Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from a web-scraping approach

Thonipara, Anita; Sternberg, Rolf; Proeger, Till; Haefner, Lukas Article — Published Version Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from aweb-scraping approach Review of Regional Research Provided in Cooperation with: Springer Nature Suggested Citation: Thonipara, Anita; Sternberg, Rolf; Proeger, Till; Haefner, Lukas (2022) : Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from aweb-scraping approach, Review of Regional Research, ISSN 1613-9836, Springer, Berlin, Heidelberg, Vol. 43, Iss. 1, pp. 69-99, https://doi.org/10.1007/s10037-022-00170-5 This Version is available at: https://hdl.handle.net/10419/312214 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ ORIGINAL PAPER https://doi.org/10.1007/s10037-022-00170-5 Rev Reg Res (2023) 43:69–99 Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from a web-scraping approach Anita Thonipara1· Rolf Sternberg2· Till Proeger1· Lukas Haefner2 Accepted: 26 June 2022 / Published online: 21 July 2022 © The Author(s) 2022 Abstract Following the “death of distance” postulate, digitalization may reduce or even eliminate the penalty of firms being located in rural areas compared with those in urban agglomerations. Despite many recent attempts to measure digitalization effects across space, there remains a lack of empirical evidence regarding the adoption of digital technologies from an explicit spatial perspective. Using web-scraping data for a sample of 345,000 small firms in Germany, we analyze the determinants of website prevalence. Comparing urban with rural areas, we show that running a website—as a proxy for the degree of digitalization of the respective firm—is highly dependent on location, whereby firms in urban areas are almost twice as likely to run websites compared with those located in rural areas. Our county-level analysis shows that a high population density, a young population and a high educational level have a positive and significant association with the probability that firms run websites. Surprisingly, we find a negative and significant association of gross domestic product per capita with website prevalence, which is driven by urban regions. There are no differences between urban, semi-urban and rural areas in terms of website up-to-dateness as well as social media prevalence. We conclude that there is a substantial digital divide and discuss policy implications. Availability of data and material The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Code availability Not applicable Lukas Haefner [email protected].de 1Institute for Small Business Economics, Georg-August-University Goettingen, Heinrich-Düker-Weg 6, 37073 Göttingen, Germany 2Institute of Economic and Cultural Geography, Leibniz University Hannover, Schneiderberg 50, 30167 Hannover, Germany K 70 A. Thonipara et al. Keywords Digital divide · Digitalization · Rural · Small and Medium-Sized Enterprises · Urban · Web-scraping Digital Divide, Websites von Handwerksunternehmen und städtischländliche Disparitäten – empirische Erkenntnisse aus einer Web- Scraping Analyse Zusammenfassung Dem Postulat des „Endes der Entfernung“ zufolge kann die Digitalisierung die Nachteile für Unternehmen in ländlichen Gebieten gegenüber Unternehmen in städtischen Ballungsräumen verringern oder sogar beseitigen. Doch trotz einiger jüngerer Versuche räumliche Digitalisierungseffekte zu messen, besteht nach wie vor ein Mangel an empirischer Evidenz bezüglich der Nutzung digitaler Technologien aus einer räumlichen Perspektive. Mithilfe einer Web-Scraping Analyse untersuchen wir die Determinanten der Verbreitung von Websites für 345.000 kleine Unternehmen aus Deutschland. Der Vergleich städtischer und ländlicher Regionen zeigt, dass das Betreiben einer Website, als Näherungswert für das Digitalisierungsniveau eines Unternehmens, stark von dessen Standort abhängt. Unternehmen in Städten betreiben fast doppelt so häufig eine Website wie Unternehmen in ländlichen Regionen. Unsere Analyse auf der Kreisebene zeigt, dass eine hohe Bevölkerungsdichte, eine junge Bevölkerung und ein hohes Bildungsniveau einen positiven und signifikanten Zusammenhang aufweist mit der Wahrscheinlichkeit, dass ein Unternehmen eine Website betreibt. Überraschenderweise finden wir einen negativen und signifikanten Zusammenhang zwschen dem Bruttoinlandsprodukt pro Person mit der regionalen Verbreitung von Websites. Dieses Ergebnis wird maßgeblich durch städtische Regionen bestimmt. Wir finden keine Unterschiede zwischen städtischen, semi-urbanen und ländlichen Regionen bezüglich der Aktualität sowie der Verbreitung von Social Media. Wir folgern, dass es einen erheblichen digital divide gibt und stellen Ansätze für politische Maßnahmen vor. 1 Introduction There is a public perception of a growing divergence in the adoption of digital technologies among urban and rural firms. Digitalization is considered to be the major technological development driving future business models and thus regional and firm-level competitiveness. Acknowledging a growing divergence in digitalization levels could warrant far-reaching innovation and digitalization support for rural firms to prevent a further polarization in competitiveness. Such support is timely since the adoption of digital technologies can be of a cumulative nature: once a firm lacks the capacity to adapt to new digital developments, further developments are even more difficult to cope with, eventually causing a further growing polarization. This is problematic because the unequal distribution of competitiveness and wealth among firms and regions is already leading to social tensions in many countries (Dijkstra et al. 2020; Rodríguez-Pose 2018). Digitalization thus poses a risk to social cohesion when new digital technologies are primarily adopted in and benefit thriving regions. Additionally, digitalization yields positive and negative impacts at the K Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from... 71 same time. For example, workers performing routine tasks face a high risk of substitution through digital technologies, while workers executing complex tasks tend to be complemented (Akerman et al. 2015). This could amplify the threat for social cohesion when thriving regions benefit from digitalization, while at the same time less-wealthy regions suffer from job degradation or job automation. Consequently, policy-makers at many geographical levels have created government programs to improve the quality and quantity of broadband access—the fundamental prerequisite to compete in digitalization—in all regions and all types of regions.1 Contrary to the threat of causing further polarization in competitiveness among firms and regions, politicians often see digitalization as an opportunity to reduce inter-regional disparities (BMI 2019;Grimes2005). Especially in regions that have previously largely been excluded from markets or value chains, digital technologies based on the internet could enable the better inclusion of firms (Galloway et al. 2011). The assumption of growing opportunities for these firms was based on the expectation of a decreasing importance of physical proximity, eventually triggering the “death of distance” (Cairncross 1997). Once internet infrastructure was sufficiently available in all regions, it was expected that firms could digitally exchange knowledge, services or even products. Firms in marginalized rural regions could thus overcome disadvantages through integration into previously-inaccessible networks and value chains, whereby these regions could consequently catch up with more