IT Use, IT Skills and Employment Opportunities of Workers 1. Introduction

Transcription

1 IT Use, IT Skills and Employment Opportunities of Workers 1. Introduction This paper examines the role of Information Technology (IT) access, IT use and IT skills in creating employment opportunities for workers. IT policies are implemented around the world with the goal of eliminating the digital divide, increasing social welfare, and promoting employment. The underlying idea behind such policies is that people who do not have access to IT are left behind and are disconnected from the labor market and job opportunities. Therefore, eliminating the digital divide and promoting universal access to IT may be a valuable approach to stimulate employment and job creation. The role of IT use and IT skills in creating employment opportunities, especially among traditionally disadvantaged socioeconomic groups in labor markets, is an important question for both research, practice, and public policy. Practical interventions that can increase IT access (such as computer and Internet) are important determinants of IT use. Then, IT use can promote employment outcomes in several ways. We summarize these factors into two main mechanisms through which IT use can affect employment opportunities: (1) directly and (2) indirectly through IT skills. The employment matching process relies heavily on the flow and quality of information (Autor 2001). IT can help improving the employment chances of workers by making more information accessible to them. From a worker s point of view, IT can make job search easier by reducing traditional geographic barriers and the cost of job search. IT use can help workers to gain employment by enabling access to more information, online job search tools, and online social and professional networks (such as LinkedIn), thus helping workers to gain employment. IT can also enable remote work settings and telecommuting that can be essential especially for workers with reduced mobility (e.g., elderly) or high preference for flexibility (e.g., single mothers). Accordingly, we consider these factors as the direct effects of IT use on the employment opportunities available to workers. Second, using IT can improve workers IT skills that are becoming an important component of human capital with the spread of IT across industries. Many IT skills can simply be acquired by access to a computer and the Internet; coupled with self-exploration, help of books or DVDs, and friends and family. Besides these informal training options, IT use can also complement and reinforce formal training received in classes and workshops. IT skills can in turn lead to more job opportunities for workers. These mechanisms are conceptualized as the indirect (or mediated) effects of IT use on employment of workers by increasing their IT skills. Therefore, to establish the relationship between IT use and employment opportunities for workers, observing their IT skills can be very important, and it is proposed to be a an important mediating link in this crucial relationship. The role of IT use in increasing employment can be even more critical in developing countries where computer and Internet access are not as prevalent as in developed countries. In developing countries, in 1

2 contrast to most developed countries, IT access, IT use, and IT skills can create a higher marginal benefit for workers and enable them to differentiate themselves in the labor market. We focus on employment opportunities in a developing country context (Turkey), and we study how IT can directly and indirectly affect employment. Hence, we use a unique data from the emerging economy (Turkey) to examine the role of IT use and IT skills in creating employment opportunities for workers across various demographics (gender, age, urban/rural location, education) and various occupations across different industries. There is limited research on the effects of IT use and IT skills on labor market outcomes, mostly due to the lack of micro-data. Among the scarce literature, IT use and IT skills are considered as two separate variables (e.g., Kuhn and Skuterud 2004, Stevenson 2009, Blanco and Boo 2010). We argue that these two variables are inter-dependent and should be simultaneously used in analysis of the overall effects of IT. Since IT skills are essential for both employee and firm success, many IS studies have analyzed the demand for IT skills and their effects over time (e.g., Green 1989, Leitheiser 1992, Todd et al. 1995, Lee et. al. 1995). However, the IS literature on the relationship between IT skills and performance is focused on IT labor or high-level professionals (e.g., Bassellier et al. 2003, Bassellier and Benbasat 2004). While these studies provide important insights for the role of IT skills in the success of IT labor and high-level workers, there is not much evidence on the nature of IT skills of non-it workers or non-specialized workers with low-level positions, particularly in developing countries. We provide a societal perspective on IT use and IT skills by using nationally representative, government administrated, large-scale data from a developing country (Turkey) in which the majority of workers are in low or medium-level jobs. We thus present a more comprehensive perspective that is not restricted to high-skilled labor (such as IT professionals or managers). This enables us to prescribe public policy implications on the role of IT use and skills in employment outcomes for various socio-economic groups. We use two individual level datasets from Turkey that are both nationally representative. The first one is a household IT use survey that contains information on the IT access of the household and IT use intensity and IT skills of the household members. We use this survey to link people s IT use and IT skills to their employment outcomes and test how this relationship varies by socio-economic groups. We supplement the IT use survey with a second household labor force survey that includes detailed information on people s current and previous occupations and industries, plus the job search behavior for the unemployed workers. The results show that there is a significant positive relationship between IT use and employment probability. Part of this relationship is derived from the direct effects. There are also significant positive indirect effects of IT use on employment probability through increased IT skills. These effects are moderated by several different demographic characteristics. We also find strong evidence that IT use improves employment opportunities for women more than men. Similarly, there are significantly larger benefits of IT use for older workers. These findings can be due to women s and older workers high 2

