more than 4percent of the variance in NVA of the individual companies. The possible reason could be the reputation the group companies are enjoying. Especially in the Indian context where family owned business groups own a major chunk of business, the impact is more pronounced. The employees feel more secure to be a part of a business group and an increase in wages act as booster doses. However employees feel insecure while working as a part of stand alone unit, in comparison to a group firm. Thus, the insecurity might reduce the expected increase in productivity. Employees might view the hike in salary with apprehension. They might perceive the hike as a tactic adopted by the company to retain the employees when the company is actually undergoing through a recession or slowdown period. The relation deteriorates while moving from low category to high category firms in the individual company. This could be an indication that the individual management owners are not able to properly manage the growth in size and hence are not able to convert their inputs into outputs in an efficient manner as they earlier used to do, when small. To express it in very crude form it means that managers in individual firms find it difficult to manage the growth in size of the firm. Further in the high category the regression coefficients are significantly different and hence prove that ownership structure determines the relationship in the high category. In all the other three cases also (low, medium and overall) t-test revealed that the coefficients were significantly different. Hence we conclude that ownership structure impacts the relationship between NVA and wages and salaries.

In the group companies the relation between size and labor productivity is totally absent and it is evident in the individual companies taken on a whole and also in the high category individual companies. But in these two the impact is very high. In the high individual companies for a percentage change in the size of the company the labor productivity goes up by a whopping 14.55 units and when taken together the productivity goes up by 3.4 units for percent increase in size. The reason for the absence of such a connection could be the same reputation the group firms are enjoying. Irresespective of their size, they enjoy repute and hence there is no reason for productivity to increase with size. In case of individual companies this could mean that with increase in size the capability of management to extract the most out of its employees increase. Overall it can be said that there is a difference exists in the impact of size on productivity between group and individual firms. However category wise analysis reveals that it is not a function of the ownership structure in the medium and low categories. It is different in the high category through.

Results, Findings and Conclusion

The study like many of its preceding studies found out that wages and salaries affects the output (NVA) of a firm. It is generally agreed upon that compensation package is the single largest motivator for the employees. As a super imposition of this notion a positive relation is established between the output and salaries and wages. This aspect cuts across the industry, present in high, medium and small firms alike. However the expected increase in output is more than proportionate increase in salary in the high category. This could possibly imply the economies of scale effect or the learning curve effect. The inability of the firms to produce a proportionate increase in its output can be attributed to the lack of resources. Though the impact varies with size the study has clearly elucidated the positive relationship. With regard to the Indian textile industry which is labor intensive, the study provides valuable insights. It can be taken for granted that the low wage structure existing in the industry is the cause of slackened performance in many of the firms. It could further be weaved from the study that size of a firm has not got significant impacts on the labor productivity of the firm. But however among the large firms it significantly affects the labor productivity. In the low category and medium category firms this could be absent because of the lack of mechanization. Labor productivity though measured as the ratio between output to wages and salaries, it should be bore in mind that the output is not a function of the labor alone. It logically follows that as companies grow in size the level of mechanization would be high. Especially in the study as industry was classified based on asset size, the argument is further substantiated. Thus to gain a true picture, the output or proportion of the output that is produced by the labor force is to be identified. This portion should be weighed against the wages and salaries. In large size firms even if the labor is into diminishing returns state the productivity of the capital goods and machines might be so high to cover up and offset for this. This particular phenomenon could have inflated the labor productivity, which was measured on the basis of the total output. In the medium and low category firms where the output is more a function of the labor than the capital goods, the relationship is not evident. Hence this could be closer to reality.

The study also proved that ownership structure determines the strength and association of relationship between NVA and wages and salaries. The relationship is more pronounced in case of the group firms and this could be attributed to the reputation of the group firms as mentioned earlier. The effect is the maximum in the high category firms where increases in salary produce a sharp increase in the output of the firm. The effect is not protruding in case individual companies due to the apprehension in the minds of the employees. The hike in salary might be viewed in suspicion when compared to the group counterparts in the industry. However the reason for this weak relationship and the argument proposed in the study should be further verified by the future researches. On the whole though the relation between size and labor productivity is a function of the ownership structure, this is not particularly visible in all the size categories. In the medium and small category firms the association between size and labor productivity is irrespective of the ownership structure.

