Fossil Fuel Subsidies in Developing Countries
Salvador Ortigueira (University of Miami)
Salvador Ortigueira (University of Miami)
Fuel Subsidies
1. Fuel subsidies can be on-budget (explicit) or off-budget (implicit).
2. On-budget subsidies are created, for example, when budgetary resources are used to make direct cash transfers to a producer or a consumer, or when publicly owned refineries and oil marketing companies are mandated to sell below the cost of production and their losses are covered by budgetary funds.
3. Funding a supply of low-priced energy from the budget entails a reduction in public expenditure in other areas, higher taxes, or public borrowing.
Salvador Ortigueira (University of Miami)
Fuel Subsidies
1. Off-budget subsidies can can take the form of tax exemptions or non-reimbursement of losses incurred by state-owned energy companies.
2. The standard procedure to estimate the size of the subsidies is to calculate the difference between an international benchmark price and the domestic retail price
3. This price-gap measure captures the aggregate effect of many subsidies at once
Salvador Ortigueira (University of Miami)
Why Developing Countries Subsidize Fuel Consumption?
1. As an anti-poverty policy: To make fuel consumption accessible to low-income households
2. Oil-producing countries subsidize gasoline to share the wealth from natural resources across the population
3. Fuel subsidies are used to maintain employment, especially in periods of economic transition
4. To fight inflation
Salvador Ortigueira (University of Miami)
Several Countries
Linking Fossil Fuel and Electricity Policies: Subregional Experience 7
Figure 3: Global Subsidies for Direct Use and Power Generation Inputs, 2011
Subsidies for direct use
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Subsidies on primary energy resources such as coal, oil, and gas affect their use in the energy mix, investment in the power sector, and, ultimately, prices and the supply of electricity to wider groups of consumers. Coal is cheap and abundant in several South and Southeast Asian countries, especially India and Indonesia, and will remain a large share of the region’s energy mix. Coal is used to produce electricity for manufacturing and households; petroleum, particularly subsidized diesel, is used mainly for transportation. Natural gas exploration and production is rapidly changing the energy landscape in both regions. In Thailand and Malaysia, gas-fired power plants will be the predominant source of electricity generation for some time.
Many developing countries are trying to increase energy prices and reduce subsidies, but governments still heavily subsidize and regulate fossil fuels. Globally, subsidies for the direct use of oil products and gas (that is, when these fuels are used in transport and industry) are by far the largest component of subsidies. Subsidies for the direct use of oil products totaled $280 billion in 2011. By contrast, subsidies for oil as an input to electricity generation contributed only $28 billion (Figure 3). Subsidies for power generation inputs are also significant, at $65 billion for gas, $32 billion for coal, and $28 billion for oil.
In most countries, the state-owned institutions that provide fossil fuels and electricity have historically been supported by government subsidies and have not been required to earn commercial rates of return. In such cases, one immediate impact of fossil fuel subsidies on power sectors has been a shortage of capacity, because financially starved state-owned energy companies cannot invest and maintain infrastructure such as refineries or new generation capacity. Another impact has been to bias
Salvador Ortigueira (University of Miami)
Several Countries
3 Measuring Subsidies Subsidies on fossil fuel consumption are prevalent in developing countries and particularly high in oil- exporting countries (Table 3). Kemp (2014) notes that three-fourths of worldwide fuel consumption subsidies in 2012 stemmed from energy-exporting countries, and Organization of the Petroleum Exporting Countries (OPEC) members accounted for over half of the total (IEA 2014a). Today, such subsidies are nonexistent or small in most OECD countries, but are important in non- OECD ones—and production subsidies that seek to expand domestic supply are important in both (OECD, OECD-IEA Fossil Fuel Subsidies and Other Support).
Table 3: Share of Consumption Subsidies in the Full Cost of Supply, 2013 (%)
Country Average
subsidization rate Country Average
subsidization rate Venezuela 92.7 Ukraine 28.9 Algeria 77.5 Nigeria 28.8 Saudi Arabia 77.3 Pakistan 23.0 Iran 77.1 El Salvador 20.9 Libyan Arab Jamahiriya 76.7 Russian Federation 20.5 Turkmenistan 65.7 India 19.9 Egypt 61.2 Malaysia 15.6 Uzbekistan 58.7 Mexico 11.9 Iraq 53.3 Gabon 8.7 Ecuador 51.2 Ghana 8.5 Bolivia 44.1 Thailand 6.7 Angola 35.9 Viet Nam 4.3 Bangladesh 33.6 China, People’s Rep. of 2.6 Kazakhstan 32.8 Korea, Rep. of 0.2 Indonesia 31.3 Colombia 0.0 Argentina 29.6
Source: International Energy Agency online database. http://www.iea.org/subsidy/index.html
Fuel subsidies can be on-budget (explicit) or off-budget (implicit). On-budget subsidies are created, for example, when budgetary resources are used to make direct cash transfers to a producer or a consumer, or when publicly owned refineries and oil marketing companies are mandated to sell below the cost of production and their losses are covered by budgetary funds. Funding a supply of low-priced energy from the budget entails a reduction in public expenditure in other areas, higher taxes, or public borrowing. In contrast, off-budget subsidies are often “hidden” and difficult to calculate. Such subsidies
Salvador Ortigueira (University of Miami)
Several Countries © OECD/IEA 2016 Fossil Fuel Subsidy Reform in Indonesia and Mexico
Page | 17
Figure 4 • Value of fossil fuel subsidies by fuel in the top 25 countries, 2014
Notes: GDP = gross domestic product; MER = market exchange rates; UAE = United Arab Emirates.
Source: IEA (2015a), World Energy Outlook 2015.
The average rate of subsidisation, i.e. the ratio of the subsidy to the international reference price, also varies significantly from country to country. The total subsidisation rate among the countries identified as subsidising fossil fuel consumption is 21%, with the maximum being in Venezuela at 93%.
Fossil fuel subsidies in ten countries account for USD 364 billion or around three-quarters of the world total. Of the 25 countries with the largest subsidies, 10 are in the Middle East or North Africa – and almost all of them are oil or gas exporters. In fact IEA estimates reveal that fossil fuel subsidies are becoming increasingly concentrated in the major oil and gas-exporting countries. For example, the share of Middle East oil exporters in the world total has risen from 35% to 40% over the last four years. (IEA, 2015a)
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USD Billion
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Total subsidies as share of GDP (MER) (top axis)
Salvador Ortigueira (University of Miami)
Mexico
1. Mexico is an oil-producing country but currently imports about half of the gasoline demanded in the country
2. Gasoline pricing policy: A pre-determined crawling gas price fixed by the government at the beginning of each year
3. A gas subsidy emerges when the international gasoline price is above the pre-determined price level
4. The subsidy, which may produce a final price below its market price, is frequently justified on economic and political grounds
Salvador Ortigueira (University of Miami)
Mexico
Author's personal copy
motor vehicle fleet than advanced countries, which increases pollution (Harrington and McConnell, 2003). All of these features call for the calculation of an appropriate gasoline tax for LDCs.
The objective of this paper is to estimate the optimal gasoline tax for a representative middle-income country. For that purpose, we apply the methodology of Parry and Small (2005) to Mexico, a prominent oil-producing LDC that heavily subsidizes gasoline consumption. The advantage of this method is the decomposition of the second-best optimal fuel tax into several components, including those related to congestion, accidents, and air pollution. As previously mentioned, these negative externalities may in fact be more severe for the economies of LDCs than for developed economies.
Our results suggest an optimal gasoline tax of $1.90/gallon at 2011 prices. The (adjusted) Pigouvian tax is the largest portion of the tax, amounting to $1.62/gallon. The accident component explains approximately one-third of the Pigouvian tax, followed by distance-related pollution damage and congestion externalities. The Ramsey tax component, arising from a relatively inelastic fuel demand, contributes another $0.28/gallon.
The optimal gas tax in Mexico is larger than the estimate reported by PS (2005) for the US (even after updating their results at 2011 prices), and that of Lin and Prince (2009) for California, but lower than that estimated by Parry and Strand (2012) for Chile. In particular, PS (2005) report an optimal tax rate of $1.01/gallon for the US at 2000 prices. This estimate increases to $1.43/gallon at 2011 prices. Lin and Prince (2009) obtain a rate of $1.37/gallon for California at 2006 prices. Finally, Parry and Strand (2012) calculate a corrective fuel tax for Chile of $2.35 per gallon at 2006 prices.
To understand what accounts for the differences with respect to PS (2005), we change each one of the parameters that is different in Mexico than in the US, one at a time. We find that distance-related pollution damage and accident costs explain the majority of the differences. The presence of fuel subsidies (typical of oil-producing countries) explains approximately 20% of the differences. Perhaps surprisingly, the lower fuel efficiency attrib- uted to an older vehicle fleet does not explain a significant proportion of the differences in tax estimates.
Using the optimal gas tax estimate, we address the effects of such a tax across income deciles in Mexico. Contrary to the conventional wisdom, we find that the fuel tax is progressive. The intuition is simple: only 9% of the poorest households demand fuel because the majority of these households (87%) do not own a car. Conversely, 85% of the wealthiest households demand fuel because 91% own at least one car.4
This paper is structured as follows. Section 2 briefly describes the fuel pricing policy in Mexico. Section 3 outlines the model, and Section 4 presents the results. Section 5 compares the results to
those obtained for advanced economies and includes a sensitivity analysis. Section 6 provides concluding remarks.
2. How is the gasoline price set in Mexico?
Mexico is an oil-producing country but currently imports about half of the gasoline demanded in the country, because of capacity constraints. Because of an inappropriate gasoline pricing policy (a pre-determined crawling gas price fixed by the government at the beginning of each year), a subsidy emerges when the international petroleum price is above its pre-determined level, as has been the case most of the time since 2006 (see Fig. 1).5 This subvention, which may produce a final price below its market price, is frequently justified on political, i.e., populist, grounds.
On average, Mexico registers the second-lowest excise tax rate, after the USA, among a sample of representative OECD countries for the period 2001–2011 (see Fig. 2).6 For the period 2007–2011, the tax is in fact negative (a subsidy). This subsidy has cost the government an average of 1.2% of GDP over the period 2007–2011, an amount equivalent to the expenditures on poverty alleviation and public health care programs in the country. These large fiscal subsidies merit careful re-evaluation, especially in this present time of fiscal
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Fig. 1. Gasoline price, 2000–2013 (Mexican pesos per gallon). Sources: US Energy Information Administration and Mexico's Energy Information System. The US price is the weekly Gulf Coast regular conventional retail price. The Mexican price is the regular conventional retail price.
Fig. 2. Excise tax rate (pre-tax fuel price), average 2001–2011. Source: Author calculations from OECD-IEA, Energy Price and Taxes, Quarterly Statistics (2012).
(footnote continued) whereas the rate in high-income countries is the lowest (8.7 per 100,000). In addition, 80% of road traffic deaths occur in middle-income countries, which have 72% of the world population but only 52% of the world's registered vehicles. In fact, nearly 70% of road deaths occur in 13 countries, 12 of which are developing countries (including Mexico).
4 The source is Mexico's Household Income and Expenditure Survey (2010). Unfortunately, the survey does not provide further information that would help to explain the gap between fuel consumption and car ownership across households. The survey only registers whether the household owns a vehicle and monthly total expenditures on fuel; it does not provide information on vehicle use or driving patterns. This gap might be due to a combination of statistical errors and to people either deciding not to use a car (car pooling) or not being able to use a car because it is not working. It is common, especially for people in lower deciles, to own old broken-down cars with the expectation of putting them back into service in the future.
5 Since 2010, the Mexican government has increased the fuel price by $0.016/ gallon every month, which corresponds to an increase of $0.19/gallon per year.
6 US data are not included in Fig. 2 because the OECD-IEA, Energy Price and Taxes, Quarterly Statistics (2012) study does not report excise taxes for this country. Only total gasoline taxes (i.e., excise plus sales taxes) in the US of $0.11/l (the average for the period 2001–2011) are reported.
A. Antón-Sarabia, F. Hernández-Trillo / Energy Policy 67 (2014) 564–571 565
Salvador Ortigueira (University of Miami)
Mexico
Southwest Economy • Federal Reserve Bank of Dallas • Third Quarter 201312
}If the goal of the subsidies is to protect the lowest income brackets, the cost to do so is heavy.
the subsidy is applied per unit of fuel, the data show that 97 percent of the assistance went to the top 80 percent of income earners in Mexico.
This is in line with other countries. The International Energy Agency (IEA) estimated that out of the $409 billion spent globally in 2010 on all subsidies covering consumption of fossil fuels (oil, natural gas and coal as well as the electricity they produced), only $35 bil- lion, or 8 percent, reached the bottom 20 percent of the income distribution.
If the goal of the subsidies is to protect the lowest income brackets, the cost to do so is heavy. Of the $15.9 bil- lion Mexico spent on subsidies in 2011, roughly $15.4 billion went to higher income groups—in other words, it cost $15.4 billion to provide about $500 mil- lion in aid to Mexico’s poorest.
Costly Subsidies Mexico’s subsidies have been
expensive on an absolute dollar basis (Chart 3, left axis).2 While Mexico’s fuel subsidies in recent years were below the peak levels in 2008 (when subsidies exceeded $20 billion), they still exceeded $15 billion in 2011. Preliminary Mexican government data suggest that the 2012 total will be close to the cost in 2011.
Sometimes it’s useful to consider the size of a subsidy relative to gross domestic product (GDP). This method
helps illustrate how big a burden the subsidy might impose on the economy, taking into account the country’s ability to pay. As a share of GDP, Mexican fuel subsidies were at least 1 percent of GDP in four of the last five years for which data are available (Chart 3, right axis). By comparison, expenditures on education amounted to 3.5 percent of GDP in 2010; health spending, 2.8 percent; and pen- sions, 1.2 percent.
Relative to other countries, Mexico typically ranks high in terms of the subsidies’ dollar value. Mexico ranked seventh in such spending in 2011, ac- cording to IEA data. Only Saudi Arabia ($46.12 billion), Iran ($41.39 billion), India ($30.86 billion), Venezuela ($21.97 billion), Iraq ($20.35 billion) and China ($18.45 billion) spent more. However, on the basis of subsidies as a percentage of GDP, Mexico ranked relatively low—19th out of 33 countries in 2011 (Chart 4).
Other Negative Impacts Subsidies work by artificially reduc-
ing prices for fuel, making it relatively cheaper than other goods. Households and firms respond, changing their economic decisions. This introduces distortions in the economy that can hinder performance. For example, households may choose to consume an outsized amount of fuel and to consume less of other goods because of pricing,
Chart
3 Mexico’s Spending on Oil Subsidies Rises Again Billions of U.S. dollars Percent of GDP
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Salvador Ortigueira (University of Miami)
Brazil
11 Lessons Learned from Brazil’s Experience with Fossil-Fuel Subsidies and their Reform
Figure 3 Estimated petroleum subsidies or revenue in Brazil and revenue from the Contribuição de Intervenção no Domínio (CIDE) levy on imported petroleum products
Notes: Positive subsidies indicate that consumers were being subsidized (Petrobas was selling products below market values). Negative subsidy values (between 2005 and 2007) show net taxes for consumers.
Source: Calculations by the author.
2.6 Future subsidy levels A group of energy bills were sent by the government to the Congress in September 2009. The main objectives of these bills are to grant the government greater control of the oil sector, increase government oil revenues and enhance the government’s ability to use this revenue for public-policy purposes.
The bills propose to replace the current concession regime by production-sharing agreements. Under these arrangements, the government would receive a share of the oil produced and will use or trade it based on government objectives. A new oil company, 100 per cent state owned, would be created to govern the large oil reserves recently identified offshore. Petrobras will become the single operator for all future oil fields discovered.
The government also intends to create a special fund with its oil revenues. This fund would be used to finance several types of programs (social, environmental, regional, educational and technological, including the support of renewable energy technologies), as well as to assist the development of domestic suppliers of equipment and services for the oil industry.
So far, there is no indication of how the oil fund or the government’s share of the oil supply will be used. Lobbyists in Congress are actively pushing for the creation of new subsidies with the revenues from the oil fund, and the government offered clear signs that it intends to use oil as a major source of revenues for achieving its economic and social objectives.9
9 Ministério de Minas e Energia (Ministry of Mines and Energy): www.mme.gov.br
Subsidies and CIDE Levy
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Salvador Ortigueira (University of Miami)
South East Asia
A Guidebook to Fossil-Fuel Subsidy Reform for Policy-Makers in Southeast Asia p.17
FOSSIL-FUEL SUBSIDIES FOR ENERGY CONSUMERS IN SOUTHEAST ASIA
1
1.2 The Scale of Fossil-Fuel Subsidies for Consumers in Southeast Asia Governments in Southeast Asia subsidize different fuels to varying extents. As shown in Figure 1, according to the International Energy Agency (IEA), Indonesia subsidizes mostly petroleum products and electricity. Malaysia subsidizes all fuel types except for coal. The Philippines have largely removed all energy subsidies, but have preferential taxation provisions for some petroleum products, such as diesel. Thailand subsidizes all energy types, while the bulk of energy subsidies in Vietnam are in the electricity sector.
