Thank you for inspiring us, motivating us and challenging us with your lives and your determination. Thank you for demonstrating what is possible with perseverance, courage, collaboration and partnership. We applaud you for being role models for your peers and demonstrating that South Africa is alive with possibility. Right now, our country needs solutions that will help us to renew and grow, but these solutions will only present themselves if we listen with our hearts to the cries and aspirations of our people.
As we enter Youth Month, we call on the young people of our nation to become socially and politically active and to help us create, overcome, endure, transform and love. I wish to convey my sincere thanks to President Jacob Zuma for his confidence that we will diligently execute the responsibilities with which he has entrusted us. Skip to main content. You are here Home » Newsroom » Speeches. Walter Sisulu was, by nature and by conviction, a unifier, a consensus builder.
It can be done. But only if we work together. Honourable Members, South Africans are evolving ways of working together in a number of areas and endeavours. Madam Speaker, There are other areas where we can see emerging the seeds of a new social compact.
One of these is the youth employment initiative, which is aimed at the challenge of youth unemployment This scheme will draw on the resources, capabilities and commitment of business, government and labour to provide paid internships for up to a million young South Africans over three years across the economy.
We are appreciative of the collaboration that is emerging on skills development. We challenge companies across the country that need these skills to adopt a TVET college.
Globally, colleges thrive when they are linked to industry. Madam Speaker, Over the course of the last year, more than , South Africans were engaged in a number of public employment programmes, where they were providing essential public services while receiving a stipend income, gaining work experience and acquiring skills.
In doing so, she became the sixth black female neurosurgeon in South Africa. Honourable Members, Another area where we have seen the value of collaboration, leadership, patience, and understanding is through the work we do through SANAC. Prudence was diagnosed with HIV in when she was a university student. She is an organiser, an agitator, an activist and a valued collaborator. We had to consider the needs of many different and diverse constituencies.
We needed to ensure that all had a place and that all had a voice. We have seen the importance of getting people to determine and own their destiny. Honourable Members, In another area of work, we started a programme to replicate high-performance, integrated delivery of services across the country.
Operation Sukuma Sakhe is a model of a government existing and living among its people. Honourable Members, As Africa Month draws to a close today, we reflect that the freedom and democracy we enjoy today is partly a consequence of the huge sacrifices that the peoples of our continent made in the struggle against apartheid.
Honourable Members, As I conclude, allow me to extend a word of gratitude to the remarkable young women who have allowed me to tell a part of their stories here today. Thank you most of all for gracing us with your presence here today. We welcome you to Parliament. We must dream, hope and build again. In Michigan, Wisconson, and Pennsylvania Trump could have achieved margins that were 0.
Although these states were not close, Ohio and Iowa would have seen a similarly sized 0. However, North Carolina and Florida, two states undergoing relatively rapid demographic shifts, were the most affected by changes to their eligible voter population. Absent these changes, Trump would have expanded his win by 0. Broadening our horizons slightly, a number of states were significantly more Democratic in than they would have been given a stable population. Georgia and Maryland were even more extreme, with margins around 1.
In fact, only two places were Republican-advantaged as a result of demographic changes since Washington, D. Both have black populations that are shrinking as a share of eligible voters while less Democratic-leaning voters who are white and college-educated, Latino, and Asian or other race are growing. The demographic churn within these states creates a unique scenario: populations that are simultaneously becoming more racially diverse and less Democratic.
The findings from these data and simulations suggest that many of the existing intra-Democratic Party debates about the path forward have missed the mark. Rather than deciding whether to focus on 1 increasing turnout and mobilization of communities of color, a key component of the Democratic base, or 2 renewing efforts to persuade and win back some segment of white non-college-educated voters and to increase inroads among the white college-educated population, Democrats would clearly benefit from pursuing a political strategy capable of doing both.
