Chapter 11: Metropolitan Government and Policies



Chapter 11: Metropolitan Government and Policies

Tasks: Mapping, Scatterplot, Historical Trends, Univariate, Cross-tabulation, Auto-Analyzer

Data: STATES, MSA, USTREND, GSS

Metropolitan Areas: Settings for Conflict

As we all know, over time the United States has experienced tremendous population growth. The accompanying graph shows the population changes that have occurred since 1800 in the U.S.

[insert MicroCase graphic 11.1]

|> Data File: |USTREND |

|> Task: |Historical Trends |

|> Variables: |2) POPULATION |

In 1800, there were slightly more than 392,000 people in the United States. Today there are well over 285 million U.S. residents!

Much of this growth has occurred in urban or metropolitan areas.

[insert MicroCase graphic 11.2]

|Data File: |USTREND |

|Task: |Historical Trends |

|> Variables: |11) %URBAN |

Here you can see that in 1790, less than 5% of U.S. citizens lived in an urban area. By 1990 nearly 80% the U.S. population lived in a metropolitan or urbanized area. As we discussed in Chapter 9, many states today are made up of large metropolitan areas. In fact, over 1/3 of the states have more than half of their populations living in metropolitan areas.

This means most of us live in one of the 267 Metropolitan Statistical Areas (MSA) identified by the United States Bureau of the Census.

[insert MicroCase graphic 11.3]

|> Data File: |MSA |

|> Task: |Mapping |

|> Variable 1: |POPULAT 00 |

|> View: |Map |

As you may recall from Chapter 9, these MSAs are located all across the country. Remember that an MSA is an urbanized or metropolitan area with a population of 50,000 or more residents. Obviously some states, like California, Florida and Texas have several MSA areas. And, of course, there are a few states that have only one MSA. For example, Bismarck, North Dakota is the only MSA in the state.

The advantage of the MSA-level data is that it shows more details of the variations that exist across the nation and within states and regions. As we probably would guess, there is great variation among MSA cities in terms of their populations. Let’s briefly examine the ranking of the population variable.

[insert MicroCase graphic 11.4]

|Data File: |MSA |

|Task: |Mapping |

|Variable 1: |POPULAT 00 |

|> View: |List: Rank |

This table shows the MSA population breakdown. After the 2000 census was taken, the largest MSA in the U.S. was the New York City area with over 21 million people. The Los Angeles-Riverside, California area ranked second in population with some 16 million, ahead of the Chicago MSA which ranked third. Recall that to be categorized as an MSA by the Census Bureau, a metropolitan area must have at least 50,000 people residing in it. Thus, as you scroll to the bottom of the rankings you will find that the smallest MSA is Enid, Oklahoma with a population of 57,813. The second smallest MSA is Casper, Wyoming with a population of 66,533 followed by Great Falls, Montana with 80,357 residents. Clearly all three meet the 50,000 population threshold.

Now, when most of us think of a “metropolitan area,” a large urban city with surrounding suburbs is what springs to mind. And, for the most part, that stereotype would apply to MSAs today. Most of them are almost entirely urban.

[insert MicroCase graphic 11.5]

|Data File: |MSA |

|Task: |Mapping |

|> Variable 1: |%URBAN00 |

|> View: |List: Rank |

Here we see the Miami, Florida MSA is 99.5% urban. The large metropolitan area, Honolulu, Hawaii, is 98.4% urban. Another metropolitan area in Florida, West Palm Beach-Boca Raton, is 98.3% urban. As you scroll down this list, you will notice that 47 of the top 50 MSAs are over 90% urban. This is the image most of us have when we think of metropolitan areas and government – large urban cities. The complete definition of “urban” is long and involved, but basically any settled area with a population of 2,500 or more people is considered to be an urban area.

Obviously people in large urban areas need places to live. The growth we have seen in the United States with people living in urban areas has meant a tremendous increase in the number of households as well. Since we are interested in household growth in cities, we will examine non-farm households.

