Week 4 - Assignment: Evaluate a Case in Public Personnel Management and Week 5 - Assignment: Recommend an Employee-Friendly Policy

WhyDoesntProductivityMattertoPublicSector.pdf

Home >Business & Finance homework help >Management homework help >Week 4 - Assignment: Evaluate a Case in Public Personnel Management and Week 5 - Assignment: Recommend an Employee-Friendly Policy

Chapter 3 The Death and Life of Productivity Management in Government

Albert Hyde San Francisco State University Frederik Uys University of Stellenbosch, South Africa

Introduction: Why Doesn’t Productivity Matter to Public Sector Human Resource Management?

“As society makes demands beyond the private sector’s ability to fulfill, government responds with two tools—regulation or money. But both approaches are incurring greater frustrations. We are coming to realize that we have a more finite resource base than previously suspected. Public expenditure may simply bid up the price rather than improve the results.

Thus we understand why it is that as government grows more expensive, not only public sector, but also total national productivity may decline. This effect is not inevitable; government is not necessarily less productive than other sectors of the economy. In fact, government often plays a catalytic role, enhancing the productivity of business. But unless government incorporates a productivity consciousness in all of its activity, it will tend to grow stagnant as it grows larger.”

—George Gilder, National Commission on Productivity and Work Quality, 1975, Public Productivity (1), 6.Review, 1

Writing for the inaugural issue of a new public sector journal in 1975 devoted to government productivity management, George Gilder warned of an impending era where the economy of the United States could be significantly threatened in terms of its competitiveness, growth, and ultimately its standard of living. Gilder was greatly concerned, as were many economists, business executives, and political leaders at that time, with the emergence of a new period of stagnation in productivity in the U.S. Coming out of the Second World War with minimal damages to its industrial infrastructure, the U.S. would become the dominant economy of the world. This was fueled by average annual rates of national productivity growth of 2.8% from the late 1940s to early 1970s. So when productivity rates fell by more than half to 1.1% in the 1970s (and, more significantly, the U.S. lagged behind emerging reindustrialized competitors Japan and Germany), and despite much national consternation could still improve to only 1.4% in the 1980s, various commissions were formed to find solutions to the “productivity crises.”

In the center of all this was the public sector. The post–World War II period in the United States is historically regarded as a new plateau for the public sector because government was bigger at all levels. Federal, state, and local governments would account for 25% of national gross domestic product and government organizations were larger; employing some 2.4 million federal, 1.5 million state, and 4.8 million local government workers1 (Shafritz and Hyde 2012: 80). Those levels beginning in the 1970s were now being seen in the context of larger U.S. economic change and global competiveness.

Victor Fuchs in his definitive economic assessment of the post–World War II era noted that this period marked the emergence of the world’s first service economy. U.S. employment would increase from 57 million jobs to nearly 75 million jobs by 1967, and the vast majority of the new jobs added to the economy would be in the service industry. Government’s now nearly 9 million workers were a significant part of a now larger American workforce where more than half provided services as opposed to producing things.

Co py ri gh t © 2 01 6. C Q

Pr es s. A ll r ig ht s re se rv ed . Ma y no t be r ep ro du ce d in a ny f or m wi th ou t pe rm is si on f ro m th e pu bl is he r, e xc ep t fa ir u se s pe rm it te d un de r U. S. o r ap pl ic ab le c op yr ig ht

la w.

EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 2/19/2022 5:33 PM via NORTHCENTRAL UNIVERSITY AN: 1945908 ; Richard C. Kearney, Jerrell D. Coggburn.; Public Human Resource Management : Problems and Prospects Account: s1229530

Among many implications, Fuchs noted two other key points: First, he noted that unlike the industrial production sector where quality of labor inputs was at best stable or declining, the quality of the labor inputs (education and skill levels) was increasing. Second, he pointed to several service industry examples and noted that while overall productivity levels showed modest annual increases, measurements of productivity, quality, and technology demanded more analysis to understand service sector differences and would require more robust measurement techniques (Fuchs 1968: 3–4). In a truly classic case, he compared beauty shops to barbershops (sorry, that is what they were called in the 1960s) and found beauty shops a benchmark of service growth, high quality and variety of services offered, and high productivity (and low idle time) while the barbershop was at best a hold-over place of stable technology, minimal range of service, and low productivity (Fuchs 1968: 6).

The student of public sector human resource management (HRM) in the modern era may well wonder what this old historical crisis about economic growth and productivity, barbershops, and the rising services industry has to do with current HRM theory and practice. After all, not many HRM books devote much attention to productivity or how it is defined and measured, much less how it can be applied to sustain performance or drive innovation. But the larger point is that for over 25 years—from 1967 to 1994—productivity was systematically measured in most of the federal government agencies and test measured across a good sample of state and local governments. Public management in the last third of the 20th century expended some credible effort in gauging labor inputs, output, and costs while grappling with how to measure the quality and value of government effort.

