1

E-commerce Impact on Iranian Manufacturing SMEs Employment

Sarvenaz Hojabr Kiani and Elsadig Musa Ahmed

Faculty of Business, Multimedia University, Malaysia

E-mails: sarvenaz777@gmail.com, elsadigmusa@yahoo.com

Sarvenaz Hojabr Kiani and Elsadig Musa Ahmed

Faculty of Business and Law, Multimedia University, Malaysia

E-mails: sarvenaz777@gmail.com, elsadigmusa@yahoo.com

Abstract

This study examines the impact of E-commerce (EC) on employment of Iranian manufacturing

SMEs by using wage, price of capital and EC as explanatory or independent variables and

employment as dependent variable. Price of capital is calculated to be use in the model (Piva and

Vivarelli, 2002; April and Pather, 2008).This study uses two years panel data (2006-07) from the

secondary data available in Statistical Center of Iran .The Stratified sampling applied as a

sampling method to select optimal sample for the regression analysis using Eviews software. The

model has six EC measurements including; number of employees using computer, number of

employees using internet, using internet to gather and offer information, e-buying and e-selling.

All of the measures of EC had positive impacts on employment indicated by highly significant

coefficients of EC.

Key words: E-commerce, Employment, Panel data, SMEs

INTRODUCTION

Globalization of SMEs through EC and their rapid changes in generating technology make new

opportunities for businesses (Love et al., 2005). This is on the situation that Iranian SMEs also

tried to adopt EC during previous years. Iran’s economy as one of the largest economies in the

Middle East although relies on oil resources, its macroeconomic performance benefits from non-

oil sector during last years’. On the other hand, in labor market, increase in unemployment is

2

posing a challenge for many developed and developing countries and Iran is no exception to this

general trend. The job shortage encourages many young skilled Iranians to leave the country

each year in the search for better employment opportunities abroad (ECO, 2009/2010; UNIDO,

2003).Due to the regional strategic importance of Iran in middle-east, adaptation and using EC

could make more benefits for Iranian SMEs and effect their employment.

In developing countries, SMEs increasingly try to use technology as effective factor and

strategy to reach the international markets (Barsauskas et.al, 2008) and EC has a great potential

for growth simulation, cost reduction, and job creation (Singh, 2008). SMEs have critical role in

countries economy including developing countries. SMEs are contributing in economic growth;

social structure and employment so they are becoming important in economical environment.

Moving through globalization and new technologies like internet and EC can create new

opportunities for SMEs (Scupola, 2001).There are several studies who have noted that EC can

bring about advantages such as reduction of cost (Poon & Swatman 1997, Quayle 2002) or even

increase competitiveness (Vescovi, 2000) and reduction of lead-time (Quayle, 2002). Some

researchers claim that EC can reduce inventory overheads and supply real-time to SMEs

(Reynolds, 2000).

According to Mohamad et al., (2009), most of the studies on developing countries are

based on upstream issues which are e-readiness, adoption, and diffusion (e.g. Abbasi, 2007;

Ghorishi, 2009; Sanayei e al., 2009) yet there are limited reported studies and researches on

downstream aspect of EC, which is impact. Although, there are some studies (e.g., Singh, 2008)

based on EC; the lack of concern on quantitative approaches is visible. Therefore, there is a gap

between empirical and theoretical studies on downstream issues on EC in developing countries,

like Iran.

In most of the studies of estimating derived demand for labor to measure the impact of

EC or ICT on employment, one of the independent variables is the price of capital. Usually,

researchers use interest rate as a proxy for this variable but because Iran is one of the Islamic

countries where interest rate is illegal, the government uses savings profit instead. But for the

reason that will become clear, for cross-sectional or short panel studies like the present study,

3

this is not a good proxy and may not lead to accurate results. Therefore, this study will try to

estimate the price of capital by using some theoretical issues and information which could be

useful data for both SMEs and government researches.

From the above mentioned problems and backgrounds based on the past studies it has

been found that there is a gap between empirical and theoretical studies on downstream issues

and lack of quantitative and comprehensive analysis of the impact of EC on SMEs contribution

to Iranian employment. Therefore, the purpose of this paper is to study the impact of EC on

employment in Iranian SMEs. The paper organized as follows: next section gives a brief review

of literature. In section 3, methodology and models are explained. In section 4, data collection

and estimation procedures are described. Section 5, includes results and discussion. Finally, the

last section is devoted to the conclusion of the paper.

Literature review

The New Economy which is in its broad concept meant as the spreading of ICT (and its other

subsets like the EC) and especially the Internet, in economic activities, is changing the labor

market. Employment is an agreement between two groups where one of them is employer and

the other is employee. An employee is someone who is in the service of the other over the

contract of express or implied, hire, written or oral. The employee has the authority to manage

the employee and the working way (Balck’s Law Dictionary, Wikipedia).

Development of ICT by providing technological revolution makes new ways of

communication between enterprises and customers. This shift labor demand employees with a

complementary knowledge of technology and looking forward to adopt which may create new

job opportunities (Mokyr, 1999). This new technology by offering lower cost of transferring

information, ease of communication and doing transaction over the Internet cause the process of

searching job and employment activities to shift to network. The new technology is creating a

labor market along side with significant changes such as, permanent increase in labor force

knowledge, employment in service sector and women’s employment, which is much more

different from the industrial labor market shaped in 20th century. Employment and searching job

via Internet have three potential benefits in economy: lowering transactional cost, more rapid

clearance of market and matching the employees searching for job with job opportunities. Most

4

of the companies pay more attention to the labor force as source of knowledge instead of

physical labor, which yet stay as a base of social reproduction and economic production

(Alasoini, 2001).

The estimates of Alan Krueger showed that in 2000, eight large employment agencies in

the U.S with 1.8 million job seekers, received $98 on average for finding a job. In contrast, 8

famous newspapers, with 1 million readers on Saturdays, received 3840 for 30 days

advertisement, Monster. Com with 3.9 million visitors in 2000 received $ 138, while at the same

time the New York Times with 1.8 million readers on Saturdays received $4500. Other estimates

showed that the cost of finding a job over Internet is one fifth compared with seeking job in

newspapers. All of these estimates confirm that cost of finding job in new environment is lower.

The result of the research by Peter Kuhn (2001) presents that there is no significant difference

between finding job over Internet and other ways of finding job in US. This may consider as the

low rate of frictional unemployment in the U.S whereas, jobless people can find job more rapidly

(Freeman, 2002).

The ICT revolution makes a phenomenon known as Skill Biased Technological Change

(SBTC), where technological change results in a greater demand for educated and highly skilled

labor. Increasing the relative wages of these workers and shifts in the composition on the

workforce in favor of that workers will be followed (OECD, 2003).

Górriz and Castel (2010) referred to some other authors and noted that; SBTC, analyses

how new technologies cause a bias concerning more skilled workers. It also produces an increase

in the demand for skilled workers, while skilled workers are needed to use the new technologies

perfectly. This study also added that, if the new skills are more costly to obtain rather than the

needed operating by old machinery then a revolution will be biased in favor of skilled workers

(Skill-Biased). It also will favor de-qualification (De-Skilling) on the time that new skills acquire

at a fewer cost than the knowledge or skills related to preexisting technologies.

