The Effects of Internet Access on Labor-Supply Decisions

The Effects of Internet Access on Labor-Supply Decisions

Sara ChampionCornerstone Research

Katrina KosecInternational Food Policy Research Institute

Christopher StantonDepartment of Finance, University of Utah

www.twcresearchprogram.com

R E P O R T S E R I E S

SUMMER 2012

For more information:

Fernando Laguarda 901 F Street, NW Suite 800 Washington, DC 20004 Phone: (202) 370-4245 www.twcresearchprogram.com twitter.com/TWC_RP

Foreword .................................................................................................................................................. 2By Fernando R. Laguarda, Time Warner Cable

Abstract .................................................................................................................................................... 3

1. Introduction ...................................................................................................................................... 4

2. Empirical Strategy and Data ......................................................................................................... 62.1 Empirical Strategy ............................................................................................................................62.2 Data ....................................................................................................................................................8

3. Analysis ............................................................................................................................................. 103.1 In the Aggregate, How has Labor Supply Varied with Internet Penetration? ......................103.2 In the Aggregate, How has Labor Supply Varied with Internet Penetration Over Time? ...... 123.3 Who Adopts the Internet and Broadband? ...............................................................................143.4 How has the Internet Influenced Labor-Supply Decisions? ...................................................16

The Propensity to Work ................................................................................................................16Hours Worked ................................................................................................................................19The Propensity to Work Full-time ...............................................................................................20Hours Worked Among those Not Working Full-time ..............................................................21The Propensity to Work Two or More Jobs ...............................................................................21Time Use..........................................................................................................................................22

3.5 How Have the Effects of the Internet Changed Over Time? ..................................................243.6 How Have the Effects of the Internet Varied Across Different Groups? ..............................24

4. Conclusion .......................................................................................................................................28

Endnotes ................................................................................................................................................29

References ............................................................................................................................................ 30

About the Authors ................................................................................................................................31

Key Contact Information ................................................................................................................... 32

Table of Contents

1 The Effects of Internet Access on Labor-Supply Decisions 1

Policy afficionados in the telecommunications space frequently make the assumption that broadband has the potential to fundamentally alter the nature of our economy. As we have noted previously, this assumption helps to drive interest in “broadband adoption,” or the use of a high-speed Internet connection (deploying technology other than dial-up) to go online. In this report, the authors help illuminate a particularly striking phenom-enon: home access to broadband is strongly correlated with employment, with the amount of hours worked, and with working from home. For this reason, we welcome the contribution of Sara Champion, Katrina Kosec, and Christopher Stanton, which assesses how Internet access at home has influenced labor-supply decisions.

In general, scholars point to three reasons why consumers who have yet to adopt broadband are not doing so: (1) the cost of a computer and a broadband connection, (2) low digital literacy (viz., knowing how to use the technology safely and feeling comfortable with it), and (3) the lack of a compelling reason to use broadband (what is frequently referred to as the “relevance” barrier). This research highlights an intriguing argument for broadband “relevance,” which is the potential for the platform to contribute to a household’s participation in the workforce. While others have studied related questions, this is the first attempt to examine the connection of Internet technolo-gies to workforce participation.

The authors find a strong connection between Internet access at home and the probability of working. Importantly, the authors don’t argue that subscribing to broadband makes it more likely that a person will find or keep a job. But they raise important points about the potential role that broadband Internet access plays in workforce participation decisions. To the extent the effects are causal, they have obviously important implications for policymakers. Even more tantalizing, the authors find that the connection between broadband Internet access and working is especially strong for women and for black men. All of which is to say, there is some compelling evidence that home broadband access, even if not causally connected to employment, is clearly relevant to under-standing the modern economy — including the amount of time and way in which people work.

There is no question in our mind that broadband Internet provides a real pathway to opportunity and economic growth. This paper showcases the important role that the Internet — and broadband in particular — can play in shaping workforce decisions and perhaps even workplace opportunity. The broadband economy needs to work for everyone. This report makes a significant contribution to explaining the role broadband might play. Is broadband “relevant” to workers in the modern economy? We think so.

We hope this report stimulates debate and encourages more thoughtful policy. As always, we look forward to your comments and feedback.

ForewordBy Fernando R. Laguarda, Time Warner Cable

The Effects of Internet Access on Labor-Supply Decisions2

The prevalence of Internet access in the home has increased markedly over the last decade. In many respects, it has changed the way people live and work. This paper examines the relationship between Internet in the home (and whether it is broadband) and various measures of labor supply using data from the Current Population Survey and the American Time Use Survey for the period 1998–2010.

After including extensive controls, we document that home Internet access is highly positively cor-related with employment, with total hours worked, and with the amount and share of work done from home. We also document that moving from dialup (lower-speed) Internet to broadband (high-speed) Internet increases the magnitude of these effects. In addition, we document that the association between home Internet access and these labor-supply measures has increased dramati-cally over time and varies with metropolitan status, educational attainment, and race. In particular, having Internet at home is associated with larger increases in employment and hours worked among women in rural areas, less-educated men and women, and black men and women.

Abstract

Note: The views expressed are those of the authors and not necessarily those of Time Warner Cable or the Time Warner Cable Research Program on Digital Communications.

The Effects of Internet Access on Labor-Supply Decisions 3

Economists recognize that workers may incur costs when participating in the labor market (Gronau, 1977). These costs may vary considerably depending on individual characteristics. For example, individuals with children may need to arrange childcare, job-search costs may be higher for groups of individuals subject to discrimination or language barriers, and commuting to a city-center may be costly for individuals living in rural areas.1 Home Internet access has the potential to reduce some costs associated with supplying labor by allowing workers to shift a portion of their work from the office to the home and supply labor according to a less rigidly-structured schedule.

Between 1998 and 2010, households became much more likely to have access to Internet at home. In 1998, 35% of adults had Internet at home, according to data on adults aged 26–55 (working age) in the United States from the Current Population Survey (CPS). But in 2010, 80% of adults had Internet at home. While less than 1% of those with home Internet had broadband in 1998, about 97% with home Internet had some form of broadband or relatively high-speed Internet by 2010. Technological advances in software and hardware during the same period — enabling workers to telecommute more effectively — were complementary to the increasing deployment of home Internet. In such an environment, it is plausible that labor-supply decisions would evolve to take advantage of the increased flexibility made possible by these technological advances.

This paper assesses how Internet access at home has influenced labor-supply decisions overall. It also examines such decisions for sub-populations, where it is expected that working is relatively costly. The main labor-supply decisions we analyze are: whether or not to work; how many hours of work to supply; and what share of that work to do from home.

Several studies examine related questions, but we know of none that address directly how Internet technologies influence micro-level labor market decisions. Crandall et al. (2007), Katz and Suter (2009) and Kolko (2010) study the economic effects of residential Internet and broadband on macro outcomes, including the unemployment rate, the average wage, output by sector and eco-nomic growth. Kamssu et al. (2004), Martin and Robinson (2007) and NTIA (2011) study factors and personal characteristics that influence residential consumers’ adoption of Internet and broad-band. Forman, Goldfarb and Greenstein (2012) show that the Internet is responsible for increasing regional wage inequality. They argue that advanced Internet access resulted in faster wage growth for counties that were already highly educated, had high income, and high pre-existing informa-tion technology (IT) intensity. They do not address labor supply or demand in isolation, however; they examine only an equilibrium outcome (the wage).

We empirically analyze a panel dataset spanning 1998-2010 constructed from yearly cross-sec-tional datasets from the CPS and the American Time Use Survey (ATUS). These individual-level datasets allow us to match data on the adoption of home Internet and home broadband Internet with various measures of individual labor supply. They also allow us to control for demographic and geographic factors that might be correlated with home Internet adoption and are likely to directly affect labor-supply decisions.

1. Introduction


We find large and statistically significant associations between Internet access at home and the probability of working. Home Internet also is positively correlated with the amount of time men and women spend working and with the share of work done from home. We find these effects are largest among those with broadband at home; that is, there is an incremental effect from having broadband as opposed to dialup (lower-speed) Internet.2

Relative to women without Internet at home, women with dialup Internet are 6% more likely to work, and women with broadband Internet are 10% more likely to work. Similarly, men with dialup Internet are 5% more likely to work. Men with broadband Internet are 9% more likely to work than men without Internet at home. Women with dialup Internet also work about 1.7 more hours per week and those with broadband about 2.8 more hours per week, than similar women without Internet. Men with dialup Internet work about 2.6 more hours per week and those with broadband about 4.0 more hours per week, than similar men without Internet. Having Internet also is associated with different patterns of work. For example, women with broadband do 37% more work from home than similar women without Internet. Men with broadband at home do 34% more work from home than similar men without Internet.

