This essay focuses on Hanna Pitkin’s understanding of what was at stake for politics and theory in her book on ordinary language philosophy, Wittgenstein and Justice (W&J). It does so by contrasting her preface to the first edition, in 1972, to the preface she wrote for the second edition in 1993, both of which I then compare to Toni Morrison’s 1992 preface to Playing in the Dark. Why Morrison? Morrison’s preface is built around a 1975 novel, The Words to Say It by Maria Cardinale. Her novel exemplifies Pitkin’s claim that ordinary language philosophy and psychoanalytic practice are deeply connected, but Morrison also pushes us beyond Pitkin to consider race and what Morrison called the “word-work” of her own creative fiction-making. By focusing on the novel’s racial subtext, Morrison’s preface presses the fact of racialized social division on Pitkin’s Wittgensteinian idea that ordinary language is a home to return to. Still, Morrison invokes the idea of “shareable language,” and the transformational possibilities in the word-work of truth-telling, in ways that suggest the resonance— and potential extensions—of Pitkin’s 1993 re-imagining of politics and theory.
{"title":"Ordinary Language and Race: Hanna Pitkin and Toni Morrison in Tandem and Tension","authors":"George Shulman","doi":"10.1086/725324","DOIUrl":"https://doi.org/10.1086/725324","url":null,"abstract":"This essay focuses on Hanna Pitkin’s understanding of what was at stake for politics and theory in her book on ordinary language philosophy, Wittgenstein and Justice (W&J). It does so by contrasting her preface to the first edition, in 1972, to the preface she wrote for the second edition in 1993, both of which I then compare to Toni Morrison’s 1992 preface to Playing in the Dark. Why Morrison? Morrison’s preface is built around a 1975 novel, The Words to Say It by Maria Cardinale. Her novel exemplifies Pitkin’s claim that ordinary language philosophy and psychoanalytic practice are deeply connected, but Morrison also pushes us beyond Pitkin to consider race and what Morrison called the “word-work” of her own creative fiction-making. By focusing on the novel’s racial subtext, Morrison’s preface presses the fact of racialized social division on Pitkin’s Wittgensteinian idea that ordinary language is a home to return to. Still, Morrison invokes the idea of “shareable language,” and the transformational possibilities in the word-work of truth-telling, in ways that suggest the resonance— and potential extensions—of Pitkin’s 1993 re-imagining of politics and theory.","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46182974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In Republic 8–9, Socrates explains how the kallipolis develops into a series of flawed regimes. Each regime is said to have a corresponding soul type; these souls are described as a lineage of fathers and sons. Socrates, then, narrates not only a political story, but what is in effect a multigenerational family saga: the story of a moral decline and fall over the course of five generations, set amidst political turmoil and revolution, covering roughly a century of narrative ground from its start in tragicomedy to its end in disaster. What are the implications of this choice to convey the change from kallipolis to tyranny through such an emotionally charged narrative? As a hybrid of generic conventions, the family saga suggests Plato’s view that existing cultural models were insufficient for understanding and reacting to constitutional breakdown. I consider two accounts Socrates offers for the relationship between his family and political narratives, discussing the interpretive difficulties raised by each, and proposing that these difficulties oblige the reader to attend closely to the details of character and plot in Socrates’s story. I treat the family saga less as an explanation of constitutional breakdown than as an affective model that attempts to make such a breakdown emotionally vivid—one that is nevertheless consistent with the Republic’s strict limits on imitative poetry. Finally, I consider the kinds of political action that the family saga might motivate in the Republic’s readers, under three sets of assumptions about Plato’s attitudes toward Athenian democracy and the kallipolis.
