1 

Approaches to Estimating Subnational Victimization Estimates 

Michael Planty 

Bureau of Justice Statistics 

810 7th St., NW Washington, DC 20531, Michael.Planty@usdoj.gov

Abstract

The Bureau of Justice Statistics (BJS) has been using the National Crime Victimization Survey (NCVS) to produce 

national-level estimates of crime since the early 1970s. Local stakeholders, however, would find the survey data 

more useful if statistics could be produced at a local level as a means to reflect local crime conditions and as a tool 

to assess police and criminal justice services. To address this demand for small area or subnational victimization 

estimates, the Bureau of Justice Statistics (BJS) has embarked on a multi-pronged approach for developing a 

portfolio of both direct and indirect estimation procedures. The overall goal is to create an approach that produces 

reliable, valid, and timely estimates that affords the Bureau the flexibility to expand or contract the focus in a data 

demanding, but fiscally uncertain environment. This paper describes the portfolio and highlights the generic area 

approach to producing subnational violent victimization estimates. Statistical and substantive limitations are 

discussed to understand the implications of using criminal victimization estimates as a measure of local crime and 

police services. 

1. Introduction

Since 1972, the National Crime Victimization Survey (NCVS) has provided national estimates of the incidence of 

crime by asking household respondents about crimes committed against them in the previous 6 months, whether or 

not they had reported them to the police. The primary focus of the survey has been on national estimates of crimes 

categorized by type of crime, on characteristics of the specific instances of crime, and on characteristics of the 

victims. 

However, national estimates of victimization are perceived to be of little value to local stakeholders in assessing 

their own needs. Local social and economic conditions, often thought to be related to crime levels and types, may 

not reflect national conditions suggesting that the national crime trend is of little relevance. Most local areas 

typically rely on data from official statistics generated from police activities, such as calls for service, recorded 

crimes, and arrests. However, much is known about the various social and policy filters associated with police 

records, most importantly, that a large portion of crime is not reported to the police. 

The production of small area estimates (SAE) of crime and victimization can serve two general purposes: 1) a 

vehicle to compare standard measures of crime across geographic areas and to the nation as a whole, and 2) a 

vehicle to assess local patterns and trends independent from police records. A third purpose that reaches beyond 

measuring the amount and type of crime that occurs, is the ability to capture citizen perceptions and attitudes about 

the status of crime and policing in their community. Subsequently SAE can be used to craft an appropriate response 

to problems and serve to allocate resources within and between communities. To address the need for reliable 

estimates at the local level, BJS has developed a research program to identify the best methods for producing 

subnational victimization estimates. 

2. Approaches to subnational victimization estimates 

To address the interest in small area estimates and to increase the value and utility of the general NCVS collection, 

the Bureau of Justice Statistics (BJS) has embarked on developing a small area estimation program using a multiprong approach that is exploring both direct and indirect estimation procedures. The specific details of many of 

these strategies are outlined in Cantor, D., Krentzke, T., Stukel, D., and Rizzo, L. (2010). Direct estimations use 

current data or current data with increased sample cases (“sample boosts”) to generate estimates directly from the 

NCVS responses. The benefit to direct estimation is that particular subnational areas are identifiable and rely on 

direct observation. For areas with small or nonexistent sample sizes, direct estimates are problematic and provide 
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unreliable estimates. Given the rare nature of crime even relatively large boosts to the sample may not produce 

measures with reliable precision. Another approach is to use indirect methods that involve modeling techniques to 

create estimates with ancillary information related to victimization at the local level. This approach uses information 

from direct sample cases when available, but primarily relies on the performance of secondary indicators to the 

produce the local victimization rate. 

The characteristics and concerns about the development and use of subnational estimates mirror those of general 

survey data collection operations. The decision to implement a particular option will rest on trade-offs between: 

reliability and precision (annual, multi-year averages); 

generalizability; 

number of and types of local areas reported out: states, MSAs, cities, counties; 

sub domain disaggregations (type of crime, reporting to police, victim and incident 

characteristics); 

timeliness; and 

costs. 

National survey collections are able to produce reliable estimates representative of the national level. The problems 

associated with producing direct estimates at finer levels, such as state, county, or city, are the costs and time 

required to collect adequate sample sizes for reliable estimates as well as designing the sample to be representative 

of the area of interest. A common solution to increase the precision of small area estimates is to aggregate data over 

multiple years in an effort to increase sample sizes. However, even with an increase in statistical power, it is not 

always the case that the national sample was designed to be representative of these smaller areas. 