Figure 1 also shows that Southeast Asia’s subsidy costs fluctuate significantly year by year, regardless of the absolute volume of subsidization or the fuels being subsidized. This is because many subsidy mechanisms do not let domestic consumer prices fluctuate fully in response to international changes; consequentially, when the world price rises, the cost of the subsidy rises too. Figure 1 illustrates this by plotting the average international oil price. The cost of subsidies for oil, gas and coal tends to follow this indicator because world oil prices are used as an index for many gas prices in Asia, and gas prices are, in turn, linked to coal, though with coal prices being the least responsive of the three. Since fossil fuels are the main input for electricity generation in most countries, price changes affect electricity subsidies too.
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FIGURE 1 | STRUCTURE OF ENERGY SUBSIDIES IN INDONESIA, MALAYSIA, THE PHILIPPINES, THAILAND AND VIETNAM, 2007–2011
Source: IISD-GSI graphic interpretation of IEA (2012) subsidy estimates, derived with the price-gap method, and BP (2012) oil price data.
Salvador Ortigueira (University of Miami)
South East Asia
A Guidebook to Fossil-Fuel Subsidy Reform for Policy-Makers in Southeast Asiap.18
FOSSIL-FUEL SUBSIDIES FOR ENERGY CONSUMERS IN SOUTHEAST ASIA
1
1.3 Fiscal Burden and Opportunity Cost As illustrated in Figure 2, the estimated value of fossil-fuel subsidies has been above two per cent of GDP for most of Southeast Asia’s biggest subsidizing countries over the past five years. This can represent a significant fiscal burden for net energy-importing countries that set a fixed price of fuel. In other cases—such as in net exporting countries, where domestic reserves are sold in reference to production cost, or when power sector subsidies are paid for through lack of investment in infrastructure—no fiscal cost is recorded. These off-budget subsidies still represent an opportunity cost, however, that is as real as their on-budget counterparts.
The opportunity cost of fossil-fuel subsidies is the money that is not spent on other priorities such as public transport and infrastructure, or improving health care and education systems. As an illustration, Figure 2 shows fossil-fuel subsidies compared to the value of budgetary deficits and surpluses. The subsidies were larger than the budgetary deficits of Indonesia in 2007–2011; Thailand in 2008, 2010 and 2011; and Vietnam in 2007 and 2011. In all other cases, the subsidies were equal to a considerable share of budgetary deficits.
FIGURE 2 | ENERGY SUBSIDIES AND BUDGETARY DEFICIT OR SURPLUS AS A PERCENTAGE OF GDP IN INDONESIA, MALAYSIA, THE PHILIPPINES, THAILAND AND VIETNAM IN 2007–2010.
Source: IISD-GSI calculations based on IEA (2012) subsidy estimates, derived using the price-gap method, and ADB (2012) data on GDP and budgetary deficits and surpluses.
Fossil-fuel subsidies Budgetary deficit Budgetary surplus
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Salvador Ortigueira (University of Miami)
South East Asia
Fossil Fuel Subsidies in Asia: Trends, Impacts, and Reforms16
Producer subsidies may have an important impact on investment decisions, and many of the subsidies aimed at producers provided a quasi-consumer subsidy. For example, state-owned electricity suppliers in Indonesia operate at a loss due to controlled consumer prices. The government provides discounted credit and loan guarantees to help finance these losses and infrastructure investment. However, credit subsidies would be unnecessary if electricity providers were permitted to sell electricity at the long-term cost plus profit. A similar situation occurs in upstream sectors in India (coal) and Thailand (natural gas for vehicles) where producers are required to sell product at below-market prices, but are partially compensated through measures that reduce their costs of production or supply.
The detailed inventories of fossil fuel subsidies provide the most comprehensive estimates of subsidies to date in India, Indonesia, and Thailand. The information from these inventories improves transparency on the true level of government finances being used to support fossil fuel consumption and production. Nonetheless, the complex nature of fuel subsidies and their questionable merit warrant an analysis of the potential impacts of subsidy reform.
Figure 7: Breakdown of Total Consumer Subsidies, 2012
Electricity Natural gas Coal Petroleum
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Salvador Ortigueira (University of Miami)
South East Asia
Fossil Fuel Subsidies in Asia: Trends, Impacts, and Reforms32
received only 18% of direct subsidy benefits and 19% of indirect benefits. By contrast, the top 20% received 48% of direct benefits and 42% of indirect benefits (Del Granado, Coady, and Gillingham 2012). However, such assessments tend to assume that the benefits of fuel subsidies are transferred to households, whereas in reality, it is often the case that low prices do not reach the intended beneficiaries because of diversion, leakage, and smuggling.
a The study reviewed data from 2005 household expenditure surveys from Bangladesh, Cambodia, India, Indonesia, Pakistan, Thailand, and Viet Nam. The precise proportions of energy products being consumed are likely to have changed since this time, given increasing world oil prices and efforts from a number of countries to encourage the use of LPG, but there is no reason to suppose that the broad proportions have changed significantly.
b A “universal” fossil fuel subsidy is one that is available to the entire population or a large majority of the population, without any attempt to target it to users defined as being “in need." The large majority of fossil fuel subsidies in Asia are universal in nature.
Box 2 continued
Figure 9: Magnitude of Fossil Fuel Subsidies Compared with Social Assistance, 2012
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Salvador Ortigueira (University of Miami)
Are Gasoline Subsidies the Right Policy?Fossil Fuel Subsidy Reform in Indonesia and Mexico © OECD/IEA 2016
Page | 12
Figure 1 • Potential unintended effects of fossil fuel subsidies
Note: CO2 = carbon dioxide.
Source: IEA (2010), World Energy Outlook 2010, www.worldenergyoutlook.org/media/weo2010.pdf.
• Increased price volatility: Fossil fuel subsidies also exacerbate energy price volatility on global markets, by dampening normal demand responses to changes in international prices.
• Black marketeering, smuggling and adulteration: Fossil fuel subsidies may also encourage black marketeering, smuggling and fuel adulteration in the case of oil products, which are easy to transport and store. Fuel shortages and flourishing black markets with high prices are common in countries with where low official prices constrain supply. In certain countries, subsidised kerosene intended for household cooking and lighting is diverted as a diesel substitute due to wide price differentials. Smuggling can also arise, since an incentive is created to sell subsidised products in neighbouring countries where prices are unsubsidised and, therefore, higher. This has been an issue for years in many parts of the world, particularly in Southeast Asia, Africa and the Middle East. The effect in subsidising countries is a substantial financial transfer to smugglers, while recipient countries experience losses from uncollected taxes and excise duties, due to reduced sales in the legitimate market. Removing subsidies would eliminate incentives both to adulterate fuels and to smuggle them across borders. In exporting countries, subsidies reduce the availability of fuels for export by driving up domestic demand. In all countries, fossil fuel subsidies ultimately undermine economic competitiveness and growth.
• Environmental effects: Fossil fuel subsidies can have varying environmental effects. In some instances, for example where subsidies enable poor communities to switch from the traditional use of biomass to modern fuels, they can have positive implications for the local environment by minimising deforestation and household air pollution. In the vast majority of cases, however, fossil fuel subsidies are counterproductive in reaching local and global environmental goals. Subsidised energy prices dampen incentives for consumers to use energy more efficiently, resulting in higher consumption and GHG emissions than would otherwise occur.
• Barriers for clean energy investments: Fossil fuel subsidies undermine the development and commercialisation of renewable energy and other technologies that could become more economically attractive. Even marginal shifts from fossil fuels to renewable energy could help to accelerate the learning effect for renewables and cause unit production costs to decline.
Encourage wasteful consumption
Hasten the decline of exports
Encourage fuel adulteration and smuggling
Discourage investment in energy infrastructure
Disproportionally benefit the middle class and rich
Increase CO emissions and exacerbate local pollution
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high prices
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Alleviate poverty
Promote economic development
Salvador Ortigueira (University of Miami)
Fuel Subsidies Are Regressive
12
targeted subsidy. Figure 6 presents the shares of the total benefits that each income group
would receive from subsidized fuel prices, separately for the total, direct, and indirect welfare
impact as well as for the direct benefit for gasoline, LPG, and kerosene. On average, the top
income quintile receives more than six times more in total subsidies than the bottom quintile.
The concentration of subsidy benefits in the hands of the top income groups is even more
pronounced for gasoline and LPG, where the top income quintile receives 27 and 12 times that of
the bottom quintile, respectively. Although the poorest households receive a much higher share
of kerosene subsidies than for other fuel subsidies, there is still substantial leakage of kerosene
subsidies to higher-income groups. Annex Table 1.6 presents the shares of the total benefits that
each income group would receive from subsidized fuel prices, disaggregated by region.
Figure 6. Distribution of Subsidy Benefits by Income Group (Percent of total subsidy benefit)
Source: Authors’ calculations based on results from reviewed studies.
Note: LPG = liquefied petroleum gas. The indirect impact is the welfare impact of higher
prices of goods and services due to an increase in the price of diesel.
The substantial leakage of subsidy benefits to the top income groups means that universal fuel
subsidies are an extremely costly approach to protecting the welfare of poor households. For
example, if we take the poorest 40 percent of households to be the target ‘‘poor” group, the cost
to the budget of transferring one dollar to this group via gasoline subsidies is about 14 dollars.
This occurs because nearly 93 out of every 100 dollars of gasoline subsidy ‘‘leaks” to the top
three quintiles. These leakages are higher in Africa and in Asia and Pacific, where poor
households’ use of gasoline and LPG is comparatively lower than in other regions (Figure 7,
Annex Table 1.6). Even for kerosene, this cost-benefit ratio is about 3 dollars.
Indirect impactTotal impact Direct impact
Gasoline KeroseneLPG
Bottom 2 3 4 Top Quintiles
Salvador Ortigueira (University of Miami)
World Bank Country Classification
Salvador Ortigueira (University of Miami)
January 15, 2017
Salvador Ortigueira (University of Miami)
FY17 Country Classification
1. As of 1 July 2016, the World Bank updated its classification of countries using their GNI per capita in 2015
1.1 Low-income economies are defined as those with a GNI per capita of $1,025 or less
1.2 Lower middle-income economies are those with a GNI per capita between $1,026 and $4,035
1.3 Upper middle-income economies are those with a GNI per capita between $4,036 and $12,475
1.4 High-income economies are those with a GNI per capita of $12,476 or more.
Salvador Ortigueira (University of Miami)
Changes with respect to previous year classification
Economy Old group New group
Cambodia Low Lower middle Equatorial Guinea High Upper middle Georgia Lower middle Upper middle Guyana Lower middle Upper middle Mongolia Upper middle Lower middle Russian Federation High Upper middle Senegal Lower middle Low Tonga Upper middle Lower middle Tunisia Upper middle Lower middle Venezuela, RB High Upper middle
Salvador Ortigueira (University of Miami)
Is GDP a Good Measure of Well-being?
Salvador Ortigueira (University of Miami)
January 12, 2017
Salvador Ortigueira (University of Miami)
Introduction
I Gross Domestic Product (GDP) is the market value of all final goods and services produced within a country in a given time period
I Typically, GDP is used as a measure of economic well-being in cross-country and across periods comparisons
I Many economists have criticized the use of GDP as a measure of economic well-being as it abstracts from
I Leisure
I Mortality
I Inequality, etc.
I So the question is: How good is GDP as a measure of economic well-being?
Salvador Ortigueira (University of Miami)
Two attempts at measuring well-being
I Objective: Construct a welfare measure that combines a more comprehensive list of variables associated with well-being
I Two attempts are: I The United Nations Human Development Index (HDI): This
index combines income, life expectancy and literacy. It first construct sub-indexes each variable and then averages them
I A consumption-equivalent measure of well-being. It combines data on consumption, leisure, inequality and mortality
Salvador Ortigueira (University of Miami)
The consumption-equivalent measure of well-being
I Example: Let us compare the US and France
I The consumption-equivalent measure of well-being in France is equal to the proportion of consumption in the US, given the US values of leisure, mortality and inequality, that would deliver the same expected utility as in France.
Salvador Ortigueira (University of Miami)
The consumption-equivalent measure of well-being
Let us start by looking at summary statistics for the three components of this measure
I Consumption
I Leisure
I Mortality rates
Salvador Ortigueira (University of Miami)
Within-country consumption inequality 2436 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
Consumption.—Figure 1 reports the standard deviation of log consumption across people in our household survey countries. We divide household expenditures equally across people in each household, and add per capita government consumption in the same year from the Penn World Tables 8.0 (Feenstra, Inklaar, and Timmer 2015). We use sampling weights and discount using US survival rates by age, analogous to the way we construct the mean of log consumption in equation (17). The resulting inequality is highest in South Africa, Brazil, and Mexico. Inequality is lower in France, Italy, and the United Kingdom than in the United States.
Leisure.—Figure 2 summarizes annual hours worked per person in our house- hold surveys. Figure 3 reports the standard deviation across people of annual hours worked.12 Hours worked are substantially lower in France, Italy, Spain, and the United Kingdom than in the United States, as has been widely noted. More novel, inequality of hours worked is lower in these same countries than in the United States.
Mortality Rates.—Figure 4 presents life expectancy in years from the World Health Organization for our baseline household survey years. It ranges from 50 in Malawi, the poorest country, to above 75 in the richest countries.
B. Calibration
To implement our welfare calculations, we need to specify the baseline utility function. (In Section IV we will explore a range of robustness checks to our choices
12 Parente, Rogerson, and Wright (2000) argue that barriers to capital accumulation explain some of the varia- tion in market hours worked. Like us, they emphasize that the gain in home production can partially offset the loss in market output. Prescott (2004) attributes some of the OECD differences in hours worked to differences in tax rates, as do Ohanian, Raffo, and Rogerson (2008).
Figure 1. Within-Country Inequality
Notes: The standard deviation of log consumption within each economy is measured from the household surveys listed in Table 1. We use survey-specific sampling weights and US survival rates across ages using an analog of equation (17), with no discounting or growth.
1/64 1/32 1/16 1/8 1/4 1/2 1 0.4
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Salvador Ortigueira (University of Miami)
Hours worked 2437JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
here.) Following the macro literature, we assume utility from leisure takes a form that implies a constant Frisch elasticity of labor supply (that is, holding the mar- ginal utility of consumption fixed, the elasticity of labor supply with respect to the wage is constant). Since labor supply in our setting is 1 − ℓ , in terms of the utility function in equation (4) this gives v(ℓ) = − θϵ _
1 + ϵ (1 − ℓ) 1+ϵ _ ϵ , where ϵ denotes the
Frisch elasticity. This leaves five parameters to be calibrated: the growth rate g , the
Figure 2. Annual Hours Worked across Countries
Notes: The measure shown here of annual hours worked per capita is computed from the household surveys noted in Table 1, using survey-specific sampling weights and US survival rates across ages as in equation (16), with no time discounting.
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France
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Indonesia
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Italy
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US South Africa
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S ta
n d
a rd
d e
vi a
tio n
o f
a n
n u
a l h
o u
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e d
Figure 3. Inequality in Annual Hours Worked
Note: See notes to Figure 2.
Salvador Ortigueira (University of Miami)
Inequality in hours worked
2437JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
here.) Following the macro literature, we assume utility from leisure takes a form that implies a constant Frisch elasticity of labor supply (that is, holding the mar- ginal utility of consumption fixed, the elasticity of labor supply with respect to the wage is constant). Since labor supply in our setting is 1 − ℓ , in terms of the utility function in equation (4) this gives v(ℓ) = − θϵ _
1 + ϵ (1 − ℓ) 1+ϵ _ ϵ , where ϵ denotes the
Frisch elasticity. This leaves five parameters to be calibrated: the growth rate g , the
Figure 2. Annual Hours Worked across Countries
Notes: The measure shown here of annual hours worked per capita is computed from the household surveys noted in Table 1, using survey-specific sampling weights and US survival rates across ages as in equation (16), with no time discounting.
1/64 1/32 1/16 1/8 1/4 1/2 1 500
550
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950
Brazil
China
Spain
France
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1/64 1/32 1/16 1/8 1/4 1/2 1 700
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China
Spain
France
UK
Indonesia
India
Italy
Mexico
Malawi Russia
US South Africa
GDP per person
S ta
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a rd
d e
v ia
ti o
n o
f a
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u a
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o u
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Figure 3. Inequality in Annual Hours Worked
Note: See notes to Figure 2. Salvador Ortigueira (University of Miami)
Life expectancy 2438 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
discount factor β , the Frisch elasticity ϵ , the utility weight on leisure or home pro- duction θ , and the intercept in flow utility u – .
