Confessions of a Wayward Academic

by Tom Corbett

  There was a phrase we heard often from the locals, “It is just now coming.” We were fully prepared intellectually to understand that the promised bus would probably arrive in five minutes. Then again, it might not arrive for five hours or five days. Still, not to worry, Sahib, “…it is just now coming.” And so, with our western cultural expectations fully in place, we would dutifully wait in the blazing noon day sun for a bus that never seemed to arrive. If nothing else, we provided great amusement to the locals with whom we worked.

  Still, a hard lesson was eventually understood, not from a training lesson but from reality. You must experience something first hand to really know it. Similarly, you must do social policy to truly appreciate what it is and what it takes to do it well. However, if you don’t have an opportunity to do that now, reading about it might be the next best thing. So, let us get started.

  CHAPTER 2

  THE HOUSE TRAINING OF A POLICY WONK

  A person who never made a mistake never tried anything new.

  —Albert Einstein

  My introduction into the policy world started with a phone call one summer night in 1971. I had accepted the unacceptable…I would have to get a grown-up job. Then, however, I bumped up against an irritating reality. People were not falling all over themselves to offer me positions that would support even my modest lifestyle while realizing my firm ambition to never accept a job that demanded heavy lifting. Shockingly, I found that there appeared to be little to no demand for my talents or my services. How could that be? After all, I was a legend in my own mind.

  For a while, I had survived as a ticket-taker at the Downer Theatre…a kind of artsy neighborhood venue located not far from campus on the east side of Milwaukee. It did not pay much, but the perks were great. I got all the free popcorn and soda I could consume. Even better, I was in charge on Thursday nights and would let in my friends for free. My popularity surged to new heights during this period but, I must confess, from a very low base.

  In truth, I was not a total deadbeat. Frank Besage, a professor of education at the University of Wisconsin-Milwaukee (UWM), was kind enough to give me a research assistantship (RA) even though technically I was no longer a student, having already completed my master’s in urban affairs. He saw some semblance of talent in me that most others overlooked. That sinecure was temporary at best, you don’t see many elderly RAs running around.

  My ticket-taker career also was fraying at the edges. Recent events suggested that longer-term career prospects in the movie world might be inadvisable. There had been several theater robberies in Milwaukee that summer. The manager called me aside one night. We need a plan, he somberly intoned, which he then laid out for me. If I was forced upstairs to the office at gunpoint, I was to use a secret knock which would signal him not to open the locked door. I paused a moment to consider his plan before responding, “Listen, you better come up with a plan B…no freaking way am I going to stand like an idiot outside the locked door with a gun at my back. You are going to open the damn door and give them the money.” He agreed, though reluctantly and after some thought. I sensed it was time to get a real job, as much as the very thought grieved me.

  Back to the phone call, it was from Professor Besage telling me that I had a job interview in Madison (the state capital) the next morning. “Great,” I responded, “what is the job?” Unfortunately, he had no idea other than it was a state civil service position of some kind. All he had was a time and a location for an interview. I was desperate, though, and could not ignore this very dubious prospect.

  Frank had asked me to accompany him to some meetings in Madison some weeks earlier to meet with several State officials regarding policy issues now long forgotten. Over lunch, my bleak career prospects came up, and one of the Wisconsin officials asked if I might be interested in working for State government. I barely knew we had a State government, but probably mumbled something to the effect that this had always been my life’s dream. Before heading back to Milwaukee, he procured some forms for me to fill out, which I did, and promptly returned to him. Then I forgot about the whole thing. My other desultory attempts to secure an adult job had led nowhere, why should this be different.

  Upon arriving in Madison that day, I entered the designated interview room to find I was about to take an oral civil service examination for the position of research analyst-social services. Three inquisitors waited to grill me. Wonderful, I thought, I know virtually nothing about research and less about social services. This ought to be quick and mercifully short as soon as they realized I was a total fraud. However, I can be loquacious, even when I am clueless about the topic at hand. The interview was rather fun, I thought.

  Sometime later, I found I was third on the hiring list, the last position the hiring supervisor could legally interview. Initially, I had been fourth, but one candidate dropped out. If they had not, my life might have been radically different. Perhaps I would still be ripping up movie tickets at some theater. Huge consequences attend to the smallest events. In any case, I was still relaxed when I met Shirley Campbell, a unit supervisor in the research section for the Wisconsin Department of Health and Family Services. Surely, she will hire one of the two more qualified candidates.

  Shockingly, she chose me. There really is no accounting for some of the atrocious decisions made by otherwise sensible people. So, in September of 1971, I began my career as a social welfare researcher, whatever the hell that was. In my mind I knew less than zip about the things necessary to do this job. But really, what is the worst that could happen? I figured I would get paid for a while until they realized the error of their ways and chucked me out the door.

