Spatially-Embedded Collective Memory and Political Behaviors

Activated History – The Case of the Turkish Sieges of Vienna

Christian Ochsner and Felix Roesel (Ifo Institute – Leibniz Institute for Economic Research at the University of Munich Dresden Branch)

Abstract: We study whether long-gone but activated history can shape social attitudes and behavior even after centuries. We exploit the case of the sieges of Vienna in 1529 and 1683, when Turkish troops pillaged individual municipalities across East Austria. In 2005, Austrian right-wing populists started to campaign against Turks and Muslims and explicitly referred to the Turkish sieges. We show that right-wing voting increased in once pillaged municipalities compared to non-pillaged municipalities after the campaigns were launched, but not before. The effects are substantial: Around one out of ten votes for the far-right in a once pillaged municipality is caused by salient history. We conclude that campaigns can act as tipping points and catalyze history in a nonlinear fashion.

URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_6586&r=his

Circulated by NEP-HIS on: 2017-11-05

Revised by Martin Söderhäll (Uppsala University)

The Turkish Siege of Vienna (1529). Collection: Vienna Museum. Source: Wikimedia Commons.

a) Summary

Is it possible for “arguably irrelevant” historical events to shape the voting behavior of a population if triggered by political campaigning exploiting said historical events? This is the main question the authors set out to answer in the paper. The authors show that political campaigning that uses stereotypes of religious and ethnic minorities can be highly effective when encountering spatially embedded collective memory utilizing a set of seemingly unique historical and societal circumstances occurring in present day Austria, among other things: 1. The pillaging of Austrian villages by Turkish troops during the Ottoman military expeditions in the 16^th and 17^th centuries. 2. The presence of one of Europe’s oldest and still existing right-wing populist parties (the Freigeitliche Partei Österreich, FPÖ) which in 2005 started to campaign against Muslims and Turks explicitly referring to “their vicious crimes during the Turkish sieges of Vienna” and 3. The arguably exogenous location of the Battle of Bisamberg which led to spatial discontinuity in the probability of villages being pillaged by the Turks north of the Danube during the second siege of Vienna in 1683.

The empirical strategy is directed towards examining if the vote share of the Austrian right-wing populist party (FPÖ) was significantly higher in villages exposed to Turkish pillaging in the 16^th and/or the 17^th centuries (i.e. in villages were the collective memory of Turkish pillages was stronger) than in villages that were not pillaged, right after the change in campaign tactics of the FPÖ in the year 2005. Using the “tools of the trade” of 21^st century economic historical research (the baseline model uses a traditional DiD approach, although the spatial fuzzy RD design using panel data, seen in section 4.3 is new according to the authors), Ochsner and Roesel find that having been exposed to pillaging in the 16^th and the 17^th centuries led to an activation effect (i.e. the average treatment effect) of 1.6-3.05 percent depending on the specification. The larger effect sizes, 2.5-3.05 percent are estimated using the spatial fuzzy RD design on the sub sample of villages west of Vienna.

 

The authors conclude that neither “a local historical record of foreign atrocities” or “a campaign that addresses the stereotypes of these foreigners” are necessary and sufficient conditions to activate any effect. However, when both conditions are met the effect is statistically significant and robust across a range of specifications. In section 7 of the paper the authors address the underlying mechanisms at work. Ochsner and Roesel find that the effects of the campaigning were stronger in small rural communities and in communities with a lower share of out-commuters. Their findings suggest that “collective memory is likely to be a function of local embeddedness”. The authors conclude their paper with a call for future research that addresses the fact that societies can evolve and interact in a non-linear manner.

b) Comments

In general, I tend to approach quasi-experimental long-run effects papers with seemingly robust and large effects on the treatment group, with a bit of skepticism. In this case however, at least from my point of view, the authors made an excellent job of convincing me of (at least) the internal validity of their study. This is in part thanks to the appealing empirical setting, which they carefully account for in the introduction, and the two following sections of the paper.  The use of pictures and references to visual remnants of history in East Austria as well as quotes of “anti-Turkish” comments in online forums and the analysis of FPÖ’s campaign content provides context to readers unfamiliar with the setting, which is great!

While I find the authors interpretation of the mechanisms at work plausible, the empirical examination of said mechanism lacks the attention to detail shown in section 4-6. Collective memory might well be a function of local embeddedness; however, the authors use the share of out-commuters from a village as a proxy for embeddedness. Arguably this variable could also be a proxy for a lot of other things such as the average income or the age structure of the population in the villages (which they do not control for in the models presented in table 13). Addressing the mechanisms at work more carefully would in my opinion further improve the paper.

