History matters: development and institutional persistence of the Habsburg Military Frontier in Croatia
MARINA TKALEC, Ph.D.*
Article** JEL: C33; E02; E26; H41; O11 https://doi.org/10.3326/pse.44.1.4
* This research was done while the author was a Visiting Fellow at the London School of Economics and Political Science Centre for research on South Eastern Europe. The author would like to thank Michael Fritsch, Josip Glaurdić, Vassilis Monastiriotis, Ivan Žilić, and participants of the LSEE Visiting Speaker Programme, as well as to the two anonymous referees for helpful comments on the paper. ** Received: June 1, 2019 Accepted: October 14, 2019
Marina TKALEC The Institute of Economics, Zagreb, Department of Macroeconomics and International Economics, Trg J. F. Kennedyja 7, 10000 Zagreb, Croatia e-mail: [email protected] ORCiD: 0000-0001-7974-9345 - nomics, they are less frequent in macroeconomic research, especially in Croatia. 1 INTRODUCTION The notion that history, previous developments and path dependency are relevant in interest academic of lot a attracted recently has context socioeconomic today’s for Northand (1973) Thomas and North by work pioneer the since Indeed, economics. (1981; 1990) in institutional economics, and Greif (1994; 2006) on the impact of persistence on culture and institutions in development, a series of recently scholars further explored have this research agenda, in terms both of research avenues and the employment of new empirical methods and identification strategies. Using the latest econometric Acemoglu, techniques, Johnson and Robinson (2001) show how the institutions of early European settlers shape economic performance today, Glaeser et al. (2004) find that growth humanand political capital institutions, accumulation improve Nunn (2008) shows a negative causal slave effect trades on from current economic Africa’s performance, while Glaeser and Shleifer (2002) that the historical initial argue level of coercion legal risk ori- explains the different gins and legal systems that shape modern socioeconomic outcomes. This type of elevated economics research as has, a rightfully, discipline into a multidisciplinary, multidimensional high impact field, and the natural explain what causes growth. become a state-of-the-art tool applied to experiments they use have In this paper we use a (quasi) natural experiment – the geographic position of the between the causal relationship – to identify Frontier in Croatia Military Habsburg of households and corruption towards trust attitudes the and institutions historical show that in history modern-day matters, Croatia. We that institutions are persis- policy- for both important, are relationships institutional deeper these that and tent that civic capital, argue i.e. trust, could be one making and We for reform design. that is usually absent from standard macroeconomic of the causal mechanisms microeco applied in common are designs research of types these While literature. Abstract on Frontier Military Habsburg long-gone of the effect the explore paper we In this and geo- Survey Transition in Life use the We Croatia. in modern institutions between the causal mechanism design to identify discontinuity graphic regression the that find We corruption. and trust towards attitudes and institutions historical worse with and poorer underdeveloped are Military Frontier of the former areas in living respondents that suggest Our results indicators. performance economic trust, a of interpersonal lower levels territory have Frontier former Military the those institutions but also tend to bribe authorities, in public of trust higher level in in Yugoslavia claim that the war with them. We when they interact often more chan- also a potential a confounding factor in the analysis but the 1990s is not just wars, harsh even survive can bribery towards attitudes that evidence find and nel events of violence. institutions collapses during extreme while trust in public - macro institutions, discontinuity, regression geographic development, Keywords: economics
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119 44 (1) 117-145 (2020) the habsburg military frontier in croatia - - - - Therefore, Therefore, by using a natural experiment approach that yields intriguing results, and develop functioning into the insights substantial provide that we we believe Although the two parts of Croatia are comparable in most demographic outcomes, demographic most in comparable are Croatia of parts two the Although we find significant differences in activity rates and economic status. The indica Becker et al. (2016) claim that trust in institutions and corruption are the channels channels the are corruption and institutions in trust that claim (2016) al. et Becker mod- affects legacy institutional and political Habsburg long-gone which through ern day Europe. Building on their work we underline along most the border, Habsburg-Ottoman of its length, was actually only the border fact that the former cordon known as the military Habsburg and the Empire Ottoman the between which and years, 350 than more for existed which Frontier, The Frontier. Military was disbanded almost 150 years ago, was a militaristic establishment ruled from further the Ottomans from making the purpose of which was to stop Vienna inroads into Europe. This historic episode split the systems for a long period and political economic population different which lived under very into two parts, that factors different to exogenous was separation the that argued be can It time. of outcomes. modern-day observable of number a in differences existing explain can In that case, the assignment of households to the treatment can be taken as ran- dom, as in an experiment. The former border cut through parts Croatia, This Serbia, exogenous Romania variation of and definedHungary. around present-day the border enables us to use regression within-country variation discontinuity and look design at effects (RDD) of the to Military Frontier on study modern beliefs and attitudes. In the following section we explain natural experiments, review the literature on advantages and challenges the argue and macroeconomics, in experiments natural discontinuity regression the explain We macroeconomics. in causality detecting of design and our overall empirical approach together with the data in section IV, after an overview of the history of the Military Frontier. Section V presents our tests. falsification and checks robustness of number a as well as results, empirical ment of the modern Croatian economy and significantly contribute to the advance the to contribute significantly and economy Croatian modern the of ment research. in macroeconomic approaches ment of methodological that these areas are Frontier and reveal former Military tors are worse for the underdeveloped, poorer, and have higher unemployment rates and worse nomic eco- performance indicators. In order to explain the observed development, we differences use three in waves of the Life in Transition Survey (LiTS), fixed and geographic RDD to estimate ordered logit effects models since the responses we measure are categorical variables. Our results suggest that respondents living public in of trust level higher a have territory Frontier Military former the in authorities, but also tend to institutions more often when bribe those they interact a statistically has Frontier Military the with affiliation that find also We them. with significant negative on effect interpersonal trust. It seems that an extreme institu such as did the tion-building not Military case only Frontier, study, instill trust in trust among people. its public institutions, but that it negatively affected - - - - - 2 NATURAL EXPERIMENTS IN MACROECONOMICS EXPERIMENTS 2 NATURAL Detecting causality in macroeconomics is a highly debated research, topic as the direction of in causality is often unclear and empirical the exact channels are advice policy poor and identification credible of lack to leading indistinguishable, (Fuchs-Schündeln and Hassan, 2016). Due to the difficulty of the task at hand, condi- on matching relies macroeconomics done in empirical work most of the tional correlations and finding highly-sophisticated ways to improve econometric models. Unlike natural and some social thescientists, macroeconomists fit of labora nor field neither as causality, detect to experiments use not do traditionally In Section VI we tackle the most obvious confounding element and a potential potential a and element confounding most obvious the tackle we VI In Section for Croatian channel Independence in and our the analysis, - estab that of War the lishment of the Republic of Serbian Krajina in the 1990s, which partially over VII con- section Finally, lapped with the territory of the former Military Frontier. cludes with a discussion of the implications of our findings formodern east-to- policies. European and with it associated west migrations tory experiments are available to them. For obvious reasons it would be impossi- ble to control a huge and complex system such as an economy of a country in a laboratory, while field experiments would be overly expensive and due to their be con- impacts it is highly unlikely there would vast socioeconomic inevitably referred literature the in alternative an is there However, out. them carry to sensus to as – natural experiments. As Fuchs-Schündeln and Hassan (2016: 4) stated, a natural to subject treatment in variation experiments quasi-random observable, provide that episodes are “... plausible identifying assumption”. An episode can be an introduction of a new flood a as such event naturally-occurring a or episode historical a measure, policy have macroeconomists experiment, laboratory a to opposed As change. climate or they are using is compa or treatment the intervention that convincingly to argue random- argue to order In assigned. randomly is it that i.e. experiment; an to rable ness they have to compare the treated and the control group and show that their while only in the fact that one group was treated differ observable characteristics the other was not. The main goal of natural experiments in macroeconomics is to identify mechanisms causal (which one cannot find in conventional macroeconomic models) in Although order standard to macroeco explain the fundamental causes of growth. nomic models seemingly provide those answers, their maximum reach is to give only approximate causes of growth that typically involve capital accumulation, mecha the down pinning at good are they while And investment. and technology, nisms of growth, they stop short of answering the why type fundamental of questions. causes The of growth on the other social structure, and civic capital as the main background players. hand, would identify institutions,