3 preference for flexible work practices that can be facilitated by IT, such as telecommuting and work-athome settings. The mediation effects via IT skills are stronger in urban locations and for educated workers, as there is usually a higher demand for IT skills in urban labor markets. We also find that IT use and IT skills complement high-skill occupations. Finally, we find that IT use and IT skills are associated with lower unemployment durations controlling for the workers job search methods, and their previous industry and previous occupation. Using nationally representative data from the context of Turkey over time, we provide evidence on the role of IT in employment of different socio-economic groups. Our findings demonstrate that the benefits from IT may not be shared equally, and IT policies can be designed and implemented with a strategy to benefit disadvantaged socio-economic groups such as female and elderly workers, and to high-skilled occupations. 2. Theoretical Development 2.1 Background Literature Research on how IT access, IT use, and IT skills shape the labor market is scarce, largely due to lack of individual level data. Kuhn and Skuterud (2004) and Stevenson (2009) used Current Population Survey data to analyze the effects of Internet job search on workers. Kuhn and Skuterud (2004) found that Internet job search is associated with lower unemployment durations. However, this relationship is eliminated and even reversed in some specifications when individual level demographics and other job search methods are held constant. Stevenson (2009) analyzed on-the-job search and found that the workers who use Internet job search methods are more likely to make an employment-to-employment transition. In an experimental study, Blanco and Boo (2010) submitted fictitious Curriculum Vitae (CVs) to real job vacancies. CVs that had IT skills listed on them had 1% higher chance to get a call back for an interview, controlling for other individual and job characteristics. These studies consider the Internet use and IT skills as different treatments; still, we argue that these two variables are interrelated and should be simultaneously considered in analysis of the overall effects of IT. To our knowledge, there are no studies on the relationship between IT use, IT skills, and employment across various socio-economic groups and occupations, particularly in developing countries. Many IS studies have analyzed what are the important IT skills are as well as how the demand for IT skills change over time (Green 1989, Leitheiser 1992, Todd et al. 1995, Lee et. al. 1995). IT skills are essential for both employee and firm success. Most of the IS literature on the relationship between IT skills and performance is focused on IT-professionals or high-level business managers. Bassellier et al. (2003) proposed a research model for understanding the nature and role of IT competence of business managers. IT skills (IT experience and IT knowledge) explained 34% of the variation in business managers plans to 3

4 champion IT (Bassellier and Benbasat, 2004). Business competence (that is broader than IT competence) was found to be important for the development of partnerships between IT professionals and their business clients (Bassellier and Benbasat, 2004). These studies provide important insights for the role of IT skills in the success of IT professionals and business managers. However, there is not much evidence on the IT skills of non-it workers and non-specialized workers who do not have high-level managerial positions. We aim to provide a comprehensive perspective by using a nationally representative data from Turkey in which the majority of the workers are in medium to low-level occupations. 2.2 Direct Effects of IT Use on Employment of Workers We expect IT use to directly increase the probability of employment. There are several means for this direct effect to occur. IT use can increase access to different sources of information and decrease the cost of job search. Online tools remove the traditional geographic barriers of search and connect workers with remote job markets. Online sources also remove the time barriers for accessing information. For example, employment agencies are one of the common job search methods; yet, they are restricted to business hours. The Internet enables constant access to job ads, online recruitment sites, social media, and other job search tools. Both unemployed and employed labor force can readily use these resources. There is some evidence on the effects of Internet job search on both the duration of unemployment and also on employment-toemployment transitions (Kuhn and Skuterud 2004, Stevenson 2009). Online social networks can provide additional job leads for workers. Garg and Telang (2013) found that people that have more ties on a professional online social network (i.e., LinkedIn) have more job leads, job interviews, and subsequent job offers; controlling for other popular types of job search tools such as employment agencies and print media. IT allows telecommuting and distant work arrangements that can be especially useful for workers with reduced mobility for health/family reasons or need/prefer job flexibility. In sum, integrating these arguments, we propose a direct positive relationship between IT use and employment opportunities. Therefore, we propose the following hypothesis for testing: H1: IT use is associated with a higher probability of employment. 2.3 Mediated Effects of IT Use on Employment through IT Skills IT use can increase employment opportunities for workers by increasing IT skills, which are predominantly valued in the labor market. The labor economics literature has shown the role of education, training, and different types of skills on labor market outcomes. Economic theory offers a systematic explanation of the relationship between training investments and the associated employment and wages of workers (e.g., Becker 1965, Mincer 1974, Hamermesh 1986). There are positive gains from education and 4

