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Elections and Electoral Systems Democracies Are Sometimes Classified in Terms of Their Electoral System

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Elections and Electoral Systems Democracies Are Sometimes Classified in Terms of Their Electoral System Elections and Electoral Systems Democracies are sometimes classified in terms of their electoral system. An electoral system is a set of laws that regulate electoral competition between candidates or parties or both. Elections are increasingly used to fill legislative and executive offices around the world. 185 of the world's 193 independent states now use direct elections to elect people to their lower house of parliament. Electoral integrity refers to the extent to which the conduct of elections meets international standards and global norms concerning `good' elections. These norms and standards are usually set out in treaties, conventions, and guidelines issued by international and regional organizations. Violations of electoral integrity are referred to as electoral malpractice. MAP 13.1 Electoral Integrity across the World in 2016 Source: Data come from the Perceptions of Electoral Integrity expert survey (PEI 4.5) and are based on national-level elections that have taken place between July 1, 2012, and June 30, 2016 (“Perceptions” 2016). Darker colors indicate higher levels of electoral integrity. 13: Elections and Electoral Systems 527 FIGURE 13.2 Electoral Integrity in Four Countries Source: Data for the star-plots come from the Perceptions of Electoral Integrity expert survey (PEI 4.5; “Perceptions” 2016). The gray star-plots indicate how the four countries score on each of the eleven categories of electoral integ- rity. They also show each country’s overall PEI score. The black dashed line indicates the average global score across the same categories of electoral integrity. each of the eleven categories of electoral integrity as well as their overall PEI score. The shaded gray area in the star-plot is large when a country scores highly on each of the eleven categories in the PEI measure. To help provide context, we overlay each country’s star-plot (filled, gray) with a star-plot (black dashed line) showing the average global score on each of the eleven categories in the PEI measure. This allows us to see where the countries are doing better or worse than the global average when it comes to electoral integrity. Although established democracies tend to have high PEI scores, there is some variation. It turns out that the United States has the lowest overall PEI score among established democ- racies (62), leaving it ranked 52nd worldwide in 2016 (Norris et al. 2016b, 12). As the top left Democracies tend to have higher levels of electoral integrity than dictatorships. There is variation, though, among both democracies and dictatorships. Electoral integrity is influenced by: • Domestic structural constraints • The role of the international community • Institutional design • Electoral management bodies Two strategies to identify election fraud: 1. Election monitoring 2. Election forensics 532 Principles of Comparative Politics BOX 13.1 THE SCIENCE OF ELECTION FORENSICS Political scientists have begun to develop tests to identify election fraud. The underlying idea is that human attempts to manipulate election results leave telltale signs that can be picked up by statistical tests (Hicken and Mebane 2015). Many of these tests focus on the frequency distribution of digits in reported vote totals. Benford’s law describes a pattern for the frequency distribution of digits in numbers that occurs in many settings (Mebane 2013, 9). Although we might think that each digit from 1 to 9 has an equal probability of appearing as the first digit in a number, this is often not the case. It turns out that in a wide variety of settings, smaller digits are more common than larger digits. To illustrate why this might be the case, Deckert, Myagkov, and Ordeshook (2011, 246) give the example of collecting house street numbers at random from a telephone book. As street numbers tend to begin with 1 (or 10 or 100) and restart at 1 after crossing a boundary or end before higher numbers are reached, addresses that start with the number 1 will be more common than those that start with the number 2, and those that start with a 2 will be more common than those that start with a 3, and so on. According to Benford’s law, the first and second digits in a number will follow the frequency distributions shown in Table 13.1. For example, the probability that the first digit in a number will be a 3 is 0.125, and the probability that it will be a 6 is 0.067. Similarly, the probability that the second digit in a number will be a 0 is 0.120, and the probability that it will be a 6 is 0.093. The mean or expected value of the first digit is 3.441, whereas it is 4.187 for the second digit. Benford’s law has been used to detect financial and accounting