ICYMI (In Case You Missed It), the following work was presented at the 2017 Annual Meeting of the American Political Science Association (APSA). The presentation, titled “The IMF and the Political Economy of GDP Data Production” was a part of the session “Economic Growth and Contraction: Causes, Consequences, and Measurement” on Sunday, September 3, 2017.
Political economists often theorize about relationships between politics and macroeconomics in the developing world; specifically, which political or social structures promote economic growth, or wealth, or economic openness, and conversely, how those economic outcomes affect politics. Answering these questions often requires some reference to macroeconomic statistics. However, recent work has questioned these data’s accuracy and objectivity. An under-explored aspect of these data’s limitations is their instability over time. Macroeconomic data is frequently revised ex post, or after the fact, and as such one could ask the same question of (ostensibly) the same data, and get different answers depending on when the question was asked.
We set out to explore the political economy of data production by examining a newly available dataset of ex post revisions to World Development Indicators (WDI) data.[1]Ex post revisions occur when newly available information changes national statistical offices’ beliefs about the nature of the economy. Those revisions extend into the past, effectively rewriting history and, in the process, providing a reasonable proxy for the inaccuracy of the initial reports. These revisions affect a wide swath of data, but we focus on Gross Domestic Product (GDP) and GDP-derived measures, like GDP per capita and GDP growth. GDP revisions are common—most GDP data available for download at the WDI are different now than they were at the time of its initial release. Normally these changes are subtle; other times they are substantial enough to condemn prior data releases as misleading.
We use these revisions to answer two related questions. First, how sensitive are political-economy relationships to GDP revisions? Should researchers worry about revisions-driven instability in the state of political-economic knowledge? We show that they should. To illustrate, we subject a simple, bivariate statistical relationships between democracy and growth to re-estimation using alternative versions of the “same” data. The democracy-growth relationship has been a topic of sufficient interest in economics and political science that instability in this relationship should give us reason for pause. Seen in this light our estimates are worrisome. As we show in Figure 1 below, our estimates are unstable across different “observation years” and further, they are unstable in ways that suggest that initial estimates were biased. Rather than simply a diminution of standard errors as more heavily revised data are introduced (which is what we would expect to see if revisions simply reduced random “noise in the data”), the estimated coefficients for Democracy change substantially across models estimated with different revisions of the same country-year GDP growth data.
Figure 1: GDP Growth ~ Democracy
Note: Figure 1 displays the relationship between GDP Growth and Democracy using the results from 21 different regression models. Plotted points represent parameter estimates, thick bars represent 90 percent confidence intervals, and thin bars represent 95 percent confidence intervals. Each point is labeled with the revision year used. The left side of the plot contains results from models estimated using the 2000-2004 data series, while the right side of the plot contains results from models estimated using the 1995-1999 data series. See paper for more details.
This finding anticipates our second question: Given the likelihood that GDP revision are non-random, what accounts for ex post revisions? What does the “political economy” of revisions look like? We show using Kolmogorov-Smirnov tests (see Figure 2) and random forest models (see Figure 3) that the International Monetary Fund (IMF) influences the magnitude of revisions for GDP and GDP-related measures. That is not entirely surprising. Our suspicion that the IMF would have such an effect is a straightforward recognition of its well-publicized efforts to provide financial and human resources to the national statistical offices of the countries in which it works. What we have “uncovered” in this exercise is simply one consequence of the IMF doing precisely what it has publically said it is doing. But this finding’s (retrospective) obviousness does not diminish its importance. Consider the empirical challenges that this presents. Political economists often ask if the IMF affects the way economies functions, but the IMF’s independent effect on the way economies are measured substantially complicates our ability to know if it does. And it doesn’t just complicate our ability to know if the IMF’s policies affect the economy, it complicates our ability to know if anything correlated with IMF participation affects the economy. Many important things correlate with IMF participation, including, for example, democracy, a country’s relationship with the UN, and whether or not a country is an ally of the United States.
Figure 2: Distributions of GDP Growth Changes
Note: Figure 2 presents compares the distributions of GDP growth revisions for years with and without IMF programs. The y-axis indicates the height of the density function and the x-axis indicates the magnitude of GDP growth revisions in percentages points. The solid green line denotes country years with an IMF program, while the dashed black line denotes countries years without a program. See paper for more details.