As a result SBTC believe that technological change favors one class of workers (such as

highly educated workers) at the expense of another class of workers. In knowledge based

companies who struggle to produce product innovations; there is a nonstop demand for

5

professional skills come together with international skills (like language skills, digital literacy

and the ability to work in an environment), which needs the use of ICT (Alasoini, 2001).

Kaushlesh (2004) had a research on giant Indian companies and found that by

establishing new technologies like ICT in these companies, the number of educated and expert

employees had a remarkable increase. Moreover, this matter created numbers of indirect jobs

which were depended on the size and the production. Large-scale enterprises in many developing

countries were not able to be successful in job-generating economic growth. Acs et al. (1996)

believes that SMEs are able to hire a large number of human capital and beside being capable for

creating new job opportunities at low cost, they are mostly labor intensive. SMEs could be a

good source of innovation because they are more flexible, dynamic and sensitive to shift of

demand. Regarding economic performance of European Union member countries, approximately

99.8% of enterprises are SMEs with 93% micro enterprises. It is obvious that SMEs’

employment growth is more than large frame. In 2001 EU commission declared that in Germany

and USA, SMEs had same result.

April and Pather (2008) referred to (Lui and Arnett, 2000; Stansfield and Grant, 2003)

who suggest that there is a difference between small and large companies in terms of EC

applications. According to Beck et al. (2003), because SMEs are more labor intensive, their

expansion boots employment growth more than large firm. Empirical evidences reveal that

although small firms have higher innovation rate, larger enterprises presents higher salaries,

stable employment and even non-wage profits. In countries with advanced education and

developed financial sectors, SMEs have higher share of employment. Yet, in countries with

higher inflation, trade and exchange rate distortions, share of employment is lower. SMEs’ share

of employment in total employment is related with higher rates of GDP growth which is their

OLS1 results (Beckinsale et al., 2004).

Theoretically it is very difficult to study the impact of the innovation and technological

changes on employment. Despite the rapid increase of information ICT in most of the developed

countries, however the impact of the ICT changes, on unemployment is ambiguous, and has been

a matter of controversy among economic analysts.

1 Ordinary Least Square

6

If ICT or EC take place, process and product innovations, affect employment

(unemployment) through two different ways. On the one hand, the decrease in demand for labor

force i.e. increase unemployment (displacement effects) and on the other hand, the creation of

new employment opportunities and hence need for more labor force (compensation effects) act

in a counteracting way. From the macroeconomic point of view, direct effect of process

innovation on labor saving should be compared with the two effects of product innovation. One

of the effects of product innovation is the labor intensity’s effect of product innovation and the

other is the stabilizer effects and price and income mechanisms that act at firms, sectoral or inter-

industries levels. The latter technological changes lowers prices, increases income (profit and

wage), and decreases unemployment (increases employment). To put in other words, for a given

level of output, process innovations that results on improvement in productivity will decrease

employment which is one of the productive factors. On the other hand, for a given level of output,

improved productivity increases demand and output due to decrease in final goods costs and

prices. The result of increase in demand and output is higher demand for labor. The first effect,

i.e., the negative relation between innovations and employment as a result of process innovation

is called displacement effect. The second effect of process innovation i.e., the positive relation

between innovations and employment is called compensation effect. Overall, since it is not clear

that displacement effect outweighs compensation effect or not, the net impact of process

innovation on employment is uncertain. But product innovation creates new products; therefore,

it has only compensation effect that leads to positive relation between innovation and

employment. For a detailed discussion on the macroeconomic relations refer to (Katsoulacos,

(1984-86); Vivarelli, 1995; Piva and Vivarelli, 2002).

The point that should be considered here is that empirical micro level results can-not

show all the macro effects of the innovation. Therefore, one cannot generalize the econometric

results obtained from micro levels. Micro evidence based on econometrics reveals the direct

effect of labor saving resulted from innovation, but shows only part of the compensation effects,

mentioned above. As a result, it is possible, that the result of empirical studies at micro level

even show a positive effect on unemployment (negative effect on employment). Consequently, it

is possible to obtain a reveres effect, by changing the level of study from micro to macro. As a

matter of fact, in order to separate the sectrol and total impact of the e-commerce as a part of the

7

ICT, on unemployment (employment), the studies should be done at different levels of

aggregation, firms, industry, sector and total, both theoretically and empirically.

For the empirical study, usually, labor input demand (employment) is derived from profit

maximization or cost minimization. For especial form of logarithmic translog production

function, which is, a function with Constant Elasticity of Substitution (CES) which is most

common choice in empirical studies on the impact of ICT or EC on employment. (See:Van

Reenen, 1997; Chennells and Van Rennen, 1999; Piva and Vivarelli, 2005), mathematical form

would be:

[ ] 1//1/)1( )()( −−− +=δδδδδδ BKANTVA (1)

Where: K is capital stock, N is labor force, VA is value added as a proxy for output, T is

parameter for neutral technology, A is parameter of technology for labor augmenting technology,

B is capital augmenting technology and δ is the elasticity of substitution. By maximizing the

profit, the labor demand equation (one of the derivatives of profit equation) obtains as below:

LnApWLnLnVALnN )1()( −+−= δδ (2)

Where: W is wage, P is the price of the product, and Ln is the natural logarithm.

However, concerning the effect of ICT or EC on employment some other researchers use

cost minimization (e.g. Matteucci and Sterlacchini, 2003) and derive demand for labor as a

function of interest rate (proxy for price of capital), wage (price of labor), output (or value-added)

and the measure of ICT or EC. It is important to note that if cost minimization is used, the price

of capital will be substituted for the price of output, P, in equation (2).If a disturbance or error

term add to the equation (2), the stochastic regression equation of demand for labor input can be

obtain. One of the main problems arising in such regression model is the correlation between

error term and independent variables. If a firm benefits from a capable manager, then he may use

employees with high knowledge alongside with high quality technology that implies the

correlation mentioned above. In the process of solving such a problem, one has to apply panel

data (Chennells and Reenen, 1999, P.18). One of the reasons for using panel data for Iranian

manufacturing SMEs can be justified by the above fact.

8

Measuring technology is another important issue which is very difficult. The traditional

way of solving such problem has been the use of trend variable. The main problem in applying

the trend is that it contains other effects such as changes in prices, changes in demand conditions,

cost shocks and etc.

Analysts presented other measures for technology estimation like the volume of research

and development (R&D) and the diffusion measures such as computer. It seems that the most

acceptable approach is constructing a new variable of capital stock. In connection with

measuring skill; the labor force can be divided in to production and non-production workers.

Another way is to use the knowledge-based measures. Some of the researchers have categorized

the employees in accordance with their functions (Wolff, 1997-2011).

Atrostic and Nguyen (2002) argue that higher amount of skilled labor could cause higher

productivity due to their ability to use, maintain and developed, use advanced technologies. They

believe that employees as well as managers need the right skills to work with modern technology

but there is a risk of their failure to make it. At the theoretical level, EC can reduce different

coordination costs of the different work processes. They improve labor productivity by assisting

enterprises to divide their tasks and simultaneously when the routine tasks automated, EC can

reduce unskilled works as well (Adekolad and Sergi, 2007, 2008).

