Tag Archives: productivity

do kids do better in private school than public?

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The answer, at least in this study, is a clear no. Kids in private school are doing better than kids in public school, but it can be entirely explained by family income.

Does Attendance in Private Schools Predict Student Outcomes at Age 15? Evidence From a Longitudinal Study

By tracking longitudinally a sample of American children (n = 1,097), this study examined the extent to which enrollment in private schools between kindergarten and ninth grade was related to students’ academic, social, psychological, and attainment outcomes at age 15. Results from this investigation revealed that in unadjusted models, children with a history of enrollment in private schools performed better on nearly all outcomes assessed in adolescence. However, by simply controlling for the sociodemographic characteristics that selected children and families into these schools, all of the advantages of private school education were eliminated. There was also no evidence to suggest that low-income children or children enrolled in urban schools benefited more from private school enrollment.

smart drugs

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This BBC article talks about how some people are using amphetamines like Adderall and Ritalin to stay focused and motivated in high pressure jobs. It clearly works, at least for short periods of time. It is not clear whether it can work longer term, because people may either need a significant recovery period to recover from use of the drugs, during which they are less focused and motivated than normal, or else they may become addicted to the drugs. But the article also points out that the new drugs are not qualitatively different from using coffee to stay focused and productive – it is just a matter of differences in degree and chemistry, and coffee has proven to be safe and even beneficial to most people.

3D model builder for construction sites

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Here’s a technology to build 3D (digital) models of what is happening on construction sites over time using data from cameras mounted on workers’ helmets. If you have a 3D model of what is supposed to be built ahead of time, you can imagine this providing the chance to compare what is being built to what was planned, giving you the ability to catch and correct mistakes in real time.

construction productivity

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The construction industry has languished in terms of productivity growth for decades. But there are ideas, some of which are mentioned in this white paper from UK firm Balfour Beatty. Many are organized around the idea of prefabricating as many components as possible offset, then bringing them in for assembly. Another way of looking at it is that construction is basically a form of (inefficient, risky and very site-specific) manufacturing, and can try to learn some lessons from other manufacturing industries.

…we know this is an industry that lives on thin margins, is plagued by time and cost overruns and inherently operates in one of the higher risk environments of any sector – risk in terms of cost, time and, above all, human safety. But do we also think of this as an industry with one of the largest opportunities of any sector to transform its model? Can we think of many industries where the size of the prize is to shift 25% of current output to a solution that radically improves speed, quality and safety – all while creating (not destroying) jobs?

Today a new generation of industrialised construction methods, including offsite and modular building techniques, are increasingly being recognised as the best way for the UK construction industry to boost productivity and plug skills shortages. And moving to these methods drives better outcomes for all stakeholders: for the customer, reducing onsite construction times and waste; for the construction supply chain, by improving quality, repeatability (and
therefore output) of infrastructure; for the workforce above all, by raising safety performance and securing long-term employment.

 

new patent trading rules to boost productivity?

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Here is one proposal to boost productivity growth from a professor at Columbia – basically tighter protections on patent use coupled with more flexible arrangements to share and lease them between parties. It sounds okay, but I have a couple questions.

First, the author sees this as an antidote to “forced technology transfer” from developed to developing companies. If I understand correctly, this is when a factory in a developing country (let’s say China) agrees to manufacture for a developed country firm, but insists they share the legal rights to the technology they are manufacturing, allowing them to possibly cut the inventor/designer out in the future. I get that this benefits the developing country, possibly at some expense to the incentive to come up with further inventions in the developed country. Maybe – but I’d like to see the evidence. Perhaps when the inventor is ready to trade his or her knowledge in exchange for cheap labor and lax regulation, he or she is ready to reap some rewards on the last invention and move on to the next one. I don’t know whether my theory or the author’s theory is more correct, but I have no evidence for either one right now so if I had any hand in policy making I would want to see the evidence for both.

