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Online media channels as drivers of entrepreneurial
activity in censored countries
Author: Douwe S. Meindertsma – S1800892 University: Rijksuniversiteit Groningen Supervisor: Dr. F. Noseleit
Co-assessor: Dr. P. de Faria Date: 20-1-2015
Word count: 6067
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1. Introduction
The Arab spring is a recent example of the formidable power of the media. Especially, online news channels, social media, blogs and online videos, played a critical role in the Arab revolutions. Interestingly, the online media seemed to mitigate the absence of a free domestic media (Howard et al., 2011). In this paper we extend the potential of free media to the field of entrepreneurship. We suggest that free media is an effective institution for accurate information and diverse ideas that enable the entrepreneur to discover opportunities that will yield a profit.
The potential of free media for social and economic outcomes is strongly established in the literature. The influential paper of Sen (1984) provides evidence that media freedom prevents famines. Media freedom is also related to improved social outcomes (Djankov et al., 2001), higher economic development (Leeson et al., 2005), and decreased corruption (Ahrend, 2002; Brunetti and Weder 2003). The recurrent argument is that free media provide greater quantities of accurate and diverse information to the citizens, than repressed media.
Despite its potential, citizens that enjoy free media are rather an exception around the world. Anno 2014 only one in seven people live in a country where government control on media is minimal and the media is not subject to heavy legal or economic pressures (Freedom House, 2014). Government motives for preventing media channels to accurately inform the citizens may be self-interest, religion, culture, ideology, or a combination. The bottom line is that governments to a certain extent prevent citizens to access the knowledge they desire. On the opposite, free media can be defined as independent from government in disseminating a varied range of information that is adequately accessible for the citizens (Weaver, 1977; McQuail, 2005).
3 hence stimulating entrepreneurial activity on the country level. In contrast, media repression hampers entrepreneurial activity, because it causes restrained and biased information flows. Taking this as a point of departure, we argue that the information flows from free media endow the entrepreneur with a unique stock of specific knowledge about the economy. Furthermore, we demonstrate that the knowledge flows from media alert the entrepreneur to existing and new information. After establishing the theoretic relationship between free media and entrepreneurial activity, we investigate the ramifications of online media channels on the relation between free media and entrepreneurial activity. We suggest that online media channels weaken government control on information flows from the media. We investigate whether global internet adoption is a significant cause.
The significance of this study lies in establishing a relation between free media and rate of entrepreneurship across countries. Entrepreneurship is one of the main mechanisms by which society converts (technical) information and knowledge into future products and services. The evidence that free media positively impacts entrepreneurial activity, provides governments with an economic incentive for policies regarding free media. Furthermore, it contributes to the understanding of entrepreneurship.
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2. Literature review and hypotheses
In this section three main subjects are discussed. First, we discuss the process of entrepreneurship. Second, we connect the information flows of the media both to the entrepreneur and the discovery of opportunities. Third, we discuss the revolution in the media landscape, with the internet as the main driver of change.
The process of entrepreneurship
Entrepreneurship can be defined as the activity that involves discovery, evaluation and exploitation of opportunities to introduce future goods and services (Shane & Venkatamaran, 2000). At the heart of entrepreneurship is the nexus between the entrepreneur and the opportunity (Venkatamaran, 1997). Indeed, without opportunities there is no entrepreneurship and without entrepreneurs the existing opportunities are not discovered. Opportunities are situations in which a combination of resources can be sold for a higher price than the cost of production (Casson, 1982). The entrepreneur is in pursuit of these opportunities. He can be defined by his alertness to observe changed conditions or overlooked possibilities that produce lucrative opportunities (Kirzner, 1985).
5 Information flows and the entrepreneur
Knowledge can be described as structured information that is situated somewhere between completely tacit and completely codified (Braunerhjelm, 2010). Knowledge is a commodity that can be transferred across time and space from where it is offered to where it is demanded. Arrow (1962) observed that knowledge has two important characteristics that distinct it from other resources. The first involves non-excludability, i.e. the difficulty or impossibility to exclude others from enjoying the good. The second characteristic encompasses non-exhaustibility, i.e. that knowledge consumption of one person does not preclude consumption of the same knowledge by others. An important implication of these characteristics is that knowledge created by one, can easily spill over to other parties who did not pay for it. Economists and sociologists emphasize the societal benefits of such knowledge spillovers, because of its scale economies (Sorenson et al. 2006). Nevertheless, private, and increasingly also public, organizations will only invest in knowledge creation when there is the prospect of sufficient rewards. Therefore appropriation mechanisms are used, such as intellectual property rights, secrecy, lead time and complementary assets (James et al., 2013). Such mechanisms are also essential in the business models of media channels. These mechanisms grant continual generation of new knowledge flows that are at the heart of new products and services.
