Immigration, Skill Mix, and the Choice of Technique * Ethan Lewis. Federal Reserve Bank of Philadelphia ** March 2006

Transcription

1 Immigration, Skill ix, and the Choice of echnique * Ethan Lewis Federal Reserve Bank of Philadelphia ** arch 2006 * I am grateful for the valuable feedback I received on this work from David Autor, Paul Beaudry, ichael Ben-Gad, arianne Bitler, Clair Brown, Benjamin Campbell, David Card, Elizabeth Cascio, Kenneth Chay, Andrew Hildreth, David Lee, Albert Saiz, and seminar participants at the University of British Columbia, U.C. Berkeley, Society of Labor Economists eetings, the IZA conference on International igration, Philadelphia Federal Reserve Bank, the University of aryland, San Francisco Federal Reserve Bank, and Drexel University. Shannon ail provided excellent research assistance. I alone am responsible for any errors. ** he views expressed here are those of the author and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System or the US Bureau of the Census.

2 Immigration, Skill ix, and the Choice of echnique Abstract U.S. manufacturing plants have become more automated in recent decades, but the extent of automation varies widely across metropolitan areas. his paper uses plant-level data to ask whether the mix of skilled and unskilled labor in a plant s metropolitan area affects its use of automation. In markets with a higher relative availability of less-skilled labor, comparable plants even plants in the same narrow (4-digit SIC) industries use less automation. his relationship is also present when examining variation in skill mix derived from historical regional settlement patterns of immigrants. hese results confirm automation substitutes for less-skilled labor, and, more importantly, support models in which producers adapt technique to factor mix. Such models may explain why immigration-induced shifts in skill ratios have little impact on relative wages in U.S. local labor markets. EL: 2, F, O3 Keywords: echnological change, immigration, skill

3 A large body of research argues recent technology advances have raised skill requirements in the U.S. labor market. Evidence for skill-biased technological change primarily consists of the association between the use of technology and the relative employment and wages of skilled workers when looking across workers (e.g., Krueger, 993), plants (e.g. Dunne et al., 2004), and industries (e.g. Autor, Levy, and urnane, 2003). Researchers also argue that the supply of skills has not kept pace with demand, enlarging the gap between the earnings of skilled and unskilled workers (e.g. Katz and urphy, 994). At the same time, however, the U.S. is in the midst of an immigration boom which has raised the proportion of workers who are less-skilled, particularly in certain parts of the U.S. Since 970, immigrants 40 percent of whom have less than a high school education (compared to 0 percent of native-born Americans) have risen from 5 to 5 percent of the U.S. workforce. Furthermore, the impact of this boom has been geographically uneven: immigrants are highly concentrated in particular labor markets, and the proportion of the workforce which is lessskilled is higher in more immigrant-dense markets. Yet study after study has found that the local labor market impact of immigration on the relative employment rates and wages of less-skilled workers is almost zero. High-immigration markets have succeeded in productively employing large amounts of unskilled workers despite the supposedly increased demand for skilled labor that the diffusion of new technologies has generated. How is this possible? One way markets may be able to absorb less-skilled immigrants is by adopting less of the new skill-intensive technologies. 2 he expectation that the local labor market impact of Borjas (994) and Friedberg and Hunt (995) provide reviews of this literature. Note that this is also despite evidence in other contexts that labor supply has a negative impact on wages (Hamermesh, 993), including evidence that immigration has an impact at the national level (Borjas, 2003). 2 Another explanation, discussed further below, is that local markets in the U.S. are each a small part of a large and integrated national economy so factor prices are insensitive to local factor mix. Adjustment to immigration, in this view, takes place through shifts in the industry mix. However, Lewis (2004b) finds shifts in metropolitan area s mix of 3-digit industries absorb at most 0 percent of immigrant-induced skill mix differences across markets.

4 immigration ought to be large derives from a standard view that production technique is invariant to input availability. However, recent models of innovation (Acemoglu, 998) and endogenous choice of technique (Beaudry and Green, 2003, 2005) show how relative wages need not respond negatively to the relative supply of different labor inputs when technique is endogenous. 3 In a version of Beaudry and Green (2005) presented below, a skill-intensive and a less-skilled intensive technique co-exist, and a market adjusts to an influx of less-skilled immigrants by increasing the relative use of the less-skilled technique just enough to employ the immigrants at existing wages. he model is identical to a standard Heckscher-Ohlin model with goods relabeled as techniques. his paper evaluates the extent to which producers adapt technology to local input supplies using detailed data from the 988 and 993 Surveys of anufacturing echnology (Ss). hese data measure the use of automation technologies which have been introduced into manufacturing in the past few decades (see Appendix able ). Like with other recent technological advances, new plant automation techniques were projected to increase the relative employment of skilled workers, or as one study put it, jobs eliminated are semi-skilled or unskilled, while jobs created require significant technical background. (Hunt and Hunt, 983, p. xii.) Doms, Dunne, and roske (997) used the S data to show that more automated plants do indeed have a higher skilled employment share. hey also showed, however, the same plants had a higher skill share well before they adopted the new technology. 4 Given this, it is appropriate to ask the extent to which causality runs from skills to technology rather than the reverse. anufacturing automation is particularly suited to evaluate the impact of immigration 3 he idea that employers adapt technology to input availability is not new (see, e.g., Solow, 962; ohansen, 959; Habbakkuk, 962) but the implications of this have mostly been neglected until recently. 4 On top of this, Luque and iranda (2005) have used the S data to show that the higher average wages paid at technologically-intensive plants comes from firm and worker unobservables rather than the effect of technology. 2