advanced regions. However, such digitalization-induced catch-up processes have hardly been witnessed, prompting some authors to revise their expectations based on a more recent perspective (Cairncross 2018). Even in the digital era, there are “mountains in a flat world”, whereby geographical proximity still matters for the location of economic activity, as Rodríguez-Pose and Crescenzi (2008) put it. One prominent explanation for the absence of catch-up processes of laggard regions is the digital divide among firms in rural and urban regions (Salemink et al. 2017), for which two levels can be distinguished (Büchi et al. 2016; Scheerder et al. 2017). The first-level digital divide refers to access: while nowadays most firms in rural regions have access to the internet, the quality of the connection often trails behind that in urban regions (Briglauer et al. 2019b; Prieger 2013), which thus limits rural firms’ opportunities of digital participation. The second-level digital divide refers to usage, whereby the adoption of not only the internet but also other digital technologies is lower in rural firms compared with their urban counterparts. This is not necessarily due to missing availability (i.e. due to the first-level digital divide) but rather to unfavorable socio-demographic characteristics of many rural regions: Research—which is mostly carried out on households rather than firms—indicates that high income, high professional qualification levels and a young population and labor force are central determinants in fostering the regional usage intensity of digital technologies (Blank 1See e.g. the Digital Agenda for Europe for an attempt at the supra-national level of the EU (https://ec. europa.eu/digital-agenda/en), the “digital agenda” for an attempt at the national level (for Germany, see the Federal Government of Germany 2018) or the “Broadband for Bavaria” initiative for the sub-national level of the German Federal State of Bavaria (https://ec.europa.eu/commission/presscorner/detail/en/ip_ 19_6630; see also Briglauer et al. 2019a). K 72 A. Thonipara et al. et al. 2018; Billon et al. 2016;Prieger2013; Schleife 2010). Often these indicators are deficient in rural regions. While an unequal access to the internet (first-level digital divide) is easily observable, it is less straightforward to determine a spatial divide in firms’ actual adoption of digital technologies (second-level). However, exactly this adoption is needed to seize the opportunities of digitalization, not only for firms but also for regions and countries as a whole (Whitacre et al. 2014a, b). Determining whether there is a second-level digital divide among urban and rural firms is therefore an important research goal to substantiate or refute political calls for a novel support of rural firms aimed at preventing a decoupling of digitalization dynamics. Empirical contributions face a number of obstacles, particularly in determining and compiling suitable indicators measuring digitalization in firms. Since digitalization is a very heterogeneous and sector-specific development, there are few common indicators that can be used to compare digitalization across industries and regions. To date, most indicators on the spatial digital divide mirror either the first-level or, when capturing the second-level, use household data or only small samples of firm data. Indices on the second-level are introduced for some study areas but most often do not reflect digitalization at a low spatial level and/or are not transferable to other study areas due to matters with data availability. In this paper, we suggest using a firm’s decision to create and promote a website in the yellow pages, its respective up-to-dateness and social media references as a proxy for its degree of digitalization. While these indicators primarily capture online marketing, we assume that its basic nature entails a firm’s propensity to conduct further digitalization measures. Put simply, the assumption is that if a firm geared at business-to-consumer (B2C) services refrains from running a website as a near-costless measure of marketing, it is highly unlikely that this firm has invested in digitizing its internal workflows, let alone that it uses advanced digital technologies in its production. We are confident that these indicators capture a firm’s basic propensity to conduct digitalization measures and hence enable us to test differences in digitalization across regions. Moreover, it can be assumed that using websites and social media has a fairly similar function for firms, even in very different (consumer-oriented) sectors. Since most other digital technologies are used quite heterogeneously across sectors, website prevalence enables us to compare larger samples of firms. Using this approach to measuring digitalization, we run a web-scraping algorithm, which gives us roughly 345,000 firm entries of the yellow pages and about 104,000 websites, which are further analyzed regarding social media references and up-to- dateness. Using the firms’ postal codes, we link county-level data for various socioeconomic indicators. This provides us with evidence regarding the occurrence and potential structural determinants of a digital divide among German SMEs. We more specifically focus the analysis on firms from the German craft sector, which is characterized by smaller, often family-led businesses, which constitute a substantial share of German SMEs and tend to lack behind in digitalization (see e.g. Proeger and Runst 2020). This article’s contribution to the literature is twofold. By harnessing hithertountapped but publicly-available data, our first contribution is of a methodological K Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from... 73 nature. Research on the second-level digital divide and its spatial effects is suffering from a scarcity of indicators proxying the prevalence of digital technologies in firms, especially aggregated at the regional level for Europe (Billon et al. 2016;Ruiz-Ro- dríguez et al. 2017). We propose a novel indicator for measuring the digitalization degree of regions that is widely accessible and does not build on the digital infrastructure but rather the actual usage of digital technologies by firms. The indicator comprises a large dataset and a wide range of sectors and is likely to be feasible for many countries without raising measurement and data accessibility problems that are inherent in indices. In providing support for Kinne and Axenbeck’s (2020) suggestion that exploiting large-scale website data offers advantages compared to traditional data sources and opens up new paths for answering (economic) research questions, our methodological contribution hence extends beyond research on the digital divide. Second, by applying this indicator, new insights into the spatial digital divide and its regional determinants are presented. While we confirm an urban-rural digital divide in website prevalence, the descriptive statistics do not show a substantial spatial divide in the technological characteristics of these websites. In line with existing literature, we find evidence that population density, the professional qualification of the workforce and a lower population age are important regional determinants that are positively associated with the regional digitalization level. Contrary to existing findings, the wealth of regions (measured by gross domestic product, GDP) is only positively associated with website prevalence in rural areas. In urban areas, GDP is negatively associated with website prevalence. Finally, our results tend to show new evidence that lacking internet access is still linked to lacking digitalization efforts in some rural regions. The remainder of this paper is structured as follows. Section two reviews the relevant literature for our research goal, before section three presents our method and dataset. Section four presents our results, while section five discusses our results and section six concludes and presents policy implications. 