{"title":"Plato the Novelist: The Family Saga in Republic 8–9","authors":"Robert Goodman","doi":"10.1086/725238","DOIUrl":"https://doi.org/10.1086/725238","url":null,"abstract":"In Republic 8–9, Socrates explains how the kallipolis develops into a series of flawed regimes. Each regime is said to have a corresponding soul type; these souls are described as a lineage of fathers and sons. Socrates, then, narrates not only a political story, but what is in effect a multigenerational family saga: the story of a moral decline and fall over the course of five generations, set amidst political turmoil and revolution, covering roughly a century of narrative ground from its start in tragicomedy to its end in disaster. What are the implications of this choice to convey the change from kallipolis to tyranny through such an emotionally charged narrative? As a hybrid of generic conventions, the family saga suggests Plato’s view that existing cultural models were insufficient for understanding and reacting to constitutional breakdown. I consider two accounts Socrates offers for the relationship between his family and political narratives, discussing the interpretive difficulties raised by each, and proposing that these difficulties oblige the reader to attend closely to the details of character and plot in Socrates’s story. I treat the family saga less as an explanation of constitutional breakdown than as an affective model that attempts to make such a breakdown emotionally vivid—one that is nevertheless consistent with the Republic’s strict limits on imitative poetry. Finally, I consider the kinds of political action that the family saga might motivate in the Republic’s readers, under three sets of assumptions about Plato’s attitudes toward Athenian democracy and the kallipolis.","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42083563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
By the final weeks of the 2022 election campaign, there was a clear consensus among pundits and political analysts that Democrats were likely to experience a shellacking in the midterm elections, especially in the House of Representatives. Republican leaders and strategists were confident that a “red wave” or even a “red tsunami” was approaching. Even more objective observers such as Chuck Todd and Mark Murray of NBC News believed that a number of indicators were clearly pointing toward large GOP gains in the House, the most prominent being President Biden’s poor approval rating, which had been stuck in the low-forties for months. While many political observers expected Joe Biden’s poor approval rating to result in big Republican gains in the 2022 election, historically, presidential approval has not been a very accurate predictor of midterm seat swing. For the nineteen midterm elections between 1946 and 2018, the correlation of net presidential approval (approval-disapproval) with House seat swing was a rather modest .66 while the correlation with Senate seat swing was a very weak .36. Presidential approval explained only 44% of the variation in House seat swing and only 13% of the variation in Senate seat swing. One indicator that has been shown to produce more accurate forecasts of both House and Senate seat swing than presidential approval is the generic ballot—a question in which voters are asked which party they plan to vote for without providing names of individual House or Senate candidates. By combining the results of generic ballot polling with the number of House or Senate seats that the president’s
{"title":"The Generic Ballot Model and the 2022 Midterm Election","authors":"A. Abramowitz","doi":"10.1086/725239","DOIUrl":"https://doi.org/10.1086/725239","url":null,"abstract":"By the final weeks of the 2022 election campaign, there was a clear consensus among pundits and political analysts that Democrats were likely to experience a shellacking in the midterm elections, especially in the House of Representatives. Republican leaders and strategists were confident that a “red wave” or even a “red tsunami” was approaching. Even more objective observers such as Chuck Todd and Mark Murray of NBC News believed that a number of indicators were clearly pointing toward large GOP gains in the House, the most prominent being President Biden’s poor approval rating, which had been stuck in the low-forties for months. While many political observers expected Joe Biden’s poor approval rating to result in big Republican gains in the 2022 election, historically, presidential approval has not been a very accurate predictor of midterm seat swing. For the nineteen midterm elections between 1946 and 2018, the correlation of net presidential approval (approval-disapproval) with House seat swing was a rather modest .66 while the correlation with Senate seat swing was a very weak .36. Presidential approval explained only 44% of the variation in House seat swing and only 13% of the variation in Senate seat swing. One indicator that has been shown to produce more accurate forecasts of both House and Senate seat swing than presidential approval is the generic ballot—a question in which voters are asked which party they plan to vote for without providing names of individual House or Senate candidates. By combining the results of generic ballot polling with the number of House or Senate seats that the president’s","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41283716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bruno Jérôme, V. Jerome, Philippe Mongrain, R. Nadeau