5 because less-skilled workers in S-covered industries, especially immigrants, are concentrated in labor-intensive assembly, welding, and other tasks that these technologies replace. (See able.) echnology data are supplemented with metropolitan area-level labor force data aggregated from Current Population Surveys and Censuses of Population microdata. he combined data show that, in two separate cross-sections, the higher the relative number of workers in a metropolitan area who were high school dropouts, the less automated the plants in the area were. In addition, between 988 and 993, plants use of technology grew more slowly where the relative number of dropouts in the local work force grew more quickly. Instrumental variables estimates are no smaller than OLS estimates. A typical estimate is that a 0 percentage point (one standard deviation) increase in the less-skilled relative supply reduces the number of technologies in use at a typical worker s plant by 0.5 technologies on a base of 6 technologies, a substantial impact. One contribution of this paper is to provide a potential explanation for why the local labor market impact of immigration is small. he modified version of Beaudry and Green (2005) presented below reduces essentially to a two-sector open-economy model, in which, like in the original, an increase in less-skilled relative supply does not affect relative wages in the long run. 5 he difference from the original model is that the economy adjusts to the change in input mix not by changing the mix of goods produced, but rather by changing the mix of techniques used to produce the same goods. An alternative interpretation of the empirical results that the observed response of technique to immigration is in fact due a shift to in industrial mix toward less-skilled intensive industries that also use less automation cannot be completely ruled out. Inconsistent with this alternative interpretation, however, controls for narrow (four- 5 Provided that the change is not so large as to move the economy outside its cone of diversification. 3

6 digit SIC) industry, and within those controls for product quality, (proxied by price, similar to Schott, 2004) have little impact on the strength of the relationship. he results of this paper also provide some indirect empirical support for a technologybased explanation for rising income inequality in the U.S. his will be elaborated upon further in the discussion section. I. heory he idea that plants adjust technique to input availability is not new. his was a feature of putty-clay models (Solow, 962; ohansen, 959) and was the core hypothesis of Habbakkuk s (962) investigation of why the U.S. mechanized production ahead of the British in the nineteenth century. However, this idea fell out of favor until it recently re-emerged in models attempting to explain why recent technological advance in the U.S. is skill-biased. odels of directed technical change (Acemoglu, 998) and endogenous choice of technique (Beaudry and Green 2003, 2005) in essence argue that skill-complementary technologies have become more prevalent as a result of the rising skills of the U.S. workforce. Acemoglu models innovation while Beaudry and Green model the choice among available techniques. In Beaudry and Green s model, firms choose between two techniques of high ( modern ) and low ( traditional ) skill-intensity. An immigration shock which raises the relative less-skilled labor induces firms to adopt less modern technique. A version of Beaudry and Green s model, modified to be appropriate for a local labor market, can be used to show how local labor markets might adapt to less-skilled immigration in a way that affects the use of technology but not relative wages. he key change from their model 4

7 is to make the supply of capital elastic, which is appropriate for a local labor market. 6 As will be seen below, this change reduces the model to essentially a two-sector Heckscher-Ohlin model, where the goods of different factor intensities have been relabeled as techniques of different factor intensities. o illustrate a simple case of the model, suppose that perfectly competitive producers have available to them modern and traditional techniques which can each be represented by a Cobb-Douglass production function: 7 Y = A L H ( ) β ( )( β ) K where {,} indexes the traditional () and modern () techniques; L represents lessskilled labor, H represents skilled labor, K represents capital used in technique ; and, β, and A are parameters with 0<, β <. Beaudry and Green s assumptions can be represented as restrictions on and β. he only assumption of theirs critical here is that the modern technique is relatively skill-intensive: ( ) β ( ) > β It is also important to emphasize that the outputs of the two techniques Y and Y are perfect substitutes there is only a single good. he price of the good is normalized to. 6 Beaudry and Green model the supply of capital as fixed, appropriate for a large national economy. 7 A Cobb-Douglass technology implies an elasticity of substitution between skilled an unskilled labor (one) which is not that different than estimates (e.g., Hamermesh, 993). his choice of technology, however, serves only the purpose of simple illustration. he results hold for any constant returns to scale technology in which one technology is relatively skill intensive. 5