We choose a common growth rate of 2 percent per year. An alternative would be to try to forecast future growth rates for each country, but such forecasts would have very large standard errors, particularly since we would need forecasts for every year over the next century. With an annual real interest rate of 4 percent in mind, we set the discount factor to β = 0.99 . Recall that there is already additional discounting inherent in the expected utility calculation because of mortality. A 4 percent real interest rate is consistent with the standard Euler equation with log preferences, 2 percent consumption growth, roughly 1 percent discounting for mortality, and 1 percent from the discount factor.
Surveying evidence such as Pistaferri (2003), Hall (2009a, b) suggests a bench- mark value for the Frisch elasticity of 0.7 for the intensive (hours) margin and 1.9 for the extensive and intensive margins combined. Chetty (2012) reconciles micro and macro estimates of the Frisch elasticity and recommends a value of 0.5 or 0.6 for the intensive margin. We consider a Frisch elasticity of 1.0 for our benchmark calibration, which implies that the disutility from working rises with the square of the number of hours worked. As we discuss in the robustness section, the results are not sensitive to this choice.
To get the weight on the disutility from working, θ , recall that the first-order condition for the labor-leisure decision is u ℓ / u c = w(1 − τ) , where w is the real wage and τ is the marginal tax rate on labor income. Our functional forms then imply θ = w (1 − τ) (1 − ℓ) −1/ϵ /c . For our benchmark calibration, we assume this first-order condition holds for the average prime-age worker (25–55 years old) in the US Consumer Expenditure Survey (CE) in 2006. We take the marginal tax rate in the United States from Barro and Redlick (2011), who report a value of 0.353 for 2006. Taking into account the ratio of earnings to consumption and average leisure
1/64 1/32 1/16 1/8 1/4 1/2 1 GDP per person
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Spain France
UK
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US
South Africa L if e
e x p
e c ta
n c y
Figure 4. Life Expectancy
Note: Life expectancy at birth in each country is measured as the sum over all ages of the probability of surviving to each age, using life tables from the World Health Organization.
Salvador Ortigueira (University of Miami)
Welfare across countries and over time 2440 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
welfare measures 22 percent higher than their incomes. The remaining countries, in contrast, have welfare levels that are typically 25 to 50 percent below their incomes. The way to reconcile these large deviations with the high correlation between wel- fare and income is that the “scales” are so different. Incomes vary by more than a factor of 64 in our sample, i.e., 6,300 percent, whereas the deviations are on the order of 25 to 50 percent.
KEY POINT 2: Average Western European living standards appear much closer to those in the United States when we take into account Europe’s longer life expec- tancy, additional leisure time, and lower levels of inequality.
Figure 5. Welfare and Income across Countries
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South Africa
GDP per person (US = 1)
GDP per person (US = 1)
W elf
are , λ
Panel A. Welfare and income are highly correlated at 0.98
1/64 1/32 1/16 1/8 1/4 1/2 1
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Panel B. But this masks substantial variation in the ratio of λ to GDP per capita
Salvador Ortigueira (University of Miami)
Welfare across countries 2442 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
penalized less.15 As we will show, this is a key place where the equivalent variation differs from the compensating variation. The compensating variation weights differ- ences in mortality by US flow utility. In the robustness section, we’ll see this leads to much larger welfare differences.
A second reason that welfare is lower than income in several countries is that average consumption—as a share of income—is low relative to the United States. Utility depends on consumption, not income. Of course, an offsetting effect is that the low consumption share may raise consumption in the future. To the extent that countries are close to their steady states, this force is already incorporated in our calculation. However, in countries with upward trends in their investment rates, our calculation will understate steady-state welfare. China is an obvious candidate for this qualification, though correcting for this has a modest effect.16
15 Table A3 in the online Appendix reports the implied value of life in each of our 13 countries. 16 See Table 8 of Jones and Klenow (2010).
Table 2—Welfare across Countries
Decomposition
Welfare λ Income log ratio Life exp. C/Y Leisure Cons. ineq.
Leis. ineq.
US 100.0 100.0 0.000 0.000 0.000 0.000 0.000 0.000 77.4 0.897 877 0.538 1,091
UK 96.6 75.2 0.250 0.086 −0.143 0.073 0.136 0.097 78.7 0.823 579 0.445 826
France 91.8 67.2 0.312 0.155 −0.152 0.083 0.102 0.124 80.1 0.790 535 0.422 747
Italy 80.2 66.1 0.193 0.182 −0.228 0.078 0.086 0.075 80.7 0.720 578 0.421 905
Spain 73.3 61.1 0.182 0.133 −0.111 0.070 0.017 0.073 79.1 0.786 619 0.541 904
Mexico 21.9 28.6 −0.268 −0.156 −0.021 −0.010 −0.076 −0.005 74.2 0.879 906 0.634 1,100
Russia 20.7 37.0 −0.583 −0.501 −0.248 0.035 0.098 0.032 67.1 0.733 753 0.489 1,027
Brazil 11.1 17.2 −0.436 −0.242 0.004 0.005 −0.209 0.006 71.2 0.872 831 0.724 1,046
S. Africa 7.4 16.0 −0.771 −0.555 0.018 0.054 −0.283 −0.006 60.9 0.887 650 0.864 1,093
China 6.3 10.1 −0.468 −0.174 −0.311 −0.016 0.048 −0.014 71.7 0.658 888 0.508 1,093
Indonesia 5.0 7.8 −0.445 −0.340 −0.178 −0.001 0.114 −0.041 67.2 0.779 883 0.445 1,178
India 3.2 5.6 −0.559 −0.440 −0.158 −0.019 0.085 −0.028 62.8 0.785 918 0.438 1,143
Malawi 0.9 1.3 −0.310 −0.389 0.012 −0.020 0.058 0.028 50.4 0.923 934 0.533 997
Notes: The table shows the consumption-equivalent welfare calculation based on equation (19). See Table 1 for sources and years. The second line for each country shows life expectancy, the ratio of consumption to income, annual hours worked per capita, the standard deviation of log consumption, and the standard deviation of annual hours worked, all computed from the cross-sectional micro data, with no discounting or growth.
Salvador Ortigueira (University of Miami)
Welfare across countries 2442 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
penalized less.15 As we will show, this is a key place where the equivalent variation differs from the compensating variation. The compensating variation weights differ- ences in mortality by US flow utility. In the robustness section, we’ll see this leads to much larger welfare differences.
A second reason that welfare is lower than income in several countries is that average consumption—as a share of income—is low relative to the United States. Utility depends on consumption, not income. Of course, an offsetting effect is that the low consumption share may raise consumption in the future. To the extent that countries are close to their steady states, this force is already incorporated in our calculation. However, in countries with upward trends in their investment rates, our calculation will understate steady-state welfare. China is an obvious candidate for this qualification, though correcting for this has a modest effect.16
15 Table A3 in the online Appendix reports the implied value of life in each of our 13 countries. 16 See Table 8 of Jones and Klenow (2010).
Table 2—Welfare across Countries
Decomposition
Welfare λ Income log ratio Life exp. C/Y Leisure Cons. ineq.
Leis. ineq.
US 100.0 100.0 0.000 0.000 0.000 0.000 0.000 0.000 77.4 0.897 877 0.538 1,091
UK 96.6 75.2 0.250 0.086 −0.143 0.073 0.136 0.097 78.7 0.823 579 0.445 826
France 91.8 67.2 0.312 0.155 −0.152 0.083 0.102 0.124 80.1 0.790 535 0.422 747
Italy 80.2 66.1 0.193 0.182 −0.228 0.078 0.086 0.075 80.7 0.720 578 0.421 905
Spain 73.3 61.1 0.182 0.133 −0.111 0.070 0.017 0.073 79.1 0.786 619 0.541 904
Mexico 21.9 28.6 −0.268 −0.156 −0.021 −0.010 −0.076 −0.005 74.2 0.879 906 0.634 1,100
Russia 20.7 37.0 −0.583 −0.501 −0.248 0.035 0.098 0.032 67.1 0.733 753 0.489 1,027
Brazil 11.1 17.2 −0.436 −0.242 0.004 0.005 −0.209 0.006 71.2 0.872 831 0.724 1,046
S. Africa 7.4 16.0 −0.771 −0.555 0.018 0.054 −0.283 −0.006 60.9 0.887 650 0.864 1,093
China 6.3 10.1 −0.468 −0.174 −0.311 −0.016 0.048 −0.014 71.7 0.658 888 0.508 1,093
Indonesia 5.0 7.8 −0.445 −0.340 −0.178 −0.001 0.114 −0.041 67.2 0.779 883 0.445 1,178
India 3.2 5.6 −0.559 −0.440 −0.158 −0.019 0.085 −0.028 62.8 0.785 918 0.438 1,143
Malawi 0.9 1.3 −0.310 −0.389 0.012 −0.020 0.058 0.028 50.4 0.923 934 0.533 997
Notes: The table shows the consumption-equivalent welfare calculation based on equation (19). See Table 1 for sources and years. The second line for each country shows life expectancy, the ratio of consumption to income, annual hours worked per capita, the standard deviation of log consumption, and the standard deviation of annual hours worked, all computed from the cross-sectional micro data, with no discounting or growth.
Salvador Ortigueira (University of Miami)
Main conclusions so far
I GDP per person is an excellent indicator of welfare across the broad range of countries: the two measures have a correlation coefficient of 0.98. Nevertheless, for any given country, the difference between the two measures can be important. Across the 13 countries analyzed, the median deviation is about 35 percent.
I Average Western European living standards appear much closer to those in the United States when we take into account Europe’s longer life expectancy, additional leisure time, and lower levels of inequality.
I Many developing countries, including all eight of the non-European countries in the sample, are poorer (in welfare) than incomes suggest because of a combination of shorter lives, low consumption shares, and extreme inequality.
Salvador Ortigueira (University of Miami)
Welfare and income growth 2444 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
person rose from 810 to 889 between 1984 and 2006. We estimate that this falling leisure reduced consumption-equivalent welfare growth by about a tenth of a per- centage point per year. According to the CE, consumption inequality rose, reducing growth by another 24 basis points.17 Finally, rising leisure inequality reduces US welfare growth another 8 basis points. Taken together, these three channels reduce consumption-equivalent welfare growth in the United States by 42 basis points per year.
Mexico and Italy exhibit similar patterns. Falling leisure reduces welfare growth by 0.17 percentage points per year in Italy and 0.23 percentage points per year in
17 The CE displays a relatively small increase in consumption inequality, as emphasized by Krueger and Perri (2006). According to Aguiar and Bils (2015), savings and Engel curves in the CE suggest that consumption inequal- ity rose as much as income inequality in the United States over this period.
Figure 6. Welfare and Income Growth (Percent)
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Panel A. The correlation between welfare growth and income growth is 0.97
Panel B. The median absolute value of the difference between welfare and income growth is 0.95 percentage points
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Salvador Ortigueira (University of Miami)
Welfare and income growth 2445JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
Mexico. Rising consumption and leisure inequality, combined, reduce growth by 0.46 and 0.30 percentage points per year in Italy and Mexico. The sum of these three forces is therefore about 0.63 percentage points per year in Italy and 0.53 percentage points per year in Mexico.
IV. Robustness
Here we gauge robustness to alternative assumptions, such as about the utility function. Table 4 shows that the gap we find between welfare and income is quite robust. More detailed results—including decompositions for France, China, and Indonesia—are available in the online Appendix.
The second row of Table 4 indicates that, if we do not discount or incorporate growth, the differences between welfare and income are somewhat smaller than in the baseline case (median absolute deviation between welfare and income of 30 percent rather than 35 percent). The reason is that life expectancy differences are less important if consumption is not growing over the life cycle (a bigger effect at 2 percent per year than the pure time discounting of 1 percent per year). If we retain growth but discount time more heavily at 4 percent per year rather than 1 percent per year, as shown in the third row, the median gap between welfare and income shrinks a little further to 28 percent.
Table 3—Welfare Growth
Decomposition
Welfare growth
Income growth Diff
Life exp. c/y Leis.
Cons. ineq.
Leis. ineq.
Russia 8.10 9.23 −1.13 0.93 −1.53 −0.29 −0.02 −0.22 (1998–2007) 65.5, 67.1 0.842, 0.745 707, 801 0.469, 0.498 997, 1,043 Brazil 4.63 3.71 0.92 1.54 −0.84 −0.06 0.06 0.23 (2003–2008) 71.2, 72.9 0.865, 0.829 845, 854 0.722, 0.720 1,050, 1,021 UK 4.42 3.12 1.30 1.16 0.12 −0.01 −0.02 0.05 (1985–2005) 75.4, 78.7 0.793, 0.827 588, 596 0.391, 0.447 860, 832 India 4.08 4.05 0.03 1.14 −1.04 0.04 −0.13 0.02 (1983–2005) 57.6, 62.8 0.973, 0.768 964, 952 0.416, 0.429 1,156, 1,149 France 3.15 2.15 1.00 1.04 0.10 −0.05 −0.16 0.07 (1984–2005) 77.1, 80.1 0.782, 0.790 480, 534 0.391, 0.422 793, 747 US 3.09 2.11 0.98 0.89 0.51 −0.10 −0.24 −0.08 (1984–2006) 75.0, 77.4 0.812, 0.892 810, 889 0.508, 0.539 1,054, 1,094 Italy 2.73 2.02 0.72 1.33 0.03 −0.17 −0.24 −0.22 (1987–2006) 76.6, 80.7 0.728, 0.719 410, 587 0.382, 0.421 782, 909 Indo. 2.65 0.39 2.25 1.43 0.81 0.18 −0.16 −0.00 (1993–2006) 62.3, 67.2 0.705, 0.780 976, 912 0.421, 0.445 1,188, 1,193 Mexico 1.87 1.05 0.82 1.09 0.26 −0.23 −0.16 −0.14 (1984–2006) 70.8, 74.2 0.838, 0.872 754, 909 0.663, 0.631 1,045, 1,101 Average 3.86 3.09 0.77 1.17 −0.17 −0.08 −0.12 −0.03 Averag e ∗ 3.14 2.13 1.02 1.15 0.11 −0.05 −0.16 −0.04 Notes: The table shows a decomposition for average annual consumption-equivalent welfare growth based on equa- tion (20). Years are shown in parentheses. Average denotes the average across the nine countries, while Average ∗ excludes Russia and Brazil. The second line for each country displays the raw data on life expectancy, the ratio of consumption to income, annual hours worked per capita, the standard deviation of log consumption, and the stan- dard deviation of annual hours worked, for the start and ending year, computed with no discounting or growth.
Salvador Ortigueira (University of Miami)
Main conclusion on welfare and income growth
I Welfare growth averages 3.1 percent between the 1980s and mid-2000s, versus income growth of 2.1 percent. A boost from rising life expectancy of about 1 percentage point per year accounts for the difference.
Salvador Ortigueira (University of Miami)
Welfare and income across countries: Macro data 2451JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
also low in Singapore and South Korea, further reducing welfare relative to income. Working hard and investing for the future are well-established means of raising GDP. Nevertheless, these approaches have costs that are not reflected in GDP.
Botswana and South Africa.—According to GDP per capita, these are relatively rich developing countries with about 20 percent of US income. AIDS, however,
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Angola
Albania
Bangladesh
Bahrain Bahamas
Bosnia/Herz.
Brazil
Botswana
Chile
Comoros
Egypt Fiji
France
Gabon
H.K.
Honduras
Ireland
Iraq
Iceland
Israel Italy
Jamaica
Kyrgyzstan
S. Korea Kuwait
Lao
Luxembourg
Macao
Moldova
Madagascar
Mali Malawi
Malaysia
Namibia
Niger
Nigeria
Norway
Oman
Poland
Portugal Qatar
Saudi Arabia
Singapore
Sao Tome/Principe
Slovakia
Sweden
Togo
Turkmenistan
Uganda
United States
Vietnam South Africa
Zambia
Zimbabwe
Costa Rica
GDP per person (US = 1)
GDP per person (US = 1)
We lfa
re, λ
Panel A. Welfare and income are highly correlated at 0.96
0
0.5
1
1.5
Angola
Albania
Armenia
Australia Austria
Bangladesh
Bahrain
Bahamas
Bosnia/Herz.
Bolivia
Barbados
Botswana
C. Afr. Rep. Switzerland
Chile
China
Cote dIvoire Cameroon
Congo
Colombia
Comoros Costa Rica
Cyprus
Denmark Egypt
Spain
Estonia
Ethiopia
Fiji
France
Gabon
U.K.
Georgia
Guinea
Greece
Guatemala
Hong Kong
Honduras
Croatia
Indonesia Ireland
Iran
Iceland
Israel Jamaica Jordan
Kenya
Kyrgyzstan
S. Korea
Kuwait
Liberia
Saint Lucia
Luxembourg Latvia
Macao
Morocco Mexico
Malta
Malawi
Namibia
Niger
Nigeria
Norway
Oman
Peru
Poland
Portugal
Qatar
Russian Fed.