Further, these characteristics often provide complex trade-offs that are not easy to understand up-front. For example, 

using current sample will have lower precision, lower generalizability, fewer areas reported out, but is the least 

expensive option. Boosting and reallocating sample of the current NCVS design would increase precision, increase 

generalizability, produce estimates for most areas, but is the most expensive and lengthy option. Other trade-offs 

need to consider schedule and timelines for deliverables; ideal unit for reporting (MSA, state, county, etc.); 

victimization measures of importance (total violence, property crime, serious violence, IPV, sexual violence, etc); 

value to stakeholders; and metrics to evaluate the performance for each option. 

The four methods currently being examined are generic areas, direct boost and reallocation, model based estimates, 

and supplementing the core collection with a companion collection. Each approach is described briefly and a detail 

example of the generic area approach is described in section 3 below. 

2.1 Generic areas 

A generic area typology is created using available geographic identifiers to create “like places” (RTI, 1984).1 As the 

term implies, these categories are not specific to any one state, city or place, rather, these generic areas represent 

places that are share the same general geographic characteristics. For example, a “city with a population of 250,000-

500,000 in the northeast” represents places such as Buffalo, NY, and Pittsburgh, PA. Issues related to the lack of 

statistical power can be addressed by aggregating multiple years. Generic areas are limited to the availability of 

geographic variables (e.g., state variables are only available on restrict-use files), the ability to obtain sufficient 

sample sizes, the homogeneity shared within categories (are places really alike?), and whether sample cases properly 

represent the targeted place. This is the least expensive option, requiring no change in the survey design. 

2.2 Direct boost and reallocation 

This approach would boost the sample sizes and reallocate the sample so that it is more suitable for subnational 

rather than national estimates. It would increase the precision and generalizabilty of small places through direct 

observation. Given the increase in sample to accommodate the small places, national estimates wouldn’t necessarily 

lose precision, but the design wouldn’t be optimal for the national estimate. This is the most expensive option as it 

 

1

 Other sources of information at the subnational level include the NCVS MSA file (Lauritsen and Schaum, 2005) 

and the availability of micro-level geo-coded restricted-use data (http://www.census.gov/ces/rdcresearch/).
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involves increases in sample cases. Details of this approach can be found in Cantor, Krenzke, Stukel and Rizzo 

(2010), Fay and Li (2012), and Fay and Li (2011). 

2.3 Indirect or model based estimates 

Indirect techniques use statistical modeling to capture information from the current sample and leverage auxiliary 

information related to victimization to produce subnational estimates. This approach is used to develop estimates for 

areas without sufficient sample for direct estimates. The relationship between ancillary data on areas and NCVS 

estimates in the areas with data are used to predict victimization rates. A critical concern is identifying proper 

covariates that perform well in the modeling efforts. In addition, since these estimates are partially or completely 

based on modeling rather than direct observation, the response from stakeholders is a concern. Details of this 

approach can be found in Li, J., Diallo, M.S., and Fay, R.E. (2012). 

2.4 Supplementing sample sizes with a companion study

A fourth option is to administer a supplemental or companion survey in local areas. Estimates are generated by 

blending the companion survey with the existing core-NCVS sample. An important challenges is to develop a 

blending methodology that accounts for the measurement differences between the two sources of information. 

Ideally this companion study would be a low cost option. Details on this approach can be found in Brick, J.M., 

Edwards, W.S., and Lohr, S. (2012). While this particular approach to sub-national estimation is expensive and on 

its face not practical for a large number of jurisdictions, it may be useful for assessing the effects of targeted 

interventions in specific jurisdictions. 

