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«Moving to opportunity voluMe 14, nuMber 2 • 2012 U.S. Department of Housing and Urban Development | Office of Policy Development and Research ...»

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Data users can conduct many different and rich analyses with O-NED, but the utility of the data depends on how much industry and geographic detail is feasible, given the simultaneous need for confidentiality and reliability. We experimented with a variety of ways to balance the tradeoffs of detail, usability, and sample size. In the end, O-NED tabulations are made for the following units of geography: Ohio; Metropolitan Status (central county of metropolitan area, other metropolitan, and nonmetropolitan); Economic Development Region;5 Metropolitan/Nonmetropolitan status by Economic Development Region; Core Based Statistical Area (CBSA); Central/Noncentral county status by CBSA; and County. The industry detail varies by geography. A cross-sector total is available for all units of geography. Major sector-level tabulations are available for all units of geography except County. Three-digit North American Industry Classification System-level tabulations are available only for Ohio as a whole.

Description of Data Files This section describes the file structure and variables included in the publicly released O-NED tabulations.6 The data are released in two formats, a SAS data file and a set of Microsoft Excel® spreadsheets.

Each observation in the data represents a unique combination of a unit of geography, a unit of industry, a type of birth or gazelle, a cohort, and an age. For each observation, the data include the number of establishments, employment, first quarter wages, and annual wages. Survival rates of these variables, both year-to-year and compared with the end of the birth year (age = 1), are also included. Each observation also includes variables to identify the observation and navigate the data, including indicators of geographic and industry detail, geography codes, sector codes, cohort, age, year of data, an indicator for type of birth and whether it is a set of births or a set of gazelles, and a suppression flag.

Exhibit 2 shows the core data elements for one observation of data. These data cover the year 2001 for entrepreneurial births from the 1997 cohort that are located in the Akron, OH CBSA and are in the manufacturing sector. The exhibit includes the values and a description of what the numbers mean for this observation. The 49 establishments remaining in this cohort after 4 years had 502 employees and paid them a total of $14.2 million from the second quarter of 2001 through the first quarter of 2002. The cohort had 25 percent more employment and 27 percent fewer establishments than in its birth year, and both employment and the number of remaining establishments declined between years 3 and 4.

Ohio’s Economic Development Regions are contiguous combinations of counties that have similar or intimately linked economic functions. They are defined by the Ohio Department of Development.

The data and documentation are available online at http://urban.csuohio.edu/economicdevelopment/ONED.html.

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Although the structure of the data is space efficient and easy to navigate, data users will find that they need to combine data across observations to calculate some statistics. For example, calculating the percentage of all entrepreneurial births from the 2003 cohort that were gazelles at age 5 requires combining the number of establishments in the cohort at age 0 and the number of gazelles in the cohort at age 5.

Example of Use The key feature of O-NED is that it measures the growth trends of cohorts of new establishments for the crucial first 5 years of existence.7 Using survival rates—the cohort’s percentage of the initial number of establishments surviving or the percentage of year-1 employment retained—is a convenient way to measure these trends. Exhibit 3 graphs establishment and employment survival rates for groups of cohorts with 5 years of complete data. Exhibit 3a shows establishment survival rates for grouped cohorts. Establishment survival rates among cohorts were similar for all years and were unaffected by whether the business was started before or during the 2001-to-2002 recession.

About 85 percent of all establishments survived to the second year. At the end of year 5, slightly more than one-half (53 percent) of all establishments were still in existence for all cohorts.

Business establishments that started toward the end of the expansionary years (1997 and 1998 cohorts) saw steep declines in employment after their third year (exhibit 3b). These establishments initially grew very rapidly but experienced a big hit during the recessionary years. For establishments that started just as the recession began (1999 and 2000 cohorts), employment also decreased with the onset of the recession. The decline in employment was at a lesser rate than the previous cohorts, however, because these establishments did not have the chance to grow as much as companies that were started in previous years. Establishments that started during the recession are grouped in the 2001 to 2003 cohorts. As seen on the graph, these establishments were not severely affected; they declined in employment at a much slower rate.