The study has opened doors to various new streams of research. It should be verified by the future researches that the findings of the study are applicable only to the particular industry or it can be extended to other industries too. Also the study adopted a quantitative approach and arrived at the findings. Future qualitative researches can inspect the reasons for the results obtained in the study like reason why the impact of wages and salaries on output of a group firm different from an individual firm, whether the reputation dimension of the group firm the root cause for this etc. The study has substantiated the common feeling that compensation package affects the output. These days as the companies have stared focusing more perks and other benefits, future researches can look into the impact of such perks on the output produced by the firm. A comparative study can also look into the difference in impact, the underlying reasons for the same etc. Though the study was carried out with diligence as far as possible it is bound by certain limitations. The greatest of them being the output measure. The output measure is not a function of the labor alone and it includes several other factors too. Thus it actually gives the impact of labor productivity when considered together with a host of several other factors. It can however be argued that separating out the portion of the output that is attributable only to the labor is near to impossible. Labor productivity is not to be measured in isolation as several factors work in harmony to obtain the desired output of a firm. Nevertheless, improving labor productivity has become an imperative for firms for long term sustenance. Knowledge organizations are affected the most by it.

References

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Xin, Meng; Frances, Perkins. Wage Determination Differences between Chinese State and Non-State Firms Asian Economic Journal, Sep98, Vol. 12 Issue 3, p295, 22p.

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Eldos Mathew Punnoose

Consultant and Visiting Faculty

Kaliserry, Chengannur, Kerala.

Madhuri Modekurti

Faculty (HR)

Manipal Universal Learning,

Ameerpet, Hyderabad.

Table I: Firm Classification Based on Averaqe Asset Size

 High Medium Low Total

Group 67 43 14 124
Individual 35 93 88 216
Total 102 136 102 340

Table II: Relation Between NVA and Wages and Salaries

 [R.sup.2] F statistic Significance

High 0.3086 44.64 <.001
Medium 0.1671 26.89 <.001
Low 0.2359 30.88 <.001
Overall 0.3938 219.6 <.001

 Partial Significance
 Regression
 coefficient

High 1.60469 <.001
Medium 0.39832 <.001
Low 0.53860 <.0001
Overall 1.69562 <.001

Table III: Relation Between NVA and
Wages and Salaries (The Log Model)

 [R.sup.2] F statistic Significance

High 0.2913 36.17 <0.001
Medium 0.1913 28.13 <0.001
Low 0.7131 156.59 <0.001
Overall 0.6357 467.65 <0.001

 Partial Significance
 Regression
 coefficient

High 0.52838 <0.001
Medium 0.37301 <0.001
Low 0.89635 <0.001
Overall 0.82787 <0.001

Table IV: Relation Between Firm Size and Labor Productivity

 [R.sup.2] F statistic Significance

High 0.162 19.34 <0.0001
Medium 0.0030 0.40 0.5296
Low 0.0008 0.08 0.7759
Overall 0.0311 10.84 0.0011

 Partial Significance
 Regression
 coefficient

High 5.98441 <0.0001
Medium 0.01379 0.5296
Low 0.85640 0.7759
Overall 1.79792 0.0011

Table V: Relation Between NVA and Wages and Salaries

 [R.sup.2] F statistic Significance

High Group 0.7439 188.80 <.0001
 Individual 0 0.00 0.9876

Medium Group 0.6324 70.53 <.0001
 Individual .0496 4.75 0.0319

Low Group 0.8191 54.34 <.0001
 Individual 0.2262 25.14 <.0001

Overall Group 0.7608 387.93 <.0001
 Individual .0404 9.02 0.0030

 Partial Significance
 Regression
 coefficient

High Group 1.84388 <.0001
 Individual 0.01784 0.9876

Medium Group 0.95926 <.0001
 Individual 0.19174 0.0319

Low Group 1.35547 <.0001
 Individual 0.52363 <.0001

Overall Group 1.79529 <.0001
 Individual 1.06096 .0030

Table VI: Relation Between Size and
Labor Productivity

 [R.sup.2] F statistic Significance

High Group .0127 0.84 0.3637
 Individual .5760 44.83 <.0001

Medium Group .0069 0.28 0.5966
 Individual .0037 0.33 0.5644

Low Group .0039 0.05 0.8329
 Individual .0033 0.28 0.5964

Overall Group .005 0.61 0.4372
 Individual .056 12.69 0.0005

 Partial Significance
 Regression
 coefficient

High Group 1.27337 0.3637
 Individual 14.55244 <.0001

Medium Group -0.60337 0.5966
 Individual 1.07608 0.5644

Low Group -0.07865 0.8329
 Individual 2.21249 0.5964

Overall Group 0.29709 0.4372
 Individual 3.38798 .0005