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121 44 (1) 117-145 (2020) the habsburg military frontier in croatia - This paper uses a natural experiment to provide evidence of historical institutions institutions historical of evidence provide to experiment natural a uses paper This having long-term effects on institutions, civic capital and economic outcomes. Natural experiments are fairly unexplored in Croatian economics and macroeco Social structure also plays a role; for example, Acemoglu, Johnson and Robinson Social structure also plays a role; for example, (2005) European show countries that that those had Western a stronger merchant class also developed property rights that lead to economic growth, a concept The literature on natural experiments in macroeconomics so far actually detected detected actually far so macroeconomics in experiments natural on literature The four fundamental causes of growth, adding luck and multiple equilibria to the institutions, social structure, and civic capital referred to above (Fuchs-Schün- of institutions deln The on and effect growth Hassan, is 2016). extensively stud- ied Acemoglu, in Johnson and Robinson (2001; 2002) who use an instrumental variable approach, and in Michalopoulos and Papaioannou (2013) who rely on This type of literature uses rather simple econo- regression discontinuity design. identifying convincingly and correctly in lies challenge their but methods, metric that the natural episode resembles an that the treatment is indeed random and experiment. Acemoglu et al. (2003) study the effect of institutions on business cycles and find that bad macroeconomic policies are no longer correlated with macroeconomic volatility once the effect of institutions is controlled for. They just tools easily replaced by other bad claim that macroeconomic policies are of deeper institutional distortions that tools and that they are rather symptoms detect to tries literature this of strand Another growth. higher of way the in stand whether replacing bad institutions would lead to preferable colonialism as such institutions historical of effects persistent the at look studies outcomes. These (Banerjee and Iyer, 2005; Iyer, 2010), forced labor systems (Dell, 2010), communism (Alesina and Fuchs-Schündeln, 2007). and nomics. But in another context, they show that history matters, and that historical historical that and matters, history that show they context, another in But nomics. institutions have long-lasting effects, which, significantly it if variable, slow-moving a is capital civic Although ignored, reform. at efforts will undermine most with others and high, with some leaving growth for capacity societies’ determines low Empire levels is The of well Habsburg civic known capital. for its reputation of “good” institutions and it is documented that the former However, neighbors. Ottoman its of that than Habsburg capital civic of levels higher enjoys territory not all Habsburg institutions were driven to build civic capital, some were estab- lished to defend the south-east borders of the Empire. Political years 350 for history Croatia in preserved Habsburgs and the that colonialism military of the type still largely influence economic performance are Frontier Military the of part formerly in Croatia, were that areas the that find We especially country. within the them, bribe to tendency higher a institutions, in trust of levels higher have poorer, and show less trust in other people (possibly related to the ethnic population mix in the area). These results are consistent with the colonial policies of the Habs- burgs who exerted highly centralized authority through its military institutions, while at the same time failing to provide adequate resources to alleviate poverty, leaving the people to resort to corruption. - - - known as Law Merchant (Milgrom, North and Weingast, 1990). On a similar note, note, similar a On 1990). Weingast, and North (Milgrom, Merchant Law as known in Russian regions in which the Holocaust significantlyreduced the sizeof the middle class, political and economic outcomes were worse even decades (Acemoglu, later Hassan and Robinson, 2011). Moving away from social structure, differences in civic capital – encompassing trust, beliefs, norms, and traditions – lead a society to different outcomes. a help Guiso, that values and beliefs shared and persistent “...those as capital civic define Sapienza and Gonzales (2011:3) activi valuable problem in the pursuit of socially the free rider group overcome type this in challenge the institutions, with intertwined is capital civic Since ties”. of research is to separate the effects in order to (2010) claim Tabellini causality. centu even dates that countries within capital civic in variation finds example for economic of level the in differences day causes modern and history in back ries development. Guiso, Sapienza and Gonzales (2016) study whether they had free city status in medieval times. It turns out cities that a longer his- in Italy and tory of self-rule is correlated with both higher civic capital and higher levels of economic development. for prerequisite a is it as capital civic of examples best the of one probably is Trust sales, financial and investment transactions, and contracts, particularly employ ment contracts. The importance of trust rises with the level of complexity in the decades detrimental so not were trust in differences if even and system, economic or centuries ago, they have certainly become so more. Algan and Cahuc (2010) isolate the effect of trust and find that higher trust among people leads to higher Gonzales and Sapienza Guiso, growth, beyond go trust of Effects capita. per GDP (2004) detect the exact mechanism that growth. In their case translates they find that trust civic – represented by financial capital development to economic and the level of sophistication of the financial system – plays a role in economic growth. The other potential channel could be that of regulation (Aghion implying correlated, negatively et are regulation government and trust where 2010), al., of higher government regulation. that lower trust increases the incidence These papers are just one piece of the puzzle as they provide evidence dif- behind factors detecting of is part other civic The growth. on impact an having capital fering levels of civic capital. Literature provides three possible factors: historical institutions, experiences of violence and conflict, and climateFuchs-Schündeln and Hassan, 2016). (as surveyed Besides (2010) Tabellini and in Guiso, Sapi- enza and Gonzales (2016), one of the most influential papers that uses historical institutions to explain different levels of trust and attitudes towards corruption is the of part once areas that show they RDD geographic Using (2016). al. et Becker and lower levels of of trust in institutions have higher levels Empire Habsburg corruption than in nearby areas that were under the Ottoman rule. Regarding the Wantchekon and Nunn outcomes, modern-day on violence of history the of effect historically were that Africa of areas in reduced are trust of levels that find (2011) affected by the slave trade while controlling for a number of observable factors. in lower is trust that finds and Europe East South in conflict studies (2014) Jancec
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123 44 (1) 117-145 (2020) the habsburg military frontier in croatia - century eroded the leverage of th centuries the Frontier was divided into into was divided Frontier the centuries th (Croatian) enjoyed privileges in the form ofari (Croatian) enjoyed privileges in the form and 19 and th Granič inhabitant was a soldier. Ethnically, the settlers were divided th (German) or (German) Grenzer Inside the Austro-Hungarian Empire, the settlers of the Military Frontier, the so- called almost equally into indigenous Croats, dominantly Catholic, and refugees, pri- marily Orthodox Serbs who were granted royal privileges to populate the Habs- the war against the Ottomans. Military Frontier and serve in burg 3 HISTORICAL BACKGROUND ON THE MILITARY FRONTIER THE MILITARY BACKGROUND ON 3 HISTORICAL Rothenberg (1960) writes that the Military Frontier existed for over 350 years, since the longest lasting Frontier on the south of Croatia was established in 1522 18 the In 1881. in disbanded and countries countries that were exposed to more frequent authority changes in the historical years. span of 500 time The life in the Military Frontier is explained in Roksandić (1988) who describes the describes who (1988) Roksandić in explained is Frontier Military the in life The cooperative (or communal) families thatsociety as being founded on a system of provided soldiers from the pool of grown men, while women engaged in agricul- ture, child and senior care as well as all remaining communal duties. Due to the Adriatic sea all the way to the elongation of the territory that spanned from the Carpathian mountains and a 1,800 km long border with Empire the (see Habsburg Figure 1), the Frontier never developed into a coherent social and economic area. did nor areas, geographic natural not were Habsburgs the to borders the Moreover, they come from historical borders, making most important social and geographic characteristics very similar on both sides of the border (Roksandić, 1988). On the social cases most in and Empire, Ottoman the with bordered Frontier the side, other on both sides of that border. and geographical characteristics significantly differed imperial policy in the Frontier area and led to the final dissolution in 1881. imperial policy in the Frontier area and nine districts: the longest-living Croatian and Slavonian Military Frontier districts Frontier Military Slavonian and Croatian longest-living