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Extended Abstract:

Tunisia’s Jasmine : A Spatial Demographic Analysis of , Violence, and Voting Patterns

Nicholas Reith, University of Texas at Austin

Abstract:

In the wake of the past two years of popular uprisings and in the Arab region, three theoretical explanations with a major demographic component have gained prominence. These three “new” theories posit 1) a youth bulge, 2) demographic disparities, and 3) the role of , respectively as likely causes of revolution and of the success of Islamist parties in . Using Generalized Spatial Two-Stage Least Squares Regression to analyze sub-national data from , preliminary results indicate that these three phenomena are not statistically significant predictors of the occurrence of protest, the timing/duration of protest, or the district vote percentage for the main Islamist party “Ennahdha.” Further analysis with both time and spatial dimensions will clarify other demographic factors that seem to be linked to protest, violence and vote outcomes, including government marginalization and women’s demographic factors.

Introduction:

The , which began in December 2010, caught most and Political Science scholars by surprise and inspired similar uprisings in other Arab countries. The success of Islamists in post- elections has however surprised few. The revolutions of the Arab Spring have offered fertile ground for research on social movements from a variety of perspectives. Three persistent theoretical explanations for the outbreak of these revolutions, all of which have a demographic component, seem to have gained significant prominence in the brief space of the past two years. These are the theories of “youth bulge,”(Hvistendahl 2011) “demographic disparities,”(LaGraffe 2012) and “digital media revolution” (Khondker 2011). Together, these can be seen as reinventions of previous Neo-Malthusian theories of “grievances” or “deprivation”(Malthus 1888; Opp 1988), “relative deprivation”(Gurney and Tierney 1982), and “technologies of freedom”(Pool 1983) respectively. To some extent, each of these has also been posited as a demographic explanation for the rise of Islamist movements, and the success of Islamist parties in elections from ’s in 1991 to , Gaza, Tunisia and in more recent times. (Brown 2006; Santos Bravo and Mendes Dias 2006; Khamis 2011; Stepanova 2011; Bunt 2003) In this paper I propose to test these three theories in the sub-national context of Tunisia by looking at the effects of the youth bulge, demographic disparities, and technological diffusion on three outcomes: , repressive anti-revolutionary violence, and elections voting patterns in time- spatial perspective.

1 of 25

Existing Approaches:

The first of the three above-mentioned theories, and the most explicitly demographic, points to the political consequences of having a “youth bulge” in the age structure. This theory harkens back to some of the earliest demographic work of Thomas Robert Malthus, and early social movement theories of “grievances” and “deprivation”. Under this heading, political scientists such as Jack Goldstone, Daniel LaGraffe, Henrik Urdal, and Richard Cincotta have found themselves allied with demographers such as John Weeks and others in advising the US government on strategic , with Goldstone claiming that “in terms of broad probabilities, demography tells you almost everything you ought to know.” (Hvistendahl 2011; Goldstone 2002; LaGraffe 2012) Although, the theory of “youth bulge” may seem simplistic or deterministic, its proponents convey its plausibility by emphasizing the deprivation side of the story with convincing national level descriptive statistics, or through cross-national data analysis. In sum, they argue, it is not only the large youth cohort between ages 15 and 29 that explains revolutions, but also their grievances in terms of lack of economic opportunities for jobs, and social opportunities for marriage.1 (Dhillon 2007) Goldstone summarizes the basic proposition well:

“The rapid growth of youth can undermine existing political coalitions, creating instability. Large youth cohorts are often drawn to new ideas and heterodox religions, challenging older forms of authority. In addition, because most young people have fewer responsibilities for families and careers, they are relatively easily mobilized for social or political conflicts. Youth have played a prominent role in political violence throughout recorded history, and the existence of a “youth bulge” (an unusually high proportion of youths 15 to 24 relative to the total adult population) has historically been associated with times of political crisis. Most major revolutions ... [including] most twentieth­century revolutions in developing countries—have occurred where exceptionally large youth bulges were present.”(Goldstone 2002)

Goldstone and other proponents of this “youth bulge” theory are in good company with macro-historical sociologists such as Charles Tilly, who have considered population growth dynamics to be one of several major factors in revolutions throughout history. (Tilly 1996)

The second of these three theories is really a corollary and slight refinement of the first. It adds a “relative deprivation” component to the first, using the terminology of “disparities.” In this variant, it is not only the large youth cohort and their grievances, but also specific relative grievances and glaring inequalities, which lead to revolution. (Al- Momani 2011).2 The main motivation for introducing this “relative” component is that,

