Increasing our Understanding of Business Dynamics through the First-Ever Census Bureau Business Management Survey

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Lucia Foster, Chief, Center for Economic Studies
Ron Jarmin, Assistant Director for Research and Methodology

How much does management matter for the success of a business? Some light can be shed on this much debated topic using results from the Census Bureau’s first-ever business management survey, the Management and Organizational Practices Survey (MOPS). This supplement to the 2010 Annual Survey of Manufactures (ASM) collected data from more than 30,000 of the approximately 50,000 manufacturing plants surveyed in the ASM. With the addition of the MOPS, the Census Bureau now provides information on manufacturing plants’ inputs, outputs, and management and organizational practices. The MOPS will be an important tool in assessing plant performance, worker productivity, and the ability of plants to succeed in a highly competitive global economy.

The MOPS represents a collaboration between Census Bureau staff and researchers from Stanford University (Nick Bloom and Itay Saporta-Eksten), the Massachusetts Institute of Technology (Erik Brynjolfsson) and the London School of Economics (John Van Reenen). The National Science Foundation also contributed to the collaboration via a grant to the academic research team.

Preliminary results from the first research paper using the MOPS, Management in America, were presented at the annual American Economic Association meetings in early January. This first paper focuses on the management component of the MOPS and provides summary statistics from the new survey and empirical exercises intended to provide validation of the survey results. The authors constructed a management score that summarizes plants’ intensity of use of structured management practices based upon the responses to the 16 management questions. Structured management practices are those that are more specific, formal, frequent or explicit.

The authors found that use of structured management practices varies across U.S. manufacturing establishments: 18 percent of establishments adopt at least 75 percent of structured management practices related to performance monitoring, targets and incentives, while 27 percent of establishments adopt less than 50 percent of these practices.

The authors also linked the survey results to other Census Bureau (and outside) data sources in order to focus on how differences in management practices are related to variations in plant performance. Plant performance metrics include productivity, profits, output growth, and exports  and measures of innovation (research and development expenditures and patents, both per employee, calculated from the Business R&D and Innovation Survey).

The figure below shows the correlations between these measures of plant performance and structured management practices. For each panel, the establishments are grouped into deciles according to their management index scores (where 1 is the decile with the lowest scores and 10 is the decile with the highest scores).  The performance measures are on the vertical axis and are the median value for each management index decile. The figures show that all of the performance measures are rising across the deciles.

Plant Performance Measures and Structured Management Practices

Plant Performance Measures and Structured Management Practices

This analysis is refined in a regression framework controlling for firm and establishment characteristics (such as capital intensity, size, education of workforce, and industry). This basic positive correlation persists even with these controls. The results provide support that the MOPS is systematically capturing meaningful content about management practices rather than just statistical noise. Future research will address causality, thus shedding light on how much management matters.

Users will be able to access MOPS results from a website that will host summary tables, a benchmarking tool where businesses can compare themselves to their peers and public-use version of the data (subject to Census Bureau approval). In addition, the full data set will be available to qualified researchers on approved projects via the Census Bureau’s secure Research Data Centers.

More on the Management and Organizational Practices Survey is available at <www.census.gov/mcd/mops>.

Management in America by Bloom, Brynjolfsson, Foster, Jarmin, Saporta-Eksten, and Van Reenen is available at <http://www2.census.gov/ces/wp/2013/CES-WP-13-01.pdf>.

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Statistics Matter

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Written By: Thomas A. Louis, PhD, Associate Director for Research and Methodology

Statistics touch every part of daily life and provide measures of most everything – the rise and fall of the tides, the size of communities and of the U.S. economy, the probability of storms, the balance of trade, the prevalence of disease, the financial cost of hurricanes, commuting patterns and time use, the effectiveness of medical treatments, performance in sporting events, the health effects of environmental exposures; plus thousands of other aspects of human behavior and natural phenomena.  The substantial societal benefits of these measures depend on their quality and relevance.

International Year of Statistics
To draw attention to the value statistics play in our lives and the importance of our profession, the statistical community has designated 2013 “The International Year of Statistics.”  Statistics is the science of learning from data, and of measuring, controlling and communicating uncertainty. It provides the navigation essential for directing the course of scientific and societal advances.

