Page 1. Long-term Economic Growth

Transcription

1 Page 1 Long-term Economic Growth

2 Economic Growth and Wages Logic and Experience Tell Us Economic Growth per Capita is a Necessary but Not a Sufficient Condition for the Improvement of Living Standards over Time We re not going to fix the wage distribution challenge without first ensuring the developed economies improve their macroeconomic performance Page 2

3 Long View of Long Term Economic Growth World Per- Capita Income in $1990 Rising standards of living for humans really begins with the industrial revolution! Page 3

4 Long Term Economic Growth: Why the Emphasis? Real GDP per Capita Black line is Real GDP /Population Page 4 If history repeats in the long run Year Factor Income , , ,584

5 Long Term Economic Growth and Distribution Key Hope: If all boats rise with the tide the table below has significant implications for raising the standard of living for households, families, and individuals. If over the next generation the mal distribution of income of the past generation continue, the political and social implications will grow. Year Factor Income , , ,584 Page 5

6 Key Concept Small Differences in Economic Growth make Big Differences to Improvements in the Standard of Living over time Illustration using Doubling of Real GDP per Capita Growth Rate Years to Double Per 1.0% % % % % % % 10 Page 6

7 Magic of Cumulative Growth Rates % Increase Economic growth rates fascinate economists because small differences in growth rates, make substantial differences in income per capita within a few years. Page 7

8 Key Concept Number of Yrs it Takes to Double GDP per Capita Growth Rate Years to Double Per 1.0% % % % % % % 10 This is why EVERY developed and less developed country wants to increase its LONG RUN growth rate: Small increases in growth rates lead to perceived and actual increases in the rate the standard of living improves Page 8

9 Since Economic Growth is So Important What explains long term economic growth? Page 9

10 Since Economic Growth is So Important A little bit of math Suppose we break GDP into workers hours (L) and how much each worker produces on average (GDP/L). GDP/L is also called labor productivity The U.S. Dept. of Labor has an entire department that tracks and studies labor productivity GDP = L * GDP/L Page 10

11 Since Economic Growth is So Important A little bit of math GDP = L * GDP/L Now consider what the above equation looks like over time In time zero GDP(0) = L(0) * GDP(0)/L(0) In time one GDP(1) = L(1) * GDP(1)/L(1) A little bit more math %D GDP = [GDP(1)-GDP(0)]/GDP(0) %DGDP %DL + %D(GDP/L) In Words: Economic Growth is explained by the growth rate of employment + the growth rate of labor productivity Page 11

12 Growth of Labor over Time Three factors explain the growth of labor input Working Age Population Growth (i.e., demographics) The share of working age population that CHOOSES to be in the labor force AND has a job or is looking for one This is called the Labor Force Participation Rate To avoid effects of recessions, when analyzing long-term growth trends economists analyze periods of relatively full employment Worker education/skills. Also known as Human Capital Usually measured in terms of education and employment experience Page 12

13 Growth of Labor Productivity over Time Three factors explain the growth of labor productivity Growth in the capital/labor ratio (a.k.a., capital deepening ) Education and training of workers Technological change These vary over time, but there are some very important conclusions from the research Page 13

14 Sources of U.S. Labor Productivity Growth Avg Annual Growth Rates (%) Source: CBO = Congressional Budget Office Page 14

15 Sources of U.S. Labor Productivity Growth About 2/3 of improvements in the standard of living is from technological change. Only 1/3 is from having more capital per worker. Page 15

16 Want to Increase the Standard of Living? Research Conclusions Virtually all of the research over 50 years points to technological change as the key source of improving the standard of living and having a skilled workforce to produce goods and services Increasing investment per worker is helpful, but its contribution is relatively small compared with technological change. It takes a large increase in investment as a share of GDP to have much effect on the growth rate of capital and the capital/labor ratio Page 16

17 Estimates of US Capital Stock Trillions of Constant $ These are only estimates and involve many imputations, but it describes the growth in household, business and government infrastructure Page 17

18 How Much Investment to Increase Growth has Small Effects Capital is the accumulation of past year s investments that is still in production Contribution of investment (to capital) is after accounting for retirements and deterioration Growth Rate of Capital Stock (%) Page 18

19 Want to Increase the Standard of Living? Research Conclusions Increasing long term growth is all about generating the rate of innovation over time If countries knew how to create Steve Jobs, they would. Social returns to higher education continue to be significant. Page 19