Saudi Arabia
Sudan
Singapore
Sierra Leone
Serbia Sao Tome/P.
Sweden
Chad
Togo
U.S.
St. Vincent
Venezuela
South Africa
Zambia
Zimbabwe
Th e r
ati o o
f w elf
are to
in co
me
Panel B. But this masks substantial variation in the ratio of λ to GDP per capita. The mean absolute deviation from unity is about 27%
Liberia
Figure 7. Welfare Using Macro Data, 2007
Salvador Ortigueira (University of Miami)
Welfare and income across countries: Macro data 2453JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
AIDS in Africa.—Young (2005) pointed out that AIDS was an humanitarian trag- edy in Africa, but that it might boost GDP per worker by raising capital per worker. Our welfare measure provides one way of adding these two components together to measure the net cost. As Young suspected, the net cost proves to be substantial. Botswana loses the equivalent of 1.1 percentage points of consumption growth from seeing its life expectancy fall from 60.5 to 52.1 years, similar to the loss in South Africa. Botswana’s growth rate falls from one of the fastest in the world at 6.27 per- cent to the much more modest 2.94 percent. Already poor, sub-Saharan Africa falls
Table 7—Welfare across Countries in 2007: Macro Data
Welfare λ Per capita
income log ratio
Decomposition
Country LifeExp C/Y Leisure C ineq. United States 100.0 100.0 0.000 0.000 0.000 0.000 0.000
77.8 0.845 836 0.658
Sweden 91.2 79.4 0.139 0.181 −0.186 0.010 0.135 80.9 0.701 807 0.404
France 91.1 70.3 0.259 0.176 −0.085 0.063 0.106 80.8 0.776 629 0.471
Japan 82.6 71.3 0.147 0.265 −0.154 −0.028 0.063 82.5 0.724 912 0.554
Norway 81.0 112.8 −0.331 0.148 −0.598 0.019 0.100 80.4 0.464 780 0.483
Germany 77.4 74.4 0.039 0.098 −0.195 0.047 0.089 79.5 0.695 687 0.506
Ireland 69.6 96.4 −0.325 0.069 −0.454 −0.022 0.082 79.0 0.536 896 0.519
Hong Kong 59.0 83.4 −0.345 0.239 −0.433 −0.151 −0.000 82.4 0.548 1,194 0.658
Singapore 56.7 117.1 −0.726 0.139 −0.685 −0.180 −0.000 80.4 0.426 1,251 0.658
South Korea 45.3 58.3 −0.252 0.078 −0.290 −0.116 0.076 79.3 0.632 1,120 0.531
Argentina 21.8 26.2 −0.181 −0.121 −0.108 0.048 −0.000 75.1 0.759 684 0.658
Chile 19.7 30.9 −0.451 0.029 −0.254 −0.026 −0.199 78.5 0.655 908 0.912
Thailand 10.9 18.1 −0.507 −0.158 −0.207 −0.043 −0.099 73.5 0.687 951 0.794
South Africa 4.5 17.4 −1.351 −0.931 −0.053 0.061 −0.427 51.0 0.801 636 1.135
Botswana 4.3 25.1 −1.767 −0.852 −0.574 −0.008 −0.333 52.1 0.476 859 1.048
Vietnam 4.0 5.9 −0.378 −0.082 −0.269 −0.020 −0.006 74.2 0.645 893 0.668
Zimbabwe 3.1 8.3 −0.972 −0.983 0.155 −0.050 −0.094 45.8 0.986 969 0.789
Kenya 1.9 2.8 −0.388 −0.394 0.104 0.059 −0.157 54.4 0.938 644 0.865
Notes: The table shows the consumption-equivalent welfare calculation based on equation (7). The second line for each country shows life expectancy, the ratio of consumption to income, annual hours worked per capita, and the standard deviation of log consumption. Results for additional countries can be downloaded at http://www.stanford. edu/~chadj/BeyondGDP500.xls.
Salvador Ortigueira (University of Miami)
Welfare and income growth: 1980-2007 (percent) 2456 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
One could carry out similar calculations across geographic regions within coun- tries, or across subgroups of a country’s population (e.g., by gender or race). Even more ambitious would be to try to account for some of the many important fac- tors we omitted entirely, such as morbidity, the quality of the natural environment, crime, political freedom, and intergenerational altruism. We hope our simple mea- sure proves to be a useful building block for work in this area.
REFERENCES
Aguiar, Mark, and Mark Bils. 2015. “Has Consumption Inequality Mirrored Income Inequality?” American Economic Review 105 (9): 2725–56.
Barro, Robert J., and Charles J. Redlick. 2011. “Macroeconomic Effects from Government Purchases and Taxes.” Quarterly Journal of Economics 126 (1): 51–102.
Becker, Gary S., Tomas J. Philipson, and Rodrigo R. Soares. 2005. “The Quantity and Quality of Life and the Evolution of World Inequality.” American Economic Review 95 (1): 277–91.
Boarini, Romina, Asa Johansson, and Marco Mira d’Ercole. 2006. “Alternative Measures of Well- Being.” Organization for Economic Co-operation and Development Social, Employment and Migration Working Paper 33.
Chetty, Raj. 2012. “Bounds on Elasticities with Optimization Frictions: A Synthesis of Micro and Macro Evidence on Labor Supply.” Econometrica 80 (3): 969–1018.
Cordoba, Juan Carlos, and Genevieve Verdier. 2008. “Inequality and Growth: Some Welfare Calcula- tions.” Journal of Economic Dynamics and Control 32 (6): 1812–29.
Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer. 2015. “The Next Generation of the Penn World Table.” American Economic Review 105 (10): 3150–82.
Fleurbaey, Marc. 2009. “Beyond GDP: The Quest for a Measure of Social Welfare.” Journal of Eco- nomic Literature 47 (4): 1029–75.
Fleurbaey, Marc, and Guillaume Gaulier. 2009. “International Comparisons of Living Standards by Equivalent Incomes.” Scandinavian Journal of Economics 111 (3): 597–624.
Hall, Robert E. 2009a. “By How Much Does GDP Rise If the Government Buys More Output?” Brookings Papers on Economic Activity 40 (2): 183–231.
Hall, Robert E. 2009b. “Reconciling Cyclical Movements in the Marginal Value of Time and the Mar- ginal Product of Labor.” Journal of Political Economy 117 (2): 281–323.
Hall, Robert E., and Charles I. Jones. 2007. “The Value of Life and the Rise in Health Spending.” Quarterly Journal of Economics 122 (1): 39–72.
−4 −2 0
2
4
6
8
10
12
Albania
Bolivia
Brazil
Bhutan
Botswana
C. Afr. Republic
China
Cote dIvoire
Congo
Comoros
Cyprus
Ecuador
Egypt
Fiji
Guinea
H.K. India
Ireland
Iran
Iraq
Jamaica
Japan
Cambodia
S. Korea
Liberia
Luxembourg
Madagascar
Maldives
Niger
Nigeria Norway
Panama
Poland
Qatar
Saudi Arabia
S. Leone Swaziland
Chad
Turkey
S. Africa Zimbabwe
Macao
Per capita GDP growth
−6 −4 −2 0 2 4 6 8
W e lf a re
g ro
w th
Figure 8. Welfare and Income Growth, 1980–2007 (Percent)
Salvador Ortigueira (University of Miami)
Development Economics Inequality and poverty
Readings: Chapter 6 in Perkins et al.
University of Miami Professor: Salvador Ortigueira
Inequality: different variables ● Income inequality
– Hard to measure for subsistence farmers in poor countries
– Not what we directly care about
● Wealth inequality – Higher than income and consumption inequality
● Consumption inequality – Measured by household expenditures – Best indicator of welfare
Measuring inequality Frequency distribution (or histogram) → Figure 6.1 ● Contains all information... ● … but does not deliver an indicator (i.e. one
number) measuring inequality. ● Skewed to right, long right tail. ● Fact: Income and consumption are roughly log-
normally distributed in most countries.
Measuring inequality Size distribution of x (income, wealth etc.) → Table 6.1 ● Share of x that goes to poorest 20%, second-
poorest 20% etc. ● Full equality: each group gets 20% of x ● Usually: quintiles (20% groups), but also: deciles
(10%), percentiles (1%) ● Gives us better comparison of inequality than
frequency distribution... ● … but still no single index.
Lorenz curve ● Generalizes size distribution ● Arrange households by levels of x on horizontal
axis ● For given level X, plot cumulative share of x earned
by households with x≤X on vertical axis. ● Example: For income 1,000$, calculate which
fraction of total U.S. Income is accounted for by households earning 1,000$ or less.
Lorenz Curve (2) ● Perfect equality: 45-degree line ● Perfect inequality: traces horizontal axis up to 100,
then shoots up to 100. ● If country A's Lorenz curve is always above
country B's curve, country A is more equal. ● If Lorenz curves intersect: ambiguous → still no single indicator!
Indicators of inequality ● Ratio of
– income share of top 20% – to income share of bottom 20% (or 40%)
● Gini coefficient (most common): Ratio of – area between Lorenz curve and 45-degree line – to area under 45-degree line (→ perfect-equality Lorenz
curve)
● → Gini=0 for perfectly equal society, ● Gini=1 if one household has all income.
Gini coefficients in reality ● Range from 0.25 to 0.60 ● → see table 6-2 ● Large variation within continents/regions... ● … but some clear patterns:
– High inequality in South America and southern Africa – Low inequality in South Asia – Low inequality in transition economies – Low inequality in rich countries (except U.S.)
Growth and inequality ● First hypothesis: Kuznets curve (Simon Kuznets,
1950s) – Inequality first rises as country industrializes... – ...then decreases as the country gets richer.
● Mechanism proposed by Kuznets: – First: Income distributed equally in agricultural
economy. – As some individuals transition to industrial sector,
they earn more → more inequality. – When all have switched, equality is restored.
Lewis's labor-surplus model → Another theory that predicts a Kuznets curve ● Two-sector model: agriculture and industrial ● Phase 1: Modern sector draws on surplus labor
from agriculture ● → low wages, high profits for firm owners ● Phase 2: When surplus labor is absorbed, have to
pay higher wages to employ workers ● → Profits fall, inequality falls
Lewis's labor-surplus model (2) ● Need inequality to have growth: Entrepreneurs
invest only if lured by high profits in phase 1. ● Attempts to re-distribute (too early) may stifle
growth
● → Kuznets' and Lewis's ideas influential until 1980s
Other theories Could inequality also be a drag on growth? ● Credit constraints:
– Poor people have no collateral → no credit – Worthwhile investments not undertaken.
● Political economy: – Very rich have power to kidnap political process – Policies that cement position of rich but hurt growth:
protection of industries, tariffs
● But: also see the reverse, see Venezuela
Kuznets curve: data ● Cross-sectional studies until 1980s supported
Kuznets curve. ● → Crucial assumption: all countries follow same
development process. ● But: More recent time-series studies provide
evidence against Kuznets curve – In some countries inequality rises with growth, in
others it falls – Striking persistence in inequality.
● → Figure 6-3
Kuznets curve: data (2) How could cross-sectional studies find inverted-U
shape? ● Coincidence: Latin America (high inequality) had
middle income ranking when studies were done. ● Cautionary tale for drawing inference from cross-
sectional data.
Causes of inequality
What, if not growth, explains variation in inequality across countries?
● Policy: – Income re-distribution, education policies. – Discrimination (e.g. apartheid in South Africa)
increases inequality.
● History: – Asset re-distribution after wars (e.g. in South Korea)
can reduce inequality. – Transition economies: Started with low inequality after
communism (then rising)
Causes of inequality (2)
● Economics: prevalent industries/technologies – Mineral wealth → higher inequality – Plantation farming (e.g. Latin America) → higher
inequality – Rice farming better suited for families (e.g. East Asia)
→ lower inequality
Measuring poverty
● Absolute poverty lines: – money needed to buy a basket of elementary goods
● Relative poverty lines: – 50% (or 40%, 60%) of the median income in a society
● → A growing economy whose Lorenz curve remains the same experiences... – … a reduction in absolute poverty,... – … but no reduction in relative poverty.
Absolute poverty lines
● Amount determined by basket of basic goods, usually to satisfy minimum calorie intake.
● → Absolute measure. ● Need to adjust for price changes over time and
across regions. ● Note: some deprivations do not enter, e.g.
– safe water – health – education
1$-a-day poverty line
● Origin: 1990 World Development Report by World Bank
● Low-income countries had poverty lines of 275$- 370$ per year in 1985 PPP $
● → adopted 365$ per year, i.e. 1$ a day ● 1985: 1.12 billion people in absolute poverty
according to this measure. ● Updated for inflation and new PPP estimates: ● e.g. 1.25$ PPP in 2005
Assessing the extent of poverty
Once a poverty line (PL) is established: ● Simplest indicator: number of people below PL ● Headcount index (HI): ratio of those below PL to
total population ● Poverty gap (PG) measures severity of poverty:
● PG = [ (PL – MC) / PL ] * HI, ● where MC = mean consumption of those below PL.
Most recent World Bank data Source: see link on Blackboard Compare to Perkins: Table 6-3 (up to 2001) ● 850 household surveys from 130 developing
countries ● 1.23 million households interviewed ● Cover 90% of population in these countries ● Main poverty line: 1.25$ PPP in 1995 ● → average of national poverty lines of 10-20
poorest countries
Most recent World Bank data
● Based on consumption whenever possible (2/3 of cases, otherwise on income)
● 2$ a day: average poverty line of all developing countries.
● 22% of population in developing countries below 1.25$ in 2008 (was 52% in 1981).
● Millennium Development Goal 1 (halving incidence of extreme poverty) reached in 2010
Recent data: by region
● East Asia: Population below 1.25$ fell from 77% (1981) to 14% (2008).
● China: Almost 500 million people left extreme poverty since 1981.
● South Asia: 61% (1981) to 36% (2008) ● Sub-Saharan Africa: 51% (1981) to 47% (2008) ● Eastern Europe and Central Asia: Negative trend
reversed since 2000.
Recent data: progress, but...
● Still 1 billion people below 1.25$ in 2015. ● Below 2$: 2.59 billion (1981) to 2.47 billion (2008) ● → Formerly extremely-poor bunch up above 1.25$,
are still vulnerable!
Criticism of 1$-a-day poverty line
● Sharp cut-off: those just above 1$ are not counted. ● 1$ or 2$ a day is very little → still see high infant
mortality, stunting above this ● 1$ may be too low in less poor countries →
different prices etc. ● But: Some researchers (Sala-i-Martin) also find
less poverty using different methods ● → still, obtains same positive time trend
Who are the poor?
● Rural poverty rates higher than urban rates – Fewer opportunities – Lack of education and health care – More vulnerable to forces of nature
● Regional variation within countries ● Minorities often at greater risk:
– Ethnic minorities in Eastern Europe – Indigenous groups in Latin America – Low castes in India
Are women poorer than men?
● Fact: women often disadvantaged – Selective abortion, less health care – → “missing women” (Amartya Sen) – Some evidence that women's nutrition is worse (but
varies across countries and regions) – Receive less education (but trend is positive) – Women work more hours (when including house work) – Legal discrimination: no ownership to land, cannot
inherit etc.
Are women poorer than men? (2) But: ● Within-household allocation of consumption hard
to measure ● Compare women-headed households to others:
– Some do worse (widows in India, single female pension recipients in Eastern Europe)...
– … but some do better than average (unmarried women in urban settings, married women with husband who sends remittances from abroad).
Strategies to reduce poverty
World Bank: World Development Report 1990 ● Promote market-oriented growth
– More labor demand → more jobs for poor – Macro stability reduces vulnerability
● Direct basic health and education services to poor – Increases their productivity
● Develop social safety nets for those who are unable to work etc. (sick, disaster victims etc.)
Criticism of growth-centered poverty- reduction strategy
United Nations Development Program: The Human Development Report (1996)
● Jobless growth ● Ruthless growth: growth that mostly benefits the
rich ● Voiceless growth: growth, but “no extension of
democracy or empowerment” ● Rootless growth: loss of cultural identity ● Futureless growth: growth on expense of future
generations (environment etc.)
Data: growth and poverty reduction
Study: Dollar & Kray (2002), see fig. 6-7 ● Sample of countries, representative of world ● ~6-year spells from 1956 to 1999 ● Result: GDP increase of 1% associated with 1.2%
increase of poorest quintile (on average) ● → Growth usually good for poor ● But: Have fair number of cases of “ruthless
growth” (or immiserizing growth) ● → often in Latin America
Pro-poor growth
● Often cited as policy objective, ... ● … but no clear definition. ● What's meant: Growth that benefits the poor more
than the average person.
Arguments for poverty-focused development strategy
Why could it be desirable to focus especially on the poor (instead of total GDP growth)?
● Common view: an additional dollar is worth more to a poor than to a rich person
● → declining marginal utility, utilitarian criterion ● Market failures most relevant to the poor ● Poor are most deprived of capabilities, should
focus on them in capabilities-oriented approach to development.