1 In fact, there was certainly a hardship component to Mohamed Boazizi’s decision to commit public suicide by self-immolation. His fruit cart and sole means of earning his living was arbitrarily confiscated. However, according to his mother, his cart had been confiscated several times before. Rather, the major reason for his drastic action was the insult of being publically slapped by a woman police officer, which he considered an affront to his honor and that of his tribe, a sentiment with which she agreed. (Abouzeid 2011) 2 According to Al-Momani (2011) The combination of socio-economic hardship, inequality, and the large “youth bulge” of Middle Eastern societies acted as a major catalyst for the new political changes of the region. The demographics of the Middle East made it ripe for revolution: 60 percent of the population is

2 of 25 for all of their cross-national statistical models, traditional “youth bulge” deprivation theories are hard pressed to explain why countries with even more youth, or even poorer youth do not have revolutions. Thus, in a sub-national twist, proponents of “demographic disparities” explanations argue that it is relative , and relative of certain groups or regions that leads to revolutionary conditions. Seen from another perspective, all of the major “deficits” identified by the recent UNDP Arab Human Development Reports, beginning in 2002, (knowledge, freedom, gender equality, and human security) are compounded by the demographic youth bulge that puts additional strain on states. (UNDP 2012)

In the specific example of Egypt, one recent paper has argued that it was the combination of two factors: first, too many unemployed university graduates with higher expectations and time on their hands, and second, the rapid rise in prices, which pushed 3 million lower middle class Egyptians below the poverty line, that explains the revolutionary events of 2011. (Korotayev and Zinkina 2011) Proponents of “disparities” explanations for revolutions also do a good job of demonstrating plausibility, thus salvaging some elements of deprivation theories. However they clash explicitly with newer theories of “,” which claim that social movements, and revolutions can only occur when there are enough organizational and financial resources available to for sustained revolt. And for all their macro-level merits, relative deprivation theories are no better at explaining the demographic characteristics of areas that support revolution. As Korotayev and Zinkina (2011) point out, there are clearly difficulties in comparing the political context of one particular Arab country (in this case Egypt) with those of other Arab countries, let alone with countries in other regions. Even protests by ostensibly similar groups may stem from different sets of grievances.

Finally, the third of these schools of new-old theories is that which puts forth the “role of digital media,” as an explanation for the Arab Spring revolutions, and which is essentially a more nuanced, networked, and updated version of Ithiel de Sola Pool’s classic thesis in Technologies of Freedom. (Pool 1983) In the less reified version of this theory, it is not precisely and that made revolutions happen. Rather the rapid scaling up of local, national and transnational networks of communication through new technology is believed to have spurred the revolutions on in a variety of ways from actually organizing protests to framing the revolutions for both domestic and international audiences. (Howard and Hussain 2011; Khamis 2011; Stepanova 2011; Ray 2011) In one theorization, Anthropologist John Postill (2011) argues that digital media transforms the political communication landscape into one of “epidemiography,” whereby political messages of protest and dissent spread like viruses, faster than authoritarian governments can contain or quarantine them. The utility of fast, mobile means of communication for

under 25 years old, with a median age of 24.1 (“The Future of the Global Muslim Population,” The Pew Forum on Religion & Public Life, 27 January 2011, http://pewforum.org/future-of-the-global-muslim - population-regional-middle-east.aspx (accessed 14 June 2011).) This statistic becomes even more significant given the 2005 Human Development Report’s finding that roughly 35 percent of the Arab population is living in poverty.

3 of 25 framing the revolutions and getting past government censors to report revolutionary events to the world is without question. However, the fact that widespread surveillance and blocking of Twitter and Facebook by Arab regimes, in addition to the Mubarak regime’s decision to shut down the entire during the most critical four days of the Egyptian revolution leave it an open question as to whether or not these technologies actually assisted in organizing many of the protests, much less caused these revolutions. Some “digital media” theories of revolution ignore the underlying demographic fact that diffusion of new communications technologies such as digital media are still dependent on hardware such as cell phones and computers, and a “digital divide” (Norris 2001) often corresponds closely to an economic one.

The three theories presented above each offer some useful elements for thinking about the demographic roots of revolutions. Yet, each of them also generates critical questions that simply cannot be answered with aggregated national and cross-national data. For example, if each theory works at a macro-political level, or in cross-national perspective, to explain revolutions or the strength of Islamist movements, do they also hold up under sub-national examination? Do local areas within a country undergoing a revolution experience protest and opposition activity more than other areas if they have higher proportions of youth in the population, or higher rates of bachelorhood, unemployment, and poverty? Do local areas with higher rates of communication technology diffusion also experience more protest and opposition activity? Is there any correlation between support for Islamist social movements and parties and the indicators proposed by these theories, youth, economic deprivation, disparities, and digital media? A number of recent studies explicitly call the claims of these three theories into question, particularly from Islamic and Arab studies specialists. (Meijer 2005; Baylouny 2004) Others argue for altogether different theories, for example, based on the robustness (or lack thereof) of the Arab state, while still tacitly accepting some of the assumptions of these three. (Bellin 2012) Few have however attempted to test these theories with quantitative sub-national data, and none have thus far done so in relation to the recent revolutions of the Arab Spring.

