LIV REUNIÓN ANUAL| NOVIEMBRE DE 2019
Que se Vayan Todos: Null and Blank Votes in Argentine Provinces (1983-2007)
Marto, Paula Guillermina Nallar, Leandro Justo
ISSN 1852-0022 / ISBN 978-987-28590-7-7 “Que se vayan todos”: Null and Blank Votes in Argentine Provinces (1983-2007)
Guillermina Marto ⇤ Leandro Nallar †‡
May, 2019
Abstract
After the restoration of democracy in Argentina, invalid votes equivalent to the sum of null and blank votes, far from representing insignificant issues, were able to powerfully twist the result of the winner in several governor elections. We estimate a Panel Corrected Standard Errors for 22 Argentine provinces between 1983-2007 for null and blank votes. My main motivation is to find the di↵erent incidences of three sets of variables: economic, social and political for each kind of invalid votes. The unemployment rate, crime rate, and reelection are the key variables in the model, but measures for education, electoral competition, and economic framework are included. Most of the variables present opposite e↵ects on null and blank votes. While the relation of unemployment and crime rate with null votes follows a withdrawal e↵ect, blank votes are related to these variables consistent with a mobilization e↵ect. However, when the crime rate has interacted with reelection the patterns of crime seem to be reversed, but unemployment maintains the sign.
Keywords: Null Votes, Blank Votes, Elections, Argentine Provinces.
JEL Codes: D72, P16.
⇤Instituto de Investigaciones Econ´omicas (INVECO), Universidad Nacional de Tucum´an (UNT).
†INVECO, UNT and Centro de Estudios Macroecon´omicos de Argentina (UCEMA).
‡We are grateful for seminar participants’ comments at Universidad Nacional de Tucum´an, and for the helpful suggestions from PhD Osvaldo Meloni. 1 Background and Motivation
In 1983, Argentina re-entered in democratic life after seven years of a dictatorship government. Since this triumph of the democracy, null and blank votes far from being presented as isolated cases occupied preponderant positions in legislative and gubernatorial elections. In fact, the phrase “Que se vayan todos” (All of them to go) was a slogan that arose spontaneously during the popular protests, pickets, and casseroles that characterized the 2001 crisis in Argentina. Surveys and political media concluded that in the legislative elections of October 14 of that year, the “voto bronca” (angry vote), i.e. blank votes or intentionally annulled, represented the 25% of the total, mostly in the City of Buenos Aires and in the Province of Santa F´e. In fact, the motto expressed the representativeness crunch and the disenchantment of the population with respect to its leaders, calling for the massive resignation of the rulers, presenting these types of votes as a protest voting measure.
For the case of gubernatorial elections, in 2003’s elections in Province of Buenos Aires, blank vote ranked second with a percentage of 14.46%, below the winner Felipe Sol´a(43.32%). Province of San Luis in the same year registered a total blank vote of 19.27% of the total, below the winner Alberto Rodriguez Sa´a(80.08%). In 2007, also in this province, blank vote ranked second again 20.86%, being the highest percentage of blank votes of the governor elections. This paper aims to contribute to the evidence modeling the null and blank votes for provincial elections for governor between 1983 and 2007.
Moreover, the importance that null and blank votes had during the electoral results of the years of my sample was significant. The margin of victory for gubernatorial elections reveals that invalid votes could have reversed the winner. Quantitatively, 15 of 23 provinces (excluding Ciudad Aut´onoma de Buenos Aires) had at least one election in which the margin of victory was less than the percentage of invalid votes. Besides, in the 22% of the Province elections between 1983-2007, invalid votes could have changed the elected governor.
In terms of previous literature on Argentine elections, ballots for presidents were analyzed during the last years by Meloni (1995), Abuelafia and Meloni (2000), Vitullo (2002), Freire (2015), but for invalid votes literature focused more on descriptions rather than empirical works, like Zicari (2014) for the case of 2001. Although there are empirical articles that center their attention on the elections for governor (Meloni (2001), Nallar and Marto (2018)), invalid votes in the governor elections for Argentina, lack theoretical and empirical analysis in the literature.
Despite the null and blank votes are di↵erentiated in their measurement for the case of Argentina, most of the literature considers that the same determinants are those that encourage their emission (Uggla (2008), Kouba and Lysek (2016), Power and Roberts (1995)). So, one objective of the document is to identify the di↵erences between the stimulus that push each one of them. At a starting point,
1 the article 101 of the Nation’s Electoral Code (Rep´ublica Argentina) define that null votes are those issued:
by uno cial ballot, or with a paper of any color with inscriptions or images of any nature, • by means of an o cial ballot that contains inscriptions and/or legends of any kind, • by means of two or more ballots of di↵erent parties for the same category of candidates, • by means of an o cial ballot that, due to partial destruction, defect or deletion, does not contain • at least the name of the party and the category of candidates to choose, without breaking or crossing,
when the envelope, along with the ballot, has included any other object, • and blank votes are those when the envelope is empty or with a paper of any color without inscriptions or any image.
Next, the paper suggests that is possible to capture systematic di↵erences in the generation of each type of invalid votes through three sets of variables: economic, social and political. Especially, attention is put on the unemployment rate and crime rate. Two opposite e↵ects in the relation with protest voting are introduced in this case. The withdrawal e↵ect, where voters decide to cast an invalid ballot as a way to protest with high levels of unemployment and crime predominates in the case of null votes. However, the mobilization e↵ect, that pushes citizens to cast valid ballots as a political protest measure prevails in the case of blank votes for these two variables. When the crime rate interacts with a dummy variable for governor reelection, the e↵ects seem to be reversed. Furthermore, most of the variables included in the estimation, such as measures of education and political environment show opposite incidence for each kind of invalid vote. Thus, my empirical results reflect that null and blank votes are not equivalent at the polls as Martins (2017), Galatas (2008) and Driscoll and Nelson (2014) pointed out.