An understanding of the basics of productivity management (i.e., goals, objectives, metrics, and applications), why productivity programs were abandoned, and how productivity management integrates technology and information resources goes beyond simple lessons learned. This chapter has three learning objectives for public sector human resource managers and students:

First, how government organizations work is still important. In a current era where high performance and outcomes-focus dominate, the tendency is to just look at results and ignore the means. But government agencies and their partners and contractors need to focus on the means the ends—inand part because they are also high-reliability organizations and because resources are going to be more limited as government budgets tighten to meet rising debt limitations. If government is to be “competitive” in an all but certain era of growing resource scarcity and chronic fiscal stress, it must be able to demonstrate some sense of “productivity consciousness.” Further, if government expenditure is going to come under increased scrutiny and fiscal pressure, it would help to have “productivity growth” re-established in public management so that it can demonstrate the return on investment for both its workforce and the intermediate outputs it uses through contractors and suppliers. Second, in this new century an increasingly loud and polarizing political debate about the role, size, debt levels, and effectiveness of government programs now includes questions and challenges about the compensation levels, work value, and performance levels of public sector workers. The latter is the essence of productivity, which might contribute essential information and objective analysis to balance the often overheated rhetoric dominating current discussions. Third, the development of much more sophisticated productivity methodologies that include capital intensity, labor composition, R&D levels, and multifactor productivity that show the effects of technology, efficiency, resource reallocation, and other capital-labor interactions. This offers public sector human resource managers new perspectives on innovations, quality, and service growth. A better understanding and potential application of current productivity metrics offer an opportunity to reassess the value proposition of government effort and its service work ethic. They also can shed light on the value of different strategies for workforce composition and work disposition; for example, are part-time or contract employees as productive as full-time employees, and are employees who telework as productive as employees who come to offices every day?

For a beginning, this chapter returns to the 1960s, a period in the United States where confidence in government was high and government agencies were expanding their roles and tackling a range of new socialCo

py ri gh t © 2 01 6. C Q

la w.

and economic problems. Overlapping interests of congressional members and political and business leaders about slowing national productivity rates and economic anxieties over rising inflation and unemployment rates would ultimately result in the establishment of a productivity measurement program for the federal government. By the early 1970s the newly reformed Office of Management Budget would take the lead in establishing a statistical reporting system that began with data that would cover about half of the federal civilian workforce for a base year in 1967 and reach almost nearly 70% by 1994 (Fisk and Forte 1997: 19–20).

How to Measure Productivity in Government and Why?

Measuring productivity is essential to any serious economy. Any nation that desires to be competitive, provide an adequate standard of living for its citizens, and generate some level of wealth transfer for its future generations begins with a goal of meeting a level of productivity growth that will cover its birth and immigration rates and provide for its elderly citizens—conventionally about 2%. Productivity growth is also traditionally correlated with compensation and employment. Throughout most of the 20th century, rates of productivity change were “procyclical”—meaning productivity rates increased during periods of economic growth and expansion but tended to contract during business downturns (McGratten and Prescott 2012).

Following the Second World War, productivity growth in the U.S. was solid and substantial, outstripping most of the international competition. When the great productivity slowdown hit the U.S. in the 1970s, the discussion of what government should do to foster productivity largely focused on what were perceived failings in government economic and regulatory policies that were seen as hampering private sector productivity growth. Critics of government pointed to deficit spending, byzantine tax systems, regulatory interventions in markets, and lack of effective public investment in research & development (R&D) and education. Some of these criticisms—or “unnecessary burdens” as they were called in a 1984 White House Conference—are “the usual suspects,” so to speak (White House Conference 1984: 4). However, underlying this reexamination of private and public sector poor performance was the recognition that the United States was in the midst of a major transition to a new economy—one based primarily on services and information—and that the old industrial management and workforce control systems and strategies were no longer adequate.

In the early 1970s, congressional interest led directly to creating a formal productivity measurement program in the federal government (Fisk and Forte 1997: 19). While the politics and institutional arrangements that surrounded the program to be launched by the Office of Management Budget, the Office of Personnel Management (then the Civil Service Commission), and the Government Accountability Office (then named the General Accounting Office) with measurement via the Bureau of Labor Statistics is interesting, the focus here is on the who, what, and how of measurement.

Once baselines were created with the measurement system, the program (usually acronymed FPMP for the Federal Productivity Measurement Program) would include about half of the civilian federal workforce at the start and reach about two-thirds of the workforce by the mid-1970s (Fisk and Forte 1997: 20). There were some major missing agencies, such as the intelligence agencies, the State Department, and large portions of the Defense Department. But even among these excepted agencies, some support functions were included for measurement (logistics and administrative support for Defense, contractors for NASA, etc.).