Before the November 2022 midterms, we proposed a model to forecast the aggregate results of elections to the U.S. House of Representatives. This model rests on two well-established traditions, that of vote-popularity functions and that of “regionalized” pooled cross-sectional time-series models. The proposed House model is inspired by the State-by-State Political Economy (2SPE) Model previously applied to presidential elections, which is based on local and national data. In 2020, the 2SPE Model gave Joe Biden 51.69% of the two-party nationwide popular vote (a 0.6-point error) and correctly predicted the winner in forty-seven states plus the District of Columbia. The House model innovates by including presidential popularity data by state formidterm elections as well as variables tracing the trajectory of
{"title":"Forecasting the 2022 U.S. House Elections with a State-by-State Model: No Red-Carpet Treatment for the Republicans","authors":"Bruno Jérôme, V. Jerome, Philippe Mongrain, R. Nadeau","doi":"10.1086/725240","DOIUrl":"https://doi.org/10.1086/725240","url":null,"abstract":"Before the November 2022 midterms, we proposed a model to forecast the aggregate results of elections to the U.S. House of Representatives. This model rests on two well-established traditions, that of vote-popularity functions and that of “regionalized” pooled cross-sectional time-series models. The proposed House model is inspired by the State-by-State Political Economy (2SPE) Model previously applied to presidential elections, which is based on local and national data. In 2020, the 2SPE Model gave Joe Biden 51.69% of the two-party nationwide popular vote (a 0.6-point error) and correctly predicted the winner in forty-seven states plus the District of Columbia. The House model innovates by including presidential popularity data by state formidterm elections as well as variables tracing the trajectory of","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47571496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charles Tien and Robyn Marasco: In your award winning 2016 book, How China Escaped the Poverty Trap, you show how China’s economic development was made possible by what you call “directed improvisation”—directives from leaders in Beijing to local officials to improvise in finding solutions to everchanging problems. Has this reliance on local level-improvisation changed under Xi Jinping in recent years? Has this model been applied to battling COVID?
{"title":"Ask a Political Scientist: A Conversation with Yuen Yuen Ang about China and Political Science","authors":"C. Tien, Robyn Marasco","doi":"10.1086/725364","DOIUrl":"https://doi.org/10.1086/725364","url":null,"abstract":"Charles Tien and Robyn Marasco: In your award winning 2016 book, How China Escaped the Poverty Trap, you show how China’s economic development was made possible by what you call “directed improvisation”—directives from leaders in Beijing to local officials to improvise in finding solutions to everchanging problems. Has this reliance on local level-improvisation changed under Xi Jinping in recent years? Has this model been applied to battling COVID?","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45894689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The prospective model of voting behavior has its genesis in the forecasting errors made in 1994. Virtually every political scientist that made a forecast was off by a good bit. One political scientist made a bet concerning the accuracy of his forecast of the Republicans only picking up in the low single digits. The actual outcome was a fifty-four-seat pickup by the Republicans. The political scientist (who shall remain nameless here) who made the single-digit forecast lost a modest sum of money. Like many articles, this project started by thinking that the others in the enterprise had made some mistakes. Many of the models make use of economic conditions when attempting to forecast election outcomes. The voting behavior literature is replete with retrospective and prospective economic models of voting behavior. Unfortunately, political forecasting with economic data necessitates parsimony because we have few cases. Consequently, the model employed here follows the literature focusing on economic expectations. Specifically, the Survey of Consumer Attitudes and Behavior has an item that asks respondents to evaluate whether they will be better off or worse off in the
{"title":"Economic Pessimism and the 2022 Election: A Postmortem","authors":"Brad Lockerbie","doi":"10.1086/725243","DOIUrl":"https://doi.org/10.1086/725243","url":null,"abstract":"The prospective model of voting behavior has its genesis in the forecasting errors made in 1994. Virtually every political scientist that made a forecast was off by a good bit. One political scientist made a bet concerning the accuracy of his forecast of the Republicans only picking up in the low single digits. The actual outcome was a fifty-four-seat pickup by the Republicans. The political scientist (who shall remain nameless here) who made the single-digit forecast lost a modest sum of money. Like many articles, this project started by thinking that the others in the enterprise had made some mistakes. Many of the models make use of economic conditions when attempting to forecast election outcomes. The voting behavior literature is replete with retrospective and prospective economic models of voting behavior. Unfortunately, political forecasting with economic data necessitates parsimony because we have few cases. Consequently, the model employed here follows the literature focusing on economic expectations. Specifically, the Survey of Consumer Attitudes and Behavior has an item that asks respondents to evaluate whether they will be better off or worse off in the","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49520583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