To the extent that these results represent causal effects, they have significant welfare implications. Typical part-time or full-time jobs require somewhat rigid hours (often 20 or 40 hours per week, respectively). Often, individuals would be better off if they had more flexibility. The ability to do work from home may have especially large welfare impacts for individuals who are to some degree constrained to their home, such as people with disabilities and those facing language barriers or transportation challenges. Our data allow us to specifically consider how the effect of the Internet on labor supply varies with metropolitan status, educational attainment and race. We find that having Internet at home is associated with larger increases in employment and hours worked among women in rural areas, less-educated men and women and black men and women.

In addition, this paper documents that the correlations between home Internet, home broadband and labor-supply decisions have become larger over time. This suggests that estimates from early years may not adequately capture the current or long-term benefits of broadband infrastructure.

Section 2 of this paper describes the empirical strategy and the data we use in the analysis. Section 3 presents and discusses our empirical findings. We first examine, in the aggregate, how labor supply has varied with Internet penetration. Second, we consider how these aggregate effects of home Internet have changed over time. Third, we consider which individual characteristics have been correlated with adopting home Internet. Fourth, we use detailed individual-level data to examine how home Internet affects various labor-supply decisions. Fifth, we examine at the individual level how the effects of home Internet have varied over time. Last, we observe at the individual level how the effects of home Internet have varied across different groups of people. Section 4 contains our conclusion.


2.1 Empirical StrategyA complete analysis of the effects of the Internet and broadband on labor-supply decisions requires detailed information of several factors: the technology people have in their homes, whether they work, how much they work and where they work (at home or at an office). This information can be used to test the hypothesis that having Internet at home (especially broadband Internet) makes people more likely to work, to work longer hours and to do a greater share of total work from home.

An ideal setting for studying the effects of having Internet at home on labor-supply decisions would be one where we divided a large number of households into two groups. One group would be pro-vided with free Internet at home (half with Internet, half with broadband Internet). The other group would not be allowed to have Internet, even if they wanted to pay for it. Since households would be assigned randomly, the groups would be almost identical in every respect except for one group having Internet at home and one not having it. For example, the two groups would have a similar average age and education level, a similar racial, ethnic and linguistic makeup, similar numbers of children at home and similar marriage rates. We would then observe whether these otherwise similar groups differed on important decisions including whether to work, how many hours to work and how to allocate working time between home and the workplace. We would also see if the behavior of those with Internet differed from the behavior of those with broadband Internet.

This experiment implies the following empirical specification:

yit= αstate-metro-year+δ Internetit+ λ Internetit × broadbandit+ Xitθ+ εit (1)

where i indexes individuals and t indexes years. yit is any labor market outcome; it could be an indicator variable for working, the total number of hours worked, or the share of work done from home, for example. Xit is a matrix of individual-level control variables such as race and educa-tion level, and αstate-metro-year represents state × metro status × year fixed effects (or, in other words, dummy variables that indicate whether an individual lives in a metropolitan (metro) area or non-metro area in a particular state in a particular year).3

The primary parameter of interest is δ. It measures the effect of home Internet adoption on labor market outcomes. The parameter λ measures whether broadband (high-speed) access has any additional effect on these labor-market outcomes that does not come from having Internet alone. That is, δ describes the effect of having Internet at home on labor-market outcomes, and δ + λ describes the effect of having broadband at home. The hypothesis that having Internet at home (and especially having broadband) makes people more likely to work, work longer hours and do a greater share of work from home implies that δ>0 and λ >0 for all three of these outcomes, yit.

Unfortunately, such an experiment does not exist. The best we can do is exploit detailed data on the technology-adoption and labor-supply decisions made by a sample of the U.S. population, where individuals are described by the geographic area in which they live, the year they were surveyed and their demographic characteristics. As we do not have true panel data (multiple observations for the same set of individuals over several years), we use a pseudo-panel approach.

2. Empirical Strategy and Data


In short, we use multiple years of individual-level data, where a different set of people is sampled in each year from the same geographic areas. This allows us to track the rollout of Internet and broadband access over time within specific geographic areas, and to study how this rollout cor-relates with labor-supply decisions.

Two problems are likely to affect this analysis. The first is the potential for omitted variable bias, in which an individual’s labor supply decisions and Internet adoption are correlated with a third, unmeasured variable. The second is the potential for observed Internet and broadband adoption to be endogenous due to reverse causality; in which case, the labor supply decisions drive Internet adoption.

An individual’s decision to adopt Internet or not is the result of factors affecting both the ability and ease of adopting Internet and that individual’s demand for Internet. We want to rely solely on variation in Internet adoption that comes from the ease of adopting Internet. Factors that influ-ence demand for Internet also may have a direct effect on labor-supply decisions. If they do, then naïve estimates of the effects of Internet adoption on labor-supply decisions would be biased by omitted variables.

There are several possible sources of omitted-variable bias. We detail three. First, an individual who is relatively highly educated may be more familiar with computers or with the process of getting connected to the Internet. However, this individual also is more likely to find a job and to have that job be a highly-skilled, highly-paid position demanding long hours and work from home. Second, people with school-age children may be more likely to adopt the Internet because their children want it or need it for school. They may, however, also feel extra pressure to work and to work more hours to pay for child-consumption items and upcoming college tuition pay-ments. Third, a family that is not English-speaking might have difficulties getting connected to the Internet due to language barriers. These same language barriers, however, also may lead them to have fewer labor-market opportunities and thus supply less labor. All three of these omitted vari-able problems would tend to upward-bias estimates of δ and λ. The magnitude of the bias would shrink as measures of education, presence of children and teenagers in the home and ethnic and linguistic background were added to equation (1), but it would be impossible to know if all biases were eliminated. This is because not all factors that directly could influence labor supply and Internet adoption are observed in the available data.

Having Internet in the home can also be a response to labor-supply decisions. This is a classic prob-lem of reverse causality. People who have a job (especially one that is high-skill, high-paying and demanding in terms of hours of work) may be better able to afford Internet at home or to require it as a part of daily life. However, we could not say that having Internet at home was what caused this individual to have such a job and make the labor-supply decisions they did. Causality may run in both directions. The best response to this type of identification problem is a valid instrument. This instrument should affect Internet adoption at home, but should be uncorrelated with factors affect-ing the demand for Internet. Unfortunately, we have so far been unable to find such an instrument.

Our empirical strategy is to reduce the magnitude of the bias of estimates of δ and λ by introduc-λ by introduc- by introduc-ing a vector of individual-level control variables into our baseline specifications, in addition to our state × metro status × year fixed effects (represented by αstate-metro-year). While it is impossible to eliminate all bias, we can learn from these estimates what the correlation is between having the Internet (whether dialup or broadband) and labor-supply decisions, after controlling for many potentially confounding factors.


The controls we include in our empirical specification are described in detail in the next sec-tion. Briefly, they include age, educational attainment, children in the household, ethnicity, race, language spoken at home and marital status. All are likely to have a direct impact on labor-supply decisions. Since they simultaneously affect decisions about whether or not to adopt Internet and broadband, they confound our efforts to isolate the unique effects of Internet and broadband. Their inclusion in our empirical specification allows us to examine how Internet (and broadband) adoption correlate with labor-supply decisions when we compare only within similar groups. In essence, our controls allow us to compare the labor-supply decisions of a married, white male, aged 46-50, living in urban Colorado in 2010, who has a child, aged 12-17, but no younger chil-dren and who has Internet at home with a man sharing all of the same characteristics except that he lacks a home Internet connection.

In addition, we were interested in seeing whether the correlation between Internet adoption and labor-supply decisions varies according to individual-level characteristics. For example, educa-tion, age, language, race and whether one lives in a rural area may be important. Such an analysis implies the following empirical specification, which can be skipped for non-technical readers:

yit= βstate-metro-year+ η Internetit+ λ Internetit × broadbandit+ μ Internetit × Zit (2) + π Internetit × broadbandit × Zit + Zitψ + Xitω + uit

where all variables are defined as before and Zit is an individual-level control variable describing a dimension along which the correlation between Internet (and broadband) and labor supply may vary, for example race.