2 Literature review 2.1 Related research 2.1.1 Definitions of digital divide In principle, a digital divide refers to the different availability, usage and usability of digital infrastructure, which may lead to gaps between people and groups of people with specific attributes within a society. Such a divide may be related to various joint characteristics of the named people or groups of people, e.g. their age (old vs. young), income (rich vs. poor) or education (university degree vs. no degree). In our paper, we follow the most common interpretation in the academic literature (see Graham 2019), which refers to spatial entities (territories or spatial units) when referring to a digital divide. These territories may be assigned to all possible spatial scales, such as the global (e.g. comparison of continents), national (comparing countries), regional (i.e. comparing sub-national ones like the 50 states in the US or the K 74 A. Thonipara et al. sixteen federal states in Germany) or the local scale (e.g. comparing the quarters of a city). In addition to these spatial units based on administrative boundaries, comparisons between urban and rural regions have attracted strong attention among scholars (see Rodríguez-Pose 2018) in recent years, partially driven by specific policy programs and interests in ensuring equal living conditions in all regions of a country (Briglauer et al. 2019a). These comparisons between urban and rural areas may be intra-national (all areas within a country), cross-national (all areas in various or all countries within the same continent) or cross-continental. A plausible—and oftenused—proxy for the rural-urban dichotomy is population density, e.g. measuring the number of inhabitants per km2, as urban areas exhibit a higher population density compared to rural areas. While this method has some weaknesses concerning comparisons between urban areas, it is a reasonable measure when comparing urban with non-urban areas (like rural ones). In fact, population density is a more precise indicator for detecting urban-rural disparities than the sheer population size of the related spatial entities. There is an important stream of literature that distinguishes explicitly or—more often—implicitly between urban and rural areas when it comes to some socioeconomic or demographic characteristics of the territories under study, i.e. based on variables of the regional context. These characteristics can be summarized by the dimensions of economic prosperity (e.g. measured by GDP per capita), human capital (e.g. measured by the share of inhabitants with an academic degree), and demographics (e.g. measured by the share of inhabitants older than 64 years). Of course, some of the values of several of these categories strongly correlate. As our paper is dedicated to German regions, the following empirical evidence may be of interest: in 2017, urban counties showed a higher annual GDP per capita (43,400 C; rural counties: 31,400 C), a lower share of inhabitants aged 65+ (20.7% in 2017; rural counties: 23.0%), and a higher share of the workforce (place of residence) with an academic degree (18.2%; rural counties: 10.2%) compared with rural counties (all data derived from INKAR2). Of course—and by definition—urban regions show higher population densities than rural ones. Therefore, it makes sense to develop hypotheses that address the aforementioned dimensions, whereby we also implicitly address the digital divide between urban and rural regions. Consequently, we focus our literature overview on the named dimensions in section 2.1.3. In the following description of the state-of-the-art research on the (spatial) digital divide, we distinguish between the first- and second-level digital divide. While the former has already provoked a lot of empirical and some theoretical research, the latter has not. We will explicitly address urban-rural differences in our overview of the literature. 2INKAR (Indikatoren und Karten zur Raum- und Stadtentwicklung [Indicators and maps on spatial and urban development]) is a free tool provided by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (see https://www.inkar.de/). K Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from... 75 2.1.2 First-level digital divide: Digital infrastructure and its spatial effects There is no doubt that in all countries there remains a gap of digital infrastructure between sub-national regions in terms of quantity (i.e. availability; Prieger 2003 for the US and Wernick and Bender 2017, for Germany) and/or quality (e.g. speed of internet connections, see Riddlesden and Singleton 2014). Moreover, the spatial diffusion process of web-based technologies varies across different geographical regions in most industrialized countries (Papagiannidis et al. 2015). When assessing the potential effects of digital infrastructure in rural areas, it is relevant to note that high-quality digital infrastructure (e.g. super-fast internet) is available more often and earlier in economically strong urban areas in a given country, which—ceteris paribus—leads to an increase in inequalities between urban and rural areas. The persistence of this divide is often ascribed to liberalized telecommunications markets and the fact that internet expansion for telecommunication providers is more profitable in urban rather than rural regions (Briglauer et al. 2019a; Grimes 2003;forthe role of municipalities in Germany, see Wernick and Bender 2017). In some cases, the spatial digital divide is even growing (Townsend et al. 2013), which is often the case particularly for novel technologies or next generation access, i.e. higher bandwidths (Briglauer et al. 2019b; Prieger 2013). Politicians have recognized this problematic divide and many policies have aimed to narrow this gap (Briglauer et al. 2019a), although they often quickly become outdated by new technological developments (Salemink et al. 2017). Regarding the spatial effects of digital infrastructure, there is some empirical evidence in the literature if inter-regional differences between sub-national regions are considered (Bertschek et al. 2015;Grimes2005; Malecki 2003). However, these empirical results are often contradictory regarding the extent, type and direction of such effects (Jung and López-Bazo 2020). This also holds true for the effects on rural vs. urban regions (for a detailed overview of the literature, see Haefner and Sternberg 2020). Some qualitative studies show positive effects of digital infrastructure for rural areas (Townsend et al. 2017). To give an example, in many rural areas in developing countries, digital infrastructure allows time to integrate such regions into global value chains and enables a low-cost and reliable exchange with extra-regional and international customers (Krone and Dannenberg 2018). However, only some quantitative studies find that these effects are also positive in a relative sense, i.e. they are stronger than the positive effects of the infrastructure in favor of urban regions (Fabritz 2013; Bertschek et al. 2015; Ivus and Boland 2015). Such relatively (compared with urban regions) strong effects of digital infrastructure are a necessary condition if digitalization is intended to be used as a regional policy instrument to reduce inequalities between urban and rural areas of a country, i.e. to achieve convergence instead of (further) divergence (Jung and López-Bazo 2020; Celbis and de Crombrugghe 2018). If urban and rural areas benefited from digital infrastructure to the same extent, then inter-regional disparities between rural and urban areas would not be reduced. By contrast, many authors argue in favor of (often economically strong) urban regions regarding the positive spatial effects of digitalization (Camagni and Capello 2005; Ciffolilli and Muscio 2018). Digital infrastructure seems to have stronger positive impacts in urban rather than rural areas K 76 A. Thonipara et al. in terms of both quantitative as well as qualitative aspects (Malecki 2002; Tranos 2016; WWG 2015). In particular, this seems to be true for young (or even new) and knowledge-intensive firms (see Mack et al. 2011; McCoy et al. 2018) and higher bandwidth levels (Briglauer et al. 2019a). 