Statistics and statisticians will play increasingly important roles as complex “big data” inform and empower our future. How will society mine the haystacks of information on social networks, time-use, economic, and other activities to benefit science and business? The answer is sound statistical practice.

Statistics inform public policy
A few examples: Each year billions of dollars are allocated to school districts based on the Census Bureau’s county-specific estimates of income and poverty, produced by combining information from the most recent decennial census, from the Current Population Survey, from the American Community Survey and administrative records.  Municipalities use these and other data sources to make decisions on transportation infrastructure.  The nation uses Economic Census statistics in setting the industry benchmarks that shape the Gross Domestic Product, our best indicator of economic health.

Growing demand
Academia, business, government, and individual stakeholders increasingly rely on data-driven decisions.  As Marie Davidian (current president of the American Statistical Association) and I highlighted in our editorial in Science (Vol. 336, April 6, 2012), substantially more statisticians and other data scientists are needed to meet the burgeoning demand to develop valid information and make sense of the data tsunami.   Success will depend on novel statistical designs and analyses, and on innovative communication strategies.

Our data-rich future demands that scientists, policy-makers, and the public be able to interpret increasingly complex information and recognize both the benefits and potential pitfalls of statistical information.  Consequently, it is a good sign that there is a strong push to promote statistics as a key component in precollege education.  We must encourage students to develop skills in describing data, developing statistical models, making inferences, evaluating the consequences of decisions, and asking questions that help calibrate quality.  These are skills that students will use throughout life, whatever their careers.  A data-driven future awaits, and statisticians must lead the way.

In my role as Associate Director for Research and Methodology at the U.S. Census Bureau, I will support our talented researchers in developing new approaches that ensure the Census Bureau remains a world leader in achieving the highest attainable quality of our statistical products. Through substantive collaboration we identify the highest priority issues, develop and evaluate approaches, then transfer the best to practice, thereby ensuring that Census statistics continue to support the public good. Visit Research@Census to learn more.

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Trends in Health Insurance Premiums for Public and Private Employers

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Written by: Alice Zawacki, Senior Economist, Center for Economic Studies

Undoubtedly, you have seen headlines or heard reports in the media comparing employee benefits between the public and private sectors.   An important component of employee benefits is health insurance.  In a current project, Tom Buchmueller (University of Michigan), Jessica Vistnes (Agency for Healthcare Research and Quality), and I are using data from the Medical Expenditure Panel Survey-Insurance Component (MEPS-IC)  to analyze recent trends in health insurance premiums and benefits for public and private sector employers.

Looking at publicly available estimates based on the MEPS-IC, we found that the gap between premiums for the public sector (state and local governments) and private employers grew dramatically from 7.5 percent in 2000 to 20.5 percent just nine years later.  The figure below shows this growing gap in premium costs for enrollees.  In 2009, the single premium per employee enrolled in state and local government health plans was $5,627 versus $4,669 for plans offered by employers in the private sector.  A more detailed analysis (not shown) indicates that the higher growth in premiums in the public sector was driven by rising premiums for local government establishments.

One possible explanation for this divergence is that private sector employers responded more to increases in health care costs and the financial pressures brought on by the Great Recession.  In our ongoing work using the MEPS-IC microdata, we will examine whether private sector employers were more likely to alter benefits in order to “buy down” health insurance premiums.  In particular, we will test the extent to which benefit changes can explain the growing gap illustrated in the figure.  Look for future postings in the Research Matters blog with more details on our results.

Average Total Single Premium per Employee Enrolled

Average Total Single Premium per Employee Enrolled

Source: Agency for Healthcare Research and Quality. 1996-2009 Medical Expenditure Panel Survey-Insurance Component Internet tables I.C.1. and III.C.1. http://meps.ahrq.gov/mepsweb/data_stats/quick_tables_search.jsp?component=2&subcomponent=1

The Census Bureau collects the MEPS-IC under sponsorship of the Agency for Healthcare Research and Quality.  The Census Bureau sponsors or co-sponsors the collection of data on health insurance in other surveys, including the American Community Survey (ACS), the Current Population Survey Annual Social and Economic Supplement (CPS ASEC), and the Survey of Income and Program Participation (SIPP). For more information, see the Census Bureau’s Health Insurance webpage.