Figure 3: Predictors of GDP Growth Revisions
Note: Figure 3 presents the results from a random forest model that examines the predictors of GDP growth revisions. The vertical axis ranks variables according to their importance for predicting GDP Growth Changes. The horizontal axis displays estimates of permutation accuracy for each variable, calculated as the difference in mean squared error between a model that is fitted using the observed values for a measure and a model that is fitted using random (but realistic) values for the same measure. This measure is then scaled to represent the percentage increase in mean square error caused by permuting the values of the variable. Positive values indicate that the variables increase the predictive performance of the model, while negative values indicate that the variables decrease the predictive performance of the variables. See paper for more details.
Of course, politics likely affects the way the economy is measured in a variety of ways that have nothing to do with the IMF. Our random forest analysis suggests that democracy might also have an effect, for example, as might public sector corruption, and it is not hard to tell a plausible post hoc story for why that might be. But our aim is not to provide a comprehensive picture of the political economy of data production, but simply to show that it exists, and that it exists in a manner that should alert us to its importance. Taking seriously the political provenance of ostensibly apolitical data is an important (and, we believe, interesting) step towards refining the state of political economy knowledge.
ICYMI (In Case You Missed It), the following work was presented at the 2017 Annual Meeting of the American Political Science Association (APSA). The presentation, titled “Big Data Innovation Transfer and Governance in Emerging High Technology States” was a part of the session “The Role of Business in Information Technology and Politics” on Friday September 1, 2017.
On August 24, 2017, India’s highest court ruled that citizens have a fundamental right to privacy. Such a ruling may serve to slowdown the government’s deployment of the Aadhaar national ID program, a robust relational database connecting each of India’s 1.3+ billion citizens with their unique 12-digit identity aimed at centralize their physiological, demographic, and digital data shadows — minute pieces of data created when an individual sends an email, updates a social media profile, swipes a credit card, uses an ATM, etc. While the government has presented the Aadhaar system as an improved channel to provide social security benefits for its nationals, India’s civil society organizations have protested it as a means of furthering government surveillance. India’s trajectory in ambitiously modernizing its high-tech toolkit for governance represents a rapidly spreading trend in the contemporary world system of 190+ nations.
Take China as an other example. China has recently mobilized its government bureaucracies to establish the worlds’ first ever, and largest, national Social Credit System covering nearly 1.4+ billion Chinese citizens. By 2020, China’s citizen management system will include each Chinese national’s financial history, online comments about government, and even traffic violations to rank their ‘trustworthiness.’ Like India’s, these unique ‘social credit’ ratings will reward and punish citizens for their behavioral allegiance with the regime’s goals by scientifically allowing the state to operationalize its vision of a “harmonious socialist society.”
Yet, the implementation of state-sponsored and ‘big data’-enabled surveillance systems to address the operational demands of governance is not limited just to the world’s largest democratic and authoritarian states. This summer, at the annual meetings of the International Communication Association (May 2017, San Diego) and the American Political Science Association (August 2017, San Francisco), the project on Big Data Innovation & Governance (BigDIG) presented findings from the first event-catalogued case-history analysis of 306 cases of mass surveillance systems that currently exist across 139 nation-states in the world system (Kostyuk, Chen, Das, Liang and Hussain, 2017). After identifying the ‘known universe’ of these population-wide data infrastructures that now shape the evolving relationships between citizens and state powers, our investigation paid particular attention to how state-sponsored mass surveillance systems have spread through the world-system, since 1995.
By closely investigating all known cases of state-backed cross-sector surveillance collaborations, our findings demonstrate that the deployment of mass surveillance systems by states has been globally increasing throughout the last twenty years (Figure 1). More importantly, from 2006-2010 to present, states have uniformly doubled their surveillance investments compared with the previous decade.
In addition to unpacking the funding and diffusion of mass surveillance systems, we are also addressing the following questions: Which stakeholders have most prominently expressed support for, benefited from, or opposed these systems, and why? What have been the comparative societal responses to the normalization of these systems for the purposes of population management in recent decades?
The observed cases in our study differ in scope and impact.
Why do stable democracies and autocracies operate similarly, while developing and emerging democracies operate differently? Access to and organization of material, financial, and technocratic resources may provide some context.
While nations worldwide have spent at least $27.1 billion USD (or $7 per individual) to surveil 4.138 billion individuals (i.e., 73 percent of the world population), stable autocracies are the highest per-capita spenders on mass surveillance. In total, authoritarian regimes have spent $10.967 billion USD to surveil 81 percent of their populations (0.1 billion individuals), even though this sub-set of states tends to have the lowest levels of high-technology capabilities. Stable autocracies have also invested 11-fold more than any other regime-type, by spending $110 USD per individual surveilled, followed second-highest by advanced democracies who have invested $8.909 billion USD in total ($11 USD per individual) covering 0.812 billion individuals (74 percent of their population). In contrast to high-spending dictatorships and democracies, developing and emerging democracies have invested $4.784 billion USD (or $1-2 per individual) for tracking 2.875 billion people (72 percent of their population).