Washington Consensus and poverty reduction (1)
Washington-Consensus policies sometimes criticized to hurt the poor:
● Market-determined wages of low-productivity workers are often very low
● Reduction in government spending can hurt poor (e.g. welfare programs)
● Cheap imports can replace goods produced by poor (e.g. cotton farmers in West Africa)
Washington Consensus and poverty reduction (2)
But: ● Budget deficits and poor monetary policy: Inflation
hurts the poor more than the rich ● Openness to trade:
– Poor country focuses on producing goods in which it has a comparative advantage
– → often labor-intensive, i.e. more low-skill jobs – → Wages increase over time (e.g. Asia)
● → Usually winners and losers from openness
How to improve opportunities for the poor?
Usually advocated: ● Basic education
– Increases productivity – Widens range of jobs available
● Health ● Spending on rural infrastructure
– Found to decrease poverty both in urban and rural areas
Controversial: land re-distribution
Income transfers
● Some individuals cannot participate in markets: the old, children, disabled etc.
● → Need transfers ● Usually two forms in developing countries:
– Regulation of food prices: distorting, also benefit the rich.
– Food-stamp programs: better-targeted, but harder to administer.
Safety nets ● Panel data: poverty is transitory rather than chronic ● Public employment schemes can alleviate poverty
temporarily – Work on public infrastructure – Low wages → target on very poor
● → Reduces variability of poor's income, found to be well-targeted and well-used.
Global inequality ● Inequality across countries is larger than inequality
within countries. – 15% of world population consumes 56% of world
output – Comparable to inequality in South Africa, Brazil
● Within-country inequality addressed by national policy-making.
Global inequality Should the reduction of between-country inequality
be a policy goal? ● To whom do people compare each other? This
often determines their perception of well-being ● → argument to focus on within-country inequality ● But: Absolute poverty should be a concern to
everybody (utilitarian criterion) ● → linked to between-country inequality
Measuring global inequality GDP-based measures: ● Compare GDP of all countries with equal
weighting – Does not account for country size – Appropriate for studies on link of countries policies and
growth
● Compare countries' GDP weighted by population – Does not account for within-country inequality
● → figure 6-8
Measuring global inequality GDP-based measures (figure 6-8): ● Unweighted Gini increased since 1980 ● Weighted Gini decreased continuously ● Reasons for discrepancy: ● Different growth experiences
– High growth in much of Asia – Low growth in Africa, Latin America, transition
economies
● Very large countries (China and India) grew
Measuring global inequality (2)
Household-based: Compute world Gini as if the world was a single country
● Advantage: Does not assume perfect income equality within-country as GDP-based measures do
● Problem: Need household surveys – Only available since 1980s – Some data issues
Measuring global inequality (3) Household-based world Gini is between 0.6 and 0.8 ● Higher than GDP-based Ginis ● Higher than usual country Ginis ● No consensus about trend from 1980-2000
● Visualization (also by region): Gapminder.org →
Downloads → Human Development Trends
Reducing global inequality Is it desirable to slow down rich countries' growth to
promote global equality? ● Would reduce global inequality if poor countries
kept growing. ● But: Might hurt poor countries' citizens
– Lower demand for poor countries' exports – Slower pace of technological progress on frontier →
poor countries can adapt fewer innovations
● → Growth is not a zero-sum game
Benefits of global-poverty reduction for rich countries
● Moral argument: relieve suffering ● Information age: poor people compare themselves
also to other nations' people. Potential for – High migration flows – Social unrest
● Improve global issues that can affect rich countries themselves: – Diseases: HIV/AIDS, avian flu etc. – Climate change
The end of poverty Jeffrey Sachs proposes global compact to end
absolute poverty by 2015 ● Rich nations commit to
– Keep markets open to exports from poor countries – Invest in global public goods (science, medicine,
agricultural technology) – Minimize impact of global warming on poor countries – Aid and debt forgiveness (controversial)
● Poor nations accountable for good governance and poverty-reduction efforts
- Development Economics�Inequality and poverty
- Inequality: different variables
- Measuring inequality
- Measuring inequality
- Lorenz curve
- Lorenz Curve (2)
- Indicators of inequality
- Gini coefficients in reality
- Growth and inequality
- Lewis's labor-surplus model
- Lewis's labor-surplus model (2)
- Other theories
- Kuznets curve: data
- Kuznets curve: data (2)
- Causes of inequality
- Causes of inequality (2)
- Measuring poverty
- Absolute poverty lines
- 1$-a-day poverty line
- Assessing the extent of poverty
- Most recent World Bank data
- Most recent World Bank data
- Recent data: by region
- Recent data: progress, but...
- Criticism of 1$-a-day poverty line
- Who are the poor?
- Are women poorer than men?
- Are women poorer than men? (2)
- Strategies to reduce poverty
- Criticism of growth-centered poverty-reduction strategy
- Data: growth and poverty reduction
- Pro-poor growth
- Arguments for poverty-focused development strategy
- Washington Consensus�and poverty reduction (1)
- Washington Consensus�and poverty reduction (2)
- How to improve�opportunities for the poor?
- Income transfers
- Safety nets
- Global inequality
- Global inequality
- Measuring global inequality
- Measuring global inequality
- Measuring global inequality (2)
- Measuring global inequality (3)
- Reducing global inequality
- Benefits of global-poverty reduction for rich countries
- The end of poverty
Development Economics States and Markets
Readings: Chapter 5 in Perkins et al.,
University of Miami Professor: Salvador Ortigueira
Problems in development process
Common experience (~before 1980): ● Heavy state intervention in economy:
– Price controls – Nationalized companies – Trade barriers
● Poor macroeconomic policies: high inflation, high budget deficits
● Exposure to external shocks, especially commodity prices
The Washington Consensus → New paradigm in the 1980s ● Backed by International Monetary Fund (IMF) and
World Bank ● Prescriptions:
– Reliance on market powers (no price controls, privatizations etc.)
– Macroeconomic stabilization: lowering inflation and budget deficits
– Reduction of trade barriers
● Builds on neoclassical economic theory
Outcomes Economic reforms towards free markets in 1980s and
1990s led to: ● High growth in some countries: China, Vietnam,
other Asian countries ● Stabilization, but low growth in other countries
(e.g. Ghana and other African countries) ● → Common view today: ● Policies prescribed by Washington Consensus
necessary but not sufficient for growth
Markets and market failures Economic theory tells us that markets are good at
allocating scarce resources: → The Welfare Theorems. ● No need for expensive central planning, decisions
are made by informed agents. ● Provide the right incentives, especially when
circumstances change ● Profit motive enhances productivity in firms ● [Ethical argument: Give economic power and
freedom to individuals.]
Market failures: overview
● Externalities: positive and negative ● Economies of scale: natural monopolies ● Imperfect information: the lemons problem ● Contracts not enforceable: institutional problems ● Missing markets: credit, insurance etc. ● Price rigidities etc. → justify macro policies ● Also: efficiency does not imply justice/equality
Market failures (1) ● Externalities
– Positive (also: “external economies”), e.g. build road, clean one's sidewalk
– Negative (“external diseconomies”), e.g. pollution, overfishing → the tragedy of the commons
– Important example: Infant industry protection → Technological spillovers as positive externality
Fix: taxes/subsidies, regulation, nationalization
Market failures (2)
● Economies of scale (=increasing returns to scale) – Natural monopolies: railroad, telecom etc. – Lead to inefficiently high prices
● Fix: government regulation (antitrust), nationalization
Market failures (3) ● Imperfect information: the lemons problem
– Example: Second-hand cars.
● Fix: quality control by government (food, e.g.) ● Contracts not enforceable: institutional problems ● Missing markets: credit, insurance etc. ● Price rigidities etc. → justify macro policies. ● Example: Central Bank intervenes to stabilize
inflation and growth.
Efficiency versus equality
● Efficient allocation may be inequitable ● Most governments pursue goal of poverty
reduction ● → Fiscal re-distribution. Trade-off between
– More equality – Less efficiency: taxation negatively affects
incentives
1950s-60s: Market pessimism ● Market failures perceived as wide-spread ● Great Depression destroyed confidence in markets
→ Keynesian ideas popular: – Active fiscal policy for stabilization – Active monetary policy
● Some successes of interventionist policies: – Soviet Union: rapid industrialization – Argentina: protectionism
→ Many developing countries embrace interventionist policies (following India).
Import substitution Trade theory of 1950s-60s (Prebisch, Singer): ● Terms of trade: price of exports compared to price
of imports ● For primary-product producers, terms of trade had
fallen for a long time ● World demand for primary products was forecast
to grow slowly ● → Export pessimism/import substitution: ● Protect local manufacturers
Means of market intervention ● Protective tariffs and import controls ● Taxes on primary exports ● Controls of prices, interest rates and exchange rate ● Minimum wage and benefit regulation ● Government ownership of key industries ● Government control over investment ● → Common in developing world until early 1970s
Shift towards markets since 1970s ● In China: after Mao's death in 1976 ● Other positive examples (1980s and 1990s):
– India, Indonesia – Bolivia, Chile – Ghana, Tanzania
● Transition to market economies after end of Soviet Union: – Eastern Europe – Central Asia...
Rise of neoclassical paradigm
Backs market-oriented, outward-looking (→ trade) development.
● Surge in neoclassical economic theory in 1970s in academia
● Success of outward-looking strategy: Hong Kong, Singapore, South Korea and Taiwan
● → Grew by exporting ● Failure of interventionist policies (see next slide)
Failure of interventionist policies ● Protective barriers supported inefficient industries ● Interest-rate controls hindered evolution of
financial sector ● Minimum wage stifled job growth ● Rent seeking and corruption: heavy regulation
favored those protected by regulation ● Inefficiencies in publicly-run companies
1980s: Debt crises Many developing countries: ● Slow growth in 1970s ● Large debt accumulated by 1980
– Failure of interventionist policies – Negative influence of oil shocks
● Large budget and trade deficits ● High inflation ● → Pressure to undertake reforms
IMF and World Bank ● Became main source of financing for poor
countries in 1980s ● IMF: Provides financing to countries with balance-
of-payment problems ● World Bank: Finances development projects ● Financing conditional on reforms
● → Washington Consensus
Main goals of reform programs ● Stabilization: Correct imbalances in
– Trade balance – Budget deficit – Money supply → inflation
● Structural adjustment: – More reliance on markets: privatization,
deregulation – Opening to trade
Macroeconomic stabilization Strong evidence that macroeconomic stability is
necessary (though not sufficient) for growth ● Inflation:
– Hurts those dependent on fixed incomes, – … especially the poor – Creates uncertainty
● Budget deficits: – Crowd out private investment – Can lead to inflation: government prints money
to pay debt
Specifics of IMF stabilization policies
● Budget consolidation: Cut spending and increase taxes
● Inflation: Control growth of money supply by restricting central-bank credit to government and commercial banks.
● Exchange rate: Devaluation or free float. ● → Makes country's exporters more competitive
and reduces balance-of-trade benefits.
Specifics of IMF policies (2)
● Removal of price controls: interest rates, food prices, fuel and utility rates etc.
● Restraining wage growth: if wages are above workers' productivity, tend to have – Less-competitive firms – Unemployment – Inflation
Conditionality
● IMF provides loans to finance balance-of-payments gap.
● → Austerity measures less drastic than if the government financed them by budget adjustments.
● Disbursement of loans is conditional on implementation of recommended policies.
Conditionality: discussion
Criticism (anti-globalization movement etc.) ● Austerity measures often painful ● Rich countries force neoclassical policy
prescriptions on poor countries. ● Rebuttal: ● Countries would have to make painful choices
anyway – don't blame doctor for disease. ● Countries had choice not to accept IMF help.
Structural adjustment
● First step: Make as many goods as possible available through markets
● → less central planning (as in former communist countries), less quantitative controls (e.g. India before 1990s).
● “Getting prices right”: Market mechanism allocates goods to highest-priority use.
● → no price controls (e.g. fuel, minimum wage)
Price controls
● Often politically motivated ● Hard to abolish if...
– ... a few individuals benefit a lot. – … the majority loses little.
● Breed corruption: – Connections determine who obtains goods – Re-sale on shadow markets
Ensuring competition
Abolish/reduce: ● Monopolies → over-pricing ● Import restrictions (tariffs, regulation) ● → allow inefficient domestic firms to survive Argument for import restrictions: ● Temporary infant-industry protection ● → But: often abused, hard to terminate
Privatization
Government-run businesses lack incentives to ● innovate, ● cut costs, ● improve quality. ● Reform example: agriculture in China and Vietnam
– Break collective farms/communes – Hand over land to individual households
● → Led to increase in agricultural output
Market-supporting institutions ● Enforceability of contracts ● → strong legal system ● Regulation of banking system ● Weed out corruption in government bureaucracy ● Property rights should be:
– Well-defined – Exclusive – Secure – Enforceable
Case: privatization in Russia in 90s
● Vouchers on state-owned firms given to – Local governments – Managers – Workers – General public
● Could be re-sold in auctions → Designed to stop stealing of public property.
Privatization in Russia: results
● Performance of privatized firms improved, but not as much as hoped for
● Cronyism in later privatization deals undermined trust in property rights (oligarchs)
The timing of reforms
● Controversial: when and how fast should reforms be carried out?
● Economic theory agrees on what policies are good...
● …but is silent about when they should be carried out.
“Shock therapy” → Implement all reforms immediately ● Idea: new political regime has short window of
opportunity when seizing power. ● Done by Poland and Russia. Results:
– State companies made big losses – Were helped by central-bank loans – Printing money → inflation – Real income of many consumers eroded
Shock therapy: Long-term outcomes Central/Eastern European countries, former Soviet
Union: ● Initial slump in GDP, only recovered to pre-reform
levels by 2000. ● But: Countries that had transformed their
economies most (e.g. Poland) recovered fastest.
Gradual approach to reform China and Vietnam: ● First: abolished price controls and returned to
household farms in agriculture ● Later: Freed up industrial sector → Creation of
small- and medium-scale industry ● These put competitive pressure on large public
companies ● High growth throughout reform process ● But: Industrial state enterprise sector was smaller
to start with (unlike in Russia)
Credibility of reforms ● Economic agents have to be convinced that reforms
are permanent ● No trust in reforms → low investment etc. ● A commitment device for government: join
international organization – Mexico: NAFTA – Central, Eastern, Southern Europe: EU – China: World Trade Organization
Reform outcomes: success stories
Especially in Asia: ● South Korea: Dismantled market controls after
1970s → high growth ● Indonesia: Highly regulated in 1970s, growth after
market-based reforms. ● Taiwan: Strong growth, based on small private
enterprises.
Reform outcomes: failures
Failed stabilization programs: ● Argentina and Brazil in 1980s ● Argentina around 2000 But: successful market-based reforms in Chile ● Poor results in African countries: Ghana,
Mozambique, Tanzania, Uganda.
Debate about results of Washington-Consensus reforms
● Not successful everywhere ● Some successful countries violated some of its
prescriptions, e.g.: – Korea: Government ownership of banks – China: Fixed exchange rate, slow pace of reform
● But: ● Prescriptions were often not fully implemented ● Time is needed for reforms to take effect
Extra: Washington Consensus in detail (1)
Article by economist John Williams in 1990: ● Fiscal discipline: budget deficit < 2% of GDP ● Public expenditure priorities: education, health,
infrastructure, essential administration. ● Tax reform: modest marginal tax rates, limit
evasion. ● Liberalization of interest rates ● Floating (i.e. free) exchange rates
Extra: Washington Consensus in detail (2)
● Trade liberalization ● Liberalization of foreign direct investment: foreign
companies bring capital and know-how ● Privatization ● Deregulation: Abolish barriers of firms into
markets, but keep sensible regulation (safety, environment, banking, judicial system)
● Secure property rights
- Development Economics�States and Markets
- Problems in development process
- The Washington Consensus
- Outcomes
- Markets and market failures
- Market failures: overview
- Market failures (1)
- Market failures (2)
- Market failures (3)
- Efficiency versus equality
- 1950s-60s: Market pessimism
- Import substitution
- Means of market intervention
- Shift towards markets since 1970s
- Rise of neoclassical paradigm
- Failure of interventionist policies
- 1980s: Debt crises
- IMF and World Bank
- Main goals of reform programs
- Macroeconomic stabilization
- Specifics of IMF stabilization policies
- Specifics of IMF policies (2)
- Conditionality
- Conditionality: discussion
- Structural adjustment
- Price controls
- Ensuring competition
- Privatization
- Market-supporting institutions
- Case: privatization in Russia in 90s
- Privatization in Russia: results
- The timing of reforms
- “Shock therapy”
- Shock therapy: Long-term outcomes
- Gradual approach to reform
- Credibility of reforms
- Reform outcomes: success stories
- Reform outcomes: failures
- Debate about results of�Washington-Consensus reforms
- Extra: Washington Consensus�in detail (1)
- Extra: Washington Consensus�in detail (2)
Development Economics Economic Growth:
Concepts and Patterns Reading: Chapter 3 in Perkins et al.