20 Education Challenges Major Policy Concerns Keeping the least affluent IN SCHOOL Providing equal educational opportunities to all income classes Keeping cost of secondary education low Public financing constraints: Recessions reduce budgets at state and local level that finance K-12, Community Colleges, and Public Universities General Economic Rule: The more we privatize the cost of education the lower the growth in human capital and the potential growth rate of the entire economy, played out over decades. Page 20

21 Cross Country Comparisons Faux Movie (Place Keeper) Page 21

22 Quick Facts - GDP Country $ Trillions % United States Europe China Japan India Brazil Canada Korea, Rep Australia Russian Federation Subtotal All Other (201) Total World Output is Highly Concentrated Page 22

23 Quick Facts GDP Per Person Page 23 Country Population Income per Population Income per Country (M) Person ($000) (M) Person ($000) Switzerland Andorra Bermuda Israel Isle of Man France Qatar Japan Norway Italy UAE Puerto Rico Australia Korea, Rep United States Bahamas, The Denmark Spain Faroe Islands Bahrain Singapore Saudi Arabia Sweden Trinidad and Tobago Iceland Slovenia Ireland Oman United Kingdom Taiwan Netherlands Portugal Canada Greece Hong Kong Estonia Austria Czech Republic Greenland Cyprus Finland Uruguay Germany Slovakia Kuwait Seychelles Belgium Barbados New Zealand Lithuania Subtotal 1, All Other 6, Total 7,

24 Population Income per Population Income per Country Country (M) Person ($000) (M) Person ($000) Switzerland Andorra Bermuda Israel Isle of Man France Qatar Japan Norway Italy UAE Puerto Rico Australia Korea, Rep United States Bahamas, The Denmark Spain Faroe Islands Bahrain Singapore Saudi Arabia Sweden Trinidad and Tobago Iceland Slovenia Ireland Oman United Kingdom Taiwan Netherlands Portugal Canada Greece Hong Kong Estonia Austria Czech Republic Greenland Cyprus Finland Uruguay Germany Slovakia Kuwait Seychelles Belgium Barbados New Zealand Lithuania Subtotal 1, All Other 6, Total 7, Quick Facts GDP Per Person GDP per Person ($000) World income is highly concentrated in a few countries. The vast majority of the word s population is living in poverty. Page 24

25 A Long View of Per Capita Income by State Top 5 Thousands of 2016$ US Bottom 5 The average incomes all rise together, but that doesn t mean they re equal across states. Page 25

26 Similar View of Selected Geographic Regions Thousands of 2016$ Page 26

27 Per Capita Income by State in 2015 Page 27 $30K - 35K $35K - 40K $40K- 45K $45K - 50K $50K +

28 Per Capita Income by State in 2015 <85% % % >115% Page 28 of U.S. Avg. Per-Capita Income

29 Relative Per Capita Income in 1980 and 2015 CT DC Per Capita Income Relative to U.S. Average in 2015 (%) MS AR AK Per Capita Income Relative to U.S. Average in 1980 (%) Page 29

30 Relative Per Capita Income in 1980 and 2015 Per Capita Income Relative to U.S. Average in 2015 (%) Per Capita Income Relative to U.S. Average in 1980 (%) Page 30

31 Color Code States relatively better off in both 1980 and 2015 States relatively worse off in both 1980 and 2015 States relatively better off in 1980 but worse off in 2015 States relatively worse off in 1980 but better off in 2015 Includes Washington D.C. Page 31

32 Relative Per Capita Income in 1980 and 2015 Page 32 Colored according to prior page

33 2016 Votes by State that Didn t Follow Pattern High relative income but voted for Trump Low relative income but voted for Clinton Page 33

34 Page 34 Income Distribution: US, Int l, and US Regional Trends

35 Income Distribution Income distribution has become a hot topic Think Trump. Think Brexit It is an example of economic structure determined by very complex market forces operating on international and domestic economies There are some first principles to consider We ll use data and recent research findings to review Page 35

36 Income Distribution Preliminary Comments Do Economists Know What an Optimal Income Distribution Is? No We know what is fairer but we don t know what s fair. So, all of the analyses are about how over time the economy has become less fair. Can Economists Claim that When Income is Distributed More Equally, Economies will Grow Faster? No There are many factors influencing why economy A grows faster than economy B in different periods of time Data on distribution of income is one factor and simply doesn t explain much of the growth differentials either over time or across countries or among regions within the U.S. Page 36