2.1.3 Second-level digital divide: Spatial effects and regional determinants As shown above, research on whether digitalization (via the internet) mainly benefits urban or rural regions (and thus whether digitalization leads to increased or reduced inter-regional disparities) yields inconclusive results. The availability of digital infrastructure (high quantity and/or high quality) is nowadays only a necessary condition for digitalization’s effects on the development of urban and rural areas (Evangelista et al. 2014; Tranos 2012) and an increase or decrease in economic disparities between the two types of areas. If this first-level component meets higher or even the highest demands, the regional economic effects would nevertheless remain very limited if people living and/or working in these areas lack the necessary motivations and skills required for exploiting the potential offered by a very high standard infrastructure. In other words, the second-level component—usage and usage skills—is decisive if a certain standard of the first-level component is provided (Evangelista et al. 2014). Studies confirm this view, showing that increased internet availability does not necessarily have positive (spatial) impacts, but rather that the usage matters (Mack and Faggian 2013; Whitacre et al. 2014a, b; Scheerder et al. 2017). Consequently, in order to answer the question asked above regarding whether digitalization mainly benefits urban or rural regions, it appears more fruitful to determine a spatial digital divide in usage rather than accessibility (Haefner and Sternberg 2020). A number of studies comparing urban and rural regions find a divide in the usage of digital technologies, which does not stem solely from lacking access in rural regions (Moriset et al. 2012;Prieger2013; Salemink et al. 2017;Townsendetal. 2013). Thus, there must be some regional determinants apart from infrastructure availability that explain this second-level digital divide. Studying the impact of these determinants on the degree of digitalization and their regional variation can thus help to better understand the digital divide. So far, the amount of theoretical or empirical research regarding the impact of regional determinants on the urban-rural second-level digital divide is rather small, particularly for firms. An important regional determinant for technology adoption that has been discussed is population density. Technologies are often developed and first adopted in cities, i.e. in densely populated areas, and subsequently diffuse to less densely populated, more peripheral areas (Salemink et al. 2017). One reason for the early adoption in cities is the fact that infrastructure expansion for telecommunication providers is more profitable in more densely populated compared to less densely populated areas (Briglauer et al. 2019a; Grimes 2003). Beyond that, the process of (digital) technology diffusion is not only starting earlier in densely populated areas, the speed of diffusion is often expected to be higher, too. Once pioneers adopt a technology, information on this technology can—due to the advantages of direct personal interaction—spill over more easily and frequently from users to non-users K Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from... 83 the German population. For them, the printed yellow pages (which is replicated in the online version) remains a central source of information. Firms that choose not to be listed forego these potential customers. The yellow pages claim (based upon market research6) that in 2017 slightly more than half of the German population over 16 years has used the yellow pages in its different formats (print, online, smartphone app). This amounts to 32.6 million persons conducting roughly 900 million searches. Craft services are the second most important domain of search requests. Overall, while the market research is a form of marketing by the yellow pages, their numbers substantiate the claim that being listed in the yellow pages continues to make sense for firms. Finally, it can be argued that firms pursuing a modern digital marketing strategy conduct search engine optimization (SEO) to be listed prominently among Google searches. An important aspect of SEO is being listed on numerous platforms, which improves a firm’s SEO score, whereby the yellow pages helps to increase this score. Therefore, digitally strong firms similarly have an incentive to be listed on the yellow pages for technical reasons (see e.g. Grappone and Couzin 2011). Nevertheless, we ultimately cannot assert that each firm has a strong incentive to use the yellow pages and enter its own website. Thus, we conclude that our dataset presents a large set of firms, while acknowledging that it might not present a fully representative picture of the craft sector; however, there are strong incentives for firms to be listed on the yellow pages and to state their website on the yellow pages. 3.2 Sample specifics Our sample comprises craft firms (following the German legal definition of craft services, see e.g. Runst and Thomä 2020). This sector is in itself heterogeneous in terms of industries as it contains a historically-grown, legal set of occupations whose defining feature is its prevalence of manual work. The craft sector is considered to be rather slow in terms of digitalization, at least when compared to other sectors characterized by SMEs. It can thus be assumed that many craft firms continue to rely on the previously-dominant information platform, namely the yellow pages. Therefore, we assume that we reach a broader, more representative sample of craft firms using the yellow pages rather than with a straightforward Google search, which would not yield firms without a website. We chose the craft sector for our analysis of the digital divide because its firms can be found all across Germany and craft firms are also traditionally present in rural regions. Most industries are uniformly distributed across different spatial units (consider e.g. hairdressers) and the firm density primarily relies on population density. Thus, choosing the craft sector is likely to yield a spatially-balanced sample. Another important reason is that craft firms have a lower technological performance than other sectors in the German economy, and they tend to be more traditional in their firm policies. Therefore, we are likely to witness a sector that is currently undergoing a digital transformation in the domain of digital marketing. 