the districts: nine situated in modern-day Croatia (1522–1881), and eight provisional districts in other countries: the Danube, Tisa, Mureş and Sava Frontiers (1702–1751), the Frontier (1762– Military Transylvanian (1751–1873), Banat Military Frontier the popula- the (1829) s.n. to According (1763–1873). Battalion Šajkaš the and 1851), tion of the Military Frontier in 1828 was reports 1,073,680, every 15 while Roksandić (1988) land allotments and were free of serfdom in exchange for permanent military ser vice and loyalty to Austrian the Habsburg crown, instead of the Hungarian-Croa- tian one that ruled the rest of The Croatia. Frontier was established to defend aris- intrud- armies Ottoman halt to eventually and Inner-Austria of possessions tocratic institution the that argues (1964) Rothenberg Empire. Habsburg the into further ing and an agricultural economyof the Military Frontier was a military establishment Frontier the of longevity the explain to continues He trade. or crafts any barely with but also because the military status of thenot only by persistent Ottoman threats Grenzer was preferred to the manorial obligations in the civil part of the Empire. Eventually, the decline of the Ottoman Empire together with rising nationalistic in the 19 aspirations and the abolition of serfdom - - - - 1 igure Source: The World of the Habsburgs (1800). of the Habsburgs The World Source: AND METHODOLOGY IDENTIFICATION, 4 DATA, 4.1 DATA modern-day in Frontier Military the of persistence long-run the analyze to order In institutions, we use all three waves of the LiTS collected by the European Bank for Reconstruction and Development (EBRD) in 2006, 2010, and in 2016. LiTS records attitudes, opinions and values in post-transition countries. In particular, the first wave (2006) assessed public attitudes, well-being and the impact of eco- nomic and political change; the second wave (2010) dealt mostly with the effects of the financial crisis; while the last wave from 2016 explored life satisfaction, corruption, and gender gaps in the labor market and business. The covariates in on the information include and they level individual the observed at are LiTS size, household status, market labor religion, education, gender, age, respondents’ number of children under age 14, and – most importantly – location of residence. waves differentiat LiTS of respondents in the three Figure 2 displays the location ing them by colors. The shaded area represents the former Military We Frontier. F in 1800 Military Frontier According to Amstadt Amstadt (1969), According the to Frontier was and super-institutionalized super- par which for need the reforms, for pressures constant under and bureaucratized, tially stemmed from the bureaucracy itself. Becker et al. (2016) showed that the Habsburg institutions established in the long-gone Habsburg - tran system, socialist the Empire Wars, – World even both survived – in Vienna from far very areas sition, and persist to Although this the day. Military Frontier established institu the Empire, the of rest the from different identity and (culture), capital civic tions, demonstrated efficacy of the Habsburg administration does notdoubt give its reason to effectiveness in establishing military colonialism. As Habsburg civic institutions (Becker et al., 2016), religion (Boeckh, 2013), and nationalism (San- herit the that assume to plausible is it century, twentieth the survived 1992), ford, as well. i.e. the Military Frontier survived age of military colonialism,
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125 44 (1) 117-145 (2020) the habsburg military frontier in croatia - . restrict restrict the analysis to modern-day Croatia because the Croatian and Slavonian districts represent the longest-lasting Military Frontier, persisting for 359 years. left out from Syrmium) is in the far east (Eastern the Slavonian district The part of of Republic Socialist the of territory the under came area the because analysis the Serbia in 1945. For each location in our dataset, we provide corresponding geo- graphic longitudes and latitudes together with information on affiliation with the sys- information geographic A (2003). Kaniški and Regan using Frontier Military between distances compute to order in data location geocode to used is (GIS) tem locations and distances to the enabling Military us Frontier to border, take differ ent subsamples of respondents around a specified distance from the border around a specified distance ent subsamples of respondents our as institutions public in trust and corruption use we (2016) al. et Becker in As public in trust on question the waves, LiTS three all In interest. of outcomes main the trust you do extent what ’To reads: and unchanged is survey the in institutions The institutions. offered the among police and courts with institutions?’ following answer categories span from ’complete distrust’ to ’complete trust’, with ’some distrust’, ’neither trust nor distrust’, and ’some trust’ in between. We exclude a ’dif- is answer the where or answer, no provide that observations of number small ficult to say’. Regarding corruption, the question of interest is ’In your opinion, payments/ unofficial make to have to you like people for necessary it is often how pos- as included are police traffic and courts both where situations?’ these in gifts sible situations. The answers proposed are: ’usu- ’never’, ’sometimes’, ’seldom’, ally’, and ’always’. For our falsification test we use a proxy forparty membership and voluntary political constructed as a dummy variable for social capital, club membership. or organization 2 igure 4.2 IDENTIFICATION AND METHODOLOGY 4.2 IDENTIFICATION Columns (5)–(8) 1 in Table report descriptive statistics for Military Frontier and non-Military Frontier separately. In terms of age, gender, household size and number of education, children under 14 there are hardly any differences religion, Columns (1)–(4) in Table 1 report descriptive statistics of our individual-level covariates. There are 13.9% respondents that live in the territory of the former Military The Frontier. average age of all respondents is slightly above 50 years, there are 59% men, most of the respondents have secondary education, and are Christian. They live in small households (mean household size is 1.8) with the mean number of children credit a under or debit a 14 has and account, equal bank a to car, a 1.4. own live they Somewhat which in above households 70% of the card, while 86% own a mobile phone. Slightly above half of households own a computer and have access to internet at home. Notes: Military Frontier in modern-day Croatia before its dissolution in 1881. We are grateful grateful are its dissolution in 1881. We before in modern-day Croatia Notes: Military Frontier Kaniški Leksikografski from zavod to Krleža Miroslav Tomislav who us provided with Military by (2003). LiTS wave 2006 is presented Regan and Kaniški from 1868–1881 shapefiles Frontier and the 2016 wave with the blue color. 2010 wave with yellow, the color, the red F the LiTS locations and 1868–1881 Frontier Military
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- - i in military w, is an indicator that takes the that is an indicator
ilw military frontier military is a set of individual and household-level con- are LiTS wave fixedWe effects. first estimate the model is a realization of the outcome variable for an individual
ilw is an indicator that takes the value 1 if a location was a part of the Mili- takes the value 1 if a location that is an indicator l for a LiTS wave w, and ilw outcome location value 1 if a location was a part of the Military Frontier and 0 otherwise. where frontier where i is an individual living in location l in a specific LiTS wave Our basic model estimates the effect of an individual living in a location that was in a location living individual of an the effect estimates Our basic model cor and of trust measures on his/her Frontier Military Habsburg the under once between between the two subsamples. there However, are significant differences in terms of respondents working for income during the last 12 months, and all six house - hold-level variables that reflect economic status. Presumably, theindicators are worse for the former Military Frontier, so we Mil- former the of areas include the that show statistics descriptive simple them These analysis. as controls in our and worse other rates have higher unemployment are poorer and itary Frontier performance indicators. economic ruption. We estimate the following equation: We ruption. tary Frontier and 0 otherwise, trol variables, and tion is slightly altered. In particular, we estimate: tion is slightly altered. In particular, We also propose We the use of a geographic RDD approach. Following Dell (2010), and Frontier as a deterministic in the Military implemented we view institutions other In latitude. and longitude i.e. position, geographic of function discontinuous words, using the historical borders of the Frontier we are effect able using to the geographic estimate RDD the approach. While RDDs are a widely and Lee 1960; popular Campbell, and (Thistlethwaite economics in strategy identification Lemieux, 2010), geographic RDD differs from the simple setup as the forcing variable is two-dimensional since location is uniquely determined by both - longi other all – identical are assumptions identification the Therefore, latitude. and tude estima the but – border Frontier Military across the constant be must covariates using all observations from the three waves in Croatia (Table 2), and then continue then and 2), (Table Croatia in waves three the from observations all using with border specifications – sample restricted to respondents livingrobust- our In 7). withinto 3 200 (Tables border Frontier Military former the from kilometers ness analysis we further restrict the samples, down to 25 kilometers around the In border. 7 Table we test the validity of our model by using different outcomes – such as trust in other people, and in private entities, and also social capital pre- sented by membership in political parties – where we find that our falsification tests hold. estimate We ordered logit models since the responses are categorical variables, assuming a constant odds ratio.