Determining what would be measured was seen as the real challenge. Agencies had to designate some form of final output. The primary focus was on some form of physical count—such as volume of mail for the Post Office, or number of inspections, claims or invoices paid, student days taught, licenses processed, health care visits, and so on. To be fair, this challenge to identify outputs was neither a formidable nor a new phenomenon for government. The prevailing budgeting system for the federal (and many state governments) coming out of the midcentury was performance budgeting, which included extensive program work output measurements both as efficiency indicators and the basis for using work measurement to establish staffing levels for programs. Performance budgeting was a precursor for productivity management; as one earlyC

op yr ig ht ©

2 01 6. C Q

la w.

budgeting textbook noted, “The contentions for the new productivity field in the 1970s are very reminiscent of the earlier claims for performance budgeting. Performance budgeting sought to establish management’s right and responsibility to ascertain how much work was being accomplished, at what cost, and for what results as measured against specified performance standards. In the 1970s the questions are still the same, only it seems they are being asked by different people” (Hyde 1978: 78).

Under the FPMP, agencies established different program output measurements and integrated them into a final organizational output index. Box 3.1 outlines the calculation elements that are part of conventional productivity measurement.

Source: Federal Productivity Measurement Program (FPMP) (1972–1994).

Labor was measured by counting the total number of employee years, compensation levels for each employee year, and a unit labor cost. The resulting calculation is then indexed at 100 for the first measurement year for the FPMP, as illustrated in which shows changing rates of federal productivity versus the privateFigure 3.1 sector over the period. Federal productivity actually rose by a respectable rate of 1.5% annually from 1967 to 1982 before slowing to a.6% annual rate during the “productivity slowdown” era from 1982 to 1994. The 1.5% rate slightly exceeded private sector productivity of 1.4% but trailed the private sector rate of 1.3% in the next period.

In commenting on what this quarter century of productivity output data shows, some major qualifications must be noted. First, the labor input in the federal productivity calculation (shown in ) was anTable 3.1 aggregate workforce input number. It did not include submeasures of capital, equipment, technology, or other factors that could affect outputs. Second, while the cost of labor input numbers did include full wage numbers (salary, benefits, incentives, etc.), qualitative submeasures of skill levels or qualifications were not included. Third, labor functions were measured in the FPMP but with an aim of showing productivity comparisons across different functions. FPMP provided average annual productivity rates for 24 federal occupational groups, with two functions showing negative productivity index rates: electric power utility personnel and medical services. These are also the two functions with the highest unit labor costs compared with the function (Finance) with the highest productivity, which had the lowest unit labor costs; this points to the sensitivity of the FPMP to wage factors.

Figure 3.1 FPMP Annual Rates of Change—Labor Productivity in Federal Government vs. U.S. Private Sector Rates, 1967–1994

Co py ri gh t © 2 01 6. C Q

la w.

Source: U.S. Monthly Labor Review, May 1997, and U.S. Bureau of Labor Statistics, Labor Productivity Database (data.bls.gov/time series/PRS85006092).

In productivity management terms, this effort by the FPMP certainly qualified as a good start. It demonstrated that federal productivity levels were certainly in line with the national experience and in the same league as the private sector. While the data qualifications weren’t trivial, there was a decent foundation to make assessments about federal productivity contributions in macroeconomic terms and solid trend data for agencies to review unit productivity performance levels.

However, in 1994, the FPMP was a victim of a major round of federal budget cuts and the Bureau of Labor Statistics suspended the measurement side of the program. Thereafter, no systematic productivity measurement would be undertaken at the federal level except for the U.S. Postal Service. It is certainly safe to say that few managerial tears were shed on the loss of the federal productivity program. And, as will be noted, the dismantling of the FPMP did not leave a vacuum. Following the National Performance Review at the outset of the Clinton-Gore administration in 1993, total quality management2 was essentially the successor to productivity management. Quality management was a better or perhaps more comfortable fit for most federal agencies, with its blend of participatory management groups and measurement methodologies that appealed to a predominantly white collar workforce and to labor groups that championed labor-management partnerships.

Before assessing the federal productivity management effort, productivity efforts at the state and local level should also be mentioned. While no systematic effort was made to report on subnational public productivity levels, there was interest in testing measurement strategies and methodologies. BLS—as their exemplary 1998 final study attests—selected ten different state and local services to develop and report productivity statistics on. While the big three (police, fire, and education) were excluded from the study—the range of services studied made quite clear that calculating productivity was both feasible and methodologically defensible. These early investigations grappled with how to determine output measurements for services ranging from more blue collar–oriented activities in enterprises (utilities and transit services) to mostly white collar (parole and corrections to employment and social services). BLS also chose three services where numerous private sector systems existed for comparison. —taken from the 1998 study—highlightsTable 3.1 the comparisons.

In the three state and local services in which public and private sector comparisons were made, public sector

Co py ri gh t © 2 01 6. C Q

la w.

productivity tracked and compared favorably. But it should be noted that these three services were among the least personnel intensive. The service area with the lowest productivity rates was local jails, although the longer-term counterpart of state prisons had better productivity rates even though labor inputs were about the same. BLS’s assessment of the corrections area (jails and prisons) is all the more interesting because it developed means to account for overcrowding. Further they pointed to the recidivism issue—which they weren’t able to factor in a meaningful way—which would clearly alter the output measurement. Another interesting distinction drawn in this productivity study was how mass transit productivity rates showed improvement when the output metric was vehicle revenue miles as opposed to number of trips.

