ECOS 3002 US Financial Idea of Uncompensated Natural Impacts Question


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ECOS3002 Development Economics
Lecture 1
Faculty of Arts and Social Sciences
School of Economics
Development Economics
Development Economics
Theory and Practice 2nd Edition
By Alain de Janvry & Elisabeth Sadoulet
Free Shipping & 20% off (code: SAV20)
ISBN: 9780367456474
ECOS3002 Development Economics
Lecture 1
Chapter 1: What is Development? Indicators and Issues
Faculty of Arts and Social Sciences
School of Economics
What is our objective?
• This class is about economic development.
• We will mostly talk about countries’ current state of development, and how to have
more or better development.
• “more” and “better” imply that we have an objective in mind, a standard we’re trying to
• This lecture/chapter is about how we conceptualize development.
The big idea: enhancing wellbeing
• A basic definition (from our textbook): “Development is about the enhancement of
human wellbeing.”
• While this seems obvious, it’s not very practically useful. People can define “human
wellbeing” in vastly different ways, as different individuals, groups, or nations may
perceive different social needs and aspirations.
• Have you seen that in your own life, perhaps as your own values come into contrast with
those of other family members (different generations) or your friends from different
cultural backgrounds – what is the ideal major, career, income level, family size, income?
Note: we won’t focus on objectives
• This class isn’t about deciding what our concept of development should be.
• That is mostly an argument for politicians, philosophers, human rights activists, the general
public, etc., to have, as it touches on ideology, which may be rooted in different baseline
assumptions about how to define “wellbeing.”
• As economists we can and should bring our own perspectives to these debates, as members of
• But our main focus as development economists is on how to achieve the development objectives
of our community, society, or nation. We largely take the objectives as given.
Seven dimensions of development
• Our textbook posts 7 dimensions of development:
1. Income and income growth.
2. Poverty and hunger.
3. Inequality and inequity.
4. Vulnerability.
5. Basic needs in education and health.
6. Environmental sustainability.
7. Quality of life.
? Does this already reflect a particular perspective on development? (e.g., western, globalist,
1. Income and growth
• In market economies, individuals can trade their time, effort, and ingenuity, for income.
• So, on this dimension, more income is better.
• But when it comes to a group, community, or society, we have a distribution of income.
We can describe characteristics of this distribution, e.g.,
• The average or the per capita (pc) income;
• The income at different percentiles;
• The income earned by different sub-groups.
1. Income and growth
• To make comparisons between countries, it isn’t enough to just add up the incomes
earned by all the people in the country, e.g.,
• Some people earn income from wages, but they can also earn returns to capital.
• For businesses, an employee’s wage is an expense that reduces their profits (which is a source of
earnings for their owners).
• People and countries have international ties, e.g., import and export, receiving and sending
remittances, etc.
• A more robust way to measure national income is based on net production.
1. Income and growth
• Gross Domestic Product (GDP): the aggregate of value-added by all firms in the country.
• This includes production for home consumption (you produce what you consume without
selling it on the market), at opportunity cost.
• Gross National Product (GNP): GDP + net factor incomes from abroad (net repatriated
profits + net remittances).
• Gross National Income (GNI): GNP, subtracting depreciation and indirect business taxes.
1. Income and growth
• We can compare countries based on GDP (or GNP, GNI), or on their per capita values,
simply by dividing:
GDPpc = GDP / population
this provides an important correction for the fact that countries may have very different
population sizes.
• To get economic growth, we just take the change in GDP, GDPpc, etc., i.e.,
GDP growth = (GDPt – GDP(t-1)) / GDP(t-1)
1. Income and growth
• In principle, to compare between countries, we could just convert GDP to a common
currency (e.g., USD), at the official exchange rate.
• However, in order to make comparisons between countries, we need to deal with the
fact that
(1) countries measure their production in their own currency,
(2) exchange rates may be driven by factors that don’t fully reflect the state of an
(3) if we want to compare well-being, equivalent goods may have different prices in
different countries.
1. Income and growth
• To deal with (2) and (3), we can:
• Use an “equilibrium exchange rate” that isn’t as affected by arbitrary fluctuations.
• Adjust the currencies for their Purchasing Power Parity (PPP). This adjusts for the fact that
similar-quality goods and services can be much cheaper in poorer countries. Residents of
these countries would look worse off than they actually are, if we don’t correct for the fact
that they pay less for some goods and services.
