Category Archives: Data

Ryan E. Carlin, Gregory J. Love, and Cecilia Martínez-Gallardo – Terrorism and presidential approval

This is a guest post by Ryan E. Carlin, Gregory J. Love, and Cecilia Martínez-Gallardo from Georgia State University, University of Mississippi, and University of North Carolina at Chapel Hill respectively

The recent terrorist attacks in Paris—as well as high-profile terrorist attacks in democratic countries far and wide, including Argentina, Spain, the U.S., and India—raise important questions about the causes terrorism and about how it can be prevented in the context of democratic politics. The attacks and the protests that have followed them also raise the question of whether leaders in democratic systems are likely to suffer political consequences—including the potential of being kicked out of office—for what citizens perceive as substantial security failures.

Much of what we know about how the public metes out blame or credit for policy outcomes comes from the field of political economy. One of the most common hypotheses in this field states that the public is more likely to hold leaders accountable for economic performance when “clarity of responsibility” is high. Under unified government, for example, citizens will be more likely to blame or credit the executive (president, prime minister, etc.) for economic policy, which they see as reflecting the executive’s vision and priorities. The perception that the economy’s fate rests in the executive’s hands makes it difficult for leaders to shift blame for failures but easy to take credit for successes. In such circumstances, when the economy roars leaders ride a wave of popularity. But when it tanks so does their public approval.

If unified government illuminates who is responsible for economic performance, divided government obscures it. When the opposition controls the legislature, economic policy must bear both the opposition’s and the government’s stamp of approval. The perception that political actors share responsibility for crafting economic policy makes citizens less likely to think executives are solely responsible for the economy – and less likely to credit or blame executive leaders for economic outcomes – under divided government

Although this hypothesis has a good track record of explaining patterns of accountability for economic outcomes, it does not explain when citizens will hold executives accountable for terrorism and security outcomes. In our work, we argue that there are two central reasons for this: first, there are important differences in who citizens perceive to be responsible for economic and national security outcomes. Second, security and economic policy failures present different opportunities for executives to create a narrative frame that benefits them. We explain each of these reasons below.

First, in most countries executives share responsibility for economic policy with other domestic and international political actors — and citizens know this and take it into account when assigning credit or blame for economic outcomes. But citizens, and most constitutions, place responsibility for terrorism and national security squarely on the shoulders of the executive. This makes it hard for leaders to shift blame for security breakdowns. Claiming that their hands were tied strains credulity. In addition, security failures create a bully pulpit from which the opposition can roundly criticize the executive and, since they share no responsibility, gives them a cheap way to score political points.

Second, although it is extremely difficult to put a positive spin on economic crises, security policy failures produce different options for executives. Under unified government, sitting executives can blame the violence on the attackers rather than on their government’s failure to prevent the attack. Threatening and evocative media coverage reinforces this frame and helps executives rally the public to their side and in support of their policy response. Thus, while unified government makes it more likely that the executive will get blamed for economic failures, it makes it less likely that terrorism and security crises will hurt executives’ public support.

Under divided government, by contrast, opposition leaders have enough resources at their disposal – speakerships, committee chairmanships, better access to the media, etc. – to allow them to counter-frame and, thus, challenge the executive’s narrative. This back and forth hurts the ability of the executive to impose its preferred narrative. As a result, when it comes to security and terrorism, divided government makes it more likely that citizens will blame the executive for a policy failure or crisis.

When it comes to terrorism and security policy, then, the classic “clarity of responsibility” hypothesis, developed to explain accountability for economic outcomes, seems to function in reverse. The contrast between presidents Alan Garcia of Peru and Alvaro Uribe of Colombia provides a good example. In mid-2007 García was faced with withering criticism from Peruvian opposition leaders in Congress and a 7.1 percentage point drop in approval following a Sendero Luminoso attack that killed 60 people. In contrast, Uribe enjoyed a united government that allowed him to shape the narrative more easily and to escape blame following attacks that killed or wounded 196 people in 2002.