Exhibit 4 provides the size and survival trends of the average birth cohort for each of the eight largest CBSAs in Ohio. This exhibit covers only entrepreneurial births, not those affiliated with an existing firm. Entrepreneurial businesses in the Columbus, OH CBSA had more employment growth than those in other CBSAs, with an employment survival rate of 111.0 percent.8 Entrepreneurial establishments represented a higher-than-typical share of total employment in the Columbus, OH CBSA, where the average cohort had 1.53 percent of total employment in its fifth year. The Cleveland, OH CBSA had the largest number of new births and the largest amount of employment, however. In five of the CBSAs, the average cohort of entrepreneurial births had less employment at the end of the fifth year than at the end of the first year.

We draw these examples from Yamoah, Austrian, and Elvery (2009).

We create the survival rates by averaging across each cohort’s survival rate and, therefore, the rates do not match what one would calculate based on the cohort sizes in years 1 and 5.

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Conclusion O-NED data described in this article are now available to anyone who wants to use them through the website of the Center for Economic Development at the Levin College at Cleveland State University. These data provide the first set of publicly available tabulations of establishment and employment survival for multiple cohorts from longitudinally linked QCEW microdata. O-NED includes tabulations down to the county level, providing more geographic detail than any comparable data. The value of these data will be determined by how analysts and researchers use it. We encourage people to dive in and use it and hope that similar data can be made available for the nation as a whole.

Acknowledgments This research was made possible by a data improvement grant from the Ewing Marion Kauffman Foundation and a grant from the President’s Initiative Fund of Cleveland State University. The Bureau of Labor Market Information of the Ohio Department of Jobs and Family Services provided the necessary data, with the cooperation of the U.S. Bureau of Labor Statistics. The authors thank all these parties.

Authors Joel A. Elvery is an assistant professor in the Department of Urban Studies at Cleveland State University.

Ellen Cyran is a senior programmer and analyst in the Center for Economic Development at Cleveland State University.

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References Ahmad, Nadim. 2006. A Proposed Framework for Business Demography Statistics. Working paper (June). Paris, France: Organisation for Economic Co-operation and Development.

Elvery, Joel A., and Ellen Cyran. 2010. Methodology and Documentation for the Ohio New Establishment Dynamics Data. Unpublished manuscript. Available at http://urban.csuohio.edu/ economicdevelopment/ONED/ONED_Method_Doc.pdf.

Knaup, Amy E. 2005. “Survival and Longevity in the Business Employment Dynamics Data,” Monthly Labor Review 128 (5): 50–56.

Knaup, Amy E., and Merissa C. Piazza. 2007. “Business Employment Dynamics Data: Survival and Longevity, II,” Monthly Labor Review 130 (9): 3–10.

Talan, David, and David R. Hiles. 2007. “Quarterly Census of Employment and Wages/Business Employment Dynamics: Data Overview.” Presentation prepared for the 2007 Kauffman Symposium on Entrepreneurship and Innovation Data, Washington, DC, November 23.

Yamoah, Afia, Ziona Austrian, and Joel Elvery. 2009. New Establishment Dynamics: Business Formation and Survival Trends in Ohio. Unpublished manuscript. Available at http://urban.csuohio.


Additional Reading

Sadeghi, Akbar, James R. Spletzer, and David M. Talan. 2009. “Business Employment Dynamics:

Annual Tabulations,” Monthly Labor Review 132 (5): 45–56.

312 Data Shop Impact A regulatory impact analysis must accompany every economically significant federal rule or regulation. The Office of Policy Development and Research performs this analysis for all U.S. Department of Housing and Urban Development rules. An impact analysis is a forecast of the annual benefits and costs accruing to all parties, including the taxpayers, from a given regulation. Modeling these benefits and costs involves use of past research findings, application of economic principles, empirical investigation, and professional judgment.