Greenan and Guellac (1996) found that, at the firm level, process innovation has a strong

negative effect on employment, but this effect is faded away at industry level. Also, the direction

of the production innovation’s effect is acceptable in firm and industry. This study has been

conducted over 15186 French industrial firms. According to Singh (2008), Internet and EC have

essential impact on work, workers and even workplace. It may generate better employment

opportunities particularly for developing countries both through improved labor facilitation and

direct employment. Researchers showed EC activities can increase employment needs for

workers involved in EC systems and website design. Vera (2002) examined EC impact on B2C

on Philippine workers and discovered that EC can create almost twenty percent additional jobs.

Therefore, EC economy has huge potential to create employment.

9

There are many empirical studies in connection with the relationship between

employment (unemployment) and technology. A few number of studies based on econometric

methods exist at industry level, but there are various studies conducted at firm level. In general, a

negative (positive) relationship between unemployment (employment) and the proxies of

production innovations measurement is observed. In Entrof and Pohlmeir (1990) study, the

negative effect of production innovation on unemployment (positive effect on employment) has

been obtained, by using the cross sectional data from 2276 West German firms in 1984 and a

dummy variable for innovation. Smolny (1998) verified this result by using panel data from 2276

West German enterprises from 1980 – 1992. Brouwer et al. (1993) found a negative relationship

between total costs of R & D and employment, using the cross-sectional data from 859 East

German industrial firms, while the result had been inversed by using only production innovation

variable.

Doms et al. (1997) found that, in US those industrial factories between (1987-1991) using

high technology have high employment. Klette and Forre (1998) estimated that there is no

relationship between employment and R&D opportunities. This study has used data from 4333

Norwegian manufacturing industries. Blanchflower et al. (1991) have obtained a positive and

significant effect of microelectronic technologies on employment, using data from 948 firms.

Blanchflower and Burgess (1998) have found a positive relationship between employment and

innovation which is measured by dummy variable and with having panel data in British and

Australian firms.

Benavente and Lauterbach (2007) proposed an empirical study at firm level for the period

1998-2001 for Chilean firms. The aim of the study was to consider the impacts of both product

and process innovation. The results of study indicated that, although the product innovation had

positive effect on employment the impact of process innovation was not significant.

In summary, in most cases, a negative (positive) relationship between unemployment

(employment) and the proxies of production innovations measurement is observed (e.g. Koenig

and et al., 1995; Entrof and pohlmeir, 1991 on German firms; Leo and Steiner, 1994 on

Australian firms; Van Reener , 1997 on British firms). The results on the process innovations are

ambiguous (See: Blanch flower and Burgess (1997) on Australian and British factories,

10

Blechinger et al. (1998) on all German firms). To summarize the results of empirical studies, it

can be said that, there are no any quite clear empirical evidence on the relationship between

technology and unemployment (employment).

Methodology and Models

Concerning the model, dependent variable is employment and value-added, wage, price of

capital and EC measures (number of employees using Internet, number of employees using

computer, using Internet to gather information, using Internet to offer information, e-buying, e-

selling) are independent variables.

As it was mentioned before, using either cost-minimization or profit-maximization by

employing Constant Elasticity of Substitution (CES) or any other production function labor

demand could be derived. Since in cost-minimization approach perfect competition is not

required (but is required in profit-maximization) and also due to availability data for variables at

firm-level, i.e. Iranian SMEs, this study takes this approach. Following Matteucci and

Sterlacchini (2003), the final version of labor demand for estimation, in logarithmic form would

in two forms of numerical and dummy variables.

When EC is numerical measure, appears in logarithmic form:

lnLit = α0 + α1lnVAit + α2lnWit + α3lnRit + α4 ln ECit + Uit (3)

i=1, 2… 378 t=1, 2 Where: L is Employment VA is Value-Added W is Wage R is Price of Capital EC is numerical measure of EC (number of employee using Internet, number

of employee using computer) Ln is natural logarithm U is error term (i.e. disturbance)

11

α1,α2,α3,α4 are elasticities

When EC is dummy variable, does not appear in logarithmic form:

lnLit = α0 + α1lnVAit + α2lnWit + α3lnRit + α4ECit + Uit

(4)

i= 1, 2… 378 t= 1, 2

Where:

L is Employment VA is Value-Added W is Wage R is Price of Capital EC is dummy variable Takes value 1 for the firms using Internet to gather information Takes value 0 for the firms do not use Internet to gather information Takes value 1 for the firms using Internet to offer information Takes value 0 for the firms do not use Internet to offer information Takes value 1 for the firms having e-buying

Takes value 0 for the firms not having e-buying Takes value 1 for the firms having e-selling Takes value 1 for the firms not having e-selling α1,α2,α3 are elastisities and α4 is coefficient of dummy variable

It is important to note that, the coefficients of qualitative (i.e. Dummy) variables

measures of EC, like the quantitative variables measures in the above models, show the impacts

of EC on employment. To clarify this point, the interpretation of dummy variable coefficient

explained in almost all of the econometrics text books including Baltagi (2011, pp.81-84),

Gujarati and Porter (2009, pp. 277-290) and Woolridge (2006, pp.230-270) can be refer.

Calculation of Price of Capital

As it was mentioned earlier, the price of capital at firm level is not estimated in many countries

including Iran. One of the novelties of this paper is that of calculating the price of capital for

Iranian SMEs. To do this, the present study assumes that production function is Cobb-Douglas

12

and exhibits constant returns to scale i.e. . Accordingly, the shares of capital and

labor from output (in a perfect competition environment) are equal to corresponding elasticities,

α and (1-α), that is:

WLQ

= α (5)

𝑅𝐾𝑄

= 1 − 𝛼 (6)

Since the data for WL the payments to L (the labor) and Q (the output) for SMEs are

available, using (5), α can be calculated. On the other hand, with known α, Q and K that is not

estimated for Iranian SMEs and will be estimated using method explained in the following

section, price of capital can be estimated using (6) as follows:

R = (1−α )QK

(7)

Estimation of Capital Stock

According to the investment theories the equation is as below:

IG = IN + D (8)

Where: IG is Gross Investment IN is Net investment D is Depreciation

In most of the developing countries like Iran IG is anticipated but not D. Mostly

calculation of depreciation and the rate of depreciation (rate of the loss of investment) is too

difficult due to lack of relevant data. Therefore, facing unknown item D, calculation of the net

investment is impossible. As a result, since the investment theories are based on net investment

and depreciation, they are not applicable for calculating capital stock (K) in this paper.

13

There are approaches like, Hojabr Kiani and Boghozian (1997) or PIM (Perpetual

Inventory Method) in Iran that are used to estimate capital stock in aggregate levels such as the

whole country, main sectors or small group industries. But the necessary information or data for

PIM method do not exist at firm-level and Hojabr Kiani and Boghozian approach is based on

search method and many functional forms thus requires intolerable calculations for enormous

numbers of firms. For these reasons there are serious problems in calculation of K for Iranian

SMEs or firms, using these approaches. The following is a suggested new approach for rough

estimation of capital stock at firm level for Iranian SMEs, which can be used for other

developing countries as well. According to the acceleration theory of investment:

IG = ∆Kt + λKt−1 (9)

Where, IG is Gross Investment, ∆Kt is growth of capital stock which is equal to net

investment,IN and λ is the rate of depreciation (i.e. depreciation, D, is equal toλKt-1).