InDiscourses, Machiavelli opined, “it is easy, by diligent study of the past, to foresee what is likely to happen in the future in any republic.” The message: there is value in exploring history to predict. A strong pedigree of political science research acknowledges the importance of path dependence, “lock-in,” and “Laws of Politics.” Such recurrences at face bode well for forecasting. Election forecasting models traditionally use political-economic variables to predict results. In 2022, we formulated a model forecast of U.S. Congressional elections with a twist—spurning any public opinion or macroeconomy measure. Instead, we tested whether historical junctures, state-level party strength, and federalism dynamics offered solid guides to the performance of the Democratic Party, historically dominant in Congress since 1946. Our analysis demonstrated that this Political History model offered credible estimates of Democrats’ performance in thirty-eight Congressional elections from 1946–2020, with out-of-sample predictions
{"title":"A Political-History Forecast Model of Congressional Elections: Lessons Learned from Campaign 2022","authors":"S. Quinlan, M. Lewis-Beck","doi":"10.1086/725252","DOIUrl":"https://doi.org/10.1086/725252","url":null,"abstract":"InDiscourses, Machiavelli opined, “it is easy, by diligent study of the past, to foresee what is likely to happen in the future in any republic.” The message: there is value in exploring history to predict. A strong pedigree of political science research acknowledges the importance of path dependence, “lock-in,” and “Laws of Politics.” Such recurrences at face bode well for forecasting. Election forecasting models traditionally use political-economic variables to predict results. In 2022, we formulated a model forecast of U.S. Congressional elections with a twist—spurning any public opinion or macroeconomy measure. Instead, we tested whether historical junctures, state-level party strength, and federalism dynamics offered solid guides to the performance of the Democratic Party, historically dominant in Congress since 1946. Our analysis demonstrated that this Political History model offered credible estimates of Democrats’ performance in thirty-eight Congressional elections from 1946–2020, with out-of-sample predictions","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44224434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Iowa Electronic Markets (IEM) are real-money, internet-based futures markets where contract prices reveal information about future events. Since 1988, the IEM has run election markets establishing a track record of accuracy. Self-selected IEM traders invest their own money and trade contracts with payoffs tied to future election outcomes. This incentivizes accurate forecasting. Prices change when price-determining traders’ beliefs change. Thus, IEM price dynamics
{"title":"Iowa Electronic Markets Seat Distribution Forecasts for the 2022 U.S. House and Senate Elections: A Retrospective","authors":"Joyce E. Berg, Thomas S Gruca, Thomas A. Rietz","doi":"10.1086/725241","DOIUrl":"https://doi.org/10.1086/725241","url":null,"abstract":"The Iowa Electronic Markets (IEM) are real-money, internet-based futures markets where contract prices reveal information about future events. Since 1988, the IEM has run election markets establishing a track record of accuracy. Self-selected IEM traders invest their own money and trade contracts with payoffs tied to future election outcomes. This incentivizes accurate forecasting. Prices change when price-determining traders’ beliefs change. Thus, IEM price dynamics","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42715298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This model was developed specifically for the Midterm Election Forecasting Roundtable at the 2022 APSA Annual Meeting in Montréal. While most House forecast models generate forecasts either at the district level, or the aggregate number of seats won by a party, this model is different. It models the number of House seats in each state won by the Democrats and generates a forecast at the end of August. With 2022 being the first post-redistricting election of the decade, the main motivation behind this model was to attempt to capture the potential impact that gerrymandering would have on state-level outcomes. Gerrymandering is most likely to occur in a state under two conditions: (1) when reapportionment leads to a change in the number of seats apportioned to the state, and (2) when the state’s redistricting process is entirely controlled by one political party. Given that, this model includes a simple dummy variable for Gerrymander Potential, which is simply 1 if both of those conditions exist in a state in the first post-redistricting election of each decade. It is also party adjusted, taking on a negative value if the state’s redistricting process is controlled by Republicans, and positive if it is controlled by Democrats. If a state’s redistricting process was subject to divided party control, handled by an independent redistricting commission, or where the maps were drawn by state courts, I assigned this variable a value of 0.