If black turnout and support rates in had matched levels, Democrats would have held Florida, Michigan, Pennsylvania, and Wisconsin and flipped North Carolina, for a to Electoral College victory. So increasing engagement, mobilization, and representation of people of color must remain an important and sustained goal of Democrats. They cannot expect to win and expand their representation in other offices without the full engagement and participation of voters who are black, Latino, and Asian American or other race.
Given the fact that the white non-college-educated voting population is almost four times larger as a share of the electorate than is the black voting population, it is critical for Democrats to attract more support from the white non-college-educated voting bloc—even just reducing the deficit to something more manageable, as Obama did in and Likewise, the apparent shift to third-party voting and potential disengagement among younger voters must be considered carefully if Democrats are to make gains against Trump and Republicans in President Trump can conceivably reconstruct his primarily white coalition from with very few changes and still eke out a narrow Electoral College victory in But this assumes that Democrats do little to either increase the turnout of voters of color or to make inroads with disaffected white Trump voters, particularly Obama-Trump voters.
Alternatively, both Trump and Republicans could expand their electoral advantages among white voters by focusing and delivering on their economic promises on infrastructure, jobs, and wages and doing more to help people with health care. Given the trajectory of the current administration, this seems unlikely and could actually lead to a schism and third-party split among Republicans. Rob Griffin is the director of quantitative analysis at the Center for American Progress.
The authors would like to thank Lauren Vicary, Emily Haynes, Will Beaudouin, Steve Bonitatibus, and Chester Hawkins for their excellent editorial and graphic design work on this report.
For this project we developed original turnout and support estimates by combining a multitude of publicly available data sources. We did this in order to deal with what we believe are systematic problems with some of the most widely available and widely cited pieces of data about elections. One of the underappreciated problems in the world of election analysis is that some of the most reliable sources of data available on demographics, turnout, and support do not play very well together.
For example, if we combine some of the best data we have on demographics with the best data we have on turnout, we find that they vary from the actual levels of turnout observed on Election Day. These estimates are fully integrated with one another and, when combined, recreate the election results observed in and Below is a more detailed description of how each was created. We started off our process by collecting detailed demographic data at the county level from the U.
The goal of this process was to produce reasonable estimates about the composition of eligible voters within a given county. Specifically, we wanted to know how many eligible voters in each county fell into each our 32 demographic groups. For example, data on the race and age distribution as well as data on the age and education level distribution within a county are available separately. To overcome this problem we employed a two-stage estimation process. We then used iterative proportional fitting IPF to make these various pieces of data that are available line up with one another.
IPF is a form of adjustment that allowed us to make individual group counts—for example, the number of eligible voters in a county who are black, 18—29 years old, and have a college degree—line up with known population margins—for example, the number of eligible voters who are black and have a college degree, the number of eligible voters who are age 18—29 and have a college degree, and the number of eligible voters who are black and age 18— At this point in the process we had estimates on the eligible voter composition of each county, but there were several notable problems.
First, the use of the 5-year ACS was necessary in order to get estimates for every county in the United States, but it provides a somewhat blurry image of the year in question.
Data from the 5-year ACS are an amalgamation of data from —, while data are from — In short, the ACS provides the necessary coverage but at the expense of giving us an accurate picture of the population as it existed in the year in question. Second, the IPF process tends to spread certain characteristics—say, citizenship—somewhat indiscriminately across groups so long as the totals line up with other margins. This is particularly problematic for something like education groups where—outside of the non-Hispanics white population—we see different rates of citizenship.
Third, the IPF process inevitably generates estimates that are logically consistent within a county given the margins that have been provided, but does not collectively add up to the number of people one can expect to belong to a given group in a state.
To address all three problems we included an additional corrective step. Using the individual-level data from the and 1-year American Community Survey, we could accurately estimate the real state-level race, age, and education level composition of eligible voters.
Logically, the numbers of eligible voters who fall into our 32 groups in the counties must add up to the number observed at the state level.