• This leads to the formula in the textbook:
PPP-adjusted GDPpc = (1 / PPPe)*GDPpcLCU
where LCU refers to the local currency, and PPPe is converts the equilibrium exchange
rate based on comparable consumption baskets.
1. Income and growth
• GNIpc is used by the World Bank to classify countries based on their stage of economic
• The World Bank classifies economies into 4 categories:
• Low income countries (LIC)
• Lower middle income countries (LMIC)
• Upper middle income countries (UMIC)
• High income countries (HIC)
•We will typically consider LIC, LMIC and UMIC as developing countries, and HIC as
developed countries, though this terminology is being phased out.
1. Income and growth
• Beyond broad critiques of using concepts like GDP and GDP growth to measure wellbeing and guide societal development, there are substantive critiques of the approach,
• It excludes good and services not transacted in the market, such as household work. Given
that globally women tend to perform this work, the contributions of women to the economy
can be seriously undercounted.
• GDP misses negative externalities like crime or social breakdown, and environmental damage.
• Though rarely used in practice, Genuine Progress Indicator (GPI):
GPI = GDP + Value of unpaid work ? Costs of crime and social breakdown ? Cost of environmental
• In the US, GPI < GDP and the gap is widening over time. 2. Poverty and hunger • While the field of development economics is about overall development of countries, in practice it focuses most on reducing poverty, on the subset of the population that is considered near or below a poverty line. • This comes back to the distribution of income and wealth – two countries with the same income (GDP) could have very different rates of poverty. E.g., • Having a few very rich people, offset by a large proportion (e.g., 20-30-40%) of the population in poverty. • Having moderate rates of extreme wealth, but also moderate rates of poverty. 2. Poverty and hunger • We typically define poverty according to a level of income that is sufficient to procure a minimal consumption bundle in a given country – a minimal caloric intake ensuring lack of hunger, clothing, housing, transport, etc, adjusted for household size. • This deals with the measurement issue that the cost of goods and services might differ between countries, and allows us to compare poverty between countries by comparing rates of poverty (e.g., % in poverty). • We will return to the issue of poverty measurement in lecture 3 (week 3), when we study Chapter 5, which provides a more comprehensive take on poverty and vulnerability. 3. Inequality and inequity • While poverty focuses on the “left tail” of the income or wealth distribution, inequality describes the entire distribution. Roughly, inequality is “the share of aggregate income hold by the top X percent of the population relative to the bottom Y percent.” • This brings the focus of economic development not just on the status of the poor, but also on how relatively well the better-off are doing. • It is not universally accepted that we should be very concerned with inequality, however it has received increased focus in recent years as many measures of inequality have increased around the world. Might high rates of inequality decrease overall growth and development? 3. Inequality and inequity • While inequality is an ex post concept (something we look at afterward), inequity is an ex ante (beforehand) concept: “the degree of equality in opportunities to generate future income or to achieve other development objectives.” • E.g., the opportunity to obtain education, as measured by the probability of achieving a level of education like high school diploma. • This is also related to Sen’s concept of “capabilities” – what people can do with their opportunity set. • We will return to the issue of inequality and inequity in lecture 3 (week 3), when we study Chapter 6, which provides a more comprehensive take on inequality and inequity. 4. Vulnerability and poverty • We can define vulnerability (to poverty, food insecurity, or hunger) as “the probability of falling into (poverty, food insecurity, or hunger) for the non-(poor, food insecure, hungry).” • This introduces the idea of risk into analyzing economic development, in particular the vulnerability to “shocks” – bad events that can increase poverty such as illness, death of a family member, crop failure, civil conflict, etc. • There is rapidly-growing empirical evidence that exposure to uninsured risks is one of the leading causes of poverty. 4. Vulnerability and poverty • Exposure to shocks can have a number of negative impacts: • People may change their behavior through anticipating them. For example, if a farmer is worried that if they try a promising new seed variety and it fails they won’t have something to fall back on, they might not try it. This is related to their degree of risk aversion (the concavity of their utility function, as you may have studied in microeconomics). • Exposure to shocks may pull people into irreversible poverty / hunger / food insecurity, aka chronic or persistent poverty. This is also known as a “poverty trap.” • Even if shocks don’t have permanent impacts, they can devastate the well-being of a household for many years. For example, a household facing a shock may sell off productive assets to survive (e.g., livestock, land, gold), and it may take them a long time to recover. 