  My career as a state-level policy wonk in Wisconsin, it turned out, really was a close-run thing. The day after I got a call from Shirley offering me the position, I was contacted by an official with the Model Cities Program in Jersey City, NJ. This program was a WOP (War-On-Poverty) initiative designed to coordinate services in distressed areas, which surely applies to Jersey City. I had been interviewed by this guy several weeks earlier but assumed nothing would come of it after hearing nothing more. He now offered me a position as a planner in that program. I often wondered where my life would have gone had the calls arrived in the opposite order. When you have no plan for life, your trajectory is often driven by idiosyncratic and seemingly serendipitous events.

  One of my favorite mantras is that you don’t know something until you experience it and, even better, try to explain it to others. Now, I would truly be learning on the fly. On many days in the beginning I felt like that new puppy in the house. I sort of knew that it was important where I pooped, but I hadn’t quite gotten the drill down yet. My ever-present infirmity known as the “imposter syndrome” was in full bloom. I always expected that the adults, finally realizing their error in judgment, would swoop down and take me back to that same breeder where they made the mistake of selecting me in the first instance.

  My supervisor, Shirley, was patient, however. She taught me several lifelong lessons…the first was to curb my tendency to write in a flowery, literary style. “From now on,” she instructed me, “no more sentences longer than ten words, and no more than a couple of four-syllable words to a paragraph.” While such rules were a challenge for me at first, I got better over time.

  One day, she confessed that the second applicant for my position was better than the first and that I was better than the second-place candidate. The civil service review committee had ranked people on the wrong attributes, in her opinion. “Good thing you could not get to number four,” I responded, “they must have been dynamite.” In those early days, I often thought on my being hired. I wondered if Shirley had been taken with my Irish charms. That hypothesis seems preposterous, but I have no other explanation for why I was hired. I doubt it involved any observable talents for the position nor any knowledge on the topic. Indeed, a mystery!

  On paper, I was to be the analyst for the “quality control” program, a federally-mandated initiat
ive to control waste and fraud in the Aid to Families with Dependent Children program (AFDC). Now known as the Temporary Assistance to Needy Families (TANF), AFDC was the singular cash-assistance program for low-income families with children. Though other income tested programs existed, this one was synonymous with the word “welfare” and had always been tainted in the public’s eye.

  AFDC caseloads were growing. Even more ominous to many, the complexion of the caseload was shading toward a darker hue. This added to the negative animus associated with the program. What once had been a system for helping sympathetic widows and other worthy poor had morphed into a worrisome trough of the public’s generosity that was being exploited by the lazy and the morally lax.

  Enter Quality Control (QC), a strategy designed both to curb abuse and enhance public confidence. Using methods adopted from manufacturing assembly lines, a monthly random sample was drawn from the statewide AFDC population. QC reviewers, stationed in regional offices across the state, would conduct a “review” of each selected case. Home visits were done to check on the household composition. In addition, birth records were reviewed; financial documents and bank accounts scoured; and any possible employment sniffed out. The review compared the local welfare worker’s handling of the case against existing rules and protocols to determine if the family was, indeed, eligible and that they were getting the correct amount of benefits for that review month. A deviation of five dollars or more, above or below the correct amount, would constitute an error. And to make sure the states were not incompetent or cheating, federal QC reviewers rechecked a random subsample of the original sample.

  This was serious business and quite an administrative expense to the state. It could be more expensive to the state if certain tolerance levels were not met. A tolerance level was a proportion of error cases which, if exceeded for a specified period, could result in very bad publicity at the least and heavy federal fines at the worst. The tolerance levels were 3 percent for eligibility levels and 5 percent each for underpayments and overpayments. Of course, states were given time to analyze what might be going wrong if tolerance levels had been exceeded before the other shoe dropped. For example, they would be expected to develop what were called profiles of error-prone cases, and to remedy the patterns of excess errors through targeted corrective action plans. If not, the threat of fiscal penalties and bad publicity loomed large. This was a program accountability initiative writ large.

  Now, remember the kerfuffle of a few years ago over the IRS use of profiling strategies to target suspicious non-profit agencies for tax audits to determine if they really were legitimate charities and not political organizations. They used certain identifiers or markers such as “Tea Party” or “Up with Marxism” in the organization’s title to identify programs for intensive audits. It seemed rational to tax officials that such programs just might be a bit more likely to claim tax exempt status as a charity even though they primarily were involved in partisan-driven activities. Conservatives went nuts with their usual conspiracy theories but with was just good strategy.

  In fact, this is the exact same principle that was used in welfare QC. You don’t want to select 100 random cases on which to focus corrective action efforts where the hit rate for an error-plagued case might be one in twenty. Rather, you want to select those cases with certain known propensities for error on which to apply your labor-intensive corrective action tactics…let us say with a hit rate of three-in-five or 60 percent. Basically, you wanted to identify error-prone cases. This is simply efficient management, which the private sector does all the time, or at least should do, to ensure that quality products go out the door.

  Anyways, here is where I came in. I would collect data from all the reviews over a six-month period, create a data base (using the long-forgotten Hollerith cards), and then prepare an analysis. There were two kinds of products. The simple one was a straightforward analysis that pretty much focused on aggregate error rates, comparisons to tolerance levels, and a global discussion of why the state was doing such a crappy job. The second was more creative and involved digging into the data to figure out why errors were occurring, where they were occurring, and what might be done about them.