[insert MicroCase graphic 11.6]

|> Data File: |USTREND |

|> Task: |Historical Trends |

|> Variables: |7) #NOFARM HH |

Here we see that in 1910, there were only 139,800 non-farm households in the United States. You will also note that from 1910 to 1970, the number of city or urban households dramatically increased over time. By 1970 we see there are more than 601,600 non-farm homes in the U.S. Since the urban population has continued to increase since the 1970s, we can safely assume the number of urban homes have increased as well.

While many of us live in an urban metropolitan area, there are some MSAs that are more rural in nature. Let’s take a look.

[insert MicroCase graphic 11.7]

|> Data File: |MSA |

|> Task: |Mapping |

|> Variable 1: |9) %RURAL00 |

|> View: |List: Rank |

As you can see, three MSAs are over 50% rural. That is, they have at least one city with 50,000 or more citizens. The Decatur, Alabama MSA is 55.3% rural. For the residents in Glen Falls, New York, 54.8% of their MSA is rural. The Danville, Virginia MSA is 52.7% rural.

Under the MSA standards set by the federal government, the county (or counties) that contains the largest city becomes the “central county.” Thus, Decatur, Alabama (which is located in northern Alabama) is the county that contains the largest city and thus the MSA is named after them. Obviously as you scroll through this table you will note that the MSAs that were more urban in makeup appear at the bottom of this chart. Essentially, rural is anything that is not urban. In some instances the term “rural” does not necessarily mean a farm or “country side.” That’s what most of this think of when we hear the term. However, rural basically includes small places and sparsely populated areas.

Ethnic and Racial Diversity

Ethnic and racial diversity are also a major characteristic of today’s metro areas. In a sense, we have always thought of our large cities as “melting pots.” Today, we might call them “metro-melting pots.” In recent years, American cities have attracted waves of immigrants from Mexico and other Latin countries, Asia, the Philippines, Haiti and a variety of other countries around the world.

Let’s take a look at this diversity in metropolitan areas and examine several race and ethnicity variables.

[insert MicroCase graphic 11.8]

|Data File: |MSA |

|Task: |Mapping |

|> Variable 1: |5) %BLACK00 |

|> View: |Map |

As we have discussed throughout this book, great variation exists among the states and localities in the U.S. The advantage of using the MSA-level map is that it shows another layer of details of the variations that exist across the nation. This map provides an MSA-level breakdown of the percentage of African Americans. It shows like the state and county maps we examined earlier in this workbook, that African Americans mostly live in the South.

Let’s see where the largest Hispanic populations are found. Do you have any guesses?

[insert MicroCase graphic 11.9]

|Data File: |MSA |

|Task: |Mapping |

|> Variable 1: |6) %HISPANC00 |

|> View: |Map |

|> Display |Legend |

As we have seen in an earlier map people of Hispanic origin are more likely to live in the southwestern part of the United States. MSAs with the darkest colors have Hispanic populations greater than 11%.

Social Problems in Cities

As mentioned earlier in this chapter, a metropolitan area is an area consisting of a large population center and adjacent communities that have a high level of economic and social interaction with that center. Generally speaking, an MSA consists of a large, urban central city and the surrounding suburbs. Let’s talk about the problems faced by these central cities.

Many of the problems of large central cities within metropolitan areas have resulted from the outward migration of industry and affluent citizens to suburban areas. This leaves a largely unskilled and poor population in the central city.

A popular notion is that a lack of jobs causes economic hardship for individuals and families and this leads to homelessness in large cities. Certainly whether we live in a metropolitan area or have simply visited one, it does appear that many urban cities have a problem with the homeless.

[insert MicroCase graphic 11.10]

|Data File: |MSA |

|Task: |Mapping |

|> Variable 1: |10) %STREETS90 |

|> View: |List: Rank |

This table shows the percent of people living in visible street locations. What exactly does this mean? Good question. Here’s how the Census Bureau defines it. This item includes the percent of people who are found at street blocks and open public locations designated by city and community officials as places where the homeless congregate at night.