Finally, the article is structured as follows. Section 2 displays the main findings in the literature above invalid votes. Next, section 3 presents the data and its descriptive statistics. Then, section 4 presents the methodology used and the results of the models. Section 5 discusses the outcomes of the models and lastly, section 6 exhibits the conclusions of the paper. An appendix includes a robustness check, some tests necessaries to check the methodology adopted and tables relevant to the construction of the data.
2 Literature Review
Many recent works have concentrated their e↵orts on evaluating the determinants of invalid votes for di↵erent countries. Taking into account that these votes are the sum of null and blank votes and
2 that the data of the countries are relieved in di↵erent ways, it is not always possible to obtain them separately. On the first section, those articles for which the dependent variable consists of the sum of null votes and blank votes will be examined. Most of them, use data from 1980, the decade in which the democracies in Latin America are re-established.
In order to evaluate invalid votes in Brazil, Power and Roberts (1995) run an OLS pooled time- series regression for 12 Legislative elections between 1945-1990. An interesting finding of this paper is that a model that incorporates socioeconomic, political and institutional variables is more powerful than one specific for each set of variables. Results from their estimation suggest that literacy rates and urbanization are important determinants of invalid votes. During the years, several studies made the analysis for di↵erent groups of countries, as follows.
Power and Garand (2007) use a cross-sectional time series data set that comprehends 80 legisla- tive elections held in 18 Latin American democracies between 1980 and 2000. Their results focus the attention on three subsets of variables: institutional variables (compulsory voting, electoral dis- proportionality raise invalid votes), socioeconomic variables (urbanization and income inequality are correlated with levels of invalid voting), and protest-democracy variables. The model is a feasible generalized least squares (FGLS) which assumes a heteroskedastic error structure across panels.
With respect to the structure of political competition, Uggla (2008) found that it provides an important explanation for the number of invalid ballots. Their estimates follow a panel corrected standards errors model corrected for AR(1) with Prais-Winsten regressions, using political and socio- economic variables for Western Europe, Australia, New Zealand, and the Americas. He also obtained that restriction of political rights is highly associated with invalid votes, as compulsory voting does as the same. An important point highlighted by the author, is that null and blank votes are treated as ’twins’, a premise that intends to be refused in my data.
Kouba and Lysek (2016) found evidence for invalid votes in new democracies of Latin America and post-communist Europe between 1980 and 2013. They estimated several panel corrected standard errors models (PCSEs) using political and control variables, such as concurrent elections, compulsory voting, and presidential reelection which foster turnout and also increase invalid voting. Interactions terms between concurrence (coded as 1 indicating concurrence of two or more elections) and literacy rate, and compulsory voting with e↵ective number of parties were introduced in the model and they resulted statistically significant. Regarding protest voting, their findings suggest that invalid votes rather than a protest measure are the product of a cost-benefit analysis of the likelihood of casting a pivotal vote.
On the second section, articles in which blank and null votes are estimated separated were pre- sented, such as the case of Portugal in legislative elections in 2011 by Martins (2017). Using a fractional regression model estimator he found that less competitiveness, more political fragmentation increase
3 blank and null percentages, but e↵ects on each one are not the same. In the case of blank votes, support for his hypothesis is found on each group of variables that he considered (electoral, economic and social), and in null votes results the case was di↵erent. For example, zones where the percentage of university graduates was high, blank ballots were more and null ballots were less. The study units of this work, the freguesias, are very similar to mine and also the di↵erentiation of both types of invalid votes.
Years before, Driscoll and Nelson (2014) estimated models for individual and aggregated data for the 2011 Bolivian elections using a multinomial logit model and a linear regression respectively. In this election, 60% of votes cast were blank or spoiled, similar to the case of Ecuador’s 2006 in which invalid ballots exceed 40% of the turnout. Authors propose that null voting was more common among politically sophisticated individuals as deliberated votes, while blank votes reflect di culties to process electoral information.
The articles described so far used as null and blank votes dependent variable, either in aggregate or disaggregated form. Nevertheless, Galatas (2008) put in the analysis only blank votes for 1999 and 2003 provincials elections in Ontario, Canada. By using political, demographic, political and variables related to nationality in an OLS model results as follows. Party competition provides an explanation for casting blank ballots, as demographic variables do too. The paper links blank ballots to political protest or social and demographic characteristics of people who cast a ballot, di↵erent from the presented by Driscoll and Nelson (2014).
In sum, both the papers of the first section and the second reveal that the invalid votes, far from being presented as isolated events, find multidimensional foundations that determine them. In the case of the second section, it is possible to distinguish between the results of the literature, one of the objectives of the work related to the di↵erentiation that exists between the factors and the way in which the null and blank votes are determined separately.
3Data
We use data from 7 consecutive government elections in 22 provinces of Argentina excluding Ciudad Aut´onoma de Buenos Aires (CABA). 1 Observations from Neuqu´en for 1983 and Province of Rio Negro for the entire period are not incorporated because of a lack of educational data. Elections of Tierra del Fuego for 1983 and 1987 are not included in my sample because this place was a National Territory at this time.2
1Argentina is compounded by 23 provinces and a federal district, the Autonomous City of Buenos Aires (CABA). 2A federal or national territory is an area under the direct and usually exclusive jurisdiction of a federation’s central or national government. It represents an area that is part of a federation but not part of any federated state (province). Tierra del Fuego elected its first governor in 1991 and CABA in 1996.