4. Vulnerability and poverty • How resilient are households to shocks and vulnerability? We can distinguish to what extent households in the same community, region or nation face the same risk. 1. Covariate risk is the component of risk that is jointly shared by households in a community, region, or nation. If everyone faces the same shock, then it’s hard to help each other, and outside help (e.g., from the government, or other nations) is needed. 2. Whereas if covariance is low, then we have idiosyncratic risk, and as different shocks hit, the less-affected ones can more easily help the more-affected ones (mutual insurance). 4. Vulnerability and poverty • There are ongoing efforts to reduce vulnerability, both through public schemes like social programs (e.g., social welfare programs like those managed by Centrelink in Australia, or Medicare), and schemes provided mostly through private markets (e.g., insurance, like homeowners or car insurance in Australia). • We will look more into these issues when we study Chapter 5 on poverty and vulnerability analysis in week 3 (lecture 3), Chapter 13 on financial services for the poor in week 7 (lecture 7) and Chapter 14 on social programs and targeting in week 9 (lecture 8). 5. Basic needs: human development • The first 4 dimensions of development so far are monetary dimensions of development. But arguably the most important measure of development is the capacity of people – their knowledge and physical well-being. • Of course, this can also plausibly contribute to monetary metrics of growth. • A number of thinkers have argued that meeting basic human needs is a key measure of development, in areas such as education, health, nutrition, sanitation and housing. However, without a singular metric like a monetary metric, there is still much subjectivity and debate over how to weight the importance of these needs. 5. Basic needs: human development • Some indicators of basic needs: • Child health: z-scores (World Health Organization). For indicators without a standard scale, we can calculate a zscore: z = (x - ?)/?, where x is an individual score (e.g., height, weight), ? is a mean value of x in a population, and ? is a standard deviation of the distribution of x in the population. To standardize across countries, ? and ? are taken from a US reference population. The two main measures are height-for-age and weight-for-age. • Global Burden of Disease (GBD) (World Health Organization). Calculated as “the gap between the current health status of a population and the ideal situation” (everyone lives to old age, disease and disability free). Measured in Disability Adjusted Life Years (DALY), where a DALY is “one lost year of healthy life due to premature mortality or to ill health or disability,” i.e., DALY = YLL + YLD. Then GBD = the share of DALY in ideal life expectancy. • Malnutrition: food insecurity (Food and Agricultural Organization). Proportion of population below minimal nutritional needs (2,800 kilocalories/person/day for adult men and 2,000 kilocalories/person/day for adult women, with moderate activity and lowest acceptable bodyweight). The depth of hunger is measured as the average distance to the nutritional norm. 5. Basic needs: human development • Some indicators of basic needs: • The classic Human Development Index (HDI) (United Nations Development Program) for country ????: 3 1 ???????????? ? ????????,???????????? ???????????????? = ? ????????,???????????? ? ????????,???????????? 3 ????=1 where ???????????? represents the value of an index of educational attainment (weighted average of literacy with primary, secondary, and tertiary gross enrollment), health (life expectancy), and income (PPP-adjusted per capita income) for country ????. • In 2010, the HDI was redefined to a multiplicative specification, allowing the indices to complement each other: 1/3 3 ???????????? ? ????????,???????????? ???????????????? = ? ????=1 ????????,???????????? ? ????????,???????????? 5. Basic needs: human development • Some indicators of basic needs: • While the HDI makes an important contribution in providing a relatively simple, cross-country comparable index of human development, it has been criticized for its arbitrariness. E.g., why include income when the idea is to have a metric of basic needs? A key justification of incomeonly measures is that money can be converted to meet other needs like education and health. Why does each category get equal weight? • Multidimensional poverty indices (MPI) are designed as an improvement over HDI, considering a broader set of measures of living standards (in addition to those in HDI, health and education, and after dropping income, it also includes access to electricity, drinking water, sanitation, flooring, cooking fuel, and assets), sets thresholds for each, and declares a household as “poor” if it is below threshold in at least 30% of categories. We’ll return to MPI in Chapter 5 on Poverty and Vulnerability Analysis. 