5 training, even after netting for demographic characteristics, direct and indirect costs of training and higher ability of more educated people (Becker 1994). On the labor demand side, there is a well-established literature on skilled-biased technical change (e.g., Acemoglu 1998, Autor et. al and Autor et. al. 2003, Acemoglu 2002, Bresnahan et. al. 2002). The skill-biased technical change model predicts that adoption of technology by firms would lead to an increase in the demand and wages for skilled labor. The basic idea behind this theory is that there are high complementarities between skilled-labor and technology. Increased IT investments by firms would lead to a higher demand for skilled labor and a higher demand for IT skills. As IT diffuses into the economy and becomes more of an essential investment for individual workers, we expect IT skills to play a more important role in human capital and in labor market. In sum, both theories that analyze human capital and skills from a labor supply and a labor demand perspective predict that having stronger IT skills would lead to greater benefits for workers. Additionally, empirical evidence shows that IT skills are associated with a higher productivity for both workers and firms. Krueger (1993) found higher wage premiums for workers that use computers at work. Black and Lynch (1996) estimated a significant and positive impact of computer skills development on productivity and labor demand, despite controlling for industry. Human capital formation theory and skilled-biased technical change theory are consistent with the IS literature on the effects of IT skills on both professionals and firms. IT personnel skills affect IS success, while IT skills constitute an important skill set in affecting IT infrastructure flexibility (Duncan 1995, Byrd and Turner 2001). The IT skills of project managers, IT professionals, and business managers were shown to have positive effects on project performance (Langer et. al. 2008, Bassellier et. al. 2000, Bassellier and Benbasat 2003). Since IT skills are positively associated with higher worker, project, and firm success, we expect having more IT skills to be associated with a higher probability of employment. IT use can be an essential training opportunity for IT skills. IT use can especially improve basic IT skills that can be learned by experimentation and do not necessarily require formal training. Therefore, familiarity and increased experience with computers can lead to improvement in IT skills. Additionally, using IT more often can increase an individual s computer self-efficacy, one s capability to use a computer (Compeau and Higgins 1995, Agarwal et al. 2000). There is a long line of literature on the antecedents and consequences of computer self-efficacy. Evidence suggests that past computer use and experience contribute to computer self-efficacy, which in turn lead to higher performance, learning and acquisition of declarative knowledge (Gist et al. 1989, Compeau and Higgins 1995, Henry and Stone 1994, Inbaria and Livari 1995, Martocchio 1992, Martocchio 1994). Hence, we theorize that higher IT use enhances IT skills by improving computer self-efficacy. Integrating these arguments, we expect higher IT use to lead to higher 5

6 IT skills, which can in turn increase employment opportunities of workers. Accordingly, we propose a mediation effect of IT use on employment through IT skills. Specifically, we hypothesize: H2: The positive relationship between IT use and probability of employment is mediated by IT skills. 2.4 Moderated Effects of IT Use on Employment We discuss the effects of IT use on employment across demographic characteristics, specifically gender, age, urban/rural location and education. Additionally, we expect different effects based on occupations since the value of IT skills will be different for occupations of different skill levels. First, we consider gender as a key demographic because gender is a determinant of several labor market outcomes, such as wages and tenure. Typically low female labor force participation rates and high unemployment rates for women are important policy concerns. In the context of IT use and labor markets, we expect gender differences to arise from women s preference for more flexible and family friendly work settings. One mechanism through which IT use can increase employment is telecommuting and flexible distant work options. We expect IT use to be more beneficial for female workers, as they often have a more crucial role in home and childcare, requiring them to have a more flexible schedule. Indeed the importance of work/family balance and flexible work settings in women s employment decisions and job satisfaction has been documented in the literature (e.g., Rousseau 1995, Scandura and Lankau 1997, Boden 1999). Therefore, we expect telecommuting and distant work settings facilitated by IT use to benefit female labor force participation more than men. We thus propose the following moderated hypothesis: H3a: The positive relationship between IT use and the probability of employment is stronger for women. Second, we consider age as a key demographic characteristic that might shape the interactions between IT use and employment probability of workers. Opportunities to retire gradually with downscaled commitment in terms of hours or working pattern are rated highly by older workers. Additionally, some older workers prefer more flexible employment as a means of bridge employment, that is the taking up of another job or work after leaving the career occupation (Loretto et al. 2005). Health problems and reduced mobility due to aging creates additional incentives for older workers to seek for flexible work settings. Telecommuting and other work-at-home opportunities can be important for elderly workers to gain employment We thus propose the following moderated hypothesis: H3b: The positive relationship between IT use and the probability of employment is stronger for older workers. Third, we expect the relationship between IT use, IT skills and employment opportunities to be different for labor force in urban versus rural labor markets. Urban and rural locations have a fairly different demand for different types of labor skills since they have a different industry and firm composition. Similar industries cluster together due to externalities and complementarities, and urban locations generally have 6