8 6 he next step is to solve for the minimum cost of producing a unit of output with each technology, given factor prices. Let w L w H, and r represent factor prices for less-skilled labor, skilled labor, and capital, respectively. he unit cost functions are: () ( ) ( ) ( )( ) r w w c r w w C H L H L β β =,, for {,} where ( ) [ ] ( ) ( )( ) [ ] ( )( ) c A β β β β = for {,}. If both techniques are in use (the economy is inside the cone of diversification ), perfect competition implies C ( ) = C ( ) = (since the output price is normalized to one and there are zero profits). In keeping with the elastic capital supply assumption, r is assumed to be exogenous. Solving for w L and w H in terms of r: (2) ( ) ( ) ( ) ( ) ( ) ( ) ( )( ) ( ) ( ) [ ] ( ) ( ) ( ) r w r c c w L L = β β β β β β β β β β β β (3) ( ) ( ) ( ) ( ) ( )( ) ( )( ) ( ) ( ) ( ) r w r c c w H H = β β β β β β β β (2) and (3) show that changes in the relative supply of skilled and unskilled labor have no effect on wages inside the cone of diversification: factor supplies do not appear in (2) and (3). his is the usual factor price insensitivity result of the two-sector model (e.g., Leamer, 995). It is depicted graphically in Figure, which shows unit isoquants of the modern and traditional methods in (H,L) space. he modern isoquant is up and to the left of the traditional one, indicating its greater skill-intensity. At any endowment point inside the cone delimited by the expansion paths of these two technologies such as (H,L) shown in Figure relative wages are

9 7 constant at the level implied by the tangent unit isocost line /w H (r) to /w L (r). Full employment is achieved by producing with a linear combination of modern and traditional methods, as indicated by the vectors leading to (H,L). Figure also shows that an increase in the relative supply of less-skilled labor reduces relative use of the modern method, i.e. the Rybczynski theorem. An influx of less-skilled immigrants which moves the input endowment to (H,L ), for example, results in a decrease in the output of modern method and an increase in the output of the traditional method. his can also be demonstrated mathematically by solving labor market clearing conditions. Let H and L represent the exogenously determined supplies of high- and less-skilled labor. By Shephard s Lemma the vector of factor demands equals the gradient of the cost function, so from (): ( ) ( ) ( )( ) ( ) L L H L H L H L w Y w r w w C Y r w w c Y r w w C Y L β β = = = =,,,, for {,}, where the last step follows from zero profits. Similarly, ( ) H w Y H β = for {,}. Substituting these into labor market clearing conditions, H = H + H and L = L + L, produces, in matrix notation: ( ) ( ) = H L Y Y w w w w H H L L β β

10 Let dl dl D denote the 2 x 2 matrix above, whose elements are each positive. hen, dh dh Y = D ( d H d L) L H depends negatively on the relative supply of less-skilled labor so long β > β which follows as D >0. But this is equivalent to the condition ( ) ( ) from the assumption that the modern technology is relatively skill-intensive. Similarly, Y depends positively on the relative supply of less-skilled labor. hus the relative use of the modern method falls with an increase in less-skilled relative labor supply, as we wanted. It also follows that the use of modern machinery, K, falls as less-skilled relative supply increases, which is the implication tested below. 8 he inessentiality of the Cobb-Douglass functional form should also be evident. 9 hough not necessary for the result above, an interesting and realistic case is one in which the modern method is also relatively capital-intensive: ( )( β ) + ( ) β > ( )( β ) + ( ) β Under this assumption, an increase in less-skilled relative labor supply also causes the capital labor ratio to fall, providing another testable implication of the model. 8 A final loose end is to show that the cone of diversification exists, i.e. that Y and Y can be both simultaneously greater than zero. he required condition is: > w L ( ) β wh H ( ) β L > his outcome is feasible by the model s assumption that the modern method is relatively skill-intensive. 9 he assumption C ( ) C ( ) > (for constant any constant returns to scale technology) is sufficient to obtain these C ( ) C ( ) 2 2 results. his is an assumption that Beaudry and Green (2005) make. 8

11 his model has the nice feature that it is consistent with the stylized fact that immigration has little impact on relative wages in local labor markets, and has an additional testable implication that immigration should reduce use of skill-complementary capital, and possibly reduce capital intensity generally. It has the drawback that by simply relabeling the modern and traditional techniques as modern and traditional goods (i.e. skill-intensive and less-skilled intensive goods in a small, open economy) one obtains the same implications; i.e., an apparent shift in production technique might really be a shift in the mix of goods (say, from low tech metal fittings to high tech machine tools). However, one can distinguish the technique from the goods interpretation of the model by looking at how the technology used to produce the same goods varies with relative labor supply. For a given good, the technique interpretation says technology depends on relative labor supply, while the goods interpretation assumes technology is invariant to relative labor supply. II. Data Surveys of anufacturing echnology he technology data used in this project come from the 988 and 993 Surveys of anufacturing echnology (S). Each polled a stratified random sample (described below) of around 0,000 manufacturing establishments with at least 20 employees in SIC industries on the use of, plans for use of, reasons for use of (or for not using) 7 categories of advanced manufacturing technologies. 0 he industries covered by the S fabricated metal products, industrial machinery and equipment, electronic and other equipment, transportation equipment, instruments and related products make up a large part of the manufacturing sector (43 percent of value added and employment in 987, according to U.S. Bureau of the Census, 989). 0 here was also a 99 survey, not used in this analysis, which polled firms on the intensity of their use of these technologies in broad categories. 9