One final contribution—worth further reflection—was BLS’s estimates of rates of labor intensity for government services. Although this was soon to change with the full arrival of the computer and Internet technology era starting around 1995, (which calculated for one baseline year, 1992) shows laborFigure 3.2 compensation as the percentage of total operating expenditures for different government functions. Human resource managers, of course, would appropriately point to functions like police, fire, and education and conclude that when over 80% of the operating budget is human resources, the quality and skill levels of those resources are paramount. Productivity management advocates would certainly concur but add that tracking the labor productivity rates of these invaluable assets is also critical.

Table 3.1 Source: U.S. Bureau of Labor Statistics, 1998, Measuring State and Local Government Productivity: 9.

While the agency productivity output and labor costs measurements were the primary quantitative emphasis, BLS also attempted more qualitative evaluations. Agency managers were surveyed about their explanation for shifts in productivity that perhaps foretold of the perceived value of FPMP as an important human resources managerial tool. Fisk and Forte in their closing assessment of the FPMP note that most agencies explained major shifts in productivity levels as driven by workload volatility and technology. In the 1970s unforeseen political, financial, or environmental events were identified as the primary driving forces that

Co py ri gh t © 2 01 6. C Q

la w.

would cause an agency to ramp up or scale down work efforts and staffing levels, which would then shifted productivity levels. Later, in the 1980s agency comments pointed to major changes in office automation and computing as major driving forces (Fisk and Forte 1997: 27). In other words, productivity measurement wasn’t seen as having much of an effect other than to register the impacts of external factors.

Figure 3.2 Personnel Compensation as Percentage of Total Operating Expenditures for Select State and Local Government Functions, 1991–1992

Source: U.S. Bureau of Labor Statistics, 1998, Measuring State and Local Government Productivity: 23.

Another political development may have also shaped this managerial disinclination toward productivity measurement. In 1985 the Reagan administration promulgated an executive order as part of his newly re-elected administration’s federal management improvement program. Entitled the President’s Productivity Improvement Program, the 23 designated primary federal agencies under OMB’s direct purview were to institute formal programs that would establish a productivity office and publish a productivity improvement plan with a formal measurement system. On the first page on the executive order draft, OMB announced that a 20% improvement goal by 1992 for all agencies would be set. Further in the document, OMB proclaimed that these implemented productivity goals would “be translated into projected cost savings” (Wright 1984).

Federal agency managers certainly understood and both resented and resisted this type of “productivity math”—where productivity gains were “pre-ordered” to be used to decrease agency budgets as opposed to increasing service quality, investing in agency capabilities, or supporting innovation efforts. NASA, having launched a major contractor and agency effort around productivity improvement a year earlier, was typical of agency response—notifying OMB that under this program they were essentially being penalized for their efforts and therefore were disinclined to participate (NASA-JSC 1985). As the Reagan administration’s political capital was diverted to other more pressing matters (the Iran-Contra affair, etc.), the OMB initiative was set aside and quietly left to expire at the end of Reagan’s second term.

There is a long history of “lapsed” public sector management efforts driven by executive mandate to reduce agency budget levels whether at the federal or state government level. Politically, programs launched from auspices of one executive are almost always let go when a new administration takes over. However, thisCo

py ri gh t © 2 01 6. C Q

la w.

instance entails the additional peril of using productivity measurement primarily as a budget tool for cost-cutting as opposed to a management reinvestment tool for service quality improvement, upgrading infrastructure or technology, or enhancing public service commitment. The latter is what makes productivity management an important management tool—its use in ensuring that economies and industries innovate and grow, and don’t stagnate.

Old Lessons Learned—New Questions Needed

As mentioned, budget cutbacks in the first two years of the Clinton administration would result in termination of the federal productivity measurement effort. In addition to changing budget priorities, new management initiatives (some call them fads) like total quality management, and lack of political support from agency managers with long memories about the ill-fated OMB 1985 productivity program, the productivity environment itself was changing. For the student of human resources management to make sense out of the change in the 1990s and in order to draw appropriate lessons for the future, five factors need to be examined.

First and foremost, national productivity improved dramatically, emerging out of its two-decade slumber. Driven primarily by new technology and capital investment, private sector productivity annual growth rates reached 2.1% in the mid-1990s and over 2.5% by 2000, as illustrates. Debates among economistsFigure 3.3 about Robert Solow’s famous query in 1987—“You can see the computer age everywhere but in the productivity statistics”—now shifted from what the problem was to what was now driving the solution and whether it would last (Brynjolfsson 1993). In the late 1990s, after productivity soared nationally and federal government budgets reached surplus levels for the first time in seemingly decades, interest in productivity plummeted.

It also should be noted that productivity measurement also changed in an effort to capture the increasing complexity of the now ascendant digital revolution. Coming out of the productivity slowdown period, there remained great concern that a services-dominated economy would hamper productivity and economic growth (Baumol, Batey Blackman, and Wolff 1989). Remarkably, economists looking at productivity trends in a so-called stagnant sector found—as a Brookings symposium of leading economists noted—“services now lead the way.” The consensus estimate was that service industries contributed over 73% of labor productivity growth in the 1990–2000 period and 76% of U.S. total productivity growth (Triplett and Bosworth 2004: 2).