University of Miami
Professor: Salvador Ortigueira
Growth since 1960
● Large differences by countries: – Industrialized countries: ~2% per year – Negative growth: Some African countries and Venezuela – Slow/moderate growth (0-3%): Most developing
countries. India, Latin America – Rapid growth (3-6% yearly): (South-)East Asia,
Botswana
● → see table 3-1 in Perkins et al.
The case of Botswana ● Very poor after independence from UK (1965) ● Very fast growth since then ● Advantage: Rich in diamonds. But: Other countries
have such resources too, and failed! ● Likely reasons for success:
– Good institutions. Invested diamond profits into infrastructure; not much corruption.
– Good macroeconomic policies (low inflation, stable fiscal policy, open to trade).
– Property rights, legal system well developed
● Problems: HIV/AIDS, inequality.
Growth: Disappointments
● Argentina: Was supposed to have better prospects than the U.S. in 1900. Now at 30% of U.S. GDP.
● Sub-Saharan Africa: Negative growth since 1960 in Nigeria, Zambia, Chad, Senegal
● Central-Eastern economies after 1990: Negative growth after transition
Factor accumulation, productivity growth and economic growth
● Basic factors of production: labor (L), capital (K) and land. ● Output determined by
– Quantity of the factors used – How efficiently they are used, i.e. technology (A)
● Economic Growth depends on: – Factor accumulation, i.e. increasing the size of the capital
stock and/or the labor force. – Productivity growth: Gains in efficiency, technological
change – → Technological change most important
● Productivity growth often entails shifting resources from producing one good to another
● In low-income countries the process of growth almost always corresponds to a major structural shift from agriculture to industry
Production function Y=AF(K,A)
● Given K (capital) and L (labor), tells us how much is produced.
● Properties: Positive, but decreasing returns to factors
● Technological improvement shifts function upward ● → Need to understand what drives factor
accumulation and technological change to understand growth.
Solow growth model (sketch)
● Focuses on capital accumulation ● Investment increases capital stock ● Investment is financed by savings ● Savings comes from current income →
consumption-savings trade-off
● → Will come back to Solow in later chapter.
How important are factor accumulation and productivity gains in explaining growth? We now explain a framework that attributes recorded economic growth to: growth in the capital stock growth of the labor force changes in overall productivity
● This approach is called the Solow growth
accounting
Solow growth accounting (1) ●Measure contribution to growth g(y) in GDP from:
– Labor growth g(l) – Capital growth g(k) – Growth in total factor productivity (TFP) a: gains
from technology and more efficiency
● g(Y) = W(k)*g(k) + W(l)*g(l) + a ● W(k)~40% is income share from capital, ● W(l)=1-W(k)~60% is income share from labor
Solow growth accounting (2) g(Y) = W(k)*g(k) + W(l)*g(l) + a ● W(k)*g(k): contribution of capital to growth ● W(l)*g(l): contribution of labor to growth ● a: contribution of technology to growth
Growth accounting (3) ● All but a are observable in data → Can back a out
(Solow residual) ● Solow residual measures contribution of
technological progress ● Can calculate contributions to growth from 3 terms
in the sum and include education ● Results (see table 3-2 in Perkins et al.):
– g(k) important in poor countries – Fast-growing countries: Also a is important – Rich countries: g(k) and a
The Solow residual Be careful with the interpretation of a! → Solow residual may not capture technological
change in the following situations: ● Financial crises ● Factors are not fully used ● War ● Extreme weather events, especially for agricultural
economies
Underlying causes for capital accumulation and technological growth ● Barro growth regressions: Try to explain variation
in growth rates across countries by – Education – Health – Policies – Political system – Geography
● Somewhat controversial (what causes what?), but give us clues on what matters
Characteristics of fast-growing countries
● Macroeconomic and political stability ● Investment in health and education ● Effective governance and institutions ● Favorable environment for private enterprise ● Favorable geography
Macroeconomic and political stability
● Macroeconomic: – Low inflation – Low government budget deficits – Appropriate exchange rate – Functioning financial markets
● Political: – No cross-border/civil war – No coups
● → Figures 3-2 and 3-3 in Perkins et al.
Investment in health and education ● Better health → workers more productive, more
investments in capital and education ● Better education:
– Workers use machines more effectively – Better at inventing and adopting technological changes – Attracts foreign investment
● → Returns to investment in girls' education found to be especially high
● → Figure 3-4 in Perkins et al.
Effective governance and institutions
● Secure property rights: More investment by (home) entrepreneurs and foreigners
● Measuring quality of governance: – Surveys by world bank – Transparency International Corruption Index
● → Figure 3-5 in Perkins et al.
Favorable environment for private enterprise
● Important in poor countries: Agriculture. Too much market intervention reduces incentives for farmers. Exampe: China.
● Infrastructure: roads, electricity, water etc. ● Lack of large firms in poor countries → productivity
losses. ● Countries open to trade grow faster
– Cheaper inputs, more markets for output – But: more exposed to shocks from abroad
● See figure 3-6 in Perkins et al
Favorable geography ● Countries in tropics grew slower
– Diseases: malaria, HIV/AIDS – Volatile weather – Poor soil
● → Figure 3-7 in Perkins et al. ● Isolation hurts: landlocked or small islands
– High transportation cost for trade ● → Figure 3-8 in Perkins et al.
Convergence: Diminishing returns
● Diminishing marginal product of capital: More machines per worker lead to ever smaller increases in output.
● Poor countries have less capital (per worker) than rich countries. Implications: – Poor countries have potential to grow faster – Growth tends to slow as country accumulates capital
● → Income levels of rich and poor countries should converge
Is there convergence in the data?
● Has happened for some countries: Japan, Korea, maybe now China (?)
● But not for most countries. ● Do poor countries grow faster on average? No, if
we look at all countries at once. ● (see figure 3-10 in Perkins et al.) ● ● → No general convergence.
Conditional convergence
● Model of diminishing returns (Solow model) assumes that everything but capital is the same across countries (technology, savings rates, population growth).
● Look at similar countries (OECD, countries open to trade): there is convergence!
● → Have conditional convergence, but no general convergence!
● See figures 3-11 and 3-12 in Perkins.
Growth and structural change ● Share of output produced in agriculture decreases,
output from industry and services increases. ● Workers move away from agriculture to
industry/services. ● Urbanization ● More goods and services are sold on markets (less
home production) ● Engel's law: Share of food spending decreases
when income increases. ● → Figures 3-13 to 3-15 in Perkins et al.
Why does urbanization occur?
● Increasing returns to scale in production: Large plants more productive than small plants
● Need large work force concentrated in one area ● Close-by suppliers, good infrastructure in cities
Should governments force resources out of agriculture?
● Negative example: China's Great Leap Forward (1950s) → Famine
● Need increases in agricultural productivity to be able to shift away workers to other sectors!
● → Important not to neglect agriculture in growth policy
- Development Economics�Economic Growth:�Concepts and Patterns
- Growth since 1960
- The case of Botswana
- Growth: Disappointments
- Factor accumulation, productivity growth and economic growth
- Slide Number 6
- Production function�Y=AF(K,A)
- Solow growth model (sketch)
- Slide Number 9
- Solow growth accounting (1)
- Solow growth accounting (2)
- Growth accounting (3)
- The Solow residual
- Underlying causes for capital accumulation and technological growth
- Characteristics of fast-growing countries
- Macroeconomic and political stability
- Investment in health and education
- Effective governance�and institutions
- Favorable environment�for private enterprise
- Favorable geography
- Convergence: Diminishing returns
- Is there convergence in the data?
- Conditional convergence
- Growth and structural change
- Why does urbanization occur?
- Should governments force resources out of agriculture?
Development Economics: Measuring Growth and Development
Reading: Chapter 2 in Perkins et al.
University of Miami Professor: Salvador Ortigueira
Measuring economic growth
● Two measures of national income: – Gross national product (GNP, also GNI): Value of
finished goods and services produced by a nation in a given year.
– Gross domestic product (GDP): The same, but for output produced within the borders of a country (GNP does not count output produced by foreigners in the country)
GDP and value added
● Value added: Incremental gain in price at a production stage.
● Equals payments made to factors of production at this stage of production (wages, profits, interest, depreciation of capital, rent for buildings etc.)
● Total value added at all stages = GDP ● → GDP measures both income and output
GDP: What is left out?
● Housework, child care → home production ● Family work on farms: GDP is adjusted for this. ● Underground economy ● Damage to environment is not accounted for
GDP: Exchange-rate conversion ● 1st approach: Convert at official exchange rate
– Distortions: Government intervention on currency markets
– Differences in price in non-traded goods and services Exchange rates do not reflect the relative prices of non-traded goods
● → Misleading! Understates poor countries' income
● 2nd approach: Purchasing power parity. Assign same value to each good across countries. – A hair cut is assigned the same value no matter if is produced in New Delhi of New York
– UN International Comparison Program: Assigns
international prices → Penn World Tables
● → see data in Perkins ch. 2
History of growth (1)
● Almost no growth in GDP per capita until 1820 → Malthusian trap: Technological advances went into higher population density
● By 1820: Western Europe and offshoots ahead of China, India and others.
● 1820: Industrial revolution, onset of modern growth: >1% per year.
● Growth rates increase towards 20th century
History of growth (2) ● Slower growth in Asia, Africa and Latin America
than in Western Europe and offshoots ● → Have record world inequality today ● Asia growing very fast since 1950 ● → Large reductions in poverty (China, India etc.) ● Lost decades:
– 1980s: Latin America, Sub-Saharan Africa – 1990s: Central and Eastern Europe
● → see figure in Perkins ch. 2
Economic Growth and Happiness ●Relationship between economic growth and happiness is weak (see figure in Perkins):
– Happiness did not increase over time in rich countries
– Relationship between the two is weak in cross- country studies, especially for rich
● Why strive for economic growth then? – Increases range of human choice – Happiness is not the only goal in life – Poor nations want material improvements
● Discussion: What do you think?
Economic development
● Amartya Sen: Goal is to expand capabilities of poor people to lead life they want to choose
● Economic development: Alleviate sources of capability deprivation – Diseases – Environmental factors: Climate, pollution etc. – Political freedom – Relative deprivation: Inequality
Measuring human development
● United Nations Development Program: Human Development Index (HDI), since 1990.
● Combines (with equal weighting): – Health: Life expectancy at birth – Education: Adult literacy and school enrollment rates. – Resources/income: GDP per capita in PPP
● → see data in Perkins ch. 2
Constructing the HDI
Each outcome (Health, Education and Income) must be converted into an index number to
permit aggregation.
Health: A nation’s life expectancy at birth is X Index of health: (X-20)/(83.2-20)
Education: Two variables: (1) Mean years of schooling achieved by adult population (>25 y.o.)
(2) Expected years of schooling for children of school-going age (based on enrollment data).
Index of (1). If mean years of schooling average X, then index is (X-0)/(13.2-0)
Index of (2).If expected years of schooling average X, then index is (X-0)/(20.6)
Income: Income is measured by GNI (PPP in USD). The conversion of income into an index: If a country
has GNI equal to X, then Index= (logX-log163)/(log108,211-log163)
Once the three sub-indexes have been constructed (health, education and income), we have to aggregate them into a single index.
Let us denote the three sub-indexes by I1, I2 and I3, then the HDI is a geometric mean of these three sub-indexes
HDI=(𝐼1 • 𝐼2 • 𝐼3)1 3⁄
HDI vs. GDP
● Strong, but not perfect correlation (see figure) ● → Economic growth is very important for
development, but other factors matter. ● HDI increased faster than GDP over time
Millennium Development Goals (MDGs)
● Specified by UN Millennium Declaration in 2000: – 1: Eradicate extreme poverty and hunger – 2: Achieve universal primary education – 3: Promote gender equality and empower women – 4: Reduce child mortality – 5: Improve maternal health – 6: Combat HIV/AIDS, Malaria and other diseases
MDGs (2)
– 7: Ensure environmental stability – 8: Develop a global partnership for development
● 18 specific targets, some quantifiable, e.g.: – From 1990 to 2015, halve proportion of people living
on less than 1$ a day. – Full enrollment in primary education by 2015
● Some policy suggestions, but not binding: Debt relief, more development aid.
MDGs: Where do we stand?
● Eradication of extreme poverty: On track. ● Primary-school enrollment: Not bad. ● Under-5 mortality: Mixed. ● Doing poorly in all: Sub-Saharan Africa. Problem:
Only 1.2% yearly growth! ● → See figures from MDG Report 2011 ● → Economic growth is central to achieve MDGs,
but we also need policy (see table in Perkins)
- Development Economics:�Measuring Growth and Development
- Measuring economic growth
- GDP and value added
- GDP: What is left out?
- GDP: Exchange-rate conversion
- Slide Number 6
- History of growth (1)
- History of growth (2)
- Economic Growth and Happiness
- Economic development
- Measuring human development
- Constructing the HDI�
- Slide Number 13
- Slide Number 14
- Slide Number 15
- HDI vs. GDP
- Millennium Development Goals (MDGs)
- MDGs (2)
- MDGs: Where do we stand?
Chapter 8
Why Isn’t the Whole World Developed?
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8 Why Isn’t the Whole World Developed?
Chapter Outline
8.1 Proximate Versus Fundamental Causes of Prosperity
8.2 Institutions and Economic Development
EBE Are Tropical and Semitropical Areas Condemned to Poverty by Their Geographies?
8.3 Is Foreign Aid the Solution to World Poverty?
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8 Why Isn’t the Whole World Developed?
Key Ideas
Proximate causes of prosperity link prosperity and poverty of nations to the levels of inputs, while fundamental causes look for reasons why there are such differences in the levels of inputs.
The geography, culture, and institutions hypotheses advance different fundamental causes of prosperity.
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3
8 Why Isn’t the Whole World Developed?
© 2015 Pearson Education, Inc
Key Ideas
Inclusive and extractive economic institutions affect economic development.
Creative destruction is integral to economic growth through technological change.
Reversal of fortune evidence provides support for the institutions hypothesis.
8.1 Proximate Versus Fundamental Causes of Prosperity
Proximate causes of prosperity
High levels of factors of production such as physical capital, human capital, and technology that result in a high level of GDP per capita.
The factors of production are proximate causes because they link high levels of prosperity to high levels of the factors, but without providing an explanation for why these factors are high.
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5
Fundamental causes of prosperity
The root reasons for the differences in the proximate causes.
The fundamental causes are ultimately the deep determinants of economic development.
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8.1 Proximate Versus Fundamental Causes of Prosperity
6
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Exhibit 8.1 Fundamental and Proximate Causes of Prosperity
8.1 Proximate Versus Fundamental Causes of Prosperity
7
Fundamental causes can be classified into three categories or theories:
Geography hypothesis
Culture hypothesis
Institutions hypothesis
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8.1 Proximate Versus Fundamental Causes of Prosperity
8
The geography hypothesis claims that differences in geography, climate, and ecology are ultimately responsible for the large differences in prosperity observed around the globe.
The following map of the world reveals that economies with low levels of GDP per capita are located in tropical areas, while those with high levels are in temperate areas (outside the tropics).
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8.1 Proximate Versus Fundamental Causes of Prosperity
9
Tropic of Capricorn
Tropic of Cancer
8.1 Proximate Versus Fundamental Causes of Prosperity
© 2015 Pearson Education, Inc
Source: http://www.geocurrents.info/economic-geography/a-global-northsouth-division-in-the-demic-framework
10
In the past, the French philosopher Montesquieu and the British economist Alfred Marshall argued that tropical climates decreased work effort.
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8.1 Proximate Versus Fundamental Causes of Prosperity
11
Economist Jeffrey
Sachs and geographer
Jared Diamond argue
that tropical climates are
more prone to infectious
diseases such as malaria
and dengue fever, which
result in poverty.
The following graph tests the geography hypothesis using cross-country data.
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8.1 Proximate Versus Fundamental Causes of Prosperity
12
© 2015 Pearson Education, Inc
8.1 Proximate Versus Fundamental Causes of Prosperity
Instructor: The graph examines the relationship between malaria exposure in 1966 (horizontal line) and real GDP per capita (vertical line) in 2010. Each dot represents a different country, with the red dots labeled. The blue line is the exponential fitted line.
If the geography hypothesis is correct, there should be a negative relationship between the two variables. The data seem to support the hypothesis, although the numerous countries with 0% and 100% make it difficult to interpret.