37 Income Distribution Preliminary Comments Given the general level of prosperity in the US and other developed nations, social welfare is improved by providing some kind of social safety net All developed countries have social safety nets The size and amount of resources devoted to providing a safety net are highly controversial in the U.S. Any social safety net involves taxing A to pay B There are legitimate concerns about incentive effects The biggest transfers in the U.S. economy: cross-generational, a.k.a., the entitlement programs: Social Security and Medicare Page 37

38 Income Distribution Preliminary Comments Equality of opportunity independent of inherited wealth and income is a generally shared social goal Recent research attempts to ask even though incomes are not distributed equally, do individuals and families change relative position over time? It is much harder to measure than the spot measures of income distribution To measure opportunity you need longitudinal data You need to track individuals and families over time Page 38

39 Income Distribution Preliminary Comments There are significant differences in measurement and implications of income vs. wealth distributions We have far more and far better data on income distribution Those measures that we do have of wealth distribution indicate wealth distributions are far more unequal than income. Capital income raises far more measurement issues than wage and salary income does With capital income there are realized and unrealized gains and losses to consider IRS only taxes realized gains and losses Individuals experience many measures of net worth that incorporate unrealized gains and losses Page 39

40 Great Recession s Impact on Net Worth Trillions of $ The Meltdown in Housing Prices had Distributional Impacts Page 40

41 Two Major Caveats None of the data that have recently used to talk about the top 1% take into account transfers. They are all pretax Recent research is using tax data to analyze the top 1% The Piketty study does not account for transfers Mobility between groups is not captured in the data shown and is much harder to analyze Some preliminary research indicates upward mobility is limited and has not improved over time and varies significantly by region. Page 41

42 Recent Research on Social Mobility Page 42 This map shows the average percentile rank of children who grow up in below-median income families across areas of the U.S. (absolute upward mobility). Lighter colors represent areas where children from low-income families are more likely to move up in the income distribution.

43 Recent Research on Social Mobility Odds of Moving from Lowest Fifth to Top Fifth Rank Area Odds Rank Area Odds 1 San Jose 12.9% 41 Cleveland 5.1% 2 San Francisco 12.2% 42 St Louis 5.1% 3 Washington D.C 11.0% 43 Raleigh, 5.0% 4 Seattle 10.9% 44 Jacksonville 4.9% 5 Salt Lake City 10.8% 45 Columbus, 4.9% 6 New York City 10.5% 46 Indianapolis 4.9% 7 Boston 10.5% 47 Dayton, 4.9% 8 San Diego 10.4% 48 Atlanta, 4.5% 9 Newark 10.2% 49 Milwaukee 4.5% 10 Manchester 10.0% 50 Charlotte 4.4% Preliminary findings This is a real world example of economic structure Page 43

44 A Method for Displaying Distribution Data with 1 Number If all households have exactly the same income and we compare the share of families or households to the share of total income, we get a 45 degree line Share of Total Income (in %) Share of Total Households (%) Page 44

45 A Method for Displaying Distribution Data with 1 Number If income by family or household varies then we get a curved line. Share of Total Income (in %) Page 45

46 The Gini Coefficient Italian statistician Corrado Gini figured out the following formula Share of Total Income Gini = A/(A+B) = 2*A Page 46

47 How Does the Gini Coefficient Work in Practice? Percentile Distribution Scenario Equal Less Equal US HH (2015) 20% 20% 10% 3% 40% 20% 15% 8% 60% 20% 20% 14% 80% 20% 25% 23% 100% 20% 30% 54% Gini Today s Distribution of Income Page 47

48 How Does This Work in Practice for 2015? % of Total Income Percentile Distribution Scenario Equal Less Equal US HH (2015) 20% 20% 10% 3% 40% 20% 15% 8% 60% 20% 20% 14% 80% 20% 25% 23% 100% 20% 30% 54% Gini Page 48

49 Income vs. Wealth Distribution 2016 Estimates Share of Total Inc or NW (%) Gini (inc)=.48 40% of HH with no or little equity Gini (nw)=.86 Caveats apply: data on wealth distributions are not as reliable as data on income. The real distribution of wealth may be far less equal than shown. Page 49

50 Reversing the Diagrams Measure of Income and Wealth Concentration Among the Wealthiest Households Top % of Housholds # of HH (MM) % of Aggregate Average Income Net Worth Income ($) Net Worth ($) 1% ,881,911 26,590,070 5% ,309 8,979,859 10% ,421 5,314,563 20% ,002 3,043,894 Totals , ,576 Median 74,935 97,300 Page 50

51 Measures of Inequality Income Ginis Less Equal Gini More Equal Page 51

52 Mean vs. Median Family Income Thousands of $2015 Page 52 Avg. Annual Growth Rates Period GDP Per Family Person Mean Median Since 1972, the increase in the standard of living has been much slower for less affluent families than more affluent families.