7 more clusters of higher-skilled intensive and IT-intensive industries. IT use is higher in urban locations where complementary resources, such as high-skilled labor and IT infrastructure, are available. Therefore, we expect IT use and IT skills to be more valuable for employment in urban locations. We propose the following moderated hypothesis for testing: H3c: The positive relationship between IT use and the probability of employment is stronger in urban versus rural locations. Additionally, the relationship between IT use and employment can be moderated by education level and occupation skill level. IT skills are more relevant for certain types of jobs; especially for jobs that involve cognitive and analytical tasks and require higher skills. This also relates to the basic idea behind skilled biased technology change theory that technology and high-skilled labor complement each other. Therefore, we expect IT skills to be a more essential part of human capital for highly educated workers compared to less educated workers. Hence, IT skills can lead to more and better job matches for more educated workers and workers that work in high-skilled occupations, as IT skills can be more critical for them. Therefore, we propose the following moderated hypothesis for testing: H3d: The positive relationship between IT use and the probability of employment is stronger for more educated workers. H3e: The positive relationship between IT use and the probability of employment is stronger for workers in high-skilled occupations. 3. Data We used two major individual level data sets from the Turkish Statistical Institute (Turkstat). One of them is Household IT use survey that includes information on IT adoption and use from 2007 to 2012, which are repeated cross-sections that are nationally representative each year. The data contains two levels of surveys. First one is a household level survey that includes information on household IT access. The second level constitutes individual surveys including detailed IT use information for each member of the household. This allows us to link household IT access with individuals IT use. Because our focus is on effects on employment of workers, we restricted our sample to working age adults (between ages 16 to 65). We also removed students and retirees who are not looking for a job that are not part of the labor force. This led to a final sample size of 89,307 observations over six years. We used this data for a portion of our main analysis. The IT use survey is a rich source for understanding individuals IT use and specific IT skills; however it does not include detailed labor market information. We used the Household Labor Force Survey that includes detailed information on employment, unemployment, job search activities, occupations and industries. This survey is the main labor force survey in Turkey, from which all the major employment and other labor related statistics are calculated. We cannot 7

8 match individuals across these two datasets. However, since both of the data sets are representative at the national and socio-economic groups level and have large number of observations, we aggregate the individuals into different cohorts, and thus have a cohort level panel data over time. We use the common cohorts in used in labor studies such as age/gender cohorts (i.e. people of the same age and same gender), and also add whether the individual lives in a rural or urban location into this cohort classification, because IT access and value of IT skills can differ significantly across urban and rural labor markets. Then we relate the IT use and IT skills of these cohorts (measured in Household IT use survey) to their employment by different industries and occupations (measured in Household Labor Force Survey). This enables us to control for the industries and the occupations of the employees and test what types of occupations IT use and IT skills complement. Another novelty of the labor force survey is to have detailed information of job search methods and duration for the unemployed; therefore we test the relationship between IT use and IT skills and unemployment duration. 3.1 IT Access, IT Use and IT Skills The data include household IT access information, such as the presence of computers and the Internet. Home IT access rate increased from around 28% in 2007 to over 50% in There are also individuallevel questions for each member of the household. These questions provide information IT use by individuals, purpose, and frequency of IT use. Computer and Internet use variables overlap together to a great extent (r=0.95, p<1%), and thus we define the level of IT use variable as level of computer and/or Internet use. This variable provides five levels of IT use frequency for each individual: 0=Do not use IT at all, 1= Uses IT less than once a month, 2= Uses IT at least once a month, 3= Uses IT at least once a week, 4= Uses IT everyday. Similarly, IT use levels increased over time. In 2007, the average IT usage level was less than a month, and increased to between less than a month and between at least one a month in A unique feature of the dataset is information on IT skills at the individual level. Individuals reported the IT skills they have using the following 8 options: 1. Copying and transferring files/folders, 2. Using copy/paste command, 3. Zipping files/folders, 4. Using formulas in a spreadsheet, 5. Connecting and installing devices to a computer (modem, scanner, etc.), 6. Connecting computers to networks, 7. Problem solving/trouble shooting involving computers and the Internet, 8. Knowing a programming language. In the main analysis, we used the total number of reported IT skills as a measure of the IT skills. The survey covers a wide variety of IT skills starting from basic office skills to advanced skills, such as knowing a programming language. These IT skills are comparable to other population level surveys in the developed countries. For example, the IT use at the Work Supplement of the Current Population Survey (CPS) in the United States renders comparable questions on IT skills, despite the statistics not being directly comparable. In the 2001 CPS survey, IT skills at work questions included: using word processing, connecting to Internet 8