6 lifetime and can be triggered at any time. For example, an entrepreneur sees a vacant church and envisions at once the idea that it could serve as a theatre.
The individual’s specific knowledge is acquired throughout life by so called knowledge corridors, such as education, social relationships, daily life and work experience (Kirzner, 1985; Venkatamaran, 1997). Because all people have different life paths, this knowledge is preeminently idiosyncratic. It is this distribution of knowledge among individuals that causes differences in valuation of new ideas and resources between economic actors (Kirzner, 1997). The entrepreneur’s conjectures lead him to belief that he can buy resources, recombine them and sell them at a profit (Schumpeter, 1934; Kirzner, 1997; Shane, 2000). Furthermore, the specific knowledge decides whether an individual will recognize an opportunity at all (Shane & Venkatamaran, 2000). Thus, specific knowledge may give one an advantage over others in discovering or creating an opportunity.
We suggest that free media act as knowledge corridors. Media inform, educate and entertain individuals by exposing them to abundant information and diverse ideas about all kinds of subjects, e.g. news, business, science, innovation, people etc. The interesting bits of information stick with the individual and can be triggered. Thus, our first argument runs that free media generate accurate information and diverse ideas, that enable the entrepreneur to discover or create an opportunity to make a profit. In contrast, repressed media will less do so, because their information flows are poorer.
Information flows and entrepreneurial opportunities
7 2000). It is simply a new idea to make money. So now we know why so many people become an entrepreneur. However, it is more interesting to know where opportunities come from. How do they come into existence? The entrepreneurship literature provides two prevalent perspectives on the sources of opportunities. The one argues that opportunities are discovered by a different view on existing market information, while the other designates new information as the source of entrepreneurial opportunities. Below we discuss these two perspectives and establish their connection with information flows.
The first perspective is based on the work of Kirzner (1973, 1985, 1997) It argues that opportunities arise from inefficiencies in societies and markets. These inefficiencies are discovered and exploited by individuals with a different view at existing information (Kirzner, 1973). These opportunities are called Kirznerian opportunities. The underlying assumption is that people have different beliefs about the value of resources and therefore make different decisions based on the existing information. Since such decisions are not always correct, errors in the shape of shortages and surpluses are created (Gaglio & Katz, 2001; Shane & Venkatamaran, 2000). The entrepreneur aims to exploit these errors or market inefficiencies. We expect that free media fulfills an important role in alerting the entrepreneur to errors and market inefficiencies. For example, a media channel alerts the entrepreneur that every year millions of tons of food waste are just thrown away. This is the existing information or status quo. Consequently, the entrepreneur makes a conjecture that the market values food waste too low. So he buys it at low cost and transforms it into biogas, which he can sell at a higher price.
8 existence of the Schumpeterian opportunity. About half of the founders of the 500 fastest growing private companies in the United States, claim that their business is a response to changed conditions (Bhide, 2000). The relevant new information for the entrepreneur originates from technological change, political and regulatory change, and social and demographic change (Shane, 2000). Technological change enables people to recombine resources in different and more efficient ways (Casson, 1995). For example, the invention of the internet allowed people to create new resource combinations that exploited this technological change (Shane, 2000). The same applies for political and regulatory change, and social and demographic change. One can think of the new resource combinations that are possible due to deregulation and the aging population. The new information alters the value of resources and triggers novel ways of using them to enhance wealth (Shane & Venkatamaran, 2000). When new information is complementary with the individual’s specific knowledge, an entrepreneurial conjecture can be triggered (Kaish & Gilad, 1987). Again, information flows from free media are a capital source of accurate information on change. The new information will trigger entrepreneurs to make conjectures about new means-ends frameworks.
In summary, we argue that media expose the entrepreneur to accurate information on both the economic status quo and new developments. Again, we expect free media to produce greater and more accurate information flows than censored media. Thus, our second arguments runs that free media alert the entrepreneur to new opportunities. On the basis of this argument and the one described in the paragraph above, we state the following hypothesis (also depicted in fig. 1):
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Fig. 1
H1 (+)
Revolution in the media landscape
A reflection on the revolution in the media landscape cannot be left out when the relationship between media freedom and entrepreneurship is investigated. The rise of the internet has introduced multiple online media channels. These channels radically changed and continue to change the media landscape. Traditional media channels, such as newspapers, television and radio, are increasingly absorbed by the online media channels. Nowadays, (inter)national newspapers, television and radio are increasingly accessed via the internet on a computer or mobile device. Furthermore, the internet instigated the enormous popularity of social media microblogging, online videos etc.