Impact Analysis of the Proposed Rule on Streamlining the Portability Process in the Housing Choice Voucher Program Yves Sopngwi Djoko U.S. Department of Housing and Urban Development The opinions expressed in this article are those of the author and do not necessarily reflect those of the U.S.

Department of Housing and Urban Development.


Proposed regulatory changes would streamline the portability process in the Housing Choice Voucher Program (HCVP) and enable public housing authorities (PHAs) to better serve families and expand housing opportunities. The proposed rule would yield intangible benefits to program participants and, if successful, increase financial transfers between PHAs. The regulatory action would not be economically significant under Executive Order 128661 and Office of Management and Budget Circular A-4,2 however, because the aggregate financial impact is far less than the $100 million annual threshold.

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Background The Housing Choice Voucher Program (HCVP) is the largest subsidized housing program in the United States that helps very low-income families, elderly people, and disabled people afford decent, safe, and sanitary housing in the private market.3 In 2010, the HCVP subsidized rents for more than 2.5 million low-income households for an estimated $17.3 billion.4 Program Description Public housing authorities (PHAs) locally administer housing choice vouchers using federal funds from the U.S. Department of Housing and Urban Development (HUD). A family that is issued a housing choice voucher is responsible for finding a suitable housing unit of the family’s choice where the owner agrees to rent under the program. This rental unit (which may include the family’s current residence) must meet minimum standards of health and safety, as determined by the PHA, which pays a housing subsidy directly to the landlord on behalf of the participating family. The family then pays the difference between the actual rent charged by the landlord and the amount that the HCVP subsidizes.5 Under certain circumstances, if authorized by the PHA, a family may use its voucher to purchase a modest home.

Eligibility The PHA determines eligibility for a housing voucher based on the total annual gross income and family size; eligibility is limited to U.S. citizens and specified categories of noncitizens who have eligible immigration status. In general, the family’s income may not exceed 80 percent of the median income for the county or metropolitan area in which the family chooses to live. By law, a PHA must provide 75 percent of its vouchers to applicants whose incomes do not exceed 30 percent of the Area Median Income.

Portability A key feature of the HCVP is the mobility, or portability, of the voucher assistance. The term portability refers to the process of leasing a dwelling unit with tenant-based housing voucher assistance outside the jurisdiction of the PHA that initially issued the family its voucher (the initial PHA). Portability allows an eligible family with a housing choice voucher to use that voucher to lease a unit anywhere in the United States where a public housing agency is operating an HCVP.

Currently, program regulations detail where a family may move and the responsibilities of the initial PHA and the receiving PHA (the PHA with jurisdiction over the area to which the family desires to move).

The HCVP is authorized by section 8(o) of the United States Housing Act of 1937 (42 U.S.C. 1473f(o)1437f(o)), and the

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PHA rent policies may specify that the PHA will use a percentage of a family’s income or some other reasonable system to determine income-based rents. In general, the total tenant payment is the highest of the following, rounded to the nearest dollar: 30 percent of the family’s monthly adjusted income, 10 percent of the family’s monthly income, the welfare rent, or the minimum rent.

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Administrative Fee The receiving PHA may either accept an incoming family to its own program (absorption) or bill the initial PHA. Under current regulation, when a voucher is in a portability billing arrangement between the initial PHA and receiving PHA, the initial PHA must pay the receiving PHA 80 percent of its administrative fee for each month the family receives assistance at the receiving PHA.

Basic Facts About the Housing Choice Voucher Program Exhibit 1 shows the total budget authority for the HCVP was about $17 billion, as of March 2011.

The HCVP had about 2.2 million units under lease at that time, and about 50,000 portable vouchers were under lease. The total housing assistance payment—which is the payment to landlords— for portable vouchers under lease was about $463 million.

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Key Provisions of the Proposed Rule The following provisions of the proposed rule are likely to have programmatic, economic, or financial effects on PHAs or other program participants.

Administrative Fee Under current regulation, when a voucher is in a portability billing arrangement between the initial PHA and receiving PHA, the initial PHA must pay the receiving PHA 80 percent of its administrative fee for each month the family receives assistance at the receiving PHA.