2.2 DataWe use two sources of data, which can be linked at the household level. First, we use data from the Current Population Survey’s (CPS) Internet and Computer Use supplements to determine whether a household is connected to the Internet and whether the household has a high-speed (broadband) connection. Individuals participating in a CPS supplement also are included in the basic CPS for that month and thus we can observe whether respondents were employed, how many hours they worked in the week preceding the survey call, and whether they held more than one job.4 The Bureau of Labor Statistics administers the CPS and is a nationally representative survey of approx-imately 50,000-60,000 households that has been conducted each month for more than 50 years. Households selected for the CPS are sampled in 8 separate months over a 16-month period.

The basic CPS provides a rich set of control variables. Our specifications include controls for age, marital status, educational attainment and race. Educational attainment is a categorical variable where possible education levels include less than a high school diploma, a high school diploma, some college but no degree, associate’s degree, bachelor’s degree and master’s degree or higher. Race is a categorical variable where possible categories include white, black or neither. Since labor-supply decisions may vary depending on how old an individual’s children are we also include three separate indicator variables for the household presence of children aged 0–5, 6–11 and 12–17. In addition we include an indicator variable for whether or not someone identifies as Hispanic and whether or not all members of the household speak only Spanish.

Second, we use information from the American Time Use Survey (ATUS). This survey is linked at the household level to the CPS and provides extremely detailed information about how individuals spend their time. In particular, it allows us to observe how many minutes someone spends on work at their workplace and separately at home. For a given individual in a household, we will know


whether that person has access to the Internet (and, more specifically, broadband Internet) from home, what that individual’s labor-market profile looks like and how many work minutes were spent at home versus at the office. After individuals complete their eighth and last CPS survey, they are eligible to be selected for the ATUS. A subset of CPS households is selected for the ATUS and from these households a random individual, aged 15 or older, is selected and asked to complete a time diary for a specified day. The specified day can be any day of the week, not just Monday through Friday, so an indicator for whether the time diary day was a work day is included in all ATUS regression specifications. Our empirical strategy requires that we use only individuals who kept an ATUS time diary and responded to a CPS Internet supplement, so that limited us to a subset of approximately 5,500 ATUS respondents.

For most analysis, we will be able to construct a six-year panel of stacked, cross-sectional data covering the years 1998, 2001, 2003, 2007, 2009 and 2010.5 For analysis using the Time Use Survey data, we will be limited to a three-year panel of stacked cross-sectional data covering the years 2003, 2007 and 2009. (We will not observe the same individuals in multiple years, but will observe a representative sample of households over multiple years.)


3.1 In the Aggregate, How has Labor Supply Varied with Internet Penetration? There are many reasons to believe that having a home Internet connection might affect individuals’ labor-supply decisions. The decision to work is a large and discrete choice that carries substantial search costs and typically requires a commitment to a non-flexible, 20-hour (part-time) or 40-hour (full-time) work week. The cost of working likely is largest for those who need to arrange childcare, face discrimination or language barriers, live in non-metro areas and have to commute relatively long distances, have disabilities or face other transportation challenges and uncertainties. We might expect these individuals to be especially likely to supply more labor in response to having Internet at home. However, do the data support this hypothesis? Do they suggest that greater Internet penetration is associated with a larger workforce or with a workforce that supplies more hours of labor?

Figure 1 summarizes, for the rural (non-metropolitan) and urban (metropolitan) portions of each state, the average share of the adult (ages 26–55) population that had adopted Internet at home and the average share of that population that worked during 1998-2010. Working is defined as supplying at least one hour of labor per week. Internet penetration is greater, on average, in urban areas than in rural areas, though there is some overlap (rural New Hampshire, for example, has higher Internet penetration than any area). Importantly, we see that having Internet at home and working are positively correlated — both overall, and among each of the sub-populations of rural and urban areas. In short, we see the highest shares of the adult population working in states with the highest Internet penetration.

Figure 1: Share of Adults Working by Share with Home Internet (Includes Adults Aged 26–55)

3. Analysis

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An important question is whether we can see these aggregate trends at the individual level. That is, do they reflect a correlation at the individual level between the decision to purchase Internet for the home and the decision to work? If so, what exactly is the magnitude of this correlation? We turn to this question in Section 3.4.

Figure 2 summarizes, once again for the rural and urban portions of each state, the average share of the adult population that had Internet at home and the average hours this population worked each week during 1998–2010. Similar to the case of the discrete decision to work (Figure 1), here we see that having Internet at home and total hours worked are positively correlated — both overall and among each of the sub-populations of rural and urban areas. It is in states with the highest Internet penetration that we see the highest number of hours of work supplied each week by adults. Of course, we are again left with the question of whether this reflects a correlation at the individual level between the decision to purchase Internet for the home and the decision of how many hours to work. We compute the magnitude of this correlation using worker-level data in Section 3.4.

Figure 2: Weekly Hours Adults Work by Share with Home Internet (Includes Adults Aged 26–55)

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People who do not work full-time have a choice to work part-time or not at all. Among non-full-time workers, we are interested in the effect that Internet has on an individual’s decision to work. Figure 3 summarizes the correlation between having Internet at home and the average share of non-full-time adult workers who worked part-time during 1998-2010. Part-time work is defined in the CPS as working fewer than 35 hours per week. As in the case of all workers, having Internet at home is positively correlated with working part-time — both overall, and among each of the sub-populations of rural and urban areas. It is in states with the highest Internet penetration that we see the highest share of part-time workers. Once again, we will return to this relationship in Section 3.4, where we will analyze the magnitude of the correlation with individual-level data.


Figure 3: Share that Work Part-Time by Share with Home Internet (Includes Adults Aged 26–55 Who Are Not Working Full-Time)

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3.2 In the Aggregate, How has Labor Supply Varied with Internet Penetration Over Time?The structure of the labor market has changed greatly in response to recent technological innova-tions, especially during the last two decades. New types of jobs and innovative ways of working (including working remotely — or telecommuting — from home) have emerged. An interesting question is have correlations between Internet at home and labor-supply decisions changed over time? In particular, is there evidence that, in the U.S., having Internet at home has become increas-ingly associated with supplying more labor over time? This might occur, for example, if any effects of having Internet on labor-supply decisions were intensified by the share of one’s colleagues and peers with Internet at home. Alternatively, this might occur if the technology available for working remotely improves over time.

Figure 4 summarizes, for seven years during 1997-2010 for which we have data, the share of working adults in each of two groups: those with Internet at home and without it. We see that in all years, the share of adults working is greater among the population with home Internet than among the population without home Internet. Most interesting, however, is the fact that the difference between these two groups’ mean rates of working has tended to increase over time. Of course, without additional controls, we cannot be sure whether this increase is merely due to different types of people taking up Internet at different points in time or actually due to an increasing correlation between Internet access at home and labor supply (while holding fixed the characteristics of people who adopt home Internet).


Figure 4: Share of Adult Population Working (Includes Adults Aged 26–55)

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Figure 5: Weekly Hours Worked (Includes Adults Aged 26–55)

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Figure 5 summarizes, for the same seven years, the average number of hours worked per week by adults in each of two groups: those with Internet at home and those without it. As in the case of the discrete decision to work, hours of labor supplied is greater for the population with home Internet than the population without it. Also, the difference between the two groups’ mean hours worked has increased. For example, in 1997, those without Internet at home worked 6 fewer hours per week than those with Internet at home. In 2010, though, those without Internet at home worked 8 fewer hours per week than those with Internet. Again, at an aggregate level, we see that the correlation between having Internet at home and supplying labor in the U.S. has intensified


in the last decade — mainly because hours supplied has decreased more among those without Internet at home than among those with Internet at home.

Figure 6 summarizes the average share of adults not working full-time who work part-time (as opposed to not at all) for the two groups: those with Internet at home and those without it. We see that in every year among non-full-time workers, the share working part-time for those who have Internet at home is greater than the share working part-time who lack Internet at home. However, unlike the results on the share of adults who work and hours worked among those who work 1+ hours, the share of part-time workers has varied little over time.

