Sociological Essay *no heavy sophisticated words*

timer Asked: Dec 13th, 2018
account_balance_wallet $25

Question description

Write a 4-5 page essay about social mobility and inequality in the U.S., drawing upon course

readings. This essay should be in 12-point font, with 1-inch margins.

Based upon research/articles on social mobility by Raj Chetty and colleagues, explain

why some children are more likely to escape poverty and have upward social mobility in

some communities than in other communities. Draw upon at least one module/topic for

this course to make your point, and cite at least 4 different course readings. Make sure

you address the following in your essay:

a. Explain what social mobility is, and explain the research by Raj Chetty that finds

that children who grew up in some communities have better outcomes than

children who grew up in other communities. (the article is attached as well as another one with more information that can be used)

b. Drawing upon Raj Chetty’s research as well as what you have read in class this

semester, explain why some children may be more likely to experience social

mobility and “move up” while other children struggle, or stay in poverty.

c. Describe two social interventions, policy changes, or programs we could

implement to help improve social mobility so that all children can have greater

opportunities to live successful productive lives. (I have attached the two programs to use, they are in powerpoint format)

Note: This take-home essay should reflect *your* writing and understanding of the topic

and course readings. You should not copy and paste the work of other people,

websites, Wikipedia, etc. Make sure you cite appropriately if you are quoting anyone. When you submit your paper it will be evaluated by plagiarism detection software.