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Falling House Prices and Labor Mobility

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Written by:  Christopher Goetz, Economist, Center for Economic Studies

Has the recent housing bust, which left approximately a third of households with negative equity, “locked” workers into their current home and unable to move for new jobs? America has long been known as a place where people are willing to relocate for new opportunities. However, obstacles to labor mobility, such as house lock, may discourage workers from changing jobs and prevent the unemployed from finding a job, possibly prolonging the slow economic recovery.

Previous studies addressing this issue lack the scope and detail to distinguish between the impact of house prices and the general effects of the recession, which also reduce migration.  My research attempted to explain this.

Using a data source with a large sample size, such as the American Community Survey (ACS), can help get around this problem, but the ACS is a snapshot that doesn’t allow us to observe people moving during one time period to the next. However, by merging employment information from the Longitudinal Employer-Household Dynamics (LEHD) jobs database, I can determine if ACS respondents later begin a new job in a different city.

Using this matched dataset, I can observe many people located in a particular metropolitan area during the same time period. This enables me to compare homeowners to renters, and see how changes in their home price differentially affect their probabilities of relocating to another city for a new job in the LEHD data. Because these workers are all exposed to the same local economic conditions, if the mobility of owners appears to decline more compared to renters when the value of their home has fallen, then we can infer that the difference is due to the changes in the owners’ home equity.

To estimate whether a respondent has negative equity in their home, I used historical price information from a real estate company to see if the value of the individual’s house has declined since the date that they moved into it.  Renters effectively serve as the control group in this setup because their migration behavior should not be directly impacted if the value of the house they are renting falls.

Results from statistical analyses using this strategy on data from 2002 to 2010 show that a homeowner with negative equity was about 20 percent less likely to move for a new job. The impact is similar if we look only at the unemployed.  For context , the attached  figure shows  that the migration rate of homeowners fell by about 60 percent over the studied time period (from 1.7 percent per quarter  to 0.7 percent ), while that of renters fell by less than half.

Owners and Renters

Note also that the migration rate fell for both owners and renters starting in 2005. During the depths of the housing crash in 2009-2010, the patterns for the two groups really began to diverge widely. This means that while migration fell in large part because of the general economic decline, the housing crash put additional downward pressure on the mobility of homeowners and could continue to do so until the market recovers.

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Fewer Children are in Private Schools, More in Charters and We’re Looking at Possible Links

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Written by: Stephanie Ewert

The majority of  U.S. schoolchildren have always attended public schools, but private schools have also educated significant numbers of children. Parents send their children to private schools for a variety of reasons, including the availability of advanced programs and extracurricular activities, religious reasons, dissatisfaction with local public schools, class size and student-teacher ratios. But as tuition costs have risen, and public charter and magnet schools have emerged, has enrollment in private schools continued to grow? Recent research suggests the answer might be “no.”

Data from several surveys show that, while overall school enrollment has been increasing, a decline in private school enrollment took place in the last decade (Figure 1). Based on the Current Population Survey, the number of students enrolled in private school, kindergarten to grade 12, went from 5.4 million in 2002 to 4.5 million in 2010.  The decline in private school enrollment occurred at all school levels but was concentrated among schools that were larger, religiously affiliated, and in cities and suburbs.

Students age 3 and older enrolled in private school, 1989-2010

Limited data make it difficult to uncover the causes of the decline in private school enrollment. However preliminary analysis suggests that growth in charter schools may be a related factor.

We compared data on private school enrollment from the Census Bureau’s American Community Survey with data on charter schools from the National Alliance for Public Charter Schools and found that the majority of states in the U.S. with a decline in private school enrollment also experienced an increase in charter school enrollment. These preliminary results call for additional research on the relationship between private and charter school enrollment.

Current data limitations prevent us from evaluating whether growth in home schooling is a factor, and the data do not suggest that the recession beginning in December of 2007 precipitated the decline. We will collect information on private, charter, and home school enrollment in the Re-engineered Survey of Income and Program Participation and perhaps this will allow us to answer some of those questions.

For more details on trends in private school enrollment and possible factors related to the observed decline, see our working paper “The Decline in Private School Enrollment.”

 

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Collecting Data on Governments – Innovation at Work!

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Written by: Carma Hogue, Assistant Division Chief, Governments Division

Today government finance, public pensions, education spending, and taxes are hot issues and in the information age – where information is readily available and more easily monitored and measured – statistics tell the stories. 

