According to a 2023 survey, Americans between 18 and 29 years of age were more likely to identify with the Democratic Party than any other surveyed age group. While 39 percent identified as Democrats, only 14 percent identified ad Republicans. However, those 50 and older identified more with the Republican Party.

This study seeks to explain state adoptions of same-day registration, with a focus on determining whether the Democratic (Republican) Party’s support of (resistance to) this impactful voting reform is driven by strategic electoral considerations. I find that states have an increased probability of enacting the reform when legislative Democrats are in the precarious position that comes with having just experienced minority status in one or both chambers. Relatedly, I demonstrate that the presence of a Republican legislature does not make adoption less likely until the size of the Black population reaches a certain threshold. In fact, provided the Black population is small enough, Republican control of the legislature encourages reform. The results offer conflicting evidence, however, that large Latino populations deter the GOP from establishing same-day registration. Considered together, the results cast doubt on the claim that either party’s position is informed by principle alone.

In this paper, we revisit the effect of ballot access laws on voter confidence in the outcome of elections. We argue that voter confidence is conditioned by partisanship. Democrats and Republicans view election laws through a partisan lens, which is especially triggered when coalitions lose. We used The Integrity of Voting data set, along with other data sets, to test our hypotheses. The sample frame for the Integrity of Voting Survey was eligible persons who voted in the 2020 Presidential elections with accessible internet email addresses. Our sample consisted of two samples from two different vendors. Surveys were conducted with 17,526 voters drawing on two independent samples of registered voters who reported voting in the 2020 Presidential election. Email addresses for registered voters in each state were purchased from L2, a commercial vendor specializing in obtaining email addresses for registered voters. Interviews were solicited from one million voters in all 50 states, with 10,770 completed interviews for a response rate of .011%. A second sample of internet interviews were solicited and completed with 6,756 2020 voters using Dynata’s proprietary select-in survey of voters in selected states with smaller populations of registered voters. A minimum of roughly 100 2020 election voters were interviewed in each state. Our state samples were weighted using a raking technique on age, race, gender, education, and vote mode demographics from the U.S. Census Bureau’s 2020 Voting and Registration in the Election of November 2020 supplement to the Current Population survey (2021), as well as party identification totals from post-election exit polls conducted by the Associated Press (2020). Surveys were conducted between the first week in December, 2020 and the first week in February 2021.

This is the dataset I used to figure out which sociodemographic factor including the current pandemic status of each state has the most significan impace on the result of the US Presidential election last year. I also included sentiment scores of tweets created from 2020-10-15 to 2020-11-02 as well, in order to figure out the effect of positive/negative emotion for each candidate - Donald Trump and Joe Biden - on the result of the election.

Details for each variable are as below: - state: name of each state in the United States, including District of Columbia - elec16, elec20: dummy variable indicating whether Trump gained the electoral votes of each state or not. If the electors casted their votes for Trump, the value is 1; otherwise the value is 0 - elecchange: dummy variable indicating whether each party flipped the result in 2020 compared to that of the 2016 - demvote16: the rate of votes that the Democrats, i.e. Hillary Clinton earned in the 2016 Presidential election - repvote16: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2016 Presidential election - demvote20: the rate of votes that the Democrats, i.e. Joe Biden earned in the 2020 Presidential election - repvote20: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2020 Presidential election - demvotedif: the difference between demvote20 and demvote16 - repvotedif: the difference between repvote20 and repvote16 - pop: the population of each state - cumulcases: the cumulative COVID-19 cases on the Election day - caseMar ~ caseOct: the cumulative COVID-19 cases during each month - Marper10k ~ Octper10k: the cumulative COVID-19 cases during each month per 10 thousands - unemp20: the unemployment rate of each state this year before the election - unempdif: the difference between the unemployment rate of the last year and that of this year - jan20unemp ~ oct20unemp: the unemployment rate of each month - cumulper10k: the cumulative COVID-19 cases on the Election day per 10 thousands - b_str_poscount_total: the total number of positive tweets on Biden measured by the SentiStrength - b_str_negcount_total: the total number of negative tweets on Biden measured by the SentiStrength - t_str_poscount_total: the total number of positive tweets on Trump measured by the SentiStrength - t_str_poscount_total: the total number of negative tweets on Trump measured by the SentiStrength - b_str_posprop_total: the proportion of positive tweets on Biden measured by the SentiStrength - b_str_negprop_total: the proportion of negative tweets on Biden measured by the SentiStrength - t_str_posprop_total: the proportion of positive tweets on Trump measured by the SentiStrength - t_str_negprop_total: the proportion of negative tweets on Trump measured by the SentiStrength - white: the proportion of white people - colored: the proportion of colored people - secondary: the proportion of people who has attained the secondary education - tertiary: the proportion of people who has attained the tertiary education - q3gdp20: GDP of the 3rd quarter 2020 - q3gdprate: the growth rate of the 3rd quarter 2020, compared to that of the same quarter last year - 3qsgdp20: GDP of 3 quarters 2020 - 3qsrate20: the growth rate of GDP compared to that of the 3 quarters last year - q3gdpdif: the difference in the level of GDP of the 3rd quarter compared to the last quarter - q3rate: the growth rate of the 3rd quarter compared to the last quarter - access: the proportion of households having the Internet access