Sources: Malaria data is from Gallup, John, and Jeffrey Sachs (1998); “Geography and Economic Development”; Brookings Papers on Economic Activity and GDP data is from Penn World Table Version 7.1
13
Test of Geography Hypothesis
0.356700003147125 0.0 0.0 0.0 0.0 0.0 0.046000000089407 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.813560009002686 0.276659995317459 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.622160017490387 0.000130000000353903 0.0 0.0 0.000880000006873161 0.245639994740486 0.0 0.938000023365021 0.0 1.0 0.759999990463257 0.0 0.266559988260269 0.0 0.095040000975132 0.0 0.0 0.400499999523163 0.578760027885437 0.0 0.26418000459671 0.0 0.00255000009201467 0.0 0.0478399991989136 0.0 0.0 0.0900000035762787 0.213499993085861 0.00193999998737127 0.0 0.0204000007361174 0.0 0.0 1.0 0.0 0.0280000008642673 0.889999985694885 0.0130000002682209 0.0 0.034000001847744 0.0379999987781048 0.4167799949646 0.0644000023603439 0.0108899995684624 0.0 0.00323999999091029 0.0 0.131860002875328 0.48743000626564 0.740000009536743 0.949999988079071 0.0 0.0 0.862999975681305 0.720480024814606 0.428970009088516 0.00528000015765429 0.889999985694885 1.0 1.0 0.0 0.0 0.896000027656555 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.632000029087067 1.0 1.0 0.980000019073486 0.980000019073486 1.0 0.046169999986887 0.860000014305115 0.0024600001052022 1.0 0.800000011920929 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.850000023841858 0.519999980926514 1.0 0.759999990463257 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.720480024814606 0.720480024814606 0.0 0.0 136248.1365 75588.11588 60175.41205 55862.41792 54790.36195 50487.52367 45866.49122 44555.22975999999 41364.99676 41239.92412 41113.58523 39978.02568 38684.72037 38585.51335 38190.63356 37103.55928 36132.38489 35612.06548999999 35556.61991 34876.73916 34268.00841 34089.0961 33705.00435 32988.84535 32241.09426 32104.92001000001 31447.22902 31299.27919 30749.3483 30111.01875 28377.45915 28088.5447 27789.63139999999 27331.49875 26609.14522999999 26034.56006 25216.3956 24902.86703 23395.99736 23101.09418 22818.46595000001 22389.89684 21850.39245 20189.30062 19782.44545 19491.10357999995 19284.29056 18755.66964 17012.13304 16705.1708 16556.75738 15635.39997 15398.29573 15067.59911 14674.82839 14656.76147 14485.51961 14136.10182 13958.25367 13503.14015 12699.95681 12524.77748 12418.93489 12351.08593 12340.33033 12303.18827 11956.05057 11939.39769 11717.66303 11510.84651 11500.08711 10857.0944 10648.53813 10589.64708 10502.94265 10437.96451 10164.05376 9895.85837799999 9675.35136599999 9638.060660999989 9474.258567 9432.057232999989 9377.618319 9070.606092 9064.885783999987 8594.189588 8538.638843 8324.386558999997 8064.722442 7628.467095999998 7538.743390999994 7536.351193 7513.191606 7414.966991 7350.149490000001 7312.67849 7129.564044 7044.365476 6966.305199 6896.368090000001 6697.727646 6617.127792 6439.16715 6263.340651 6226.772647000001 6168.61922 6105.322079 6091.21587 5944.932957 5411.157654 5107.542184 5100.637732 4853.826756 4810.407014999999 4536.523606 4477.551533 4462.946339 4151.743 822 4069.84135 4063.350521 4003.527241 3966.039158 3946.397675 3916.614015 3792.662419 3743.844217 3692.334198 3664.667568 3622.421889 3591.838616 3579.585202 3522.954407 3477.308781 3193.901161 2780.049264 2774.470665 2680.012576 2643.482226 2623.715095 2410.547437 2392.894517 2297.054831 2289.817151 2288.22226 2253.75032 2094.278303 2081.528655 1973.86698 1938.575462 1892.050829 1764.447524 1748.110073 1695.4525 1615.71421 1517.238716 1469.307153 1410.011917 1394.738264 1371.013355 1330.639095 1283.670551 1271.46968 1246.761329 1178.486386 1176.867108 1145.23877 1119.446102 1101.747559 1048.599213 1025.218612 997.9699260999988 933.5405185999994 929.9250863999994 856.2210252999994 798.4121349999994 787.7003452 781.2569103 732.8525575999988 702.5809121999994 680.4287547 655.6087685 588.7788164 587.9976075999994 521.9902509999994 461.7454491 458.7361567999993 396.169121 319.0431206 240.5498984Percentage of the population exposed to malaria in 1966
Real GDP per capita in 2010 (proportional scale)
The culture hypothesis claims that different values and cultural beliefs are ultimately responsible for the large differences in prosperity observed around the globe.
© 2015 Pearson Education, Inc
8.1 Proximate Versus Fundamental Causes of Prosperity
14
In 1905, the German socialist Max Weber argued that Protestant beliefs lead to a greater work effort, higher savings, and increased income.
The following graph tests the Protestant work ethic hypothesis using cross-country data.
© 2015 Pearson Education, Inc
8.1 Proximate Versus Fundamental Causes of Prosperity
15
© 2015 Pearson Education, Inc
8.1 Proximate Versus Fundamental Causes of Prosperity
Instructor: The graph examines the relationship between the percentage of the population that is Protestant in 1900 (horizontal line) and real GDP per capita (vertical line) in 2010. Each dot represents a different country with, the red dots labeled. The brown line is the exponential fitted line.
If Weber’s Protestant Work Ethic hypothesis is correct, there should a positive relationship between the two variables. There is little evidence in support of this hypothesis given the many rich countries with 0 Protestants.
Sources: Religion data is from the World Religion Database and GDP data is from Penn World Table Version 7.1
16
Test of Weber's Protestant Work Ethic Hypothesis
0.001 0.014 0.0 0.014 0.0 0.99 0.752 0.002 0.482 0.0 0.63 0.562 0.01 0.027 0.601 0.509 0.988 0.995 0.004 0.103 0.884 0.611 0.992 0.97 0.078 0.001 0.002 0.017 0.367 0.84 0.001 0.833 0.771 0.0 0.001 0.002 0.001 0.001 0.056 0.004 0.0 0.052 0.0 0.0 0.0 0.074 0.002 0.541 0.029 0.27 0.0 0.008 0.001 0.415 0.85 0.005 0.004 0.001 0.012 0.009 0.45 0.155 0.005 0.009 0.006 0.003 0.016 0.003 0.005 0.12 0.198 0.001 0.003 0.004 0.022 0.011 0.108 0.875 0.0 0.0 0.013 0.001 0.405 0.738 0.009 0.001 0.001 0.154 0.252 0.001 0.05 0.001 0.017 0.856 0.538 0.195 0.0 0.877 0.003 0.0 0.0 0.0 0.001 0.0 0.001 0.0 0.002 0.005 0.056 0.0 0.43 0.003 0.724 0.0 0.023 0.0 0.012 0.0 0.0 0.001 0.001 0.007 0.546 0.0 0.503 0.001 0.0 0.005 0.0 0.0 0.009 0.0 0.011 0.0 0.0 0.002 0.003 0.006 0.0 0.0 0.033 0.002 0.16 0.0 0.0 0.0 0.002 0.005 0.0 0.001 0.0 0.024 0.074 0.001 0.0 0.0 0.021 0.001 0.004 0.002 0.0 0.023 0.0 0.0 0.0 0.039 0.0 0.0 0.0 0.0 0.0 0.003 0.187 0.0 0.013 0.0 0.0 0.0 0.0 0.055 0.0 0.022 0.006 0.0 0.001 0.852 0.512 0.019 0.036 0.016 0.003 0.0 0.045 136248.1365 75588.11588 60175.41205 55862.41792 54790.36195 50487.52367 45866.49122 44555.22975999999 41364.99676 41239.92412 41113.58523 39978.02568 38684.72037 38585.51335 38190.63356 37103.55928 36132.38489 35612.06548999999 35556.61991 34876.73916 34268.00841 34089.0961 337 05.00435 32988.84535 32241.09426 32104.92001000001 31447.22902 31299.27919 30749.3483 30111.01875 28377.45915 28088.5447 27789.63139999999 27331.49875 26609.14522999999 26034.56006 25216.3956 24902.86703 23395.99736 23101.09418 22818.46595000001 22389.89684 21850.39245 20189.30062 19782.44545 19491.10357999995 19284.29056 18755.66964 17012.13304 16705.1708 16556.75738 15635.39997 15398.29573 15067.59911 14674.82839 14656.76147 14485.51961 14136.10182 13958.25367 13503.14015 12699.95681 12524.77748 12418.93489 12351.08593 12340.33033 12303.18827 11956.05057 11939.39769 11717.66303 11510.84651 11500.08711 10857.0944 10648.53813 10589.64708 10502.94265 10437.96451 10164.05376 9895.85837799999 9675.35136599999 9638.060660999989 9474.258567 9432.057232999989 9377.618319 9070.606092 9064.885783999987 8594.189588 8538.638843 8324.386558999997 8064.722442 7628.467095999998 7538.743390999994 7536.351193 7513.191606 7414.966991 7350.149490000001 7312.67849 7129.564044 7044.365476 6966.305199 6896.368090000001 6697.727646 6617.127792 6439.16715 6263.340651 6226.772647000001 6168.61922 6105.322079 6091.21587 5944.932957 5411.157654 5107.542184 5100.637732 4853.826756 4810.407014999999 4536.523606 4477.551533 4462.946339 4151.743822 4069.84135 4063.350521 4003.527241 3966.039158 3946.397675 3916.614015 3792.662419 3743.844217 3692.334198 3664.667568 3622.421889 3591.838616 3579.585202 3522.954407 3477.308781 3193.901161 2780.049264 2774.470665 2680.012576 2643.482226 2623.715095 2410.547437 2392.894517 2297.054831 2289.817151 2288.22226 2253.75032 2094.278303 2081.528655 1973.86698 1938.575462 1892.050829 1764.447524 1748.110073 1695.4525 1615.71421 1517.238716 1469.307153 1410.011917 1394.738264 1371.013355 1330.639095 1283.670551 1271.46968 1246.761329 1178.486386 1176.867108 1145.23877 1119.446102 1101.747559 1048.599213 1025.218612 997.9699260999988 933.5405185999994 929.9250863999994 856.2210252999994 798.4121349999994 787.7003452 781.2569103 732.8525575999988 702.5809121999994 680.4287547 655.6087685 588.7788164 587.9976075999994 521.9902509999994 461.7454491 458.7361567999993 396.169121 319.0431206 240.5498984Percentage of the population that is Protestant in 1900
Real GDP per capita in 2010 (proportional scale)
Almost 20 years ago, the Harvard political scientist Samuel Huntington talked of a “clash of civilization” between the West and Islam.
The following graph tests the Clash of Civilization hypothesis using cross-country data
© 2015 Pearson Education, Inc
8.1 Proximate Versus Fundamental Causes of Prosperity
17
© 2015 Pearson Education, Inc
8.1 Proximate Versus Fundamental Causes of Prosperity
Instructor: The graph examines the relationship between the percentage of the population that is Muslim in 1900 (horizontal line) and real GDP per capita (vertical line) in 2010. Each dot represents a different country with the red dots labeled and blue line is the exponential fitted line.
If Huntington’s clash of civilization hypothesis is correct, there should be a negative relationship between the two variables. There is no evidence in support of this hypothesis, given that the fitted brown line has a zero slope.
Sources: Religion data is from the World Religion Database and GDP data is from Penn World Table Version 7.1
18
Test of Clash of Civilizations
0.996 0.0 0.999 0.22 0.0 0.0 0.0 0.61 0.0 0.997 0.003 0.0 0.003 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.003 0.002 0.0 0.001 0.038 0.0 0.0 0.0 0.0 0.0 0.0 0.833 0.128 0.0 0.0 0.997 1.0 0.0 1.0 0.0 0.938 0.0 0.216 0.001 0.0 0.0 0.984 0.089 0.0 0.001 0.0 0.0 0.0 0.001 0.206 0.0 0.0 0.003 0.001 0.939 0.488 0.0 0.0 0.0 0.0 0.005 0.1 0.172 0.0 0.773 0.109 0.0 0.0 0.0 0.893 0.981 0.008 0.0 0.0 0.003 0.001 0.015 0.084 0.0 0.006 0.0 0.0 0.051 0.002 0.0 0.0 0.0 0.685 0.0 0.866 0.0 0.0 0.875 0.0 0.395 0.102 0.0 0.068 0.811 0.0 0.895 0.063 0.942 0.022 0.0 0.069 1.0 0.4 0.01 0.0 0.831 0.0 0.0 0.0 0.964 0.0 0.0 0.01 0.137 0.035 0.007 0.0 0.984 0.001 0.002 0.995 0.985 0.821 0.0 0.62 0.0 0.05 0.963 0.0 0.977 0.02 0.983 0.05 0.259 0.0 0.0 0.7 0.0 0.0 0.656 0.36 0.05 0.81 0.034 0.07 0.07 0.01 0.02 0.99 5 0.002 0.3 0.1 0.1 0.999 0.15 0.58 0.03 0.04 0.005 0.246 0.03 0.004 0.5 0.451 0.999 0.02 0.002 0.002 0.006 0.0 0.0 0.0 0.0 0.0 0.0 0.032 0.0 0.0 0.097 136248.1365 75588.11588 60175.41205 55862.41792 54790.36195 50487.52367 45866.49122 44555.22975999999 41364.99676 41239.92412 41113.58523 39978.02568 38684.72037 38585.51335 38190.63356 37103.55928 36132.38489 35612.06548999999 35556.61991 34876.73916 34268.00841 34089.0961 33705.00435 32988.84535 32241.09426 32104.92001000001 31447.22902 31299.27919 30749.3483 30111.01875 28377.45915 28088.5447 27789.63139999999 27331.49875 26609.14522999999 26034.56006 25216.3956 24902.86703 23395.99736 23101.09418 22818.46595000001 22389.89684 21850.39245 20189.30062 19782.44545 19491.10357999995 19284.29056 18755.66964 17012.13304 16705.1708 16556.75738 15635.39997 15398.29573 15067.59911 14674.82839 14656.76147 14485.51961 14136.10182 13958.25367 13503.14015 12699.95681 12524.77748 12418.93489 12351.08593 12340.33033 12303.18827 11956.05057 11939.39769 11717.66303 11510.84651 11500.08711 10857.0944 10648.53813 10589.64708 10502.94265 10437.96451 10164.05376 9895.85837799999 9675.35136599999 9638.060660999989 9474.258567 9432.057232999989 9377.618319 9070.606092 9064.885783999987 8594.189588 8538.638843 8324.386558999997 8064.722442 7628.467095999998 7538.743390999994 7536.351193 7513.191606 7414.966991 7350.149490000001 7312.67849 7129.564044 7044.365476 6966.305199 6896.368090000001 6697.727646 6617.127792 6439.16715 6263.340651 6226.772647000001 6168.61922 6105.322079 6091.21587 5944.932957 5411.157654 5107.542184 5100.637732 4853.826756 4810.407014999999 4536.523606 4477.551533 4462.946339 4151.743822 4069.84135 4063.350521 4003.527241 3966.039158 3946.397675 3916.614015 3792.662419 3743.844217 3692.334198 3664.667568 3622.421889 3591.838616 3579.585202 3522.954407 3477.308781 3193.901161 2780.049264 2774.470665 2680.012576 2643.482226 2623.715095 2410.547437 2392.894517 2297.054831 2289.817151 2288.22226 2253.75032 2094.278303 2081.528655 1973.86698 1938.575462 1892.050829 1764.447524 1748.110073 1695.4525 1615.71421 1517.238716 1469.307153 1410.011917 1394.738264 1371.013355 1330.639095 1283.670551 1271.46968 1246.761329 1178.486386 1176.867108 1145.23877 1119.446102 1101.747559 1048.599213 1025.218612 997.9699260999988 933.5405185999994 929.9250863999994 856.2210252999994 798.4121349999994 787.7003452 781.2569103 732.8525575999988 702.5809121999994 680.4287547 655.6087685 588.7788164 587.9976075999994 521.9902509999994 461.7454491 458.7361567999993 396.169121 319.0431206 240.5498984Percentage of the population that is Muslim in 1900
Real GDP per capita in 2010 (proportional scale)
The institutions hypothesis claims that differences in the way societies organize themselves and shape the incentives of individuals and businesses (so-called economic rules of the game) are ultimately responsible for the large differences in prosperity observed around the globe.
© 2015 Pearson Education, Inc
8.1 Proximate Versus Fundamental Causes of Prosperity
19
© 2015 Pearson Education, Inc
8.1 Proximate Versus Fundamental Causes of Prosperity
Instructor: The graph examines the relationship between the rule of law in 1995 (horizontal line) and real GDP per capita (vertical line) in 2010. Each dot represents a different country, with the red dots labeled. The blue line is the exponential fitted line.
If the institutions hypothesis is correct, there should a positive relationship between the two variables. There is strong evidence in support of this hypothesis, given that the fitted blue line is positively sloped, with the observations clustered close to it.