To explore this idea empirically, in our research we look at how acts of terrorism influence executive support in 18 Latin American democracies in the context of either unified or divided government. We use historical data that covers the period from 1980 to 2007. For terrorism casualties we use data from START’s Global Terrorism Database; data on clarity of responsibility come from Witold Henisz’s Political Constraints Database; presidential approval data come from our own Executive Approval Database; economic data are taken from the International Monetary Fund’s International Financial Statistics and the Economic Commission for Latin America and the Caribbean’s CEPALSTAT; and controls for level of democracy are from the Center for Systemic Peace’s Polity IV Project. If our argument is correct, terrorist attacks should reduce presidential approval less under unified government than under divided government.

This is exactly what we observe.

Figure 1. The Effects of Inflation and Terrorism on Presidential Approval Conditional on Clarity of Responsibility

Martinez-Gallardo-Fig-1

Source: Carlin, Love & Martinez-Gallardo (2015).

Figure 1 shows the relationship between inflation (right panel) and casualties attributed to terrorism (left panel) and presidential approval at different levels of clarity of responsibility (or the degree to which government authority is divided). The Figure highlights our main finding: while presidents facing an opposition congress (low clarity of responsibility) can somewhat avoid blame for a failing economy, executives under the same circumstances are likely to be punished for security failures. In other words, the consequences of an economic crisis for executives’ approval are worse under divided government, when voters have a hard time deciding whom to blame for policy outcomes. In contrast, security crises are more likely to dent executives’ approval when the government is unified because they cannot credibly shift blame.

The conditionality of accountability we examine in this research and in a previous paper focused on political scandals, highlights the challenges involved in understanding democratic accountability. Our work suggests it is important to look not only at the political environment – whether the government is unified or divided—but also at the nature of the particular policy issue. By taking both factors into account, performance accountability can be viewed as a complex relationship between issue ownership, blame shifting, and the effectiveness of executive and opposition narratives. In particular, the issue ownership executives have over security policy is clearly a blessing and a curse. Executives can often feel emboldened and less constrained by the legislature when dealing with matters of national security and physical safety. However, when a country suffers a security failure, executives find it hard to shift blame onto an opposition legislature, and instead face vocal criticisms that tend to lower their public support.

The first version of this post appeared at the LSE USApp– American Politics and Policy blog: http://bit.ly/1xy0Scr

kuleto1Ryan E. Carlin – Georgia State University
Ryan Carlin is an Associate Professor of Political Science at Georgia State University. His main research field is comparative political behavior, especially in Latin America. His other research interests include natural disaster politics, social preferences, rule of law, and political institutions.

Love

Gregory J. Love – University of Mississippi
Greg Love is an Associate Professor of political science at the University of Mississippi. He specializes in the politics of Latin America and developing countries broadly. One of his specific research interests is how political careers develop and change in transitional democracies and how these changes affect the quality of governance. He has conducted extensive field research in Mexico and Chile for this and other projects.

Ceci

Cecilia Martínez-Gallardo – University of North Carolina at Chapel Hill
Cecilia Martínez-Gallardo is an Assistant Professor of Political Science at the University of North Carolina at Chapel Hill. Her teaching and research interest are in Latin American political institutions, especially government formation and change. Her work focuses on the political and institutional factors that affect coalition politics in these countries. She has also worked on government formation and stability in Western Europe and as well as policy reform in Latin America.

 

Fernando Casal Bértoa – Party Systems and Governments Observatory (PSGo): A New Research Tool

This is a guest post by Fernando Casal Bértoa from the University of Nottingham.

cv-photo

Have you ever wondered who governs the countries of Europe? Would you like to know who governed your country more than a century ago? Are you not sure about the partisan affiliation of ministers in your neighboring states? Are you interested in discovering how has the (economic and financial) crisis affected the composition of European governments and party systems?