6. Sustainability and use of natural resources • Some of the measures thus far consider time-dependent dimensions of development within a person’s lifetime – e.g., people in school might not be contributing to GDP at the time, but they can produce a lot more later, reducing vulnerability might require up-front investments that reduce GDP, but greatly reduce the risk of people being worse of for sustained periods after facing shocks. • Even further on the time dimension: what about caring about the wellbeing of future generations? Sustainability is defined as “the concern with intergenerational equity: that the wellbeing of future generations should not be inferior to that of the current generation, as a consequence of the current generation’s behaviour toward the use of natural resources and the environment.” 6. Sustainability and use of natural resources • While this sounds plausible in principle, in practice it is hard to implement: • Just as it is hard to find a universal definition of wellbeing for present generations, even moreso for anticipating the wellbeing of future generations. • Even if we can solve this definitional problem, we need to think about that future wellbeing will flow out of natural resources. • If and when we address these challenges, we need to resolve the inevitable tradeoff between the wellbeing of the current generation and the wellbeing of future generations. • We will return to these issues in lecture 12 (week 13), when we study Chapter 15, on sustainable development and the environment. 7. Quality of life • The aforementioned 6 categories (income growth, escape from poverty, equality and equity, reduction of vulnerability, basic needs in education and health, and environmental sustainability) lend themselves relatively well to quantitative analysis, either based on money metrics, quantification of human development, or extending these ideas across generations to consider sustainability. • However, many more theories of human wellbeing and development have been developed, much of this work going beyond the scope of ECOS3002. • Our textbook additionally raises two further concepts of quality of life. 7. Quality of life • The Nobel-prize winning economist Amartya Sen is known for his work including on famines, poverty measurement, and the “capability approach.” • In his books Commodities and Capabilities (1985) and Development as Freedom (2000), he develops the idea of development as a process of expanding freedoms. In a logical framework. • Greater freedoms derive from capabilities (“the choices that a person makes among “functionings” that could be achieved, and the freedoms he or she has in exercising such choices”). • Functionings are in turn determined by “entitlements,” “the set of alternative commodities and services that a person can command in a society using the totality of rights and opportunities that he or she faces.” 7. Quality of life • Entitlements are the fruits of a developed society – public goods, personal characteristics, asset endowments, social norms, environmental conditions, etc. • Sen helped push the definition of development within the economic profession beyond monetary metrics to consider issues of freedom of choice. Monetary metrics could fail if there are differences in freedom – e.g., a wealthy member of a discriminated group may have a lower level of wellbeing than an average member of a favored group in society. • Under Sen’s approach, progress in development isn’t just about raising GDPpc, it’s about attacking sources of capability deprivation and expanding the set of capabilities. 7. Quality of life • As a second alternative, we have William Easterly’s (1999) Indicators of Quality of Life. Easterly is another prolific writer on economic development with a series of popular and influential books. • Easterly (1999) proposes a set of 81 additional indicators beyond income, adding to education, health, and inequality measures around individual rights and democracy, political stability and peace, and absence of “bads” (fraud, terrorism, crime, pollution, etc). • This can be seen as related to the multidimensional approach. Subjective measures • Unfortunately there is a tradeoff between comprehensiveness of how we measure development and how easy it is to measure and derive comparisons. Single-index measures (e.g., money) are relatively easy to measure and compare, but may miss important things. • Another approach is to collect subjective measures of well-being, such as measuring concepts like “happiness.” E.g., ask people on a scale of 1-10, “All things considered, how satisfied are you with your life as a whole these days?” • The famous “Easterlin paradox” (Easterlin, 1974) involved showing that there is not a strong correlation between income and happiness in industrialized countries. Subjective measures • However, more recent work (e.g., Deaton, 2008) shows that rising GDPpc in developing countries does increase happiness. • It appears that income tends to increase happiness below PPP-adjusted $10,000 USD per capita but not beyond, as other factors may play a greater role in driving happiness once basic needs to be met beyond this income level. • Meanwhile, the evidence on whether income growth increases well-being is relatively problematic and the positive evidence is relatively weak. MDGs and SDGs • The “development goals” represent a global effort to define minimum standards of development and set up global efforts and coordination to achieve them. • The Millenium Development Goals (MDGs) were declared in September, 2000, and involved attempting to achieving 8 goals by 2015, including To eradicate extreme poverty and hunger To improve maternal health To achieve universal primary education To combat HIV/AIDS, malaria, and other diseases To promote gender equality