Figure 6: Share of Part-Time Workers (Includes Adults Aged 26–55).2

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In Section 3.5, we explore in more detail how the correlation between having Internet (and broad-band Internet) at home and labor-supply decisions has changed over time using individual-level data. In this way, we shed light on whether these U.S.-wide, aggregate trends can be observed in data on individual decisions about whether to purchase Internet (or broadband) and how much labor to supply.

3.3 Who Adopts the Internet and Broadband? Table 1 examines individual characteristics associated with the presence of household Internet. Two dependent variables are examined: a dummy variable for household Internet and a dummy variable for household broadband.

The most detailed regressions (containing the full set of age, race, marriage, education and chil-dren controls, plus state x metro × year fixed effects) explain about 28 percent of the variation in household Internet take-up and 45 percent of the variation in broadband Internet take-up. The detailed results are very sensible. Within a state-metro status-year group, the presence of Internet and broadband Internet are monotonically increasing with education.


Table 1: Factors Correlated with Adults (Aged 26-55) Having Internet and Broadband at Home

(1) (2) (3) (4) (5) (6) (7) (8)

Dependent Variable: Dummy – Internet at home Dummy – Broadband at home

Female Male Female Male

Mean: 0.668 0.661 0.414 0.413

Dummy – aged 31-35 0.039 0.007 0.026 -0.012 0.019 0.000 0.010 -0.012

(0.004)*** (0.004)* (0.004)*** (0.004)*** (0.004)*** (0.004) (0.004)** (0.004)***

Dummy – aged 36-40 0.053 0.011 0.039 -0.021 0.022 -0.001 0.013 -0.021

(0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004) (0.004)*** (0.004)***

Dummy – aged 41-45 0.056 0.015 0.041 -0.027 0.017 -0.005 0.002 -0.036

(0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004) (0.004) (0.004)***

Dummy – aged 46-50 0.037 0.008 0.032 -0.036 0.007 -0.008 -0.006 -0.046

(0.004)*** (0.004)** (0.004)*** (0.004)*** (0.004)** (0.004)** (0.004)* (0.004)***

Dummy – aged 51-55 0.003 -0.008 0.008 -0.052 -0.018 -0.023 -0.029 -0.065

(0.004) (0.004)* (0.004)* (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)***

Dummy – U.S. native 0.033 0.026 0.048 0.051 0.026 0.022 0.043 0.045

(0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)*** (0.004)***

Dummy – not white or black -0.019 -0.024 -0.005 -0.022 0.006 0.002 0.013 0.000

(0.005)*** (0.005)*** (0.006) (0.005)*** (0.005) (0.005) (0.005)** (0.005)

Dummy – black -0.251 -0.171 -0.216 -0.151 -0.148 -0.096 -0.125 -0.081

(0.004)*** (0.004)*** (0.005)*** (0.004)*** (0.003)*** (0.003)*** (0.004)*** (0.004)***

Dummy – Hispanic -0.237 -0.136 -0.252 -0.152 -0.162 -0.096 -0.178 -0.108

(0.005)*** (0.005)*** (0.005)*** (0.005)*** (0.004)*** (0.004)*** (0.004)*** (0.004)***

Dummy – Spanish at home -0.222 -0.136 -0.212 -0.126 -0.121 -0.067 -0.109 -0.053

(0.009)*** (0.009)*** (0.009)*** (0.008)*** (0.007)*** (0.007)*** (0.008)*** (0.007)***

Dummy – married 0.144 0.148 0.087 0.088

(0.003)*** (0.003)*** (0.002)*** (0.003)***

Dummy – HS diploma 0.171 0.161 0.098 0.095

(0.005)*** (0.005)*** (0.004)*** (0.004)***

Dummy – some college 0.287 0.288 0.173 0.184

(0.005)*** (0.005)*** (0.004)*** (0.004)***

Dummy – Associate degree 0.312 0.311 0.191 0.192

(0.005)*** (0.006)*** (0.005)*** (0.005)***

Dummy – BA degree 0.391 0.383 0.252 0.258

(0.005)*** (0.005)*** (0.004)*** (0.004)***

Dummy – Masters or higher 0.419 0.415 0.274 0.283

(0.005)*** (0.005)*** (0.005)*** (0.005)***

Constant 0.673 0.311 0.656 0.320 0.437 0.213 0.430 0.218

(0.005)*** (0.006)*** (0.005)*** (0.006)*** (0.005)*** (0.006)*** (0.005)*** (0.006)***

Observations 176301 176301 164082 164082 176301 176301 164082 164082

R-squared 0.19 0.28 0.18 0.28 0.42 0.46 0.41 0.45

Notes: Robust standard errors in parentheses. All specifications include state × year fixed effects, and dummies for having a child aged 0-5, aged 6-11, and aged 12-17. * significant at 10%; ** significant at 5%; *** significant at 1%.Source: Current Population Survey (CPS), 1998, 2001, 2003, 2007, 2009, and 2010 data.


In addition, all of the coefficients on ethnicity dummies are negative, indicating that whites are more likely to have home Internet than other ethnic groups. From columns 2 and 4, we see that black females are 17.1 percentage points less likely to have Internet and 9.6 percentage points less likely to have broadband Internet when compared to otherwise similar white females. (Black males are 15.1 percentage points less likely to have Internet and 8.1 percentage points less likely to have broadband Internet than white males.) Similar patterns hold for Hispanics. Hispanic females are 13.6 percentage points less likely to have Internet and 9.6 percentage points less likely to have broadband Internet when compared to otherwise similar non-Hispanic females. (For Hispanic males relative to otherwise similar non-Hispanic males, the respective numbers are 15.2 percent-age points and 10.8 percentage points).

Finally, married adults are much more likely to have home Internet than single adults. We see that married men and married women are approximately 15% more likely to have Internet than otherwise similar unmarried men or women. Married men and women are approximately 9% more likely to have broadband Internet than unmarried men or women. It is logical that individu-als who share a household with someone would be more likely to have Internet (or broadband Internet) since the price of home Internet does not depend on the number of individuals in a household, yet there are more people who can benefit from it.

3.4 How has the Internet Influenced Labor-Supply Decisions? In Section 3.1, we presented some aggregate trends using data from the rural and urban portions of each U.S. state. We showed that having Internet at home is positively correlated with working, with total hours worked and with the share working part-time among those not working full-time. These correlations raise the question of how having Internet at home (and whether it is dialup or broadband) affects individual-level decisions about whether to work, how much to work and where to work. Does Internet at home actually increase productivity? Does it improve worker utility by allowing for a more flexible balance between work done at home and work done at the office?

In this section, we present analyses of individual-level data investigating the relationship between Internet and broadband adoption and labor-supply decisions from the CPS and the ATUS. The CPS data, as described in Section 2, cover the years 1998, 2001, 2003, 2007, 2009 and 2010. The ATUS data are available for 2003, 2007 and 2009. In the analyses, we run separate regressions for men and women to allow all covariates to have heterogeneous impacts on labor-supply decisions by gender. Indeed, much of the labor economics literature takes men and women’s labor-supply decisions to be sufficiently different as to warrant separate analysis.

We analyze several labor-supply decisions: (1) the propensity to work, (2) hours worked by adults (which is zero for adults who do not work), (3) hours worked by adults who work at least one hour per week, (4) the propensity to work full-time (35 hours or more) by adults who work at least one hour per week, (5) the propensity to work more than one job by adults who work at least one hour per week, (6) hours worked by adults who do not work full-time (i.e. who work less than 35 hours per week), (7) minutes worked at home, (8) minutes worked at work and (9) share of work done from home. This section describes the association between having Internet at home (includ-ing whether it is dialup or broadband) and each of these labor-market outcomes, in turn. We analyze (1) through (6) using data from the CPS and (7) through (9) using ATUS data.