As you will note from this table, many cities have a fairly significant percentage of people living in street locations. The Santa Barbara, California MSA has the most people living in visible street locations with 26%, followed by Redding, California at 20%. Ranking third, in Yuma, Arizona 15% live in visible street locations.

These are high percentages of people living on city streets. As you scroll down this table, you will note that many cities have 1, 2, 3 and 4 percent of their citizens living on the street. While these may not seem like very large percentages, keep in mind we are talking about large urban areas. So, a city of 75,000 people with a 2% homeless problem still has 1500 people.

In most major metropolitan areas, the middle-class has increasingly chosen to move to the suburbs. This has left cities with poor families who cannot afford to leave. Let’s analyze two maps at once.

[insert MicroCase graphic 11.11]

|Data File: |MSA |

|Task: |Mapping |

|> Variable 1: |16) POOR FAM00 |

|> Variable 2: |22) F$>35K00 |

|> View: |Map |

Just like poverty in the states, there is great variation among metropolitan areas in terms of their wealth and poverty rates. The top map reveals the percent of families below the poverty level. Along with great variation among poverty rates in MSAs, there are also regional differences in terms of poverty. Notice that the percent of families living in poverty is particularly high in the southwest and southern regions.

If you examine the ranked list for this map, you will discover that several metropolitan areas in Texas and New Mexico have over 20% of their families living in poverty. In fact, the Mcallen-Edinburg, Mission, TX MSA has 31.3% of its families below the poverty level. This is closely followed by Brownsville-Harl.-S.Benito, TX (28.2%) and Laredo, TX (26.7%). Rounding out the top five MSAs with high levels of families in poverty are El Paso, TX (20.5%) and Las Cruces, NM (20.2%). Several of the other MSAs with high levels of families in poverty are found in California, Louisiana, Georgia and Arkansas. By contrast, there are several MSAs in which 5% or less of their families live below the poverty level. These metropolitan areas appear to be located in the upper Midwest and northeast regions of the U.S. They include Rochester, MN (3.8%), Sheboygan, WI (3.7%) and Appleton-Oshkosh-Neenah, WI (3.2%).

The bottom map displays the percent of families with incomes above $35,000. Not surprisingly, this map is nearly the opposite of the top map. For instance, notice the dark areas in the top map tend to be colored lighter in the bottom map and vice versa.

For many middle class families, the decision to move out of the central metropolitan city to the suburbs (or somewhere else) is based on their families. Many of the moves to suburban areas have been explained as moves that were made for the benefit of the children.

An exodus of people out of the central city has meant an exodus of jobs as well. People have left to get away from traffic congestion, taxes and crime.

Let’s take a look at crime in metropolitan areas.

[insert MicroCase graphic 11.12]

|Data File: |MSA |

| Task: |Mapping |

|> Variable 1: |18) CINDEX00 |

|> View: |Map |

In the case of crime, the regional distinctions are apparent. We can see that metropolitan areas in the Northeastern and upper Midwest parts of the country have less reported serious crimes than the other regions of the U.S.

Now let’s examine the ranked list for this map.

[insert MicroCase graphic 11.13]

|Data File: |MSA |

|Task: |Mapping |

|Variable 1: |18) CINDEX00 |

|> View: |List: Rank |

Here we can see the regional distinctions a bit more clearly. Many of the MSAs with high numbers of serious crimes reported to the police are located in the South. As you can see, Myrtle Beach, SC leads the way with 8079.8 serious crimes reported to police per 100,000 population. At the bottom of this list you find Johnstown, Pa where 1973.9 serious crimes were reported.

As you scroll through this list another you may think of another connection among these metropolitan areas. Many of the cities on this list of serious crimes are homes to universities and some also have large tourist areas. Obviously, Myrtle Beach, SC is a tourist destination. Tucson, AZ is the home of the University of Arizona and so on down the list. It has become increasingly true in most large tourist areas and always the case in university towns that the safety and security of tourists and college students is a great concern. Many metropolitan areas have to deal with a variety of factors that can impact crime in their jurisdictions. Today, it is one of the most challenging problems to the potential growth of MSAs.