Obviously U.S. productivity growth improved dramatically, as shows. Not quite as obvious wasFigure 3.2 why, given the new domination of services in the U.S. economy. During the 1990s the American economy added more than 19 million jobs while manufacturing goods production sectors were basically flat. This doesn’t mean that manufacturing productivity decreased. Quite the opposite: Since 2000, U.S. manufacturing jobs have declined by over 30% while manufacturing output has increased by almost 50% (de Rugy 2011). Basically, the U.S. manufacturing labor force has dropped to under 12 million workers who are now producing the equivalent total output as the previous 17 million workforce.

Figure 3.3 U.S. National Rates of Average Productivity Growth, 1947–2011, Private Sector Nonfarm business (excludes all levels of government)

Co py ri gh t © 2 01 6. C Q

la w.

Source: Bureau of Labor Statistics (2011).

U.S. Manufacturing: Output vs. Jobs Since 1975

To many economists and management analysts, it was clear that something else was in play. It became increasingly obvious that the impact of “other dimensions” of productivity had not been adequately measured before. To be fair, organizational purchases of capital—even computers and other technology investments—were part of the productivity equation that included the total costs of labor and capital equipment. As economists debated both if and when the investments made by the U.S. in both the private and public sector would materialize up to the mid-1990s productivity turnaround, pressure mounted to augment the methodology for measuring productivity. The resulting metric called stillmulti-factor productivity produced an output ratio per labor hour, but it was expanded to include labor-capital interactions to estimate what contributions were made by technology, other efficiency actions, and resource reallocations.

Currently—if one looks at the 2011 multifactor productivity trends from the U.S. Bureau of Labor Statistics of U.S. national averages (excluding government services)—the following larger view is possible (Figure 3.4 ). The introduction of multifactor productivity not only enlarged the organization view of capital and labor resources, it also provided a means for assessing different strategies for human resource investments. Capital intensity also included a separate breakout for the contribution of information processing equipment and software. So, for example, a state government’s motor vehicle registration and licensing department could reassess how to align its technology support, capital equipment ratios, workforce mix of service employees and contractors, and Internet services provision to achieve the most optimal productivity levels.

Figure 3.4 National Productivity Growth Rates for Private Nonfarm Business Sector, 1987–2011

Co py ri gh t © 2 01 6. C Q

la w.

Source: Bureau of Labor Statistics, May 9, 2012 Office of Productivity and Technology.

A second factor entailed internal shifts in public sector workforces. The movement towards a super-majority white collar workforce had been underway for some time. Government agencies at all levels contracted out support functions and blue collar jobs, accelerating this trend. By the mid-1990s, the federal workforce was below 15% blue-collar positions; by 2010 the percentage went below 10%. But much more importantly, government workforces were becoming more highly compensated as average grade levels increased. Productivity measurements capture this of course when labor costs are attached to labor hours. So when viewing the FPMP statistics in , both the effects of annual salary increases given across the board toTable 3.1 the workforce and rising costs from promotions and labor compositions are in play.

Figure 3.5 shows a 50-year decadal perspective of how the federal workforce has shifted from a 50%–25% split between the lowest six grades and the highest five grades, By the end of the FPMP, the split was 30% for lowest grades and 45% for the top five for white collar workers. In 2014, the top five grades accounted for just under 62% of the federal workforce. Of course, those grade increases reflect higher education levels, greater skill qualifications, longer tenure, and an older force. But similarly, productivity measurement using today’s methodologies are capable of measuring impacts of labor composition and if in place might have been useful in assessing the impacts of these shifts. For example, one factor often mentioned in looking at current workforce dynamics in government is contract management. Instead of framing the question in terms of staffing—that is, aligning employee grade levels with the level and award amounts of contracting—the productivity question might produce a different assessment of the optimal mix of organizational and contractual staffing.

Figure 3.5 Grade Level Change in the Federal Government in the Civilian White Collar Workforce by Decade, 1962–2014

Co py ri gh t © 2 01 6. C Q

la w.

Source: Compiled by the authors using federal employment data from (2002, 2012,www.fedscope.opm 2014), the 1962, 1972 Civil Service Commission Federal Civilian Workforce Statistics Report, and the 1982 and1992 Office of Personnel Management Federal Civilian Workforce Statistics Reports.

So externally the national productivity picture brightened, and internally the labor structure of many government organizations shifted toward a high quality in terms of human factors workforce. Two other factors emerged in the 1990s that pushed the demise of productivity management. The advent of quality management, already mentioned, in effect superseded productivity. Quality management in the public sector also got some help from a major effort in the American service industry to embrace the principles of quality management. Telecommunications, banking, insurance, and even health care organizations began to develop their own versions of quality with a distinct service focus. These industries all had major counterparts in the public sector at federal, state, and local levels (along with being suppliers, contractors, and partners), and they strongly encouraged benchmarking and sharing of best practices with government agencies. Many of these service industry corporations helped fund studies on quality practices among state and local governments and set up advisory committees to help launch government-wide efforts.