LOVE, MARRIAGE, AND FAMILY LIFE Readings: Annette Lareau – Unequal Childhoods: Class, Race and Family Life Brought to you by, Sarah Gailey, Alica Sek, Javi Meliton, Jennifer Eicholtz, Karen Guzman, and Xarlie Facio • Factors that influence middle-class and working-class child rearing – – • Middle-class v working-class – Concerted cultivation vs natural growth • Long term consequences – who benefits Labor Time Our social problem is.. • HOMEWORK!!! • Inadequate access to after school programs for the children of low income families. • Technology may not be accessible for low income families • Low income families/parents are so busy with work & everyday life, or may not understand the material so no way of helping their child(ren) with homework. • PowerMyLearning Connect is a collaborative school wide platform that helps parents understand course material, kids are currently learning, to help them with homework. • PowerMyLearning is committed to helping students, teachers, and families in under-served school communities in Greater Los Angeles. What does it do?! ★ PowerMyLearning’s principles set us apart from other personalized learning organizations in four ways: 1. 2. 3. 4. Student ownership Capacity Building Strengthening relationships Learning Science ★ Services empower parents to become stronger! ★ The program partners with schools! ★ It has a feature software: Family Playlist (cool!) • Featured in the New York Times • • • • • Smith for The New York TimesKatrina Hernandez, 12, worked on Web-based math activities at I.S. 528 Bea Fuller Rodgers School in Washington Heights. • In the article “A Digital Tool to Unlock Learning” by David Bornstein, he introduces us to a middle school in Washington Heights, New York known as the Bea Fuller Rodgers Middle School technology to low-income students who are struggling with in their academics. The Non-Profit Organization known as CFY contributed to this middle school 6th grade class take home computers in order to project their learning and strive their knowledge at greater levels. CYF even incorporated a 4 hour training course for the students and family members in order to be knowledgeable in using the PowerMyLearning platform Teachers and staff were at first hesitant in using this database and even were giving push back towards the program due to it may be another resource set up for failure. However, the teachers were taken back at how much it was helping the students and even saw a well amount of progress in the children's’ academics. PowerMyLearning has various curriculums and activities for the students in order them to be well involved in the subject they are struggling in. Teachers and Parents can even create “Playlists” to keep track of their children's academic progress and see where they need improvement as well. Children are becoming so engaged in this platform that they are taking the initiative on creating playlists to let their teachers and parents know where they are struggling so they can get assistance in order to bring to their parents and teachers attention of where they can improve on. Featured in the New York Times (continued) ● This program is helping students become well rounded and engaged in their academic courses in order to succeed and grow. ● The article also speaks about how children would be so embarrassed about asking questions because they felt distraught, frustrated, and discouraged when they didn’t understand the subject ● Now after being engaged in these online resources they feel the encouragement and enthusiasm to ask questions and engage with their teachers and classmates because they feel encouraged and excited to understand it and from they can continue to return to their activities so they can move onto the next level. ● Parents are becoming more involved with their children's education because the training course from CYF not only starts at the beginning of the year but now children and parents are building a bond when it comes to education. ● The success of this platform continues to grow that Gates, Kellog, and Broad foundation have funded $7 million dollars so it can continue to succeed for the children. • Funding • The following are those who have donated starting at $200,000 up to $1,000,000 and above! • • • • • • • • • • • • • • • • • • • • • The funding of PowerMyLearning has been award grants, donations, and funding by many generous people, groups, and organizations The Bill & Melinda Gates Foundation Oak Foundation Carnegie Foundation of New York CY Press Funding Laura and John Arnold Foundation Eli and Edythe Board Foundation Kellogg Foundation, W.K. Microsoft** U.S. Department of Commerce Bank of America Charitable Foundation Deutsche Bank Americas Foundation New York City Council New Profit, Inc. Chan Zuckerberg Initiative The Joseph H. Flom Foundation Robin Hood Foundation SAP SCE Morgridge Family Foundation News Corp Blank Family Foundation Funding (continued) Have been funded for the amount of $100,000-$199,999 • • • • • • • • • • • • • • • • • Bloomberg Philanthropies/America Achieves Charter Communications Cisco Systems Critelli Family Foundation Goizueta Foundation Google Eric Gural Johnny Carson Foundation Koret Foundation The Learning Accelerator NewSchools Venture Fund New York Community Trust Rose Hills Foundation Salesforce** Verizon Foundation Weingart Foundation Zeist Foundation Funding (continued) Have contributed $50,000-$99,999 • • • • • • • • • • • • • • • • • • • • • • • All Points North Foundation Annenberg Foundation Arthur M. Blank Family Foundation Con Edison The Francis L. and Edwin L. Cummings Memorial Fund Joseph Drown Foundation Dwight Stuart Youth Fund Gregory Gould The Green Foundation Kalpi Kadaba NBCUniversal Foundation News Corp Robert Ronus Robert W. Woodruff Foundation Rose Hills Foundation Mark Taper Foundation The O’Shea Family Foundation Speedwell Foundation SunTrust Bank Foundation Toyota