The decision of whether to move from a given area or stay is a complex and difficult one for many metropolitan residents. Confronted with neighborhood problems, those who can afford to move may simply leave for the suburbs, particularly if political action such as joining a neighborhood association appears unlikely to resolve the problems.

[insert MicroCase graphic 11.14]

|> Data File: |GSS |

|> Task: |Univariate |

|> Primary Variable: |39) LOCALGVT |

|> View: |Pie |

As you can see, over 50% of Americans believe that it is not likely that they would be able to persuade their local government to make some improvement in their community.

And, as you may recall, most people don’t participate in the activities of their neighborhood group or association.

[insert MicroCase graphic 11.15]

|Data File: |GSS |

|Task: |Univariate |

|> Primary Variable: |11) GRPNEI |

|> View: |Pie |

In fact an overwhelming 84% of Americans have not become involved in their neighborhood group. Clearly metropolitan residents do not see their government as being able to make positive changes to their communities and do not see the value in participation in order to bring about positive change. Thus, it appears that many residents simply “vote with their feet” and move to places that better meet their needs and preferences. However, those people and families that do leave make the problems worse because it deprives a neighborhood of the human capital and economic resources it needs in order to improve.

In most major metropolitan areas, the white middle-class has increasingly chosen to move to the suburbs. This has often been called “white flight.” This has left cities with African-Americans and other minority groups denied the option to leave because of poverty and the higher cost of moving and living in the suburban areas.

[insert MicroCase graphic 11.16]

|> Data File: |MSA |

|> Task: |Mapping |

|> Variable 1: |14) BLK POOR00 |

|> Variable 2: |15) HISP.POR00 |

|> Views: |Map |

As is fairly obvious, these two maps are quite similar. The top map displays the percent of Blacks below the poverty level. The bottom map displays the percent of Hispanics living in poverty. From these two maps it appears the Blacks and Hispanics experience poverty in many of the same metropolitan areas across the country.

Now let’s look at the ranked list for these maps.

[insert MicroCase graphic 11.17]

|Data File: |MSA |

|Task: |Mapping |

| Variable 1: |14) BLK POOR00 |

|Variable 2: |15) HISP.POR00 |

|> Views: |List: Rank |

When you examine the ranked lists for these maps you will discover there are a large majority of metropolitan areas in which over 20% of the African-American and Hispanic populations live below the poverty level. For Eau Claire, WI, 45.8% of its Black population lives in poverty while in State College, PA 44.7% of Hispanics are below the poverty level. In fact, no MSA has less than 8% of the minority population living in poverty. In Honolulu, HI, 8.1% of the African American community lives below poverty while in Fort Walton Beach, FL and Cedar Rapids, IA 10.4% of Hispanics live below the poverty level.

Of course, many people living below the poverty level are not simply minorities. There are many poor whites living in the nation’s urban centers.

[insert MicroCase graphic 11.18]

|Data File: |MSA |

|Task: |Mapping |

|> Variable 1: |13) WHT POOR00 |

|> View: |List: Rank |

However, while this is true, it appears from the previous analysis that African Americans and Hispanics experience poverty in much greater percentages than whites. As you discover in this ranked table, there are many MSAs with white poverty levels below 10%. In Sheboygan, WI, for example, 4.1% of its white population lives in poverty. On the other hand, McAllen-Edinburg, Mission, TX has the highest rate of white poverty at 35.2%.

Despite these poverty rates, there have been recent efforts by metropolitan governments to encourage many middle-class people and families to return.