At the federal level, when the September 1993 National Performance Review report was issued, quality management was not a primary reference point. However the report’s second chapter—“Putting Customers First”—was quality management 101 from top to bottom. The administration issued Executive Order 12862 embedding all of these quality expectations into agency management requirements. All federal agencies dealing with the public were required to identify their customers, set quality standards for service, survey their customers, and act to make government services “equal to the best in business.” Unlike the aforementioned formal productivity programs where improvements might be translated into cost savings and staffing reductions, quality improvements were reinvestments in the agency’s performance.

Quality management also was highly compatible with the aims and natural interests of a highly skilled workforce. Quality management called for very high levels of workforce participation or what was generally called “empowerment.” Workers at all levels were expected (and trained) to join together in any number of variations of quality groups or project efforts to analyze quality problems (improvement teams) or to devise new solutions (process redesign teams). Most of these teams operated outside of the classic formal hierarchical and representation structures of government bureaucracy. Many of the efforts included contractors, partners, and even client and customer groups.

Essentially, the core dimensions of quality management—internal process measurements, external customer focus, employee participation, and contractor involvement—were all highly compatible with the public management premises and goals embodied in the Reinventing Government movement. For much of the

Co py ri gh t © 2 01 6. C Q

la w.

http://www.fedscope.opm

decade, quality management was seen as a preferred framework for organizational change that emphasizes work groups and processes with a customer focus that was superior to more formal organizations focused on work through functional specializations. But formal quality management would face a similar fate as productivity with the presidential election change in 2000. One of the first acts of the Bush administration was to issue an executive order ending labor-management partnerships. While the executive order neither prevented government agencies from labor consultations nor promoting quality program aspects, the management emphasis at the federal level shifted to competitive government, technology innovations, new personnel systems, and new budget priorities.

A fourth factor—the emergence of performance results management—also played a pivotal role in productivity’s demise as both management change strategy and methodology for assessing performance. Following passage of the Government Performance Results Act in 1993, federal agencies went through a five-year trial period putting in a budgeting system that asked agencies to prepare five-year strategic plans with performance goals based on outcomes. Indeed, reliance on outputs—the core numerator in productivity metrics—was seen as a problem with underperformance. Both the Office of Management and Budget and the GAO (then the General Accounting Office—soon to be renamed the Government Accountability Office) championed this new direction.

There were few dissenters. It was difficult to argue with the strong current of performance management—or, as an assessment of the demise of FPMN by the research staff at the Minneapolis Federal Reserve Bank quoted one Beltway expert, “I don’t care how fast a government worker goes through a pile of paper until I know whether the pile of paper needs going through in the first place . . . . productivity numbers tell me nothing until I have a measure of the benefit” (Wirtz 2000: 6). So the federal emphasis (and many state governments likewise pursued performance results budgeting variations) was on measuring the effects—or social outcomes—of government programs. Productivity was equated with more simplistic efficiency while performance was to be best understood in terms of measuring effectiveness.

This is a pivotal issue that productivity management has always recognized but been unable to reconcile. One of the most influential early management theorists in productivity—Michael Packer3—addressed this in an MIT white paper in 1982 aptly titled “What’s Wrong with Organizational Productivity Analysis?” Packer sorted through the different measurement issues highlighting the degrees of difficulty and reliability in various service industry and government organizations, especially those with substantial R&D efforts, intelligence roles, or those that produce more intangible outputs. He also noted all the objections that managers would have about measurement and data analysis methods, especially if the numbers were going to be used to make comparisons to other private sector entities. But his point was that managers weren’t going to be impressed by simply knowing how the organization’s current productivity rates were trending. Packer urged that organizational productivity data be used to interpret the range management flexibilities and potential scale of improvement and innovation in the same vein that business enterprises use market research and economics (Packer 1982: 9–10).

Packer’s concerns are still valid today. If human resource managers want to understand how much progress is being made in pursuit of organizational goals and concurrently how effective the use of human, capital, technology, and information resources is, they will need analytical tools for measuring productivity. Productivity analytical tools go beyond simply adopting a vocabulary of management efficiency used to proclaim that new initiatives (the movement to cloud computing in government comes readily to mind) will make the workforce more productive.

But perhaps a case example is needed to illustrate this point. Federal agencies today are striving to comply with requirements to allow teleworking in their agencies. Most use surveys of workers in their teleworking programs that show higher job satisfaction, more time spent doing task work, and less time doing administrative work. A study at the Patent and Trademarks Office found teleworking employees processed more patent applications per year than their in-office counterparts, according to the Commerce Department Office of Inspector General. That makes them more or equally productive, except that the Inspector General noted that teleworkers didn’t process applications at a greater rate; they simply reviewed patents for more

Co py ri gh t © 2 01 6. C Q

la w.

hours than their office-bound counterparts (U.S. Dept. of Commerce 2012). Of course, the program is successful on a number of other fronts, but in terms of Packer’s organizational productivity analysis framework challenge, the questions still remain: How productive is teleworking and how do you know?