We once again employed IPF to make the frequencies in the counties collectively line up with the frequencies at the state level. These were used as our final estimates for eligible voter composition in each state.
The process of creating county-level and turnout rates for each of our 32 demographic groups began by generating state-level estimates for these groups.
Using data from the and November Supplement of the Current Population Survey, or CPS, we ran cross-nested multilevel models that estimated the turnout rate for each year, state, race, age, and education level group represented in the data. We then fed those state-level turnout estimates into the eligible voter counts we generated in the previous step. This provided us with an initial estimate of how many people turned out to vote in a particular county in each year.
At this point the difficulties we previously described became apparent—the estimated number of voters from a given county will inevitably deviate from the real number who voted. Once again, we employed IPF at the county level to force these counts to match up with one another, increasing or decreasing the turnout rates for our 32 groups until the two aggregate vote counts aligned.
Instead of treating the numbers as completely accurate, we view this process as something that helps us generate more precise state-level estimates. Namely, this process takes advantage of geographic segregation at the county level to selectively adjust turnout rates between demographic groups rather than applying a blanket correction at the state level. Looking at Figure A1—which shows the share of eligible voters in each county who are white and do not have a college degree—we can see that there are some places where more than 80 percent of the population falls into that demographic category.
To the extent that our 32 demographic groups are non-randomly distributed across a state, this process will selectively push and pull their turnout rates. While the estimates within any given place may be off, we believe this discriminatory adjustment provides a better state-level picture. Combining our eligible voter estimates with our turnout rates, we could generate counts for the number of individuals in each county who voted and belonged to one of our 32 demographic groups.
The state-level compositions we reported throughout the paper are simply aggregations of these county counts. We feel that these estimates are superior to the ones typically reported from the November Supplement of the Current Population Survey for two reasons. First, the multilevel modeling previously mentioned helps produce better estimates for small populations across the country.
Second, when compared to the ACS the CPS would appear to systematically underrepresent the number of eligible voters in the population who are white and do not have a college degree. As can be seen in Table A1—which compares the composition estimates from the CPS November Supplement and the 1-year ACS—white non-college-educated citizens age 18 or older are underrepresented by 1.
Given the superior sample size of the ACS, we believe it provides a more accurate picture of the eligible voter population, particularly at the state level.
Assuming it is more accurate, post-stratifying our turnout estimates from the CPS onto the ACS eligible voter counts should provide a more accurate picture of the electorate. The process of creating county-level and Democratic and Republican support rates for each of our 32 demographic groups began by generating state-level estimates for these groups. Using publicly available data from the American National Election Study and the Cooperative Congressional Election Study in and , as well as one of the post-election surveys from by Center for American Progress, we ran cross-nested multilevel models that estimate the turnout rate for each year, state, race, age, and education group represented in the data.
We then fed those state-level support estimates into the voter counts we generated in the previous step. This provided us with an initial estimate of how many people voted Democratic, Republican, and third party in a particular county in each year. Once again, the difficulties we described became apparent—the estimated number of Democratic, Republican, and third-party votes from a given county will inevitably deviate from the real election results.
We employed IPF at the county level to force these counts to match up with one another, increasing or decreasing the support rates for our 32 groups until the aggregate vote counts aligned. Instead of treating the numbers as completely accurate, we view this process as something that helps us generate more precise state-level estimates than previous methodologies.
We see the strengths and weaknesses of this process in the same light as we previously described in the turnout explanation above. Geographic segregation at the county level lets us selectively push and pull the support rates of our groups around rather than applying a blanket correction at a higher geographic level. The estimates within any given place may be off, but we believe this discriminatory adjustment provides a better state-level picture.
For this simulation we used the eligible voter composition within each county, but substituted the black turnout rates and party support rates with their counterparts. All other racial and educational groups were assigned their original turnout and support levels. Vote counts were then aggregated to the state level and reported. For this simulation we used the eligible voter composition within each county, but substituted the white, non-college-educated party support rates with their counterparts.