3. Generic areas 

This section of the paper highlights the direct generic area approach to estimating violent victimization estimates at 

the subnational geography levels for regions, jurisdictions of a specific population size, and metropolitan areas 

(MSA). A generic area typology or taxonomy is created by these geographic identifiers to create “like places”, 

which are areas that share one or more characteristics. Based on this typology comparisons are made between place 

typologies and national victimization rates to identify similar patterns and points of divergence. Information about 

other geographic units such as state or city is currently limited because of concerns over respondent confidentiality 

and disclosure risks as well as the sample design since the NCVS sample is designed to optimize generating national 

crime estimates.2 The primary advantage of the generic area approach is the use of existing data, which are readily 

available from the NCVS public-use files. A drawback to this approach is that annual estimates can be prone to 

relatively lower statistical precision due to small sample sizes and possibly lower levels of external validity. In 

addition, estimates are not specific to a unique area, so specific places such as states, cities or counties are not 

identifiable. The next sections describe the typology and examine the variability of these estimates as measured by 

the standard error and relative standard error. 

3.1 Methodology 

The report uses NCVS data from 1996-2010. The violent victimization rate includes attempted and completed rape, 

sexual assault, robbery, and both aggravated and simple assault. Victimizations are classified based upon detailed 

characteristics of the event provided by the respondent. During data processing an algorithm classifies each incident 

into a type of crime, based upon the entries on a number of items on the survey questionnaire. This process ensures 

that similar events will be classified using a standard procedure rather than relying on the victim or interviewer to 

make these determinations. If an event can be classified as more than one type of crime, a hierarchy is used which 

classifies the crime according to the most serious event that occurred. The hierarchy is: rape, sexual assault, robbery, 

assault, burglary, motor vehicle theft, theft. Therefore, the NCVS creates a standard definition across all places in an 

attempt to increase comparability. The three geographic variables used in the analysis are place size, region, and 

urbanicity or metropolitan area. 

Place size. The relationship between place size and crime has been examined by a number of researchers 

(Ackerman, 1998; Chamlin and Cochran, 2004; McDowall and Loftin, 2009; Rotolo and Tittle, 2006). Generally, 

 

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 Is it possible that direct boosts and reallocations of sample could change these constraints e constraints. 
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research has found larger places have higher crime rates, but there is some evidence that the strength of this 

relationship has become weaker over time. Place size uses population size to define areas. Eight categories are used: 

Not in a place 

Under 10,000 

10,000-49,999 

50,000-99,999 

100,000-249,999 

250,000-499,999 

500,000-999,999 

1,000,000 or more 

Region. Regional differences have been examined in relation to crime rates in terms of structural, social, and 

economic variation (Loftin and Hill, 1974; Messner, 1983; McCall, P., Land, K., and Cohen, L., 1992). For this 

analysis regional areas divide the states into four groups or census regions: 

Northeast - Includes the 9 states of Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New 

York, Pennsylvania, Rhode Island, and Vermont. 

Midwest - Includes the 12 States of Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, 

Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. 

South - Includes the District of Columbia and the 16 States of Alabama, Arkansas, Delaware, Florida, 

Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, 

Tennessee, Texas, Virginia, and West Virginia. 

West - Includes the 13 states of Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, 

New Mexico, Oregon, Utah, Washington, and Wyoming.

Urbanicity and Metropolitan Statistical Area (MSA). Urban areas or cities have been an intense focus of 

criminological research and theory (Bachman, 1992; Feld, 1991; Gibbs, 1979; Lauritsen and Schaum, 2005; Laub, 

1983; McDowall and Loftin, 2009; Skogan, 1977b, 1977a; Shaw and McKay 1942). We create three measures of

urbanicity based on MSA status: 

Rural area - A place not located inside the Metropolitan Statistical Area. This category includes a variety 

of localities, ranging from sparsely populated rural areas to cities with populations less than 50,000 

Suburban areas - A county or counties containing a central city, plus any contiguous counties that are 

linked socially and economically to the central city. On data tables, suburban areas are categorized as those 

portions of metropolitan areas situated "outside central cities." 

Urban areas - The largest city (or grouping of cities) in a Metropolitan Statistical Area (see definition of 

Metropolitan Statistical Area). 

These three variables are used to create a typology subnational “generic areas.” As the term implies, these categories 

are not specific to any one state, city or place, rather, these generic areas represent places that all share the same 

general geographic characteristics. For example, one category is a “city with a population of 250,000-500,000 in the 

northeast” representing places like Buffalo, NY, and Pittsburgh, PA. Along with violent victimization rates, standard 

errors and the relative standard error (RSE) are shown as measures of reliability. Large standard errors or RSEs are 

associated with less precision and go toward the eventual decision to accept a particular estimate as fit for use. 