4 Argentina returned to democracy on October 30th, 1983, after 7 years of a dictator government with Ricardo Alfonsin elected as President. The same date, gubernatorial elections were held in each province, with the exception of CABA and Tierra del Fuego, which were National Territories at this time. Since this year, every four years citizens cast a governor ballot in most of the 24 jurisdictions, except provinces where their governments were interrupted by Federal Interventions. 3 Several provinces like Tucum´anand Catamarca in 2001, Corrientes and Santiago del Estero in 1993, Corrientes in 1997 and Santiago del Estero in 2005 registered Federal Interventions during the years considered in the estimation. In summary, 151 cases of elections are included in the sample.
3.1 Dependent Variable
Taking into account that Argentine data for government elections are separated into blank and null votes, we consider each type of casting a ballot as a di↵erent dependent variable in order to test contrasting political e↵ects.
On one hand, the first dependent variable, null votes, measures invalid votes as a percentage of the total number of voter cast in the first/only rounds of an election for governor. Histogram and kernel density plot for the distribution of the null votes are presented to characterize the dependent variable. As the plot reflects, the distribution is positively skewed showing an average percentage of 0,8% of null votes and a standard deviation of 0.59%, with a minimum equal to 0% and a maximum equal to 4.08% corresponding to Tierra del Fuego in 2007. The relative variance is equal to 0.74.
Figure 1: Distribution of null votes governor elections in 22 provinces for the period 1983-2007. The solid line represents the kernel density plot for the distribution.
3Article 6 of the Argentine Constitution states: ’The federal government intervenes in the territory of the provinces to guarantee the republican form of government or to repel foreign invasions, and upon request of its authorities created to sustain or re-establish them, if they have been deposed by sedition or by the invasion of another province’. The application of a Federal Intervention means that the President with the assent of the National Congress selects aFederal Interventor to govern the province until the next election.
5 As a result of taking averages for each province during the entire period, the map of the Figure 2 suggests that provinces with the lowest percentage of null votes are San Luis (0.24%) and Chaco (0.38%). On the contrary, at the top end, Neuqu´en (1.37%) and Tierra del Fuego (1.66%) represent the provinces where null votes were higher during 1983-2007.
Figure 2: Average null votes by province for governor elections for the period 1983-2007.
On the other hand, the second variable, blank votes, quantifies null votes as a percentage of the total number of voter cast in the first/only rounds of an election for governor. It is possible to recognize a positively skewed distribution in the histogram and kernel density plot, which is similar to the distribution of the null votes. The mean for the entire sample is 4.07% and the standard deviation is 3.55%, displaying a minimum of 0.10% and a maximum of 20.86 % resulting from San Luis in 2007. The second highest percentage was also in San Luis in 2003, recording a 19.27% of blank votes for this election. The relative variance is equal to 0.87.
6 Figure 3: Distribution of blank votes governor elections in 22 provinces for the period 1983-2007. The solid line represents the kernel density plot for the distribution.
As one can see in the map of the Figure 4, at the low end, provinces such as Corrientes (1.17%) and Santiago del Estero (1.19%) have average blank ballot rates of less than 1.5%. However, on the extreme provinces like Chubut (6.59%), Buenos Aires (7.1%) and San Luis (7.65%) showed the highest average rates.
Figure 4: Average blank votes by province for governor elections for the period 1983-2007
7 Regarding to the di↵erence of the percentage of null and blank votes in each province, San Luis and Buenos Aires show the highest gaps between average null votes and average blank votes. Tucum´an and Santiago del Estero show the lowest.
3.2 Independent Variables
My expectations parallel those of Power and Garand (2007), who suspect that null and blank voting is associated with a number of diverse variables that rather than juxtapose them as mutually exclusive explanations, they merge simultaneously to trace voter behavior. The core of this article is to plot the di↵erent e↵ects of the variables for each one type of invalid votes. In accordance with this hypothesis, three subsets of independent variables are included in the estimation: economic, social, and political.
Table 1: Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max ISAP 151 90.84 28.34 25.94 219.75 ISAP Growth Rate 151 5.53 8.55 -16.51 46.26 Unemployment 151 9.07 4.58 1.30 20.80 College 151 5.41 2.18 1.90 11.80 Illiteracy 151 1.31 0.67 0.07 4.10 Crime rate 151 16.48 8.73 2.86 48.46 ENPC (Golosov) 151 2.19 0.48 1.12 3.54 Reelection 151 0.34 0.47 0.00 1.00 Same Party 151 0.54 0.50 0.00 1.00 Date 151 0.33 0.47 0.00 1.00 Closeness 151 17.00 15.93 0.23 84.54
3.2.1 Economic Variables
The first economic variable included is ISAP (Provincial Activity Composite Index)4 whose variations are obtained as a weighted mean of the variations of several variables that strongly correlate to the economic performance 5. Given the fact that ISAP represents a very good proxy of GSP, the hypothesis could be extended to this variable.
A higher GSP per capita (and in fact ISAP) should be correlated with lower invalid ballots (Power and Garand, 2007). As long GSP measures the provincial added value on a territorial basis, ISAP is
4See Munoz and Trombetta (2015). 5We also considered the possibility of include GSP instead of ISAP, but the last one allows to a homogeneous comparison of the economic behavior of the Argentine Provinces. Due to ISAP is available only after 1997, the variable used is constructed with ISAP for 1997-2007, and extended to 1983 with GSP variations.