6A summary of the respective study by gfk research from 2020 can be found at the yellow pages: https://www.gelbeseiten.de/gsservice/Newsroom/Pressemitteilungen/Pressemeldungen-national/Knappjeder-fuenfte-Nutzer-von-Gelbe-Seiten-sucht-nach-einem-Handwerker. K 84 A. Thonipara et al. Thus, our indicator is likely to capture marked spatial patterns in the craft sector that similarly occurred and have continually disappeared in other sectors in recent years. Therefore, using a digitally relatively backward sector using the indicator of website prevalence is likely to show us transformative processes that have occurred in other sectors and are occurring in similar sectors. We would not suggest that our sample is representative of all firms in the yellow pages or all firms in Germany, but rather for the craft sector, which is an integral part of the German economy and had (official numbers for 2019) 1 million firms, 5.58 million employees—which translates to about 12% of all German employees—and an overall revenue of 640 billion Euros.7Thus, overall we would suggest that our sample is representative of the craft sector and other sectors with smaller firms addressing private customers and a lower propensity to conduct digitalization measures. Overall, our sample comprises 346,361 firm entries, of which 104,460 (30.2%) have entered a website in the yellow pages that was online and working in June 2018. We downloaded firm data from 44 different industries with quite heterogeneous numbers of firms and levels of website prevalence. An overview of the industries and respective number of observations is documented in Table 4 in the online appendix. We use 44 industries, which are the relevant craft occupations in the yellow pages. There are small craft industries that are formally part of the craft sector with very few firms in Germany, which have not been included in the analysis, for example ropemakers. Overall, our goal was to represent the craft sector on the yellow pages using the respective industries, which then yields 346,361 firms. The web-scraping procedure further yielded information on the existence of a Facebook/ Twitter/Instagram reference in the website, as well as information on the most recent update and the use of HTML5 and HTTPS. At the regional level, there was no limitation during the web-scraping procedure, and thus all firms of the respective industries were used, irrespective of their regional origin. We used the postal codes associated with the firm entries and matched this information to the respective counties. This approach leads to inaccuracies in some cases, whereby the associated error can be assumed to be uniformly distributed over all counties/postal codes. With firms clustered at the county level, we matched a number of additional county-level regional indicators, namely population density, the share of employees without a professional qualification, the share of employees with an academic education, the revenue of craft firms, annual GDP per capita, rates of regional in-migration and out-migration8, increase/decrease of the population 7The data is from the German Federal Statistical Office, and thus our sample includes one-third of all officially-listed firms in German craft chambers (for a summary see https://www.zdh.de/daten-fakten/ kennzahlen-des-handwerks/, last access: 27.10.2020). However, the official number of 1 million firms should be considered inflated, as non-trivial numbers of factually-inactive firms continue to be listed in the official records. Further, the yellow pages contain firms and industries primarily addressing private customers. This excludes firms, e.g. in the building sector, working as contractors for industry firms or public organizations, which makes the yellow pages irrelevant for these firms as they tend to react to public biddings. While there are no exact numbers to substantiate this claim, we are confident that our sample encompasses all active German craft firms addressing private customer and excludes inactive yet listed firms and those primarily working in B2B contracts or for the public sector. 8As the rate of regional out-migration is highly correlated with the rate of regional in-migration we decide to only use the latter rate in the econometric analyses. K Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from... 85 aged 65 and older, the number of employees in craft firms,9and finally the share of households with high-speed internet access.10 Using the web-scraping data as well as the additional regional indicators, we can analyze the regional disparities in website prevalence, social media prevalence and the up-to-dateness of websites and show potential drivers of the differences using regional indicators. 3.3 Methodological approach/quantitative analysis In a first step, we use descriptive statistics to analyze the share of firms running websites, social media as well as the up-to-datedness of the websites for each regional category (rural, semi-urban, urban). For this purpose, the dataset obtained by our web-scraping procedure is used. The second part of the analysis comprises a regression analysis. As our dependent variable y (website prevalence) is a binary outcome dependent variable—which is “1” if a company has a website entry in the yellow pages and “0” if not—we apply a linear probability model taking the following form: yDˇ0Cˇ1X1Cu(1) Estimating the equation: b PDcpr Œy D1jx Dx0ˇ(2) The linear probability model models the probability of a firm having a website, meaning that website prevalence “y” equals “1” in dependence of×. This again is equal to a functional form of F(x’ß). × is a vector of independent variables. This vector includes the explanatory variables which were introduced in the hypotheses in section 2.3. (population density, share of employees with an academic education, share of employees without formal education, increase/decrease of the population aged 65 years or older, GDP per capita). Besides these variables we include a number of further variables: The firm’s industry: as there are industries which are more technology affine than others (electrician vs. mason). The share of households with access to broadband internet (<50 Mbit/s): if the share of households with less than 50Mbit/s is greater in a region we expect the probability of a firm having a website to be lower. 9More specific information on the variables is presented in online appendix Table 5. 10 To determine a region’s access to high-speed internet, we use a dataset from the TÜV Rheinland and the German Federal Ministry of Transport and Digital Infrastructure at the county level. The dataset gives us the share of households that can access an internet speed of 50Mbit/s, which we use as the broadband internet variable. For urban counties, there is essentially no variation in the data as essentially all households can access 50Mbit/s, there is little variation for semi-urban counties and strong variation for rural counties. K 86 A. Thonipara et al. Sales revenue in the craft sector: as regions with higher sales revenues in the craft sector could possibly have a higher level of digitalization. Increase/decrease of birth rate: increasing birth rates may be an indicator for a younger population which would explain a higher level of digitalization as younger people tend to use digital devices and communicate digitally. Regional in-migration (as well as regional out-migration used in the robustness tests): we assume that regions with higher regional in-migration have a higher level of website prevalence as the influx of people to a region goes along with a certain degree of anonymity. Hence, firms would have to invest into a website in order to be recognized by new inhabitants. Share of employees in the craft sector in a region’s labor force: this variable could be used