Region, place size and MSA. Table 1 shows 3-year rolling average violent victimization rates per 1,000 persons and 

90% confidence intervals by combination of region, MSA and population size. Table 2 presents the standard errors 

and relative standard errors (RSE) for the estimates. Figure 1 shows that most estimates have a RSE between 5 and 

20 percent, a range considered to be a reasonable level of reliability. 
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Figure 1. NCVS violent victimization rate standard errors by relative standard errors (RSE), 2008-2010 

 

0 

5 

10

15

20

25

30

35

40

45

50

0 2 4 6 8 10 12 14 16

RSE 

Standard error of the victimization rate 

4. Discussion 

BJS is currently considering options for generating small area victimization estimates. The need for subnational 

victimization estimates has been recognized for almost as long as the NCS and NCVS have been collecting data. 

BJS is currently exploring ways to generate these important statistics. One method is through generic area 

estimation. This paper presents findings about generic area violent victimization rates for region, MSA, and 

population size. Point estimates and measures of reliability indicate that most places showed very similar patterns 

over time, however, there is some evidence that mid-size cities between 100,000-499,999 experienced larger 

declines than other places. While these generic area statistics can provide greater insights to victimization at a 

subnational level, they are some important limitations to consider about use. 

The value of police and victimization statistics for local stakeholders is determined in part by estimate reliability and 

timeliness, but also validity as it relates to purpose or use. Like any measure, local victimization estimates have 

limitations. Police statistics represent the amount of crime that occurs in a specific place or jurisdiction. It includes 

crime that occurs against all persons in that area, including crimes against the homeless, tourists, folks visiting 

business or entertainment districts. Police statistics also include commercial crime, as well as victimless crimes such 

as drug use, traffic citations, and disorder. The most severe limitations on police statistics is that many crimes that 

occur within a jurisdiction go unreported or are reported but not recorded by the police (Skogan, 1977; Hart and 

Rennison, 2003). While a large percentage of crime goes unreported to the police, police statistics do capture 

primarily those events that occur within a jurisdiction. 

Unlike police statistics NCVS victimization statistics reflect the amount of crime the local residential household 

population has experienced in a particular time frame. The actual incident may have occurred in a very different area 
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and not necessarily in their current neighborhood. For example, residents may be victimized at work, school, or 

while on vacation or travel. The NCVS survey provides information on where the incident occurred, however, it 

does not capture specific information on the region, population size, or MSA status. Therefore, while we know if the 

incident occurred in a different place than their current residential area, we have limited information on where to 

classify the event. 

Figure 2 shows the percentage of crimes that occur in a different place from the victim’s local residential community 

as measured by the NCVS. In 2010, about 81% of urban victims were victimized in their local community compared 

to 70% of suburbanites. This is due in part to commuting patterns of suburbanites to work and entertainment districts 

outside of their communities. This type of variation is important to consider in terms of the use of victimization 

surveys. Even if the residential household crime location was all in their current neighborhood, victimization 

surveys still could miss a significant portion of crime that happens to non-residents. 

Figure 2. Percentage of violent victimizations reported to have occurred within the victim’s residential area, 2010

This study is an important first step in better understanding local patterns of victimization. More work is needed is 

developing reliable, cost-effective approaches to generating subnational victimization estimates. Future research 

would include the following steps and considerations. 

How valid are the generic areas in relation to population totals compared to other sources such as the

American Community Survey? The sample is not explicitly stratified or controlled to these refined subareas 

so it is possible to misrepresent the actual coverage, size and distribution of such smaller populations.

Use of more specific area identifiers including the geo-coded microfiles available at the restricted-use 

Census Research Data Centers. These more detailed area codes could be used to refine the classification

typology in an effort to create more homogeneous or policy relevant groups. 

Exploration of other classification systems that include socio-economic and demographic measures similar 

to the U.S. Nielsen PRIZM (http://www.tetrad.com/demographics/usa/claritas/prizmne.html) project and 
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the British Crime Survey ACORN project.3 For example, ACORN is a geodemographic classification 

system that sorts small areas or neighborhoods into 17 groups and 56 types. This taxonomy describes the

local area in terms of population change, wealth, education, labor market status, urban status, family 

structure, and housing types. The five-group categories are: 

Wealthy Achievers – wealthy executives, affluent older people and well-off families. 