If for a period of time gross investment,IG does not fluctuate much (i.e. the condition of

relatively stable period), there is an assumption that the capital output ratio is fixed. This

assumption is one of the basic assumptions in economic growth theories, which it seems to be

fairly reasonable in this study, because at least for short period in past the trend of gross

investment in Iranian SMEs have been relatively stable. Therefore, the capital-output ratio,KQ

assume to be constant α. Given this assumption, and substituting for K in equation (9) we have:

IG = αQt − αQt−1 + α λQt−1 (10)

Or:

IG = αQt + α(λ − 1)Qt−1 (11)

If equation (11) estimate as a multiple regression equation without the intercept, the estimated

value of α can be used to calculate series of,Kt the capital stock of Iranian SMEs using

𝐾𝑡𝑄𝑡

= 𝛼.

Data collection and estimation procedure

14

The data used in this study are secondary data developed by Statistical Center of Iran (SCI)

based on survey conducted by this department for the period of 2006 and 20072. In this study

population is Iranian Small and Medium Enterprises (SMEs) among manufacturing industry

firms. Following the definition of Statistical Center of Iran (SCI), firms among 10 – 100

employees are the target SMEs of this research. Since the questionnaires among approximately

12,000 firms have been distributed by statistical center of Iran and the raw data is available for

two years, this study used appropriate sampling method to select the sample. This data consist of

Gross Value of Output, total number of employed persons as well as number of skilled and

unskilled labor which is considered to be a good measure of human capital as the fact is that

most of those workers in these companies are family owners who do not receive regular salaries.

This data also includes total number of SMEs, EC facilities, wages and salaries.

Although a panel of two years is a short panel, the following studies could justify the

usage of such a short panel (Atrostic and Nguyen (2002, 2 years panel), Maliranta and Rouvinen

(2003, 3 years panel), Maliranta and Rouvinen (2006, 2 years panel), Criscuolo and Waldron

(2003, 2 years panel), Farooqui (2005, 4 years panel), Gujarati and Porter (2009, 2 years panel),

Wooldridge (2002, 2006,2 years panel)).

This study uses one of the probability type sampling methods which is Stratified

Sampling. In a stratified random sample, first the population divides into subpopulations, which

is called strata. Then, one sample is selected from each of these strata. The collection of all

samples from all strata gives the stratified random sample. (Mann, 1998)

One type of panel model has constant coefficients, referring to both intercepts and slopes.

If, in this research, there are neither significant SME effects nor time effects, we can pool all of

the data and run an Ordinary Least Squares (OLS) regression model. This model is called pooled

regression or common effects model.

Another type of panel model would have constant slopes but intercepts that differ

according to the cross-sectional (group) unit- for example, the SMEs. While the intercept in

cross-section (group) specific, for example, form SME to SME in this study differs, it may or

2 Note that only this two year data for EC measures is available from SCI.

15

may not differ over time. These models are called Fixed Effects Models (FEM), due to the fact

that the differences are fixed and not random. Still there exists another type of fixed effects

model which could have constant slopes but intercepts vary over time. In this case, the model

would have no significant SME differences but might have different time effects. There is

another fixed effects panel model where the slope coefficients are constant, but the intercept

varies over SME as well as time. Another type of fixed effects model has differential intercepts

and slopes. This kind of model has intercepts and slopes that both vary according to the SME.

There is also fixed effects panel model in which both intercepts and slopes might vary according

to SME and time.

All of the fixed effects models can be formulated using dummy variables. For this reason

the models of this kind are called Least Squares Dummy Variable (LSDV) model. The number

of dummy variables should be one less than the number of cross-sectional units, i.e., n-1, “to

avoid falling into the dummy-variable trap (i.e., the situation of perfect collinearity)”. (Gujarati

and porter, 2009, p.597)

The first step in estimating panel data models is to choose between pooled and fixed

effects models. To do this restricted F (i.e. Leamer F statistics) test is used. (Gujarati and porter,

2009; Baltagi, 2005; Greene, 2003; Gujarati, 2003)

𝐹 = 𝑅𝑅𝑆𝑆−𝑈𝑅𝑆𝑆

𝑁−1�𝑈𝑆𝑆𝑅

𝑁𝑇−𝑁−𝐾� (12)

“This is a simple Chow test with the restricted residual sum of squares (RRSS) being that of OLS

on the pooled model and the unrestricted residual sum of squares (URSS) being that of the

LSDV regression” (Baltagi, 2005, p. 13) .In (12), N is the number of cross-sectional units, T is

number of times and K is number of independent variables.

One problem with LSDV approach arises when we have too many cross- sectional units,

which is the case in this study due to the large number of SMEs. In this case, model would have

too many dummy variables which will produce multicollinearity and also degree of freedom of

model will be reduced significantly. To overcome this problem, one can eliminate dummy

16

variables by differencing sample observations around their sample means. This approach

produces what is called within group estimators. (Gujarati and Porter, 2009; Greene, 2003;

Baltagi 2005, 2008) Another approach is to use the first difference of variables in both sides of

regression equation. This is called first difference method. Since in this research we have a short

panel with only two time periods, it is very important to state the following:

“It may be pointed out that the first difference and fixed effects estimators are the same

when we have only two time periods, but if there are more than two periods, these estimators

differ” (Gujarati and Porter, 2009, p.602) .To support the above statement, Gujarati and Porter

(2009) claim that “the reasons for this are rather involved” and refer to Wooldridge (2002).

There is another model called random effect model (REM). Here, the difference in

intercepts 3(or slopes), are random rather than being fixed. As mentioned before, the first step in

panel data regression analysis is to choose between pooled regression model and FEM using

Leamer F statistic. Now if one chose FEM, the second step is to select between FEM and REM.

Hausman test, which is very popular and explained in detail in many econometrics text books

including Greene (2003), Davidson and Mackinnon (2004), Baltagi(2005,2008), could be used to

choose between FEM and REM. To use a time series for prediction, assumption of stationary for

variables is required. A variable is stationary if it’s mean, variance and covariance does not

change over time, and i.e. the main characteristics are stable. If variables of time series model

are non-stationary i.e. have unit-root, then the usual t, F and 𝑅2 are not valid. In this case

probability of having spurious (non-sense) regression is high i.e. we may conclude there is

relation between unrelated variables. But there are some cases where there is a valid regression

results among non-stationary variables. In this case it is said that the variables are cointegrated.

(Gujarati-Porter, 2009 and Enders, 2010)

Although; Unit-Root and Cointegration test has become very popular but the tests for

them are for time-series not for Cross-sectional series. For short panel like this research with

only two time dimension Unit-Root and Cointegration tests are not required (Baltagi 2005, p.237,

p.247). Moreover, for large N and very small T (i.e. large cross-section and very small time, like

3 In panel data regression analysis, assuming different intercepts is much more popular than assuming different slopes. Concerning this research due to the lack of data there are a limited number of studies in the literature who have used panel data approaches (i.e. FEM or REM). However all of these models are constructed to have different intercepts.

