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This article, written at the start of April 2021, is a personal reflection on what has and hasn't worked in remote/online education. I have drawn on my own experience of teaching over the course of the past year, observations of classroom practice I have undertaken as a mentor and middle leader with responsibility for teaching and learning in my school, and conversations I have had with colleagues in my school and elsewhere; it is, therefore, highly anecdotal, and the reader is asked to bear in mind the fact that, like many others, my journey into online teaching was enforced by the closure of schools during the first nationwide lockdown in March 2020. My core aim during both lockdowns was to provide for my students the best experience possible until such a time as we could all return to the physical classroom. As it became clear towards the end of 2020 and the start of 2021 that we were going to need to return to remote education, I began to think more deeply about the strategies I was employing in my online teaching, how effective they were for my students, and what I might do to maximise their learning experience and outcomes.
We develop a computational approximation to the intensity of a Gibbs spatial point process having interactions of any order. Limit theorems from stochastic geometry, and small-sample probabilities estimated once and for all by an extensive simulation study, are combined with scaling properties to form an approximation to the moment generating function of the sufficient statistic under a Poisson process. The approximate intensity is obtained as the solution of a self-consistency equation.
My principal contribution comes from the proposal, with my colleague Graham Hitch, that the capacity to think, to learn, and to plan for the future all depend on a temporary memory system that can be divided into a small number of separate components. It was important to us that the resulting theoretical model could then be applied outside the laboratory to help understand a range of issues of practical importance. Our original idea has been more successful than we dared hope, with the term “working memory” having occurred in the title of over 170,000 papers – not all, of course, accepting our own theory, although our initial paper has been quoted more than 10,000 times. The idea of working memory combines two essential features – temporary memory, and its attentional control – both of which are limited in their capacity. Theorists vary in their emphasis on memory or attention, but all accept the need for both.
My own approach began with an emphasis on temporary storage and was strongly influenced by the controversy during the 1960s as to whether human memory involved separate short-term and long-term memory systems, or whether a single system could explain everything.
I began my research career in long-term memory, researching the way in which postal codes could be constructed so that people would find them easy to remember. I was working at the Medical Research Council Applied Psychology Unit in Cambridge, which specialized in linking the development of theory with its practical application. Having worked on postal codes, I was next given the task of attempting to improve methods of measuring the quality of telephone lines. The standard method was simply having people listen to potentially confusable words.
I proposed that having both to discriminate and to remember the words might make the task more sensitive, particularly since my boss, Conrad, had shown that similarity of sound made sequences much harder to recall. For example, a sequence such as b g c t d would be harder to remember in the right order than k w y q x. I decided to use words since this also allowed me to incorporate similarity of meaning, contrasting sequences such as man mat cat cap map or huge big wide long tall. I expected adding noise would make similar items much more difficult to remember in the right order. It did not.
I was very pleased to be invited to contribute to this book marking the foundation of a center for the study of autobiographical memory in Aarhus, but somewhat daunted, given that I have not worked in the area for a number of years. I have, however, just written a chapter on the topic in a recent textbook (Baddeley, Eysenck, and Anderson, 2009), but had emerged from my review feeling that the field still seemed somewhat fragmented and atheoretical. This seemed a good opportunity to help celebrate the foundation of a center that explicitly aims to change that perception.
Labels and definitions
I do not subscribe to the view that concepts and theories require precise definition before they can be fruitfully applied. On the contrary, my view of precise definitions is that they are possible only when one has a good understanding of the broad area, and, in the case of autobiographical memory, this is not yet. However, I think that the way in which a concept is labeled can have a major effect on its acceptance and subsequent popularity, an effect that can be positive, but may also lead to confusion if different people use the same labels in different ways. I think this is the case in the study of autobiographical memory.