8 a proxy of the level of expenditure or provincial income that captures many dimensions of economic activity through several indicators (e.g., supermarket sales, jobs declared, average wages, car patents). This premise perceives null and blank votes as a channel to protest against the political and economic system, so a good economic performance would raise people satisfaction level discouraging the intention of cast an invalid ballot.
The second variable aims to evaluate the influence of the dynamic of the economic performance on the electoral behavior, especially in invalid votes. Therefore we employ the annual growth rate of ISAP, which is highly correlated with the annual growth rate of GSP (more than 95% of correlation in 11 provinces).
Third, the unemployment rate6 is included in the estimation as one of the key variables. Martins (2017) in his article delimit the di↵erence between the e↵ects on blank and null votes and propose that unemployment is relevant but only for the explanation of blank variations and in more urban areas, where more sophisticated voters reside. Kouba and Lysek (2016) obtained that economic hardships channel protest motivations into casting an invalid vote. Conversely, Uggla (2008) who denotes social marginalization through unemployment found no e↵ects on invalid ballots.
In terms to establish the relationship between unemployment and turnout, two opposite mecha- nisms prevail in the literature. They can be extended to the connection between unemployment and invalid votes. On one hand, the withdrawal e↵ect (Rosenstone 1982) presents higher rates of invalid votes when unemployment is high, which reveals that voters give up of the economic hardship and don’t participate in elections. For instance, this approach goes in the line of a “depressing disenchant- ment” mechanism or an exit model (Kouba and Lysek, 2016), where cast an invalid vote reflects an exit of the political and economic system.
On the other hand, in the mobilization e↵ect, invalid votes are lower when the economy is under- going a bad situation (Scholzman and Verba, 1979), in other words, cast a ballot is a way of protesting against the incumbent government of their economic hardship. Indeed, the incentives to cast a valid vote raises in this case. Galatas (2008) found that the unemployment rate is consistently strongly and inversely related to the casting of blank ballots and Martins (2017) expresses that the mobilization e↵ect predominates for null and blank votes, but not statistically significant in the first one.
3.2.2 Social Variables
In order to evaluate the bottom and the top end of the distribution of the educational characteristics of the electorate, the percentage of people over 15 years without instruction as a measure of illit- eracy, and the percentage of people with university complete degree were collected. Both were
6INDEC.
9 constructed using Permanent Household Survey (EPH) from INDEC.7
Kouba and Lysek (2016) suggest that low levels of literacy are related with a low level of political information, make that voters may spoil their ballot without any intention or just leave it blank. Furthermore, Power and Garand (2007) infer that greater literacy should reduce the likelihood of voters to cast an invalid ballot and Uggla (2008) explicit that literacy is the ability to make an informed choice, so high levels of illiteracy may conduce larger numbers of spoiled ballots. All of these articles found a negative relationship between education and invalid ballots in their results. Nevertheless, Power and Roberts (1995) and Muszynski (1985) in their studies of Brazilian elections found that blank and spoiled ballots are associated with lower levels of education and urbanization. Disaggregating by null and blank votes, the message of Driscoll and Nelson (2014) is that many blank and null votes are cast intentionally and motivated by political concerns. But null voting persists between politically sophisticated individuals. Martins (2017) supports the idea that people with a university degree prefer to vote blank rather than null.
In my data, college covaries positively with the percentage of null and blank votes and illiteracy co- varies negatively, being the correlation coe cient stronger in the case of blank votes and heterogeneous between provinces and time.
In Argentina, as Meloni (2010) pointed out, the crime rate has grown 74% during the 1980s after the return of democracy. In the early 1990s a big descend took place, but in 2002 crime rate have reached 3573 o↵enses per 100,000 inhabitants, showing an upward trend. Emblematic cases, such as Maria Soledad Morales in the province of Catamarca (1990), Walter Bulacio in the City of Buenos Aires (1991), Omar Carrasco in Neuqu´en (1994) and Jose Luis Cabezas in Buenos Aires (1997), influenced the decision of the voter at the polls. This social obstacle as is the crime involves infringements to political rights and civil liberties may increase blank and spoiled ballots as a sign of distrust in the regime. Regarding the importance of this indicator in Argentina, the crime rate is presented as the other key variable in this paper. It also can follow the mobilization e↵ect, where the intention to cast a valid ballot remains when the crime rate is high, or the withdrawal e↵ect, that increases null and blank votes when citizen security is very a↵ected. Power and Garand (2007) use the number of acts of revolutionary violence during the election year and obtained a positive relation between invalid votes and this variable because those people directly a↵ected by a more “decadent environment” prefer to maintain a more active democratic participation. We define crime as the rate of growth of the number of o↵enses per 100 inhabitants in a given district i in the period t. Data on crime rate were obtained from Direcci´onNacional de Pol´ıtica Criminal. 7Data for some elections before 2003 were not available, so we used proxies for those observations. A table in the Appendix presents the corresponding years.
10 3.2.3 Political Variables
Same is defined as 1 when the provincial and national government are from the same party in the voting years and 0 otherwise. First, a coincidence of national and provincial parties in governmental power may induce that the voter perceives low their possibilities to choose. Second, citizens can extend the characteristics of the ruling party at the country level to those of the ruling party in the province or vice versa, in order to speculate the features of the next mandate. Given the fact that Argentine provinces receive transfers from the National Government, if the sign of these parties is the same, a better relationship between them can be associated with improving the provincial situation and discouraging invalid votes.