Now a quick answer to all these questions, and more, is possible thanks to a new research project at the University of Nottingham: namely, the Party Systems and Governments Observatory (PSGo), a new research interactive tool (whogoverns.eu)[1] where data on government formation and party system institutionalization in 48 European democratic states since 1848 can be found. European indicates those countries stretching from the Atlantic to the Urals. Democratic refers to those countries displaying (1) a score of 6 or higher in the Polity IV index, (2) universal suffrage elections (including universal male suffrage only, when historically appropriate), and (3) governments formed and/or relying on a parliamentary majority, rather than on the exclusive will of the head of state. States includes those countries recognized by either the United Nations or the Council of Nations.[2]

In particular, and as it follows from the table below, the number of years per country varies between just one (e.g. Czechoslovakia’s Third Republic and the Kingdom of Serbs, Croats and Slovenes) and more than a century (e.g. Norway or Denmark). Secondly, the number of political regimes taken into account varies between just one (e.g. Belgium or the Netherlands) and four (France and Greece). Thirdly, the number of electoral cycles taken into account varies between just one (e.g. Greece’s post-WWII Kingdom or Poland’s First Republic) and thirty-three (Switzerland). Finally, the number of cabinets taken into account varies between just one (Czechoslovakia’s Third Republic) or two (e.g. Belarus or Kosovo) and ninety-seven (France’s Third Republic).

European democracies (1848-2014)

Country Period Country Period
Albania 2002- Kingdom of SHS 1921
Andorra 1993- Kosovo 2008-
Armenia 1991-1994 Latvia (post-WWI) 1920-1933
Austria (1st Republic) 1920-1932 Latvia (post-1989) 1993-
Austria (2nd Republic) 1946- Liechtenstein 1993-
Belarus 1991-1994 Lithuania 1993-
Belgium 1919- Luxembourg 1920-
Bulgaria 1991- Macedonia 1992-
Croatia 2000- Malta 1964-
Cyprus 1978- Moldova 1994-
Czechoslovakia (1st Rep) 1918-1938 Montenegro 2007-
Czechoslovakia (3rd Rep) 1946 The Netherlands 1918-
Czech Republic 1993 Norway 1905-
Denmark 1911-1934 Poland (2nd Republic) 1918-1926
Estonia (post-WWI) 1921-1934 Poland (3rd Republic) 1991-
Estonia (post-1989) 1992- Portugal (1st Republic) 1919-1925
Finland (post-WWI) 1917-1930 Portugal (3rd Republic) 1976-
Finland (post-WWII) 1945- Romania 1996-
France (2nd Republic) 1848-1851 Russia 2000-2006
France (3rd Republic) 1876-1940 San Marino (post-WWI) 1920-1923
France (4th Republic) 1946-1957 San Marino (post-WWII) 1945-
France (5th Republic) 1968- Serbia 2001-
Georgia 2004- Slovenia 1993-
Germany (Weimar Rep) 1925-1932 Spain (Restoration) 1900-1923
Germany (post-WWII) 1949- Spain (2nd Republic) 1931-1936
Greece (King. of George I) 1875-1914 Spain (post-Francoist) 1979-
Greece (2nd Republic) 1926-1936 Sweden 1917-
Greece (post-WWII) 1946-1948 Switzerland 1897-
Greece (3rd Republic) 1975- Turkey (post-WWII) 1946-1953
Hungary 1990- Turkey (post-1960 coup) 1961-1979
Iceland 1944- Turkey (post-1980 coup) 1983-
Ireland 1923- Ukraine 1994-
Italy 1948- United Kingdom 1919-

In terms of government composition, the database contains information on cabinet duration (i.e. dates of formation and termination), the names of the various ministerial offices as well as of the people[3] appointed to occupy them, and the partisan affiliation of each minister at the time a particular cabinet is appointed.[4]

In accordance with the party government literature (Müller and Strøm, 2000), the database records changes of government in three different instances:

a) change in the partisan composition of the government coalition,
b) change in the prime minister, and
c) celebration of parliamentary elections.

In case of electoral coalitions, the database also displays information about the partisan affiliation of the ministers belonging to the different parties within the coalition. In those instances when two or more political formations merged to form a new party, the partisan affiliation of the ministers belonging to the parties merged is also shown.