as a proxy for competitiveness. If there are more employees in the craft sector in a region firms have to compete for customers but also for qualified workers. Hence, employees in the craft sector could have a positive effect on website prevalence. Firm size: One could argue that larger firms are more likely to have a higher level of digitalization. If larger firms are more likely to be situated in urban areas, the effects we find would only reflect the effects of firm size on the website prevalence instead of urban/semi-urban/rural regions (Kinne and Axenbeck 2020). We decide to include only firm size as a variable in the model as firm sales correlate with firm size. Data on firm size is not available on firm level as the firms do not report the number of employees on their website. Instead, we use data on average firm sizes by industries and by county from the sixteen Statistical Offices of the German Federal States.11 A list of the variables used, their description as well as descriptive statistics can be found in Tables 4, 5 and 6 in the online appendix. We specify robust standard errors clustered by the German counties, which capture their unobserved heterogeneity. We have also applied a logit model using a logistic distribution function as well as a probit model using a standard normal distribution function, whereby the results (and marginal effect sizes) in all three models yield similar results and effect sizes. We decide to report the results of the linear probability model estimated by ordinary least squares which allows effect sizes to be directly interpreted. We run five separate specifications: (I) using all firms in all regions while excluding broadband access, (II) using all firms and all variables, using the sub-samples for (III) firms in rural counties, (IV) firms in semi-urban counties and (V) urban firms including all variables. We decide to split the dataset in order to run the last three specifications instead of only using dummy variables for each region type as we are particularly interested in the region type specific effect each variable has on website prevalence. 11 In the rare cases that an industry-county combination comprises only very few firms, the average firm sizes have often been omitted by the Statistical Offices due to data privacy reasons. In these cases we proxy the omitted values by inserting the average size of the industry in Germany and adjust it by the relative average firm size of all industries in the respective county. K Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from... 87 To confirm whether our results are robust, we conduct a number of robustness tests. First of all, Table 15 in the online appendix presents a correlation matrix of the main variables. The correlation matrix including all industries is not reported for space reasons. However, their correlation coefficients are smaller than 0.01 in the majority of cases and can hence be neglected. In our robustness tests, we drop step by step variables that show higher correlation coefficients. Overall, we conduct six robustness test specifications, each time using the different regional sub-samples (see Tables 9–14 in online appendix). The results remain robust throughout all robustness tests. By matching regional data with firm-specific data received from web-scraping, the model is likely to suffer from the modifiable areal unit problem (MAUP). We acknowledge this problem and therefore run several robustness tests on different aggregation levels (county and regional planning level)12 for each regional category (whole sample, rural, semi-urban, urban) to ascertain whether the results remain robust. As Table 16 in the online appendix shows, the results remain robust regardless of the aggregation level chosen. Hence, we are confident that MAUP does not confound our results.13 4Results 4.1 Descriptive results The basic finding regarding website prevalence is shown at the county-level applying the respective classification of all German counties into rural, semi-urban and urban ones.14 Fig. 1shows how website prevalence differs between the three types of regions, with urban counties showing roughly a double share of website prevalence compared with rural counties. One can clearly see that rural firms have a substantially lower propensity to conduct digital marketing by establishing a website compared with urban firms. Thus, there is a clear and strong digital divide in terms of website prevalence, which warrants further analysis. Thus, there is a clear and strong digital divide in terms of website prevalence, which warrants further analysis. However, as shown in Fig. 2, different from indicators of economic prosperity or economic growth, there is no clear east-west pattern or differences across Federal states. Urban areas, more or less, show higher values than the rural ones, independent from the Federal state. Furthermore, by comparing firms’ propensity to update their websites, we can use the broad classification of counties in rural, semi-urban and urban counties. Recall 12 Using a smaller regional level is not possible due to data availability of the variables used. 13 Another possibility to circumvent this problem is using a multilevel approach as used in Tranos and Stich (2020) or López-Bazo and Motellón (2018). However, multilevel modeling is not applicable in our case as we do not have any firm-level explanatory variables except for the variable controlling for the firm’s industry. 14 This classification is based upon population density and the role of large cities in the respective region. See https://www.inkar.de/documents/Erlaeuterungen%20Raumbezuege19.pdf for details. Last access 2021/11/24. K 88 A. Thonipara et al. 20% 30% 37% 0% 5% 10% 15% 20% 25% 30% 35% 40% Rural counties Semi-urban counties Urban counties ybetisbewagni nnursmriffoerahs regional classification Fig. 1 Share of firms running websites classified by rural, semi-urban and urban counties that the “last update” variable is common among older website formats and that it can only be obtained from a low share of websites. Therefore, the two proxies HTML5 and HTTPS can be used to comment on the up-to-dateness of websites. They can both be interpreted as follows: HTML5 has been introduced roughly four years prior to the data collection process and has become the respective standard in website design. If a firm has conducted a substantial technical update of their website to the current format in the past four years, it has most likely used the format. HTTPS shows that the owner of a website has invested into the communication security of the website in the past years and hence it can be regarded as another proxy of up- to-datedness. Table 1. provides an overview of the share of websites featuring the respective variable according to the classification of the respective county. Interestingly, unlike in website prevalence itself, there are very small differences between urban, semi-urban and rural regions in terms of up-to-dateness of the websites. While urban firms tend to have slightly higher shares on the three indicators, the differences are weak at best. Thus, rural firms are less likely to run websites, although such firms tend to keep their website similarly up-to-date as urban firms. Table 1 Website update indicators Region type (no. of firms) Update 2018 HTML5 HTTPS Urban 0.247 (10,040) 0.573 (48,164) 0.296 (48,164) Semi-urban 0.236 (5780) 0.558 (29,059) 0.287 (29,059) Rural 0.239 (3721) 0.551 (17,561) 0.291 (17,561) The number reported in brackets represents the number of firms for which data could be retrieved for the specific variable. Hence, the results have to be read as follows: we were able to retrieve 10,040 observations on the last update of a firm’s website in urban regions. 