Some important features of the online media channels are interconnectedness, accessibility to senders or receivers, interactiveness, ubiquity and delocatedness (McQuail, 2005). Another important characteristic of online media is low cost of publishing and access. It is the receiver, who decides what kind of information he wants, when he wants it and how he wants it. It is clear that the online media shift the power from the sender to the receiver.
We suggest that this power shift has important implications for government control on the media. Online media channels may well act as a substitute for domestic media that is censored by the government. First, because online media channels overcome geographic and political boundaries and have no central owner or location (Castells, 2001). As a result, online media channels are more difficult to control for governments. This argument is supported by research of the Freedom house, which shows that internet freedom scores 10 points or more better (on a scale of 100), than general media freedom in 34 out of 60 studied countries (Freedom House, 2014). A second argument runs that the accessible knowledge content on
10 the internet is many times greater and more diverse than the knowledge flows from traditional media channels. For example, the internet provides dozens of diverse channels on business news, while a domestic newspaper or television channel only provide one. This is a clear incentive for citizens to look up the desired information on the internet. Third, the interactiveness of the internet allows people to search for the specific information they desire, for example by using Google. Traditional media provide no personalized information. On the base of these three arguments, we expect that online media channels will weaken the relationship between media repression and entrepreneurial activity. Accordingly, our hypothesis is as follows:
H2: The strength of the relationship between media repression and rates of entrepreneurship across countries weakens over time.
The undermining effect of the internet
11 H3: Internet adoption has a significant interaction effect on the weakening relationship between media repression and rates of entrepreneurship across countries.
3. Methodology
In this section we present the collected data and explain the equations for the empirical analysis.
Data collection
We collected data on 70 countries over the period of 2004-2012. For our dependent variable we use the measure of new business density from the World Bank indicators. The variable is often employed as a measure of entrepreneurial activity cross-country (Sobel et al. 2010; Dutta et al., 2009). Its number reflects annual new business registrations per 1,000 people ages 15-64.
For the explanatory variable of media freedom, we use the press freedom index of Freedom House. Since 1980 Freedom House annually reports on the state of the media in countries around the world. Their indices are widely used by academics for research purposes. Freedom House ranks each country on a scale from 0 (best) to 100 (worst). The classification is based on a set of 23 methodology questions, which are divided in three subcategories: the legal environment, the political environment and the economic environment. A country is classified as free, partly free or not free, based on the extent to which a free flow of news and information is allowed.
12 finds evidence that better institutional conditions tend to increase entrepreneurship across countries. We use the economic freedom index from the Fraser institute, which ranks countries on a scale from 0 (worst) to 10 (best). We also include annual GDP growth, costs of starting a business and population density, as possible explanations for entrepreneurial activity cross-country. These latter three control variables are collected from the World Bank indicators. Definitions and sources of all 7 variables and a list of the 70 countries are provided in Appendix 1.
Specification of models
To test H1 we use a fixed effects regression. This method is used to analyze longitudinal data with repeated measures on both dependent and independent variables. The advantage of the fixed effects model is that it controls for time-invariant characteristics of the measured entities, which cannot be observed or measured (Torres-Reyna, 2007). As a result, it prevents omitted variable bias. To run the fixed effects regression method, a dummy variable must be created for each group, except one. Consider the following basic fixed effects model, in which 𝑌𝑖𝑡 is the dependent variable and where i=entity and t=time
𝑌𝑖𝑡 = 𝜇𝑡+ 𝛽𝑋𝑖𝑡+ 𝑎𝑖 + 𝑢𝑖𝑡 (1)
Further 𝜇𝑡 is an intercept, 𝑋𝑖𝑡 presents the independent variable, 𝛽 stands for the slope of the independent variable, 𝑎𝑖 is the entity specific effect and 𝑢𝑖𝑡 is the residual term. When we
apply this model to our analysis we have
13 that may influence the level of entrepreneurial activity, e.g. time-invariant cultural characteristics, location characteristics, etc.
We test H2 by including a time trend t to reflect changes in entrepreneurial activity over time in equation (2). The equation becomes
𝐸𝑛𝑡𝑟𝑖𝑡 = 𝜇𝑡+ 𝛽1𝑃𝐹𝑖𝑡+ 𝛽2𝑡𝑡+ 𝛽3𝑃𝐹𝑖𝑡𝑡𝑡+ 𝛽4𝐶𝐵𝑆𝑖𝑡+ 𝛽5𝐺𝐷𝑃𝑖𝑡+ 𝛽6𝑃𝐷𝑖𝑡+ 𝑎𝑖+ 𝑢𝑖𝑡 (3) The interaction term between press freedom and the time trend allows us to estimate changes in the effect of press freedom over time. Thus, a significant and positive coefficient for β3 indicates that the impact of press freedom becomes less pronounced over time.