The Propensity to WorkTable 2 presents OLS regressions with the goal of measuring the propensity to work as a function of having Internet and broadband Internet at home. Columns (1) and (3) (for females and for


Table 2: The Relationship between Having Internet and Broadband at Home and the Propensity to Work, by Gender

(1) (2) (3) (4)

Dependent Variable: Dummy – in labor

force and working

Female Male

mean = 0.708 mean = 0.841

Internet 0.089 0.045 0.093 0.043

(0.003)*** (0.003)*** (0.003)*** (0.003)***

Internet × broadband 0.041 0.025 0.043 0.029

(0.004)*** (0.004)*** (0.003)*** (0.003)***

Dummy – aged 31-35 -0.005 -0.003

(0.004) (0.004)

Dummy – aged 36-40 -0.006 -0.007

(0.004) (0.004)*

Dummy – aged 41-45 -0.004 -0.025

(0.004) (0.004)***

Dummy – aged 46-50 -0.013 -0.039

(0.004)*** (0.004)***

Dummy – aged 51-55 -0.053 -0.077

(0.005)*** (0.004)***

Dummy – married -0.047 0.093

(0.003)*** (0.003)***

Dummy – HS diploma 0.169 0.098

(0.005)*** (0.004)***

Dummy – some college 0.202 0.117

(0.006)*** (0.005)***

Dummy – Associate degree 0.260 0.139

(0.006)*** (0.005)***

Dummy – BA degree 0.253 0.170

(0.005)*** (0.005)***

Dummy – Masters or higher 0.317 0.175

(0.006)*** (0.005)***

Dummy – children aged 0-5 -0.126 0.012

(0.003)*** (0.003)***

Dummy – children aged 6-11 -0.038 0.011

(0.003)*** (0.002)***

Dummy – children aged 12-17 0.013 0.020

(0.003)*** (0.003)***

Constant 0.619 0.509 0.756 0.678

(0.002)*** (0.007)*** (0.002)*** (0.006)***Observations 176301 176301 164082 164082R-squared 0.02 0.07 0.04 0.08

Notes: Robust standard errors in parentheses. All specifications include state × year fixed effects, and dummies for being a U.S. native, black, not black or white, Hispanic, and having Spanish be the only language all household members speak. * Significant at 10%; ** significant at 5%; *** significant at 1%.Source: Current Population Survey (CPS), 1998, 2001, 2003, 2007, 2009, and 2010 data.


males, respectively) show correlations when the only control variables are state × metro status × year fixed effects. The inclusion of these fixed effects, of course, absorbs all time-invariant charac-teristics unique to a given state × metro status × year (call it a “cell”). For example, urban Colorado in 2010 had a particular set of Internet prices and labor-market characteristics that would affect anyone living in urban Colorado in 2010. By including a dummy for urban Colorado in 2010 (and dummies for every other possible cell — a total of almost 600 dummies), we only compare individuals in this cell with other individuals in the same cell. Columns (2) and (4) (for females and for males, respectively) show correlations when we add controls for age, marital status, race, ethnicity, language spoken at home, education and the presence of children in the household.

What is first apparent is that having Internet at home is positively correlated with working in all specifications. This correlation is even stronger in the case of broadband Internet, where we see an incremental effect of Internet being relatively high-speed as opposed to dialup. These observed correlations are highly statistically significant. It is also apparent that the correlations between having Internet and working are much smaller in magnitude in the specifications of Columns (2) and (4) — which contain the full set of control variables — than they are in Columns (1) and (3), which lack them. This is consistent with the channels of upward bias due to omitted variables discussed in Section 2.

In general, the coefficients on the control variables are rather intuitive: women with young chil-dren are less likely to work, men with young children are more likely to work, propensity to work is increasing in educational attainment for both genders, married women are less likely to work, while married men are more likely to work and people are less likely to work as they age. As these variables likely are correlated with Internet and broadband adoption and as they appear to have statistically significant effects on working, their inclusion in the regression can help us reduce the bias in estimates of the effect of Internet and broadband on propensity to work.

If we take Columns (2) and (4), with the full set of controls, to be our baseline specifications, we see several things. Relative to women without Internet at home, women with dialup Internet are 6% more likely to work. This is because the probability of working is 4.5 percentage points higher when women have Internet, which is about 6% of 0.71 — the baseline probability of a woman working. However, women with high-speed (broadband) Internet are 10% more likely to work — an effect we can observe by adding the coefficients on Internet and Internet × broadband. This is because the probability of working is 7.0 percentage points higher when women have broadband Internet, which is about 10% of the baseline probability of a woman working. Similarly, men with dialup Internet are 5% more likely to work and men with broadband Internet are 9% more likely to work than are men without Internet at home.

These effects are large relative to the mean rate at which women and men work, 84.1% and 70.8%, respectively — especially in the case of women. Of course, we must take care not to interpret the estimates necessarily as causal effects. Nonetheless, they suggest a large correlation between Internet adoption and working decisions — one that is reduced in magnitude but not nearly eliminated by controlling for extensive individual-level controls.

That broadband Internet has an incremental effect on the propensity to work — above and beyond the effect of dialup, or lower-speed Internet — is itself an interesting, though not unexpected, finding. This effect is non-trivial relative to the effect of dialup; for both men and women, having broadband leads to a more than 50% increase in the effect of dialup (i.e. 0.025 is more than 50% of 0.045, and 0.029 is more than 50% of 0.043). It is intuitive that individuals who are able to work more efficiently due to having a high-speed Internet connection are most likely to adapt their behavior.


Hours WorkedTable 3 presents OLS regressions with the goal of measuring the correlation between hours worked and having Internet and broadband Internet at home. All specifications contain a full set of control variables and state × metro status × year fixed effects, given the discussion earlier in this section. Columns (1) and (2) consider the full sample of adults aged 26–55, and present results for females and then for males, respectively. Columns (3) and (4) consider only working adults (i.e. at least one hour per week) and present results for females and then for males, respectively.

Among all adults, having Internet at home is positively correlated with hours worked last week for both females and males. This correlation is even stronger in the case of broadband Internet, where we again see an incremental effect of Internet being high-speed as opposed to dialup. Women with dialup Internet work about 1.7 more hours per week and those with broadband about 2.8 more hours per week, than similar women without Internet. Men with dialup Internet work about 2.6 more hours per week and those with broadband about 4.0 more hours per week, than similar men without Internet.

The findings are somewhat different when instead of examining all adults (whether they work or not), we consider only adults who work at least one hour per week. Running regressions using this smaller sample of adults is important and instructive. In short, we want to know if people with Internet work more hours simply because Internet draws people to work who would otherwise not work (the extensive margin of the Internet’s possible effect), or if people with Internet work more hours because those who would already work now work even more (the intensive margin). When we run regressions on working adults, having Internet at home does not appear to have a signifi-cant effect on the number of hours women work (though having broadband is associated with about 15 minutes more work per week). Among working men, those with dialup Internet work approximately one additional hour per week. Those working men with broadband work about 1.2 more hours per week than similar working men without Internet.

These findings — though they cannot be interpreted as causal — are suggestive that a large part of the Internet’s effect on men’s labor supply comes through the extensive margin. That is, it is

Table 3: The Relationship between Having Internet and Broadband at Home and Hours Worked, by Gender

(1) (2) (3) (4)

Dependent Variable:

Hours worked last week (among all adults)

Hours worked last week (among adults working 1+ hours)


mean = 26.43 mean = 36.72 mean = 37.34 mean = 43.69

Internet 1.668 2.608 -0.029 0.894

(0.150)*** (0.154)*** (0.108) (0.107)***

Internet × broadband 1.088 1.345 0.257 0.303

(0.167)*** (0.170)*** (0.124)** (0.120)**

Observations 176301 164082 124803 137912

R-squared 0.09 0.10 0.05 0.04

Notes: Robust standard errors in parentheses. All specifications include state × year fixed effects, and dummies for age, race, whether only Spanish is spoken at home, education, marital status, and presence of children at different ages (0–5, 6–11, and 12–17). * Significant at 10%; ** significant at 5%; *** significant at 1%.Source: Current Population Survey (CPS), 1998, 2001, 2003, 2007, 2009, and 2010 data.


likely to come through drawing men to work who otherwise would not. In the case of women, nearly all of the Internet effect on labor supply seems to come through the extensive version. That is, the Internet is associated with women being more likely to work, but not with working women supplying additional hours of work. This difference between the genders also highlights the importance of separately considering women and men’s labor-supply decisions in future work on the labor-supply effects of the Internet.