It is possible that in a hyper-globalizing environment increasingly characterized by non-state economic (e.g., multi-national corporations) and political (e.g., transnational terror organizations) activity, nation-states have both learned from and mimicked each other’s investments in mass surveillance as an increasingly central activity in exercising power over their polities and jurisdictions. It is also likely that the technological revolution in digitally-enabled big data and cloud computing capabilities as well as the ubiquitous digital wiring of global populations (through mobile telephony and digital communication) have technically enabled states to access and organize population-wide data on their citizens in ways not possible in previous eras. Regardless of the impetuses for increases in mass surveillance efforts, our research aims to provide empirical support to advance theory and guide policy on balancing security needs and privacy concerns at a time where many governments are ambitiously upgrading their governance systems with unbridled hi-tech capabilities.
ICYMI (In Case You Missed It), the following work was presented at the 2017 Annual Meeting of the American Political Science Association (APSA). The presentation, titled “Inequality is Always in the Room: Language and Power in Deliberative Democracy” was a part of the session “Is Deliberation War by Other Means?” on Thursday, August 31, 2017.
Posted by Catherine Allen-West
In a new paper, presented at the 2017 APSA meeting, Arthur Lupia, University of Michigan, and Anne Norton, University of Pennsylvania, explore the effectiveness of deliberative democracy by examining the foundational communicative acts that take place during deliberation.
Political parties across the developed and developing world increasingly rely on some form of primary to select nominees for legislative elections. The candidate selection process within political parties is crucial for shaping the extent to which voters can control elected representatives, as parties are important intermediaries between citizens and government.
The scientific literature has only scratched the surface in examining how primary elections operate in new democracies and what the implications of the system may be, both for the quality of candidates presented to the electorate and for general election outcomes. The process for selecting a candidate varies on several dimensions, including the restrictiveness of the rules for who may seek a nomination and who selects nominees.
Ghana has held regular, concurrent elections for president and a unicameral parliament since its transition to democracy in 1992. The two parties that dominate elections in Ghana’s parliament are the National Democratic Congress (NDC), the current ruling party, and the New Patriotic Party (NPP), the current opposition. The two parties pursue similar policy, but they differ in their ethnic bases of support. Voting is not exclusively along ethnic lines, but, in Ghana, ethnicity remains a strong determinant of vote choice overall.
One reason that Ichino and Nathan looked at primaries in a new democracy is that patronage is prevalent in these settings. The primaries are a contest over who becomes the more important local patron in a given constituency rather than a contest over policy issues and ideology. In these scenarios, voting in primaries can involve extensive vote buying through the distribution of private goods. In Ghana, aspirants (politicians seeking their party’s nomination) woo voters with gifts of flat screen TVs, new motorbikes, payment of school fees for their children and more. This system inherently benefits aspirants with immense personal wealth and the political connections to distribute these goods to the right people. It also disadvantages aspirants who are outsiders who may better represent the interests of the party membership, like women or people from different ethnic groups.
So, given Ghana’s recent reforms that expanded the size of the electorate, Ichino and Nathan argue that this expansion will have positive effects on democratic representation through two changes:
Vote buying becomes more difficult both logistically and financially due to the sheer size of new electorate.
The expanded primary electorate will include new voters from groups that have been underrepresented in local party leadership positions.
These changes in turn will affect what types of politicians choose to compete in primary elections and the types of politicians who win nominations. In particular, the researchers hypothesize that more female politicians and politicians from groups that are usually excluded from power will compete for nominations and that nominees will be more likely to come from these marginalized groups. Ultimately, a larger pool of people- representing a more diverse distribution of preferences and interests- has a viable path to a nomination, and thus to elected office.
Their results show just that: With the new reforms, the total number of aspirants increased (including women and other ethnic groups) and more of these aspirants went on to become the party nominee. Also interesting to note: the number of aspirants with a private sector background (which indicates more personal wealth to put towards vote buying) decreased significantly.
This figure shows the effects of reforms on the number of aspirants (total, women aspirants only, and then aspirants from different sets of ethnic groups).
Overall, the reforms increased the probability that the nominee will be from a non-plurality (local minority) ethnic group by 18 percentage points on average and reduced the probability that the nominee will be from the party’s core ethnic coalition by 12 percentage points on average.
This figure shows the effects of reforms on the characteristics of the selected nominee (whether that was the incumbent, someone who was a government official, etc.)