4 of 25 Data:

As mentioned in the introduction, in this paper, I propose to test these three theories in the sub-national context of Tunisia by looking at the effects of the youth bulge, demographic disparities, and technological diffusion on three outcomes: protests, repressive anti-revolutionary violence, and elections voting patterns in time-spatial perspective. To do so, I use a unique merged and geo-coded dataset of four sources:

1) Tunisian demographic and economic data from the 2004 census3 2) Media accounts of major protests during the revolutionary period from the revolutionary period 17 December 2010 through 14 January 20114 3) Humanitarian data on individuals killed and wounded during the conflict provided by a recent Tunisian Government Commission (forthcoming)5 4) Official vote counts and elections data from the first post-revolutionary Tunisian elections in October 2011.6

Methods:

This draft paper also proposes a novel analytic methodology rather rare in demographic research, and rarer still in research on revolutionary and democratic outcomes. This method is that of spatial auto-regression using Generalized Spatial Two-Stage Least Squares. The advantage of this method over multiple regression with any standard estimator, is that GS2SLS can account not only for heteroskedastic disturbance terms resulting from the different population sizes of various districts, but also for the potentially more important spatial dependencies that result from adjacency or proximity of districts, thus providing more accurate standard errors and significance tests. (Chi and Zhu 2007) In these preliminary analyses of protest and elections outcomes in 257 Tunisian administrative districts, spatial weights of inverse distance have been used, in addition to an adjustment for heteroskedasticity.7 All of these analyses were conducted in

3 This census data was collected in 2004 and finalized in 2005 and in some cases 2006, about 5 years before the Tunisian Revolution. I am currently working on obtaining more recent district-level statistics for certain indicators such as unemployment. However, an unpublished report by a major development agency produced with up-to-date data in 2009 indicates that many of the trends highlighted in the census were still valid, at least at the regional and state level. 4 This data was gathered from a variety of English, French and media sources from the US, Europe, the Middle East and Africa, including Al-Jazeera, Le Monde, Christian Science Monitor, Jeune Afrique, and a number of others. Although this protest data is quite complete, I will continue to review a few additional news sources prior to the upcoming IUSSP meeting. 5 Data have already been collected, but are being coded and merged with the other geo-coded data sources. 6 It is worthwhile to mention here that these official vote counts were only publicly released by the Tunisian Government in February 2012 after much pressure from European Union elections monitors, and even then were only provided online in html format in Arabic. It took a couple of months of data cleaning and translation to get them into a useable format to analyze them in this draft paper. 7 The method of modeling spatial dependencies chosen for the preliminary analyses in this draft paper is that of inverse distance. This makes sense for two practical reasons. First, there are two islands in the model comprising 4 districts, which would be lost using a matrix of adjacency weights, unless their weights were manually calculated to make them appear adjacent to the mainland. Second, given the vast differences in geographic size of the Tunisian administrative districts, a weight by distance from the center point of one

5 of 25 STATA 12, using the sppack commands spmat for creating weighting matrices and spreg for spatial autoregression. (Drukker et al. 2012)

Going one step further, in several upcoming analyses of the already included protest data, and the forthcoming violence data,8 I will perform GS2SLS spatial regression on time- series data, also weighting for dependencies that may occur across both space and time in protest and violence outcomes as the Tunisian revolution unfolded. Spatial dependencies in the explanatory variables are easy to see by glancing quickly at any of the demographic cloropleth maps provided in the appendices, where clumping of districts with similar colors indicates shared demographic characteristics. However, a pointed example of dual spatial and time dependencies can be seen on page 18, in the second cloropleth map depicting the “Timing/Duration of Protests.” In this map, it is clear how the protests spread outward from the center of the country, and then jumped to far-flung districts throughout several phases of the revolution. It is to be expected that protests or violence may be affected not only by the events and characteristics of neighboring districts, but also by the events around the country at any given point in the revolution when underwent surges or lulls. Thus, including a time-series component to these spatial regressions, will lend further precision to these models.

Dependent Variables:

At this stage of the analysis, I have run 2 sets of models, each with both GS2SLS and OLS regression models in order to compare coefficients and standard errors. In the first set of models, I analyze “protest” as the dependent variable, coded as 0/1 for districts in which there were major protests reported by the media during the revolution. A total of 45 out of 257 districts were mentioned in media reports as experiencing significant protest. I also analyze “Timing/Duration of Protest” as a proxy measure for the political risk climate associated with protest at various points during the revolution. Since it is nearly impossible to get accurate or consistent counts of the number of people attending each protest, this offers an alternative measure of the “intensity” of protest. Since media did report on the earliest date when the protests were known to have begun in each location, I created this “Timing/Duration of Protest” variable as a count from January 14th, 2011 when the revolution culminated in the flight of former President Ben Ali backwards to the day on which a particular district began to experience protests. 9 In this instance then, this variable represents an increasing scale of political risk associating more risk with earlier protests, which often occurred in political backwaters, than with