[insert MicroCase graphic 11.19]

|Data File: |MSA |

| Task: |Mapping |

|> Variable 1: |22) F$>35K00 |

|> View: |List: Rank |

As we again look at the percentage of families with incomes greater than $35,000 we see that many metropolitan areas in the Midwest have high percentages of middle-class and wealthier families. In the MSAs of Minneapolis-St. Paul, MN and Madison, WI each have more than 80% of their families with income greater than $35,000. Also, these Midwestern areas are known for the successful downtown renewal efforts. In metropolitan areas around the country, local governments and developers are attempting to preserve and resurrect their downtown areas. This “urban renewal” has become a priority for many large urban areas. The goal is to develop new shopping areas and preserve and revitalize historic neighborhoods in an effort to lure middle-class families back to the central city. Of course the danger is that increased housing costs in these upscale areas will simply force poor Blacks and Hispanics to other urban neighborhoods.

Costs of Metropolitan Government

Large metropolitan areas require a great amount of upkeep and maintenance. Of course, this costs money – taxpayer money. This only makes sense. There are roads to build and repair; there is the upkeep of parks and recreation areas; also utility costs for government buildings, salaries for government employees and many other costs. These are all magnified in an MSA.

[insert MicroCase graphic 11.20]

|Data File: |MSA |

|Task: |Mapping |

|> Variable 1: |33) GOV.$/CP87 |

|> View: |Map |

For example, here’s a map of MSAs and the money spent on local government payroll (per capita). Take note that the dark areas of the map are the MSAs that spend the most, per capita, on local government. These dark colored MSAs are spread throughout the U.S. But there appears to be a cluster of them in the upper Midwest (great lakes area) and the Northeast. As we learned earlier in this workbook, per capita figures can sometimes be misleading but if you examine the ranked list for this map you get the impression that, generally speaking, residents in these two regions have higher per capita costs than those folks who live in the South.

Problems and Conflicts in Metropolitan Areas

Another crisis for large metropolitan cities is simply the deterioration of urban life. In many parts of the U.S., big city life is characterized by high crime rates, drug and alcohol addiction, poor city services, failing school systems, racial conflicts, traffic congestion, air pollution, unemployment, slum housing and a variety of other problems.

Metropolitan governments spend millions of dollars to solve these problems, or least alleviate them. Do people feel this is a wise investment by their governments?

[insert MicroCase graphic 11.21]

|>Data File: |GSS |

|> Task: |Univariate |

|> Primary Variable: |29) BIG CITY$ |

|> View: |Pie |

As you can see, 45.4% of Americans believe too little is spent to solve the problems of big cities. However, 39.9% believe the amount spent is just right while another 14.8% feel we are spending too much money on urban city problems.

It sometimes appears that living in a large American city isn’t worth it – they are the prime targets for terrorist attacks, businesses have found it more profitable to move to the suburbs and “downtown” commerce is failing. Crime, terrorism and traffic jams don’t make good slogans for encouraging people to invest their lives in the city.

Living in a metropolitan area means that you are living in an area concentrated with people of all different economic, educational and occupational characteristics in a few square miles. All these people living in close proximity to one another is known as “population density.” Simply measured, population density is the number of persons per square mile. Let’s take a look.

[insert MicroCase graphic 11.22]

|> Data File: |MSA |

|> Task: |Mapping |

|> Variable 1: |34) DENSITY 00 |

|> View: |List: Rank |

As you can see, there is great variation among metropolitan areas in terms of population density. And, of course, when we think about metropolitan areas we generally envision an urban city filled with a large number of people living closely together. So, it should not be surprising to most of us that the New York-New Jersey-Long Island MSA ranks number one for population density. At the other end of the spectrum, we should not be surprised to find the Casper, WY MSA ranked last.

Cities with particularly high population densities are often considered to be overpopulated and over crowded. However, the extent to which this is true depends on several factors including the quality and availability of housing and local infrastructure.

Metropolitan cities often are confronted with problems of conflict. Because a metropolitan area consists of a large number of different kinds of people living closely together and these people all have different occupations, incomes and ethnic ties, the potential for differing views on public issues is inevitable.

