Broadband data
This paper focuses on the first generation of broadband, which became available in Germany around the year 2000. By far the most important broadband technology in Germany was and is DSL (digital subscriber line), which accounts for over 90 percent of broadband subscriptions (Bundesnetzagentur 2013). Compared to older technologies (dial-up, ISDN), DSL drastically increased data transfer rates (from at most 128 kb/s to at least 384 kb/s). The main explanatory variable in this paper is DSL availability. It is measured as the share of households, at the municipality-year level, for which a DSL connection is technically available, but not necessarily adopted (used). The data are obtained from the Broadband Atlas Germany (Federal Ministry of Economics and Technology 2009).
First-generation DSL was rolled out rather rapidly in Germany, because it was based on the telephone network—that is, an existing infrastructure. Figure 4 in “Appendix” illustrates that overall, Germany reached a high level of DSL availability within the period of observation. As illustrated by Fig. 1, the largest cities (e.g. Berlin, Hamburg, Munich, and the Rhein-Ruhr area) were well served with DSL throughout this period. In contrast, a large number of municipalities in rural Western Germany and Eastern Germany gained DSL access only in this period. More precisely, there are considerable differences even between neighbor municipalities that do not differ much in other respects.
These sharp differences in DSL expansion between municipalities offer an opportunity to identify causal relationships between local broadband availability and labor market outcomes, even though broadband is provided by private firms and therefore, local broadband availability should be endogenous with regard to economic outcomes. However, by relying on the pre-installed telephone network, there were some unforeseen and therefore quasi-random technical obstacles to DSL provision in some municipalities. These technical obstacles have been exploited by Czernich et al. (2011), Falck et al. (2014), Bauernschuster et al. (2014), and others to construct instrumental variables (IVs). This study applies a similar IV approach, yet with the focus on German establishments’ labor demand, an outcome previously not addressed in the empirical literature.
Instrumental variable approach
To identify the causal relationship between DSL availability and employment growth, this study seeks to exploit sources of exogenous variation in the former variable. There exist two such sources, one each for rural Western Germany and urban Eastern Germany, which potentially allow for the construction of instrumental variables.
Turning first to Western Germany, the key to the identification strategy is that during the observation period of this study, DSL was supplied using “fiber-to-the-node” (FTTN) technology. This means that only the more central part of the telecommunications network was already equipped with “fast” fiber wires. In contrast, the decentral part of the network (the “last mile”), which reaches from the so-called main distribution frames (MDFs) to the households, was still served via copper wires. The telephone network, including the MDFs, was installed in the 1960s by the (West) German Federal Postal Services, then the state-owned monopoly provider of telephone service. The Federal Postal Services, not subject to competition and not profit-oriented, installed the network such that telephone service could be provided universally throughout Germany. The location of the MDFs was determined by the availability of lots or buildings were they could be placed (cf. Falck et al. 2014). Most importantly, the precise locations of MDFs were irrelevant for the feasibility and quality of telephone service, and even pre-DSL internet service (dial-up and ISDN). Thus, MDF locations were determined quasi-randomly at a small spatial scale, in the sense that it did not matter whether a MDF was some kilometers closer or further away from its users. Therefore, it is rather unlikely that MDF locations, chosen in the 1960s, are correlated with local amenities and services—e.g., closeness to town centers, public transport stations, or centers of economic activity (such as shopping centers and industrial districts)—that could matter for employment (let alone employment growth) in the 2000s.
To provide some descriptive evidence, the circa 8000 MDFs were allocated relatively densely and evenly across the country (see Fig. 5 in “Appendix”), especially in rural areas. As indicated by Fig. 6 in “Appendix”, municipalities above and below the distance threshold are frequently located right next to each other, and therefore likely very similar. Furthermore, there are very few municipalities whose entire territory is more than 4.2 km distant from the MDF (cf. Fig. 3 in Sect. 5.2), due to the dense allocation of MDFs even in the most rural areas. Finally, some municipalities got connected to a rather far-away MDF (more than 4.2 km from the centroid) even though there is a closer MDF (less than 4.2 km from the centroid). This would not probably not have happened had distance played any important role for telephone service.
With the introduction of broadband internet, however, distance to the MDF suddenly became important. When DSL was introduced in the 2000s, the main provider (Deutsche Telekom) defined the minimum signal strength required for DSL service to be 55 dB, which translates into a copper-wire distance of approximately 4.2 km. Thus, beyond 4.2 km from the MDF, Deutsche Telekom did not provide DSL until, years later, at least part of the copper wires became replaced by fiber wires. Other providers had to rely on the same network and therefore could not provide DSL either. Although it would have been technically possible for Deutsche Telekom to create additional MDFs, this did not happen: DSL was rolled out in the early 2000s purely using the then-existing telephone network, supposedly for economic reasons—installing new wires would have been very costly, since telecommunication wires are installed subsurface in Germany (see also Falck et al. 2014).
The 4.2 km distance threshold can therefore be used as a binary IV for local broadband availability. To obtain an unambiguous measure of the distance between a municipality and its MDF, one can use the centroid (geographic center) of the municipality. Thus, the following IV is defined for each municipality m:
$$IV_{m} = \left\{ {\begin{array}{*{20}c} {1 \ if \ municipality \ centroid \ > \ 4.2 \ km \ from \ assigned \ MDF} \\ {0 \ otherwise.} \\ \end{array} } \right.$$
Municipalities with \(IV_{m} = 1\) are thus expected to have a lower DSL availability.