USA Foundation Weingart Foundation Wells Fargo Foundation Wonderful Foundation Some that have contributed from the range of $50,000-$99,999 (List was too long to include all the foundations but here are some of the many!) Companies in the Technology World have also contributed! Such as: ● 2DBoy ● Apple, Inc. ● Celestia ● Dell EMC ● DIRECTV ● Digital Directions International, Inc. (DDI) ● Disney/ABC Television Group ● DreamBox Learning, Inc. ● Los Angeles Dodgers Foundation ● E-Line Media ● AT&T ● Federation of American Scientists ● Aurelius Capital Management, LP. ● Global Kids ● William C. Bannerman Foundation ● Google ● Barker Welfare Foundation ● Green Eclipse Software ● John W. Carson Foundation ● Kaplan, Inc. ● CDI Computers ● ● MIT Media Lab ● CenturyLink ● NaturalSoft, Ltd. ● Change Happens Foundation ● Interactive Classics (Tigor Media) ● Cisco Systems ● Sensory Software ● Cisneros Group ● Teaching Matters, Inc. ● Coca-Cola Enterprises (CCE) ● The JASON Project ● Comcast ● The Liemandt Foundation ● CompTIA ● WebbIE Evaluation: Impact Study • PowerMyLearning schools received coaching for math teachers, workshops for educators, support for school leaders, and family learning workshops. • PowerMyLearning schools outperformed comparison schools in math proficiency by an average of 7 percentage points each year. – greater increase in math proficiency as compared to their respective comparison schools regardless of school size • 94% of teachers improved at using data to drive their instruction and supporting student agency. • 95% of parents in partner schools become more confident they can help their child learn. Case study • The pilot completely transformed family engagement in the 6th grade at South Bronx Preparatory, with 91% of families participating in the initiative. • Research shows that family engagement is crucial to student achievement, and what matters the most is how families support their children’s learning at home. • 100% of participating families agreed that Family Playlists helped them understand what their child was learning in school • 92% of participating families and 100% of the teachers agreed that Family Playlists helped them develop a stronger relationship with each other • 83% of students agreed that Family Playlists led to better conversations with their family about what they were learning in school • costs: – – Limitations and drawbacks – Requirements? Inaccessible to all poor folks (i.e. families from immigrant backgrounds) Speaks on technological advances • Applied to other social groups – Surface level-- does not address further factors • Public support – The privatization of education • Board members’ background references • • • Bornstein, David. “A Digital Tool to Unlock Learning.” The New York Times, The New York Times, 19 Sept. 2012, ing/?_r=0. Our Supporters.” PowerMyLearning, “Strong Learning Relationships | Education Technology.” PowerMyLearning,
Executive Summary, April 2015 The Impacts of Neighborhoods on Intergenerational Mobility Childhood Exposure Effects and County-Level Estimates Raj Chetty and Nathaniel Hendren, Harvard University To what extent are children’s opportunities for upward economic mobility shaped by the neighborhoods in which they grow up? We study this question using data from de-identified tax records on more than five million children whose families moved across counties between 1996 and 2012. The study consists of two parts. In part one, we show that the area in which a child grows up has significant causal effects on her prospects for upward mobility. In part two, we present estimates of the causal effect of each county in the United States on a child’s chances of success. Using these results, we identify the properties of high- vs. low-opportunity areas to obtain insights into policies that can increase economic opportunity. Part 1: Do Neighborhoods Matter for Economic Mobility? In previous work (Chetty, Hendren, Kline, and Saez 2014), we documented substantial variation in rates of upward income mobility across commuting zones (aggregations of counties analogous to metropolitan areas) in the United States. This geographic variation could be driven by two very different sources. One possibility is that neighborhoods have causal effects on upward mobility: that is, moving a given child to a different neighborhood would change her life outcomes. Another possibility is that the observed geographic variation is due to systematic differences in the types of people living in each area, such as differences in race or wealth. Distinguishing between these two explanations is essential to determine whether changing neighborhood environments is a good way to improve economic mobility or whether policy makers should focus on other types of interventions. The ideal experiment to test between these two explanations and identify the causal effects of neighborhoods would be to randomly assign children to different neighborhoods and compare their incomes in adulthood. We use a quasi-experimental approximation to this experiment that relies on differences in the timing of when families move across areas. Figure 1 illustrates our approach and results. As an example, consider a set of families who move from Cincinnati to Pittsburgh. Children who grow up in low-income families (at the 25th percentile of the national distribution) in Cincinnati from birth have an income of $23,000 on average at age 26, while those in Pittsburgh have an income of $28,000. Now consider the incomes of children whose families moved from Cincinnati to Pittsburgh at some point in their childhood. Figure 1 plots the fraction of the difference in income between Pittsburgh and Cincinnati that a child will on average obtain by moving at different ages during childhood. Children who were nine years old at the time of the move (the earliest age we can analyze given available data) capture 50% of this