This basic human resource management question deserves more than subjective answers. In another detailed assessment of teleworking using national government employee survey data, Mahler provides a sterling examination of the benefits of teleworking programs and questions whether there may be a rift between those who are and those who are not allowed to participate. The survey results point to strong agreement that those who telework report higher levels of job satisfaction and improved personnel productivity (Mahler 2012: 413). But how do they know, since there are no basic quantitative measurements of organizational, unit, or much less individual productivity in place? No disparagement of teleworking or any other form of flexible work arrangements using new technologies is intended; the point is simply to reinforce the need for organizational productivity measurement, especially in government services.

There remains an unranked fifth factor that, despite a great amount of activity that occurred and continues to be made, is of less certain significance. This would include organizational change management strategies based on participatory management in the workplace. When these “change strategies” have been charted in the private sector, results in terms of productivity management are mixed.

Some change management strategies have pursued linking compensation to productivity. Results here have generally followed Blinder’s conclusion that changing the way workers are treated increases productivity more than changing compensation practices (Blinder 1990: 13). A 1999 NBER–MIT metastudy on productivity improvement concluded that progressive human resource policies and practices produced little net organizational productivity benefits, as increased labor costs tended to offset increases in productivity improvements, where they were measured (Lester 1999) or even resulted in lower performance and diminished organizational reputation (Keating et al. 1999).

Other multiple organizational case reviews are more positive, as Black and Lynch have noted in a 2004 Federal Reserve Bank of San Francisco research note. They found that those organizations supporting workplace innovations—specifically work teams, more flexible job definitions, and up-skilling of the workforce—tended to be more productive than traditional organizations (Black and Lynch 2004: 2), These efforts also have multiple objectives—to support workforce retention, enhance morale, and promote engagement and commitment to organizational values. Of course, in government agencies where productivity is no longer measured, these are the only managerial objectives that remain.

A Concluding Note and a Postscript

This chapter, despite its odd title, began with three objectives and a hope. The objectives were to recast productivity measurement methods and management strategies to promote better understanding of several key debates about whether governments are competitive, workforces are compensated appropriately, and organizations are using their resources optimally.

It’s already clear that the national debate about the size and role of government is most likely to be argued on political grounds. Whatever the shape of the 2016 federal budget, discretionary program spending or the remaining programs after entitlements and interest requirements are destined to be further crowded in the coming decade. This was apparent back in 2006 when McKinsey published a study calling for a renewal of the federal productivity program so that federal productivity could be part of what they called “performance transparency” (Danker et al. 2006). That the study was basically ignored, even by the largely pro-business Bush administration, proves once again that sector productivity comparisons are neither compelling nor convincing.

However, the organizational productivity challenge is going to be of increasing interest. As the public-private pay comparability debate continues, human resource managers are going to face increasing pressure (andC

op yr ig ht ©

2 01 6. C Q

la w.

media scrutiny) to explain how staffing, productivity, and compensation levels are linked. Debates about the necessary numbers of police, teachers, nurses, and other public work functions are going to go deeper than arguing trends in crime rates, test scores, and health care outcomes. Many government functions are already on the defensive about why well-intentioned efforts are not always translating into improved outcomes. Governments may find that to obtain additional resources to achieve better results, they will have to demonstrate that their good intentions are matched by high productivity levels and optimal use of resources. This will become even more apparent as technology alters every aspect of work from content to methods to work skill competencies.

Public managers may well want to revisit the current quality of performance paradigm in which being responsive and delivering services that meet citizen preferences seems to be all that matters. The means (i.e., productivity) in which organizations determine that the right things are being done using the right mix of resources most efficiently is also essential. Again, this is going to be even more critical as technology and connectivity transform the production and service processes. Public services, especially those that are human interaction intensive, are not going away. However, productivity measurement can provide human resource managers with critical information about how to use technology shifts in support of the next stages of public service innovation. Hopefully, the need for organizational productivity metrics and opportunity for using multifactor productivity analysis in the public sector will bring productivity management back to the forefront of human resources management.

Finally, it is altogether fitting and indeed ironic, that there is a renewed debate about national productivity levels. In this new decade since 2010, U.S. productivity growth rates have slumped dramatically, to dismal levels, even below the terrible 1970s (Blinder 2014):

While economists have been somewhat surprised by this and there is disagreement about the causes, this time there is consensus about the long-term consequences and potential negative effects. Governments will also find that they are part of the debate about what to do. Robert Solow, of the “computers and productivity” linkage mentioned earlier, noted in a recent interview that what is most different now is the recognition that what drives productivity growth most is management differences. Paraphrasing Solow, it’s not how capital intensive or technological advances that matter most. It’s “failure in management decisions”—the inability or unwillingness to rethink and reallocate tasks within organizations to compete successfully (Solow 2014). Of course, that reallocation of tasks to an organization’s workforce is the essence of human resources management and a reminder of why productivity measurement matters.

Notes

1. The 9 million total government workers in the 1970s compares with 14 million total full-time government workers (there are also over 5 million part-time employees) according to the last available census of government in 2012. About 90% of that growth has been in state and local government (U.S. Census 2014).