12 he S technologies, described in Appendix able, include processes used both in production and non-production activities, but most of the technologies are for use on the shop floor. any also appear to replace raw labor, such as automated inspection (alternatively handled by semiskilled production inspectors ), automated materials handling, and robots. his intuitive assessment of the role of these technologies fits with research showing a positive association between the use of these technologies and the skills of workers at the plant (Doms, Dunne and roske, 997) and by field work evaluating the impact of these technologies (Bartel et. al., 2003). It is also supported by research showing a negative association between computer use and use of labor in routine tasks (Autor, Levy, urnane, 2003). he S surveys also recorded other establishment characteristics, such as plant size, plant age, ownership, production type, military contractor status. hese are listed in Appendix able 2. he responses were in categories. Rather than drop observations that did not respond to one of these plant characteristics questions, I treated non-response as a separate category of response to each question. he strata used to create each of the S samples consisted of 3-digit SIC industry by class size cells. here were three class sizes, defined by employment: 20 to 99, 00 to 499, more than 500 employees. (Plants with fewer than 20 employees were not in the survey). Within each strata, a simple random sample was taken, and a weight was recorded equal to one over the sampling rate for that strata. (he average sampling rate was about one-fourth.) hough the S was in theory a random sample, it was also a small sample. o insure that the present analysis would be geographically representative, I constructed new sample weights to properly reflect the geographic distribution of plants in the S-universe. I merged each plant in the S to the prior-year (987 and 992) Census of anufactures. I then constructed new 0

13 strata 2-digit SIC industry by class size by metropolitan area. he equivalent to the original S weights would be to construct, in each of my new strata, a weight equal to the number of plants in the Census of anufacturers universe divided by the number of plants in the S sample. However, this is not what I did. For the purpose of studying the impact on the labor force, I wanted weights that were representative of employment, not plants. So instead, I created a weight equal to strata employment in the Census of anufactures divided by the number of plants in the S. III. Empirical Approach he initial analysis will consist of regressions of technology use on the relative supply of less-skilled labor in the local work force, regressions of the form: (4) jcn = j + θls c + β X jcn + ε jcn where jcn represents the use of technology at plant n in industry j in city c; j represents a vector of industry dummies; and LS c represents the relative supply of less-skilled labor in city-c. X jcn is a vector of plant characteristics. he slope coefficient, θ, measures the impact of less-skilled supply on the use of technology. If the theoretical model presented above is correct then θ will be negative in sign; under the null that technology is the same in all locations it is zero. he most important set of control variables in this regression are the industry dummies, j. Industries vary in their use of technology and the skill mix: electrical machinery, for example uses both more technology and more skilled labor than the average S-covered erging the Ss to the prior-year Census of anufactures had another purpose: it allowed me to merge in information about the plant not available in the S, such as employment (which is available only in categories in the S).

14 industry. Also, open economy models predict differences in worker mix across markets are absorbed by differences in traded industry mix. An immigration-induced increase in the share of workers who were unskilled, for example, according to trade theory raises the share of the economy s output produced in unskilled-intensive sectors, which could show up as a lesser use of technology. Including industry dummies is equivalent to asking how much local skill ratios shift the method by which the same industries produce. Plant size, measured by a continuous employment variable from the prior-year Census of anufactures (987 or 992), will also be controlled for in some regressions. Dunne (994) showed that the relationship between the use of technology and plant size was strong, while the relationship with another factor one might suspect was important, plant age, was weak. In the current context, it is nevertheless not entirely clear that a plant s size should be controlled for. After all, a plant s size may be endogenous, a channel through which local workforce skills affect the use of technology. herefore, the regression without size controls is also of interest. he Surveys of anufacturing echnology also contain several other plant characteristics variables, described in Appendix able 2, which will be controlled for in some regression specifications. One characteristic of interest is product price. Schott (2004) showed that even though there is little international specialization across four-digit industries, countries with a low relative supply of capital or skilled labor tend to specialize in lower quality products within fourdigit industries. Schott used unit values as a proxy for product quality in his analysis. o capture this possibility, I will include specifications that interact product price categories, indexed by p, with industry: (4 ) jcpn = jp + θlsc + ε jcpn 2

