Will Comprehensive Immigration Reform Pass in the House?
Conclusion: The road to CIR is an uphill climb in the House, as just around 200 representatives are likely to vote yes. The data suggest a low of 183 and a high of 203. However, the results also show that 33 representatives who are likely to vote no on CIR are electorally vulnerable based on their November 2012 electoral margin of victory. These representatives are thus likely to be the locus of grassroots efforts to pass CIR.
As the details of the House comprehensive immigration reform bill begin to emerge, it is an opportune time to ask ourselves what the likelihood of passage is in the 435-member House of Representatives. Before proceeding to the analysis, I recap the events that have brought us to this point (jump below if you’ve already read the Senate post).
Will comprehensive immigration reform (CIR) pass in 2013? In a recent whirlwind of events since President Obama’s reelection, the Senate “gang of 8” introduced its draft principles on immigration reform, the President then addressed the nation describing his own vision of reform just one day after the gang of 8 unveiled their blueprint, the White House promptly released more details of the president’s proposal immediately after his speech, the House Judiciary Committee recently held its first hearing on the issue, and then the Senate Judiciary Committee did the same. The momentum that has been building towards CIR, which started well before last November’s election, has shown no signs of slowing down. However, momentum itself is no guarantee that reform will happen.
As a matter of politics, the key question is whether there are enough votes in Congress? More specifically, are there 60 filibuster-proof votes in the Senate and 218 votes in the House? While there are no crystal balls to tell us how legislators will ultimately vote, the recent history of immigration politics in the U.S. provides sufficient information to make informed predictions, not only about how current members of Congress are likely to vote on CIR, but also about what the final bill is likely to include (or not include).
Three key factors are worth noting:
- First, while the current push for immigration reform represents its own distinct stage, many of the key actors are still the same. Nearly 200 representatives who are currently in the House were also in office during the contentious debate over H.R. 4437 in 2005. In the Senate, nearly 50 sitting Senators were also around during the 2006 immigration reform effort. This provides a rich pool of voting history to analyze and learn from.
- Second, while we still do not know the details of the Senate or House bills, a) the degree of overlap between the gang of 8’s principles, the President’s proposal, and the 2006/2007 Senate CIR bills, coupled with b) the many lingering stand-alone immigration-related bills that have been introduced in Congress over the past few years, suggests that while the devil remains in the details, many of the details are already known. This is not to suggest that 2013’s bill will perfectly mirror previous reform efforts. Rather, it is to say that in analyzing the hundreds of amendments to CIR 2006 and 2007 and all of the immigration-related bills that have been introduced in Congress since 2005, we can create a map of the road ahead.
- Third, the November presidential election showed that immigrant communities – and communities of color more generally – are increasingly flexing their political muscle. This adds confidence to our ability to use the demographic makeup of states and districts to inform how we think legislators are likely to vote on CIR.
In analyzing over 10,000 roll call votes on immigration-related legislation in the House from H.R. 4437 to present, and in identifying the votes that most closely approximates the House immigration reform proposal – that is, a bill that includes a path to citizenship for DREAMers, but no “special” path for others – the data suggest that between 183 and 203 representatives are likely to vote for CIR. The models accurately predict up to 94.4% of all key votes analyzed. A full list of all House Representatives broken down into into their predicted categories of support and opposition can be found here: CIR House Analysis for CIR Blog.
Ending the story here would paint a bleak portrait. However, the numbers mentioned above does not mean that CIR is not possible in the House. If we assume that those who are predicted to “lean yes,” in fact, vote yes, this brings us to 203 votes. The question then becomes, how can this number be increased?
- Getting all Democrats on Board with CIR. In looking at representatives who are predicted to “lean no,” 1 is a Democrat. In additional there are 4 Democrats who are predicted as “solid no” votes. Switching these no votes to yes votes is no easy task given the voting records of these representatives. However, getting all Democrats on board with CIR represents one practical approach
- Electorally vulnerable no votes. There are 33 representatives who are predicted to vote no and are electorally vulnerable – meaning the representative won by a margin of less than 10 points during the November 2012 elections. In looking more closely at electorally vulnerable no votes, we see that 4 are Democrat and the rest are Republican. These representatives are spread across 21 different states.
In all, there is no easy road to CIR in the House and all paths to it are paved with obstacles. However, the data point to several alternate routes. Stay tuned for more detailed analyses of the electorally vulnerable no vote districts.
The underlying assumption of the statistical models used to estimate likely support and opposition is that representatives who represent diverse districts, particularly those with large foreign-born and Hispanic/Latino and Asian populations, are more likely to support a CIR bill that includes a path to citizenship than Representatives in mostly white districts – while considering other factors. One way to interpret these results is thus: to the extent that Representatives are responsive to the changing demographics of their districts, we can expect them to vote as the model predicts. However, it is not yet clear whether changing demographics – even given the November 2012 elections – will parlay into more legislative support for CIR.
Step 1 of this analysis models the final passage vote on H.R. 4437, amendment 667, which would have made the bill even more restrictive, and the DREAM Act vote in 2010. Here, I estimate several models. Model 1 focuses on the racial and ethnic characteristics of districts while controlling for other factors (e.g., the party affiliation, southern state). Model 2 focuses the racial and ethnic backgrounds of representatives. Model 3 focuses on the racial and ethnic characteristics of districts, the racial and ethnic background of representatives, and the interaction between race/ethnicity and geography. I then construct categories of likelihood, wherein each predicted probability is characterized as a “solid yes” vote, “leans yes,” “leans no,” and “solid no” vote. These categories take into account the confidence intervals around each predicted probability. Figure 1 provides a graphical illustration.
Figure 1. Predicted probabilities and 95% confidence interval for all 435 Representatives
Step 2 critically evaluates the predicted probabilities obtained in step 1 by analyzing the actual voting record on immigration-related bills for each Representative (where a robust voting record exists). For example, if a Representative voted for H.R. 4437, for amendment 667 and against the DREAM Act, I expect the Representative to be categorized as “solid no” when it comes to supporting a CIR bill with a path to citizenship. Conversely, if a Representative voted against H.R. 4437, against amendment 667, and for the DREAM Act, I expect the Representative to be categorized as “solid yes.” Model 3 correctly classified 134 out of 142 Representatives, which represents a match rate of just over 94%.
Step 3 expands the analysis in step 1 to the full history of voting on immigration-related bills in the House from H.R. 4437 to present as an additional check. The determinants of past votes on immigration policy are analyzed using a logistic regression model: Pr (Voteit = 1|Xit) = πit(β0 + β1Xit + εit) where Xit represents a vector of explanatory variables, including key demographics characteristics, and εit represents the error term. In the House, there are over 10,000 observations to analyze. Predicted probabilities are then obtained from m = 1,000 simulations for each current member of the House using the estimated models.
Step 4 synthesizes all of the data – actual voting records and predicted probabilities – and assigns each Senator to one of the four categories of likelihood described above.
Special thanks to Hillary Kosnac, Cat Benson, Brana Vlasic, Cameron Kaveh, and Sierra Graves for wonderful research assistance.