Intercropping systems that include legumes can provide symbiotically fixed nitrogen (N) and potentially increase yield through improved resource use efficiency. The aims of the present study were: (a) to evaluate the effects of different legumes (species and varieties) and barley on grain yield, dry matter production and N uptake of the intercrop treatments compared with the associated cereal sole crop; (b) to assess the effects on the yields of the next grain crop and (c) to determine the accumulation of N in shoots of the crops in a low-input rotation. An experiment was established near Edinburgh, UK, consisting of 12 hydrologically isolated plots. Treatments were a spring barley (Hordeum vulgare cvar Westminster) sole crop and intercrops of barley/white clover (Trifolium repens cvar Alice) and barley/pea (Pisum sativum cvar Zero4 or cvar Nitouche) in 2006. All the plots were sown with spring oats (Avena sativa cvar Firth) in 2007 and perennial ryegrass in 2008. No fertilizers, herbicides or pesticides were used at any stage of the experiment. Above-ground biomass (barley, clover, pea, oat and ryegrass) and grain yields (barley, pea and oat) were measured at key stages during the growing seasons of 2006, 2007 and 2008; land equivalent ratio (LER) was measured only in 2006. At harvest, the total above-ground biomass of barley intercropped with clover (4·56 t biomass/ha) and barley intercropped with pea cvar Zero4 (4·49 t biomass/ha) were significantly different from the barley sole crop (3·05 t biomass/ha; P<0·05). The grain yield of the barley (2006) intercropped with clover (3·36 t grain/ha) was significantly greater than that in the other treatments (P<0·01). The accumulation of N in barley was low in 2006, but significantly higher (P<0·05) in the oat grown the following year on the same plots. The present study demonstrates for the first time that intercrops can affect the grain yield and N uptake of the following crop (spring oats) in a rotation. Differences were also linked to the contrasting legume species and cultivars present in the previous year's intercrop. Legume choice is essential to optimize the plant productivity in intercropping designs. Cultivars chosen for intercropping purposes must take into account the effects upon the growth of the partner crop/s as well as to the following crop, including environmental factors.
Localization microscopy techniques based on localizing single fluorophore molecules now routinely achieve accuracies better than 30 nm. Unlike conventional optical microscopies, localization microscopy experiments do not generate an image but a list of discrete coordinates of estimated fluorophore positions. Data display and analysis therefore generally require visualization methods that translate the position data into conventional images. Here we investigate the properties of several widely used visualization techniques and show that a commonly used algorithm based on rendering Gaussians may lead to a 1.44-fold loss of resolution. Existing methods typically do not explicitly take sampling considerations into account and thus may produce spurious structures. We present two additional visualization algorithms, an adaptive histogram method based on quad-trees and a Delaunay triangulation based visualization of point data that address some of these deficiencies. The new visualization methods are designed to suppress erroneous detail in poorly sampled image areas but avoid loss of resolution in well-sampled regions. A number of criteria for scoring visualization methods are developed as a guide for choosing among visualization methods and are used to qualitatively compare various algorithms.
How much should the State spend on health? One might doubt this is a question for economists – perhaps medical professionals should decide on patients' needs and treatment costs or politicians should be asked about the desires of the electorate and its taxpayers. But economists can offer helpful tools with which to debate questions of this nature. No country has a blank chequebook, and public budgeting is a tense business of allocating scarce resources amongst competing needs. Economists can contribute helpfully to the debate on how resources might be best used by showing the relative costs and benefits of different strategies. This can help to reconcile, or at least to prioritise, diverse and sometimes conflicting objectives. Moreover, the question of how much governments should spend on health care is usually nested within the wider debate to which economists have long been central – namely, to what extent should the State be involved in the economy at all. In most countries this topic can be relied upon to produce heated argument from all ends of the political spectrum, in tones that are often highly ideological, and polemical.
Developments in housing markets have wide-ranging implications for the macro-economy. Increases in house prices will boost household wealth, thereby boosting consumption and fostering increases in aggregate demand. Increases in house prices may also affect consumer confidence and expectations, increasing general optimism within the economy. On the other hand, rigidities in housing markets have crucial implications for labour mobility, employment and unemployment. For example, housing market volatility will limit the ability of people to move in search of better jobs: Oswald (1997) observed that countries with fastest growth in home ownership in the 1980s and 1990s had the fastest growth in unemployment and attributed this to the labour immobility of owner occupiers in depressed housing markets, particularly those home owners who face negative equity (a situation which occurs when mortgage borrowings on a property exceed the value of the mortgaged property).
The affordability of housing will also affect householders' decisions to invest in housing: as house prices rise and incomes fall, people will choose other alternatives (e.g. renting, living with family) and cannot afford to enter the housing market either because their incomes are too low and/or because they cannot get easy access to mortgage credit.