The proposed rule would set the maximum amount the initial PHA is required to pay at 100 percent of the receiving PHA’s administrative fee rate. This change prevents a receiving PHA with a lower administrative fee from profiting from an initial PHA with a higher administrative fee.

Mandatory Absorption of Portability Vouchers To ensure that a PHA uses its available budget authority to the maximum extent possible, and to reduce the number of portability billing arrangements between agencies, this proposed rule would

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require a PHA that (1) is using 95 percent or less of its available budget authority and (2) has a leasing rate of less than 95 percent to absorb incoming portability families until the percentage of available budget authority or the leasing rate is at least 95 percent.

Briefing Families on Housing Choice Currently, many PHAs supply new tenants with a briefing packet that includes a map of the PHA jurisdiction, area schools, and relevant community organizations, as well as landlords or owners who have expressed interest in participating in the HCVP. This proposed rule would require that the briefing packet include materials identifying housing opportunities in areas where the eligible low-income (ELI) rate is less than 15 percent, both within the jurisdiction of the PHA and in neighboring jurisdictions.

The proposed rule also requires the PHA to identify owners known to the PHA who are interested in participating in the HCVP or available, eligible units known to the PHA that are located in areas outside areas of ELI concentration. To comply with the proposed rule, PHAs in metropolitan Fair Market Rent areas would also be required to include a current list of other community organizations or programs that help families find units outside areas of ELI concentration.

Cost-Benefit Analysis The implementation of the proposed rule would generate certain benefits to HCVP participants by increasing family choice in locating and securing suitable housing. The proposed rule would also generate some administrative and compliance costs to PHAs. It is estimated, however, that the benefits would largely outweigh the costs. The financial flows that the proposed rule would cause are addressed in the financial transfer section of this article.

Benefits of the Proposed Rule The HCVP portability policy helps ensure that families have the opportunity to relocate to pursue increased or new employment opportunities or to gain access to higher performing schools for their children. An efficient portability process also helps ensure that victims of domestic violence and stalking have access to the resources necessary to relocate to a safe, stable home away from an abuser.

It is difficult to quantify the effects of mobility on the welfare of a program participant, and the jury is still out on the valuation of existing programs with some mobility component, such as the Moving to Opportunity (MTO) for Fair Housing demonstration6 and the Gautreaux desegregation program in Chicago. Although, in principle, improved ability to port should result in better employment outcomes from greater access to available jobs, the MTO results do not allow us to quantify such benefits. Improved ability to port will presumably result in better matches between consumer needs and the locational amenities. Again, we do not know how to quantify.

See http://www.nber.org/mtopublic/ for a comprehensive database on MTO research.

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Costs of the Proposed Rule The rule would not have any effect on the program budget at the national level per se. The portability billing arrangements proposed by this rule, however, may place some additional administrative burden on PHAs. Organizational costs may be associated with agreements and consolidating PHA operations, databases, and documents. For example, the proposed rule would require that the briefing packet include materials identifying housing opportunities outside areas of concentrated poverty both within the jurisdiction of the PHA and in neighboring jurisdictions. PHAs would also be required to identify owners interested in participating in the HCVP or available, eligible units located outside areas of concentrated poverty. To comply with the proposed rule, the PHA would also be required to include a current list of other community organizations or programs that help families find units outside areas of concentrated poverty.

Low-income people make choices based on information they know, including the choices informing their budget constraint. Because the budget constraint of low-income people is tightly binding, they may economize on information gathering as well; that is, they may decide not to gather information on the benefits of options beyond their budget constraint. The selection of a household into the HCVP loosens the household’s budget constraint along the housing dimension. The intent of the information disclosure rule discussed in this report is to provide new voucher households with an expanded information set commensurate with their new budget constraint. It in no way cancels out the information on cost and other disadvantages of location in high-income neighborhoods that led the household to select a house in a low-income neighborhood before entering the HCVP. Households with vouchers will make rational decisions based on the combination of their own information and the information that the PHA provides. As for households without vouchers replacing voucher tenants who have moved out of units in low-income neighborhoods, in keeping with Glaeser, Kahn, and Rappaport (2000), it presumes that such moves are at least not welfarereducing for nonsubsidized tenants operating within their budget constraints.