difference, leading to an income of approximately $25,500 as adults. Children who move from Cincinnati to Pittsburgh at later ages have steadily declining incomes, relative to those who moved at younger ages. Those whose families moved after they were 23 experience no gain relative to those who stayed in Cincinnati permanently. Figure 1 shows that every extra year a child spends in a better environment – as measured by the outcomes of children already living in that area – improves her outcomes, a pattern we term a childhood exposure effect. We find equal and opposite exposure effects for children whose families moved to worse areas. Further, we find analogous exposure effects for a broad range of other outcomes, including college attendance and the probability of having a teenage birth. Executive Summary, April 2015 FIGURE 1 Effects of Moving to a Different Neighborhood on a Child’s Income in Adulthood Notes: This figure plots the percentage gain from moving to a better area by the age at which the child moves. For example, children who move at age 9 have outcomes that are about 50% between the outcomes of children who grow up permanently in the origin and destination areas. The key assumption underlying the analysis shown in Figure 1 – the assumption that is necessary to make it as good as the ideal randomized experiment – is that families who move from Cincinnati to Pittsburgh when their children are young are comparable to those who move when their children are older. This assumption would not hold if, for instance, families who move to better areas when their children are young are more educated or have higher wealth than families who move later. We implement a series of tests to assess the validity of this assumption and evaluate the robustness of our quasi-experimental methodology. First, we compare siblings within the same family, and show that the difference in siblings’ outcomes is proportional to the difference in their exposure to better environments. When a family with two children moves from Cincinnati to Pittsburgh, the younger child does better than the older child on average. Second, we show that one obtains similar estimates of exposure effects when analyzing families displaced by events outside their control, such as natural disasters or local plant closures. Finally, we exploit differences in cities’ effects across subgroups to develop sharper tests for exposure effects. For example, some areas – such as those with high crime rates – generate significantly worse outcomes for boys than girls. We find that when a family with a boy and a girl moves to such an area, their son’s outcomes worsen in proportion to the number of years he grows up there, but their daughter’s outcomes change much less. Similarly, some areas are particularly good at producing “superstars” – children who reach the top 10% of the income distribution – even though they don’t produce better outcomes on average. We find that children who move to such areas when young are themselves more likely to become superstars, but do not have higher incomes on average. Executive Summary, April 2015 Since it is unlikely that other factors would reproduce all of these patterns, we conclude that the pattern in Figure 1 reflects the causal effect of neighborhoods on children’s long-term outcomes. This result has several important policy implications. First, it shows that the neighborhood environment during childhood is a key determinant of a child’s long-term success. This suggests that policy makers seeking to improve mobility should focus on improving childhood environments (e.g., by improving local schools) and not just on the strength of the local labor market or availability of jobs. Second, Figure 1 shows that the incremental benefits of exposure to a better area do not vary with a child’s age. Moving to a better area at age 9 instead of 10 produces the same incremental improvement in earnings as moving to that area at age 15 instead of 16. This finding is particularly important in light of recent discussions about early childhood interventions, as it is shows that there are significant returns to improving children’s environments even at older ages. Part 2: County-Level Estimates of Causal Exposure Effects The first part of our study establishes that neighborhoods matter for intergenerational mobility, but does not directly identify the causal effect of any given area. In the second part of our analysis, we estimate the causal childhood exposure effect of every county in the U.S. by studying the outcomes of children who moved between counties at different ages. To understand how we estimate these effects, consider families in the New York metro area. If we were to find that children who moved from Manhattan to Queens at a young age do better as adults, we can infer that Queens has positive causal exposure effects relative to Manhattan. Building on this logic, we use data on movers across the full set of counties in the U.S. to estimate the effect of spending an additional year of childhood in each county. We construct these estimates separately by parent income level, permitting the effects of each area to vary with the family’s income. Table 1 shows the causal effects of the top 10 and bottom 10 counties among the 100 largest counties in the U.S for children growing up in families at the 25th percentile of the national income distribution. The estimates represent the percentage change in earnings from spending an additional year of one’s childhood in the relevant county relative to the national average. TABLE 1 Causal Exposure Effects: Top 10 and Bottom 10 Among the 100 Largest Counties For Children with Parents at 25th Percentile of the Income Distribution Rank  Earnings (%) per year of exposure Rank  Earnings (%) per year of exposure 1 DuPage, IL 0.76% 91 Pima, AZ -0.61% 2 Snohomish, WA 0.72% 92 Bronx, NY -0.62% 3 Bergen, NJ 0.71% 93 Milwaukee, WI -0.62% 4 Bucks, PA 0.66% 94 Wayne, MI -0.63% 5 Contra Costa, CA 0.61% 95 Fresno, CA -0.65% 6 Fairfax, VA 0.60% 96 Cook, IL -0.67% 7 King, WA 0.57% 97 Orange, FL -0.67% 8 Norfolk, MA 0.54% 98 Hillsborough, FL -0.67% 9 Montgomery, MD 0.52% 99 Mecklenburg, NC -0.69% 10 Middlesex, NJ 0.43% 100 Baltimore City, MD -0.86% Executive Summary, April 2015 For example, each additional year that a child spends growing up in DuPage County, IL raises her household income in adulthood by 0.76%. This implies that growing up in DuPage County from birth – i.e., having about 20 years of exposure to that environment – would raise a child’s earnings by 15% relative to the national average. In contrast, every extra year spent in the city of Baltimore reduces a child’s earnings by 0.86% per year of exposure, generating a total earnings penalty of approximately 17% for children who grow up there from birth.1 There is considerable variation across counties even within metro areas. Figure 2 presents a map of the causal exposure effects for counties in the New York City area for children growing up in families at the 25th percentile. The estimates range from an earnings loss of -0.54% per year of childhood spent in Manhattan (New York County) to an earnings gain of 0.25% per year in Hudson County, NJ and 0.71% per year in Bergen County, NJ. Concretely, this implies that children in lowincome families who move from Manhattan to Hudson County, NJ when they are born earn 16% more as adults on average.2 Figure 2: Causal Exposure Effects by County in the New York Combined Statistical Area For Children with Parents at 25th Percentile of the Income Distribution Notes: This figure shows the percentage change in household earnings caused by spending an additional year growing up in each county for children with parents at the 25th percentile of the national income distribution. Lighter colored areas are areas that generate larger earnings gains. To download statistics for your county, visit 1 These estimates are based on data for children born between 1980-86 and who grew up in the 1980’s and 1990’s. We find that neighborhoods’ effects generally remain stable over time, but some cities have presumably gotten better in the 2000’s, while others may have gotten worse. 2 Most families at the 25th percentile of the national distribution (roughly a household income of $30,000 for a family with teenage children) who live in Manhattan are in Harlem. Hence, the comparison is effectively between the effects of growing up in Harlem vs. an area with relatively low house prices in New Jersey. Executive Summary, April 2015 The causal effects of counties are typically smaller in percentage terms for children who grow up in high-income families, but remain substantial. For instance, for children growing up in families in the top 1% of the income distribution, we estimate that every extra year of childhood spent in Manhattan reduces their earnings by 1.08% relative to Westchester. Areas that produce better outcomes for children in low-income families are, on average, no worse for those from high-income families. This finding suggests that the success of the poor need not come at the expense of the rich, implying that social mobility is not a “zero-sum game.” Neighborhoods matter more for boys than girls. For example, every extra year of childhood exposure to Baltimore reduces earnings by 1.39% for low-income boys, but only 0.27% for girls. Areas with high crime rates and a large fraction of single parents generate particularly negative outcomes for boys relative to girls. There are also significant gender differences related to marriage rates. For example, Northern California generates high levels of individual earnings for girls, but produces lower levels of household income because fewer children get married in their 20s. Our estimates of causal effects at the county and commuting zone (CZ) level are strongly correlated with the raw estimates of intergenerational mobility reported in Chetty, Hendren, Kline, and Saez (2014), but there are several significant differences. For example, children who grow up in New York City have above-average rates of upward mobility. However, the causal effect of growing up in New York City on upward mobility – as revealed by analyzing individuals who move into and out of New York – is negative relative to the national average. This negative effect of growing up in New York is masked when one simply studies the average outcomes of children who grow up there because families who live in New York tend to have unusually high rates of upward mobility. In particular, New York has a very large share of immigrants, and we find that immigrants have higher rates of upward mobility independent of where they live. This example shows that part of the variation in mobility across areas is driven simply by the characteristics of the people who live in those areas, which is why it is important to identify each area’s causal effect as we do in this study. What are the properties of areas that improve upward mobility? Within a given commuting zone, we find that counties that have higher rates of upward mobility tend to have five characteristics: they have less segregation by income and race, lower levels of income inequality, better schools, lower rates of violent crime, and a larger share of two-parent households. We also find that areas with a larger African-American population tend to have lower rates of upward mobility. These spatial differences amplify racial inequality across generations: we estimate that one-fourth of the gap in intergenerational mobility between blacks and whites can be attributed to the counties in which they live. Lastly, we examine whether one has to pay a higher rent to live in an area with greater upward mobility. In the nation as a whole, we find weak correlations between rents and upward mobility. However, in large metro areas – especially those with high levels of segregation and sprawl – counties that offer better prospects of upward mobility are much more expensive. For example, Chicago has one area with a high level of upward mobility – DuPage County – which is also one of the most expensive counties in the area. There are, however, some “bargains” even in the largest cities: for example, Hudson County in the New York metro area and Snohomish County in the Seattle area both offer high levels of upward mobility with relatively low house prices. The high housing prices that families often must pay to achieve better outcomes for their children may partially explain the persistence of poverty in large American cities. One approach to addressing this problem is to provide subsidized housing vouchers that enable families to move to better (e.g., lower-poverty) neighborhoods. In a companion paper (Chetty, Hendren, and Katz 2015), we show that the Moving to Opportunity experiment – which randomly assigned families subsidized Executive Summary, April 2015 housing vouchers to move to low poverty areas – significantly improved long-term outcomes for children who moved at young ages, providing direct support for such policies. Of course, given limits to the scalability of policies that seek to move families, one must also find methods of improving neighborhood environments in areas that currently generate low levels of mobility. Our study does not directly identify which policies are most successful in achieving this goal, but our findings provide support for policies that reduce segregation and concentrated poverty in cities (e.g., affordable housing subsidies or changes in zoning laws) as well as efforts to improve public schools. The broader lesson of our analysis is that social mobility should be tackled at a local level by improving childhood environments. Much remains to be learned about the best ways to make such improvements. We hope the county-level data constructed here will ultimately offer new solutions to increase opportunities for disadvantaged youth throughout the United States. Works Cited Chetty, R., N. Hendren, P. Kline, and E. Saez. “Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States.” Quarterly Journal of Economics 129(4): 15531623, 2014. Chetty, R. N. Hendren, and L. Katz. “The Long-Term Effects of Exposure to Better Neighborhoods: New Evidence from the Moving to Opportunity Experiment” Harvard University Working Paper, 2015.
Executive Summary, January 2014 The United States is often hailed as the “land of opportunity,” a society in which a child's chances of success depend little on her family background. Is this reputation warranted? We show that this question does not have a clear answer because there is substantial variation in intergenerational mobility across areas within the U.S. The U.S. is better described as a collection of societies, some of which are “lands of opportunity” with high rates of mobility across generations, and others in which few children escape poverty. We present a new portrait of social mobility in the U.S. by compiling statistics from millions of anonymous earnings records. Our core sample consists of all children in the U.S. born between 1980-82, whose income we measure in 2011-12, when they are approximately 30 years old. Using these income data, we calculate two measures of intergenerational mobility. The first, relative mobility, measures the difference in the expected economic outcomes between children from high-income and low-income families. The second, absolute upward mobility, measures the expected economic outcomes of children born to a family earning an income of approximately $30,000 (the 25th percentile of the income distribution). We construct measures of relative and absolute mobility for 741 “commuting zones” (CZs) in the United States. Commuting zones are geographical aggregations of counties that are similar to metro areas but also cover rural areas. Children are assigned to a CZ based on their location at age 16 (no matter where they live as adults), so that their location represents where they grew up. When analyzing local area variation, we rank both children and parents based on their positions in the national income distribution. Hence, our statistics measure how well children do relative to those in the nation as a whole rather than those in their own particular community. We find substantial variation in mobility across areas. To take one example, children from families at the 25th percentile in Seattle have outcomes comparable to children from families at the median in Atlanta. Some cities – such as Salt Lake City and San Jose – have rates of mobility comparable to countries with the highest rates of relative mobility, such as Denmark. Other cities – such as Atlanta and Milwaukee – have lower rates of mobility than any developed country for which data are currently available. Next, we analyze what drives the variation in social mobility across areas. The spatial patterns of the gradients of college attendance and teenage birth rates with respect to parent income across CZs are very similar to the pattern in intergenerational income mobility. The fact that much of the spatial variation in children's outcomes emerges before they enter the labor market suggests that the differences in mobility are driven by factors that affect children while they are growing up. We explore such factors by correlating the spatial variation in mobility with observable characteristics. We begin by showing that upward income mobility is significantly lower in areas with larger African-American populations. However, white individuals in areas with large AfricanAmerican populations also have lower rates of upward mobility, implying that racial shares matter at the community (rather than individual) level. One mechanism for such a community-level effect of race is segregation. Areas with larger