Unit roots, non-stationarity and spurious regressions
Data issues include:
Measuring the New Economy
In this chapter we will look at the relationship between computing investment and venture capital financing in the New Economy. Computing investment has been essential to the growth of the New Economy: Yang and Brynjolfsson (2001) argue that computerisation is the most pervasive technological change this era. IT (information technology) investments promoted improved macroeconomic performance, culminating from large increases in productivity and growth in the 1990s onwards, particularly in the US. Increasing GDP growth was accompanied by reduced volatility in GDP. This is because IT innovations played a key role in promoting greater flexibility; for example, innovations such as price comparison sites (e.g. dealtime.com and kelkoo.com) increased micro-economic flexibility via increased price transparency.
The New Economy grew rapidly from the 1990s onwards and its growth was enabled by venture capital injections. Venture capital is of particular importance because young entrepreneurs are responsible for a substantial proportion of the innovative New Economy investments. These entrepreneurs do not have profits retained from existing production. So venture capital funds are important in providing them finance for their new investments.
In this chapter we will explore the relationship between New Economy investment and venture capital funding.
Tragedy of the commons and environmental externalities
Game theory and the Prisoner's Dilemma
Tourism life-cycles and sustainable development
Econometric issues include:
Breusch–Godfrey LM and Durbin–Watson tests for autocorrelation
Generalised Least Squares (GLS) and the Cochrane–Orcutt procedure
Data issues include:
Measuring the environment
The tourism industry has great potential to boost incomes, growth and development in poorer countries. World Travel and Tourism Council (WTTC) estimates of the economic contribution made by tourism in 2005 include revenues of $6201.5 billion, employment of 221.6 million workers and an 8.3% share of GDP. For the Caribbean alone, a region heavily dependent on its tourism industry, 2005 estimates indicate that tourism has contributed 15% of GDP, 15% of jobs and $45.5 billion of revenue.
This growth in the tourism industry has not come without a cost. If tourism is not carefully planned then there will be negative consequences for the natural environment and for socio-economic and cultural institutions more generally. Even when judged in terms of prospects for the tourism industry alone, if the environmental resources on which tourism depends are exploited and destroyed by rapid growth in tourism demand then the contribution of tourism to sustainable development in the long term will be limited. In managing a tourism industry that will be sustainable in the future, policy makers need to take account of the environmental consequences.
In Chapters 2–4, we introduced the basic OLS techniques associated with simple regression analysis (SRA). But, as explained in Chapter 1, OLS is the Best Linear Unbiased Estimator (BLUE) – i.e. the most reliable and accurate estimator of parameters – only when the Gauss–Markov (GM) assumptions are satisfied. In many cases, GM assumptions will be violated and OLS will be inadequately simple; for example the omission of important variables will generate biases in SRA. And there may be other econometric problems further compromising the BLUEness of OLS.
In Part II we introduce multiple regression analysis (MRA), i.e. regressions on models with more than one explanatory variable. We also outline some commonly used econometric tests and procedures, for example the diagnostic tests used to discover violations of the GM assumptions. We will also introduce some procedures for correcting violations of GM assumptions.
The test of our progress is not whether we add to the abundance of those who have much; it is whether we provide enough for those who have too little.
US President Franklin D. Roosevelt, 2nd inaugural address, 1937.
Economic issues include:
Poverty reduction – the role of investment and trade
Millennium Development Goals (MDGs)
Development economics and development institutions
Econometric issues include:
Running OLS regressions ‘by hand’
Single hypothesis tests: Student's t test
Point estimates and confidence intervals
Goodness of fit and the coefficient of determination
Data issues include:
Quantitative indicators of development
A qualitative indicator – the Human Poverty Index
How can we reduce poverty and improve living standards for the poor? There are no easy answers and in addressing the problems of global poverty, the world's heads of state met in September 2000 to discuss a universal framework for development, agreeing on targets to promote development. The then 189 UN member states agreed to the eight Millennium Development Goals (MDGs) listed in Box 2.1. Poverty reduction was one of these primary goals and the UN member states pledged to halve world poverty by 2015. These goals were reaffirmed at the World Summit held in September 2005, and the issue has been kept alive ever since by a number of high-profile awareness-raising and fund-raising events, backed by a diverse range of people – rockstars included.