9 and using , using calendar on computer, using spreadsheets, and programming. Even though the prevalence of IT skills is much lower in Turkey then the US, we can infer that IT skills that are relevant at the society level are similar. It should be also considered that these IT skills are more basic than IT skills of specialized labor such as IT professionals (besides perhaps programming skills), since the governmental data in Turkey are at the overall population level that consist of people that mostly have low to medium educational levels and occupations (versus IT professionals and business managers). 3.2 Industries and Occupations We used the Household Labor Force survey to obtain information of workers current and past occupations and industries, as well information on job search methods and job search durations for the unemployed. Each worker s industry is coded at four-digit level according to the International Standard of Economic Activities in the European Union. We use the main industry categories such as manufacturing, retail and wholesale trade, agriculture, information etc. Occupations are coded at the four-digit level according to the International Standard Classification of Occupations (ISCO-88). 1 We use the major occupation groups and their associated skill-level categories to test whether IT use and IT skills have different effects for workers in different occupations. ISCO determines the skill level for each occupation group by analyzing the degree of complexity of constituent tasks and skill specialization - essentially the field of knowledge required for competent performance of the constituent tasks. There are four ISCO skilllevel categories where 4 th level is the highest skill category and 1 st is the lowest skill category. The IT orientation of the occupation is corresponds to the occupations that are in the 4 th and 3 rd skill level categories and also based on the occupation descriptions. Table 1: ISCO-88 Occupation Categories Major Group ISCO skill-level IT-oriented occupation 1 Managers 4th, 3rd Yes 2 Professionals 4th Yes 3 Technicians and associate professionals 3rd Yes 4 Clerical support workers 2nd No 5 Service and sales workers 2nd No 6 Skilled agricultural, forestry and fishery workers 2nd No 7 Craft and related trades workers 2nd No 8 Plant and machine operators and assemblers 2nd No 9 Elementary occupations 1st No 1 The detailed description of each category and other related information can be found at: 9

10 4. Empirical Specification and Results We start by using individual-level moderated mediation models to identify the direct and indirect effects of IT use on employment probability, and how these effects change based on demographics and labor market characteristics. The indirect effects are mediated by IT skills. We developed multiple mediator (IT skills) and moderator models for key demographics (gender, age, urban/rural, education), to analyze the relationship between of IT use, IT skills and employment probability. We estimated three equations simultaneously for IT use, IT skills, and employment status. (1) IT use equation: = (2) IT skills equation: = (3) Employment equation: = For all specifications, we included individual level (, country level ( ) and time controls. Year fixed effects ( were added in all equations to account for shocks to the national economy and labor markets experienced by all individuals. For IT use, we expected IT access to be an important determinant. IT use can be also different for different age groups and education levels. Gender can play a role in in the intensity of IT use as well. IT skills depend on the IT use levels, and therefore we expected IT use to be a key variable in the second equation. We further used two other variables to identify the equation for IT skills. Computer use and help of others was shown to be important determinant of computer self-efficacy (Compeau and Higgins 1995). We used other household members average IT use, and other household members average age as proxies of support and help available for IT use and gaining IT skills at home. In the third equation of our system, we used employment status as the dependent variable (binary variable where =1 if the individual is employed). Main independent variables of interest are IT use and IT skills. Probability of employment also depends on several individual demographics. We also controlled for whether the individual is the head of the household or not. As documented by the labor economics literature, the effects of education and age are not linear and usually a squared term is included to address potential non-linear effects. On top of the year fixed effects, we further controlled for yearly national unemployment rate and GDP growth rates that play a role in the labor market. 10

11 4.1 Endogeneity The relationship between IT access, IT use, IT skills and employment can be characterized by endogeneity. There are at least two sources that can play a role. Reverse causality can take place if employment precedes IT use and IT skills, as people can learn IT skills at their jobs. We address this issue by using additional questions on how skills are acquired. This information is only available for 2007, 2008 and We remove the people who reported that they have acquired IT skills at work or at a workshop initiated by their employers (around 8% of the whole sample). The results are similar when we remove the people who report gaining IT skills at their jobs or employer-initiated workshops. There can also be an unobserved confounding factor that affects IT use, IT skills and employment probability at the same time, such as the general competence level of the individual. In other words, if an individual is smarter, which cannot be observed, she/he will me more likely to be employed, have higher IT use and IT skill levels. This is a common problem in the labor economics literature in employment status and wage rate estimations as general capabilities of people usually cannot be observed. We address this issue by creating a proxy for this unobserved skill/capacity of the individual by calculating residual education. This is the difference between an individual s education and the average education level of his cohort. Cohort is defined as people of the same age, gender and urban/rural location in the sample. Additional education received compared to the cohort creates a proxy for individuals general capability level. This variable can be positive (for people that have higher education than their cohort), and negative (for people that have lower education than their cohort). We found similar results when controlling for residual education. Additionally, in the second part of our empirical results we use cohort-level analysis that alleviates some of the endogeneity concerns by reducing the measurement error and create a cohort that can be tracked over time. This analysis also enables to test the effect of IT skills on different occupations controlling for industries. We conduct further robustness checks where we find that IT use and IT skills are also correlated with lower unemployment durations (controlling for the job search methods used, previous occupation and industry of workers as well as all the relevant demographics). 4.2 Direct and Indirect Effects of IT Use on Employment Table 2 presents the direct and indirect effects of IT use on employment probability. Column 1 presents the first model with IT use as the dependent variable. In Column 2, IT skills are determined by IT use and other individual characteristics, and Column 3 presents the marginal effects of IT use and number of IT skills with employment status of the workers as the dependent variable. IT access is an important determinant of IT use level. Individuals with home IT access use IT at one higher-level out of 5 levels of 11