The results suggest that, in Ghana, the reforms opened up important positions in the party to previously under-represented groups. This work is important because it advances our understanding of the nature and effects of primary elections in new democracies, contributes to research on institutional reforms that can improve the political incorporation of women, and also shows how internal political dynamics within parties shape the connections between parties and ethnic groups in setting where ethnic competition is prevalent.
Post developed by Catherine Allen-West in coordination with Josh Pasek
ICYMI (In Case You Missed It), the following work was presented at the 2016 Annual Meeting of the American Political Science Association (APSA). The presentation, titled “Motivated Reasoning and the Sources of Scientific Illiteracy” was a part of the session “Knowledge and Ideology in Environmental Politics” on Friday, September 2, 2016.
At APSA 2016, Josh Pasek, Assistant Professor of Communication Studies and Faculty Associate at the Center For Political Studies presented work that delves into the reasons that people do not believe in prevailing scientific consensus.
He argues that widespread scientific illiteracy in the general population is not simply a function of ignorance. In fact, there are several reasons why an individual may answer a question about science or a scientific topic incorrectly.
They are ignorant of the correct answer
They have misperceptions about the science
They know what scientists say and disagree (rejectionism)
They are trying to express some identity that they hold in their response
The typical approach to measuring knowledge involves asking individuals multiple-choice questions where they are presumed to know something when they answer the questions correctly and to lack information when they either answer the questions incorrectly or say that they don’t know.
Pasek suggests that this current model for measuring scientific knowledge is flawed, because individuals who have misperceptions can appear less knowledgeable than those who are ignorant. So he and his co-author Sedona Chinn, also from the University of Michigan, set out with a new approach to disentangle these cognitive states (knowledge, misperception, rejectionism and ignorance) and then determine which sorts of individuals fall into each of the camps.
Instead of posing multiple-choice questions, the researchers asked the participants what most scientists would say about a certain scientific topic (like, climate change or evolution) and then examined how those answers compared to the respondent’s personal beliefs.
Across two waves of data collection, respondent answers about scientific consensus could fall into four patterns. They could be consistently correct, change from correct to incorrect, change from incorrect to correct or be consistently correct.
This set of cognitive states lends itself to a set of equations producing each pattern of responses:
Consistently Correct = Knowledge + .5 x Learning + .25 x Ignorance Correct then Incorrect = .25 x Ignorance Incorrect -> Correct =.5 x Learning + .25 x Ignorance Consistently Incorrect = Misperception + .25 x Ignorance
The researchers then reverse-engineered this estimation strategy for a survey aimed at measuring knowledge on various scientific topics. This yielded the following sort of translations:
In addition to classifying respondents as knowledgeable, ignorant, or misinformed, Pasek was especially interested in identifying a fourth category: rejectionist. These are individuals who assert that they know the scientific consensus but fail to hold corresponding personal beliefs. Significant rejectionism was apparent for most of the scientific knowledge items, but was particularly prevalent for questions about the big bang, whether humans evolved, and climate change.
Rejectionism surrounding these controversial scientific topics is closely linked to religious and political motivations. Pasek’s novel strategy of parsing out rejectionism from ignorance and knowledge provides evidence that religious individuals are not simply ignorant about the scientific consensus on evolution or that partisans are unaware of climate change research. Instead, respondents appear to have either systematically wrong beliefs about the state of the science or seem liberated to diverge in their views from a known scientific consensus.
Pasek’s results show a much more nuanced, yet at times predictable, relationship between scientific knowledge and belief in scientific consensus.
Post developed by Catherine Allen-West and Ozan Kuru.
ICYMI (In Case You Missed It) the following work was presented at the 2016 Annual Meeting of the American Political Science Association (APSA). The presentation, titled “Motivated Reasoning in the Perceived Credibility of Public Opinion Polls,” was part of the session “Surprises: A Magical Mystery Tour of Public Opinion and Political Psychology” on Saturday, September 3, 2016.
Polls have been an integral part of American democracy, political rhetoric, and news coverage since the 1930s. Today, there are new polls reported constantly, showing public opinion on a range of issues from the President’s approval rating to the direction of the country. Polls remain relevant because numbers and statistical evidence have always been regarded as sound evidence to support one’s beliefs or affirm their affiliations; similarly, polls are supposed to provide relatively objective information in politics.
However, despite their importance and ever-increasing prevalence, polls are often heavily criticized, both by the public and politicians, especially when they fail to predict election outcomes. Such criticisms and discounting of poll credibility is important, because people’s perceptions of polls matter. In such an environment, the perceived credibility of polls becomes an important issue for the public’s reception of poll findings, which then determines the likelihood of any meaningful impact of their results.