People near the bottom of the social and economic ladder may look at police and spending on police differently from the people at the top of the ladder. Homeowners and renters probably have different views on property taxation. Families with children and those without children may have different ideas about the school system. Some metropolitan areas attract the young; others attract the old. Different metro areas rate better than others on such things as culture, restaurants, travel and shopping variety. And so it goes. All these differences make up the social and political environment in the metropolitan area.

You will explore some of these differences in the exercise that follows.

WORKSHEET: CHAPTER 11: Metropolitan Government and Politics

REVIEW QUESTIONS

. Based on the first part of this chapter, answer True or False to the following items:

A. Today, most of us live in one of the 267 Metropolitan Statistical Areas (MSA) identified by the United States Bureau of the Census. T F

B. Most MSAs are almost entirely rural areas. T F

C. Ethnic and racial diversity are major characteristics of today’s metropolitan areas. T F

D. In most major metropolitan areas, the middle-class has increasingly chosen to move to the suburbs. T F

E. Recently there have been efforts by metropolitan governments to encourage as many people living in poverty as possible to move to the area. T F

F. Cities with extremely high crime rates and population densities are often considered great vacation spots to “get away from it all.”

EXPLORIT QUESTIONS

As you have learned, the populations of state and cities have changed dramatically throughout the course of U.S. history. Early on we were a country where everyone lived on rural farms. But today, a majority of us live in large metropolitan cities. Let’s take a look at this change over time.

|> Data File: |USTREND |

|> Task: |Historical Trends |

|> Variables: |3) #URBAN |

|> Variables: |4) #RURAL |

1. The number of people living in urban areas has exceeded the number of people living in rural areas since 1920. TRUE FALSE

2. Which statement(s) describe the results on this graph? (Select all that apply)

a. In 1990, the number of people in urban areas exceeded the number of those in rural areas by less than 10 million.

b. 1n 1990, the number of people in urban areas exceeded the number of those in rural areas by more than 100 million.

c. Following WWII (1945), more people lived in urban areas than in rural areas.

d. The numbers of people living in urban areas increased much more during the 1800s than the numbers of people living in rural areas.

Now, let’s look at spending for big cities in greater detail. Using the AUTO-ANALYZER task, let’s see if there are differences among groups on spending for solving urban problems.

|> Data File: |GSS |

|> Task: |Auto-Analyzer |

|> Variable: |29) BIG CITY $ |

|> View: |Univariate |

3. Among all respondents, _________% feel the government spends too little to solve the problems of big cities.

For each of the demographic variables listed, indicate whether there is a significant effect. If so, indicate which category is most likely and least likely to own a homefeel the government spends too little to solve the problems of big cities.

|Socio-Demographic Variable |Is the Overall Effect Significant? |Category Most Likely |Category Least Likely |

|Age | Yes No | | |

|Education | Yes No | | |

|Sex | Yes No | | |

|Region | Yes No | | |

|Party | Yes No | | |

|Race | Yes No | | |

4. Which statement best describes the characteristics of a person most likely to believe too little is spent to solve big city problems?

a. African-American Democratic male with a high school diploma living in an Eastern state.

b. African-American Democratic woman without a high school diploma living in an Eastern state.

c. White Republican male with a college education living in a Midwestern state.

d. White Democratic woman with a high school education living in a Southern state.

5. Which statement best describes the characteristics of a person least likely to believe too little is spent to solve big city problems?

a. White Republican male with a college education living in a Southern state.

b. White Democratic woman with a high school education living in a Southern state.

c. African-American male without a high school diploma living in an Eastern state.

d. African-American woman with a high school diploma living in a Western state.