The Propensity to Work Full-timeColumns (1) and (2) of Table 4 present, for women and men, respectively, regressions of a dummy for working full-time (i.e. 35+ hours per week) on dummies for having Internet and broadband at home. All specifications analyze the labor-supply decisions of adults aged 26-55, and contain our full set of controls and fixed effects. In these regressions, we include only adults who are working (i.e. supplying at least 1 hour of labor per week). Among adults who want to and are able to work, is having Internet (and broadband) at home associated with a higher likelihood of working full-time?

Having Internet at home actually is negatively associated with working women doing so full-time, although the effect is very small. Column (1) shows that while on average, 80% of women who work do so on a full-time basis, working women with Internet at home are 1.2 percentage points less likely to work full-time and women with broadband are 0.5 percentage points less likely to work full-time than are similar women without Internet. The finding on Internet is significant at the 1% level, though the finding on broadband is not significant at conventional levels. Of course, these effects cannot necessarily be interpreted as causal. It may be, for example, that having Internet at home and working women’s propensity to work full-time are influenced by spousal income (e.g., women with high-earning spouses are better able to afford Internet and less pres-sured to work full-time). Regardless, we do not find evidence that — conditional on deciding to work — the Internet (or broadband) makes women more likely to do that work full-time.

The findings reverse for working men with Internet at home. While on average, 86.9% of men who work do so full-time, working men with Internet at home are 0.6 percentage points more likely to

Table 4: The Relationship between Having Internet and Broadband at Home and the Propensity to Work Full-time, Part-time and Multiple Jobs, by Gender

(1) (2) (3) (4) (5) (6)

Dependent Variable:

Dummy – full time (35+ hours) (among adults work-

ing 1+ hours)

Dummy – part-time worker (among adults working

0-34 hours)

Dummy – two or more jobs (among adults with

1+ jobs)

Female Male Female Male Female Male

Mean: 0.800 0.869 0.413 0.408 0.067 0.061

Internet -0.012 0.006 0.058 0.056 0.009 0.013

(0.004)*** (0.003)** (0.005)*** (0.008)*** (0.002)*** (0.002)***

Internet x broadband 0.007 0.008 0.015 0.021 0.001 0.000

(0.005) (0.003)** (0.006)** (0.009)** (0.002) (0.002)

Observations 124803 137912 87708 44239 129139 141451

R-squared 0.04 0.02 0.07 0.08 0.02 0.01

Notes: Robust standard errors in parentheses. All specifications include state × year fixed effects, and dummies for age, race, whether only Spanish is spoken at home, education, marital status, and presence of children at different ages (0–5, 6–11, and 12–17). * Significant at 10%; ** significant at 5%; *** significant at 1%.Source: Current Population Survey (CPS), 1998, 2001, 2003, 2007, 2009, and 2010 data.


work full-time. In addition, men with broadband are 1.4 percentage points more likely to work full-time than are similar men without Internet. While these effects are small in some sense, they are statistically significant at the 5% level. They also highlight the importance of separately estimating relationships between technology adoption and labor supply decisions for women and for men.

Hours Worked Among those Not Working Full-timeThose who do not work full-time are in many senses a separate class of individuals. Among this class, some choose to supply no labor (59% for both genders), while others work part-time, sup-plying between one and 34 hours of labor per week (41% for both genders). What explains this choice?

The decision to work might be, for example, a discrete choice of whether to work 0 hours, 20 hours or 40 hours per week. Those who want to work fewer than 20 hours per week may simply decide to work 0 hours. Since there are reasons to believe that having Internet at home makes it more feasible to work at least one but fewer than 20 hours per week, we might expect people who do not want to work full-time to join the labor force in greater numbers when they have the flexibility that home Internet lends. We also might expect broadband to intensify this effect since Internet speed has the potential to improve the feasibility and functionality of at-home work.

In Table 4, Columns (3) and (4) analyze the working decisions of adults aged 26-55 who do not work full-time. They show that — for women and for men, respectively — having Internet at home is associated with a higher likelihood of working part-time. Among women not working full-time, those with home Internet are 14% more likely to work part-time, and those with broad-band are 18% more likely to work part-time, compared to similar women without home Internet. The effects are similar for men: those with home Internet are 14% more likely to work part-time, and those with broadband are 19% more likely to work part-time, compared to similar men with-out home Internet.

These numbers come from the fact that 41% of adults not working full-time work part-time and having Internet at home is associated with a 5.8-percentage-point increase in the likelihood of a woman (5.6-percentage-point increase in the likelihood of a man) to work part-time. Also, hav-ing broadband is associated with a 7.3-percentage-point increase in the likelihood of a woman (7.8-percentage-point increase in the likelihood of a man) working part-time. The effects of Internet are statistically significant at the 1% level and those of broadband are significant at the 5% level. These effects are quite large and suggest different working behaviors among those not working full-time who have Internet and those who do not have Internet.

The Propensity to Work Two or More JobsA potentially important way in which having Internet could influence labor supply is by allowing workers to take a second (or third) job. A number of jobs can be performed outside of a typical office setting, including many that can be performed entirely from home using the Internet. For example, oDesk.com — currently the largest online remote labor-market platform — connects employers with workers for short-term projects where output is delivered electronically. Individuals with home Internet can tap into such labor-market opportunities to more flexibly choose the number of hours they want to work each week. For example, if someone wants to work 45 hours/week but has a job that requires only 40 hours/week, a worker can find a second job to make up the other 5 hours. The costs of commuting to a job that is only 5 hours of work per week and the difficulty of finding such a job may lead this person to work only 40 hours/week at one job. With home Internet, however, this person may easily find 5 hours of work per week on a platform such as oDesk.com, which involves no commuting costs.


In Table 4, Columns (5) and 6) analyze adults who have at least one job. They show that — for women and for men, respectively — having Internet at home is associated with a higher likelihood of having two or more jobs. Among women with at least one job, those with Internet at home are 13% more likely to work 2+ jobs, compared to similar women without home Internet. For men with at least one job, those with Internet at home are 21% more likely to work 2+ jobs, compared to similar men without home Internet. This is because about 6.7% of women (6.1% of men) with at least one job work 2+ jobs and having Internet at home is associated with a 0.9-percentage-point increase in the likelihood of a woman (1.3-percentage-point increase in the likelihood of a man) having 2+ jobs.

Importantly, there appears to be no incremental effect on the propensity to work 2+ jobs if one has broadband Internet. The coefficients on Internet × broadband, for women and men, are small in magnitude and statistically insignificant. Thus, women with dialup Internet see similar effects as do women with broadband. The same is true for men with dialup vs. broadband.

These effects are not small. They suggest a powerful role for Internet technology in influencing how people work and how flexibly they work. Of course, these findings fail to tell us whether workers with 2+ jobs perform part of one or both of those jobs online. It could be, for example, that having Internet at home is simply a convenience that frees up more time for working in the office. For example, paying bills, managing one’s finances, and shopping online could generate significant time savings that simply allow workers to spend more hours each week in the office. To better understand home Internet’s effects on time use, we now turn to an analysis of data on time use from the ATUS.

Time UseWe examine the effects of home Internet and home broadband on three variables: minutes worked from home each day; minutes worked from work each day; and the share of work done from home. If Internet is associated with a higher propensity to work, or with a propensity to work more hours and more jobs — as suggested earlier in this section — we would expect to see this in how time is spent, as well. It also is interesting to analyze if — aside from the decision of how many hours to work — the Internet is associated with working those hours in different locations. This could represent an additional utility to individuals, if working implies less disutility by virtue of the ability to perform it from home.

Table 5 uses data from the ATUS on time spent working, which are linked to data from the CPS on labor supply and Internet-adoption decisions. ATUS data are based on time diaries kept by individual workers, so we can use them to analyze whether having home Internet affects how individuals spend an average (Monday – Friday) working day.

Having Internet and broadband at home always is positively correlated with minutes worked at home, minutes worked at work and the share of work done from home, though the findings are not always significant. Columns (1) and (2) show results for females (first without, and then with individual-level controls). Columns (3) and (4) show the same results for males. The results for females and males are somewhat different, with Internet having the greatest effect for women (and very little incremental effect from having broadband), while Internet has little effect on men but broadband has a large incremental effect. The variability in these results may be at least in part due to the relatively small sample of individuals who completed ATUS time diaries and also responded to a CPS Internet supplement survey.