fraud (Cho and Gaines 2007). The general idea is that individuals who fabricate numbers have a tendency to do so uniformly. As a result, one can compare the frequencies with which different digits appear as the first number in financial accounts with the expected probabilities for those digits from Benford’s law. Significant deviations would indicate “suspicious” numbers and possible fraud. Scholars have adopted the same basic idea to try to identify electoral fraud in voting returns (Cantu and Saiegh 2011), though they tend to focus on the distribution of the second digit rather than the first digit (Mebane 2006, 2008; Pericchi and Torres 2011). For example, Mebane (2013) examined electoral returns from 45,692 ballot boxes in the 2009 presidential elections in Iran and found that the frequency distribution of the second digits in the vote totals for the incumbent president, Ahmadinejad, was suspicious. Rather than focus on Benford’s law, other scholars have argued that fair elections should produce voting returns that have uniformly distributed 0–9 last digits. Using this method, Beber and Scacco (2012) Benford’s Law: The Frequency Distribution of First TABLE 13.1 and Second Digits 0 1 2 3 4 5 6 7 8 9 Mean — 0.301 0.176 0.125 0.097 0.079 0.067 0.058 0.051 0.046 3.441 0.120 0.114 0.109 0.104 0.100 0.097 0.093 0.090 0.088 0.085 4.187 Political scientists typically distinguish between electoral systems based on their electoral formula. 1. Majoritarian 2. Proportional 3. Mixed An electoral formula determines how votes are translated into seats. FIGURE 13.3 Electoral System Families Electoral System Families Majoritarian Proportional Mixed Plurality Absolute Majority List PR Single Transferable Independent Dependent Vote Single-Member District Plurality Alternative Vote Coexistence Correction Single Nontransferable Vote TRS: Majority-Runoff Supposition Conditional Block Vote Fusion Party Block Vote Borda Count Quota Divisor Modified Borda Count Limited Vote Hare D’Hondt TRS: Majority-Plurality Hagenbach-Bischoff Sainte-Laguë Droop Modified Sainte-Laguë Imperiali Reinforced Imperiali Note: These are all of the electoral systems used in national-level legislative elections around the world (Bormann and Golder 2013, 362). TRS refers to “two-round systems.” A majoritarian electoral system is one in which the candidates or parties that receive the most votes wins. A single-member district plurality system (SMDP) is one in which individuals cast a single vote for a candidate in a single-member district. The candidate with the most votes wins. 536 Principles of Comparative Politics most votes, even if this is not a majority of the votes, is elected from the district. SMDP systems are sometimes referred to as “first-past-the-post.” This name, though, is misleading as it suggests that a candidate is elected once she gets past a certain vote total. In theory, a candidate can win in an SMDP system with as few as two votes if all the other candidates win only one vote each. An example of the operation of an SMDP system in the Bath con- stituency in the United Kingdom in the 2015 legislative elections is shown in Table 13.2. Ben Howlett of the Conservative Party won the most votes and was, therefore, elected as the Member of Parliament for this district. SMDP electoral systems have both strengths and weaknesses. Perhaps the greatest strength of SMDP systems is their simplicity. This means that they are easy for voters to understand. It also means that they are easy and relatively inexpensive to administer. A sec- ond strength of SMDP systems has to do with the fact that only one representative is elected in each district. Having only one representative per constituency means that responsibility for what happens in the district lies squarely with that person. In other words, SMDP systems make it easy for voters to identify who is responsible for policies in their district and there- fore to hold them accountable in the next election. By making it easier for voters to hold representatives accountable, SMDP systems create incentives for representatives to perform well in office. As a result, SMDP systems tend to produce high levels of constituency service and close bonds between constituents and their representatives. Despite these strengths, SMDP electoral systems have many critics. Some critics point to the fact that SMDP systems have the potential to produce unrepresentative outcomes. As our example in Table 13.2 illustrates, it is possible for a candidate to win without obtaining a majority of the votes; in fact, 62.2 percent of Bath voters did not support the winning candi- date. It is worth noting that candidates can win in SMDP systems with even lower vote shares than that obtained by the winning candidate in Bath. As an example, the winning candidate in the Kerowagi constituency in Papua New Guinea won with just 7.9 percent of the vote in the 1987 legislative elections (Cox 1997, 85).
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