district to the center point of another may be a more accurate approximation of “influence” or “dependency” than strict adjacency. However, during future diagnostics, I will test more rigorously for spatial dependencies to choose the model weights that best reflect the underlying geographic and demographic data. 8 As mentioned previously, this data on repressive anti-revolutionary violence has been collected and is in the process of being coded and merged with the other data in this study. 9 Although both protests and repression continued for some days after the revolution, the enormous political opening and celebrations after Ben Ali’s departure make post-January 14th Tunisia scarcely comparable with pre-January 14th Tunisia. In addition, major media ceased to report reliably on protests in most locales after this date, partly because protests were so common, and partly because the revolution became less newsworthy once it had succeeded.

6 of 25 later protests, which were enormous and occurred in more cosmopolitan centers. Of the 45 districts mentioned in media reports, protests ranged in duration from a minimum of 1 day in Cite El Khadra in the capital city , to 29 days in the town of where the revolution began on December 17th, 2010, with a mean of 16 days.10

Another possible way to measure the “intensity” of protest is to measure the human toll in terms of violence. New data that I am in the process of merging with the existing geocoded dataset provides precise information on those individuals killed and wounded during the period of the revolution, including the day and location of the incident, context of the incident (protest or other), age, gender, and of the aggrieved, and responsible party (police, etc.). This data is provided by a Tunisian government report released in early May 2012. While I am still conceptualizing my models for these outcome variables, these will certainly contain both spatial and time components, and will also test the extent to which the three theories elaborated in this paper fit in explaining repressive anti-revolutionary violence in the Tunisian case.

One possible critique of a demographic and spatial approach to explaining revolutionary protest and violence outcomes is that individuals are not bound in space to one district. It is possible and indeed likely that some individuals may have transported themselves from one district to another in order to engage in protests. However, the spatial distribution of protests even in more rural districts and media reports of protests occurring in front of local government buildings in various parts of the country tend to support the idea that this may have only been an issue in the capital city where metropolitan transportation made mass transportation easy, and the architectural symbols of the Ben Ali regime were tempting targets of protest. Even there, several poorer and less noteworthy districts experienced significant protests. Moving beyond these revolutionary incidents then, I propose a third set of outcomes that is less susceptible to geographic disturbances of this sort. This is the analysis of elections outcomes, particularly the district-level percentages of votes for the major Islamist party, Ennahdha vs. parties associated with candidates from the Former Regime. Coming so soon (10 months) after the end of the revolutionary period, these elections can be considered as a referendum on the revolution.

As noted in the brief preceding literature review, theories of “youth bulge,” “demographic disparities,” and “digital media,” have been applied to social movements as well as revolutions, and have in recent times been used to explain the success of Islamist social movements and political parties, particularly in recent post-Arab Spring elections. In the case of Tunisia, the country’s first transparent democratic elections were those of October 2011 for the country’s Constituent Assembly, which would draft a new constitution. In most of the country’s 268 districts, up to 100 different “electoral lists,” similar to parties vied for the handful of seats in each district. Many of these were local parties that only appeared in one or possibly two adjacent Gouvernorats (states) out of

10 It must be noted that the continuity of protests from their first data until the end of the revolution cannot be verified. However, this seems a safe assumption, since no media reports indicated that any of the protests stopped during the middle of the revolution due to repression. Rather, the general image that emerges is of a gradually increasing geographic scope, and size of protests until the crescendo of Ben Ali’s departure.

7 of 25 the 24 in the country, and thus only in a handful of districts. There were initially more than 700 electoral lists, which after combining duplicates and misspellings yielded 575 unique lists, of which approximately 100 were considered major parties for which some platform or ideology could be ascertained. For the sake of analysis, I further simplified and classified these 575 parties into one of four main political blocks, which reflect underlying tendencies more than ideologies. The first block consists of only one party, the main Islamist party “Ennahdha,” who won about 37% of the vote nationwide, gaining them the largest minority of 89 out of 217 seats. The next block consists of parties that were known to have sponsored some candidates linked to the former regime. These parties were identified through two key internet sources, which have tracked details on most of the major Tunisian parties in the past two years. (Fhimt.com 2012; Wikipedia contributors 2012) Twenty parties fell into this category of former regime parties, which garnered an average of 18% of the vote across the country and a number of seats in the Assembly. A third group of parties are what I consider “local parties.” I defined these geographically as parties that only appeared on the ballot in one of the 24 Tunisian states, or in two adjacent states. Three hundred and forty-one parties were “local parties,” and collectively they got 8% of the vote across the country. And finally, the last block include “other parties,” that do not fall into one of the three former categories. This group includes 213 parties that range from left to right across the political spectrum and are opposition, but are neither local nor Islamist. Together, this group got 37% of the vote and a substantial number of seats in the new Assembly. While most analysts have been content with examining the broad conclusions of the Ennahdha victory and its alliance with Leftist opposition parties, here I am more interested in ascertaining the demographic characteristics of districts which voted more heavily for one party or another. To return to our central question then, do youth, disparities, and technological diffusion explain the Islamist vote in sub-national perspective?