For women (taking the specification with full controls as our baseline), having Internet or broad-band at home is associated with about 8 additional minutes of work at home daily. Given that


Table 5: The Relationship between Having Internet and Broadband at Home and Time Use, by Gender

(1) (2) (3) (4)

Female Male

Dependent Variable: Minutes Worked at Home during Monday – Friday

Mean: 21.82 21.82 30.91 30.91

Internet 10.328*** 7.760*** 4.087 0.581

(2.767) (2.881) (4.701) (5.390)

Internet x broadband 1.878 -0.247 13.365** 10.042*

(3.110) (3.066) (5.368) (5.814)

Observations 6,582 6,581 5,264 5,263

R-squared 0.080 0.088 0.107 0.114

Dependent Variable: Minutes Worked at Work during Monday – Friday

Mean: 239.68 239.68 355.24 355.24

Internet 20.708* 10.500 24.809* 14.010

(11.473) (11.882) (14.264) (14.859)

Internet x broadband 0.534 -1.632 -6.255 -8.647

(10.618) (10.567) (12.951) (12.936)

Observations 6,582 6,581 5,264 5,263

R-squared 0.215 0.233 0.278 0.284

Dependent Variable: Share of Work done at Home during Monday – Friday

Mean: 0.14 0.14 0.10 0.10

Internet 0.084*** 0.065*** 0.022 0.001

(0.021) (0.021) (0.018) (0.019)

Internet x broadband 0.030 0.011 0.061*** 0.046**

(0.023) (0.022) (0.018) (0.019)

Observations 2,646 2,645 2,833 2,832

R-squared 0.241 0.268 0.212 0.230

Education Effects N Y N Y

Marital Status Effects N Y N Y

Children by Age Group Effects N Y N Y

Notes: Robust standard errors in parentheses. All specifications include state × metro status × year fixed effects, age group dummies, race dummies, and Spanish-language dummies, along with an indicator that the survey day was Monday-Friday (as opposed to a weekend day). All regressions include adults aged 26–55. Sources: American Time Use Survey (ATUS) and Current Population Survey (CPS), 2003, 2007, and 2009 data.


the average woman in the ATUS sample does approximately 22 minutes of work from home each day, this represents about a 37% increase in work from home daily. For men, having broadband at home is associated with about 11 additional minutes of work at home each day. Given that the average man does some 31 minutes of work from home each day, this represents about a 34% increase in work from home each day. Both having Internet and broadband Internet at home have positive but insignificant effects on minutes spent working at work, when we examine specifica-tions with our complete set of individual-level controls (Columns 2 and 4).

There also is evidence that having Internet and broadband at home is associated with shifts of work from the office to the home. For women, having Internet at home is associated with a 6.5 percent-age point increase in the share of work done from home. As the average woman does 14% of work at home, this is a large effect; it implies a 46% increase in the share of work done from home. The incremental effect of broadband is positive but insignificant, suggesting no significant differences between women with simple Internet and those with broadband Internet. For men, having Internet at home has no effect on the share of work done from home, but having high-speed broadband at home is associated with a 4.7-percentage-point increase in the share of work done from home. As the average man does 10% of work at home, this is large; it implies a 47% increase in the share of work done from home.

All of this evidence suggests that the Internet and especially broadband Internet are associated with men and women working differently. They shift some of their (now larger) work load to the home, possibly a reason why they are able to work more hours. Working from home is a more flexible arrangement that can allow for multi-tasking and working a non-typical number of hours and set of jobs.

3.5 How Have the Effects of the Internet Changed Over Time? Table 6 examines how the effects of Internet and broadband have changed over time. The results strongly suggest that the correlation between home Internet and labor supply has increased over time. For female participation in the labor force the results are striking. For women, the estimated effect of Internet on propensity to work after 2003 is nearly double the estimated effect of Internet on propensity to work prior to 2003. In addition, there is an incrementally positive effect of having broadband after 2003.

Female hours of work do not appear to be affected by Internet or broadband Internet prior to 2003, but after 2003, there is a large incremental effect captured through the broadband post-2003 interaction. The same is true for male hours of work except that there is a small positive effect of Internet on hours worked prior to 2003.

These results suggest that the effect of home Internet has increased over time. Just as e-commerce didn’t displace traditional retail immediately, it appears that the effect of home Internet and broad-band infrastructure is cumulative and increasing, especially in its relationship to employment.

3.6 How Have the Effects of the Internet Varied Across Different Groups? This section documents interactions between home Internet and demographic characteristics. These results are mainly presented as interesting stylized facts, which may spur future research. Table 7 displays the results for different subgroups.


Panel A of Table 7 displays results with interactions between the Internet dummy and metro sta-tus. A negative coefficient on metro status suggests that the correlation between Internet at home and labor market outcomes is greater for individuals living in rural areas. The data suggest that this is true for women on the extensive margin. That is, women overall are more likely to work if they have Internet, but the magnitude of the effect is greater for women in rural areas who are 2.7% more likely to work than similar women in urban areas.

This evidence is consistent with the notion that women in rural areas can more easily perform work at home or search for work due to home Internet. We are not, however, able to disentangle these two competing explanations using time use data because of small sample sizes for rural women in the time use surveys. Hours worked does not appear differentially responsive to Internet depending on metro status, so it is likely that at least part of this effect is due to the increasing ease of job search. There are no differential effects with respect to metro status for men.

Panel B displays interactions with education. In all specifications, for men and women, having a B.A. degree or higher reduces the impact of having Internet. Individuals with a bachelor’s degree and home Internet are 2–2.5% less likely to work than similar individuals without home Internet. This is somewhat surprising, but it is consistent with several potential explanations. First, hours and labor force participation likely will be high for individuals with a B.A. degree, regardless of the presence of the Internet, so the incremental effect of Internet may be more limited for highly educated individuals. Another possibility is that home Internet allows less-educated individuals to more effectively search for work, whereas individuals with a college degree likely have pre-existing offline job search networks. Finally, the Internet may effectively allow individuals to engage in

Table 6: Changes Over Time in the Relationship between Having Internet and Broadband at Home and Labor-Supply Decisions, by Gender

(1) (2) (3) (4)

Dummy – in labor force and workingHours worked last week

(among adults working 1+ hours)


Mean: 0.708 0.841 37.34 43.69

Internet 0.032 0.037 -0.106 0.892

(0.004)*** (0.003)*** (0.121) (0.119)***

Internet x after 2003 0.036 0.012 0.076 -0.262

(0.009)*** (0.008) (0.272) (0.269)

Internet x broadband -0.012 0.006 -0.151 -0.022

(0.006)** (0.004) (0.187) (0.173)

Internet x broadband x after 2003 0.039 0.031 0.646 0.719

(0.009)*** (0.008)*** (0.296)** (0.285)**

Observations 176301 164082 124803 137912

R-squared 0.08 0.08 0.05 0.04

Notes: Robust standard errors in parentheses. All specifications include state × year fixed effects, education dummies, age dummies, children dummies (dummy for children aged 0-5, 6-11, and 12-17), and dummies for being married, a U.S. native, black, not black or white, Hispanic, and having Spanish be the only language all household members speak. * Significant at 10%; ** significant at 5%; *** significant at 1%.Source: Current Population Survey (CPS), 1998, 2001, 2003, 2007, 2009, and 2010 data.


