WT Week 1 Flashcards
Motivation
Beyond GDP? Welfare across Countries and Time (Jones and Klenow, 2016)
To compare living standards of people in different countries taking into account welfare measures such as consumption, leisure, inequality, and mortality using the standard economics of expected utility.
Special settings
Beyond GDP? Welfare across Countries and Time (Jones and Klenow, 2016)
GDP is a flawed measure of economic welfare as many factors affect living standards within a country that are incorporated imperfectly, if at all, in GDP.
Theory
Beyond GDP? Welfare across Countries and Time (Jones and Klenow, 2016)
Compare welfare across countries using a common specification for preferences. A utilitarian expected utility calculation giving equal weight to each person. The welfare metric could be an equivalent variation (by what proportion to adjust to equal welfare) and a compensating variation (by what factor to increase to raise welfare) producing different results especially for poor countries. The paper reports the equivalent variation.
Empirical design
Beyond GDP? Welfare across Countries and Time (Jones and Klenow, 2016)
- Calculate consumption-equivalent measure of welfare to see what proportion of consumption in one country, given its values of these factors, would deliver the same expected utility as values in another country and make an individual indifferent between two countries? It is assumed that utility from leisure takes a form that implies a constant Frisch elasticity oflabour supply (holding the marginal utility of consumption fixed, the elasticity of labour supply with respect to the wage is constant).Various existing data sets (Penn World Table, UNU-WIDER World Income Inequality Database etc.) are used for data for a much wider set of countries. Evidence suggests that calculations using publicly-available multi-country datasets are potentially informative as they closely match the results derived from extensive data for the 13 aforementioned countries.
- propose a summary statistic for the economic well-being of people in a country. Our measure incorporates consumption, leisure, mortality, and inequality, first for a narrow set of countries using detailed micro data, and then more broadly using multi-country datasets.
- Each component we introduce plays a significant role in accounting for these differences, with mortality being most important.
Data
Beyond GDP? Welfare across Countries and Time (Jones and Klenow, 2016)
Detailed micro data from household surveys for 13 countries to provide a measure of welfare. Publicly available multi-country datasets to construct cruder welfare measures for 152 countries. The authors omitted morbidity (disease prevalence), the quality of the natural environment, crime, political freedom and intergenerational altruism.
Key findings
Beyond GDP? Welfare across Countries and Time (Jones and Klenow, 2016)
- GDP per person is an excellent indicator of welfare across the broad range of countries: the two measures have a correlation of 0.98. Nevertheless, for any given country, the differencebetween the two measures can be important. Across 13 countries, the median deviation is about 35 percent. Welfare is more dispersed than income.The way to reconcile these large deviations with the high correlation between welfare and income is that the “scales” are so different. Incomes vary by more than a factor of 64 in oursample, i.e., 6,300 percent, whereas the deviations are on the order of 25 to 50 percent.
- 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.
- Many developing countries, including all eight of the non-European countries in our sample, are poorer than incomes suggest because of a combination of shorter lives, low consumption shares, and extreme inequality.
- Welfare growth averages 3.1 percent between the 1980s and mid- 2000s, versus income growth of 2.1 percent, across the seven countries for which we have household surveys during these periods. A boost from rising life expectancy of about 1 percentage point per year accounts for the difference. An important contributor to welfare growth is the increase in life expectancy (Table 3)
- Western Europe looks considerably closer to the United States, emerging Asia has not caught up as much, and many developing countries are further behind.
Interpretation / policy implications
Beyond GDP? Welfare across Countries and Time (Jones and Klenow, 2016)
Developing countries should focus on inequality issue. Through the findings of the paper, it seems that most developing countries ought to not simply focus on boosting incomes but boosting health and life expectancy directly.
Motivation
How Much Should We Trust the Dictator’s GDP Growth Estimates? (Martínez, 2022)
To use night-time light data to assess the overstatement of economic growth in autocracies in self-reported GDP figures
Special settings
How Much Should We Trust the Dictator’s GDP Growth Estimates? (Martínez, 2022)
Given governments/their bodies are responsible for measuring GDP, autocratic countries might wish to overstate economic growth. This is permissable by the lack of strong democratic opposition/media to create checks and balances on government statistics. Creates misrepresented economy and might suggest that their economic successes are unrealistic.