24.7% of these firms updated their website in 2018. Differences in the number of firms for different variables are due to data availability K Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from... 89 Fig. 2 Website prevalence in German counties Table 2 Share of social media plugins in the websites Region type (no. of firms) Facebook Instagram Twitter Urban (48,164) 0.309 0.059 0.179 Semi-urban (29,059) 0.297 0.045 0.171 Rural (17,561) 0.311 0.047 0.177 A similar descriptive analysis was conducted regarding social media prevalence, asshowninTable2. Urban firms tend to have slightly higher shares of social media plugins, yet no substantial effects were discovered. Thus, our core descriptive result is that firms in rural counties have a substantially lower propensity to run websites. However, those rural firm running websites show a similar disposition to keep their websites updated and connected to social media. Building upon these descriptive results, we run several regressions aiming to determine the driving factors behind the strong difference in website prevalence. As online annex Table 4 reveals, there are some differences in terms of the share of craft firms with and without websites across industries, the range covers values K 90 A. Thonipara et al. between 18.3% and 51.6%. However, the standard deviation is not extraordinarily large and there are no correlations between the absolute number of firms for particular industries on the one hand and the respective share of website availability on the other. 4.2 Regression analysis The linear probability regression model analyzes which regional variables increase the probability of a firm having a website. The results of the different regression specifications are presented in Table 3. Turning to the interpretation of our results, we identify some regional determinants that are significantly associated with website prevalence. We find that population density has a significantly positive association with website prevalence for the overall sample, both including and excluding broadband access. In terms of effect sizes, an increase of one inhabitant per square kilometer increases the webpage prevalence by 0.01%, which can be considered a moderate effect. Population density can therefore be seen as one determinant for the second-level digital divide between rural and urban firms and the respective markets (Fig. 1). However, this does not warrant a simple interpretation of this result. The positive association with population density could either be due to the greater availability of the technology and network effects in cities or it could be interpreted as a rational business decision Table 3 Regression output (short) to explain website prevalence Independent variables All variables, except broadband internet All variables Rural Semiurban Urban Broadband internet (over 50Mbit/s in 2017) – 0.0004* 0.0040*** –0.0017*** –0.00471*** Population density 0.0001*** 0.0001*** –0.0001*** 0.00003*** 0.0001*** Share of employees with acad. Education 0.0074*** 0.0071*** 0.0064*** 0.0135*** –0.0021* Share of employees without formal educ –0.0112*** –0.0116*** –0.0231*** –0.0003 –0.0562*** Sales revenue in craft sector 0.0022*** 0.0022*** 0.0030*** –0.001*** 0.0024*** GDP per capita –0.0030*** –0.0030*** 0.0013*** –0.0013*** –0.0023*** Birth rate 0.0032*** 0.0031*** –0.0004* –0.0024*** 0.0085*** Rate of reg. in-migration 0.0001*** 0.0001* –0.0005*** 0.0007*** 0.0010*** Increase/Decrease of inhabitants aged 65+ –0.0157*** –0.0158*** 0.0110*** –0.0125*** –0.0484*** Employees in craft sector overall –0.0028*** –0.0024*** 0.0006 0.0128*** –0.0109*** Firm Size 0.0016*** 0.0016*** –0.0001 0.0023*** 0.0013** Constant 0.161*** 0.183*** –0.291*** 0.286*** 1.373*** Observations 296,228 296,228 73,948 93,376 128,901 R20.07 0.07 0.10 0.06 0.11 The full regression output including industries is reported in Table 7 in the online appendix *p< 0.10, **p< 0.05, ***p<0.01 K Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from... 91 such that rural firms prefer a more direct, non-digital interaction with their customers and vice versa. Result 1: There is evidence in favor of H1, implying a digital divide between urban and rural firms. Regarding the level of professional qualification among employees at the place of residence in the regional workforce, we find that higher shares of academicallytrained employees are associated with higher website prevalence, and vice versa higher shares of employees without professional qualification are associated with lower website prevalence. Both effect sizes are fairly strong given that a one-per- cent increase of the respective share leads to a 0.7%/1% in(de)crease in webpage prevalence. This hints at the knowledge-based nature of the current technological change through digitalization, whereby employees, firms and regions with a stronger emphasis on higher education and knowledge transfer are more innovative, while those without are threatened by digitalization. Result 2: Higher levels of professional qualification in the regional workforce are associated with a higher website prevalence, which provides evidence in favor of H2. Connected to this result is the regions’ development of the number of inhabitants of pension age (65+ in Germany). We find that counties with a high increase of inhabitants in pension age have overall lower levels of website prevalence, which can be interpreted as resulting from the different affinity to using digital technologies among older generations, in the private as well as the occupational context. All effect sizes are fairly strong, with a one-percent increase in the share of inhabitants 65+ leading to around one percent decrease in webpage prevalence. However, when looking at the differentiation between rural, semi-urban and urban counties, we find that a higher increase of older persons in rural regions is associated with higher website prevalence, meaning that the overall effect is driven by semi-urban and urban counties. Thus, while the overall result corresponds to previous results, the evidence is mixed when employing a regional differentiation. Result 3: The development of the number of inhabitants aged 65+ is negatively associated with website prevalence, which provides evidence in favor of H3. Further, it has been hypothesized that GDP per capita has a positive association with website prevalence, which can be explained from a capability as well as an affordability perspective regarding firms and customers. However, the results of our analysis provide evidence of a negative association of GDP per capita in the overall sample and particularly in urban regions with moderate effect sizes. Hence, even less-wealthy regions can have high levels of website prevalence. Nonetheless, in rural areas the effect is the reversed, as GDP per capita is positively associated with the probability of running a website. Result 4: GDP per capita is positively associated with website prevalence in rural areas only. In urban areas, GDP per capita is negatively associated with website prevalence. Consequently, there is mixed evidence regarding H4. Building upon these central results, our analysis addresses a number of additional variables and their association with website prevalence. The results suggest that other regional factors may partially explain the likelihood of having a website. First, we ask whether regional in-migration drives website prevalence, whereby this K 92 A. Thonipara et al. variable proves