black populations tend to be more segregated by income and race, which could affect both white and black low-income individuals adversely. Indeed, we find a strong negative correlation between standard measures of racial and income segregation and upward mobility. Moreover, we also find that upward mobility is higher in cities with less sprawl, as Executive Summary, January 2014 measured by commute times to work. These findings lead us to identify segregation as the first of five major factors that are strongly correlated with mobility. The second factor we explore is inequality. CZs with larger Gini coefficients have less upward mobility, consistent with the “Great Gatsby curve” documented across countries (Krueger 2012, Corak 2013). In contrast, top 1% income shares are not highly correlated with intergenerational mobility both across CZs within the U.S. and across countries. Although one cannot draw definitive conclusions from such correlations, they suggest that the factors that erode the middle class hamper intergenerational mobility more than the factors that lead to income growth in the upper tail. Third, proxies for the quality of the K-12 school system are also correlated with mobility. Areas with higher test scores (controlling for income levels), lower dropout rates, and smaller class sizes have higher rates of upward mobility. In addition, areas with higher local tax rates, which are predominantly used to finance public schools, have higher rates of mobility. Fourth, social capital indices (Putnam 1995) -- which are proxies for the strength of social networks and community involvement in an area -- are very strongly correlated with mobility. For instance, high upward mobility areas tend to have higher fractions of religious individuals and greater participation in local civic organizations. Finally, the strongest predictors of upward mobility are measures of family structure such as the fraction of single parents in the area. As with race, parents' marital status does not matter purely through its effects at the individual level. Children of married parents also have higher rates of upward mobility if they live in communities with fewer single parents. We find modest correlations between upward mobility and local tax and government expenditure policies and no systematic correlation between mobility and local labor market conditions, rates of migration, or access to higher education. We caution that all of the findings in this study are correlational and cannot be interpreted as causal effects. For instance, areas with high rates of segregation may also have other characteristics that could be the root cause driving the differences in children’s outcomes. What is clear from this research is that there is substantial variation in the United States in the prospects for escaping poverty. Understanding the properties of the highest mobility areas – and how we can improve mobility in areas that currently have lower rates of mobility – is an important question for future research that we and other social scientists are exploring. To facilitate this ongoing work, we have posted the mobility statistics by area and the other correlates used in the study on the project website. WHERE IS THE LAND OF OPPORTUNITY? THE GEOGRAPHY OF INTERGENERATIONAL MOBILITY IN THE U.S. Raj Chetty, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez Is America still the “land of opportunity”? We show that this question does not have a clear answer because the economic outcomes of children from low income families vary substantially within the U.S. Some cities have rates of upward income mobility comparable to the most mobile countries in the world, while others have lower rates of mobility than any developed country. These geographical differences in upward mobility are strongly correlated with five primary factors: segregation, income inequality, local school quality, social capital, and family structure. For further information, see the non-technical summary and the complete paper. Note: This map shows the average percentile rank of children who grow up in below-median income families across areas of the U.S. (absolute upward mobility). Lighter colors represent areas where children from low-income families are more likely to move up in the income distribution. To look up statistics for your own city, use the interactive version of this map created by the New York Times. Upward Mobility in the 50 Biggest Cities: The Top 10 and Bottom 10 Rank 1 2 3 4 5 6 7 8 9 10 San Jose, CA San Francisco, CA Washington DC, DC Seattle, WA Salt Lake City, UT New York, NY Boston, MA San Diego, CA Newark, NJ Manchester, NH Odds of Reaching Top Fifth Starting from Bottom Fifth 12.9% 12.2% 11.0% 10.9% 10.8% 10.5% 10.5% 10.4% 10.2% 10.0% Rank 41 42 43 44 45 46 47 48 49 50 Cleveland, OH St. Louis, MO Raleigh, NC Jacksonville, FL Columbus, OH Indianapolis, IN Dayton, OH Atlanta, GA Milwaukee, WI Charlotte, NC Odds of Reaching Top Fifth Starting from Bottom Fifth 5.1% 5.1% 5.0% 4.9% 4.9% 4.9% 4.9% 4.5% 4.5% 4.4%