Date is a dummy variable that takes 1 when governor elections are held on the same day as the presidential elections and 0 otherwise. The national constitution determines that gubernatorial elections should be held every four years. With respect to presidential elections, from 1983 to 1989, they were held every six years until the constitutional amendment of 1994 diminishes the presidential period to four years. In this context, the probability of concurrence of elections for both levels of government increased. Nonetheless, in most provinces, governors are allowed by law to shift provincial elections various months in the same year.
In order to test if the fact that the incumbent governor postulate for reelection changes the patterns of the electoral decision, We also include a dummy variable that takes 1 when the outgoing governor is running for the reelection and 0 when new candidates present to the position. An issue is that Argentine provinces present greater heterogeneities in their social variables. In connection with this point, We will test joint e↵ects including an interaction term of crime rate and reelection, so as to asses if the attempt to reelection in provinces with higher levels of crime cause an upturn in null and blank ballots as a form of protest. This variable looks important in the determination of null and blank votes given that 15 of 22 provinces and 9 of the 22 provinces had during the entire period at least 2 and 3 presentations for reelection respectively. Entre R´ıos, Mendoza, Santa F´eand Corrientes were the only that in every election new candidates compete.
Regarding the structure of political competition, Golosov (2010) designed a measure that provides the number of hypothetical equal-size parties, e↵ective number of parties. It follows that the con- figuration assumed by the Argentine party system at its origin and maintained during the next two democratic periods was that of a predominantly polarized bipartisan system (Abal Medina and Suarez Cao, 2010). As party systems get fragmented into smaller parties, voters feel a weaker connection between their votes (Franklin, 2004) and less power of decision. On the opposite, in more competitive elections, the probability of casting a blank ballot will be lower as the chances of casting the deter- mining vote are greater (Galatas, 2008). Here my expectations are related to a positive association between invalid votes and this measure.
11 4 Model
The dataset used in this estimation is a cross-sectional time-series. Panels are unbalanced8 with a total of 151 observations of 22 groups in a range of 5 to 7 per group and an average of 6.86. Given that ISAP is highly correlated with col (0.64) a variance inflation factor test (VIF) for higher-order multicollinearity was taken and it showed a mean of only 1.67 which is far below threshold (10.00) for multicollinearity problems. 9 Beck and Katz (1995) propose the panel corrected standard errors model (PCSE) in which the disturbances are assumed to be heteroskedastic. In my model, the disturbances are also assumed to be autocorrelated within panel.10 The autocorrelation parameter is di↵erent for each panel and computed from the residuals of an OLS regression across all panels. In order to check robustness, a model using feasible generalized least squares (FGLS) is presented in the Appendix.
5 Results
Model 1 and 2 present estimations for null and blank votes respectively. Comparing the coe cients for unemployment and unemployment square as Martins (2017) and Veiga (2012) perform for the case of turnout in Portugal, results are the following. In the case of null votes withdrawal e↵ect (coe cient = 0.09) predominates over mobilization e↵ect (coe cient = -0.002) for unemployment. For the other key variable of this paper, the crime rate also follows the withdrawal e↵ect rising null votes on average to 0.03 percentage points when the crime rate increases in one percentage point. These results indicate lower confidence in the regime of these electoral environments. In summary, the null vote rate increases where unemployment and crime are rising.
In terms of the other economic variables, both ISAP level and ISAP growth rate that attempt to comprise economic hardship, are negatively correlated with cast null ballots, but those coe cient are not statistically significant. Following with social variables, a one percentage point more of people with a university degree is expected to decrease on average 0.06 percentage point of null votes. The estimation shows that illiteracy seems to present a higher negative e↵ect on null votes than college, but the coe cient is not statistically significant.
Finally, political variables such as the e↵ective number of parties and the same party are positive and statistically significant in the estimation for null votes. An increase of one point of an e↵ective party, rise in average 0.14 percentage points of null votes. Moreover, if the party of the president is the same as the governor at the time of the election, null votes increase in 0.18 percentage points, while if the election of the governor is held on the same day of president’s election, null votes diminish
81983’s elections for Neuqu´en and Tierra del Fuego are not in the sample. 9College presented the highest variance inflation factor value (3.31). No other variable exceeded the value of 2. 10Matsusaka and Palda (1999) explicit that past voting behavior exhibit some degree of persistence on current electoral choices.
12 Table 2: Panel Corrected Standard Errors
VARIABLES Model 1 Model 2 Model 3 Model 4 Null Blank Null Blank
ISAP -0.00368 -0.00187 -0.00248 -0.00563 (0.00273) (0.0136) (0.00268) (0.0124) ISAP Growth Rate -0.00543 0.00844 -0.00359 0.0101 (0.00431) (0.0240) (0.00416) (0.0234) Unemployment 0.0877*** -0.520*** 0.0968*** -0.617*** (0.0315) (0.157) (0.0303) (0.163) Unemployment2 -0.00224* 0.0209*** -0.00266** 0.0253*** (0.00130) (0.00735) (0.00125) (0.00744) College -0.0628** 0.435*** -0.0728*** 0.483*** (0.0267) (0.153) (0.0248) (0.159) Illiteracy -0.0772 0.747* -0.0911* 0.702* (0.0559) (0.426) (0.0550) (0.406) Crime rate 0.0316*** -0.0531* 0.0368*** -0.106*** (0.00515) (0.0320) (0.00513) (0.0338) ENPC (Golosov) 0.137* -0.165 0.119* 0.0749 (0.0727) (0.366) (0.0698) (0.391) Reelection 0.0629 -0.453 0.396** -2.108*** (0.0802) (0.374) (0.157) (0.695) Same Party 0.184*** -1.256*** 0.188*** -1.303*** (0.0694) (0.438) (0.0698) (0.422) Date -0.271*** 2.559*** -0.282*** 2.801*** (0.0831) (0.451) (0.0819) (0.457) Closeness 0.00115 0.0466*** 0.00157 0.0507*** (0.00227) (0.0138) (0.00218) (0.0138) Crime rate x Reelection -0.0192*** 0.104*** (0.00706) (0.0344) Constant 0.612* 0.497 0.562* 0.710 (0.347) (1.525) (0.336) (1.506) R-squared 0.652 0.579 0.671 0.614 Year Fixed E↵ect YES YES YES YES Panel-corrected standard errors in parentheses. *** p < 0.01, ** p<0.05, * p<0.1
13 in 0.27 percentage points. Indeed, the voter error model does not find support in the data. Reelection and closeness are positive which is consistent with the literature review, but they are not statistically significant.
