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The Theory Of Economic Growth ... The Theory Of Economic Growth dc.type: Print - Paper dc.type: Book ... PDF download. download 1 file
- Contents
- Why This Book?
- For Whom?
- Competition and Entry
- Education and Distance to Frontier
- Macroeconomic Policy and Growth
- The Neoclassical Growth Model
- The AK Model
- Y AK
- = K −δ
- Y α t = K di it ∑ 0
- A it Kα , it 0
- Y A − α α t = K
- t A = t a A t
- I BASIC PARADIGMS OF GROWTH THEORY
- K K0
- Y α
- G A A g
- g → G as t → ∞
- 1.2.3 Conditional Convergence
- 1.2.3.1 Convergence in Growth Rates
- c dc
- 1.3.1.2 The Canonical Euler Equation
- 1.4 Conclusion
- Dynamic Optimization Using the Hamiltonian
- e − u c t dt ⎡ ∫0 ⎤ ⎣ ( ) ⎦
- Problems
- 4. The Golden Rule of capital accumulation and dynamic ineffi ciency in the Solow model
- 6. Measuring the speed of convergence in the Solow model
- 2.1 Introduction
- = K sA = −δ
- g K K
- c c 1 α − − ε = ρ − Ak α
- 2.3.4 Concluding Remarks
- 2.4 The Debate between Neoclassical and AK Advocates, in a Nutshell
- 2.5 An Open-Economy AK Model with Convergence
- 2.5.1 A Two-Sector Closed Economy
- 1 Y K α X − α
- 2.5.4 Concluding Comment
- 2.6 Conclusion
- 3. *Justifi cation for the AK model: Human capital
- 4. Justifi cation for the AK model: Government expenditure (based on Barro, 1990)
- 3.2 Endogenizing Technological Change
- A Simple Variant of the Product-Variety Model
- 1 L − α x α i x − i
- t dt
- t dt R
- ( Π ) λ t 2 w L − t 2
- + ( −υ ) α υ M
- 3.4 Conclusion
- 4.2.2 Production and Profi ts
- 4.2.3 Innovation
- 4.2.4 Research Arbitrage
- 1 − α 1 π = ( χ − )( α χ )
- 1 1 − α α − x A ∂ it = α ( it L x ) it
- 4.3.2 Innovation and Research Arbitrage
- 5. The continuous-time version of the Schumpeterian model, I
- 6. The continuous-time version of the Schumpeterian model, II
- 5.2 Measuring the Growth of Total Factor Productivity
- B B G k
- B B 1 A A
- 5.4 Capital Accumulation and Innovation
- 5.4.1 The Basics
- 1 A it x α − it
- 5.4.2 Innovation and Growth
- 5.4.4 Implications for Growth Accounting
- Problems
- 6.1 Introduction
- 6.2.1 Basic Setup
- 1 p A − α xα 1 − it = α it it
- π = 1 f ( γ − 1 ) ( + θ ) ψ
- 6.2.4.1 Credit Multiplier and R&D Investment
- 1 A − α = A K
- g sA
- j = j
- t sA
- t ( = Y s g K t )−δ
- 6.4 The Empirical Findings: Levine’s Survey, in a Nutshell
- 6.5 Conclusion
- 3. Liquidation value and R&D
- 4. *Income distribution and human capital
- 7.2.4 Convergence and Divergence
- β j μj
- β j μj
- X ⎟ u ⎠
- 8.3.2.3 The Market-Size Effect on Relative Productivity
- s = s ) α s − s ⎤ ⎦
- 8.4 Appropriate Technology and Productivity Differences*
- 8.4.3 Skill-Biased Technical Change
- 8.4.4 Explaining Cross-Country Productivity Differences
- H S L S H L
- 8.5 Conclusion
- 6. **Appropriate technology and growth (based on Basu and Weil 1998)
- 9.2.1 General-Purpose Technologies in the Neoclassical Model
- x = x ( ωt + 1 ) =α ωt 1 +
- L n
- 1 n μ +λ
- 9.3.2 Explaining the Increase in Within-Group Inequality
- 1 ω >ω 1
- 10.1 Introduction
- The Effects of a Productivity Increase
- Level Increase
- Y L y *
- Steady Productivity Growth
- Agriculture and Manufacturing
- Y Y Y
- m − m = ( − ) m
- Sustainable Growth
- y Y L A L L
- s h eθ
- h * u e θ δ =
- 10.3.2 Physical-Capital Accumulation
- s 1 α− κ = δ + g
- A = φ ( t )
- S = AL
- = K L
- 10.5 Conclusion
- 3. Property rights
- 6. **Nonbalanced growth (based on Kongsamut, Rebelo, and Xie 2001)
- 11.2 Do Institutions Matter?