As we discussed in the first part of the chapter, people have differing views on many metropolitan issues and all these differences make up the political environment in the metropolitan area. Using the AUTO-ANALYZER task, let’s see if there are differences between homeowners and renters.

|Data File: |GSS |

|Task: |Auto-Analyzer |

|> Variable: |29) OWN HOME? |

|> View: |Univariate |

6. Among all respondents, _________% own their own home.

For each of the demographic variables listed, indicate whether there is a significant effect. If so, indicate which category is most likely and least likely to own a home.

|Socio-Demographic Variable |Is the Overall Effect Significant? |Category Most Likely |Category Least Likely |

|Age | Yes No | | |

|Education | Yes No | | |

|Sex | Yes No | | |

|Region | Yes No | | |

|Party | Yes No | | |

|Race | Yes No | | |

7. Which statement best describes the characteristics of those most likely to be homeowners?

a. People with a college education, between the ages of 30 and 49, live in the South, vote Democrat, and are White.

b. Women with a college education, between the ages of 30 and 49, live in a Western state, vote Democrat and are African-American.

c. Men with a college education, between the ages of 30 and 49, live in a Midwestern state, vote Republican and are White.

d. People who have at least a high school education, are at least 50 years of age, live in a Southern state, vote Republican and are White.

8. Which statement best describes the characteristics of those least likely to own a home and be renters?

a. Women with a college education, between the ages of 30 and 49, live in a Western state, vote Democrat and are African-American.

b. People with less than a high school education, under the age of 30, vote Independent, live in a Western state, and are African-American.

c. People with a high school education, between the ages of 30 and 49, live in a Eastern state, vote Republican, and are White.

d. Men with a college education, between the ages of 30 and 49, live in a Midwestern state, vote Republican and are White.

Low income, low educated, unskilled people are often concentrated in large metropolitan cities. Across the country, this population is often single mothers. Let’s map the percentage of female headed households below the poverty level.

|> Data File: |MSA |

|> Task: |Mapping |

|> Variable 1: |17) F.HEAD P00 |

|> View: |Map |

9. Which of the following regions or states have comparatively high percentages of female headed households below the poverty level? (Check all that apply).

o Southwest (Arizona, New Mexico, Oklahoma, etc.)

o New York City and surrounding areas (from Connecticut to New Jersey)

o Midwest (Ohio, Indiana, Missouri)

o South (Alabama, Florida, Mississippi, Georgia)

o California

o Texas

o Louisiana

|> View: |List: Rank |

10. How many of the 267 MSAs have female headed households in which at least 30 percent are below the poverty level? ________

11. What MSA has the highest percentage? __________

12. What is the percentage for this MSA? _________

13. Which MSA has the lowest percentage? _______________________

14. What is the percentage for this MSA? _______________________

Describe two political issues that may be particularly important in cities that have high percentages of female headed households living below the poverty level.

1.

2.

Now, map the percentage of people who speak a non-English language at home.

|Data File: |MSA |

|Task: |Mapping |

|> Variable 1: |7) %NON-ENG00 |

|> View: |List: Map |

15. Which states or regions are the MSAs located that have comparatively high percentages of people who speak a non-English language at home? (Check all that apply).

o Southwest (Texas, Arizona, New Mexico, etc.)

o New York City and surrounding areas (from Connecticut to New Jersey)

o Midwest (Ohio, Indiana, Missouri)

o South (Alabama, Mississippi, Georgia)

o California

o Florida

|> View: |List: Rank |

16. How many of the 267 MSAs have populations in which at least 10 percent of the residents speak a language other than English at home? ________

17. What MSA has the highest percentage? __________

18. What is the percentage for this MSA? _________

Let’s compare two maps: one that shows the percentage of families below the poverty level (in 2000) and the other that shows local government money spend on education per capita (1987).

|Data File: |MSA |

|Task: |Mapping |

|> Variable 1: |16) POOR FAM00 |

|> Variable 2: |26) EDUC$/CP87 |

|> View: |Map |

19. Do these two maps look quite similar, nearly opposite or neither? (Circle one)

20. Write in the value of the Pearson correlation coefficient. r = ____.

21. Is the relationship statistically significant? Yes No

22. Is the relationship positive or negative? Positive Negative

. Based on the results, what do you conclude?