17

the case of this study) usual panel procedure ignoring unit-root and cointegration is

recommended (Gujarati-Porter, 2009 and Enders, 2010).

Results and Discussion

Following procedure that was explained earlier, equation 3 estimated and according to the results

of measure of EC defined as number of employees using Internet, Leamer F statistic is 8.311

with prob (p-value) 0.00 which indicates rejection of the common effects model in favor of the

Fixed Effects Model (FEM). Hausman chi-squared test has value 27.021 with prob 0.000 which

indicates rejection of the Random Effects Model (REM) in favor of the FEM. Finally, White test

indicates that estimated equation faces heteroskedasticity problem. According to econometrics

knowledge, if there is heteroskedasticity or autocorrelation (also called spherical errors) in panel

data models, one should use Generalized Least Square (GLS). Table 1 of appendix summarizes

the final results of this part using Estimated Generalized Least Squares (EGLS) method.

Taking a close look at table 1 of appendix, it can be seen that all of the coefficients are

highly significant and EC (number of employees using Internet) has positive impact on

employment of manufacturing SMEs in Iran. Also output has positive impact and wage and price

of capital have negative impacts on employment of Iranian manufacturing SME. These results

are perfectly consistent with related theories.

The results can be interpreted as follows: one percent increase in the value-added would

increase employment by 0.047 percent. One percent increase in wage would decrease

employment by 0.149 percent. One percent increase in price of capital would decrease

employment by 0.161 percent. The most important and promising result is the impact of EC

(number of employees using Internet), which indicates that, one percent increase in one of the

EC measures (i.e. the number of employees using Internet) would increase employment of

Iranian manufacturing SMEs, by 0.041 percent, in other words, increase of number of employees

using Internet would increase employment.

Again, following the procedure used in previous section, equation 3 estimated employing

another measure of EC. Based on the results of measure of EC defined as number of employees

18

using computer, Leamer F statistic is 7.949 with prob (P-value) 0.00 which indicates rejection of

the common effects model in favor of Fixed Effects Model (FEM). Hausman chi-squared test has

value 36.799 with prob 0.00 which indicates rejection of the Random Effects Model (REM) in

favor of FEM. White test indicates that estimated equation faces heteroskedasticity. Therefore,

Estimated Generalized Least Square (EGLS) method is used. Table 2 of appendix summarizes

final results of this part.

Looking at the results of table 2 of appendix, it can be seen that all of the coefficients are

highly significant with p-value 0.000. These results indicate that number of employees using

computer has a positive impact on employment of Iranian manufacturing SMEs. Also, output has

positive impact; wage and price of capital have negative impacts on employment. These results

are perfectly consistent with related theories.

The results can be interpreted as follows: one percent increase in the value added would

increase employment by 0.143 percent. One percent increase in wage would decrease

employment by 0.602 percent. One percent increase in price of capital would decrease

employment by 0.227 percent. Finally, EC measured, as number of employees using computer

would increase employment of Iranian manufacturing SMEs by 0.067 percent.

Following the procedure used in previous sections, equation 4 estimated with probably

most accurate and reliable measure of EC (i.e. e-selling). The reported results show that, Leamer

F statistic is 8.883 with prob (p-value) 0.00 which indicates rejection of the common effects

model in favor of Fixed Effects Model (FEM). Hausman chi-squared test has value 13.710 with

prob 0.00 which indicates rejection of Random Effects Model (REM) in favor of FEM. Model

selection criterions also like Hausman test selected FEM. White test indicates that estimated

equation faces heteroskedasticity. Therefore, this research used Estimated Generalized Least

Squares (EGLS) method. Table 3 summarizes final results of this part, i.e., EGLS results of FEM

of equation 4.

Looking at the results reported in table 3, it can be seen that all of the coefficients are

highly significant (with prob 0.000). These results are proof that EC (e-selling) has positive

19

impact and value-added has positive impact on employment of Iranian manufacturing SMEs.

Also, wage and price of capital have negative impact on employment. These results are perfectly

consistent with both related theories and empirical findings.

The results can be interpreted as follows: one percent increases in value-added would

increase employment by 0.058 percent. One percent increase in wage would decrease

employment by 0.029 percent. One percent increase in price of capital would decrease

employment by 0.010 percent. As it was mentioned earlier, perhaps e-selling could be

considered as one of the best, accurate and reliable measures of EC. Table 3 shows that the

coefficient of EC variable is positive; which indicates that manufacturing SMEs using Internet

for selling electronically have higher employment in average. The coefficient of EC when is

dummy variable can be used to calculate the rate of growth of dependent variable in level (which

is rate of growth of employment here) and calculate the impact of EC on employment. To do this:

Rate of growth of employment due to e-selling= 𝑒0.013 − 1 = 1.013 − 1 = 0.013

Therefore, SMEs having e-selling could raise their employment by 0.013.

Finally, following procedure that was explained earlier, equation 4 estimated and

according to the results of the measure of EC (i.e. e-buying); Leamer F statistic is 8.835 with

prob (p-value) 0.00 which indicates rejection of the common effects model in favor of the Fixed

Effects Model (FEM). Hausman chi-squared test has value 14.905 with prob 0.00 which

indicates rejection of the Random Effects Model (REM) in favor of FEM. Finally, White test

indicates that estimated equation faces heteroskedasticity problem. According to econometrics

knowledge, if there is heteroskedasticity or autocorrelation (also called spherical errors) in panel

data models, one should use Generalized Least Square (GLS). Table 4 summarizes the final

results of this part using Estimated Generalized Least Squares (EGLS) method.

Taking a close look at the table 4, it can be seen that all of the coefficients are highly

significant. These results show that EC (e-buying) has positive impact on employment of

manufacturing SMEs in Iran. On the other hand, value-added has positive impact and wage and

price of capital have negative impacts on employment. The results can be interpreted as follows:

20

One percent increases in value-added would increase employment by 0.389 percent. One percent

increase in wage would decrease employment by 0.879 percent. One percent increase in price of

capital would decrease employment by 0.579 percent. Following the method of calculation in

previous section the impact of EC on employment is:

Rate of growth of employment due to e-buying = 𝑒0.235 − 1 = 1.265 − 1 = 0.265

Therefore, SMEs having e-buying could raise their employment by 0.265.

Following the procedure used in previous sections, equation 4 estimated with another

measure of EC, using Internet to offer information and the reported results show that, Leamer F

statistic is 9.173 with prob (p-value) 0.00 which indicates rejection of the common effects model

in favor of Fixed Effects Model (FEM). Hausman chi-square test has value 9.493 with prob 0.00

which indicates rejection of Random Effects Model (REM) in favor of FEM. Model selection

criterions also like Hausman test selected FEM. White test indicates that estimated equation

faces heteroskedasticity. Therefore, Estimated Generalized Least Squares (EGLS) method is

used. Table 5 summarizes final results of this part, i.e., EGLS results of FEM of equation 4 using

the above EC measure.

Looking at the results reported in table 5, it can be seen that all of the coefficients are

significant. These results show that EC (using Internet to offer information) has positive impact

on employment of manufacturing SMEs in Iran. Moreover, value-added has positive impact and

wage and price of capital have negative impacts on employment. These results are perfectly

consistent with both related theories and empirical findings.