2. Although the literature on quality management in the public sector is extensive—beginning with quality circles in the 1980s merging into full blown total quality management programs in the 1990s, an Executive Order mandating customer service quality standards and reviews—that goes beyond the scope and space allotted for this review.

3. Michael Packer died in the World Trade Center in New York City during the September 11 terrorist

la w.

attacks; he was delivering a keynote address at a conference there.

References , , and . . , Baumol William J. Sue Anne Batey Blackman Edward Wolff 1989 Productivity and American

. : .Leadership Cambridge, MA MIT Press

, and . . , Black Sandra A. Lisa M. Lynch 2004 Workplace Practices and the New Economy. Federal Reserve , .Bank of San Francisco, CSIP Notes Number 2-4-10 April

. . : ., Blinder Alan S. 1990 Paying for Productivity Washington, DC Brookings Institution Press, 13

. . , ., Blinder Alan S. 2014 The Unsettling Mystery of Productivity Wall Street Journal November 24

. . . , Brynjolfsson Erik 1993 The Productivity Paradox of Information Technology: Review and Assessment , .Communications of the ACM December

, , , and , . , Danker Tony Thomas Dohrmann Nancy Killefer Lenny Mendonca L. 2006 How Can American , .Government Meet Its Productivity Challenge? McKinsey & Company July

, , and . . , Dertouzos Michael L. Richard K. Lester Robert M. Solow 1989 Made in America: Regaining the . : .Productive Edge (MIT Commission on Industrial Economy) Cambridge, MA MIT Press

. . . , De Rugy Veronique 2011 U.S. Manufacturing: Outputs vs. Jobs since 1975 Mercatus Center White Paper, , .George Mason University January

. . , Fisk Donald M. 1985 The Federal Productivity Measurement System: The Process and Selected Statistics , ( ).Management Science and Policy Analysis Journal-Letter 2 3

, and . . . , Fisk Donald Darlene Forte 1997 The Federal Productivity Measurement Program: Final Results , .Monthly Labor Review , May 19–28

. . : , Fuchs Victor. 1968 The Service Economy New York National Bureau of Economic Research/Columbia , .University Press 1968

. . . , Gilder George 1975 Public Sector Productivity National Commission on Productivity and Work Quality , ( ), – , .Public Productivity Review 1 1 4 8 6

la w.

. . In . , Hyde Albert C. 1978 Performance Budgeting Government Budgeting: Theory, Process, Politics Oak : , – .Park, IL Moore Publishing 77 81

, et al. . , , Keating Elizabeth K. 1999 Overcoming the Improvement Paradox.” European Management Journal ( ), – .17 2 120 134

. . : ., Lester Richard K. 1999 The Productive Edge New York Norton

. . . , Mahler Julianne 2012 The Telework Divide: Managerial and Personnel Challenges of Telework Review of , ( ), – .Public Personnel Administration 32 4 407 418

, and . . , McGratten Ellen R. Edward C. Prescott 2012 The Labor Productivity Puzzle. Federal Reserve Bank of , Working Paper 694, .Minneapolis May

. . NASA-JSC 1985 An Assessment of NASA’s Productivity Management Program—Johnson Space Center. , .University of Houston Clear City Consulting Report, ed. Albert C. Hyde July

. . . : National Academy of Sciences 1979 Measurement and Interpretation of Productivity Washington, DC .NAS

. , Packer Michael B. 1982 What’s Wrong with Organizational Productivity Analysis? MIT Laboratory for , .Manufacturing and Productivity, Paper LMP-MRP-81–15 July

, and . . , ed. : ., Shafritz Jay M. Albert C. Hyde 2012 Classics of public administration 7th Boston Wadsworth

. . . , ., Solow Robert 2014 Prospects for Growth: An Interview McKinsey Quarterly September

, and (Eds.). . , Triplett Jack E. Barry Bosworth 2004 Productivity in the U.S. Services Sector: New Sources of . : .Economic Growth Washington, DC Brookings Institution Press

. . U.S. Census 2014 2012 Census of Governments: Employment Summary Report, G12-CG-EMP, edited by , .Lisa Jessie and Mary Tarleton March 6

. . U.S. Department of Commerce, Office of Inspector General 2012 The Patent Hoteling Program Is . . .Succeeding as a Business Strategy Report OIG-12-018-A February

2 01 6. C Q

la w.

. Bulletin 2495. .Examples from Eleven Studies June

. . .White House Conference on Productivity Productivity Growth: A Better Life for America April

. . . ., Wirtz Ronald 2000 Icebergs and Government Productivity. Minneapolis Federal Reserve Bank June Available at https://www.minneapolisfed.org/publications/the-region/icebergs-and-government

. Accessed on .-productivity , November 7 2012

. . , Wright Joseph R. Jr. 1984 Memorandum on Productivity Improvement Program Office of Management and , .Budget September 27

la w.

https://www.minneapolisfed.org/publications/the-region/icebergs-and-government-productivity