12 Table 2: Direct and Mediated effects of IT Use on Employment (1) (2) (3) VARIABLES DV: IT Use Level DV: IT Skills DV: Employment Status IT Use 0.753*** 0.045*** (0.004) (0.001) IT Skills 0.010*** (0.001) Household IT Access 1.001*** (0.009) Age *** *** *** (0.000) (0.000) (0.001) Female *** *** 0.448*** (0.009) (0.011) (0.003) Urban 0.257*** 0.039*** 0.160*** (0.009) (0.011) (0.003) Years of Education 0.159*** 0.100*** 0.005*** (0.001) (0.001) (0.001) HH age except individual 0.008*** (0.001) HH IT use except individual 0.036*** (0.005) Age Squared 0.001*** (0.000) Years of Education Squared *** (0.000) Household Head *** (0.003) GDP Growth Rate 0.021*** (0.001) Country Unemployment Rate 0.088*** (0.001) Employment Status 0.478*** 0.087*** (0.010) (0.011) Year Fixed Effects Yes Yes Yes 12

13 Observations 89,307 89,307 89,307 Adj. R-squared use. IT use is in turn an important determinant of IT skills. On average, IT use increases employment probability directly and also through IT skills. One level increase in IT use (out of 5) is associated with 4.5 % higher probability of employment directly, and with 1.5% higher probability of employment through one standard deviation increase IT skill. These findings support both H1 (direct effects of IT use on probability of employment), and H2 (indirect effects of IT use on probability of employment via IT skills). 4.3 Moderating Effects by Demographic Characteristics We further analyze the hypothesized moderating effects of key demographics (gender, age, urban/rural location, and education). Table 3 presents only the employment equation component of our system of equations (similar to column 3 of Table 2) for brevity, where different moderators are introduced. Same set of controls in Table 2 is also included in Table 3. Column 1 presents the results with gender as a moderator. The direct effects of IT use on male and female workers have the same magnitude (4.2% higher employment probability) and not statistically different from each other. We find that the mediation effects of IT use is 5.4 % increase in probability of employment for female workers for one more standard deviation of IT skills, and it is significantly (p<.01) higher than the male workers. These results suggest that overall the relationship between IT use and employment is stronger for female workers, thereby supporting H3a. Column 2 presents the moderated results for age. Since age is a continuous variable, we report the effects evaluated at three different points: One standard deviation below the mean, at the mean and one standard deviation above the mean. For age, the direct effect of IT use for different age groups is around 4% higher probability of employment and it is not statistically different across the groups. However, the mediation effects through IT skills are significantly stronger for older workers. Interestingly, one level increase in IT use is associated with 3.9% higher probability of employment for workers around 50 years old (one standard deviation higher than average age), 3.0% higher probability of employment for workers of average age of 37, and 1.1% higher probability of employment for younger workers around 24 years old (one standard deviation lower than the average age). These differences are statistically significant at p<.01, thus supporting H3b. In Column 3 moderator is whether the individual worker resides in an urban versus a rural labor market. Both the direct and indirect effects of IT use on the probability of employment are stronger in urban labor markets. One level increase in IT use is associated with 1.5% higher probability of employment in rural locations, and 5.5% higher probability of employment in urban locations directly; these effects are 13