2009 State and Local Government Expenditures

The U.S. Census Bureau’s Governments Division collects data on federal, state and local government and constantly researches new ways to make data collection more efficient and the data more precise.

On March 15, 2012, the Council of Professional Associations on Federal Statistics held a workshop on censuses and surveys of governments.  Attendees at the conference included representatives from academia, the private sector, several federal statistical agencies, and members of a 2007 Committee on National Statistics panel on government statistics.

Governments Division staff presented their research, as well as planned research, on a host of topics.  We believe many readers will find this research to be of interest:

For more information on the papers, see http://www.census.gov/govs/pubs/research_reports.html

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Using Historical Census Data to Reveal Migration Patterns of the Young, Single, and College Educated

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Written by: James Fitzsimmons, Assistant Division Chief, Population Division

Between 1965 and 2000, the young, single, and college-educated population in the United States—the “YSCE” population—migrated in patterns that were often at odds with those of other segments of the nation’s population.

In general, larger metropolitan statistical areas were more likely to have consistent net in-migration of the YSCE population, while smaller metros, micropolitan statistical areas, and areas outside of metros and micros were more likely to experience YSCE net out-migration.  These findings were often opposite those for the total population. Within metro areas, migration to principal cities also was a hallmark of the YSCE population.

Other findings reported in the recently released Population Division working paper Historical Migration of the Young, Single, and College Educated: 1965 to 2000, authored by Justyna Goworowska and Todd Gardner, included the fact that less than one-fifth of states saw consistent net in-migration of the YSCE population during that period.  About half of states, on the other hand, experienced consistent net out-migration of the group.

The working paper’s focus on migration of the YSCE population, a group with outsized human capital and potential impact on population growth, was possible thanks to the Census Bureau’s Historical Census Files Project. That project has recovered all available microdata from the 1960, 1970, and 1980 censuses, and it is in the process of harmonizing these files with ones from the 1990 and 2000 censuses.

The central outcome of the historical files project is a time series of anonymized historical decennial census microdata files available to researchers within the Census Bureau as well as to those with approved projects through the Census Bureau’s national network of secure Research Data Centers.

An eventual project goal is to extend the historical microdata holdings to earlier censuses, but at present the full range of data gathered from the “long form” of five consecutive censuses, along with documentation, is at hand for researchers with approved projects.  In its analysis of migration patterns of the YSCE population, the working paper has shed light on only one of a long list of potential subjects that would lend themselves to further study with the historical microdata series.

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Calibrated Bayes Modeling at the Census

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Written by: Roderick Little, Associate Director for Research and Methodology and Chief Scientist

Federal statistics have a rather schizophrenic view of survey inference. The preferred approach for inferences about descriptive population quantities from large surveys is the so-called “design” or “randomization” based approach, where population values are treated as fixed and uncertainty is based on probabilistic selection of the sample. This approach is widely attributed to the famous 1934 paper by Jerzy Neyman, and the Census Bureau was a pioneer in putting it into practice, led by Morris Hansen and others.

The design-based approach does not work well for situations where the survey information is limited and the so-called “direct” estimates it produces are noisy, like small area estimation. It also falls down for problems such as missing data where response cannot be considered random. An alternative is the modeling paradigm, which bases inference on a statistical model for the population values. It is also widely practiced at the Bureau. Indeed, economists and other social scientists are trained as modelers and are often somewhat mystified by the design-based approach. This leads to controversies over such matters as how and when design weights need to be included in the analysis.

I favor the approach known as “calibrated Bayes,” where all inferences are based on Bayesian models, but models need to be chosen that have good repeated sampling properties. To me everything is modeling, but some models make limited assumptions and lead to answers similar to “direct” design-based approaches, others make stronger modeling assumptions to allow useful estimates for situations where direct estimates are too noisy. I have argued that this approach is more unified than the existing paradigm and provides a valuable way forward for official statistics. See “Calibrated Bayes, an Alternative Inferential Paradigm for Official Statistics” for more details.

This may seem a rather abstruse topic, but it’s fun to think about, and fundamental since it underlies nearly everything we do. What’s your view?

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Take Your Best Shot– (and may the best model win)!