Financial Transfers Although the financial effect of the proposed rule is marginal, it does have the potential to create substantial financial transfers among PHAs.

Mandatory Absorptions In this proposed rule, HUD is proposing mandatory absorptions of portability vouchers when a PHA is using 95 percent or less of its available budget authority and has a leasing rate of less than 95 percent. It is HUD’s position that this approach would ensure that PHAs are using their available budget authority to the maximum extent possible while also reducing the number of portability billing arrangements.

Administrative Fee Under the current regulation, when a voucher is in a portability billing arrangement between the initial PHA and receiving PHA, the initial PHA must pay the receiving PHA 80 percent of its administrative fee for each month the family receives assistance at the receiving PHA. With the removal of potential barriers to mobility, an increase in the number of portability vouchers is

Cityscape 317Djoko

expected and, thus, an increase in the amount of administrative fee transfers between PHAs. Given that PHAs are not required to report on interagency administrative fee transfers, data on such activity are not available.

The proposed rule would set the maximum amount the initial PHA is required to pay at 100 percent of the receiving PHA’s administrative fee rate. In other words, the initial PHA would reimburse the receiving PHA for the lesser of (1) 80 percent of the initial PHA’s ongoing fee or (2) the full amount of the receiving PHA’s administrative fee. This change eliminates the incentive for a receiving PHA with a lower administrative fee to bill an initial PHA with a higher administrative fee to receive a higher administrative fee than what they would normally earn from HUD. This action should reduce portability billings for those PHAs for which 80 percent of the receiving PHA’s fee is more than 100 percent of their own administrative fee. For example, assume a receiving PHA’s administrative fee is $60. Under current rules, if a family moves to the receiving PHA’s jurisdiction from an initial PHA that receives $100 in administrative fees for a housing voucher, the receiving PHA may bill the initial PHA for $80, which is $20 more than the PHA would earn if it simply absorbed the voucher. Under the proposed rule, the receiving PHA will receive $60 regardless of whether the receiving PHA bills the initial PHA or absorbs the family into its own program.

Conclusion As presented in this analysis, the proposed changes to the portability process provided by the current HCVP regulations would not have a significant incidence on the national program budget (although there would be some amount less billed to HUD without the excess charges) nor have an economically significant effect on the economy, as defined by Executive Order 12866 (Regulatory Planning and Review). Although the proposed rule would not result in an economically significant effect, the proposed changes would yield certain intangible benefits to program participants and, if successful, increase financial transfers between PHAs. The primary purpose of this rule is to streamline the portability process and, in doing so, alleviate some of the administrative complications that families and PHAs both face with the current portability process.

Acknowledgments The author thanks Alastair W. McFarlane and Kurt G. Usowski for encouraging the development of this article and providing helpful comments on the original economic analyses of the proposed rule.

Author Yves Sopngwi Djoko is a senior economist in the Economic Development and Public Finance Division at the U.S. Department of Housing and Urban Development, Office of Economic Affairs.

References Glaeser, Edward L., Matthew E. Kahn, and Jordan Rappaport. 2000. Why Do the Poor Live in Cities? NBER Working Paper No. 7636. Cambridge, MA: National Bureau of Economic Research.

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Office of Management and Budget (OMB). 2003. Circular A-4. September 17, 2003. Available at http://www.whitehouse.gov/omb/circulars_a004_a-4.

———. 1996. Economic Analysis of Federal Regulations Under Executive Order 12866. January 11,

1996. Available at http://www.whitehouse.gov/omb/inforeg/riaguide.html.

U.S. Department of Housing and Urban Development (HUD). 2011. Budget Authority by Program.

Available at http://hud.gov/budgetsummary2011/budget-authority-by-prog.pdf.