Second, and this is related, the author equates technology with knowledge. That might make sense in certain industries, for example drugs and chemicals. In many other industries, as much or more knowledge exists in the minds of experienced human beings than exists in a written-down form. Many forms of engineering are an example, because engineering by definition is using existing knowledge and experience to solve new problems without completely obvious solutions. If it takes decades of education/training/experience to get an individual to this point, even with the available written-down knowledge, there is not a whole lot of risk if that written-down knowledge leaks out. There is probably also very little value in patenting or otherwise protecting it, and much to be gained by making it freely available.

adjusting productivity/GDP for ecosystem services

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Here’s a new paper on a method of adjusting productivity/GDP (they seem to use the terms interchangeably, which confuses me) for ecosystem services and natural capital depletion.

Environmentally Adjusted Multifactor Productivity: Methodology and Empirical Results for OECD and G20 Countries

This paper extends the analytical framework for measuring multifactor productivity in order\ to account for environmental services. A growth accounting approach is used to decompose a pollution-adjusted measure of output growth into the contributions of labour, produced capital and natural capital. These indicators allow the sources of economic growth, and its long run sustainability, to be better assessed. Results presented here cover OECD and G20 countries for the 1990–2013 period, and account for the extraction of subsoil natural assets and emissions of air pollutants and greenhouse gases. The main findings suggest that growth in OECD countries has been generated almost exclusively through productivity gains, while BRIICS countries have drawn largely on increased utilisation of factor inputs to generate additional growth. Regarding natural capital, in countries such as Russia, Saudi Arabia, and Chile, reliance on subsoil assets extraction has contributed to a significant share of income growth. Results also point to a shift towards more environmentally friendly production processes in many countries. In fact, most OECD countries have decreased their emissions over the last two decades, and these pollution abatement efforts result in an upward adjustment of their GDP growth rates, allowing for a more accurate assessment of their economic performance.

It’s a little hard to tease out (from the abstract, since I haven’t read the paper) whether this means we are turning the corner and becoming more sustainable as a planet, or simply becoming more unsustainable at a slower rate than the past. I suspect it is the latter – so while it might be good news, it doesn’t necessarily mean that we are on a sustainable path.

the French AI strategy

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Other countries (than the United States) are developing strategies for how artificial intelligence will affect work, productivity, and growth in the near future.

France’s national strategy also reveals that Macron’s government is wrestling with how to ensure that AI supports inclusivity and diversity, and to make certain that its implementation is transparent. The French aren’t just theorizing; they’re taking action. France plans to invest 1.5 billion euros (almost $1.8 billion dollars) in the next five years in artificial intelligence research. The French are looking to create their own AI ecosystem, train the next generation of scientists and engineers, and make sure that their workforce is prepared for an automated future.

France isn’t alone. Last month, the European Union’s executive branch recommended its member states increase their public and private sector investment in AI. It also pledged billions in direct research spending. Meanwhile, China laid out its AI plan for global dominance last year, a plan that has also been backed up with massive investment. China’s goal is to lead the world in AI technology by 2030. Around the world, our global economic competitors are taking action on artificial intelligence.

It’s therefore striking that the United States doesn’t have a national artificial intelligence plan.

The fact that I don’t find it striking reflects my lowered expectations more than anything. We don’t really have a strategy for infrastructure or education either, for example.

one large or many smaller cities for maximum productivity

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This paper looks at data from 306 cities in China to identify trends in how different sizes and densities of cities relative to each other affect economic productivity. The interesting finding is that it is best to have either one big low-density city or many smaller high-density ones.

How did urban polycentricity and dispersion affect economic productivity? A case study of 306 Chinese cities

This article aims to assess the impacts of urban spatial structure on economic productivity. Drawing upon detailed gridded population data of 306 Chinese cities at the prefecture level and above, we identify their urban (sub)centers through exploratory spatial data analysis, construct indicators to measure their degrees of polycentricity and dispersion, and model the impacts of spatial structure on urban productivity. A regression analysis reveals that economic productivity is significantly associated with urban spatial structure. Conditioning on other factors, higher degrees of dispersion are associated with lower level of urban productivity whereas the effects of polycentricity depend on urban population density. Less densely populated cities are likely to have higher productivity levels when they are more monocentric, while urban productivity of cities with high population density tend to benefit from a more polycentric structure. The paper concludes with spatial planning implications.