Source: Life in Transition Survey (LiTS) 2006, 2010 and 2016; see main text for details. for text main see 2016; and 2010 2006, (LiTS) Survey Transition in Life Source:
Household number of children under 14 under children of number Household 0.748 1.343 0.809 1.416 7 1 0.757 1.353
Household size (equivalent scale) (equivalent size Household 0.619 1.752 0.656 1.774 4.500 1.000 0.624 1.755
Household has access to internet at home at internet to access has Household 0.494 0.581 0.494 0.420 1 0 0.497 0.558
Household has a computer a has Household 0.488 0.608 0.497 0.437 1 0 0.493 0.584
Household has a mobile phone mobile a has Household 0.330 0.875 0.440 0.739 1 0 0.351 0.856
Household has a credit/debit card credit/debit a has Household 0.413 0.782 0.493 0.587 1 0 0.433 0.750
Household has a bank account bank a has Household 0.448 0.723 0.476 0.654 1 0 0.452 0.713
Household has a car a has Household 0.444 0.730 0.489 0.609 1 0 0.452 0.713
Household-level variables Household-level
Other 0.087 0.008 0.064 0.004 1 0 0.085 0.007
Muslim 0.095 0.009 0.174 0.031 1 0 0.109 0.012
Christian 0.306 0.895 0.260 0.928 1 0 0.300 0.900
Jewish 0.026 0.001 0.000 0.000 1 0 0.024 0.001
Buddhist 0.026 0.001 0.000 0.000 1 0 0.024 0.001
Religion (omitted category: atheist) category: (omitted Religion
Post-graduate degree Post-graduate 0.161 0.027 0.143 0.021 1 0 0.159 0.026
Higher professional degree (university, college) (university, degree professional Higher 0.352 0.145 0.276 0.083 1 0 0.343 0.136
Professional, vocational school or training or school vocational Professional, 0.377 0.171 0.366 0.159 1 0 0.375 0.170
Secondary education Secondary 0.499 0.464 0.492 0.410 1 0 0.498 0.456
Compulsory schooling education schooling Compulsory 0.347 0.140 0.396 0.195 1 0 0.355 0.147
Education (omitted category: no degree) no category: (omitted Education
Worked for income during last 12 months 12 last during income for Worked 0.500 0.498 0.481 0.360 1 0 0.500 0.479
Male respondent Male 0.493 0.582 0.486 0.619 1 0 0.492 0.587
Age of respondent of Age 17.605 49.957 17.534 52.420 95 18 17.613 50.299
Individual-level variables Individual-level
Part of Military Frontier Military of Part 0.000 0.000 0.000 1.000 1 0 0.346 0.139
(8) (7) (6) (5) (4) (3) (2) (1)
Mean SD Min Max Mean SD Mean SD
Both sides Both Military Frontier Military Non-Military Frontier Non-Military
Descriptive statistics Descriptive
1 1 T able
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129 44 (1) 117-145 (2020) the habsburg military frontier in croatia - - - - is a set of LiTS wave fixed denotes denotes a flexible (polynomial) function of geographic
ilw outcome Our results show that living in a location that was once part of the that in a location living Our results show that 1 Although minority status is an important determinant, the question from the first LiTS ’Do you consider your consider you ’Do LiTS first the from question the determinant, important an is status minority Although self as a member of an ethnic minority in this country?’, is not repeated in the two subsequent waves. Based and dummy minority a construct to able were we ethnicity?’ your is ’What wave third the from question a on sug- they and request on available are results These only. waves LiTS third and first the for analysis our repeat number small a in significant is dummy minority The robust. are waves LiTS three all on results the that gest of cases and the sign and size of all other remains The coefficients unchanged. only change is in the statisti of our Military Frontier variable that goes down var cal significance in some cases with trust as the outcome believe this is due to small sample size that reduces statistical power. We iable. location – either longitude and latitude or distance In to order border. to pinpoint we our propose effect, bandwidths, varying starting distances with to 200 border, kilometers. down to 25 the border and narrowing from both sides of kilometers Military Frontier is positively associated with higher trust in courts and police. when we control Interestingly, for the fact that the respondent actually interacted a statistically find we months, 12 last the in police traffic the with or court the with significant, strong, andpositive relationship between living on former Military Frontier territory and unofficial payments/gifts – bribes –made to courts and to the traffic police. In other words, respondents seem to have a higher level of trust they when often more institutions those bribe to tend also but authorities, public in interact with them. Becker Similarly, et al. (2016) found that living in ex-Habs- effect opposite an found they but police, and courts in trust increases territory burg there is Frontier Military The bribery. of intensity the decreases it that bribery, for fore somewhere in between the institution-friendly Habsburg Empire bribery-friendly manifestation of persistent economic under-development east of and the of presence the by confounded even possibly case this in Croatia, of part civil the Ottomans (for details on the relationship between corruption, socialism, and the Ottomans see Uberti, 2018). 1 5.1 BORDER SPECIFICATION In order to make our treatment and control groups more comparable, we continue by restricting our sample to the border specification of respondents living within 200 This kilometers slightly from shrinks the the border. total number of observa- tions, while the number of locations by which we cluster falls from 164 to 159, preserving most of the statistical power. According to Table 3 the effects found persist, and remain robust. In three cases the point estimate is slightly lower sug- Regarding present. differences location unobserved be might still there that gesting the size of the estimated effects, marginal effects presented in Appendix suggest Table A1 that when holding of other variables the at their means, living on the former Military Frontier territory increases the probability of moving to a higher 5 RESULTS In the first part of our analysisprimary wedifferent 164 from estimateobservations 3,361 theuses 2 model Table onCroatia. in thehouseholds whole sample of waves. LiTS three the from collected residence of places or (PSU) units sampling The four models presented control for respondents’ age, gender, and affiliation. religious denotes denotes individual and household-level covariates, effects, effects, while - - Yes Yes (4) 164 0.469* 0.131 3,302 0.046 0.644*** (0.003) (0.094) (0.258) (0.123) -0.018*** traffic police Bribes to Yes Yes 164 (3) 0.090 3,272 0.029 0.547*** 0.835*** courts (0.003) (0.099) (0.165) (0.259) -0.008** Yes Yes 164 (2) 3,411 0.273** 0.075 0.019 0.010*** police (0.061) (0.002) (0.137) Trust in Trust Yes Yes 164 (1) 0.392** 3,361 0.016 courts (0.064) (0.189) -0.001 (0.002) -0.120* 2 2 able Male respondent Used service in last 12 months Age of respondent Controls for religious affiliation (6 categories) Pseudo-R Controls for LiTS wave No. of observations No. of locations Part of Military Frontier T police and corruption in courts and Trust category category of trust in courts by 3.8 percentage points, on average across the five cat- egories. For the remaining measures, the average are effects marginal equal to 2.8 percentage points for the trust in police, 6.8 percentage points for bribes to courts, and 4.2 percentage points for bribes to the traffic As police. there are five answer categories, their average share amounts up to 20% suggesting that mar The average. on 14–34% by categories institutions public the moves Frontier the Military respondents’ to comparison in large are affiliation Frontier Military for effects ginal types. are somewhat smaller than some religion although they age and gender, Table A2 Table provides details on the marginal effects by specific answer categories. of reporting increases the probability territory Frontier in former Military Living some trust in courts by 5.1 percentage points, to bribes and Regarding points. percentage decreases 7.2 by courts the in distrust probability complete reporting of courts, Military Frontier is associated with