and empower women To ensure environmental sustainability To reduce child mortality To develop a global partnership for development MDGs and SDGs • Each MDG had specific targets and dates for achieving those targets. Progress towards these goals was uneven – some countries achieved many goals, some didn’t achieve any. •The Sustainable Development Goals (SDGs) succeeded the MDGs in 2016, and are intended to be achieved by 2030. They are a set of 17 interlinked global goals, and in 2017 the UN General Assembly set specific targets for each goal, and indicators to measure progress toward each target (usually between 2020 and 2030, though some have no end date). • You can learn more about the SDGs here ( and review the SDG tracker here: ECOS3002 Development Economics Lecture 1 Faculty of Arts and Social Sciences School of Economics ECOS3002 Development Economics Lecture 1 Introduction to Impact Evaluation and RCTs (~early sections of Chapter 4) Faculty of Arts and Social Sciences School of Economics ECOS3002 Development Economics Lecture 1 Introduction to Impact Evaluation and RCTs Overview Faculty of Arts and Social Sciences School of Economics Why evaluate impact? •Organizations funding and implementing international development projects increasingly want to evaluate “impact.” •Familiar approaches like M&E, process evaluations, qualitative assessments, etc, can verify implementation activities and provide critical diagnostic insights. •However, such methods generally cannot tell us whether a project is improving the ultimate outcomes we care about – health, income, welfare, well-being, etc. Impact Evaluations (IEs) can fill this gap. •Furthermore other methods can’t cleanly identify the magnitude of the impact of a project (how much does productivity increase, profitability or income go up, etc). • Critical for cost-benefit analysis: compare program costs to real program benefits •Detailed data collection and measurement also provides insights into why and how programs work that we might not get from a less intrusive approach. The goal of impact evaluation: causal inference •The goal of an impact evaluation is typically to answer a question like: what is the (quantitative) effect of X (a “treatment”) on Y (an outcome)? In other words, how much would Y increase (or decrease) on average, purely due to X alone, all other things equal? •This sounds easy in principle: just compare people, firms, farms, etc, that have the treatment, with those that don’t. •The challenge comes from the fact that in many of the impact evaluation contexts we care about, human choices and/or other systems intervene to allocate treatment: • Governments and NGOs choose who to give social, health, educational or benefits or programs to, and people choose or not to accept or seek out these benefits • People choose whether or not to proceed in school • Financial institutions choose who to lend to or offer other financial services • Governments or private sector firms choose where to implement infrastructure projects • Etc And the allocation of treatment may be a function of characteristics that also influence outcomes. Classic example: returns to education •Education is one of the largest areas of government expenditure, so massively important to know the returns to education, to optimize investment. •The naïve approach would be to compare people who have more or less years of education. •Can run a regression (best fit of a line to the data): Y = a + b*X + e where Y is income, and X is years of education. Would ‘b’ tell us the true average annual return to education? Classic example: returns to education •Education is one of the largest areas of government expenditure, so massively important to know the returns to education, to optimize subsidization, investment, regulation, etc. •The naïve approach would be to compare people who have more or less years of education. •Can run a regression (best fit of a line to the data): Y = a + b*X + e where Y is income, and X is years of education. Would ‘b’ tell us the average return to education? No! People can choose when to stop schooling, and the system may also have built-in barriers (e.g., entry exams). This can lead people who have innate characteristics that make them better at school to stay longer. If these innate characteristics can also drive earnings independent of X, then ‘b’ is not the “true” effect of schooling on earnings. Classic example: returns to education Classic example: returns to education Observables affecting X and Y Treatment: education Outcome: income Unobservables affecting X and Y Classic example: returns to education •The problem with our regression in this case is that there are likely to be unobservable characteristics that lead people to get more years of schooling and drive earnings (independent of schooling). •This is often called “innate ability” but could be lots of things: ambition, drive, social influences (family), genetics, etc, etc. •In our naïve regression, these characteristics go into e: Y = a + b*X + e where Y is income, and X is years of education. •So let’s just measure these characteristics? It’s generally accepted that that’s a fool’s errand: ? In many datasets where we want to evaluate impact, these variables don’t exist. ? Even if we collect the data, testing for a large range of characteristics would be incredibly expensive and we don’t have good tests for some of