Sources: Rule of law data are from Daniel Kaufmann, Aart Kraay, and Pablo Zoido-Lobatón (1999), "Aggregating Governance Indicators," World Bank Policy Research Working Paper No. 2195, and GDP data are from Penn World Tables 7.1
20
Test of Institutions Hypothesis
0.432828605175018 1.80476212501526 0.785017728805542 1.30816960334778 0.186528027057648 1.90802180767059 1.24161052703857 0.570452809333801 1.5347820520401 0.651625394821167 1.73465085029602 1.94789326190948 0.886804223060608 1.8534232378006 1.72507786750793 1.72624933719635 1.79335010051727 1.71563792228699 1.277291297912599 1.54075300693512 1.67805683612823 1.61080956459045 1.83661603927612 1.94984447956085 0.710722744464874 0.84117329120636 1.37017238140106 1.41041707992554 0.420637130737305 1.1757698059082 0.844504594802856 1.1550589799881 1.83645343780518 1.37680542469025 -0.810991048812866 1.12858700752258 0.844865143299103 1.11948215961456 0.780486047267914 0.48836612701416 0.977839946746826 0.717035591602325 1.21641409397125 0.181433230638504 1.21534907817841 -0.867702484130859 0.232497274875641 0.916382908821106 0.550769567489624 0.691580 533981323 0.818506956100464 -1.23701369762421 -0.947520017623901 -0.255102217197418 0.237811163067818 0.977839946746826 0.400522887706757 -1.19897305965424 -0.796081304550171 -0.194353759288788 1.18857431411743 0.157578408718109 0.0615431778132915 -0.0301678385585546 -1.04941248893738 0.477511793375015 -0.571051597595215 0.494036465883255 -0.890450894832611 0.602821230888367 -0.202100813388824 -0.228439852595329 -0.318855911493301 -0.528242707252502 -0.104243069887161 0.93790602684021 -0.318126976490021 0.553430199623108 0.0615431778132915 -1.0722633600235 -0.531147360801697 -0.0828116983175278 -0.870571613311768 0.0503517836332321 -1.25627255439758 -0.398913979530334 -0.326107919216156 0.551757097244263 -0.532949388027191 -0.3569315969944 -0.885850548744202 0.0641089826822281 -0.654276192188263 0.569450199604034 -0.405120968818665 -1.04480743408203 0.0615431778132915 -0.394784569740295 -0.447187125682831 -1.1181458234787 0.767070770263672 -1.14629149436951 -0.589997231960297 -0.751857280731201 -0.109054982662201 -1.01156377792358 -0.509918451309204 -0.416858434677124 -1.62113177776337 -1.2573789358139 0.0220411662012339 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-0.638212919235229 -1.733420252799987 -1.43674373626709 -0.495393633842468 -1.36217641830444 -0.7055662 8704071 -1.23101246356964 -1.77930331230164 -1.35974144935608 -0.793829202651978 -0.67115306854248 -0.546539902687073 -0.802152693271637 -0.441953897476196 -1.50438463687897 -0.338129639625549 -0.876254439353943 -2.28264141082764 -2.10647869110107 -1.48360228538513 -0.8459632396698 -1.99948024749756 1.50538122653961 0.857940256595612 1.50538122653961 0.977839946746826 1.50538122653961 -1.36828327178955 1.24161052703857 0.806923627853394 1.68902397155762 -0.0772425979375839 136248.1365 75588.11588 60175.41205 55862.41792 54790.36195 50487.52367 45866.49122 44555.22975999999 41364.99676 41239.92412 41113.58523 39978.02568 38684.7203 7 38585.51335 38190.63356 37103.55928 36132.38489 35612.06548999999 35556.61991 34876.73916 34268.00841 34089.0961 33705.00435 32988.84535 32241.09426 32104.92001000001 31447.22902 31299.27919 30749.3483 30111.01875 28377.45915 28088.5447 27789.63139999999 27331.49875 26609.14522999999 26034.56006 25216.3956 24902.86703 23395.99736 23101.09418 22818.46595000001 22389.89684 21850.39245 20189.30062 19782.44545 19491.10357999995 19284.29056 18755.66964 17012.13304 16705.1708 16556.75738 15635.39997 15398.29573 15067.59911 14674.82839 14656.76147 14485.51961 14136.10182 139 58.25367 13503.14015 12699.95681 12524.77748 12418.93489 12351.08593 12340.33033 12303.18827 11956.05057 11939.39769 11717.66303 11510.84651 11500.08711 10857.0944 10648.53813 10589.64708 10502.94265 10437.96451 10164.05376 9895.85837799999 9675.35136599999 9638.060660999989 9474.258567 9432.057232999989 9377.618319 9070.606092 9064.885783999987 8594.189588 8538.638843 8324.386558999997 8064.722442 7628.467095999998 7538.743390999994 7536.351193 7513.191606 7414.966991 7350.149490000001 7312.67849 7129.564044 7044.365476 6966.305199 6896.368090000001 6697.727646 6617.127792 6439.16715 6263.340651 6226.772647000001 6168.61922 6105.322079 6091.21587 5944.932957 5411.157654 5107.542184 5100.637732 4853.826756 4810.407014999999 4536.523606 4477.551533 4462.946339 4151.743822 4069.84135 4063.350521 4003.527241 3966.039158 3946.397675 3916.614015 3792.662419 3743.844217 3692.334198 3664.667568 3622.421889 3591.838616 3579.585202 3522.954407 3477.308781 3193.901161 2780.049264 2774.470665 2680.012576 2643.482226 2623.715095 2410.547437 2392.894517 2297.054831 2289.817151 2288.22226 2253.75032 2094.278303 2081.528655 1973.86698 1938.575462 1892.050829 1764.447524 1748.110073 1695.4525 1615.71421 1517.238716 1469.307153 1410.011917 1394.738264 1371.013355 1330.639095 1283.670551 1271.46968 1246.761329 1178.486386 1176.867108 1145.23877 1119.446102 1101.747559 1048.599213 1025.218612 997.9699260999988 933.5405185999994 929.9250863999994 856.2210252999994 798.4121349999994 787.7003452 781.2569103 732.8525575999988 702.5809121999994 680.4287547 655.6087685 588.7788164 587.9976075999994 521.9902509999994 461.7454491 458.7361567999993 396.169121 319.0431206 240.5498984Rule of law (-2 is lowest; +2 is highest)
Real GDP per capita in 2010 (proportional scale)
Institutions have three important features:
They are determined by individuals.
They place constraints on behavior.
They shape human behavior by determining incentives.
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8.1 Proximate Versus Fundamental Causes of Prosperity
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The “natural experiment” of the two Koreas provides a test of the institutions hypothesis.
In the 1940s, North and South Korea were a single country, with a unified language, culture, and geography.
In 1947, the country was split into two countries along the 38th parallel by an agreement between the United States and the Soviet Union.
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8.1 Proximate Versus Fundamental Causes of Prosperity
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© 2015 Pearson Education, Inc
8.1 Proximate Versus Fundamental Causes of Prosperity
Exhibit 8.2 GDP per Capita in North and South Korea (in PPP-adjusted 2005 constant dollars)
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By 2010, North Korea was an economic “disaster,” with a GDP per capita of $1,500, while South Korea was an economic “miracle,” with a GDP per capita of close to $30,000.
What happened?
Was it geography? Was it culture? Was it institutions?
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8.1 Proximate Versus Fundamental Causes of Prosperity
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The South Korean autocratic government of Syngman Rhee and Park Chung-hee adopted a market-based economy, providing incentives for investment in physical and human capital.
The North Korean dictatorship of Kim Il-Sung and his son Kim Jong-Il adopted a strict communist system (called Juche) that outlawed private property and banned markets.
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8.1 Proximate Versus Fundamental Causes of Prosperity
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8.2 Institutions and Economic Development
Economic institutions
Aspects of society’s rules that concern economic transactions.
Economic institutions include:
Protection of property rights and ownership
Impartiality of the justice system
Financial arrangements between savers and borrowers
Regulations concerning new businesses or occupations
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8.2 Institutions and Economic Development
Inclusive economic institutions
Institutions that support and encourage economic transactions and, as such:
Protect private property
Uphold law and order
Allow and enforce private contracts
Allow free entry into new lines of business and occupations
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8.2 Institutions and Economic Development
Extractive economic institutions
Institutions that remove resources from the economy and, as such:
Do not protect private property
Do not enforce private contracts
Interfere with the workings of markets
Restrict entry into new lines of business and occupations
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8.2 Institutions and Economic Development
Political institutions
The aspects of society’s rules that determine who holds political power and what types of constraints are placed on them.
In North Korea, political power in the past lay completely with Kim Il-Sung and then Kim Jong-Il, and it now lies with Kim Jong-Un.
In South Korea, political power is spread between an elected president and Parliament.
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The experience in Eastern Europe provides another example of the institutions hypothesis.
In 1948, Czechoslovakia (forcibly) became communist, with extractive institutions, while Austria followed a market system with inclusive institutions.
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8.2 Institutions and Economic Development
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© 2015 Pearson Education, Inc
8.2 Institutions and Economic Development
Exhibit 8.3 GDP per Capita in Austria and the Neighboring Czechoslovakia Since 1948 (in PPP-adjusted 2005 constant dollars)
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By 1989, Czechoslovakia had a GDP per capita of $12,066, while Austria had a GDP per capita of $22,514—almost twice as high!
What happened?
Was it geography? Was it culture? Was it institutions?
© 2015 Pearson Education, Inc
8.2 Institutions and Economic Development
Instructor: The real GDP per capita data are from Penn World Tables 7.1
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After the fall of Communism, the Czech Republic and Slovakia with their inclusive institutions are starting to close the GDP per capita gap with their neighbor Austria.
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8.2 Institutions and Economic Development
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Extractive economic institutions tend to support inefficient firms and prevent entrepreneurs with new ideas from entering the market.
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8.2 Institutions and Economic Development
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We rank potential entrepreneurs in descending order, according to the return they could earn by starting a business.
The return-to-entrepreneurship curve is the downward-sloping blue line on the next slide, which shows the number of entrepreneurs (horizontal axis) against their return (vertical axis).
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8.2 Institutions and Economic Development
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© 2015 Pearson Education, Inc
8.2 Institutions and Economic Development
Exhibit 8.4, Panel (a) How Extractive Economic Institutions Reduce the Number of Entrepreneurs
Instructor: The downward-sloping blue line is the return-to-entrepreneurship curve, which shows the number of entrepreneurs against their return.
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We then add the opportunity cost of the entrepreneurship, which is assumed to be the same for all entrepreneurs.
The opportunity cost line is the red horizontal line drawn at a constant opportunity cost.
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8.2 Institutions and Economic Development
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© 2015 Pearson Education, Inc
8.2 Institutions and Economic Development
Exhibit 8.4, Panel (a) How Extractive Economic Institutions Reduce the Number of Entrepreneurs
Instructor: The horizontal red curve is the opportunity cost schedule, which indicates the value to the entrepreneur of her best alternative.
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The intersection of the two lines gives us the equilibrium return and the equilibrium number of entrepreneurs.
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8.2 Institutions and Economic Development
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© 2015 Pearson Education, Inc
8.2 Institutions and Economic Development
Exhibit 8.4, Panel (a) How Extractive Economic Institutions Reduce the Number of Entrepreneurs
Instructor: The intersection of the two curves at point E1 gives the equilibrium where 700 entrepreneurs choose projects with a return of $50,000 or greater.
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Extractive institutions have two main impacts:
Weak property rights and legal enforcement prevent the entrepreneur from capturing the full returns they create.
This shifts the return-to-entrepreneurship line to the left.
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8.2 Institutions and Economic Development
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© 2015 Pearson Education, Inc
8.2 Institutions and Economic Development
Exhibit 8.4, Panel (b) How Extractive Economic Institutions Reduce the Number of Entrepreneurs
Instructor: Extractive economic institutions shift the return-to-entrepreneurship curve to the left due to (a) weak property rights and (b) lack of contract enforcement. As a result, the new equilibrium is E2, where only 300 entrepreneurs undertake projects.
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Barriers to entry in the marketplace increase the cost of entering the market.
This shifts the opportunity cost line upward.
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8.2 Institutions and Economic Development
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© 2015 Pearson Education, Inc
8.2 Institutions and Economic Development
Exhibit 8.4, Panel (c) How Extractive Economic Institutions Reduce the Number of Entrepreneurs
Instructor: Extractive economic institutions also shift the opportunity cost curve upward because they erect barriers to entry for entrepreneurs. As a result, the new equilibrium is E3, where only 100 entrepreneurs undertake projects.
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Why would a country adopt extractive economic institutions if they retard economic growth?
The notion of political creative destruction predicts that economic growth destabilizes existing regimes and reduces political power.
Therefore, rulers such as Kim Jong-Un of North Korea use extractive economic institutions to maintain political power.
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8.2 Institutions and Economic Development
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Inclusive economic institutions allowed the Industrial Revolution, with all of its complex social and economic processes, to occur in England in the late 18th century and in the United States in the 19th century.
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8.2 Institutions and Economic Development
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Evidence-Based Economics Example
Question: Are tropical
and semitropical areas
condemned to poverty by
their geographies?
Data: Urbanization and population density rates in 1500 and urbanization rates and GDP per capita in 2010.
© 2015 Pearson Education, Inc
8 Why Isn’t the Whole World Developed?
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In late 15th century, the European powers “colonized” Africa, Asia, Australasia, Latin America, and North America.
Although there is no GDP per capita data for that time, we can measure the living standards of these former colonies by using urbanization and population density rates in 1500.
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8 Why Isn’t the Whole World Developed?
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Countries that generate sufficient agricultural surplus and a transportation and trading network can support a larger population per square mile in general and a larger urban population in particular.
What, then, is the relationship between prosperity in 1500 and prosperity today, measured as GDP per capita?
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8 Why Isn’t the Whole World Developed?
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© 2015 Pearson Education, Inc
8 Why Isn’t the Whole World Developed?
Exhibit 8.6 The Reversal of Fortune Using Urbanization
Instructor: The data for urbanization rates in 1500 do not contain the former colonies in sub-Saharan Africa.
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© 2015 Pearson Education, Inc
8 Why Isn’t the Whole World Developed?
Exhibit 8.7 The Reversal of Fortune Using Population Density
Instructor: The data for population density rates in 1500 do contain the former colonies in sub-Saharan Africa.
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The two graphs show what we call “reversal of fortune”: Former colonies that were poor in 1500 are rich today, while former colonies that were rich in 1500 are relatively poor today.
What happened?
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8 Why Isn’t the Whole World Developed?
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The settlers in the poor, temperate areas of North America, Australasia, and Argentina developed inclusive economic institutions due to low settler mortality rates.
The settlers in the rich tropical areas of Mexico, Peru, India, and Morocco adopted extractive institutions due to high settler mortality rates.
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8 Why Isn’t the Whole World Developed?
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Evidence-Based Economics Example
Question: Are tropical and semitropical areas condemned to poverty by their geographies?
Answer: No. Although tropical areas tend to have lower GDP per capita levels today, the reason is not the geography of the tropics but rather the fact that extractive institutions were adopted in these areas.
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8 Why Isn’t the Whole World Developed?
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Many people, like the singer
Bono, actress Angelina
Jolie, and economist Jeffrey
Sachs, argue that increased
spending on foreign and
development aid by the
West is needed to alleviate
poverty around the world.
In the book The End of Poverty, Sachs argues that extreme poverty can be eliminated by the year 2025 through carefully planned and targeted development aid.
8.3 Is Foreign Aid the Solution to World Poverty?
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8.3 Is Foreign Aid the Solution to World Poverty?
Surprisingly, most economists contend that foreign aid has been ineffective, on the whole, in alleviating poverty.
Dambisa Moyo states in her book Dead Aid that more than $1 trillion of aid has gone to Africa, and yet most of the recipients of this aid are not better off, and some are even worse off.
Why?
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1. The amount of foreign aid provided is not large enough to lead to sizable increases in physical capital and educational attainment and, more importantly, does not impact technology or the efficiency of production.
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8.3 Is Foreign Aid the Solution to World Poverty?
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8.3 Is Foreign Aid the Solution to World Poverty?
2. In practice, much foreign aid does not get invested in education or new technologies but is captured by corrupt government officials.
Many studies indicate that only about 10% to 15% of foreign aid actually reaches its intended destination.
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8.3 Is Foreign Aid the Solution to World Poverty?
3. If the root of poverty is extractive economic institutions, then foreign aid given within these institutions will not fix the fundamental causes of poverty.
Think about why most people insist on donating money through charitable organizations rather than giving directly to the needy individuals.
© 2015 Pearson Education, Inc
Instructor: The answer to why most people insist on donating money through charitable organizations is that the donors want a mechanism to ensure that the money is spent on “correct” things, such as food, shelter, and schooling, rather than “incorrect” things, such as alcohol, drugs, and maybe video games.
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