Theory
How Much Should We Trust the Dictator’s GDP Growth Estimates? (Martínez, 2022)
GDP and NTL both positively correlated with real economic activity, with NTL being much less vulnerable to government manipulation.
Empirical design
How Much Should We Trust the Dictator’s GDP Growth Estimates? (Martínez, 2022)
“Maps night lights data to GDP
True GDP growth rate is some noise measure which deponds on output of a country
Reported GDP growth rate depends not only on the true GDP growth rate, but also on who is responsible for reporting
Assess whether there’s a difference between elasticity of GDP wrt NTL between democracies and autocracies.”
Data
How Much Should We Trust the Dictator’s GDP Growth Estimates? (Martínez, 2022)
“Political regimes: primary source is Freedom in the World Index which scores countries on 0-6 scale
NTL: National Oceanic and Atmospheric Administration, nightly observations of the globe between 20:30-22:00 covering 1992-2013
GDP: World Development Indicators from World Bank (1992-2013), heavily relient on self-reporting of national statistical agencies of member countries”
Key findings
How Much Should We Trust the Dictator’s GDP Growth Estimates? (Martínez, 2022)
“Table 1 shows NTL are a strong predictor of GDP; with an average NTL elasticity of GDP is 0.3
A 1 unit increase in the FiW index is associated with an increase of 0.02 units in the NTL elasticity of GDP
Autocracies overstate yearly GDP growth by about 35% (i.e. a true growth rate of 1% is reported as 1.35%)
Reruning regreesion on subcomponents of GDP show political regime only affects investment and government spending (but NTL is predictive across all 5)
- this is because these two metrics are reported by the government and thus difficult to verify
Dictators exaggerate growth estimates further when true growth is low
Countries overstate GDP growth more when they cross a threshold for foreign aid”
Interpretation / policy implications
How Much Should We Trust the Dictator’s GDP Growth Estimates? (Martínez, 2022)
Caution needs to be taken when considering autocratic GDP growth figures, might suggest changing the assessment of criteria-based funding schemes as there is a natural incentive to manipulate figures (might also suggest that the effect of these programmes is overstated given the allocation to economies that might have a different effect on the future GDP)
Motivation
Measuring economic growth from outer space
Use satellite data to on night lights to accurately measure the rate of income growth in countries to determine accuracy in officially reported figures.
Special settings
Measuring economic growth from outer space
Official data is self reported, economies may use different measures (difficulty in building price indices), satellite data provides a standardised measure and highlights differences in growth by regions within countries, for example, rural vs central areas in countries.
Theory
Measuring economic growth from outer space
GDP and NTL both positively correlated with real economic activity, with NTL being much less vulnerable to government manipulation.
* Governments tempted to lie about how well the economy is doing and given that GDP is common, this is more attractive.
* Incentive to lie is greater in autocracies, as they lack accountability
* Government is the main source of information for public spending and investment
Empirical design
Measuring economic growth from outer space
Simple averages across satellites within pixel years. Use transformed data from satellite imagery to draw conclusions. Using maps lights growth as a proxy for GDP growth and obtaining an optimal weight for this to be combined with national accounts. These optimal weights change according to the availability and volume of national accounts.
For countries with poor national income accounts, the optimal estimate of growth is a composite with roughly equal weights on conventionally measured growth and growth predicted from lights.
* Interaction between night light senstivity and world indicator: to measure autocracy
develop a statistical framework
Data
Measuring economic growth from outer space
- Night time lights: Satellite imagery from the US Air Force, currently have data sets for 30 satellite years covering 1992 to 2008. Findings for 30 countries give new figures of income growth for the years 1992-2006.
- Political regimes: Freedom in the world index by freedom house. Values range from 0 to 6 with lower values corresponding to greater enjoyment of political rights and civil liberties
Key findings
Measuring economic growth from outer space
“Estimates attained differ from official data by up to 3% annually. For developing countries with weaker statistical infrastructure, placing equal weighting on conventionally measured GDP growth and NTL measured growth offers an optimal estimate of true income growth.
* We show, for example, that coastal areas in sub-Saharan Africa are growing slower than the hinterland.
* Autocracies overestimate by 35%
Interpretation / policy implications
Measuring economic growth from outer space
- developing countries should incorporate NTL data into GDP growth measurements
- NTL data can identify regions experiencing slower growth, which can allow for better allocation of govt resources”