53 US Family Income Trends Thousands of $ Less Equal % More Equal Page 53

54 Gini vs. Mean/Median Family Income Gini Coefficient They re all providing similar information: incomes are becoming less equally distributed and have since the mid 70s Page 54

55 Measures of Inequality Mean Income by HH Quintile Real Mean Income ($000) Top 5% Top 20% 60-80% 40-60% 20-40% Bottom 20% The standard of living has been increasing for the top 5% and not much more Page 55

56 Measures of Inequality - Wages Page 56 Econ Report of the President

57 Real Wages vs. Productivity Growth Index Jan 47 =1.0 BLS measures indicate a break occurring in early 1970s with prior trends aligning growth in real labor compensation and productivity Page 57

58 Trickle Down? Index Gini Two representations of the same phenomena: productivity gains not passed onto wages and family incomes becoming less equal over time beginning ~ 1973 Page 58

59 Measures of Inequality - International Page 59 Econ Report of the President

60 Family Income Trends US Regions Thousands of Constant $ Mean Income Less Equal Mean/Median % More Equal Page 60

61 Family Income Trends by Race Less Equal Gini Index More Equal While differences exist at a point in time, the trends for declining equality of income is occurring across all races Page 61

62 Causes Ongoing Research to Understand Why Has Pretax Distributions of Income become Less Equal in the last 40 Years. Hypotheses: Weakening of unions? Illegal immigration? International trade? Returns to job skills? Research has not found a smoking gun from these sources It is the one hypothesis that has generated results Wages and returns to education have been bifurcating on an international scale These are Structural Trends: Forces in the Economy Shaped by Technological Change and Changes in Economic Structure Page 62

63 Causes (cont.) % Change in Hourly Earnings Pct. Change in Real Hourly Earnings Males Females Page 63

64 Mitigating Public Policies Social Safety Net Social Security Medicare/Medicaid/Affordable Care Act Food Stamps & other welfare programs Earned Income Tax Credit Progressive Income Tax Schedules (potentially) Estate Tax Property Taxes Proposition 13 has created long list of winners and losers Public Education (K-12) and Community Colleges Federal Grants for College Page 64

65 Cross Country Comparisons Tax & Public Expenditures as Mitigating Factors Page 65

66 Page 66 US Taxes and Distributional Effects

67 Page 67 US Taxes

68 Page 68 US Taxes

69 Data on Mitigating Public Policies Pre vs. Post Tax Gini Indices Among Advanced Economies Trend of both pretax and after tax income is becoming less equ Gini Index of HH Income Spread between lines indicates the impact of taxes on income equality hasn t changed but mitigates some of the distributional consequences of market economies Page 69

70 Data on Mitigating Public Policies: US vs. CAN Pre vs. Post Tax Gini Indices Canada pretax distribution has been becoming less equal but measure of inequality is more offset by its nation s tax policies, which are more progressive than U.S. Page 70

71 Data on Mitigating Public Policies: US vs. UK Pre vs. Post Tax Gini Indices UK pretax distribution has been becoming less equal but measure of inequality is more offset by its nation s tax policies, and spending policies. Page 71

72 Data on Mitigating Public Policies: US vs. UK Pre vs. Post Tax Gini Indices French pretax distribution of income has been relatively constant and its post-tax has become slightly more equal. Page 72

73 International Data on Mitigating Public Policies Pre vs. Post Tax Gini Indices US UK SP JAP IT CAN FR SWE DEN POST TAX PRETAX Page 73

74 Page 74 World Gini Indexes 2014

75 What Do These Trends Tell Us? Not all boats rise with the tide Almost all major advanced economies have experienced the same changes in pretax (a.k.a. earnings ) distribution of income All regions in the U.S. have experienced similar trends All advanced economies mitigate the distributional effects of the economic structure, some more than others The major factors influencing distributional outcomes are inherent in the economy. The economic forces impact pre-tax distributions are not a function of policy Policies are confined to the redistributive powers of national public taxing and spending policies. Page 75