a 16.9 percentage points lower proba- bility of reporting the category of never having to bribe, a 7.3 percentage points higher probability of having to bribe sometimes, and higher probability of usually having to bribe the courts. a 3.6 percentage points Notes: Coefficients and standard errors from ordered logit estimation. Dependent variable in logit estimation. Dependent variable in ordered from errors Notes: Coefficients and standard institu following the trust you do extent what “To question the to answer is (2) and (1) columns tions?” Column (1): The courts. Column (2): The police. Answer categories are: 1 = Complete are: Answer categories The police. tions?” Column (1): The courts. Column (2): trust; 5 = Complete trust. trust nor distrust; 4 = Some distrust; 2 = Some distrust; 3 = Neither - set to missing in regres are stated know/Not applicable/Not to say/Don’t Category 6 = Difficult how opinion, your “In question the to answer is (4) and (3) columns in variable Dependent sions. situa- in these payments/gifts you to have to make unofficial like for people necessary is it often Answer (4): Interact with the traffic police. tions?” Column (3): Interact with the courts. Column Always. Category 6 = Never; 2 = Seldom; 3 = Sometimes; 4 = Usually; 5 = 1 categories are: at clustered errors Standard set to missing in regressions. know/Refusal to say/Don’t = Difficult *significance at 10, **5, ***1 percent. in parentheses: are the level of PSU or place of residence Survey (LiTS) 2006, 2010 and 2016; see main text for details. Life in Transition Source:
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131 44 (1) 117-145 (2020) the habsburg military frontier in croatia - - Yes Yes 159 (4) 3,213 0.047 0.652*** 0.118 0.448* (0.003) -0.018*** (0.094) (0.124) (0.260) traffic police Bribes to Yes Yes 159 (3) 3,183 0.084 0.029 0.599*** 0.813*** courts -0.007** (0.010) (0.167) (0.003) (0.261) Yes Yes 159 (2) 0.010*** 0.285** 3,319 0.068 0.018 police (0.063) (0.002) (0.139) Trust in Trust Yes Yes 159 (1) 0.383** 3,272 0.016 courts -0.119* (0.066) (0.002) (0.188) -0.001 2 3 able Controls for LiTS wave No. of observations Male respondent Used service in last 12 months Controls for religious affiliation (6 categories) No. of locations Age of respondent Pseudo-R Part of Military Frontier umns (1) and (2) is answer to what the extent question do “To you trust the following institu- 1 = Complete are: Answer categories The police. tions?” Column (1): The courts. Column (2): trust; 5 = Complete trust. trust nor distrust; 4 = Some distrust; 2 = Some distrust; 3 = Neither - set to missing in regres stated are know/Not applicable/Not to say/Don’t Category 6 = Difficult how opinion, your “In question the to answer is (4) and (3) columns in variable Dependent sions. situa- in these payments/gifts you to have to make unofficial like necessary for people is it often Answer tions?” Column (3): Interact with the courts. Column (4): Interact with the traffic police. Always. Category 6 1 = Never; 2 = Seldom; 3 = Sometimes; 4 = Usually; 5 = categories are: at clustered errors Standard set to missing in regressions. know/Refusal to say/Don’t = Difficult *significance at 10, **5, ***1 percent. in parentheses: are the level of PSU or place of residence Survey (LiTS) 2006, 2010 and 2016; see main text for details. Transition Life in Source: - sample: respond Border estimation. logit ordered from errors and standard Notes: Coefficients col in variable Dependent border. Frontier Military former the from km 200 within living ents T specification police: border and corruption in courts and Trust 5.2 GEOGRAPHIC REGRESSION DISCONTINUITY DESIGN DISCONTINUITY REGRESSION 5.2 GEOGRAPHIC The results of 4 our with geographic the RDD two-dimen are presented Table in sional RDD in latitude and longitude in the upper panel, and the one-dimensional one-dimensional the and panel, upper the in longitude and latitude in RDD sional RDD with distance to border in the bottom panel. For the two-dimensional case we present four different specificationswith either the quadratic, linear, cubic or specifi- linear, reference, The location. geographic of function polynomial quartic border the from results the resembles clearly it and row first the in shown is cation signifi- statistical of lot a polynomials, of orders higher At 3). (Table specification poly- for controlling that suggest (2018) Imbens and Gelman as but – lost is cance nomials of orders above two leads to noisy estimates and poor coverage of confi- remains case one-dimensional The estimate. linear the prefer we – intervals dence and linear the for persists effect The bribes. to related outcomes the for only robust the quadratic case, but also when we add an interaction term (distance to border case. Military Frontier dummy) to the linear multiplied with the - (4) 0.711** 0.450* 0.460* 0.402 0.287 0.356 3,213 0.458*** (0.253) (0.341) (0.332) (0.469) (0.295) (0.301) (0.146) traffic police Bribes to (3) 0.825* 0.697 0.869*** 3,183 0.902** 0.781** 0.929** 0.773** courts (0.270) (0.445) (0.485) (0.715) (0.395) (0.371) (0.372) (2) 0.267* 0.055 3,319 police -0.196** -0.076 -0.077 -0.210 (0.147) (0.191) -0.021 (0.199) (0.249) (0.206) (0.184) (0.083) Trust in Trust (1) 0.329* 0.225 0.214 0.275 0.352 0.241 3,272 courts (0.311) (0.192) (0.232) -0.006 (0.243) (0.244) (0.285) (0.052)
4 able (reference specification) Polynomials in latitude and longitude Polynomials Linear polynomial Quadratic polynomial in latitude and longitude Cubic polynomial in latitude and longitude Quartic polynomial in latitude and longitude in distance to border Polynomials in distance to border Polynomials in distance Linear polynomial No. of observations Quadratic polynomial Quadratic polynomial in distance to border Interacted linear polynomial in distance to border T design discontinuity regression Geographic fore narrow the bandwidth from the initial 200 kilometers to 150, 100, 50, and finally to 25We kilometers. perform this exercise with the approaches taken in 3 and 4 Tables (the two-dimensional linear RDD), results of which are presented in the first rowsTable of 5, i.e. for the 200 kilometer case. The point estimates appear to be rather stable when we decrease the bandwidth, and we can claim safely that the results are robust down to 50 kilometers At around the the border. narrowest band though (25 kilometers), only the result for bribes to courts holds, although the coefficients for the traffic police are statistically significant at the 10.9% and 10.3% levels. As mentioned earlier, there are significant differences are These group. control the in someand treatment the between variables household-level individual and 5.3 ROBUSTNESS CHECKS the to analysis our subject we results, significant statistically find do we Although bias-variance tradeoff and vary the distance to border that decides on the sample size and how close to the discontinuity – there border – we run our We analysis. Notes: All reported Notes: All estimates coefficients reported are of the variable “Part of Military in Frontier” a and stand- 3. Coefficients variables shown in Table includes all control that model specification 200 km from within living sample: respondents Border estimation. logit ordered from errors ard Standard border. based on GIS-computed distance from border, the former Military Frontier *significanceat parentheses: in are residence of PSUorplace of level the at clustered errors 10, **5, ***1 percent. Survey (LiTS) 2006, 2010 and 2016; see main text for details. Life in Transition Source:
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133 44 (1) 117-145 (2020) the habsburg military frontier in croatia - - fect. MILITARY COLONIALISM, INTERPERSONAL TRUST TRUST COLONIALISM, INTERPERSONAL MILITARY CAPITAL AND SOCIAL tion with the Military Frontier has in a trust the for estimate point statistically the while 7), significantTable of (1) (column negativetrust interpersonal effect on trade unions is also negative. Becker et al. (2016) for example did not find a sta- tistically significant for effect interpersonal trust, so it leads us to believe that the instill only not did study, case institution-building extreme an as Frontier, Military trust in its own public institutions, but that it negatively affected trust (1994) Greif on and intuition historical on between reflect paper this in results The people. as it Vienna had good reason to trust Frontier Military of the the population that Croatia, of part civil the from lords feudal governments, local from them protected from paying taxes. Meanwhile, the people that migrated to and most importantly, and East of the culture the them with brought East the from Frontier Military the Ottomans as And well. since the Ottoman Empire, unlike the Habsburg Empire, persisted. bribery that surprising that not is it 2018), (Uberti, law by ruled not was fami extended in trust higher for ground the laid mix population ethnic the Also, ethnic groups. lies and lower trust across different Finally, Finally, we run the analysis on interpersonal the trust from coming is and effect the that corroborate to trust services, public in trust in of instead private entities, public institutions and not from some pre-existing social life of the region. The Military Frontier in its approach did not interfere with the existing way of living, but channeled its power through find newly-built that institutions. We the affilia 5.4 presumably presumably endogenous to the Military Frontier as they mostly reflect the- eco nomic status. In Table 6 we show the results when we include these additional control variables: working for income status, household size, number of children car, a owning as such property household for controls and urbanization, 14, under a bank account, a mobile phone, and/or a Our computer. results indicate that the results are arguably very stable; the variance does increase a bit, and the estimates point are somewhat smaller for the trust outcomes, and larger for the bribes outcomes. It is therefore safe to assume that none of the controls included could ef channel of the military colonialism be a convincing alternative 51 51 (6) 105 148 157 159 105 148 157 159 clusters able 4) No. of 967 967 (5) 2,178 3,054 3,232 3,272 2,178 3,054 3,232 3,272 observations (4) 0.605 0.482* 0.447* 0.484* 0.527 0.450* 0.451* 0.449* 0.469* 0.448* (0.371) (0.261) (0.258) (0.253) (0.329) (0.253) (0.268) (0.265) (0.261) (0.260) traffic police Bribes to (3) courts 0.839* 0.813*** 0.853*** 0.895*** 0.735* 0.869*** 0.850*** 0.825*** 0.830*** 0.813*** (0.456) (0.309) (0.276) (0.271) (0.394) (0.270) (0.279) (0.264) (0.263) (0.261) (2) 0.015 0.264 0.257* 0.282* 0.117 0.267* 0.213 0.268* 0.289** 0.285** police (0.185) (0.175) (0.150) (0.147) (0.183) (0.146) (0.150) (0.141) (0.139) (0.139) Trust in Trust (1) courts 0.004 0.227 0.300 0.356* 0.131 0.329* 0.401** 0.367* 0.403** 0.383** (0.232) (0.196) (0.192) (0.192) (0.243) (0.192) (0.195) (0.188) (0.188) (0.188) 5 <25 <50 <25 <50 able <100 <150 <200 <100 <150 <200 RDD specification with linear polynomial in latitude and longitude (as in T linear polynomial in latitude and longitude RDD specification with in km) border (distance from Border specification (as in Table 3) (distance from border in km) border from Table 3) (distance (as in specification Border Notes: All reported Notes: All estimates coefficients reported are of the variable “Part of Military in Frontier” a 3. The number of observa- in Table variables shown control specification that includes all model to the average number of observations/ (5) and (6) refer in columns tions and clusters reported at the level of PSU or place clustered errors clusters in all four model specifications. Standard at 10, **5, ***1 percent. *significance in parentheses: are of residence Survey (LiTS) 2006, 2010 and 2016; see main text for details. Life in Transition Source: T border Frontier Military around bandwidths Different
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135 44 (1) 117-145 (2020) the habsburg military frontier in croatia - Yes Yes Yes Yes Yes 159 (4) 0.051 3,213 0.474* 0.094 0.046 0.587*** 0.216* 0.476* (0.254) -0.038 (0.073) (0.101) -0.014*** (0.125) (0.094) (0.125) (0.095) -0.004 (0.194) (0.260) traffic police Bribes to Yes Yes Yes Yes Yes 159 (3) 0.033 3,183 0.880*** 0.195* 0.024 0.589*** 0.198* 0.110 0.825*** (0.274) courts -0.097 (0.074) (0.114) -0.004 (0.004) (0.104) (0.171) (0.101) (0.225) (0.265) Yes Yes Yes Yes Yes 159 (2) 0.059 0.173** 0.045 0.229* 0.006** 0.220 0.022 3,319 (0.143) (0.052) (0.077) (0.065) -0.043 (0.112) -0.179** (0.085) (0.137) (0.003) police Trust in Trust Yes Yes Yes Yes Yes 159 (1) 0.116** 0.089 0.036 0.324* 0.280 0.020 3,272 (0.188) (0.055) (0.121) (0.079) -0.094 (0.064) -0.048 (0.077) (0.185) -0.004 (0.002) courts 2 6 able Controls (as above) RDD specification with linear polynomial in latitude and longitude RDD specification with linear polynomial Part of Military Frontier No. of locations Pseudo-R Controls for religious affiliation (6 categories) Controls for LiTS wave Controls for household property (4 variables) Controls for education level (6 categories) No. of observations Household number of children under 14 Male respondent Used service in last 12 months for income Worked during last 12 months Urban area Household size (equivalent scale) Age of respondent Border specification Border Part of Military Frontier ses: *significance at 10, **5, ***1 percent. ses: *significance at 10, **5, ***1 percent. Survey (LiTS) 2006, 2010 and 2016; see main text for details. Transition Life in Source: Notes: Coefficients and standard errors from ordered logit estimation. Sample: respondents living logit estimation. Sample: respondents ordered from errors Notes: Coefficients and standard from based on GIS-computed distance border, Frontier former Military the 200 km from within do extent what “To question the to answer is (2) and (1) columns in variable Dependent border. Answer (1): The courts. Column (2): The police. you trust the following institutions?” Column 4 = 1 = Complete distrust; 2 = Some distrust; 3 = Neither trust nor distrust; categories are: applicable/Not know/Not say/Don’t to Difficult = 6 Category trust. Complete = 5 trust; Some Dependent variable in columns (3) and (4) is answer set to missing in regressions. stated are to the question “In your opinion, how often is it necessary for people like you to have to make (4):unofficial payments/gifts in these situations?” Column (3): Interact with the courts. Column 1 = Never; 2 = Seldom; 3 = Sometimes; are: Answer categories police. with the traffic Interact know/Refusal set to missing in Always. Category Difficult 6 = to say/Don’t 4 = Usually; 5 = in parenthe are of PSU or place of residence at the level clustered errors Standard regressions. T variables control Additional - - Yes Yes 159 159 (4) civic 0.116 0.118 0.103** 0.105** 3,370 3,370 (0.149) (0.152) organizations Membership in Membership Yes Yes 159 159 (3) 0.016 0.052 0.059 0.010 3,370 3,370 parties (0.105) (0.105) political Yes Yes 159 159 (2) 0.008 0.009 3,196 3,196 -0.141 -0.126 (0.195) (0.198) trade unions Trust in Trust society. Grenzer Yes Yes 158 158 (1) 0.013 0.015 3,243 3,243 -0.299 -0.329* (0.185) (0.190) other people other 2 2 7 able Border specification Border Part of Military Frontier 6) Table Controls (as in RDD specification with linear polynomial in latitude and longitude RDD specification with Part of Military Frontier No. of observations Controls (as in Table 6) Table Controls (as in No. of locations No. of observations Pseudo-R No. of locations Pseudo-R Banerjee and Iyer (2005) and Iyer (2010) for example found that British colonial British that found example for (2010) Iyer and (2005) Iyer and Banerjee cance at 10, **5, ***1 percent. cance at 10, **5, ***1 percent. Survey (LiTS) 2006, 2010 and 2016; see main text for details. Life in Transition Source: ism in India had long-term adverse impacts on the social structure areas, in of the sense affected that they are now less able to exert their influence on policy makers to obtain an appropriate amount of expenditure on public goods. Spanish forced labor systems in Peru have a persistent negative effect on modern house- hold consumption, most probably also due to lower access to education, to due Frontier Military health Habsburg the that seems It 2010). (Dell, goods public and its system authoritarian centralized was efficient in installing institutions, but due to its militaristic nature, not particularly interested or successful in caring for the social and economic fabric of the As a different falsification test, we also use proxies for social capital as our out- find we 7), Table of (3) (column parties political in membership Regarding comes. membership on effect positive small a is there but effect, significant statistically no Notes: Columns (1) and (2): Coefficients and standard errors from ordered logit estimation. logit estimation. ordered from errors Notes: Columns (1) and (2): Coefficients and standard estimations. Sample: probit from errors and standard effects Columns (3) and (4): marginal based on GIS- border, the former Military Frontier living within 200 km from respondents to the answer is (2) (1) and columns in variable Dependent border. from distance computed what extent question do “To you trust the following [...]