For this dummy variable to be a valid instrument, it must not affect labor market outcomes except through its effect on DSL availability. One should therefore ask whether the location of economic activity (i.e., firm location) depends on the nearby presence of an MDF, at least concerning firms whose business strongly depends on fast internet access. In the rural (Western German) setting which this study focuses on, this might be less of a concern, given that information-intensive economic activities tend to concentrate in urban areas. Nevertheless, to alleviate potential problems of endogenous firm location, in this study the Western sample is restricted to establishments founded before the observation period (2005–2010). For these establishments, DSL availability can plausibly be regarded as given, and hence their locations can be regarded as exogenous. A technical prerequisite of this IV approach, and its main caveat, is that it only applies to municipalities without an own MDF, which are rather rural. More urban municipalities have at least one own MDF, so the distance between the municipality centroid and the MDF is ambiguous. These municipalities are therefore excluded from the sample.
Deviating slightly from Falck et al. (2014), the identification strategy in this study focuses on the distance between each municipality and its originally assigned MDF (i.e., the MDF to which the municipality’s households were connected for telephone service in the 1960s). For a minor share of municipalities with an assigned MDF above the threshold, this distance was in fact not an obstacle to DSL rollout, namely if there was another, nearer MDF than the originally assigned one. These municipalities could, after all, obtain DSL by getting connected to the nearer MDF. Falck et al. (2014) exploit this setting to construct a second IV, namely another distance-threshold dummy with respect to the nearest MDF, given that the originally assigned MDF’s distance is above the threshold. However, this results in an even smaller and more rural subsample of municipalities. Given the aim of this study (identifying establishment-level employment growth effects), such a subsample seems too small and selective, considering that establishments and employment concentrate in more urban areas. Therefore, only the first distance threshold (referring to the originally assigned MDF) is used to create an IV, and the (few) municipalities above this threshold, but within reach of a nearer MDF, are dropped from the sample. For a descriptive comparison of sample municipalities to all other municipalities, see Table 13 in “Appendix”.
The relevance of the instrument can be displayed as a relationship between municipal DSL availability (in year t) and the distance to the municipality’s MDF. In Fig. 2, municipality-year observations are grouped into bins (each containing 100 observations, except for the last bin) ordered form nearest to furthest from the MDF. As the graph shows, DSL availability decreases sharply at the 4.2 km threshold. The last bin (at > 10,000 meters from the MDF) contains only one observation and therefore can be regarded as an outlier.
In Eastern Germany (the former German Democratic Republic), there was another historic “accident” that also caused some municipalities to receive DSL service much later than others, namely the installation of the so-called OPAL (optical access line) technology in the early 1990s (see Falck et al. 2014 and Fig. 5 in “Appendix”). Although OPAL was considered state-of-the-art at the time, the technology was incompatible with broadband, and thus turned out to be a disadvantage for DSL rollout. Therefore, analogous to the distance threshold used for Western German municipalities, a municipal-level OPAL dummy could be used to split the sample into a technologically disadvantaged group and a “lucky” group of municipalities. However, descriptive statistics indicate that the OPAL dummy is correlated with municipalities’ labor market indicators, which might directly affect establishments’ employment growth. It is therefore not a valid IV for DSL availability in the present context (unlike in the case of Falck et al. (2014), where the outcome of interest is voting behavior). Therefore, in the following, only the IV approach for Western Germany is pursued.
Measurement and identification
Besides the above-discussed problem of endogeneity (the main motivation for the proposed IV approach), another rationale for employing IV regression is to address measurement error in the explanatory variable (see Angrist and Pischke 2009, p. 127 sqq. and Hausman 2001), which biases OLS estimates towards zero. Here, the main source of measurement error is that DSL availability is observed only at the municipality level, while employment growth is observed at the establishment level. By using variation in DSL availability driven by the instruments, attenuation bias can be alleviated. However, there is also measurement error in the instruments themselves, namely if a municipality is classified as being above the distance threshold, while the establishment location is in fact below the threshold, or vice versa. Yet, this problem can be addressed by using alternative instruments (see robustness checks).
Regarding the interpretation of the estimates, the IV estimator identifies the local average treatment effect (LATE) for “compliers” (Angrist and Pischke 2009, p. 154 sq.). In this context, compliers are establishments in municipalities which have lower (higher) broadband availability if the technical friction used as an IV is (not) in place (Imbens and Angrist 1994). Considering the massive negative association between the IV and municipal broadband availability (see also descriptive statistics below), non-compliance appears to be a negligible problem. Municipalities could hardly circumvent the technical obstacle from which the IV is constructed, particularly not within a few years. At the establishment level, one potential problem of non-compliance does arise: Large customers can obtain broadband service independently of the municipality-wide DSL provision, via private leased lines. Yet, leased lines are affordable only to larger establishments. According to Fabritz (2013), 82 percent of German establishments use the local DSL infrastructure. This share is likely to be even higher in the sample at hand, which omits urban Western Germany and therefore the bulk of Germany’s large establishments (the average size of the sample establishments is about ten full-time employees). Thus, only few establishments in the sample are likely to be non-compliers.
Another caveat regarding the interpretation of the estimates is that only the availability, but not the adoption of broadband is observed. As discussed by Czernich (2014), the effects of broadband availability are necessarily closer to zero than the effects of its adoption if, as seems likely, broadband affects firms through its actual use by firms. At the same time, however, note that the observation period captures a relatively late stage of DSL rollout (the late-coming municipalities), when the DSL technology was already well established and easily affordable even to households. Most establishments therefore likely adopted DSL as soon as it became available, so the difference between availability and adoption should be small, and the estimated effects of DSL availability should largely reflect the effects of DSL adoption.