As a final comment, the results provided by the authors raises many interesting questions. The possibility to activate history in places were a collective memory of past events is present by campaigning could be utilized by a range of actors. In this day and age when the costs for highly customized political advertising (on social media platforms for example) is lower than ever before, “activating history” could be utilized by political parties (or other interest groups) in locations were the probability of a positive effect is higher, whilst other (less controversial) campaigning strategies could be used in other locations. The fact that the authors implicitly raise the awareness of how distant history in subtle ways can influence our opinions is truly a good thing.

(Superstar) Firms and Inequality

The Fall of the Labor Share and the Rise of Superstar Firms

By: David Autor (MIT, NBER and IZA), David Dorn (University of Zurich and IZA), Lawrence F. Katz (Harvard University, NBER and IZA), Christina Patterson (MIT) and John Van Reenen (MIT, NBER and IZA)

Abstract: The fall of labor’s share of GDP in the United States and many other countries in recent decades is well documented but its causes remain uncertain. Existing empirical assessments of trends in labor’s share typically have relied on industry or macro data, obscuring heterogeneity among firms. In this paper, we analyze micro panel data from the U.S. Economic Census since 1982 and international sources and document empirical patterns to assess a new interpretation of the fall in the labor share based on the rise of “superstar firms.” If globalization or technological changes advantage the most productive firms in each industry, product market concentration will rise as industries become increasingly dominated by superstar firms with high profits and a low share of labor in firm value-added and sales. As the importance of superstar firms increases, the aggregate labor share will tend to fall. Our hypothesis offers several testable predictions: industry sales will increasingly concentrate in a small number of firms; industries where concentration rises most will have the largest declines in the labor share; the fall in the labor share will be driven largely by between-firm reallocation rather than (primarily) a fall in the unweighted mean labor share within firms; the between-firm reallocation component of the fall in the labor share will be greatest in the sectors with the largest increases in market concentration; and finally, such patterns will be observed not only in U.S. firms, but also internationally. We find support for all of these predictions.

URL: http://EconPapers.repec.org/RePEc:iza:izadps:dp10756

Distributed by NEP-HIS on: 2017‒05‒28

Review by: Sebastian Fleitas (University of Arizona)

In the last few years, inequality has been at the center of many political and academic debates. It turns out that, although less mentioned in these debates, the rapid growth of some developing countries in the last decades has actually decreased global inequality. But then, why is there a big debate about inequality? The issue is that, on the other hand, inequality in developed countries has been increasing over time. From the perspective of the functional distribution of income between labor and capital, one of the indicators of this increase in inequality is that the labor’s share of GDP has been falling in the United States and other countries in recent decades. These forces have generated winners and losers. As economist Branko Milanovic points out with his famous “elephant chart,” the middle class of the world and the very rich of the world are the two groups whose incomes have increased more rapidly. In contrast, it can be easily seen that there are large groups of people uncomfortable with increased inequality. Moreover, the factors assumed to be causing inequality have taken a vital role in political debates and recent elections.

“Elephant Chart”: Lakner & Milanovic (2016)

In this context, it is extremely important to understand what is driving these changes in inequality. There are different approaches to understand the increase in inequality in developed countries. The two main perspectives point to the importance of top incomes and changes in the tax system (e.g. Piketty and Saez, 2014), on one hand, and to changes in the labor market, mainly related to the incorporation of technological change that is more favorable to skilled workers (e.g. Autor, 2014), on the other. More recent approaches have begun to more directly incorporate the role of firms. For example, a growing literature estimates models to separate the firm’s and employee’s contributions to wage differences via double fixed-effects models, with many studies finding that firm wage effects account for approximately 20% of the overall variance of wages and have had an increasingly important role over time (e.g. Card et al., 2016). However, while we can all see that “superstar firms” like Apple, Microsoft, Google or many others in different sectors of the economy are growing very quickly, we still do not know what their effect of inequality is.

Do these “superstar firms” increase inequality because they are responsible for the decrease in labor’s share? The paper by Autor, Dorn, Katz, Patterson and Van Reenen addresses exactly this issue. If we are interested in understanding the role of firms in the increase in inequality, it is particularly important to answer the question of whether the decrease in labor’s share of income can be explained by technological changes occurring within firms, or if it is better explained by a rise of “superstar” firms, which tend to use new technologies and are more capital-intensive. The main argument of the authors is that markets have changed in such a way that firms with superior quality, lower costs, or greater innovation get disproportionately high rewards relative to previous periods. Since these “superstar firms” have higher profit levels, they also tend to have a lower share of labor in sales and value-added. Therefore, as these firms gain market share across a wide range of sectors, the aggregate share of labor falls. In this way, “superstar firms” are one of the drivers of the decrease in labor’s share (in favor of capital’s share) of value added.