?” Column (1): Other people. Column 1 = Complete distrust; 2 = Some distrust; 3 = Neither Answer categories are: unions. (2): Trade know/ trust. Category 6 = Difficult to say/Don’t trust nor distrust; 4 = Some trust; 5 = Complete Dependent variable in columns (3) set to missing in regressions. Not applicable/Not stated are is you answer a to member the of question a “Are political party?”. Dependent variable in col- organizations?”. civic/voluntary (other) of member a you “Are question the to answer is (4) umns *signifi parentheses: in are residence of PSU or place of level the at clustered errors Standard T in organizations and membership trust Interpersonal
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137 44 (1) 117-145 (2020) the habsburg military frontier in croatia century was geographically th preserved with relatively more Serbs living in the former Military Frontier area. the collapse of With socialist systems around Europe, Croatia opted for independ- Republic so-called the of proclamation the spurring 1991, in Yugoslavia from ence Frontier. Military former the of area the of much covered which Krajina, Serbian of endured it and occupation, military fire, under thus was area the 1990s, the During extreme human and infrastructure losses. Taking this rather recent historical epi- sode into consideration, one has to argue that it is possibly the war in Yugoslavia that is driving our results, although and the not Fron- the former Military Frontier, conflict in the 1990s in the first place. tier was a channel that led to the violent 6 WAR YUGOSLAVIA IN 6 WAR In this section we recognize that there is possibly a confounding element and potential a channel in our analysis and induced migrations explore east-to-west massive if the our to Due results shock. endogenous hold this account if we take into Ottomans the with border the on composition ethnic the conquests, Ottoman the by strat- defense the to due also but migration, to response a As changed. permanently and eastern the on cordon military a organized Vienna noblemen, Austrian of egies south eastern borders of Croatia. It used the influx of mostly Serbian refugees to popu- be to was area The following. the of consisted that policy migration a create - institutional with together certain rights granted be would that refugees by lated ized ethnic and religion tolerance in the area of the Military Frontier in exchange for military obligations. The settlers were given land and they were manorial freed obligations – from serfdom – as opposed to the civil part of the Empire. The special status persisted until the dissolution of the Frontier in 1881 when its terri- tory (and population) was unified withA Croatia. hundred years later, one could from the 19 still observe that the ethnic composition in civic The organizations. latter could possibly be explained by the typical life in in than longer much territory Frontier Military in the persisted that communities to confirm this hypothesis. research is needed Croatia, but further the rest of - Yes Yes 147 (4) 0.043 0.567 0.123 0.637*** 2,994 -0.019*** (0.003) (0.348) (0.100) (0.130) traffic police Bribes to Yes Yes 147 (3) 0.023 0.804** 0.051 0.509*** 2,957 courts (0.344) -0.009*** (0.003) (0.106) (0.178) Yes Yes 147 (2) 0.020 0.153 0.064 0.010*** 3,080 police (0.139) (0.067) (0.002) Trust in Trust Yes Yes 147 (1) 0.016 0.325 3,036 courts -0.127* (0.068) (0.230) -0.002 (0.002) 2 8 able Male respondent Part of Military Frontier Age of respondent Used service in last 12 months Controls for religious affiliation (6 categories) Controls for LiTS wave No. of observations No. of locations Pseudo-R We therefore put our investigation under further scrutiny, and create two geograph- two create and scrutiny, further under investigation our put therefore We ical subsamples. According to Šterc and Pokos (1993) forces UN we of arrival the were on frontline able the behind were to that locations household identify LiTS in 1992. The first subsample therefore excludes the areas behind the frontline on the arrival of UN forces in 1992 (Table 8); this way we completely exclude the any for only significant statistically although robust, are results The areas. occupied bribes to courts outcome. The bribes to traffic police issue10.3% level, and that of trust in courts at the is significant15.7% level. When we exclude only at the the area that was behind the frontline in 1992 and that used to be part of the former Military Frontier (Table 9), the results are even more convincing as outcomes become both statistically bribe significantand strongly positive. What one might conclude is that the attitudes towards bribery are more persistent as they survive events. extreme during collapses institutions public in trust while wars, harsh even able in columns (3) and (4) is answer to the question “In your opinion, how often is it necessary it is often how opinion, your “In question the to answer is (4) and (3) columns in able payments/gifts in these situations?” Column (3): for people like you to have to make unofficial 1 Answer categories are: with the traffic police. Interact with the courts. Column (4): Interact to say/ Always. Category 6 = Difficult 5 = = Never; 2 = Seldom; 3 = Sometimes; 4 = Usually; at the level of PSU clustered errors Standard to missing in regressions. know/Refusal set Don’t *significance at 10, **5, ***1 percent. parentheses: in are or place of residence and Pokos (1993); see Survey (LiTS) Šterc 2006, 2010 and 2016 and Life in Transition Source: main text for details. Notes: Coefficients and standard errors from ordered logit estimation. Sample: areas behind the Sample: areas estimation. logit ordered from errors and standard Notes: Coefficients (1) and (2) columns in variable Dependent excluded. 1992 are in UN forces of arrival at frontline what is extent answer do to you the trust question the “To following institutions?” Column (1): distrust; 2 = Some dis- 1 = Complete Answer categories are: The courts. Column (2): The police. 6 = Difficult trust. Category Complete trust; 5 = Some nor distrust; 4 = trust trust; 3 = Neither vari Dependent regressions. in missing to set are stated applicable/Not know/Not say/Don’t to T and police in courts and corruption Trust