Taking into account the results for blank votes, most of the coe cients are opposite to the e↵ects of null votes, displaying that null and blank votes are empirically di↵erent (Driscoll and Nelson, 2014). Regarding the unemployment rate, for blank votes, the mobilization e↵ect (coe cient= -0.52) prevails over the other one (coe cient= 0.02) displaying that people decide to cast a valid ballot when economic conditions are worsening. As Galatas (2008) pointed out in areas with high levels of unemployment, individuals tend to cast votes for some party of the electoral system rather than cast a blank ballot. This can be extended for the crime rate e↵ect on blank votes, where a one percentage point more of crime rate, diminish on average 0.05 percentage point of blank ballots.
In addition, for economic variables, the coe cient of ISAP presents the same sign that null votes, but it is not statistically significant. Nevertheless, the ISAP growth rate is positively associated with blank votes, but its coe cient is also not statistically significant, which implies that is no e↵ect of the business cycle on electoral behavior. With respect to social variables, in contrast to the e↵ect in null votes college is positively associated and statistically significant as Martins (2017) found. Comparing the coe cients of college of both invalid votes, value for blank votes is greater of the presented in the case of null votes, throwing a bigger e↵ect.11 Lack of educational abilities such as read and write, reflected in illiteracy rate is positively correlated with blank votes, performing a coe cient of 0.75 statistically significant. Moreover is observable, that people without instruction produce a larger incidence e↵ect in blank votes than highly educated people.
Lastly, assessing for the political variables, values of same party and date coe cient evidence an inverse reaction. The big positive e↵ect of the dummy variable date, following Kouba and Lysek (2016) might result that people who voted for a candidate in the presidential elections are also those which feel the civic duty to turnout. Casting a blank ballot at the concurrent governor election may reflect that motivation even though the individual has no preference for any governor candidate. The variable Same party is also strongly correlated with the variations in cast blank ballots, with a negative coe cient, contrary to my expectations. In this model is also possible to visualize measures of electoral competition as a determinant of invalid votes, through the margin of victory that We defined as closeness. Less competitive elections, i.e. when the di↵erence is fewer, blank votes increases in 0.05 percentage points, which is consistent and similar to Kouba and Lysek (2016) findings.
In order to test how people react in their vote if the crime rate increase and the current governor is a candidate again, an interaction term between these two variables is included. Remembering that for null votes the sign of the coe cient of reelection is positive and for null votes is negative
11The coe cient for null votes of college is -0.06 and for blank votes is 0.43.
14 (not statistically significant for neither case of invalid voting), Model 3 and 4 display the estimation including this interaction which seems to give a turn between mobilization and withdrawal e↵ects. For the case of null votes (Model 3), where withdrawal e↵ect prevail for the crime rate in model 1, the interaction term show that when the crime rate increases in 1 percentage point and the outgoing governor is running for the reelection, people tend less to invalid their vote in 0.02 percentage points controlling for the rest variables. They prefer to resort to a more active democratic participation in concordance with Mobilization E↵ect. Model 4 presents the e↵ects for blank votes using the same variables but including the interaction term. For this estimation, the Mobilization e↵ect that prevailed in model 2 for the variable crime rate seems to turn to withdrawal e↵ect when the actual governor is running for the reelection. The coe cient of the interaction term is 0.10, which is positive and greater than the obtained for null votes.In the Appendix, total marginal e↵ects for both invalid votes conclude that the interaction term is not able to twist the sign of the entire e↵ect, but represents a good approximation.
6 Conclusions
This paper focuses their e↵orts on modeling null and blank votes of 151 cases of governor elections in Argentine provinces between 1983-2007. A panel corrected standard errors is ran using three sets of variables: economic, social and political in order to find the determinants of these types of invalid votes. Argentine history presents blank and annulled ballots as a way to protest against political and economic contexts, such as legislative elections in 2001. Moreover, several governor elections included in my sample showed a high percentage of invalid votes that greatly a↵ect electoral results. Other stylized facts ranked null and blank votes in seconds and third places for these elections raising the importance of analyzing them.
Given the fact that during 1983 and 2007 elections display provinces with high blank votes but a low percentage of null votes and vice versa, as San Luis and Buenos Aires, the question to answer is the following: Are null and blank votes empirically di↵erent? My results present that while null votes obey a withdrawal e↵ect in the relation with the unemployment rate and crime rate, which means a positive sign, blank votes follow a mobilization e↵ect, discouraging blank ballot when unemployment and crime are high. Furthermore, variables such as college complete which measures education, same party, the concurrence of presidential and governor elections, and the probability of reelection got opposite signs in their coe cients for each type of invalid votes. The models are successful in their prediction, showing high r-squared, which improves when the interaction between crime and reelection is included. This term that intends to catch how people act when the crime rate is been modified in scenarios which the outgoing governor is running for reelection, turns the e↵ect of the crime rate. Despite the coe cient of the interaction fails to twist the sign of the total marginal e↵ect of crime,
15 we expect to find more determinants of these types of invalid ballots, considering that this is the first paper that investigates null and blank votes for the Argentine Provinces.