Before they start developing the evidence for this argument, the authors clearly document the fall in labor’s share of GDP in the United States and other developed countries. After that, they formalize their main argument in a model of “superstar firms,” in order to derive the set of predictions that will be taken to the data. With this model in hand, the authors use several sources of information (U.S. Economic Census, KLEMS, UN Comtrade Database, and others) to run a series of regressions and decompositions to analyze the testable predictions of the model. First, the authors find that sales concentration levels have risen in most sectors. Second, they show that the larger decreases in labor’s share are observed in industries where concentration has increased the most. Third, by comparing the weighted and unweighted mean of labor’s share, the authors conclude that the fall in labor’s share has an important component of reallocation between (and not within) firms. Furthermore, they find that the between-firm reallocation of labor’s share is greatest in the sectors that are concentrating the most. Finally, these patterns are not only present in the US but also in many European countries.

Overall, all of these findings are consistent with the idea of a rise of “superstar firms” that have lower labor’s share, and which have gained more importance by concentrating large shares of sales in different sectors of the economy. It should be noted, however, that the authors do not provide a clean causal identification of the superstar firm model. The empirical exercises are done carefully and controlling for the factors that can more clearly affect the tested relationships. The use of fixed effects and trends by industry allow the authors to obtain identification exclusively from the acceleration or deceleration of labor’s shares and concentration conditional on these controlled trends. Thus, any potential threat to this identification strategy would have to come from other factors not captured by these trends or fixed effects and which are correlated with industry concentration and inequality.

This paper makes a major contribution by pointing out the role of “superstar firms” in explaining increasing inequality and opens some avenues for future research in a direction that had not been typically considered in the literature. In this sense, a particularly interesting direction would be to use the matched employer-employee databases with census data on sales to test if industry concentration has impacts on the firm component of wages and the within and between firm decomposition in each sector.

Finally, the paper addresses the question of what is the driver of the growth of these “superstar firms.” The main debate here is whether the rise of these “superstar firms” and industry concentration are associated with competitive forces, or if they are a signal of an economy with competition problems. Increased concentration can be a result of technological changes: some sectors could be introducing technologies that have a “winner takes all” aspect. An alternative, more worrisome story is that leading firms are less exposed to competition because they can create barriers to entry or have more lobbying power. The authors provide evidence that is somewhat comforting about this point. They show that concentration is greater in industries experiencing faster technical change, approximated either by patent activity or by total factor productivity growth. However, this evidence is still subject to debate. It could be the case that these originally innovative firms are now using their market power to generate barriers to entry. This can be even more important in some technology sectors where network effects generate an important advantage to the innovators. I think this discussion is actually one of the main directions where this stream of research can be expanded and complemented in the future. In this sense, for example, sector-specific partial equilibrium models could allow formalizing the product and labor markets under innovation dynamics, and such models could be estimated using data for specific industries and structural econometrics estimation techniques.

To sum up, I think that this paper makes a major contribution by pointing out the effect of “superstar firms” on the decrease of labor’s share of GDP, and therefore increased inequality in developed countries. Additionally, this paper opens several avenues for future work in order to generate more evidence consistent with the “superstar firms” model and, critically, to understand its causes and consequences at the individual micro level, especially using matched individual and firm level databases and sector-specific analysis. To understand the relationship between firms and inequality is a key task in a world of “superstar firms,” and these are key inputs for the discussion of, for example, the roles of tax policies, labor market institutions and their relationship with the increasing heterogeneity of firms.

REFERENCES

Autor, D. (2014). Skills, Education, and the Rise of Earnings Inequality Among the “Other 99 Percent.” Science 344 (6186), 843-851.

Card, D., Cardoso, A. R., Heining, J., & Kline, P. (2016). Firms and Labor Market Inequality: Evidence and Some Theory. National Bureau of Economic Research Working Paper 22850

Lakner, C., & Milanovic, B. (2016). Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession. World Bank Economic Review 30(2), 203-232.

Piketty, T., & Saez, E. (2014). Inequality in the Long Run. Science 344(6186), 838-843.