Urban Prosperity – prosperous professionals, young urban professionals and students living in 

town and city areas. 

Comfortably Off – young couples, secure families, older couples living in the suburbs and 

pensioners. 

Moderate Means – Asian communities, post-industrial families and skilled manual workers. 

Hard Pressed – low-income families, residents in council areas, people living in highrise and 

inner-city estates. 

Disclaimer 

The views expressed here are the author’s and do not reflect those of the Bureau of Justice Statistics.

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Table 1. Point estimates and 90% confidence intervals, 1996-98 and 2008-2010, 3-year averages 

 

Rate per 1,000 `

1996-98 90% CI 2008-10 90% CI 1996-98 90% CI 2008-10 90% CI

Northeast Urban 10,000 to 49,000 109 21.1 36 13.1 South Urban 10,000 to 49,000 44 8.4 21 8.6

50,000 to 99,000 68 12.3 25 8.4 50,000 to 99,000 98 12.4 25 5.6

100,000 to 249,000 61 11.8 30 11.8 100,000 to 249,000 75 8.5 23 4.7

250,000 to 499,999 71 19.1 25 13.9 250,000 to 499,999 64 7.7 37 7.8

500,000 to 999,999 45 19.7 40 22.0 500,000 to 999,999 70 10.3 25 5.4

1 million + 54 6.3 14 3.2 1 million + 56 10.7 37 8.1

Total 62 5.2 20 3.2 Total 69 4.6 28 2.9

Suburban Not in a place 39 4.6 20 3.4 Suburban Not in a place 55 4.3 14 2.1

Under 10,000 57 7.2 26 5.1 Under 10,000 57 6.7 16 3.7

10,000 to 49,000 46 5.5 16 3.5 10,000 to 49,000 74 6.2 22 3.4

50,000 to 99,000 47 10.9 19 8.5 50,000 to 99,000 67 13.7 15 6.4

100,000 to 249,000 13 13.9 16 12.3 100,000 to 249,000 45 12.5 21 9.3

Total 46 3.4 20 2.3 Total 61 3.5 17 1.7

Rural Not in a place 62 10.2 24 7.3 Rural Not in a place 39 4.3 19 3.7

Under 10,000 61 14.5 44 17.5 Under 10,000 32 5.4 21 4.9

10,000 to 49,000 91 27.1 40 15.8 10,000 to 49,000 40 8.4 23 6.7

Total 65 8.4 31 6.7 Total 37 3.4 21 2.9

Midwest Urban 10,000 to 49,000 62 14.0 63 16.4 West Urban 10,000 to 49,000 133 18.5 29 10.2

50,000 to 99,000 69 11.8 30 6.6 50,000 to 99,000 73 12.2 32 7.6

100,000 to 249,000 80 10.3 41 8.4 100,000 to 249,000 89 12.1 31 5.6

250,000 to 499,999 125 16.8 42 10.1 250,000 to 499,999 81 9.4 32 6.6

500,000 to 999,999 102 14.3 28 8.1 500,000 to 999,999 83 12.2 24 6.8

1 million + 76 11.0 32 10.0 1 million + 57 8.7 22 5.2

Total 85 6.1 37 4.2 Total 82 5.5 28 3.1

Suburban Not in a place 50 5.6 26 4.4 Suburban Not in a place 68 8.4 18 4.4

Under 10,000 72 8.4 17 4.2 Under 10,000 83 11.2 19 5.1

10,000 to 49,000 63 6.3 27 4.2 10,000 to 49,000 79 7.5 20 3.8

50,000 to 99,000 63 11.9 30 8.8 50,000 to 99,000 67 8.0 23 5.6

100,000 to 249,000 64 21.4 22 17.2 100,000 to 249,000 65 10.1 26 6.2

Total 61 4.1 25 2.7 Total 73 4.6 21 2.4

Rural Not in a place 43 5.7 9 2.7 Rural Not in a place 50 8.0 16 7.9

Under 10,000 38 5.8 19 5.2 Under 10,000 81 13.6 21 6.9

10,000 to 49,000 84 12.2 22 7.0 10,000 to 49,000 35 11.0 53 13.0

Total 48 4.4 14 2.7 Total 56 6.5 30 5.7
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Table 2. Standard errors and relative standard errors, 1996-98 and 2008-2010, 3-year average 