Independent Variables

In both sets of analyses above, I use an explanatory variable model that includes demographic, educational, economic and household characteristics. Control variables include: Population, Population Density, Urbanization, Average Household Size, Net Migration, Percentage of Rudimentary Houses, and Percentage of Houses Lacking Basic Services. I test various aspects of the “Youth Bulge” hypothesis with variables including Relative Cohort Size, Youth Sex Ratio, Percentage of Single Men, and Male Rate. I also test aspects of the “Disparities” hypothesis with variables that look at the relative deprivation of male youth including Male Youth University Education Rate, Male Relative Cohort Unemployment, and Percentage of Unemployed Male University Graduates. Finally, I provide one test of the “Digital Media” hypothesis by testing technological diffusion in the form of the Percentage of Households with a telephone, a mobile phone, or a computer.

8 of 25 Preliminary Findings: Maps:

A series of maps beginning on page 18 details the geo-spatial distribution of both outcome variables (protests, and voting patterns) and independent variables (demographic, economic, educational, and technological). Of particular interest are the map of Protests Duration on page 18 which details the spread of protests during the revolution, and the maps of voting percentages for Ennahdha and the Former Regime Parties on pages 18 and 19, which show the regions of the country where each political block faired best. Protests began in the central district of Sidi Bouzid on December 17th, 2010 and slowly radiated outward to neighboring districts after several days of protest, then spread to the North and Northwest, eventually reaching the capital of Tunis in the Northeast after 10 days. From there protests became more national in scope, especially after headquarters of the UGTT, the Tunisian Workers’ Union came out in support of on December 28th, 2010. Some of the last protests to occur were in the Southeast of the country, a region highly dependent on tourism. Meanwhile, the elections maps show that Ennahdha faired best in the Southern and a handful of Central districts, as well as in certain areas near the Northeastern Capital of Tunis. Former regime parties garnered their highest vote percentages in the Central and Eastern districts, which had long been their bases of power and from whence both Tunisian Presidents Habib Bourguiba and had originated, specifically Monastir and Sousse respectively.

Noting the spatial distribution of protests and votes for these two major blocks of parties, the following maps show selected demographic, economic, educational and technological connectivity indicators by district. Only a few of the many variables available in the 2004 census are included here. Without giving a detailed description of these independent variables here, suffice it to say that they display clear spatial distribution tendencies that indicate regional demographic and development disparities well known to scholars of Tunisia, which were reconfirmed by an unpublished report by a major development agency as recently as 2009. Two important points are worth mentioning here, regarding the bearing of these maps on the theory and analysis of this paper. First, the bivariate relationships between explanatory variables and outcomes can be visualized in these maps, where one can see the characteristics of districts that protest or voted one way or another. Second, the clear clumping of demographic characteristics that include multiple neighboring districts, gives a graphic explanation of the need to account for spatial dependencies through spatial regression techniques.

Preliminary Findings: Regression Results

Preliminary regression results of my full models predicting Protest, Protest Timing/Duration, and Voting Percentages can be found in the tables on pages 15-17. Models are presented both as Generalized Spatial Two-Stage Least Squares and as Ordinary Least Squares regressions in order to compare the differences in coefficients and standard errors resulting from accounting for both heteroskedastic disturbances and spatial dependencies. In bivariate simple OLS regressions of variables testing the three theories of “youth bulge,” “disparities” and “digital media,” coefficients were statistically

9 of 25 significant and in the direction predicted by each of these theories for predicting protest, protest timing/duration, and voting for the Islamist party Ennahdha. However, after including control variables and all three sets of test variables, none of these three theories significantly predicts protest or timing/duration of protest. Only the percentage of single men is weakly associated with timing/duration of protest in OLS regression, but this effect disappears when weighting for spatial dependencies. In terms of voting for the Islamist party Ennahdha, the “youth bulge” measured as Relative Cohort Size does seem to be a substantively and statistically significant predictor of voting for the Islamist party, and an equally significant predictor against voting for the former regime. However, the negative coefficient for Youth Sex Ratio, even if only significant at the .1 level, hints that the Islamist party tended to do better in districts with higher proportions of young women than men. Districts with Single Men voted more for the Former Regime, and districts with more Unemployed Young Men or more Unemployed Male University Graduates voted less for them, but neither group seemed to be a factor in districts voting for the Islamist party. In terms of technology, the Percentage of Households with a Computer seemed to be a factor in districts voting for the Former Regime, Other and Local Parties, but not for the Islamists. In contrast, having a simple landline Telephone seemed to be a reasonably substantive and wildly statistically significant predictor for districts voting for Ennahdha.