{"title":"A State-Level U.S. House Election Forecast Model for 2022: Modeling the Potential Effects of Gerrymandering","authors":"Jay A. DeSart","doi":"10.1086/725244","DOIUrl":"https://doi.org/10.1086/725244","url":null,"abstract":"This model was developed specifically for the Midterm Election Forecasting Roundtable at the 2022 APSA Annual Meeting in Montréal. While most House forecast models generate forecasts either at the district level, or the aggregate number of seats won by a party, this model is different. It models the number of House seats in each state won by the Democrats and generates a forecast at the end of August. With 2022 being the first post-redistricting election of the decade, the main motivation behind this model was to attempt to capture the potential impact that gerrymandering would have on state-level outcomes. Gerrymandering is most likely to occur in a state under two conditions: (1) when reapportionment leads to a change in the number of seats apportioned to the state, and (2) when the state’s redistricting process is entirely controlled by one political party. Given that, this model includes a simple dummy variable for Gerrymander Potential, which is simply 1 if both of those conditions exist in a state in the first post-redistricting election of each decade. It is also party adjusted, taking on a negative value if the state’s redistricting process is controlled by Republicans, and positive if it is controlled by Democrats. If a state’s redistricting process was subject to divided party control, handled by an independent redistricting commission, or where the maps were drawn by state courts, I assigned this variable a value of 0.","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49646313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To forecast the 2022 congressional races, we returned to our structural model, which we have utilized to good effect since 2010. Our forecasts for 2022 were published before the election. The model rests on strong theory and expresses itself in a political economy equation. For the House, which we focus on in this brief essay, midterms are assumed to be referenda on the president and the incumbent party, where voters reward or punish according to key economic and political issues, as measured by aggregate indicators. These ex ante national forecasts we adjust a bit, to account for local conditions via expert judgement. In 2022, our Structure-X forecast for the House foresaw a Democratic loss of thirty-seven seats. This prediction is correct in that it foretells the ruling party would experience a net loss, so upholding the “iron law” of midterm incumbent performance. Moreover, this loss technically falls, just barely, within the 95% confidence interval for the structural OLS equation (i.e., 37 1/2 (1.96 # 18.82) 5 [.11 to 73.84].) Thus, when strictly judged as an outlier, it lands on the line. Nevertheless, there is no denying the error of twenty-eight seats looks large (given the actual Democratic loss of nine seats). Here we begin to assess the source of
{"title":"Referendum Model Forecasts: Trump and the 2022 Midterm Errors","authors":"C. Tien, M. Lewis-Beck","doi":"10.1086/725242","DOIUrl":"https://doi.org/10.1086/725242","url":null,"abstract":"To forecast the 2022 congressional races, we returned to our structural model, which we have utilized to good effect since 2010. Our forecasts for 2022 were published before the election. The model rests on strong theory and expresses itself in a political economy equation. For the House, which we focus on in this brief essay, midterms are assumed to be referenda on the president and the incumbent party, where voters reward or punish according to key economic and political issues, as measured by aggregate indicators. These ex ante national forecasts we adjust a bit, to account for local conditions via expert judgement. In 2022, our Structure-X forecast for the House foresaw a Democratic loss of thirty-seven seats. This prediction is correct in that it foretells the ruling party would experience a net loss, so upholding the “iron law” of midterm incumbent performance. Moreover, this loss technically falls, just barely, within the 95% confidence interval for the structural OLS equation (i.e., 37 1/2 (1.96 # 18.82) 5 [.11 to 73.84].) Thus, when strictly judged as an outlier, it lands on the line. Nevertheless, there is no denying the error of twenty-eight seats looks large (given the actual Democratic loss of nine seats). Here we begin to assess the source of","PeriodicalId":46912,"journal":{"name":"Polity","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42962111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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