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139 44 (1) 117-145 (2020) the habsburg military frontier in croatia Yes Yes 155 (4) 0.044 0.577* 0.124 0.615*** 3,143 (0.343) -0.018*** (0.003) (0.095) (0.126) traffic police Bribes to Yes Yes 155 (3) 0.821** 0.045 0.513*** 0.026 3,106 courts (0.340) -0.009*** (0.003) (0.103) (0.173) Yes Yes 155 (2) 0.018 0.141 0.009*** 0.067 3,230 police (0.142) (0.002) (0.064) Trust in Trust Yes Yes 155 (1) 0.015 0.288 3,184 courts (0.231) -0.001 (0.002) -0.129* (0.067) 2 9 able Part of Military Frontier Age of respondent Male respondent Used service in last 12 months Controls for religious affiliation (6 categories) Controls for LiTS wave No. of observations No. of locations Pseudo-R T and police in courts and corruption Trust Our identifying assumption is that the Military Frontier randomly split the country for a period of more than 350 years and established economic different and politi- cal systems on the two sides of the border. We obtain robust evidence that the 7 CONCLUSIONS The goal of this research was to use a natural experiment in macroeconomics to identify a causal mechanism of a specificfirst the is politicalThis growth. of systemcauses fundamental the of onsome reveal to modern-day order in capital civic andgrowth of mechanics simple beyond goes it as Croatia, for done research such provides evidence that history matters and that institutions are persistent and have macroeconomics modern in goal this achieve to standard The impacts. far-reaching is to use natural experiments and pair them with methods borrowed from microe- conometrics. The literature so far has offered that growth is causally related with institutions, social structure, and civic capital, both between and within countries. Notes: Coefficients and standard errors from ordered logit estimation. Sample: areas behind the Sample: areas estimation. logit ordered from errors and standard Notes: Coefficients excluded. are Frontier Military former of territory the on 1992 in forces UN of arrival at frontline trust you do extent what “To question the to answer is (2) and (1) columns in variable Dependent Answer categories Column (1): The courts. Column (2): The police. institutions?” following the Some trust; 5 trust nor distrust; 4 = Neither Some distrust; 3 = distrust; 2 = Complete 1 = are: set to stated are know/Not applicable/Not to say/Don’t = Complete trust. Category 6 = Difficult “In question the to answer is (4) and (3) columns in variable Dependent regressions. in missing payments/ unofficial make to have to you like for people necessary is it often how your opinion, with the courts. Column (4): Interact with the gifts in these situations?” Column (3): Interact = 5 Usually; 4 = Sometimes; = 3 Seldom; = 2 Never; = 1 are: categories Answer police. traffic Standard regressions. in missing to set know/Refusal say/Don’t to Difficult 6 = Category Always. *significanceat parentheses: in are residence of PSUorplace of level the at clustered errors 10, **5, ***1 percent. and Pokos (1993); see Survey (LiTS) Šterc 2006, 2010 and 2016 and Life in Transition Source: main text for details. - Disclosure statement statement Disclosure by the author. No potential conflict of interest was reported former former Military Frontier area is poorer than the rest of the country, and that this finding can be attributed to lower interpersonal trust, but These are effects not institutions only and statisticallyhigher incidence of bribery. higher trust in public significant, but also quantitatively reasonable as he Military Frontier moves the trust in public institutions categories by 14–34% The on limitation average. to our for used or generalized be cannot therefore and events, past on builds it that is study speculating about the future. However, parallels can be drawn. For example, one Empire) has now the center of the Habsburg (as Vienna that the role of can argue east- on decisions day modern that and centers, political other by replaced been just to-west migrations are once again in hands of politicians. As history teaches us, and centuries. lasting for decades effects far-reaching these decisions have of the economy and insight into the functioning substantial This study provides explains factors that cause persistent differences in economic outcomes within Croatia. History matters, be it in the form of a military cordon, episodes of vio- Any reform or economic policy tool utilized without or migration. lence and war, taking into account institutions, social structure and culture, the will change to order be in because tool, ineffective ineffective another by replaced repeatedly and are societies and Institutions causes. underlying the into look must one trajectory, persistent, but they also change, sometimes driven and internally, sometimes by exogenous events. Existing research teaches us that both persistence and change are equilibrium outcomes (Acemoglu, Since 1995), equilibrium. and ’bad’ a of that out country a small move to steps enough not usually and are reforms gradual both policy makers and institutions are inherently endogenous, providing recom mendations is naturally a complex task, one that asks for an empirically realistic theory of comparative growth based to closer step one is elements endogenous those between links causal the on explores institutions. Empirical research that for policy advice. building a more realistic theoretical framework
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able Part of Military Frontier Age of respondent Male respondent Atheist Buddhist Jewish Muslim Other religion LiTS wave 2010 LiTS wave 2016 Used service in last 12 months Notes: Average absolute marginal effects of each independent variable for model specifications specifications model for variable independent each of effects marginal absolute Average Notes: mean. Dependent variable in columns 3, holding the other variables at their in Table presented (1) and (2) is answer to what the extent question do “To you trust the following institutions?” 2 1 = Complete distrust; Answer categories are: Column (1): The courts. Column (2): The police. 4 = Some trust; 5 = Complete trust. Category 6 == Some distrust; 3 = Neither trust nor distrust; Dependent set to missing in regressions. know/Not applicable/Not stated are Difficult to say/Don’t - neces it is often how opinion, your “In question the to answer is (4) and (3) columns in variable in these situations?” Column payments/gifts you to have to make unofficial like sary for people Answer categories are: with the traffic police. (3): Interact with the courts. Column (4): Interact to say/ Always. Category 6 = Difficult 5 = 1 = Never; 2 = Seldom; 3 = Sometimes; 4 = Usually; missing in regressions. know/Refusal set to Don’t Survey (LiTS) 2006, 2010 and 2016; see main text for details Life in Transition Source: APPENDIX T effects absolute marginal Average
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145 44 (1) 117-145 (2020) the habsburg military frontier in croatia (4) (2) 0.025 0.022 0.023 0.044 0.027 0.028 0.105 0.024 0.056 0.019 0.005 0.042 police traffic police Trust in Trust Bribes to (3) (1) 0.169 0.072 0.024 0.038 0.051 0.006 0.038 0.053 0.073 0.036 0.007 0.068 courts courts A2 able Never Complete distrust Some distrust Neither trust nor distrust Some trust Complete trust effect marginal absolute Average Seldom Sometimes Usually Always effect absolute marginal Average Notes: Average absolute marginal effects of each independent variable for model specifications specifications model for variable independent each of effects marginal absolute Average Notes: average presents row The last mean. at their variables the other 3, holding Table in presented A1. in Table same number as presented categories and is the effect across absolute marginal trust you do extent what “To question the to answer is (2) and (1) columns in variable Dependent Answer categories Column (1): The courts. Column (2): The police. institutions?” following the Some trust; 5 trust nor distrust; 4 = Neither Some distrust; 3 = distrust; 2 = Complete 1 = are: set to stated are know/Not applicable/Not to say/Don’t = Complete trust. Category 6 = Difficult “In question the to answer is (4) and (3) columns in variable Dependent regressions. in missing payments/ unofficial make to have to you like for people necessary is it often how your opinion, with the courts. Column (4): Interact with the gifts in these situations?” Column (3): Interact = 5 Usually; 4 = Sometimes; = 3 Seldom; = 2 Never; = 1 are: categories Answer police. traffic know/Refusal set to missing in regressions. Always. Category 6 = Difficult to say/Don’t Survey (LiTS) 2006, 2010 and 2016; see main text for details. Life in Transition Source: T by category Frontier of Military effects Marginal