14 significantly different at the p<.01 level. The mediation effects through IT skills are also significantly different in urban locations because IT skills are more valued in urban labor markets. These results support H3c. Column 4 presents the moderated results for education, which indicate that the mediation effect is stronger for more educated workers as they could be a better fit for jobs that require IT skills. One level increase in IT use is associated with 3% higher probability of employment for workers with one standard deviation below mean education levels, 4.5% higher probability of employment for workers with average education levels, and 6% higher probability of employment for workers with education levels of one standard deviation higher than the average. Table 3: Moderated Effects by Demographics DV: Employment Status VARIABLES (1) (2) (3) (4) IT Use Level 0.042*** 0.046*** 0.015*** 0.028*** (0.002) (0.004) (0.003) (0.003) IT Use Level x Female (0.002) IT skills *** *** 0.005** 0.020*** (0.001) (0.003) (0.002) (0.003) IT skills x Female 0.054*** (0.002) IT Use Level x Age (0.000) IT Skills x Age 0.001*** (0.000) IT Use Level x Urban 0.040*** (0.003) IT Skills x Urban 0.014*** (0.003) IT Use Level x Education 0.003*** (0.000) IT skills x Education 0.002*** (0.000) Year Fixed Effects Yes Yes Yes Yes Observations 89,307 89,307 89,307 89,307 Adj. R-squared Standard errors in parentheses. *** p<0.01, ** p<0.05, * p< Cohort Level Analysis and Moderating Effects by Occupation In this section, we combine the Household IT use survey and Household Labor Force survey. Since the individuals cannot be matched across the surveys, we exploited the representative nature of the surveys to create age/gender/urban vs rural cohorts. We summarize the IT use and IT skills variables for people of the same age, gender and residing in urban or rural location. Similarly, labor market variables such as the employment status, occupations, industries, unemployment duration and job search methods are 14

15 summarized at the same age/gender/urban vs rural cohorts. Then we match the cohorts across the two samples and conduct cohort level analysis. For example, one observation in this analysis is women who are 35 years old living in urban locations. We pooled all the different cohorts and relate the average IT use and average IT skills to the average employment rate and average rate in different occupations using all the cohorts as different observations. We restrict the sample to working age population from 16-65, and combinations of different ages with gender and urban/rural location lead to 200 cohorts in a given year. We have the Household Labor Force data between 2007 and 2011, therefore cohort level analysis leads to a total of 1000 observations. In Table 4, we regressed cohort level average employment rate on cohort-level average IT skills and other characteristics. We are interested in whether the relationship between IT skills and employment rate varies by occupations. The IT skills can complement certain jobs more than others. In column 2, we add the interaction of IT skills with IT-oriented occupation category (skill level 3 and skill level 4 based on the ISCO-88 classification, as described in the data section). In column 4 we add more detailed groups and use the four skill groups of ISCO-88 classifications. Omitted category is skill level 1, which is the least skilled occupation category. We see that IT skills have significant and positive interaction terms with Skill level 3 and skill level 4 occupations. These are more high-skilled occupations, confirming that IT skills complement the higher skilled tasks and occupations. 5. Discussion We contribute to the literature by providing a theoretical model and empirical evidence on the relationship between IT use, IT skills, and employment probability of workers, using a nationally representative dataset from a developing country. It has been historically hard to acquire such micro-level data with large number of observations, including information on people s IT use combined with IT skills and employment status. This has been one of the reasons why the empirical evidence on role of IT on employment outcomes has been limited. We utilize government-administered household and individual level IT use data from Turkstat that include information on stratified segments of the population and labor force, including primarily low and medium level jobs. The representative nature of the data allow us to infer the importance of IT, not only for IT workers or high-skilled IT labor, that have been studied in the IS literature, but also for the overall labor force. Second, our study also relates to the literatures on the digital divide. There is an extensive stream of research analyzing the determinants of individual and regional IT adoption, and how IT adoption affects various socio-economic groups (e.g., Dewan et al. 2005, Kaufmann and Techatassanasoontorn 2005, Barki et al. 2007, Agarwal et al. 2009). We thus contribute to the literature by establishing the link between IT access, IT use, and IT skills, and by analyzing their effects on employment across socio-economic groups, specifically traditionally disadvantaged demographics. The digital divide is a major concern especially for 15

16 Table 4: IT Use and Employment (Cohort level) DV: Employment Rate VARIABLES (1) (2) (3) (4) IT Use 0.047*** 0.024*** (0.006) (0.006) IT-oriented occupation 0.170* *** (0.099) (0.091) IT Use* IT-oriented occupation 0.723*** (0.040) Skill level *** 0.277*** (0.071) (0.070) Skill level ** *** (0.206) (0.229) Skill level *** (0.130) (0.123) IT skills*skill level (0.049) IT skills*skill level *** (0.126) IT skills*skill level *** (0.067) Secondary School *** *** *** *** (0.027) (0.028) (0.028) (0.029) High School *** *** *** *** (0.037) (0.036) (0.037) (0.033) Higher Education 0.659*** 0.808*** 0.526*** *** (0.092) (0.100) (0.110) (0.108) Age *** *** *** *** (0.001) (0.001) (0.001) (0.001) Female *** *** *** *** (0.014) (0.019) (0.018) (0.016) Urban *** *** *** (0.020) (0.023) (0.021) (0.019) Year Fixed Effects Yes Yes Yes Yes Industry Yes Yes Yes Yes Occupation No Yes Yes Yes Observations 1,000 1,000 1,000 1,000 Adj. R-squared women and elderly, as they tend to have lower IT access and use. Interestingly, we found that women and older workers can benefit significantly more from IT use and IT skills. These findings emphasize the importance of mitigating digital divide and its economic impacts on traditionally under-privileged groups in the labor market by promoting IT use. 16