To summarize these preliminary findings, none of the three theories investigated in this research seemed to be a significant predictor of districts that protested, or the timing/duration or political risk associated with their protests. The “youth bulge” does seem to predict voting for the Islamist party Ennahdha, however women may have been more of a demographic factor in this than men. Communications technology is not a significant predictor of protest, but Mobile Phones and Computers did predict some voting patterns, albeit not those for Ennahdha. Some interesting findings also resulted from control variables for general deprivation and household services.

These results are only a first approximation, but it is expected that further analyses will support these findings that contradict these three theories of revolution at the sub-national level. Further work will be undertaken to improve these analyses and finalize a draft paper prior in the next couple of months. Next steps include:

1) Coding and merging of further data on revolutionary violence, to add a third set of outcomes to those on protest and elections. 2) Possible inclusion of more recent data for some variables from the Tunisian Institute of Statistics 3) Refinements to coding and merging to retain more than 257 cases (districts) in the dataset, and up to a maximum of 268. 4) Further refinements to the baseline and test models, including possible factor scores to combine some variables relating to young men or technology. 5) Additional adjustments to factor analysis models based on confirmatory analysis. 6) Testing of some other demographic factors, particularly relating to young women and older cohorts in order to consider other hypotheses not elaborated by these three theories.

10 of 25 7) Running incremental models, which will begin with a baseline model, adding in new sets of variables to test each theory one at a time and all together. 8) Further refinement of the estimator and weights, including comparison of adjacency weights (with manual weights to place the islands adjacent to the mainland) vs. the distance weights currently used. 9) Inclusion of a time-series component and weights with both protests and violence. 10) Extensive diagnostics to look for outliers and influential cases, as well as to test for assumptions violations such as heteroskedasticity, and time and spatial dependencies. Here, these three have been assumed because of the nature of the data, but it will be worth further investigation in order to ensure the best estimator and regression technique is chosen for the data.

Pseudo-Conclusion:

In guise of conclusion, I would like to acknowledge some of the limitations of this study, as well as to point out some of its strengths. First, as with any study, there are data limitations. The census data only includes certain explanatory variables and misses some potentially interesting predictors such as individual and community religiosity, level of tribal identity, and a number of others that researchers could wish for. Further, the fact that these data date from at least 5 years before the revolutions is not the most ideal. This is mitigated by regional-level confirmation of the same patterns in development data from 2009 previously mentioned. Other potential issues with data could stem from incompleteness of outcome variables due to media bias toward larger demonstrations, or government oversight in missing some casualties of violence. The elections did experience some “irregularities,” which were well documented and vote counts were adjusted in only a few cases of fraud, but on the whole they were deemed free and fair by both domestic and international observers, and thus the data can be considered reliable. Beyond these collection issues, it is important to avoid the pitfalls of ecological fallacy. In other words, just as cross-national studies make potentially faulty assumptions about human behavior based on national-level data, it would be dangerous to speculate about individual protest or voting behavior based on aggregate district-level data. Crude media accounts, images, and videos as well as data on violence indicates that young men may indeed have been heavily represented among protesters. However, this could also reflect a regime/police bias toward targeting young men and a media bias toward reporting the most dramatic protest events. There are simply no large sample surveys of Tunisian protesters or voters that would enable us to draw such detailed conclusions.

With all of these caveats, I believe this proposed study presents a number of merits in that it provides a unique sub-national dataset on the first Arab Spring revolution in Tunisia. Further, the techniques of spatial and time-series regression provide both a good fit for the data and a novel use of methods rarely applied in demography or in studies of social movements and revolutions. Finally, by calling into question several major theories of revolution, and opening up avenues for potential alternative theory building, this study stands poised to add a theoretical contribution as well.

11 of 25 References

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14 of 25 Preliminary Results: Protests Generalized Spatial Two- Ordinary Least Squares Stage Least Squares Regression