The results can be interpreted as follows: One percent increase in the value-added would

increase employment by 0.016 percent. One percent increase in wage would decrease

employment by 0.090 percent. One percent increase in price of capital would decrease

employment by 0.158 percent. As mentioned earlier using Internet to offer information like e-

selling, could be considered as one of the best, accurate and reliable measures of EC. Table 5

shows that the coefficient of EC variable is positive; which indicates that manufacturing SMEs

using Internet to offer information have higher employment in average. The coefficient of EC

21

when is dummy variable can be used to calculate the rate of growth of dependent variable in

level (which is rate of growth of employment here) and calculate the impact of EC on

employment. To do this:

Rate of growth of employment due to using Internet to offer information =

𝑒0.080 − 1 = 1.083 − 1 = 0.083

Therefore, SMEs using Internet to offer information could raise their employment by 0.083.

Following procedure that was explained earlier, equation 4 estimated and according to

the results of this measure of EC (i.e. using Internet to gather information), Leamer F statistic is

8.338 with prob (p-value) 0.00 which indicates rejection of the common effects model in favor of

the Fixed Effects Model (FEM). Hausman chi-squared test has value 27.019 with prob 0.00

which indicates rejection of the Random Effects Model (REM) in favor of FEM. Also, based on

the model selection criterion, Fixed Effects Model (FEM) is selected. Finally, White test

indicates that estimated equation faces heteroskedasticity problem. According to econometrics

knowledge, if there is heteroskedasticity or autocorrelation (also called spherical errors) in panel

data models, one should use Generalized Least Square (GLS). Table 6 summarizes the final

results of this part using Estimated Generalized Least Squares (EGLS) method.

Taking a close look at the table 6, it can be seen that all of the coefficients are highly

significant. These results show that EC (using Internet to gather information) has positive impact

on employment of manufacturing SMEs in Iran. On the other hand value-added has positive

impact and wage and price of capital have negative impacts on employment of Iranian

manufacturing SMEs, are valid with very high confidence. The results can be interpreted as

follows: one percent increases in value-added would increase employment by 0.136 percent. One

percent increase in the wages would decrease employment by 0.487 percent. One percent

increase in the price of capital would decrease employment by 0.237 percent. The coefficient of

EC variable is positive; which indicates that manufacturing SMEs using Internet for gathering

information have higher employment in average. The coefficient of EC when is dummy variable

can be used to calculate the rate of growth of dependent variable in level (which is rate of growth

of employment here) and calculate the impact of EC on employment. To do so:

22

Rate of growth of employment due to using Internet to gather information =

𝑒0.454 − 1 = 1.574 − 1 = 0.574

Therefore, SMEs using Internet to gather information could raise their employment by 0.574.

This result is consistent with the results of positive impact of EC on employment confirmed by

most of others studies.

Conclusion

According to the results of estimation, all of the measures of EC, namely, number of employees

using Internet, number of employees using computer, e-selling, e-buying, using Internet to offer

information and using Internet to gather information, all have positive impacts on employment

indicated by highly significant coefficients of EC. Table 1 show that the coefficient of EC

(measured by number of employees using Internet) is highly significant. This is an indication of

EC impact as; one percent increase in number of employees using Internet would increase

employment by 0.041 percent. Table 2 shows that the coefficient of EC (measured by number of

employees using computer) is highly significant. This is an indication of EC impact as, one

percent increase in number of employees using computer would increase employment by 0.067

percent. Table 3 shows that the coefficient of EC (measured by e-selling) is positive, which

indicates that Iranian manufacturing SMEs using Internet for selling electronically have higher

employment in average. The rate of growth of employment due to e-selling is equal to 0.013

percent. Therefore, Iranian SMES having e-selling could raise their employment by 0.013

percent. Table 4 shows that the coefficient of EC (measured by e-buying) is positive, which

indicates that Iranian manufacturing SMEs using Internet for buying electronically have higher

employment in average. The rate of growth of employment due to e-buying is equal to 0.265

percent. Therefore, Iranian SMEs having e-buying could raise their employment by 0.265

percent. Table 5 shows that the coefficient of EC (measured by using Internet to offer

information) is positive, which indicates that Iranian manufacturing SMEs using Internet for

offering information have higher employment in average. The rate of growth of employment due

to offering information by using Internet is equal to 0.083 percent. Therefore, Iranian SMEs

using Internet for offering information could raise their employment by 0.083 percent. Table 6

shows that the coefficient of EC (measured by using Internet to gather information) is positive,

23

which indicates that Iranian manufacturing SMEs using Internet to gather information have

higher employment in average. The rate of growth of employment due to gathering information

by using Internet is equal to 0.574 percent. Therefore, Iranian SMEs using Internet for gathering

information could raise their employment by 0.574 percent. The coefficients of value-added,

price of capital and wage are all highly significant in all of the regression equations. These

results indicate that value-added has positive impact and wage and price of capital have negative

impacts on employment. The above findings are perfectly consistent with related theories.

In summary, econometrics findings analyzed in this paper are perfectly consistent with

underlying theories. The results show that EC impact on employment with different measures of

e-commerce) are positive.

References

Abbasi , Alireza. (2007). Information Technology Policy Program. College of Engineering, Seoul National

University, Seoul, Korea.

Acs, J. Zoltan., Carlsson, Bo., and Thurik, Roy. (1996). Small Business in the Modern Economy. Wiley-Blackwell

Publisher, 1 Ed.

Adekola, Abel., Sergi. Bruno.S. (2007). International economic organizations and the new economic order. World

Review of Science, Technology and Sustainable Development, Vol.4, No.1, PP.56-72.

Adekola, Abel., Sergi. Bruno.S. (2008). Particulars of US Information Technology and Productivity Lessons for

Europe. International Journal of Trade and Global Markets, Vol.1, No. 2.

Alasoni, Tuomo. (2001). Challenges of Work Organization Development in the Knowledge-Based Economy -With

a Special Reference to E-Commerce, DG Employment & Social Affairs. By The European Work Organization

Network EWON.

April. D. Graham., and Parther, Shaun. (2008).Evaluating service quality dimensions within e-commerce SME.

Electronic Journal Information systems Evaluation, Vol. 11, Issue. 3, PP.109-124.

April. D. Graham., and Parther, Shaun. (2008).Evaluating service quality dimensions within e-commerce SME.

Electronic Journal Information systems Evaluation, Vol. 11, Issue. 3, PP.109-124.

Atrostic, B. K., and Nguyen, S. V. (2002). Computer Networks and US Manufacturing Plant Productivity: New

Evidence from the CNUS Data, Center for Economic Studies, US Census Bureau. Washington, DC.

Baltagi, Bandi. H. (2005). Econometric analysis of panel data.3rd edition. Wiley Europe Book.

Baltagi, Bandi.H. (2011). Econometrics. 5th edition, Spring Heidelberg Dordercht London, New York, PP. 81-84.

Barsauskas, Petrns. Sarapovas, Tadas. and Cvilikas, Aurelijus. (2008).The evaluation of EC impact on business

efficiency, Baltic journal of management.

