Cultural attitudes, economic shocks and political radicalization

The renowned Lars von Trier’s movie ‘Dogville’ has a particularly economically interesting plot that has the following storyline. A young foreign woman, who has her origins in a society with anti-village attitudes but does not share these attitudes, arrives in a small closed world – a village named Dogville. Initially, the woman provides her labor in exchange for food and shelter in a classical supply and demand model. However, her bargaining power on the labor market decreases due to distorted information about her being an outlaw person (actually the truth is she is being chased by her anti-village past associates). The market responds to the shock from this information and her salary is decreased. Over time, her economic and, gradually, also her social rights deteriorate. Ultimately, the basic minimum of human dignity is breached – the young woman is brought to bartering her body in exchange for shelter under complete loss of personal freedom (she agrees to be kept chained, as part of the transaction). This shocking marginalization of the woman leads her to take a radical turn in her choices. She decides to cooperate with her own old enemies (from whom she was trying to escape at her first place when coming to seek shelter in Dogville). Together with them, she sets on fire and completely destroys the place ‘Dogville’ which had impoverished her beyond the minimum threshold of acceptability.

We assume that the radical response to a negatively changing economic context (e.g., a shock) is morally not sanctioned owing to what is known in behavioral economics as a fudge factor or in Becker’s (1968) economic interpretation as a moral cost–benefit analysis. Clearly, the above mentioned local ‘tunnel effect’ activates the drive (Herzog 2015) for basic survival. And therefore, people may even perceive their increased aggressiveness/hostility as morally justifiable under extreme conditions that previously would have been regarded as excessive (Dharmapala et al. 2009; Becker 1968). The moment that the previously unthinkable moral constraint switches to another level of tolerance or acceptance is an illustration of a classical tipping point (Lamberson and Page 2012). This is the tipping point at which we assume that the mechanism of the above mentioned ‘tunnel effect’ starts operating. There are other potential mechanism through which radicalization may be affecting negatively local economic productivity.

As mentioned above, one highly plausible such mechanism might be the impact of cultural gravity on the human capital accumulation in a place and migration (see Tubadji and Nijkamp 2015), where the radicalization of the place decreases the attractiveness of a place for footloose human capital. Alternatively, the cultural impact on growth through the six degrees of connectedness in the social network which affects local innovation (see Tubadji and Nijkamp 2016 for an extensive description of this potential channel) might be the way how radicalization decreases the connectedness in the social network and thus makes a place less innovative and therefore less economically prosperous. Also, many other known or novel economic mechanisms sensitive to cultural impact may be affected by radicalization and thus generate a serious impact on local economic development.

Alternatively, we re-estimated all tests with the share of voters for the ultra-left party KKE as a dependent variable. The results however, were never statistically significant for the KKE-related variables. The rest of the model was stable and performed similar to the reported results employing the percentage voted for the Chrisi Avgi. On the one hand, a reason for this may be the Greek specific reality, where the origin of the left and right parties and their relation with the Monarchy from the past might be carried further in time by cultural persistence. It could be also that there is something more universal in the ethos of the right and left political ideologies, that can be generalized outside the Greek context. Namely, left ideology might be associated with concern for the common good and right ideology is more concerned with the private interest. There is work related to this matter suggesting that places with less risk-averse attitudes are more prone to develop populist political preferences (see Pastor and Veronesi 2018). This matter is worth further investigation before a definite answer for the reasons behind the different results obtained with ultra-left and ultra-right voting can be stated.

We conducted all estimations alternatively using the dummy variable capturing the start of the austerity period as an approximation for the negative economic shock. These results appeared to be very consistent with the ones presented here.

The results obtained with the use of the two alternative measures are consistent. The results using the adjusted budget deficit are only more prominent as magnitudes.

The sectoral location quotient (LQ) is a measure of concentration of a particular industry in a region compared to the national average presence of this industry. We have these calculated on NUTS3 level and they are obtained following Florence (1939) using the formula: where IS represents the specialization index; Es stands for local employment in the given sector of interest; Est represents the employment in this sector in the country; Er stands for total local employment in all sectors; and Et denotes total employment in the country. We have six LQ variables for every regions for the six sectors: (1) agriculture; (2) energy and manufacturing; (3) construction; (4) distribution, transport and common services; (5) finance and other services; and vi) non-market services. The sectoral location quotients are calculated for every year in each region. They are labeled respectively as variables: ‘agriculture’, ‘industry’, ‘construction’, ‘trade and retail’, ‘finance’, ‘non-market sector’.

This includes EPEN voters for the years, before Chrysi Avgi evolved from EPEN and became founded as an official party.

The share of ND voters cannot be assumed directly inversely related to the share of other parties per se, as the number of parties in the different elections varies as well as the number of voting people and the fact that ND sometimes wins and sometimes loses the elections. What however can be reliably expected to be linearly captured by the share of the ND party in a locality, is a proxy for the particular right-minded-political culture in the locality.

To use an interaction term in this 2SLS setting, we first obtain the purged predicted value of ‘center-right (%)’from a 2SLS for model (2) without interaction term. Then, we multiply this purged value with ‘adj. budget deficit’ in order to obtained a cleaned from endogeneity interaction term between the economic and cultural triggers.

The variable ‘center-right (%)’ captures the standard pro-right orientation as opposed to the ultra-right orientation. With the center-right orientation, the radical right party shares the right ideology, but the ultra-right party is an extreme form of ideology and in this sense it might have similarity with extreme left parties. Disentangling which part of the ultra-right motivation is leading (its right orientation or its radical orientation) is important. If the economic shock causes an increase in the extreme votes, we will observe an increase in the share of votes for the ultra-right and ultra-left parties. If the austerity radicalizes only the right oriented voters, we will see an increase among the shares of center- and ultra-right voters, but not of the percentage of ultra-left voters. This clarifies the economic meaning and political ideological and attitudinal difference behind the reaction captured in the increase of the voting for the ultra-right party that we analyze here.

The relationship of this instrument with the left extreme party KKE is of course opposite in sign.

It is noteworthy that while Table 2a–d presents different quantifications and use different sets of controls, indifferent of measurement alternatives tried, the results obtained are always consistent and lead to a very similar economic interpretation.

As many countries, Greece experiences strong agglomeration around its capital city—Athens—and its corresponding NUTS3 region—Attika. Also some of the regions such as Crete, Peloponnese and Thessaly also group as an outstanding gear of development different from the rest of Greece (see for example Ioannides and Petrakos 2000) on these topics. Yet, the regional economic disparities and agglomerations regard the GVA (productivity levels) and population concentrated in the NUTS3. We do address these variables directly in our analysis, so basically the disparities between the units are very clearly quantified and their effects are reported. We don’t know why the NUTS 3 regions are having disparities, but we clearly learn how their disparities are associated with their radicalization under economic shocks and given their cultural heritage in political conservatism.

The results across all specifications with and without this last battery of controls are consistent. The use of the latter however reduces the number of observations because it is available for a shorter period of years. Thus, we present in Table 2 both types of results. The full list of tables of various tried specifications with diverse lists of controls is available upon request.

When we use the cultural trigger as a treatment we include in the matching also the economic trigger as an explanatory variable and the historic cultural variable in order to take away the endogeneity effect; when we use the economic trigger as a treatment, we use the cultural variable as a regressor in the matching model.

Tables with these descriptive statistics are available upon request.