Regression

VARIABLES Protests Protests Duration Protests Protests Duration Total District Population 4.88e-06*** 8.36e-05*** 4.82e-06*** 8.58e-05*** (Count) (1.22e-06) (2.31e-05) (1.18e-06) (2.09e-05) District Population Density -3.67e-07* -1.93e-06 -3.06e-07 -5.01e-06 (Count/Sq. Km) (2.12e-07) (3.50e-06) (3.71e-07) (6.57e-06) Urban Population 0.00287** 0.0352* 0.00268* 0.0403 (Percentage) (0.00128) (0.0205) (0.00143) (0.0253) Average Household Size -0.127 -1.888 -0.124 -1.866 (Number of Individuals) (0.106) (2.035) (0.0970) (1.718) Net Domestic & International Migration -0.0124*** -0.162*** -0.0116*** -0.177*** (Positive or Negative Percentage of Population) (0.00249) (0.0339) (0.00240) (0.0425) Relative Cohort Size 0.118 -5.304 0.0777 -4.318 (Count Age 15-29 / Count Age 30-59) (0.372) (7.288) (0.370) (6.556) Youth Sex Ratio 0.207 0.526 0.218 -0.751 (Count Men Age 15-29 / Count Women Age 15-29) (0.328) (6.117) (0.352) (6.231) Single M en 0.0172 0.367 0.0167 0.383* (Percentage of Male Population - All Ages) (0.0122) (0.226) (0.0117) (0.207) Male Youth Unemployment Rate 0.00638 0.0217 0.00614 0.0174 (Percentage of Men Ages 20-29 Unemployed) (0.00441) (0.0806) (0.00447) (0.0791) Male Relative Cohort Unemployment 0.0596 2.312 0.0628 1.999 (Count of Unemp. Men 20-29/Count Unemp. Men 30-59) (0.0809) (1.464) (0.0838) (1.484) Male Youth University Education Rate 0.00415 0.151 0.00455 0.151 (Percentage of Men Age 19-24 with University Degree) (0.00733) (0.130) (0.00593) (0.105) Unemployed Male University Graduates -0.00755 -0.0919 -0.00734 -0.0838 (Percentage of Male University Grads Unemployed - All (0.0111) (0.175) (0.0118) (0.209) Ages) Rudimentary Housing -0.0808*** -1.512*** -0.0867*** -1.320*** (Percentage of Rudimentary Housing out of Total Units) (0.0235) (0.388) (0.0201) (0.356) Factor Score for Lack of Government Household Services 0.00990*** 0.192*** 0.00956*** 0.193*** (% of households not connected to electricity, gas, water, (0.00342) (0.0621) (0.00292) (0.0517) and sewage services) Telephones -0.00301 -0.0505 -0.00267 -0.0786 (Percentage of Households with a Land Line Telephone) (0.00347) (0.0631) (0.00309) (0.0548) Mobile Phones 0.00529 0.0979 0.00513 0.122 (0.00447) (0.0782) (0.00439) (0.0776) (Percentage of Households with at least One Mobile Phone Computers -0.00539 -0.215 -0.00613 -0.236 (Percentage of Households with a Computer (0.00998) (0.177) (0.0105) (0.185) Constant -0.940** -13.08* -0.899* -12.50 (0.421) (7.682) (0.487) (8.628) Observations 257 257 257 257 rho_2sls -0.142 0.350 k 20 20 R Squared 0.215 0.223 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

15 of 25 Preliminary Results: Voting Patterns Generalized Spatial Two-Stage Least Squares Regression VARIABLES % Ennahdha % Former Regime % Other Parties % Local Parties

Total District Population 1.70e-05 -5.73e-06 5.83e-06 -3.12e-05 (Count) (2.49e-05) (2.21e-05) (9.28e-06) (2.05e-05) District Population Density -5.05e-06 5.17e-06 -1.11e-06 -1.82e-06 (Count/Sq. Km) (3.28e-06) (3.89e-06) (1.53e-06) (2.50e-06) Urban Population 0.0209 0.00150 -0.00640 0.00316 (Percentage) (0.0332) (0.0300) (0.0162) (0.0330) Average Household Size 5.467** -2.440 0.313 -4.120* (Number of Individuals) (2.282) (1.728) (0.874) (2.120) Net Domestic & International Migration 0.0192 -0.0145 0.0144 -0.0269 (Positive or Negative Percentage of Population) (0.0424) (0.0390) (0.0134) (0.0238) Relative Cohort Size 23.33*** -15.76* -6.534 8.010 (Count Age 15-29 / Count Age 30-59) (8.279) (8.744) (4.895) (7.349) Youth Sex Ratio -13.97* -8.131 -2.378 20.43*** (Count Men Age 15-29 / Count Women Age 15-29) (7.642) (6.999) (4.435) (6.685) Single M en -0.431 0.777** 0.181 -0.556* (Percentage of Male Population - All Ages) (0.280) (0.332) (0.165) (0.309) Male Youth Unemployment Rate 0.00440 -0.210** 0.0799 0.0820 (Percentage of Men Ages 20-29 Unemployed) (0.104) (0.0927) (0.0535) (0.0970) Male Relative Cohort Unemployment 0.359 0.689 0.772 -1.397 (Count of Unemp. Men 20-29/Count Unemp. Men 30-59) (2.000) (1.627) (1.019) (1.653) Male Youth University Education Rate 0.0995 -0.0994 -0.00469 -0.0202 (Percentage of Men Age 19-24 with University Degree) (0.120) (0.107) (0.0624) (0.114) Unemployed Male University Graduates 0.0625 -0.397* 0.157 0.204 (Percentage of Male University Grads Unemployed - All Ages) (0.286) (0.223) (0.140) (0.252) Rudimentary Housing 0.341 -0.173 0.250* -0.352 (Percentage of Rudimentary Housing out of Total Units) (0.354) (0.336) (0.143) (0.235) Factor Score for Lack of Government Household Services -0.225*** 0.151* 0.0387 0.0831 (% of households not connected to electricity, gas, water, and sewage services) (0.0761) (0.0778) (0.0500) (0.0743) Telephones 0.382*** -0.249*** -0.0268 -0.0677 (Percentage of Households with a Land Line Telephone) (0.0715) (0.0596) (0.0344) (0.0729) Mobile Phones 0.124 0.0105 -0.143*** 0.0788 (Percentage of Households with at least One Mobile (0.0956) (0.0997) (0.0489) (0.105) Computers -1.325*** 0.504*** 0.302** 0.366* (Percentage of Households with a Computer (0.182) (0.175) (0.152) (0.216) Constant 7.595 16.34 11.76* 52.10*** (11.96) (9.970) (7.036) (10.84) Observations 257 257 257 257 rho_2sls 0.650 0.231 0.994 0.798 k 20 20 20 20 R Squared Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