As we discussed earlier in this chapter, many families have also left for the suburbs to find cleaner air, better housing, less crime and improved schools. As we noted before, “better schools” and crime are two of the top reasons people cite for moving their family from the central city to the suburbs. Let’s see if we can see if education spending has any effect on reported crime.

|Data File: |MSA |

|> Task: |Scatterplot |

|> Dependent Variable: |18) CINDEX00 |

|> Independent Variable: |26) EDUC$/CP87 |

|> View: |Reg.Line |

23. What is the correlation coefficient for these results? r = _____

24. Is the relationship positive or negative?

25. Are these results statistically significant? Yes No

Based on the above results, how would you summarize the relationship between these variables?

Earlier in this chapter we learned how some MSAs spend more per capita on government payroll expenditures than others. This time let’s see if great government spending is related to greater debt for a metropolitan community. For this analysis we will compare per capita government payroll spending among the MSAs with per capita government debt by MSA.

|Data File: |MSA |

|> Task: |Mapping |

|> Variable 1: |32) DEBT$/CP87 |

|> Variable 2: |33) GOV.$/CP87 |

|> View: |Map |

26. Do these two maps look quite similar, nearly opposite or neither? (Circle one)

27. Write in the value of the Pearson correlation coefficient. r = ____.

28. Is the relationship statistically significant? Yes No

29. Is the relationship positive or negative? Positive Negative

Based on the results, what do you conclude about the relationship between local government payroll spending and local government debt?

Metropolitan areas in the U.S. are major contributors to the nation’s economy. Home building is also a good indicator of economic growth. We know that many metropolitan areas in the Sunbelt states have experienced tremendous growth over the last decade. Let’s compare the Sunbelt region to the rate of new home construction.

|>Data File: |STATE |

|> Task: |Mapping |

|> Variable 1: |138) SUNBELT |

|> Variable 2: |65) NEW HOMS00 |

|> View: |Map |

30. Do these two maps look quite similar, nearly opposite or neither? (Circle one)

31. Write in the value of the Pearson correlation coefficient. r = ____.

32. Is the relationship statistically significant? Yes No

33. Is the relationship positive or negative? Positive Negative

Describe two political issues that may be of particular importance to metropolitan areas that are experiencing high levels of new home construction.

Because a metropolitan area consists of a large number of different kinds of people living closely together the potential for differing views on public issues is inevitable. For example, people near the bottom of the economic ladder may look at spending on police differently from people at the top. Let’s find out.

We would hypothesize that wealthy people in metropolitan areas would want more spending for police. What might be two reasons for making this hypothesis?

1.

2.

|> Data File: |MSA |

|> Task: |Scatterplot |

|> Dependent Variable: |31) POLICE$/C87 |

|> Independent Variable: |22) F$>35K00 |

|> View: |List: Rank |

34. What is the correlation coefficient for these results? r = _____

35. Is the relationship positive or negative?

36. Are these results statistically significant? Yes No

Based on the above results, how would you summarize the relationship between these variables?

Do the results support our hypothesis? YES or NO

On Your Own

1. As we have discussed in this chapter, a great majority of Americans live in metropolitan areas. Therefore, large urban areas should have significant political influence in today’s elections. Is this the case? Do voters in metropolitan areas affect elections? Let’s find out.

Write a hypothesis concerning the relationship between living in a metropolitan area and voter turnout. Then using the STATES data file, select the SCATTERPLOT task and use 93) URBAN USA as the independent variable and 41) TURNOUT00 as the dependent variable to test your hypothesis. What does your analysis say about the relationship between living in an urban area and voting? Be sure to support your conclusions with evidence.

2. In this chapter we learned that many metropolitan areas have high percentages of density. What impact does density have on metropolitan communities? Do metropolitan areas that have high percentages of density also tend to attract families with lower incomes? Using the MSA data file, select the SCATTERPLOT task and use 34) DENSITY 00 as the independent variable and 21) F$ ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download