them. Classic example: returns to education •Another way to see this is to take a graphical approach. •Again: the key challenge comes up if some factor(s) that are hard to measure drives both (1) selection, and (2) outcomes. Income We don’t know where this is Ideally we want to get rid of this middle bit (the “selection bias”), so the true effect of the treatment is just the difference in outcomes between the two groups 10 years of schooling 16 years of schooling Modern approach to impact evaluation: have a research design •The modern approach to impact evaluation gives up on trying to run “kitchen sink regressions” (regressing Y and X and a lot of other stuff, to control for sources of selection bias) Y = a + b*X + c*[Kitchen_Sink] + e In some cases this may be the best we can do, but it often leads to undesirably weak or misleading results, and quite an unreliable guide for policy. •In other words, interpreting such results as valid estimates of the true causal effect of the treatment relies on strong assumptions. ? Our kitchen sink approach requires us to assume that our paltry or ill-measured set of controls fully controls for all selection bias, and that the functional form of the regression is properly specified. •Rather, the modern approach is to understand, and ideally control, the allocation of treatment (the selection process), as much as possible. This allows us to make the weakest possible assumptions about our ability to control for selection bias, and hence to generate the most reliable (i.e., believable) possible results. The so-called “gold standard” of impact evaluation: the RCT •The most direct way to control treatment assignment is to do it ourselves as researchers, in a way that minimizes the chances that treatment assignment is related to any characteristics of the treatment units. •The best way to do this is randomization. •In the simplest case, we run an RCT by taking a list of eligible participants, and randomizing them into two groups: treatment and control. •For example, list the participants in an Excel sheet, generate a new column with random numbers (e.g., “=rand()”) and then ‘sort’ on the randomlygenerated variable, taking the top 50% of observations in treatment, and the bottom 50% into control. RCTs are desirable for their design simplicity •This is particularly desirable because then we don’t need to rely on fancy econometrics (which often relies on various assumptions) to identify causal effects. We can just run the following, where X is now our randomly-assigned treatment variable (X =1 for treatment, X = 0 for control), and ‘b’ will be the true causal effect of the treatment: Y = a + b*X + e •So can display results in a simple table or bar chart, comparing a to b. •Can add controls if we want, to be more efficient by removing some of the variation in Y, but we don’t need it to deal with selection bias. •This means that RCTs rely on very weak assumptions. Many methods can help us estimate causal effects, but the stronger the assumptions it relies on, the less credible it is. RCTs rely on the weakest identifying assumptions. The “gold standard”: the RCT •The randomized control trial (= RCT) is considered the “gold standard” of impact evaluation, because it most convincingly deals with the selection bias problem. •For this reason, RCTs are advocated as long as they are ethical, feasible, and the cost justifies the knowledge created. Income 10 years of schooling 16 years of schooling RCTs aren’t the only credible way to do an impact evaluation •There are other credible ways to do an impact evaluation. •In economics these are known as “quasi-experimental” methods because they try to imitate what a pure experiment does – separating treatment from the characteristics of the treated units. •Common methods in applied economics include: • Regression discontinuity design (RDD) • Differences-in-differences (DiD) • Instrumental variables (IV) •While we can learn a lot from these methods, they all suffer drawbacks relative to RCTs: ? RDD only estimates a local average treatment effect ? DiD relies on assumptions about counterfactual trends ? IV relies on an untestable assumption, the exclusion restriction RCTs have become increasingly popular in (development) economics The “credibility revolution” •The increased use of RCTs has been at the forefront of a “credibility revolution” in empirical economics: an increase in the use of experiments (RCTs) and quasi-experimental methods, to study causal questions. •“Design-based” empirical work has become increasingly common – rely on clean, transparent, research designs that minimize heavy modeling assumptions. •Overall, empirical work has come to dominate theoretical work, though theory always has an important role to play. The “credibility revolution” in development economics •While Banerjee, Duflo, and Kremer officially won the Nobel Prize “for their experimental approach to alleviating global poverty,” unofficially I think that their main influence, especially Duflo, was in pushing a credibility revolution in development economics. •In the 1990s, development economics was dominated by theorical work, macro-empirical analyses (so-called cross-country regressions), and a strand of microeconomic empirical research by researchers like Angus Deaton and Christopher Udry. •Today the large majority of leading researchers are empiricists. •If not running RCTs, then conductin