In terms of party systems, and closely following the party politics literature (Bartolini and Mair, 1990; Huntington, 1968; Lijphart, 1999; Mainwaring and Scully, 1995; Sartori, 1976), the database contains operationalisations and measurements for six different classic indicators:

                a) party system institutionalisation, calculated in four different periods (pre-WWI, inter-war, post-WWII, and post-1989),
                b) party institutionalization, calculated according to average party age as well as Lewis’ (2006) index,

c) electoral volatility, measured by Pedersen’s (1979) index,
d) the effective number of (electoral and legislative) parties, measured by Laakso and Taagepera’s index,

e) the number of “new” parties, with at least 0.5 per cent of votes,

f) polarization, calculated as the percentage of votes obtained by anti-establishment-parties, and

g) electoral disproportionality, measured by Gallagher’s (1991) index.

All in all, the database covers 166 years, 66 different historical political regimes, roughly 670 elections, and more than 1600 cases of government formation.

Finally, and for those interested in more than plain data, the Observatory also runs a blog where country experts post their knowledgeable opinions on the latest process of cabinet formation (for example in Bulgaria, Ukraine, Kosovo, Romania), including inside analyses on coalition negotiations, possible government alternatives, future outcomes and expectations, and the like.

[1] See also https://twitter.com/whogovernseu or https://www.facebook.com/whogovernseurope.

[2] As a result, the Turkish Republic of Northern Cyprus is not included.

[3] Senior, but not junior (i.e. deputy), ministers are recorded.

[4] Simple government reshuffles (i.e. change of ministers without proper “governmental change”, see above) are not recorded.

Fernando Casal Bértoa is a Research Fellow at the University of Nottingham (UK). He is also co-director of the Centre for Comparative and Political Research at the School of Politics and International Relations. Before he was a Post-doctoral Fellow at the University of Leiden in The Netherlands. He studied Law at the University of Navarra (Pamplona, Spain) and Political Science at the University of Salamanca (Spain). After specializing in Eastern and Central European Studies at the Jagiellonian University (Cracow, Poland), he obtained his PhD at the European University Institute (Florence, Italy). His work has been published in Party Politics, Government and Opposition, International Political Science Review, South European Society and Politics, or East European Politics.

A new dataset of presidential power scores

This is a post by David Doyle and Robert Elgie

Since the beginning of the 1990s there has been ongoing debate about the relative effect of different regime types, specifically presidentialism, semi-presidentialism and parliamentarism. From the very beginning of this debate, there has been an acknowledgement of variation in presidential power not just across these three regime types, but also within each regime type. This within-type variation causes a problem for cross-national studies. If the within-type variation is large, then estimating the effect of regime types themselves may lead to spurious results. For this reason, Crisp, Desposato and Kantha (p. 447) have stated that there is a “need for more explicit theoretical depictions of the institutional variation in the class of regimes referred to as presidential as well as the need for a systematic empirical exploration of the impact of that diversity on regime performance” (1).

In this context, some observers have preferred to estimate the effect of presidential power on outcomes rather than the effect of regime type more broadly. For example, Hicken and Stoll (p. 1114) note that the power of the Colombian president has varied over time as a result of constitutional amendments, even though Colombia has maintained a presidential regime throughout. As a result, they prefer to measure variation in presidential power over time and estimate the effect of such variation. They note: “our overall index of presidential powers reveals variation within each type of regime that the simple trichotomy [of presidentialism, parliamentarism, and semi-presidentialism] obscures. It is this greater level of precision that leads us to prefer the index”.

However, while there is now a well-established literature demonstrating that variation in presidential power has consequences across a range of political and economic outcomes, there are many different measures of presidential power. In fact, we have identified 19 separate and original measures of presidential power, plus a further 16 studies that used one of these measures with different countries, time periods, and/or scores from the original study.

This range of measures raises a number of issues. Individual measures are sometimes poorly correlated with each other, meaning that findings are sensitive to the particular measure of presidential power that is used. There is also a considerable loss of information across the set of measures as a whole as countries are included in some measures but not others and then for only certain time periods. More generally, as Jessica Fortin has recently shown, there are no theoretical priors to tell us which indicators of presidential power we should choose or how the scores for the individual indicators should be aggregated (3).