15 where jp represents a vector of industry x product price dummies. hough there are only six price categories in the data, they allow further, albeit crude, disaggregation of the data to test whether the use of technology differs across plants producing similar quality products. easuring Skill ix he primary measure of less-skilled relative labor supply used in this paper will be high school dropouts per high school equivalent. he number of high school equivalents, defined here as the number of workers who are high school graduates plus one-half the number of workers with some college (-3 years college) education, is a commonly used skill aggregate in research on skill biased technological change (for example, Autor, Levy, and urnane, 2003; Katz and urphy, 992). 2 Examining this skill margin the very low educated relative to those with high school and vocational training has two motivations. First, it is the margin on which foreign-immigration to U.S. labor markets has the strongest influence, and a major goal of this paper is to understand how immigrants are absorbed into U.S. labor markets. Second, it is a relevant skill margin to affect the use of the mostly production automation related technologies covered by the S. Hunt and Hunt s (983) survey of the potential impact of robotics, for example, talks about the loss of less-skilled jobs in favor of mostly vocationally trained workers and some engineers. his margin also seems appropriate in light of the occupations of dropouts and native-born workers in S-covered industries (SIC 34-38). able, which was computed using the 990 census, shows dropouts are highly concentrated in labor-intensive production occupations assemblers, welders, and inspectors which the automated technologies covered 2 In this formulation, those with some college education are thought of as supplying labor inputs equivalent to half a high school educated worker and half a four-year college graduate worker. he qualitative results of this paper do not depend on the weight given to some college workers. 3

16 by the S might be reasonably argued to replace. Half of native-born dropout workers hours are concentrated in ten occupations. Supporting the treatment of immigrants as substitutes for natives in this context, the top occupations immigrant dropouts are the same, though they are more concentrated in assembly occupations than natives are. In contrast, only 43 percent of high school educated workers hours and 26 percent of some college educated workers hours (and 7 percent of college-graduate workers hours) are in these same jobs more educated workers have a greater presence in supervisory, managerial, and non-production tasks. 3 Dropouts have a significant presence in these sectors: roughly nine percent of native-born and foreign-born workers are employed in these sectors (able ). In addition, variation across cities in dropouts per high school equivalent appears to be reflected in the skill ratios at manufacturing plants in S industries. Figure 2 plots dropouts per high school equivalent in S industries against the dropouts per high school equivalent in the city s labor force overall (for my sample of cities, described below). he relationship does not appear to deviate from the 45 degree line in either 988 or 993. ore generally, Lewis (2004b) finds an approximately one-for-one relationship between citywide dropout share and dropout share in narrow industries. Figure 2 also demonstrates the tremendous variation across labor markets in the relative supply of less-skilled labor. I will also examine the impact of other relative skill supply measures on the use of these technologies. In light of the association between the use of these technologies and college share at the plants in the S (Doms et. al., 997) one might be tempted to look also at the influence of college-educated relative supply. College graduates have little presence in production 3 Similar patterns also emerge in looking at a longer list of occupations say, the top 20. Bartel et. al. (2003) attempt to learn about the impact of new technologies on the skill requirements of production jobs through site visits to several plants in a variety of the same industries covered by the S. hey find that new technologies increasingly require soft skills communication and problem-solving skills in addition to math, literacy, and to some extent computer skills. hey argue these are skills which can be acquired in high school. 4

17 occupations and instead tend to work in high-skill white-collar jobs in management, engineering, computer programming, and sales and marketing. 4 Nevertheless, the influence of college relative supply will also be examined in a robustness section. Identification Some argue that the use of new technologies, including the ones covered by the S, raise relative demand for skilled labor. Dunne and Schmitz (995), for example, show plantlevel average wages rise with the use of S-technologies. Doms et al. (997) find this, too, but, in contrast, find little evidence that changes over time in the use of S technologies were associated with faster growing employment share of skilled workers. Instead, Doms et al. find that plants that adopted more technology had more skilled workers prior to adoption. Nevertheless, if it is true that technology raises skill demand, one might be concerned about interpreting θ from (4) as the causal impact of skill supply on technology use. Less-skilled workers might seek out low-tech markets where the relative demand for less-skilled labor is higher, generating a spurious correlation between technology use and local skill ratios. o address this concern, I instrument for LS c. his instrument takes advantage of the impact that the immigration boom has had on the skill mix of U.S. cities, particularly those where immigrants concentrate most heavily. According to the 990 Census of Population, 4 percent of immigrant workers during the 980s were high school dropouts, compared to 5 percent of native-born workers in 990 (Lewis, 2004b). hus where immigrants have become numerous, they can have substantial impact on the relative proportion of workers who are low 4 he top ten occupations, by hours worked in 990, of college graduates in S industries are: managers and administrators (8.9%), electrical engineers (9.0%), aerospace engineers (5.7%), sales representatives (4.8%), mechanical engineers (4.4%), computer systems analysts (4.4%), accountants and auditors (4.%), marketing, advertising and PR managers (3.8%), computer programmers (3.5%), and production supervisors (3.3%). 5