For this simulation we used the eligible voter composition within each county, but substituted the Latino party support rates with their counterparts. For this simulation we used the turnout rates and party support rates for every racial and educational group, but substituted the eligible voter composition in each county with its counterpart. Ruy Teixeira , John Halpin.
Ruy Teixeira. In this article. The product of this analysis is the following for each of those 32 groups: County-level estimates of eligible voter composition County-level turnout estimates County-level estimates of voter composition County-level party support estimates These estimates are fully integrated with one another and, when combined, recreate the elections results observed in and How much did differential turnout rates between white voters, including those who are college educated and those who are not college educated, and voters of color, including those who are black, Latino, and Asian American or other race affect the outcome of the election?
What exactly happened with the white vote, especially the white college-educated and white non-college-educated vote? How large is this latter group of voters compared to others? Was there a big surge in support among white non-college-educated voters for Donald Trump, or not? How well did Hillary Clinton do with white college-educated voters compared to President Barack Obama?
What exactly happened with the racial minority vote? Did Republicans do better or worse with black, Latino, and Asian American or other race voters? How did these turnout and support dynamics by group influence the outcomes in key Electoral College states such as Florida, Michigan, North Carolina, Ohio, Pennsylvania, and Wisconsin?
If black turnout and support rates in had been equal to black turnout and support rates in , what would the results have looked like in ? What about Latino margins? If white non-college-educated support for Democrats had been equal to white non-college-educated support for President Obama in , what would the results have looked like in ? What do these results and simulations tell us about party strategies for the election and beyond? If a winning Presidential candidate dies or becomes incapacitated between the counting of electoral votes in the Congress and the inauguration, the Vice President elect will become President, according to Section 3 of the 20th Amendment.
Title 3 of the United States Code establishes procedures for the Electoral College process and requires that States settle any controversies regarding their electors at least 6 calendar days before the meeting of the electors. It is up to Congress to determine what to do in the event one or more States cannot meet the statutory deadlines. However, the Constitution does not require that States appoint electors based on the popular vote, so a State may be able to resolve the controversy under State law, appoint electors, and issue a Certificate even if a recount is pending.
Even if a State is unable to resolve a controversy by the statutory deadline, nothing prevents the State from appointing electors. Resolving controversies before the statutory deadline eliminates the potential for one type of challenge during the counting of the votes in Congress. See 3 U. It is important to remember that the President is not chosen by a national popular vote.
The Electoral College vote totals determine the winner, not the statistical plurality or majority a candidate may have in the national popular vote totals. Electoral votes are awarded on the basis of the popular vote in each state. Note that 48 out of the 50 States award Electoral votes on a winner-takes-all basis as does the District of Columbia. In a multi-candidate race where candidates have strong regional appeal, as in , it is quite possible that a candidate who collects the most votes on a nation-wide basis will not win the electoral vote.
In a two-candidate race, that is less likely to occur. This also occurred in the presidential election, where George W. Bush received fewer popular votes than Albert Gore Jr. Trump received fewer popular votes than Hillary Clinton, but received a majority of electoral votes. In , even though millions more individuals voted for the Democratic candidate than the Republican candidate in CA, PA, and TX if you add the votes from the 3 States , the Democratic party was only awarded the electors appointed in CA.
Because the Republican candidate won the State popular vote in PA and TX, the Republican party was awarded 3 more total electors than the Democratic party. Total - 15,, Democratic votes cast vs 12,, Republican votes cast for the national popular vote, but 55 Democratic electors vs 58 Republican electors appointed based on each State's popular vote.
By Alexander Winning , James Macharia. He has promised to fight rampant corruption and revitalize the economy, a message hailed by foreign investors. The party once led by Nelson Mandela is now deeply divided. He smiled and hugged other party officials as the results were read out.
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