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Written by: Nancy Bates, Senior Researcher for Survey Methodology, Associate Directorate for Research and Methodology

On August 31, the Census Bureau launched a nationwide prize competition under Section 105 of the America COMPETES Reauthorization Act of 2011, Public Law 111-358 (2011). The contest – dubbed the Census Return Rate Challenge – encourages teams and individuals to compete for prize money for predicting 2010 Census mail return rates. The contest ends on November 1st.

The challenge is to create a statistical model that accurately predicts 2010 Census mail return rates for small geographic areas (census block groups). Nationwide, 79.3% of households that received a 2010 Census mail form completed it and mailed it back. However, the level of mail  return varied greatly by geography.  The Census Return Rate Challenge asks participants to model these variations using predictive variables found in the updated 2010 Census Planning Database.

The 2010 Census Planning Database is a block-group level database that assembles a range of geographic, housing, demographic, and census operational data extracted from the 2010 Census and 2006-2010 American Community Surveys.  Participants are provided a sample of the database upon which to build their models.

The Census Bureau will use the winning models for planning purposes for the decennial census and for demographic sample surveys. Participants are encouraged to develop and evaluate different statistical approaches to proposing the best predictive model for geographic units.

We hope the competition will generate new ideas on predicting census return rates as well as attract a range of talent and expertise. To help attract this talent, prizes are offered.

Prizes:

1st place: $14,000 / Visualization $1,000
2nd place: $7,500
3rd place: $2,500

Rules and Information

Contest details, rules, and eligibility guidelines are available at http://www.kaggle.com/c/us-census-challenge.

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Demography at the U.S. Census Bureau

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By Howard Hogan, Ph.D.
Chief Demographer

 

Demography is literally “Writing about people.”  However, it has come to mean the quantative or statistical study of human populations. As such, most of what the U.S. Census Bureau does can be described as “demography,”  from the taking of the census every 10 years, to collecting information on race, ethnicity, employment, income, poverty, commuting, health, crime victimization… the list would be long.

A more narrow definition of demography focuses on the processes which determine the growth and composition of human populations. These processes are the most fundamental of all human activities:  birth, death and movement. Demography is the oldest of the social sciences, tracing its origin to 1662, when John Graunt analyzed the death rolls of London.

Each of these processes is greatly influenced by age, time, and what demographers refer to as cohort. A cohort is a group of people who experience the same event at the same time. The most famous cohort is the people born during the Baby Boom, from July 1946 through June 1964.

In all human populations, the chance of dying follows a predictable pattern. The probability of dying is relatively high just after birth, and then falls to a low in the early teen age years, slowly rises during the adult years, and increases dramatically with old age.  This overall pattern seems to be determined by basic biology. In studying the chance of dying at a given age, demographers typically analyze the conditional probability: when a person has reached a given birthday, what is the chance he or she will not survive to the next birthday?

Of course, while the general pattern may be the same, the level of mortality can differ greatly, as can the specific details. These are determined by things such as nutrition, public health, access to health care, and smoking. Thus, demographers quickly return to wider issues that affect humans.

Similarly, fertility tends to follow a general pattern, with few births to women under 16 or over 40, with the peak generally in the 20s. This much is driven by biology. However, the exact level and pattern is driven by customs of marriage and social expectations as well as factors such as nutrition and access to birth control. Of course, all of these factors are related to wider issues such as education, class, income, race and ethnicity. To understand fertility, the demographer must again tackle a wider set of issues.

Population movement is the least biologically driven of the three basic processes. There is a general tendency for young adults to be more mobile than young children or older adults, but population movement can be driven by economics (jobs), laws, availability of housing as well as crisis-driven movement due to natural or man-made disasters. One needs only to remember Hurricane Katrina to see how quickly population movement can take place.

How do Census Bureau demographers use these concepts?  They:

What is happening demographically around the world will affect the United States in many ways. So, the demographers at the U.S. Census Bureau  do not restrict their work to only the U.S. population. They have an active program to gather and study population statistics from around the world to inform Federal agencies as well as U.S. businesses of these trends.  A fine example of this is the report “An Aging World,” describing the effects of reduced fertility and mortality around the world to produce a population that is, on average, older than ever experienced in human history.

Births, marriage, deaths, movement, aging — demographers study the processes that affect us all.

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