16 7 Bibliography
Abal Medina, J. Suarez Cao, J. (2003). Mas alla del bipartidismo: El sistema argentino de • partidos. Iberoamericana, III, 9, 65-87
Abuelafia, E. Meloni, O. (2000). Determinantes Economicos de los Resultados Electorales en la • Argentina. Evidencia a partir de Datos en Panel. Asociaci´onArgentina de Econom´ıa Pol´ıtica, C´ordoba.
Blais, A. and A. Dobrzynska. (1998). Turnout in Electoral Democracies. European Journal of • Political Research, 33, 239-61.
Blais, A. and K. Carty. (1990). Does Proportional Representation Foster Voter Turnout?. • European Journal of Political Research, 18, 167-81.
Beck, N., Katz, J. N. (1995). What to do (and not to do) with time-series cross section data. • American Political Science Review, 89, 634-647.
Codigo Electoral Nacional. Decreto N 2135. • http://servicios.infoleg.gob.ar/infolegInternet/anexos/15000-19999/19442/texact.htm
El voto bronca lleg´oa casi 4 milliones (The angry vote reached almost 4 million. 2001, October • 16). Clarin (Buenos Aires), p. 1. in Uggla, F. (2008). Incompetence, Alienation, or Calculation?: Explaining Levels of Invalid Ballots and Extra-Parliamentary Votes. Comparative Political Studies, 41(8), 1141ˆa1164.https://doi.org/10.1177/0010414007301702
La elecci´on: El descontento con la clase pol(The choice: Discontent with the political class. • 2001, October 15). Clarin (Buenos Aires).
https://www.clarin.com/politica/voto bronca protesta fuerte triunfo capital santa fe Downs, A. 1957. An Economic Theory of Democracy: Harper Collins Publishers. • Driscoll, A., Nelson, M. J. (2014). Ignorance or Opposition? Blank and Spoiled Votes in • Low-Information, Highly Politicized Environments.
Political Research Quarterly, 67(3), 547ˆa561.https://doi.org/10.1177/1065912914524634 • Franklin, M.N., (2004). Voter Turnout and the Dynamics of Electoral Competition in Estab- • lished Democracies Since 1945. Cambridge University Press, Cambridge.
Freire, S.(2015). Do private campaign contributions a↵ect electoral results? An examination of • Argentine national elections.Asociaci´onArgentina de Econom´ıa Pol´ıtica, Salta. ISBN 978-987- 28590-3-9.
17 Golosov, G. V. (2010). The E↵ective Number of Parties: A New Approach. Party Politics, • 16(2), 171ˆa192.
Matsusaka, J. and F. Palda. (1999). ”Voter Turnout: How much can we explain?” Public • Choice, 98, 431-46.
Meloni, O. (1996). Empleo, desempleo y elecciones. El caso de la elecci´onpresidencial argentina • 1995. Asociaci´onArgentina de Econom´ıa Pol´ıtica, Salta.
Meloni, O. (2001) Gobernadores y Elecciones: A¿Esˆ ˆanegocioˆaser austero?. Evidencia a partir • de Datos en Panel. Buenos Aires.
Meloni, O. (2011). Is there an electoral-motivated crime rate cycle? Evidence from Argentina. • Asociaci´onArgentina de Econom´ıa Pol´ıtica, Mar del Plata.
Martins, R. (2017). The Blank and the Null: An examination of non-conventional voting choices. • CeBER Working Papers. NAˆ 6.
Nallar L., Marto, P. (2018). Which factors discourage people to vote? Turnout in Argentine • Provinces. Asociaci´onArgentina de Econom´ıa Pol´ıtica, La Plata.
Que se vayan todos. (All of them to leave]). n.d. https://es.wikipedia.org/wiki/Quesevayantodos. • Rosenstone, Steven J. (1982). Economic Adversity and Voter Turnout. American Journal of • Political Science 26 (1): 25-46.
Schlozman, K. Verba, S (1979) Injury to Insult: Unemployment, Class, and Political Response. • Cambridge, Massachusetts: Harvard University Press.
Vitullo, G. (2002). Participacion electoral, comportamiento politico y desestructuracion social • en Argentina y Brasil. CLACSO.
Zicari, J. (2014). Las elecciones Legislativas del 2001. Entre el voto bronca y el final del gobierno • de la Alianza. Universidad de Buenos Aires-Conicet.
Zulfikarpasic, A. (2001). Le vote blanc: abstention civique ou expression politique? Revue fran • ¸caise de Sci. politique 51 (1), 247e268.