24

Beck, T., A. Demirguc – Kunt., and R. Levine (2003). SMEs Growth and Poverty: Cross- Country Evidence.

WORLD BANK, Working Paper.

Beckinsale, M., and Levy, M. (2004). SMEs and Internet Adoption Strategy: Who do SMEs listen to? The European

IS Profession in the Global Networking Environment. Proceedings of the 12th European Conference on Information

Systems, Turku, Finland: Turku School of Economics and Business Administration.

Benavente, Jose Miguel., and Lauterbach, Rodolfo. (2007). The Effect of Innovation on Employment, Evidence

from Chilean Firms. Forthcoming in the European Journal.

Blanchflower, D., and Burgess, S.M. (1998). New Technology and Jobs: Comparative Evidence from a Two

Country Study. Economics of Innovation and New Technology, Vol.5, PP.109-138.

Blanchflower, D., and Burgess. (1997). New Technology and Jobs: Comparative Evidence from a two Country

Study. Economics of Innovation and New Technology, Vol.6, No.1/ 2.

Blenchinger, D., Kleinknecht, A., Licht, G and Pfeiffer, F. (1998). The Impact of Innovation on Employment in

Europe- An Analysis using CIS Data, ZEW-Dokumentation 98-02, Mannheim.

Brouwer, E., Kleinknecht, A., and Reijnen, O.N. (1993). Employment Growth and Innovation at the Firm Level An

Empirical Study. Journal of Evolutionary Economics, Vol.3, No.2, PP.153-159.

Chennells, L., Van Reenen, J. (1999). Has Technology Hurt Less Skilled Workers? An economic survey of the

effects of technical change on the structure of pay and jobs. IFS working paper, W 99/27.

Criscuolo, C., and Waldron, K. (2003). E-commerce and Productivity. Economic Trends 600.

Davidson, R. and MacKinnon, J.G. (2004). Econometric Theory and Methods. New York: Oxford University Press.

Doms, M., Dunne, t. and Troske, K. (1997). Workers, Wages, and Technology. Quarterly journal of Economics,

Vol.112, PP.253-289.

ECO trade and development plan, (2009-2010), IRAN Country Partnership Strategy.

Enders, Walter. (2010). Applied Econometric Times Series. Book. 3th Edition. ISBN 978-0-470-50539-7.

Entorf, H., and Pohlmeir, W. (1990). Employment, Innovation and Export Activities, in (J. P. Florens, Ed.)

Microeconomics: Surveys and Applications, London: Basil Black Well.

Freeman, R.B. (2002). The Labor Market in the New Information Economy. Oxford Review of Economic Policy,

No.18, PP.288-305.

Ghorishi, Maryam. (2009). E-commerce adoption model in Iranian SME's: investigating the causal link between

perceived strategic value of e-commerce & factor of adoption. University essay from Luleå/Business Administration

and Social Sciences.

Gorriz, Carmen Galve., and Castel, Ana Gargallo. (2010). The relationship between human resources and

information and communication technologies: Spanish firm-level evidence. Journal of Theoretical and Applied

Electronic Commerce Research, ISSN 0718-1876, Vol. 5, Issue 1.

Greenan, N., and Guellec, D. (1996). Technological innovation employment reallocation. INSEE-DESE working

paper, G 9608, Paris.

Greene, W. H. (2003). Econometric Analysis. 5th ed. Upper Saddle River: Prentice Hall, PP. 285, 291, 293, 304.

25

Greene, W. H. (2003). Limited Version 8 Econometric Modeling Guide, Vol. 1. Plainview, NY: Econometric

Software, pp. E8_1-E8_98; E8_26-E8_30.

Greene, W.H. (2011). Econometric Analysis. Prentice Hall, 7th Edition.

Gujarati, D. (2003). Basic Econometrics. 4th Ed. New York: McGraw Hill.

Gujarati, D.N. and Porter, D.C. (2009). Basic Econometrics. 5th edition, McGraw- Hill Companies, Inc.

Hojabr Kiani, Kambiz., and Hojabr Kiani, Sarvenaz. (2011).Comparing the effect of ICT on employment in the

industry sector of Zanjan and Hamedan provinces. Journal of Quantitative Researches in Management.

Hojabr Kiani, Sarvenaz. (2004).The Impact on GDP and Labor productivity in Iran. Payknour Journal, Economic

and Accounting, Vol.2, No.4.

Katsoulacos, Y.S. (1984). Product Innovation and Employment. European Economic Review, No.26, PP.83-108.

Katsoulacos, Y.S. (1986). The Employment Effect of Technical change: A Theoretical study of new technology and

the labor market. Book, Brigthon, Wheatsheaf.

Klette, T.J., and Eorre, S.E. (1998). Innovation and Job Creation in a Small open Economy: Evidence from

Norwegian Manufacturing Plants 1982-92. Economics of Innovation and New Technology, Vol.5, PP.247-272.

Krueger, A. B. (1993). How Computers Have Changed the Wage Structure: Evidence from Micro Data. Quarterly

Journal of Economics, Vol.108, PP.33-60.

Kuhn, Peter., and Mikal Skuterude. (2001). Does Internet Job Search Reduce Unemployed workers’ Jobless

Durations. Santa Barbara Working Paper.

Leo, H., and Steiner, V. (1994).Innovation and Employment at the Firm Level.

Love, Peter E.D., Irani, Zahir., Standing, Craig., Lin, Chad., and Burn, Janice M. (2005). The enigma of evaluation:

benefits, costs and risks of IT in Australian small–medium-sized enterprises. Information & Management, Vol.42,

Issue 7, PP. 947–964.

Lui, C., and Arnett, K.P. (2000).Exploring the Factors Associated with Web site Success in the context of Electronic

Commerce. Information and Management, Vol.38, N.1, PP. 23-33.

Maliranta, M., and Rouvinen, P. (2003). Productivity Effects of ICT in Finish Business. Helsink: ETLa,

Elinkeinoelaman Tutkimuslaitos, the Research Institute of the Finnish Economy, Discussion Papers, and No.852.

Maliranta. M., and Rouvinen. P. (2006). Information Mobility and Productivity: Finnish evidence. Economics of

Innovation and New Technology, Vol.15, No.6.

Mann, S. Prem. (1998). Introductory Statistics. Third Edition. John Wiley and Sons, INC.

Matteucci, Nicola and Sterlacchini, Alessandro. (2003). ICT and Employment Growth in Italian Industries. Working

Papers 193.

Mohamad, Rosli, Ismail., and Noor, Azizi. (2009).E-commerce practices among SMEs: a review of major themes

and issues. Business e-Bulletin, Vol.1, Issue 1, PP.7-13.

Mohamad, Rosli., and Ismail, Noor Azizi.(2009). Electronic Commerce Adoption in SME: The Trend of Prior

Studies, Journal of Internet Banking & Commerce; Vol. 14, Issue 2.

Mokyr, J. (1999). The British Industrial Revolution: An Economic Perspective. West view Press, Boulder.

26

OECD. (2003).Information and Communications Technologies ICT and Economic Growth. Evidence from OECD

Countries, Industries and Firms.

Piva M.C. and Vivarelli M. (2002). The Skill Bias: Comparative Evidence and an Econometric Test. International

Review of Applied Economics, vol.16, No.3, PP.347-358.