17 This study relates to the literature on IT skills on employee, project, and firm performance. Many studies in the IS literature provide evidence for the importance of IT skills for IT managers and business executives success (Langer et al. 2008, Bassellier et al. 2000, Bassellier and Benbasat 2003). We extend these studies by analyzing a larger pool of workers at the national labor force level that are not limited to specialized IT labor and high-level managers. As IT use proliferates rapidly in firms, all employees will soon be expected to have some IT skills and familiarity with IT. IT skills would still be of utmost importance to IT employees, however with technological expansion, it is also important to analyze the role of IT skills for all members of the labor force going beyond specialized labor. Our results have several public policy implications, especially relevant for developing and emerging labor markets. The digital divide is a serious policy concern because of the common belief that lack of access to computers and the Internet has adverse economic and social costs for the people who are left behind. Public policies that promote household computer/internet access and use are especially common in developing and emerging economies where computer and Internet penetration is lower compared to developed countries. The IT policies in Turkey launched around in 2003 and there are several public policy initiatives that aim to increase labor productivity and employment through IT use expansion, along with other important outcomes such as improving IT infrastructure, expanding IT use in businesses and households, improve education and health care. There are also several policies around the world clearly aim at improving such important socio-economic outcomes, including employment. The effects of IT use on employment are shown to differ by gender, age, urban/rural locations, education, and occupations/industries. We find significantly higher benefits of IT use for women and elderly, especially in urban areas. One potential route for explanation is that women have a higher preference for more flexible work settings due to their role at home and family care. Decreased labor force participation and higher rates of discouraged and displaced workers among the aging population have been an important policy concern. Flexible work structures and telecommuting enabled by IT can be important for elderly workers with limited mobility due to health issues. Thus, IT use can lead to increased labor participation and employment opportunities for female and older workers. Additionally, these effects would be stronger among who live in urban locations. On the other hand, we find that educated and workers in high-skilled occupations benefit more from IT use, so these policies can potentially further increase the inequality between educated and less-educated workers. Therefore, policy makers should take into account possible unequal distribution of benefits. The IT policies can be designed in a manner to stimulate employment for certain targeted groups that not privileged in labor markets. 17

18 5.1 Limitations and Suggestions for Future Research This study has limitations arising from data constraints that create opportunities for future research: First, our data is pooled cross section over time, which does not allow to track individuals. Therefore, we cannot infer longitudinal effects at the worker level. An individual panel data can provide opportunities to address causality especially by observing the timing of changes in IT use, IT skills and employment. Second, we focus solely on employment and unemployment durations as outcomes. Another interesting question might be how IT use and IT skills affect workers compensation. IT use and IT skills can also lead to an employment-to-employment transition, such as moving to a job with higher wages and being promoted. However, this type of employment-to-employment transition is not captured in this study. Future research would be needed to analyze the effects IT use and skills on other interesting labor market outcomes such as wages, occupations, tenure, and promotions. Third, we use a unique data set from Turkey, which includes variables that are not usually available in publicly available sources, that presents an opportunity to study several features of IT and employment of workers, and interactions with key demographics. Our findings are provided from a developing country context. There are differences between developed and developing countries in several aspects of IT provision, access and use. We believe that our results apply more for developing and emerging country contexts where IT are not as prevalent as in developed nations; but future research from different countries would be needed to empirically support these arguments. Finally, in this study, we attempted to untangle a small piece of the complex dynamics between the relationship between IT use, IT skills and employment, and we hope that it will stimulate future research on this important question of how IT affects important labor market outcomes. 5.2 Concluding Remark The role of IT access and use on economic and social outcomes has been an important question in the Information Systems and labor economics literatures. Unemployment rates are a major concern for public policy makers and society in general. IT policies generally aim at increasing employment though providing universal access and reducing the digital divide. We provide a theoretical model and empirical evidence on the importance of IT use in the labor market using a unique nationally representative data source from a developing country that enables a broader societal view. This study is a small step in analyzing the important inter-relationship among IT access and use, IT skills, and workers employment outcomes, particularly among disadvantaged socio-economic groups (women, less educated, people in rural areas, elderly). Notably, we find that interventions that increase IT access and use can especially benefit socioeconomic groups (women, older workers) who are typically disadvantaged by IT and in the labor market. 18