18 skill. o help see this and to motivate the instrument, the dropout share in city c is written to show the impact of recent immigration: (5) S D c = N N D c c = N N D I, c I, c + N + N D 0, c 0, c S = D I, c N N I, c I, c + S + N D 0, c 0, c N 0, c where D N c is the number of dropout workers and N c is the total number of workers in city c. his is disaggregated into recent immigrant arrivals, N I, c, and other workers (native-born and earlier immigrant arrivals), N 0, c. In markets where recent immigrants represent a significant fraction part of the population N, N is sizeable and are disproportionately dropouts I c c D D S I c S0, c, > they can have a significant impact on local skill mix. It would be inappropriate, however, to use the recent immigrant dropout share ( D S I, c ) or population share ( N I, c N c ) as instruments, as the locations of recent immigrants could also be endogenous. Instead, I construct instruments using the 970 metropolitan area locations of immigrant groups from different parts of the world. 970 largely precedes the modern wave of less-skilled immigration, and around that time the presence of foreigners in the U.S. population reached a low point since at least 850, at 4.7 percent (Gibson and Lennon, 999). Despite immigrants small numbers in 970, their locations in that year can predict where new immigrants in the 980s settle because of the strong tendency of new immigrants to settle into existing enclaves (e.g., Bartel, 989). Since Altonji and Card (99) researchers have often taking advantage of this fact to construct instruments for immigration based on lagged immigrant 6

19 density. 5 he argument for such instruments validity is that the persistence of regional immigration patterns derives from new immigrants preference to resettle with family and much of U.S. immigration is family-based or to be in a culturally familiar environment. For example, Gonzales (998) finds that wages are lower and rents are higher in heavily exican markets, consistent with the presence of other exicans being an amenity valued by exicans. Another reason to use instruments of this type is that similar ones instruments have been used in other research to demonstrate that local skill ratios have little impact on relative wages (Card, 200) but nevertheless have a large impact on skill ratios in narrow industries (Lewis 2004b). Using the same source of local skill mix variation to evaluate the impact on the use of technology allows these different results to be linked in a common model. he main instrument reassigns recent immigrant arrivals to their 970 enclaves and constructs from this a predicted D S I, c. Let g = G index country-of-origin groups that partition all immigrants. Define I as the total number of immigrants from g who settled g anywhere in the U.S. in some recent period (say, ); let D I g represent the total number of high school dropout immigrants from g who settled in the U.S. during the same period. Recent immigrants are apportioned to their 970 enclaves as follows: I gc,970 I Nˆ D gc,970 I, c I g g, Nˆ I, c I g I I g,970 g,970 D g 5 Bartel grouped immigrants into three broad world regions ( Asians, Hispanics, and Europeans ). Altonji and Card (99) were the first to use Bartel s observation to develop an instrument for immigration, but numerous others have done so since. Card s (200) formulation of the instrument is most similar to the one used here. his style of instrument has also been outside the context of the labor market impact of immigration. Albert Saiz (2003), for example, takes this approach in order to evaluate the impact of immigration on house prices and rents. 7

20 where I I gc,970 g,970 represents the share of immigrants from country g living in metropolitan area c in 970 the enclave measure. 6 ˆ and N I, c ˆ therefore represents the predicted counts of D N I, c immigrants and immigrant dropouts arriving in c if recent immigrants settled in the same cities as they did in 970. he main instrument will simply be the ratio of these two, or the proportion dropout among recent predicted immigrants: (6) Nˆ D D I, c I, c Nˆ I, c Sˆ = Appendix able 3 lists the 6 world regions the g index in equations above used to construct the instrument. Columns () (4) lists the number of recent (roughly the previous five years) immigrants from each region in 988 and 993 the years of the S surveys and the number of recent immigrants who are dropouts. 7 hese are the I g and D I g, respectively, and were computed using 990 and 2000 censuses. he instrument apportions these recent immigrants to the metropolitan area locations of immigrants from the same part of the world in For example, the top cities where the largest and most unskilled recent immigrant group, exicans, lived in 970 include Los Angeles (32%), Chicago (7%), Houston 6 I is the total number of immigrants from g in the U.S. in 970, and g,970 gc, 970 I is the number from g living in c 7 For 988, recent immigrants are defined as those who report having arrived , measured in the 990 census of population. For 993, recent immigrants are defined as those who arrived , measured using the 2000 Census of Population. Only working age migrants with at least one year of potential work experience and in the labor force are included in the counts. he population weights in each Census were used to compute the counts. 8 he locations of immigrants in 970 are measured using the 970 Census of Population. etropolitan areas in the 970 Census were constructed using county groups, with a county group included in a metropolitan area s definition if a majority of its population resided inside the 990 boundaries of the metropolitan area. 970 County population estimates were obtained from U.S. Dept. of Commerce, Bureau of the Census (984). he 990 boundaries of the metropolitan areas appear at 8