Piva M.C. and Vivarelli M. (2002). The Skill Bias: Comparative Evidence and an Econometric Test. International

Review of Applied Economics, vol.16, No.3, PP.347-358.

Piva, Microdata Mariacristina., and Vivarelli, Marco. (2005).Innovation and Employment: Evidence from Italian

Micro data. Journal of Economics, Vol.86, No.1, PP. 65-83.

Poon S., and Swatman, P. (1997). The Internet for Small Businesses: An Enabling Infrastructure Fifth Internet

Society Conference, PP.221-231.

Quayle, M. (2002). E-commerce: The challenge for UK SMEs in the Twenty-First Century. International Journal of

Operations and Production Management, Vol.22, No.10, PP.1148-1161.

Reynolds, J. (2000). E-commerce: A Critical Review. International Journal of Retail and Distribution Management,

Vol.28, No.10, PP.417-444.

Sanayei, A., M.S.Torkestani and P.Ahadi. (2009). Readiness Assessment of Iran’s Insurance Industry for

Ecommerce and E-insurance Success. International Journal of Information Science and Management.

Scupola, A. (2001). Adoption of Internet-based electronic commerce in Southern Italian SMEs. 1st Nordic Workshop

on Electronic Commerce, Halmstad, Sweden.

Singh, Sumanjeet. (2008). Impact of e-commerce on economic models; little to lose; more to gain. International

Journal of Trade and Global Markets, Vol.1, No.3, PP. 319-337.

Singh, Sumanjeet. (2008). Impact of Internet and E-commerce on the labor market. Indian Journal of Industrial

Relations (IJIR), Vol.43, No.4, pp. 633-644.

Smolny, W. (1998). Innovations, Prices, and Employment: A Theoretical Model and an Empirical Application for

West-German Manufacturing Firms. Journal of Industrial Economics, Vol.3, PP. 359-381.

Stansfield, M., and Grant, K. (2003). An investigation into issues influencing the use of the Internet and Electronic

Commerce among Small-Medium Sized Enterprises. Journal of Electronic Commerce Research, Vol.4, No.1,

PP.15-33.

UNIDO. (2003). Strategy Document to enhance the contribution of efficient and competitive small and medium-

sized enterprise sector to industrial and economic development in the Islamic republic of Iran.

Van Reenen, J. (1997).Employment and Technological Innovation: Evidence from UK Manufacturing Firms.

Journal of Labour Economics, Vol. 15, PP. 255-84.

Van Reenen, J. (1997).Employment and Technological Innovation: Evidence from UK Manufacturing Firms.

Journal of Labour Economics, Vol. 15, PP. 255-84.

Vera, R. (2002). The Employment Impact of Business to Customer E-Commerce on Philippine Worker. APEC Study.

Vescovi, T. (2000). Internet Communication: The Italian SME Case. Corporate Communications: An International

Journal, Vol.5, No.2, PP. 107-112.

27

Vivarelli, M. (1995). The Economics of Technology and Employment: Theory and Empirical Evidence, Book,

Aldershot, Elgar.

Wolff, Edward N. (1997). Spillovers, Linkages and Technical Change. Economic Systems Research, Vol. 9, No.1,

PP.9-23.

Wolff, Edward N. (2011). Spillovers, Linkages, and Productivity Growth in the US Economy 1958-2007. NBER

Working Paper Series, Working paper 16864.

Wooldridge, J. M (2002). Econometric Analysis of Cross Section and Panel Data. MIT Press.

Wooldridge, J. M (2006). Introductory Econometrics: A Modern Approach. 3rd Edition, THOMSON South-Western

Publishing, chapter 7, PP.230-270.

Appendix Table 1: Estimation results of the impact of EC (Number of employees using Internet) on Employment

Independent Variable

Fixed Effects- Estimated Generalized Least Squares (EGLS)

Coefficient Standard Error t-Statistics Prob

Constant ln VA lnW lnR lnEC Leamer F Hausman chi-squared

5.361 0.535 10.023 0.000 0.047 0.012 3.786 0.000 -0.149 0.032 - 5.018 0.000 -0.161 0.015 -10.102 0.000 0.041 0.001 52.547 0.000 8.311 0.000 27.021 0.000

Source: Estimated by using equation No. 3 Table 2: Estimation results of the impact of EC (Number of employees using computer) on Employment

Independent Variable

Fixed Effects- Estimated Generalized Least Squares (EGLS) Coefficient Standard Error t-Statistics Prob

28

Constant ln VA lnW lnR lnEC Leamer F Hausman chi-squared

11.030 1.220 9.040 0.000 0.143 0.015 9.720 0.000 - 0.602 0.072 - 8.363 0.000 - 0.227 0.016 - 14.431 0.000 0.067 0.009 7.738 0.000 7.949 0.000 3.799 0.000

Source: Estimated by using equation No.3 Table 3: Estimation results of the impact of EC (E-selling) on Employment

Independent Variable

Fixed Effects- Estimated Generalized Least Squares (EGLS) Coefficient Standard Error t-Statistics Prob

Constant ln VA lnW lnR EC Leamer F Hausman chi-squared

1.678 0.084 20.012 0.000 0.058 0.001 37.137 0.000 - 0.029 0.004 - 7.222 0.000 -0.010 0.002 - 4.032 0.000 0.013 0.001 9.738 0.000 8.883 0.000 13.710 0.000

Source: Estimated by using equation No. 4

Table 4: Estimation results of the impact of EC (E-buying) on Employment

Independent

Variable Fixed Effects- Estimated Generalized Least Squares

(EGLS) Coefficient Standard Error t-Statistics Prob

29

Constant ln VA lnW lnR EC Leamer F Hausman chi-squared

11.508 1.548 7.434 0.000 0.389 0.010 38.057 0.000 - 0.879 0.087 -10.066 0.000 - 0.579 0.016 -36.580 0.000 0.235 0.030 7.848 0.000 8.835 0.000 14.905 0.000

Source: Estimated by using equation No. 4 Table 5: Estimation results of the impact of EC (Using Internet to offer information) on Employment

Independent Variable

Fixed Effects- Estimated Generalized Least Squares (EGLS)

Coefficient Standard Error t-Statistics Prob Constant ln VA lnW lnR EC Leamer F Hausman chi-squared

4.828 0.772 6.255 0.000 0.016 0.009 1.786 0.074 - 0.090 0.043 - 2.107 0.035 - 0.158 0.005 -29.243 0.000 0.080 0.038 2.072 0.040 9.173 0.000 9.493 0.000

Source: Estimated by using equation No. 4 Table 6 Estimation results of the impact of EC (Using Internet to gather information) on Employment

Independent Variable

Fixed Effects- Estimated Generalized Least Squares (EGLS)

Coefficient Standard Error t-Statistics Prob

30

Constant ln VA lnW lnR EC Leamer F Hausman chi-squared

8.832 0.918 9.622 0.000 0.136 0.013 10.032 0.000 - 0.487 0.055 -8.789 0.000 - 0.237 0.016 - 15.093 0.000 0.454 0.019 23.687 0.000 8.338 0.000 27.019 0.000

Source: Estimated by using equation No. 4