Table 7: Analysis of How the Relationship between Having Internet at Home and Labor Supply Decisions Varies with Age, Education, Metropolitan Status, and Race

(1) (2) (3) (4)

Dummy – in labor force

and workingHours worked last week

(among adults working 1+ hours)


mean = 0.708 mean = 0.841 mean = 37.34 mean = 43.69

Panel A: METRO INTERACTIONS

Internet 0.072 0.058 0.197 0.873

(0.006)*** (0.005)*** (0.183) (0.195)***

Internet x dummy for metro area -0.019 -0.001 -0.139 0.209

(0.007)*** (0.006) (0.206) (0.213)

R-squared 0.07 0.08 0.05 0.04

Panel B: EDUCATION INTERACTIONS

Internet 0.061 0.062 0.184 1.193

(0.003)*** (0.003)*** (0.103)* (0.101)***

Internet x dummy for a BA degree -0.021 -0.021 -0.409 -0.722

(0.006)*** (0.005)*** (0.196)** (0.204)***

R-squared 0.07 0.08 0.05 0.04

Panel C: AGE INTERACTIONS

Internet 0.095 0.072 0.641 1.395

(0.004)*** (0.004)*** (0.126)*** (0.124)***

Internet x dummy for being born before 1964

0.016 0.032 -0.061 0.469

(0.005)*** (0.005)*** (0.164) (0.161)***

R-squared 0.05 0.06 0.04 0.04

Panel D: BLACK INTERACTIONS

Internet 0.097 0.082 0.428 1.596

(0.003)*** (0.003)*** (0.097)*** (0.093)***

Internet x dummy for being black 0.046 0.055 1.103 0.390

(0.008)*** (0.008)*** (0.220)*** (0.265)

R-squared 0.05 0.06 0.04 0.04

Observations 176301 164082 124803 137912

Notes: Robust standard errors in parentheses. All specifications include state × year fixed effects, education dummies, age dummies, children dummies (dummy for children aged 0-5, 6-11, and 12-17), and dummies for being married, a U.S. native, black, not black or white, Hispanic, and having Spanish be the only language all household members speak. * Significant at 10%; ** significant at 5%; *** significant at 1%.Source: Current Population Survey (CPS), 1998, 2001, 2003, 2007, 2009, and 2010 data.


routine services work, like data entry, from home. This type of work typically is concentrated among individuals with less than a B.A.

Panel C displays age interactions. Surprisingly, the results suggest that the Internet has a greater effect for older individuals in the sample. Older men and women with home Internet are more likely to work than similar men and women who are younger. Older men with home Internet work an additional half hour relative to younger men with home Internet. This may be spurious, as older individuals who take up the Internet likely are technologically savvy and employable. On the other hand, it may be that home Internet provides older individuals with limited mobility the flexibility to work when they otherwise would not be able to.

Panel D includes interactions for black individuals. The coefficient on the indicator for being black and also having home Internet is significant and positive for females’ labor-force participa-tion and hours of work. The coefficient also is positive for black males’ labor-force participation. The effects are large — black individuals with home Internet are 6.5% more likely to work than similar black individuals without home Internet. Black women with home Internet likely will work approximately one additional hour as compared to black women without home Internet. These results suggest that black individuals who take up home Internet likely will have superior labor-market outcomes. Unfortunately, at this point, we cannot determine whether this is a selection effect, where the most-able members of these demographic groups adopt Internet or a treatment effect caused by the Internet. One possible causal explanation to be tested in future work is the possibility that black individuals face less discrimination when securing and performing work over the Internet.


Taken together, these results suggest that individuals with Internet at home are more likely to work, to work longer hours and to do a larger amount and share of work from home than are similar individuals who do not have home Internet. High-speed Internet increases the magnitude of these effects. In addition, we document that the association between home Internet and these labor-supply measures has increased dramatically over time and varies with metropolitan status, educational attainment and race. In particular, having Internet at home is associated with larger increases in employment and hours worked among women in rural areas, less-educated men and women and black men and women.

The potential for home Internet and improved telecommuting technology to improve welfare is nontrivial. Although our estimates cannot necessarily be interpreted as causal, they are consistent with the notion that the Internet has given workers more flexibility and in doing so has improved welfare. If the observed effects on propensity to work are even partially causal, these findings also have important implications for the U.S. economy, as gross domestic product would be affected by a substantial increase in labor-force participation.

Future work should investigate to what degree these estimates are causal and further explore subpopulations that are especially likely to benefit from the opportunity to work from home. In particular, if suitable data become available, it would be interesting to see how the disabled have been affected by these technological advances.

4. Conclusion


1. See Connelly (1992) for an analysis of childcare costs.2. Note that our analysis is confined to studying the effect of relatively high-speed (broadband) Internet versus basic

dialup access and does not make further distinctions on the basis of speed or other service characteristics.3. With 50 states, having state × metro status × year fixed effects implies having 100 fixed effects (50 × 2) for each year

of data included in the analysis. With 6 years of data, for example, this is 600 fixed effects.4. The CPS Internet supplement is a set of supplemental questions asked of CPS basic survey participants. Supplemental

questions related to the Internet were asked in October 2003, 2007, 2009, and 2010, in September 2001 and December 1998.

5. For some calculations we also include data from the October 1997 CPS, which included an Internet supplement, but did not contain a question allowing us to determine whether a household had access to broadband Internet, separate from Internet generally.

Endnotes


Brynjolfsson, Erik and Lorin M. Hitt. (2003). “Computing Productivity: Firm-Level Evidence.” The Review of Economics and Statistics 85(4): 793-808.

Connelly, Rachel. (1992). “The Effect of Child Care Costs on Married Women’s Labor Force Participation.” The Review of Economics and Statistics 74(1): 83-90.

Crandall, Robert, William Lehr, and Robert Litan. (2007). “The Effects of Broadband Deployment on Output and Employment: A Cross-sectional Analysis of U.S. Data.” Working Paper.

Forman, Chris, Avi Goldfarb, and Shane Greenstein. (2012). “The Internet and Local Wages: A Puzzle.” American Economic Review 102(1): 556-575.

Freeman, Richard. (2002). “The Labour Market In the New Information Economy.” NBER Working Paper 9254.

Gronau, Reuben. (1977). “Leisure, Home Production, and Work – the Theory of the Allocation of Time Revisited.” Journal of Political Economy 85(6): 1099-1123.

Kamssu, Aurore, Jeffry Siekpe, James Ellzy, and Aurora Kamssu. (2004). “Shortcomings to Globalization: Using Internet Technology and Electronic Commerce in Developing Countries.” The Journal of Developing Areas 38(1): 151-169.

Katz, Raul and Stephan Suter. (2009). “Estimating the Economic Impact of the Broadband Stimulus Plan.” Working Paper.

Kolko, Jed. (2010). “Does Broadband Boost Local Economic Development?” Public Policy Institute of California Working Paper.

Krueger, Alan. (1993). “How Computers Have Changed the Wage Structure: Evidence from Microdata, 1984-1989.” Quarterly Journal of Economics 108(1): 33-60.

Martin, Steven and John Robinson. (2007). “The Income Digital Divide: Trends and Predictions for Levels of Internet Use.” Social Problems 54(1): 1-22.

References


Sara Champion is an associate at Cornerstone Research, an economic consulting firm, where she works on antitrust, general damages, labor and health care cases. Champion received her Ph.D. from Stanford University’s Graduate School of Business’ Economic Policy and Analysis program. At Stanford, her primary research fields were labor economics, public finance and personnel economics. Her dissertation, Educational Output and Management Practices in Teaching, examined the labor market for teach-ers. She received an M.S. in Statistics and a B.A. in Economics and Statistics from the University of Illinois. She lives in Palo Alto, California.

Katrina Kosec is a post-doctoral research fellow with the Development Strategy and Governance Division of the International Food Policy Research Institute (IFPRI) and the Abdul Latif Jameel Poverty Action Lab (JPAL) South Asia. She is an applied microeconomist working at the intersection of development economics, political economy, and public economics. Ms. Kosec holds a Ph.D. in Political Economics from Stanford University.

Before joining IFPRI, Ms. Kosec worked with organizations including the Human Development Group at the World Bank, the AEI-Brookings Joint Center for Regulatory Studies, the U.S. Department of State, and the Development Research Group at the World Bank.

Christopher Stanton is an applied microeconomist in the Department of Finance at the University of Utah. He teaches the doctoral sequence in econometrics, and has research interests in labor economics, the econom-ics of technology and digitization, applied econometrics, and corporate governance.

Stanton earned an M.A. in Political Science and a B.A. in International Studies, Math and Economics from Emory University. He received a Ph.D. from the Stanford Graduate School of Business in 2011. Prior to gradu-ate school, he worked in economic and litigation consulting at the Brattle Group in San Francisco, CA.

About the Authors


Sara Champion Cornerstone Research 1000 El Camino Real, Suite 250 Menlo Park, CA 94025-4327 (650) 470-7099 e-mail: [email protected]

Katrina KosecInternational Food Policy Research Institute 2033 K Street, NWWashington, DC 20006(202) 862-4663e-mail: [email protected]

Christopher Stanton Department of Finance University of Utah 1655 East Campus Center Drive Office: SFE8117 Salt Lake, Utah 84112 Office phone: (801) 581-9487 e-mail: [email protected]

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Documents

The Effects of Internet Access on Labor-Supply Decisions