We agree with Fortin’s analysis. However, we draw a different conclusion from her. She concludes very skeptically, effectively questioning whether any measure of presidential power is likely to be valid. By contrast, we assume that most social science concepts, such as voter turnout, social equality, corruption, and so on, suffer from equivalent problems of construct validity. Therefore, we should not give up on the effort to generate a dataset of presidential power scores. Instead, we should focus on the reliability of the data that underpins the concept we are trying to capture.

We wish to generate a time-series cross-sectional dataset of presidential power scores with country years as the units of observation. To do so, we choose not to construct a new measure of presidential power from scratch. Instead, we draw upon the comparative and local knowledge already embedded in the existing measures of presidential power that we identified. To maximize the reliability of the scores we derive them solely from measures that are based on institutional indicators of presidential power and on the basis of a method that accounts for potential idiosyncrasies of country scores in the existing measures. In addition, we report the standard errors and the confidence intervals for all the country years in our measures, providing information with which scholars can make an informed choice about whether or not a particular country should be included in an estimation and which of our measures might best be used in comparative studies.

The paper outlining our full methodology will soon appear in British Journal of Political Science and is available in advance here. In the meantime, we are making available the full set of presidential power scores, including standard errors and confidence intervals for each country time period, in a separate page at the header of this blog. We also provide more detail about the scores.

Overall, we encourage people to keep developing new measures of presidential power and to update existing measures for as many countries and as long a time period as possible. One of the advantages of our approach is that new country scores can be easily incorporated into the method we have used, creating the potential for country coverage to be further extended, for existing country scores to be updated, and for cross-national measures to become even more reliable.

(1) Crisp, Brian F., Scott W. Desposato, and Kristin Kanthak. 2011. Legislative Pivots, Presidential Powers, and Policy Stability.” Journal of Law, Economics and Organization 27 (2): 426-452.

(2) Hicken, Allen, and Heather Stoll. 2008. “Electoral Rules and the Size of the Prize: How Political Institutions Shape Presidential Party Systems.” Journal of Politics 70 (4): 1109-1127.

(3) Fortin, Jessica. 2013. “Measuring presidential powers: Some pitfalls of aggregate measurement.” International Political Science Review 34 (1): 91-112.

New data set of Facebook posts

This blog is now seven weeks old. Thank you for visiting the page and for coming back!

As you may know, in addition to this blog we also run a Facebook Page where we post links to breaking news items. These Facebook posts go automatically to our Twitter account (@prespow). Do please visit and ‘like’ our Facebook Page and follow us on Twitter.

Since we began on 4 October, we have posted nearly 350 news items on our Facebook Page, or around 50 per week. We have also retweeted additional news stories from our Twitter account.

Increasingly, researchers are using social media to collect data for research projects. For example, Thomas Sedelius and Olga Mashtaler recently used posts on The Semi-presidential One blog as one of their sources for identifying presidential/prime ministerial conflict in Central and Eastern Europe (‘Two decades of semi-presidentialism: issues of intra-executive conflict in Central and Eastern Europe 1991–2011’, East European Politics, DOI:10.1080/21599165.2012.748662).

In this spirit, we are making openly available the data set of our Facebook posts. The data set includes the title of the post, the date of the post, and the link to the original news story.

The data are fully searchable. So, for example, they can be searched for references to presidents in particular countries, or for references to the exercise of a particular type of presidential power. In addition, the links allow researchers to go back to the source of the Facebook post to verify the story or to research the item further.

The data set is available on request as a .csv document. This means that it can be opened in Excel, Numbers or an equivalent programme. If you wish to receive a copy of the data, then please just e-mail Robert Elgie (robert.elgie@dcu.ie).

In return, we ask two things. First, if you are on Facebook and have not already ‘liked’ our Page, then please do so. Second, and more importantly, if you use the data set in any publications or papers, then please acknowledge it in the following way: Robert Elgie, Lydia Beuman, Cristina Bucur, David Doyle, Philipp Köker, Sophia Moestrup, Paola Rivetti, and Fiona Yap, 2013-, Presidential Power Project, http://presidential-power.com.

If you have any reflections on the data or the blog generally, then please feel free to leave a comment or contact Robert Elgie directly (robert.elgie@dcu.ie).

Thank you again for visiting.