Heteroscedasticity tests: the Goldfield–Quandt test, White's test
Weighted Least Squares (WLS)
Data issues include:
Measuring innovation and knowledge: an innovativeness index
Working with outliers
Innovation is essential for economic growth and development, whether for countries that are technological leaders at the frontier of knowledge or for countries that are ‘latecomers’, catching up on technical advances made elsewhere. Globalisation has interacted with rapid technological change and trade has become increasingly open and competitive, transport costs have fallen, and capital and labour have become more and more mobile. International competitive pressures have become more pronounced and all countries, rich and poor, have had to innovate constantly.
Innovation can also involve finding new ways to do the same old things – for example in introducing new processes that can reduce the price and/or raise the quality of traditional products – such as wine. Overall, being innovative is important everywhere; it has important implications for developing countries as well as developed ones (UNCTAD, 2007); it is as important to individual firms as it is to nations as a whole. It is also important to individual people – we need to keep investing in and refreshing our personal human capital; in modern economies for example, there are many good reasons (social as well as professional) to be computer literate, i.e. to ‘invest’ in our human computing capital.
Permanent income hypothesis and life-cycle hypothesis
Econometric issues include:
Autoregressive distributed lag (ARDL) models
Error correction models (ECMs) and cointegration
Granger causality tests
Data issues include:
When income rises do we spend more or save more? This question most famously excited the interest of the British economist, John Maynard Keynes (1883–1946) during the depression years of the 1930s, but it is of equal importance today. Consumption is the single largest element in household spending and has significant implications for demand and the subsequent health of the economy. Recognition of this role of household spending was evident in the flurry of newspaper articles in late 2001 and early 2002, when many commentators feared the world was headed for recession, if not a complete depression. ‘The shoppers who saved the nation’ was a typical headline in 2005: journalists who had previously expressed shock at the high debt held at stores and on credit cards by the average household, now applauded the fact they were continuing to spend and thereby maintaining the multiplier effects that might ward off the nation's – and indeed the world's – slide into recession. Policy-makers struggled to estimate precisely the extent to which an extra dollar's spending would trickle through the economy to keep the country's producers, manufacturers and retailers afloat.
This chapter provides a brief and intuitive summary of the econometric theory that underlies OLS estimation, including:
The data generating process and sample regression functions
Types of data
Ordinary Least Squares (OLS) estimation
Measuring correlation and goodness of fit
The Gauss–Markov theorem
Properties of estimators
Running Regressions is about the quantitative analysis of observed behaviours and phenomena using economic theory, probability and statistics. By bringing these different elements together we can improve our understanding of people, firms and countries – in the past, the present and the future. For those readers who approach the subject with trepidation, it may be worth remembering this practical goal.
The skills that are required are not intrinsically difficult if a systematic approach is followed. Not least, our intention in Running Regressions is to illustrate commonly used techniques in an imaginative and interesting way. This chapter provides a brief introduction to concepts and techniques that are then worked through as practical examples in the following chapters.
Finally, whilst we focus on economics, finance and development studies in our selection of topics, the approach can be used in analysing a wide range of all real-world situations. The techniques can be, and are, used in all the social and natural sciences.
Models and data
The aim of econometric analysis is to understand the data generating processes (DGPs) that underlie economic systems and human behaviour. These DGPs are like the engines in a car and they propel socio-economic actions and events.
Poverty and armed conflict are the norm for a large proportion of the world's poor. In moderating the socio-economic impacts of these conflicts, the military as an institution plays a complex role. It may bestow some benefits in countries with institutions that are otherwise underdeveloped and perhaps it is not surprising that a well-organised and powerful public institution, when engaged in peaceful activity, should have positive impacts in countries with otherwise underdeveloped institutions. A lot of empirical work has been done, following Benoit (1978), to show that defence spending necessitated by real or potential armed conflicts encourages the development of human skills and essential infrastructure within poor economies, thus alleviating poverty. Benoit's study led to a range of further studies, some of which questioned Benoit's methodology and findings, and others which investigated the relationship between defence and economic growth using different methodologies.
Whilst the military as an institution may have a positive impact in peacetime, what about the direct impacts of war and conflict? What are its causes and consequences? In this chapter we will start to illuminate these questions by assessing evidence about the relationship between the incidence of war and relative poverty.