Standard errors

1996-98 RSE 2008-10 RSE 1996-98 RSE 2008-10 RSE

Northeast Urban 10,000 to 49,000 12.8 11.7 8.0 21.9 South Urban 10,000 to 49,000 5.1 11.5 5.2 24.3

50,000 to 99,000 7.4 10.9 5.1 20.4 50,000 to 99,000 7.5 7.7 3.4 13.6

100,000 to 249,000 7.1 11.7 7.1 23.8 100,000 to 249,000 5.2 6.9 2.9 12.6

250,000 to 499,999 11.6 16.4 8.5 33.2 250,000 to 499,999 4.7 7.4 4.7 12.7

500,000 to 999,999 12.0 26.8 13.4 33.1 500,000 to 999,999 6.3 8.9 3.3 13.1

1 million + 3.8 7.1 2.0 13.9 1 million + 6.5 11.5 4.9 13.4

Total 3.2 5.2 1.9 9.5 Total 2.8 4.1 1.8 6.5

Suburban Not in a place 2.8 7.0 2.0 10.2 Suburban Not in a place 2.6 4.8 1.3 9.2

Under 10,000 4.4 7.7 3.1 12.0 Under 10,000 4.1 7.1 2.3 13.8

10,000 to 49,000 3.4 7.3 2.1 13.2 10,000 to 49,000 3.8 5.1 2.0 9.1

50,000 to 99,000 6.6 14.1 5.2 26.8 50,000 to 99,000 8.3 12.4 3.9 26.4

100,000 to 249,000 8.4 64.6 7.5 45.7 100,000 to 249,000 7.6 16.9 5.6 27.4

Total 2.1 4.6 1.4 7.1 Total 2.1 3.4 1.1 6.2

Rural Not in a place 6.2 9.9 4.5 18.9 Rural Not in a place 2.6 6.7 2.2 11.5

Under 10,000 8.8 14.6 10.6 23.9 Under 10,000 3.3 10.2 3.0 14.1

10,000 to 49,000 16.5 18.2 9.6 24.3 10,000 to 49,000 5.1 12.7 4.1 17.5

Total 5.1 7.9 4.1 13.3 Total 2.1 5.5 1.8 8.5

Midwest Urban 10,000 to 49,000 8.5 13.7 10.0 15.9 West Urban 10,000 to 49,000 11.3 8.4 6.2 21.3

50,000 to 99,000 7.2 10.3 4.0 13.4 50,000 to 99,000 7.4 10.2 4.6 14.6

100,000 to 249,000 6.2 7.8 5.1 12.5 100,000 to 249,000 7.4 8.3 3.4 11.1

250,000 to 499,999 10.2 8.2 6.2 14.8 250,000 to 499,999 5.7 7.1 4.0 12.4

500,000 to 999,999 8.7 8.5 4.9 17.3 500,000 to 999,999 7.4 8.9 4.2 17.2

1 million + 6.7 8.7 6.1 18.7 1 million + 5.3 9.2 3.1 14.1

Total 3.7 4.3 2.5 6.9 Total 3.3 4.1 1.9 6.6

Suburban Not in a place 3.4 6.8 2.7 10.1 Suburban Not in a place 5.1 7.5 2.7 14.9

Under 10,000 5.1 7.0 2.5 14.8 Under 10,000 6.8 8.2 3.1 16.1

10,000 to 49,000 3.8 6.0 2.6 9.4 10,000 to 49,000 4.5 5.7 2.3 11.4

50,000 to 99,000 7.2 11.4 5.3 17.6 50,000 to 99,000 4.9 7.3 3.4 15.1

100,000 to 249,000 13.0 20.3 10.4 46.5 100,000 to 249,000 6.2 9.4 3.8 14.4

Total 2.5 4.1 1.6 6.5 Total 2.8 3.8 1.5 7.0

Rural Not in a place 3.5 8.0 1.7 19.3 Rural Not in a place 4.9 9.8 4.8 29.5

Under 10,000 3.5 9.3 3.2 16.9 Under 10,000 8.3 10.2 4.2 20.0

10,000 to 49,000 7.4 8.8 4.3 19.1 10,000 to 49,000 6.7 19.1 7.9 15.1

Total 2.7 5.5 1.6 11.2 Total 4.0 7.1 3.5 11.7