18 8 Appendix
Specification for educational data construction. Source: EPH-INDEC
Marginal E↵ects
Unemployment Rate:
2 yit = ↵ + uit + uit + ✓zit + "it
dy/du= + u¯
Null: .0877 -.0022* 8.965= .0677 •
Blank: -.5203+.0209*8.965= -.3329 •
19 Table 3: Feasible Generalized Least Squares
VARIABLES (1) (2) (3) (4) Null Blank Null Blank
ISAP -0.00117 -0.00567 -0.00104 -0.00796 (0.00187) (0.0128) (0.00192) (0.0122) ISAP Growth Rate -0.00249 0.0173 -0.00259 0.00797 (0.00351) (0.0207) (0.00357) (0.0211) Unemployment 0.0496** -0.507*** 0.0569** -0.559*** (0.0236) (0.160) (0.0246) (0.159) Unemployment2ˆ -0.00133 0.0179** -0.00171 0.0205*** (0.000992) (0.00718) (0.00105) (0.00717) College -0.00529 0.456*** -0.0135 0.551*** (0.0259) (0.164) (0.0272) (0.165) Illiteracy -0.0603 0.392 -0.0630 0.474 (0.0483) (0.385) (0.0501) (0.379) Crime rate 0.0265*** -0.00380 0.0301*** -0.0420 (0.00437) (0.0302) (0.00480) (0.0317) ENPC (Golosov) 0.224*** -0.0382 0.223*** 0.132 (0.0647) (0.389) (0.0668) (0.389) Reelection 0.0389 -0.460 0.243* -1.963*** (0.0621) (0.344) (0.138) (0.636) Same Party 0.209*** -1.068*** 0.246*** -1.256*** (0.0631) (0.385) (0.0642) (0.384) Date -0.291*** 2.730*** -0.298*** 2.850*** (0.0711) (0.418) (0.0725) (0.414) Closeness 0.00284 0.0321** 0.00251 0.0365*** (0.00229) (0.0136) (0.00228) (0.0135) Crime rate x Reelection -0.0131* 0.0939*** (0.00769) (0.0328) Constant 0.220 0.0594 0.200 -0.149 (0.276) (1.697) (0.273) (1.664) Observations 151 151 151 151 Number of ID 22 22 22 22 Year Fixed E↵ect YES YES YES YES Standard errors in parentheses. *** p < 0.01, ** p<0.05, * p<0.1 20 Null with interaction: .0968 -.0027*8.965= .07292 •
Blank with interaction: -.6166 + .02525*8.965= -.3902 •
Interaction term: Crime rate x Reelection y = ↵ + cr + ⇡R + ⇢cr R + ✓z + " it it it it ⇤ it it it Reelection: dy/dR= ⇡ + ⇢cr¯ ifR =1
Null: .396-.0192*16.4308= .0806 • Blank: -2.1081+.1043*16.4308 = -.3948 •
If R=0 marginal e↵ects equal to 0.
Crime Rate: dy/dcr= + ⇢R if R=1 dy/dcr= + ⇢
Null: .0367-.0192= .0176 • Blank: -.1059+ .1043 = -.0017 • if R=0 dy/dcr=
Null: .0367 • Blank: -.1059 •
21 Table 4: Test for collinearity
Variable VIF SQRT VIF Tolerance R- Squared ISAP 1.99 1.41 0.5034 0.4966 ISAP Growth Rate 1.23 1.11 0.8117 0.1883 Unemployment 1.35 1.16 0.7405 0.2595 College 1.67 1.29 0.5983 0.4017 Illiteracy 3.31 1.82 0.3021 0.6979 Crime rate 1.81 1.35 0.5522 0.4478 ENPC (Golosov) 1.65 1.28 0.6074 0.3926 Reelection 1.38 1.18 0.7237 0.2763 Same Party 1.33 1.15 0.7541 0.2459 Date 1.08 1.04 0.9246 0.0754 Closeness 1.59 1.26 0.6291 0.3709 Mean VIF 1.67
Eigenvalue Cond Index 1 8.4109 1 2 0.8558 3.1349 3 0.7624 3.3215 4 0.5804 3.8068 5 0.4281 4.4325 6 0.341 4.9667 7 0.2951 5.3387 8 0.1664 7.1093 9 0.0871 9.8241 10 0.0346 15.5969 11 0.0275 17.4858 12 0.0108 27.9639 Condition Number 27.9639 Eigenvalues & Cond Index computed from scaled raw sscp (w/ intercept) Det(correlation matrix) 0.0608
Kleinbaum, Kupper, and Miller (1988) suggest that VIFs above 10.0 and CIs above 30.0 indicate multicollinearity requiring remedial attention. So, the variables of the model donˆatpresent collinearity problems.
22 Test for autocorrelation The following tests reject the null hypothesis of no first-order autocorrelation.
Table 5: Model 1
Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F (1; 21) 5.511 Prob > F = 0.0288
Table 6: Model 2
Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F(1;21) 3.663 Prob > F = 0.0694
Table 7: Model 3
Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F(1;21) 5.96 Prob > F = 0.0236
Table 8: Model 4
Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F(1;21) 3.47 Prob ¿F = 0.0765
Test for Heteroskedasticity
The following tests for heteroskedasticity reject the null hypothesis of homoskedasticity.
23 Table 9: Model 1
Modified Wald test for groupwise heteroskedasticity in cross-sectional time-series FGLS regression model
H0: sigma(i)2 = sigma2foralli chi2 (22) = 1431.25 Prob>chi2 = 0.0000
Table 10: Model 2
Modified Wald test for groupwise heteroskedasticity in cross-sectional time-series FGLS regression model
H0: sigma(i)2 = sigma2foralli chi2 (22) = 552.65 Prob>chi2 = 0.0000
Table 11: Model 3
Modified Wald test for groupwise heteroskedasticity in cross-sectional time-series FGLS regression model
H0: sigma(i)2 = sigma2foralli chi2 (22) = 2141.26 Prob>chi2 = 0.0000
Table 12: Model 4
Modified Wald test for groupwise heteroskedasticity in cross-sectional time-series FGLS regression model
H0: sigma(i)2 = sigma2foralli chi2 (22) = 343.43 Prob>chi2 = 0.0000
24