  In the beginning, I was slogging my way through a lot of this stuff…getting comfortable with Hollerith cards, punching up my own data, figuring out the best way to code and store information, and learning basic data manipulation commands to run simple computations. They should have hired an eight-year-old for this stuff since, at the time, I could not reset the time on my VCR if it was blinking 12:00 AM after a brief power outage. Come to think of it, I still can’t reset the time on those damn machines. But I struggled through this early learning period, and no one seemed to notice my incompetence. People liked me even then, and that trait excuses many sins.

  I did figure out that I was rather clever on the bigger, more conceptual stuff. For example, I developed my own way of coding data that apparently was sufficiently attractive to catch on with other states when the feds picked up on my innovation. I also argued that the QC system should be used to collect data for broader management purposes since you had all these well-paid case reviewers driving around the state poking into a representative sample of the welfare population. I had some success with that broader vision for the system, but not as much as I had hoped.

  My great success came in getting our error rate down, and here I do justifiably take considerable credit. Many states exceeded the federal tolerance rates in the beginning and faced the “threat” of federal sanctions. Now the feds did a lot of talking about doing these error profiles, and then spending a lot of time and money training local workers to handle such cases better. And we did play along, developing a research proposal to assess the effect of alternate training regimens on error rates. I probably pushed this research project along just to see if I could pull it off.

  One distinct memory remains from this attempt at a conventional strategy. My phone rang. It was a call from a colleague named Louise Bakke. “The feds are on my line,” she sounded a bit panicked, “they like our training ideas, but want to know more about our research design.” They are not the only ones, I mused silently, but told her I would come down to her office. Fortunately, this was located at the other end of the building which gave me time to collect my thoughts. I grabbed a copy of that thin classic (for that era) volume on research methods by Stanley and Campbell (or was it Campbell and Stanley), and slowly walked down the hall as I flipped through the book. On getting to her office I said to Louise, “I’ll handle this.” I took the receiver from her and laid out a methodology as if I had given the matter far more thought than the four or five minutes it took to reach her office. While my ploy seemed to work for the moment, we eventually had to fly out to D.C. to agree upon a more rigorous experimental design. We did get the money, though. It was my first success at bringing in research money. Now, that was fun. I thought.

  I quickly concluded that all this interest in training was for show. The real action would be elsewhere. My reasoning was simple, and I won’t claim to be the only one to see this rather obvious point. Calculating eligibility and especially grant levels were very complicated tasks back then. If you were to look at the manual governing welfare case decision-making, the detail was daunting. To calculate what was called the benefit guarantee, each need item (or necessary expenditure) was calculated separately—clothes, rent, utilities, and so forth. The same for work expenses, if there were any, that would be offset against available income. How much for gas, parking, uniforms, child care, or whatever? As the decision points mounted, the probability of error in a given month (since circumstances change all the time) grew exponentially.

  Two options presented themselves. Increasing local discretion was one. That is, change all “shalls” in the manual to “mays.” If a rule is discretionary, how can there be an error? Since there already were “mays” in the rule books of that era, why not add a few more? How could a rule be violated if it were discretionar
y? Then, it wasn’t really a rule, at least in the conventional sense. But that gave too much leeway to locals who could easily abuse it, that approach was a non-starter. The second option proved more alluring—reduce the number of decision points. This was an approach clearly within state control and less likely to raise the eyebrows of the feds who were always looking over the State’s shoulders looking for nefarious schemes.

  But let me start with one of my more intriguing stratagems. As I looked at the data, and chatted with the case reviewers in the field, I noticed something interesting. Welfare used an asset test for determining eligibility, an amount of accumulated resources which, if the permitted maximum were exceeded, got you kicked off the rolls. The intent was to ensure that only the truly destitute obtained aid and thus meet the goal of target efficiency where benefits were directed to the truly needy. Federal rules permitted states to set the limit as high as $2,000 but Wisconsin had a much more stringent test of $1,000.

  This more stringent standard caused a problem. A family might obtain a modest amount of money, through part-time work for example, and put it in the bank to cover upcoming expenses. If the QC reviewer happened to check on a day after a deposit to the bank had been made, they might find assets that ran over the allowable limit by a few dollars and thus rendering the case ineligible for assistance. The case would remain in error even if the situation was rectified the next day when more bills were paid. It was just like the rules of golf, rigid and inflexible. There were no do overs.

  So, I made the following argument—raise the limit to the federal level. Then variations in cash resources during a month would be less likely to exceed the asset limit at an inopportune moment. This resonated with management, and the change was made. On paper, the eligibility error rate went down, which was duly reported as a savings to the state. No one mentioned that caseloads probably rose since additional families could now meet the more liberal asset standard. In welfare, perception was everything. I suppose we could have advised clients to hide excess money under the mattress, but I doubt the feds would have considered that an appropriate corrective action. Interesting philosophical point, though. Is an error an error if it is never discovered?