To test H3, we include Iuse in equation (3), which measures the interaction effect of internet adoption on the relationship between media repression and new business density, controlling for the time trend 𝑡𝑡. The equation becomes as follows:
𝐸𝑛𝑡𝑟𝑖𝑡 = 𝜇𝑡+ 𝛽1𝑃𝐹𝑖𝑡+ 𝛽2𝐼𝑢𝑠𝑒𝑖𝑡+ 𝛽3𝑃𝐹 ∗ 𝐼𝑢𝑠𝑒𝑖𝑡+ 𝛽4𝐶𝐵𝑆𝑖𝑡+ 𝛽5𝐺𝐷𝑃𝑖𝑡+ 𝛽6𝑃𝐷𝑖𝑡+ 𝛽7𝑡𝑡+ 𝑎𝑖 + 𝑢𝑖𝑡 (4)
4. Results
In this section we present the empirical results for our three hypotheses. The summary statistics of the variables are depicted in the appendix (table 2).
Our first hypothesis states that higher levels of media freedom positively impact rates of entrepreneurship across countries. We tested this hypothesis by means of the fixed effects regression displayed in equation (2). Table 1 below presents the results of the fixed effects regression.
Table 1 The impact of press freedom on new business density
14 New business density (log)
Press freedom index (log) -.182929 Economic freedom index (log) 1.661032***
GDP growth .0130845***
Cost of business start-up (log) -.199015*** Population density (log) -45.31232***
Constant -45.31232***
R-squared (within) 0.3007
***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level.
The findings show no significant impact of press freedom on new business density across countries. Although the coefficient is negative, it is nog significant (β=-0,18, p=>0,10). Thus, higher levels of media freedom show no positive impact on rates of entrepreneurship across country. Hypothesis 1 is not supported. Interestingly, all the control variables that we included are significant at the 1% level and have the anticipated outcome. The model explains 30% of all the changes on new business density over time at the country level, as is shown by the R-squared (within) term.
15 Fig. 1: Correlation press freedom (log) and new business density (log) over time
Table 2 Changes over time in the press-nbd relationship
Independent variable Dependent variable
New business density (log) Press freedom index (log) -44.50711***
Economic freedom index (log) 1.428023***
GDP growth .0131364***
Cost of business start-up (log) -.1834933*** Population density (log) -.7162976***
Time trend .0221272***
Constant 104.6453***
R-squared (within) 0.3146
***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level.
Table 2 above presents the results of the fixed effects regression of equation (3). The findings show a positive coefficient for the interaction term between press freedom and the time trend, that is significant at the 1% level. From the model it follows that every consecutive year in the
16 period 2004-2012, ceteris paribus, new business density increases with 0.22. Thus, over the years the relationship between media repression and entrepreneurial becomes less pronounced. H2 is supported. Interestingly, by including the time trend, the explanatory power of the model increases substantially from 30.1% to 31.5%.
The results of equation (4) are presented in table 3 below. We find that the interaction effect of internet adoption is positive and significant at the 10% level. A one percent increase of internet adoption results in 0,086 higher new business density. H3 is supported. Internet adoption has a significant interaction effect on the relationship between media repression and new business density.
Table 3 Interaction effect of internet on the press-nbd relationship
Independent variables Dependent variable New business density Press freedom index (log)
Internet use (log)
-.3424704* -.2273106 Interaction effect
Economic freedom index (log)
.0857181* 1.357315***
GDP growth .0130721***
Cost of business start-up (log) -.1625262*** Population density (log) -.5978304**
Constant -19.6607
R-squared (within) 0.3172
17 When we depict the outcome of equation (4) in the graph below (fig. 2), it can be clearly seen that higher internet adoption rates weaken the impact of media repression on new business density. Finally, by including the interaction effect of the internet, the explanatory power of our model increases to 31.7%. It is clear that adding both the interaction effect of internet adoption and the time control, results in a substantial increase of the explanatory power of our model.