16 of 25 Preliminary Results: Voting Patterns Ordinary Least Squares Regression VARIABLES % Ennahdha % Former Regime % Other Parties % Local Parties

Total District Population 3.74e-05 -2.75e-05 1.33e-05 1.33e-05 (Count) (2.76e-05) (2.88e-05) (1.50e-05) (1.50e-05) District Population Density 4.04e-06 3.74e-06 -4.45e-06 -4.45e-06 (Count/Sq. Km) (8.66e-06) (9.06e-06) (4.71e-06) (4.71e-06) Urban Population -0.00680 0.0163 0.00684 0.00684 (Percentage) (0.0333) (0.0348) (0.0181) (0.0181) Average Household Size 7.042*** -3.657 1.562 1.562 (Number of Individuals) (2.265) (2.369) (1.231) (1.231) Net Domestic & International Migration 0.0394 0.00187 -0.00131 -0.00131 (Positive or Negative Percentage of Population) (0.0560) (0.0586) (0.0305) (0.0305) Relative Cohort Size 7.556 -3.681 -3.768 -3.768 (Count Age 15-29 / Count Age 30-59) (8.645) (9.043) (4.699) (4.699) Youth Sex Ratio -21.66*** -0.0740 -5.191 -5.191 (Count Men Age 15-29 / Count Women Age 15-29) (8.216) (8.594) (4.466) (4.466) Single M en -0.00177 0.350 0.00912 0.00912 (Percentage of Male Population - All Ages) (0.273) (0.286) (0.149) (0.149) Male Youth Unemployment Rate 0.00504 -0.273** 0.0762 0.0762 (Percentage of Men Ages 20-29 Unemployed) (0.104) (0.109) (0.0567) (0.0567) Male Relative Cohort Unemployment 0.522 -0.493 1.634 1.634 (Count of Unemp. Men 20-29/Count Unemp. Men 30-59) (1.957) (2.047) (1.064) (1.064) Male Youth University Education Rate 0.0522 -0.0269 0.0969 0.0969 (Percentage of Men Age 19-24 with University Degree) (0.139) (0.145) (0.0753) (0.0753) Unemployed Male University Graduates -0.194 -0.415 0.392*** 0.392*** (Percentage of Male University Grads Unemployed - All Ages) (0.276) (0.289) (0.150) (0.150) Rudimentary Housing 0.870* -0.196 0.209 0.209 (Percentage of Rudimentary Housing out of Total Units) (0.469) (0.491) (0.255) (0.255) Factor Score for Lack of Government Household Services -0.246*** -0.0186 0.102*** 0.102*** (% of households not connected to electricity, gas, water, and sewage services) (0.0682) (0.0714) (0.0371) (0.0371) Telephones 0.459*** -0.445*** 0.0256 0.0256 (Percentage of Households with a Land Line Telephone) (0.0722) (0.0755) (0.0393) (0.0393) Mobile Phones 0.0430 0.144 -0.134** -0.134** (Percentage of Households with at least One Mobile (0.102) (0.107) (0.0556) (0.0556) Computers -1.154*** 0.347 0.192 0.192 (Percentage of Households with a Computer (0.244) (0.256) (0.133) (0.133) Constant 5.650 36.45*** 5.496 5.496 (11.38) (11.90) (6.184) (6.184) Observations 257 257 257 257 rho_2sls k R Squared 0.469 0.271 0.283 0.283 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

17 of 25 Map Appendices: Summary of Geographic Distribution of Outcome and Explanatory Variables

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