21 (4%), El Paso (4%), and Anaheim (4%). he instrument predicts these cities have a large predicted dropout share. Enclaves of more skilled immigrant groups will help predict a low dropout share. he instrument is a strong predictor of differences in the dropouts/high school equivalents across markets in 988 and 993. he bivariate relationship between the instrument and dropouts per high school equivalent in 988 or 993 is shown in the first and fourth column of able 2, respectively, and in Panel A of Figure 3. An area s dropout share is estimated in each area using Current Population Survey merged outgoing rotation group files (ORGs). 9 F-stats exceed 60. his strength of this relationship reveals both the influence that immigration has on local skill supply and the strength of immigrant enclaves in attracting continued migration from the same part of the world, even 20 years later. 20 he heavy inflow of exicans in recent decades contributes strongly this relationship, but it does not drive it by itself. Columns (2) and (5) of able 2 show that the share of a city s population that was exican-born in 970 enters the first-stage regressions significantly and separately from the main instrument. Finally, supporting the validity of the instrument, controls for employment growth during the period in which the immigrant flows are measured, added in columns (3) and (6), do not significantly affect the first stage. 2 In the estimates below I use both ˆ and the share of the population that is exican born in 970 as instruments. D S I, c uses the average of the ORGs, and 993 uses the average of the ORGs. Only those of working age (age 6-65) with at least one year of potential work experience who reported being in the labor force were included in the calculation. CPS final person weights were used in the computations. 20 here is a long-running debate over whether immigration actually tilts the skill mix in markets where they settle, or whether natives respond to the labor market competition by moving out. wo recent papers include Borjas (2005) and Card and DiNardo (2000). his is not a concern here per se because if immigration had no impact on skill mix there would be no first stage. 2 Employment is total private non-farm employment from the county business patterns county summary files. For the 988 regression, employment growth is measured during , the same years in which the immigrant flows are measured. Employment growth is measured for 993. Controls for the wages and employment rates of high school dropouts and graduates are also insignificant and have little effect on the first stage. he 2SLS 9

22 A potential concern about the validity of the instrument is that its correlation with unskilled labor share reflects long-run differences in the relative demand for unskilled labor in different cities. A exican enclave might have been established in Los Angeles, for example, because of high demand for less-skilled labor in that city, which encouraged exican settlement there. While this cannot be completely ruled out, one fact that suggests otherwise is the lack of correlation between the instrument and the dropout share in 980, shown in Panel B of Figure While there was significant immigration to the U.S. during the 970s, immigrants were apparently not numerous (or unskilled) enough by 980 to have a major impact on the skill mix of most U.S. cities. Even Los Angeles, which is labeled in Figure 3, had a moderate proportion of dropouts in his is consistent with the correlation between the instrument and dropout share in 988 and 993 as deriving from a supply shock generated by the huge increase in immigration volume during the 980s. ore generally, though, it means the instrument identifies variation due to skill mix changes since 980. his is useful because the technologies studied in this paper were hardly used at all 980 (American achinist (983)). hus the level of use in 988 or 993 can roughly be construed as a change in use since 980 and the crosssectional regressions are thus similar in interpretation to a first difference since regressions below use first-stage specifications in columns (2) and (5), though results are robust to using the other specifications. Interestingly, for example, employment growth enters significantly in the reduced form faster growing places adopt more technology but employment growth is nearly orthogonal to skill share. 22 he regression coefficient is 0.2 with an F-stat of.23 for the 988 instrument and 0. with an F-stat of 0.88 for the 993 regression. 23 he outlier at the top of the figure is callen, X whose skill mix was indeed influenced by the heavy presence of exican labor; but this seems more like the exception. 24 In fact, I have estimated this explicitly, replacing LS c with LS c where the change in dropouts/high school equivalent is measured since 980. his change has little effect on the empirical results. 20

23 Other Empirical Issues In most of the regressions below, the dependent variable will be simple count of the number of the 7 technologies in use by the plant. 25 Although this summary potentially masks some interesting variation, a number of studies using these data (including Doms et al., 997) have summarized technology use in this way. 26 It might seem appropriate to also look at the principle component of 7 technology dummies, but this turns out to be almost exactly equal to that simple count. hat is, principle component analysis suggests an equal weighting of each of the technology dummies. he count or principle component also captures nearly 40 percent of the variation in the use of individual technologies. 27 It would be desirable to know not just how much the local skill supply affects whether a technology is used, but also how much of it is used. his type of information is available for a limited number of the technologies in the 993 survey, and will be used in some regressions. In addition, I will evaluate whether the less-skilled labor supply influences a continuous measure of the capital intensity of plants. In order to obtain the correct standard errors, the regressions were run in two steps: first, the number of technologies was regressed on plant characteristics and city dummies; second, the estimated city dummies adjusted city level averages were regressed on the city s dropout share. Regressions were weighted to be representative of employment; correctly interpreted, therefore, they measure the impact of citywide dropout share on the number of technologies at 25 I assume, as the Census Bureau did throughout most of the reports they published on the results of the S (989, 994), that non-response to any technology use question indicates that the plant is not using that technology. 26 Beede and Young (998), however, illustrate the potential pitfall of this summary measure: they find that the effect on productivity, employment, and earnings vary by technology, and sometimes even differing in sign. I also find some heterogeneity, but, in contrast, I cannot reject that the impact of the local dropouts/h.s. equivalent on the use of these technologies is uniformly negative. Given this, the effect on the number of technologies concisely sums up the total effect. 27 here does not seem to be significant variation in the impact of less-skilled share on the use of the different technologies. (See Appendix able A5.) 2