Fig. 2 Undermining effect of internet adoption on media repression
5. Discussion and conclusions
Free media preeminently disseminate accurate information and diverse ideas. The entrepreneur is dependent on accurate and diverse information to discover and pursue opportunities that generate a profit. Therefore, we suggested that free media positively impacts entrepreneurial activity across countries. Based on the literature, we provided two ways by which the entrepreneur benefits from information flows generated by free media. First, we argued that information flows of free media endow the entrepreneur with specific knowledge about markets, places, people, resources etc. Second, we asserted that free media
low (25 percentile) high (75 percentile)
-. 4 -. 2 0 .2 E ff ec t of m ed ia rep res s ion on n bd
18 produce accurate reporting on existing information as well as new information, thereby contributing to discovery of both Kirznerian and Schumpeterian opportunities. Despite our theoretical foundation, our results showed no significant positive impact of media freedom on the rate of entrepreneurship across countries. This contradicts results of Dutta et al. (2009), who do observe a positive impact of free media on entrepreneurial activity in a similar research setting. A possible explanation could be the difference in analyzed time period. Respectively, Dutta et al. analyzed data over the years 2000-2005, while we measured the time period of 2004-2012. This explanation is in line with our supported H2, which states that the relationship between free media and rate of entrepreneurship weakens over time. Our outcomes indicate, as predicted, that the relationship between media freedom and entrepreneurial activity becomes less pronounced over the time period of 2004-2012.
Next, we suggested that increasing internet adoption provides a significant explanation for the weakening media freedom-entrepreneurial activity relationship, because it is able to circumvent government control. And indeed, our results indicate that higher internet adoption rates weaken the effect of media repression on entrepreneurial activity across country. These findings are interesting, because they indicate that growing internet adoption undermines government control on media output. Because of online media channels, real media freedom in repressed countries is higher than media freedom in theory. This finding is corroborated by the Freedom House, who state that online media ensure a growing availability of knowledge in countries with repressed media (Freedom House, 2014).
19 Markman et al. (2005) empirically demonstrate that basic knowledge created by universities is exploited by start-ups and spin-offs. In our suggestions for future research we come back to this point.
We believe that our findings have important implications for public policy. First, the outcomes advocate policy aimed at providing internet access to citizens in censored countries. Second, policy should aim at guaranteeing internet freedom and fight growing trends of internet censorship by suppressing governments. Such policy can be instigated by the international community or countries that aim to reform their media policy. European Parliament already passed resolutions in 2014 that call on the EU to take the lead in ensuring worldwide media freedom. Our study can contribute to a response to this call. Also non-governmental institutions, such as the World Bank and the OECD can play a role in facilitating internet adoption internet freedom. They may provide loans or invest in internet infrastructure in countries where the right of access to accurate and diverse information is violated. An example of a promising initiative in this, is the global internet-access initiative of Facebook-founder Mark Zuckerberg.1
We conclude with the remark that, indirectly, growing internet adoption and increasing media freedom may also have a strong influence on social and economic outcomes in a country. Efficient and effective political and economic markets require accurate information (Stiglitz, 2000). Positive outcomes of better access to accurate and diverse information may be further democratization, increased human rights, economic growth, and reduction of poverty.
6. Limitations and future research
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21 Appendix: List of variables, summary statistics and countries
Table 1 Variable definitions and sources
Variable Definition Function Source
New business density new registrations per 1,000 people ages 15-64
Dependent variable
World Bank Indicator
Press freedom Index Ranking of media freedom on a scale from 0 (best) to 100 (worst) Independent variable Freedom House
Internet users Internet users per 100 people
Interaction term World Bank Indicator Economic freedom
index
Control variable Frazer institute GDP growth Annual % GDP growth Control variable World Bank Indicator Cost of business
start-up procedures
Cost of business start-up procedures as a % of GNI per capita
Control variable World Bank Indicator
Population density People per square km of land area
Control variable World Bank Indicator
Table 2 Summary statistics
Variable Obs. mean std. dev. min max
22
Table 3 Countries
1. Albania 15. Chile
29. Hong Kong
SAR China 43. Netherlands
57. Slovak Republic 2. Algeria 16. Colombia 30. Hungary
44. New
Zealand 58. Slovenia 3. Argentina 17. Costa Rica 31. Iceland 45. Nigeria 59. South Africa 4. Armenia 18. Croatia 32. India 46. Norway 60. Spain
5. Australia
19. Czech
Republic 33. Indonesia 47. Pakistan 61. Sweden 6. Austria 20. Denmark 34. Ireland 48. Panama 62. Switzerland 7. Bangladesh 21. El Salvador 35. Israel 49. Peru 63. Thailand 8. Belgium 22. Finland 36. Italy 50. Philippines 64. Tunisia 9. Belize 23. France 37. Jordan 51. Romania 65. Turkey 10. Bolivia 24. Georgia 38. Kazakhstan
52. Russian
Federation 66. Ukraine 11. Botswana 25. Germany 39. Mauritius
53. Rwanda 67. United Arab Emirates
12. Brazil 26. Ghana 40. Mexico 54. Senegal
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