to be significantly positive for the overall sample. This result seems plausible as the majority of in-migrants within advanced countries are young adults who migrate for tertiary education or in the early stages of their careers, as is the case for this article’s study area. This group tends to have profound digital skills compared to older persons, particularly through life-long learning (“digital natives”). Regions with a higher share of in-migrants should thus have a stronger propensity to use digital technologies like websites for digital marketing. On the opposite side, counties with a high regional out-migration are likely to lose younger inhabitants with digital skills, thus making firm-level digitalization less relevant. Our results support this interpretation as the rate of out-migration in several robustness checks is negatively associated with website prevalence. Second, we find an overall positive association of the development of the birth rate with website prevalence. This, again, points at the importance of a young population (the parents) for the regional level of digitalization. However, this result is driven by urban regions, where an increased birth rate is associated with higher website prevalence. We find a negative significant association in semi-urban and no significant association in rural areas. Third, we controlled for the availability of high-speed internet. While the speed of 50Mbit/second is not needed to run a website, the availability of high-speed internet is likely to positively affect a firm’s willingness to run a website by making investments in digitalization more rewarding overall for firms, which should also affect website prevalence. For customers, it might increase internet usage overall and thus foster a stronger demand for online information on firms, thus prompting them to provide such information. For the entire sample, there is a weak positive effect for broadband access. However, when looking at rural regions, there is a clear and significant positive effect, while it is significantly negative in semi-urban and urban counties. This result can be partly traced back to the respective broadband variable: there is only substantial variation in terms of households’ access to internet with 50Mbit/second for rural counties. For semi-urban and urban counties in Germany, there are very few cases with an internet speed of less than 50Mbit/second. Thus, only the rural sub-sample should be interpreted. Accordingly, rural counties with good high-speed internet infrastructure are significantly more likely to have high shares of websites in craft firms than rural counties with poor internet infrastructure. It goes without saying that this result says little about causality; rather, there might be a causal link between the existence of larger and innovative firms in particular rural counties, whose political pressure has led to broadband access and whose existence leads to the internal migration of high-skilled employees who require non-relational information on craft firms. Last but not least, the county’s average firm size has a significantly positive effect on the probability of having a website. Hence, the larger a firm, the more likely it has a website. Now, if larger firms are more likely to be situated in urban areas, our results would only reflect the effects of firm size differences across region types on the website prevalence. We can rule out this argument by using a robustness test and presenting additional evidence. First of all, the effect size of the variable firm size is small and the fit increase is also comparatively low (see Table 17 in online appendix). Secondly, when looking at the region type specific effects, firm K Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from... 99 Riddlesden D, Singleton AD (2014) Broadband speed equity: a new digital divide? Appl Geogr 52:25–33. https://doi.org/10.1016/j.apgeog.2014.04.008 Rodríguez-Pose A (2018) The revenge of the places that don’t matter (and what to do about it). Camb J Reg Econ Soc 11:189–209. https://doi.org/10.1093/cjres/rsx024 Rodríguez-Pose A, Crescenzi R (2008) Mountains in a flat world: why proximity still matters for the location of economic activity. Camb J Reg Econ Soc 1(3):371–338 Ruiz-Rodríguez F, Lucendo-Monedero AL, González-Relaño (2017) Measurement and characterisation of the Digital Divide of Spanish regions at enterprise level. A comparative analysis with the European context. Telecomm Policy 42:187–211. https://doi.org/10.1016/j.telpol.2017.11.007 Runst P, Thomä J (2020) Does occupational deregulation affect in-company vocational training?—Evidence from the 2004 Reform of the German Trade and Crafts Code. J Econ Stat 240(1):51–88 Salemink K, Strijker D, Bosworth G (2017) Rural development in the digital age: a systematic literature review on unequal ICT availability, adoption, and use in rural areas. J Rural Stud 54:360–371. https:// doi.org/10.1016/j.jrurstud.2015.09.001 Scheerder A, von Deursen A, van Dijk J (2017) Determinants of Internet skills, uses and outcomes. A systematic review of the second- and third-level digital divide. Telemat Inf 34(8):1607–1624. https:// doi.org/10.1016/j.tele.2017.07.007 Schleife K (2010) What really matters: Regional versus individual determinants of the digital divide in Germany. Res Policy 39:173–185. https://doi.org/10.1016/j.respol.2009.11.003 Sternberg R, Krymalowski M (2002) Internet domains and the innovativeness of cities/regions—Evidence from Germany and Munich. Eur Plan St 10(2):251–274 The Federal Government of Germany (2018) Shaping Digitalization. Implementation strategy of the Federal Government. The Federal Government of Germany, Berlin Townsend L, Sathiaseelan A, Fairhurst G, Wallace C (2013) Enhanced broadband access as a solution to the social and economic problems of the rural digital divide. Local Econ 28:580–595. https://doi.org/ 10.1177/0269094213496974 Townsend L, Wallace C, Fairhurst G, Anderson A (2017) Broadband and the creative industries in rural Scotland. J Rural Stud 54:451–458. https://doi.org/10.1016/j.jrurstud.2016.09.001 Tranos E (2012) The causal effect of the Internet infrastructure on the economic development of European city regions. Spat Econ Anal 7(3):319–337. https://doi.org/10.1080/17421772.2012.694140 Tranos E (2016) The internet: its geography, growth and the creation of (digital) social capital. In: Shearmur R, Carrincazeaux C, Doloreux D (eds) Handbook on the geographies of innovation. Edward Elgar, Cheltenham, pp 356–369 Tranos E, Stich C (2020) Individual internet usage and the availability of online content of local interest: a multilevel approach. Comput Environ Urban Syst. https://doi.org/10.1016/j.compenvurbsys.2019. 101371 Vicente MR, López AJ (2011) Assessing the regional digital divide across the European Union-27. Telecomm Policy 35(3):220–237. https://doi.org/10.1016/j.telpol.2010.12.013 Wernick C, Bender CM (2017) The role of municipalities for broadband deployment in rural areas in Germany: an economic perspective. Digiworld Econ J 1st Quart 105:91–110 What works Centre for local economic growth (WWG) (2015) Evidence review 6—broadband Whitacre B, Gallardo R, Strover S (2014a) Does rural broadband impact jobs and income? Evidence from spatial and first-differenced regressions. Ann Reg Sci 53:649–670. https://doi.org/10.1007/s00168- 014-0637-x Whitacre B, Gallardo R, Strover S (2014b) Broadband’s contribution to economic growth in rural areas: moving towards a causal relationship. Telecomm Policy 38:1011–1023. https://doi.org/10.1016/ j.telpol.2014.05.005 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. K