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320 Impact Comparing Public Housing and Housing Voucher Tenants With Bayesian Propensity Scores Brent D. Mast Correction The volume 14, number 1 issue of Cityscape contained an error on page 64 in the article titled, “Comparing Public Housing and Housing Voucher Tenants With Bayesian Propensity Scores,” by Brent D. Mast. The article stated that, “Of [public housing] tenants, 58.2 percent have rent burdens between 28 and 31 percent, as do 72.7 percent of [Housing Choice Voucher Program] tenants.” The sentence should have read, “Of [public housing] tenants, 72.7 percent have rent burdens between 28 and 31 percent, as do 58.2 percent of [Housing Choice Voucher Program] tenants.” The author thanks Bill Jacobs for bringing the error to his attention.

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Contents Symposium: Moving to Opportunity Guest Editor: Jens Ludwig Guest Editor’s Introduction

Acknowledgment of Extraordinary Obligations

Moving to Opportunity: Why, How, and What Next? by Mark D. Shroder and Larry L. Orr...31 Achieving MTO’s High Effective Response Rates: Strategies and Tradeoffs by Nancy Gebler, Lisa A. Gennetian, Margaret L. Hudson, Barbara Ward, and Matthew Sciandra......... 57 MTO: A Successful Housing Intervention by Jennifer Comey, Susan J. Popkin, and Kaitlin Franks

The Long-Term Effects of Moving to Opportunity on Adult Health and Economic Self-Sufficiency by Lisa Sanbonmatsu, Jordan Marvakov, Nicholas A. Potter, Fanghua Yang, Emma Adam, William J. Congdon, Greg J. Duncan, Lisa A. Gennetian, Lawrence F. Katz, Jeffrey R. Kling, Ronald C. Kessler, Stacy Tessler Lindau, Jens Ludwig, and Thomas W. McDade. 109 The Long-Term Effects of Moving to Opportunity on Youth Outcomes by Lisa A.

Gennetian, Matthew Sciandra, Lisa Sanbonmatsu, Jens Ludwig, Lawrence F. Katz, Greg J.

Duncan, Jeffrey R. Kling, and Ronald C. Kessler

Making MTO Health Results More Relevant to Current Housing Policy: Next Steps by Thomas D. Cook and Coady Wing

Constrained Compliance: Solving the Puzzle of MTO’s Lease-Up Rates and Why Mobility Matters by Kathryn Edin, Stefanie DeLuca, and Ann Owens

Increasing the Value of MTO Research for Housing Policy Development by Edgar O. Olsen

Moving Neighborhoods Versus Reforming Schools: A Canadian’s Perspective by Philip Oreopoulos

Commentary: MTO’s Contribution to a Virtuous Cycle of Policy Experimentation and Learning by Margery Austin Turner

Point of Contention: Defining Neighborhoods The Tyranny of Census Geography: Small-Area Data and Neighborhood Statistics by Jonathan Sperling

Defining Neighborhoods in Space and Time by Ralph B. Taylor

Defining Neighborhoods for Research and Policy by Claudia Coulton

Dynamic Geography: The Changing Definition of Neighborhood by Marc S. Buslik..... 237 Refereed Papers Geographic Patterns of Serious Mortgage Delinquency: Cross-MSA Comparisons by Lariece M. Brown, Hui-Chin Chen, Melissa T. Narragon, and Paul S. Calem

The Housing Needs of Rental Assistance Applicants by Josh Leopold

Departments Graphic Detail: Geographic Patterns of Regional Unemployment Versus Unemployment Compensation in the United States—2009 by Ron Wilson

Data Shop: Introducing the Ohio New Establishment Dynamics Data by Joel A.

Elvery and Ellen Cyran

Impact: Impact Analysis of the Proposed Rule on Streamlining the Portability Process in the Housing Choice Voucher Program by Yves Sopngwi Djoko

Correction: Comparing Public Housing and Housing Voucher Tenants With Bayesian Propensity Scores by Brent D. Mast

Referees 2011–12

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