I loved this piece when I was 20, I still have the 7″ by Les Disques du Crepuscule: “In re don Giovanni” by Michael Nyman

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Solid Africa political overview from Steven Feldstein

Africa often brings policy surprises. Two of the most significant political moments in Africa—the street demonstrations that toppled Compaoré in Burkina Faso, and the surprisingly free and fair elections in The Gambia that ended Jammeh’s rule—were wholly unanticipated. Smart and capable diplomats on the ground made all the difference in preventing either situation from barreling into a full-blown crisis. The right support and resources from Washington helped U.S. diplomats reinforce these unexpected democratic opportunities and facilitate peaceful transitions. U.S. efforts have made a crucial difference in Africa during both the George W. Bush and Barack Obama administrations. They saved lives, prevented mass atrocities, brokered peaceful solutions, and helped create the conditions for more prosperous futures. The new administration should continue this engagement.

Source: Why the Trump Administration Should Not Overlook Africa – Carnegie Endowment for International Peace

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Where Economics is going: Increasingly integrated into decision-making in a routine way, as in decisions about whether to go to jail while awaiting trial

We examine how machine learning can be used to improve and understand human decision-making. In particular, we focus on a decision that has important policy consequences. Millions of times each year, judges must decide where defendants will await trial—at home or in jail. By law, this decision hinges on the judge’s prediction of what the defendant would do if released. This is a promising machine learning application because it is a concrete prediction task for which there is a large volume of data available. Yet comparing the algorithm to the judge proves complicated. First, the data are themselves generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the single variable that the algorithm focuses on; for instance, judges may care about racial inequities or about specific crimes (such as violent crimes) rather than just overall crime risk. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: a policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0% with no increase in crime rates. Moreover, we see reductions in all categories of crime, including violent ones. Importantly, such gains can be had while also significantly reducing the percentage of African-Americans and Hispanics in jail. We find similar results in a national dataset as well. In addition, by focusing the algorithm on predicting judges’ decisions, rather than defendant behavior, we gain some insight into decision-making: a key problem appears to be that judges to respond to ‘noise’ as if it were signal. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.

Source: Human Decisions and Machine Predictions

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President Engh’s remarks on SCU budget shortfalls

An interesting thing happened on the way to implementing Sustaining Excellence. Our operating budget for FY17 inherited more challenges from the previous year than originally estimated. In FY16 we ended up with a $4.5 Million net operating loss — the first operating loss in over 20 years. If repeated, such a deficit would have a negative impact on the University’s financial rating. Think of it this way. When you manage your household finances, you thoughtfully plan a budget for the year by making careful estimates of your income and projected expenses. All goes well until something unexpected occurs. Maybe the transmission on your car falls out. Maybe the orthodontist announces that your child needs braces. Possibly your water heater explodes. You suddenly face an unforeseen bill that blows the budget, and you have to adjust your spending to cover the costs. In planning for our current year budget, FY 17, our original calculations of needs to operate the university was short $8.4 Million. We reworked this budget and balanced it by June 2016, with a reduced merit increase, a 5% reduction in operating expenses, and a small contingency. Believing all was well, in the Fall we discovered a reduced enrollment in certain graduate programs and an under-realization of gift funds held in various departments to support operations. Just like your household, we had to adjust. This meant an additional 1.25% reduction across all cabinet areas in operating expenses. This was not quite the perfect storm, but stormy enough. These cuts in operating funds have caused a number of our colleagues to question the university’s financial health. Some have even speculated that we face a financial crisis. Others wonder about the possibility of a further budget shortfall. For these reasons, the annual Budget Forum, one week from today, will be a particularly important opportunity to learn more and to ask questions. There you will hear a deeper explanation of the issues of cash flow, construction, pledged donations, and university debt capacity.

My few comments.  (1) I hate analogies of budgets of large complex organizations to households.  Such analogies are shrouded in an earnest language of “let me help you children understand” but the real intent is to obfuscate with a poor analogy.  The intertemporal budget constraint (i.e. over time) of a large, complex organization is usually quite different from that of a household.  (2) This sentence: “In planning for our current year budget, FY 17, our original calculations of needs to operate the university was short $8.4 Million.”  “Needs” seems to be referring to “revenue flows.”  That is, “Our projected revenue flows were $8.4m below projected expenses.”  Odd choice of language.  We “need” this money!  (3) A 6.25% cut in total budget (he isn’t clear) amounts to about $28m.  Pres. Engh attributes the shortfall to declining graduate enrollment, but let’s say graduate students pay $35,000 in tuition per year, then enrollment would have had to go down by 800 students (almost one third of total enrollments).  Is that what happened?  How did our leadership allow graduate enrollments to decline so precipitously?  Where is the urgent task force to restore graduate enrollments?

Source: State of the University Address, 2017 – Public Addresses – Selected Writings – Office of the President – Santa Clara University

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Why can’t I listen to Sigur Ros The Nothing Song for more than two hours? Why didn’t they play it for ten hours?

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Light television viewing if you stream Netflix

I enjoyed The OA immensely, even if at times the show is unsatisfying.  It is like Netflix had carefully analyzed my viewing profile and said, “Let’s make a series that mixes Lost, Arrival, Lars von Trier, I see dead people, Maureen McHugh, etc.” All my favorite themes.  This genre (let’s call it melancholy fiction about our mysterious universe MFMU) always has a problem with endings, and The OA wisely chooses to not have an ending, and instead simply has the series drive away with someone shouting “Take me…!”

Midway through I made the mistake of reading a bit about the two writers, and realized they had cast themselves in the series, and suddenly the acting seemed more like “look at me” and less professional.  I think writer-directors should stay away from casting themselves in the lead roles.

A little bit more editing would have been good, at times a little repetitious. And the show never explains why the OA repeatedly switches demeanor from dysfunctional (when interacting with people) to highly functional (in her “group” of five).

Leslie and I both enjoyed Mozart in the Jungle.  With Gael Garcia Bernal, it is an over-the-top classical music romp of a soap opera.  It really helped that I have started to learn piano and have learned to read music (at beginner level, naturally)… but I find my music appreciation has really changed.

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Kwei Quartey’s Murder at Cape Three Points

Quartey’s detective series, now in its third title, featuring Darko Dawson is pretty good.  I enjoyed Murder at Cape Three Points The mystery here is a double-murder that involves the nascent offshore oil industry.  Lots of nice description of Takoradi, Cape Three Points (I want to live there!), middle class Ghanaian life, and corruption issues.  Brisk pacing, and good writing in the first half of the mystery keep you going.

I do think Quartey could benefit from better editing.  The second half really gets bogged down.  Like many mysteries, this one fizzles out at the end, and is unconvincing in its resolution.

The novel is a good introduction to Ghanaian development issues, though.  Maybe I will assign to class!

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