24 the average employee s plant, but nevertheless they will frequently be described below as the impact at the average plant. 28 he regressions were run across 43 cities for which all the necessary data were available. 29 able 3 shows the means of the dependent variables used in the regressions. In 988, the average employee in the S-universe in these cities was at a plant using six of these technologies; by 993 this had risen only slightly, to 6.2 technologies. ost of the technologies actually declined in use between 988 and 993; the growth in use is confined to computer-based technologies listed in categories I and V of Appendix able. 30 In both 988 and 993 there is also wide variation across plants in the use of technology; the standard deviation is nearly as large as the mean. Not shown in this table is that more than ten percent of this variation is accounted for by variation across labor markets, even when holding constant industry mix. 3 IV. Estimates for 988 and 993 able 4 presents estimates of (4). Columns () and (3) show OLS estimates of θ for 988 and 993, respectively. he first row shows OLS estimates with no additional controls. he coefficient for 988 says that when the relative supply of dropouts rises by 0 percentage points slightly less than one standard deviation the average plant in the city uses fewer technologies. A similar estimate is obtained in 993 data. his relationship may partly reflect 28 he employment weights are described in the data section. Note that another approach to obtaining the correct standard errors is simply to cluster on metropolitan area. his produces the same point estimates and slightly smaller standard errors. 29 he biggest loss of metropolitan areas comes from the requirement that each area be observable in the 970 Census of Population, which is used to construct the instrument. Another restriction is that there be at least one plant in the both the 993 and 988 S surveys, which knocks out an additional 5 metropolitan areas. 30 cguckin et al. (998) also found the increase in use was confined to these categories of technology. 3 his figure is the amount by which the R 2 increases in going from a plant-level technology regression without city dummies to one with city dummies. 22

25 differences in industry mix across locations: areas with more unskilled labor may have more low-technology types of industries. he second row therefore controls for detailed industry, dividing S plants into 6 four-digit industries. his does not weaken the relationship. Even within narrow industries, therefore, the use of these technologies varies strongly with the local skill share. o further control for product quality within industry, the third row interacts fourdigit industry with the product-price categories. Schott (2004) shows that product price is likely a decent proxy for its factor content. Indeed, the industry x product price controls absorb nearly half of the variation in technology use across plants. Nevertheless, the influence of local skill supply is robust to controls for this proxy for product in both years, suggesting the adjustments occur within product price x industry categories. One might argue that what is really going on is that the use of technology influences the skill composition of the local workforce: low-skill workers are attracted to markets where, for some reason, the use of these (potentially) labor-replacing technologies is lower. o find out if this is the case, I now turn to instrumental variables estimates, using the instrument ˆ D S I, c described in equation (6), and the share of the population that was exican-born in 970. wostage least squares (2SLS) estimates are presented in columns (2) and (4). hese are larger than the OLS estimates. In other words, if anything dropouts differentially live in markets with higher technology use, biasing OLS estimates toward zero. ore likely, the CPS supplies a noisy estimate of skill shares, and so OLS estimates are attenuated relative to 2SLS. 32 he last five rows of able 4 present specifications with other plant-level controls. he fourth row shows a specification which controls also for plant employment, entered as a sixth- 32 It may also be that immigration-induced less-skilled labor supply has a larger impact on technology use than lessskilled labor supply generally, a point which will be returned to in the discussion below. 23

26 order polynomial. 33 Dunne (994) showed plant size has a strong influence on the use of these technologies, though in this context, where plant size may be endogenous, it is not necessarily appropriate to use it as a control variable. Nevertheless, conditional on plant size one continues to find a significant, albeit reduced in magnitude, influence of local dropout shares on technology use. he next row adds the first four plant-level controls listed in Appendix able 2 plant age, nature of manufacturing process, product price, and product market entered as dummy variables for each category of response. he coefficient on the skill supply variable remains significant in all four columns. he next row adds military contractor variables (controls 5-7 in Appendix able 2). ilitary contractors generally use more of these technologies (U.S. Bureau of the Census, 989, 994), but regional differences in the presence of military contractors do not drive the relationship between technology and local skill supply. Other controls are available only in the 993 S. It asked about foreign ownership and how much of a plant s production was exported to foreign countries; prior research has found both are associated with higher plant productivity (Bernard et. al., 2002) and technology use (U.S. Bureau of the Census, 994). hese controls have little impact on the estimates. Also available are controls on the nature and difficulty of worker training, and whether research and development occurs at the plant. One might interpret these as proxies for frictions which may affect the adoption of new technology and be correlated with skill shares. For example, managers at plants that do their own R&D may be more aware of new technologies; plants that do their own training may be able to adapt more quickly to changing technology; both may be more prevalent in more skilled locations. he last row of the table, however, shows that these controls have little impact on the estimates. 33 erms beyond sixth order were never found to be significant and results are insensitive to their inclusion. 24