By Grayson Jitmetta, Kimberly Flores, Kelsi Grau, Stacey Arias, and Annel Guerra The Problem ● Kindergarteners lacking prosocial skills is a problem everywhere, but happens more commonly in low-socioeconomic areas. ● Penn State University published a study in 2015 that found that kindergarten kids who lacked social and communication skills were less likely to graduate and find success as an adult compared to kids that had developed good social and communication skills. ● Kindergarten kids who lacked prosocial skills were more inclined to partake in criminal behavior as they aged. ● Children who scored high on social skills were four times as likely to graduate from college than those who scored low. Continued ● Schools focus too much on reading, math instructions, and test preparations. ○ Leaves little room for other education goals ● In a national survey conducted in 2015, more than 90 percent of schoolteachers said it was important for schools to promote the development of students’ social and emotional skills (sometimes called 21st century skills, noncognitive skills, or character education). ○ But many struggle to integrate this kind of teaching in their classrooms. Collaborative for Academic, Social and Emotional Learning ● ● CASEL ○ CASEL was formed in 1994 with the goal of establishing high-quality, evidence-based social and emotional learning (SEL) as an essential part of preschool through high school education. ○ Based in Chicago ○ Both CASEL and the term “social and emotional learning” emerged from a meeting in 1994 hosted by the Fetzer Institute. ○ More focused on socioeconomically disadvantaged areas Social and emotional learning (SEL) is the process through which children and adults acquire and effectively apply the knowledge, attitudes, and skills necessary to understand and manage emotions, set and achieve positive goals, feel and show empathy for others, establish and maintain positive relationships, and make responsible decisions. ○ SEL was introduced as a framework that addresses the needs of young people and helps to align and coordinate school programs and programming. How it Works ● Teach students, step by step, to manage their behavior and get along with their classmates. SEL can include helping students to understand what they’re feeling, show empathy for others, create healthy relationships and make responsible decisions. ● District partners use SEL as a systemic framework to drive change at all levels, from the district office, to the classroom, to the home. ● CASEL has produced a two-volume guide to social and emotional learning programs with a preschool and elementary edition and a middle and high school edition. ○ Schools will apply to recieve these guides and integrate the programs into their school system. Key components of SEL implementation in schools ● Instruction and opportunities to practice and apply an integrated set of cognitive,affective and behavioral skills. ● Learning environments characterized by trust and respectful relationships. ● Coordinated implementation that reinforces classroom,schoolwide,out of school, and at home learning activities. ● Developmentally and culturally appropriate behavior supports. ● Systematic and sequential programming from preschool to high school. ● Ongoing monitoring and evaluation of implementation for continuous improvement. Who Funds it? ● Private philanthropists, government agencies, and individual donors. ● 1440 Foundation ● Allstate foundation Good Starts Young ● Rockefeller Philanthropy Foundation ● The Wallace Foundation ● TCH Legacy INC Studies/Evaluations ● “A 2015 national study published in the American Journal of Public Health found statistically significant associations between SEL skills in kindergarten and key outcomes for young adults years later in education, employment, criminal activity, substance use, and mental health.The study concluded that early prosocial skills decreased the likelihood of living in or being on a waiting list for public housing, receiving public assistance, having any involvement with police before adulthood, and ever spending time in a detention facility.” ● “Comparison of end of year school records showed higher marks for children in the intervention(group taught prosocial skills) as compared to control group(were not taught prosocial skills) on indicators of learning, socialemotional development, and health. Notably, these differences emerged for second semester report card grades assigned approximately 3 months after the end of the intervention.” (Retrieved from ○ Seven classrooms were recruited from six different elementary schools within a public school district in a medium-sized Midwestern city ○ Sample size of 99 students in elementary school ○ Conducted in socioeconomically disadvantaged area Relationship to Course Material ● James Heckman – Skill Formation and the Economics of Investing in Disadvantaged Children ○ Social skills or “soft” skills are very important in early childhood education ○ ● Children from lower socioeconomic backgrounds do not have a great foundation in non-cognitive skills because their families are not instilling these skills at home Sean Reardon – The Widening Academic Achievement Gap between the Rich and the Poor ○ Family income is a good predictor of educational achievement but educational achievement is also a good predictor of future wages ○ ● If we can help students to increase their level of educational achievement regardless of their family background, then we are giving them better chances of future upward social mobility Doug Downey and Benjamin Gibbs – How Schools Really Matter ○ “High- and low-socioeconomic status students gain academic skills at about the same rate during the school year. The gaps in their skills develop during the summer.” ○ This means that students are lacking not in what they are learning academically, but how they progress in nonacademic ways ○ ● “High- and low-socioeconomic status students gain academic skills at about the same rate during the school year. The gaps in their skills develop during the summer.” CASEL looks to meet the needs of these children by providing them with the skills that their middle-class or upper-class schoolmates already have from outside the classroom Limitations ● Implementing the program takes years ● Difficult to apply it to all districts at once ● Although all 50 states have implemented Social emotional learning to their learning goals that doesn't mean it’s systematically and strategically supported. References ❏ ❏ ❏ ❏ ❏ ❏ ❏ Grusky, D. and Hill, J. (2018). Inequality in the 21st century. Boulder: Routledge, pp.176-186. ❏ Downey, D. and Gibbs, B. (2010). How Schools Really Matter. Contexts, 9(2), pp.50-54.

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