- Growth-Maximizing Strategy
- Decentralized Equilibrium
- t + γ t − 1 ( ) 1 −
- ˆ η −η
- 11.4 Conclusion
- 12.2.1 Basic Environment
- 12.2.2 Technology and Innovation
- 1 p − 1 = 1 − γ
- 12.3.1 Environment
- 1 1 − α Y A α t it x it di ∫ 0
- 2 c cz A 2 it = it
- p = − π γ ( − )
- 12.3.4 The Effect of Labor Market Regulations
- 12.3.5 Main Theoretical Predictions
- 12.4 Conclusion
- Problems
- AK α H β
- L L
- Yss = ⎜ β ⎟ j AH ⎝ j ⎠
- h − α −β ⎞ = ⎜ ⎟ ⎝ δ ⎠
- Development Accounting2
- Y L A K Y 1 − α − β H Y 1 − α −β AX = ( ) ( ) =
- g 1 u
- 13.2.3 Threshold Effects and Low-Development Traps
- 13.4 Schumpeter Meets Gerschenkron
- w u w
- Cross-U.S.-State Evidence
- 13.5 Conclusion
- t u = t k t
- 14.3 Short-versus Long-Term Investments
- 14.3.1 The Argument
- Timing and Payoff Functions
- Cyclicality of R&D under Imperfect Credit Markets
- t ≤ μ t π ( t )
- + z w
- Credit Constraints, Volatility, and Growth
- Φ, s r = φ , s ∀ 0 , 1 ∈[ ]
- 14.4.2 Analysis
- 14.4.3 Equilibrium Dynamics
- 14.5 Conclusion
- 15.1 Introduction
- g ˆ 1
- 15.3 Opening Up to Trade, Abstracting from Innovation
- 15.3.1 The Experiment
- L * X
- Scale Effect
- * di ⎤ ) φ ⎥ ⎦
- Backwardness
- t ( +α ) t
- 15.4.1 Step-by-Step Innovation
- 15.4.2 Three Cases
- μA γ ( π − ( − τ ) φ ( μA )
- 15.4.7 How Trade Can Enhance Growth in All Countries
- 15.4.8 How Trade Can Reduce Growth in One Country
- 15.5 Conclusion
- 16.3 Schumpeterian Growth with an Exhaustible Resource
- p x ∂ L 1 1 A − α x α − 1 − α Rφ
- 16.4 Environment and Directed Technical Change
- Production with a Given Allocation of Labor
- d = ( −α ) α d
- 16.4.3 Taxing Dirty Production
- c −α L c = 1 A ( −α ) α c
- c + ε z = 1
- d + ε =
- 1 = z 1 1 ε αβγ
- = Y Y
- Appendix: Optimal Schumpeterian Growth with an Exhaustible Resource
- 1 R 1 R
- t A 1 − α it x α it di ∫ 0
- 17.3.2 Entry and Incumbent Innovation
- Equilibrium Growth Rate for Given Redistribution Policy
- Political Equilibrium and the Effect of Inequality on Growth
- 17.6 Conclusion
- 18 Looking Ahead: Culture and Development
- 18.1 What We Have Learned, in a Nutshell
- 18.2 Culture and Growth
- A Toy Model
- The Argument
- The Doepke-Zilibotti Model
- B 1 B ,
- 18.3.1 Growth through the Lens of Development Economics
- j K = j k L j
- y α j = A j k j
- Moving Beyond Endogenous Growth Models?
- 18.4 Conclusion
- A.1 The Simple Regression Model2
- i u
- Δ = β1Δ
- A.3 Multiple Regression Analysis
- A.4 Inference and Hypothesis Testing
- i + u
- + δ 1 + e
- A.6 Fixed-Effects Regressions5
- x jt + αj
Preface Why This Book? For Whom? Outline of the Book Acknowledgments
To learn about economic growth you need formal theory, for organizing the facts, clarifying causal relationships, and drawing out hidden implications. In growth economics, as in other areas of economics, an argument that is not disciplined by a clear theoretical framework is rarely enlightening. Our experience with graduates and undergraduates at B...
The book is aimed at three main audiences. The fi rst is graduate students. The book can be taught in its entirety in a one-semester graduate growth course. It can also be used as part of a growth and development sequence, in which case one can start with the fi rst four chapters, then move on to the chapters on fi nance (and wealth inequality), co...
Innovation is a vital source of long-run growth, and the reward for innovation is monopoly profi t, which comes from being able to do something that your rivals haven’t yet been able to match. Economists since Schumpeter have argued that this analysis implies a trade-off between growth and competition. Tighter anti-trust legislation would reduce th...
Figure I.6 shows how growth depends upon a country’s proximity to the world frontier productivity, respectively, for countries that invest mostly in primary and secondary education (upper graph) and for countries that invest more in tertiary Domestic productivity (per worker GDP) relative to U.S. productivity Domestic productivity (per worker GDP) ...
Figure I.7 shows how macro policies in the United States and the euro area react to booms and recessions. Short-term interest rates are depicted on the vertical axes, whereas the structural defi cit is depicted on the horizontal axes. We see that the United States reduces its interest rates and increases budget defi cits a lot during recessions, wh...
The primary reference in growth economics is the neoclassical paradigm. The success of this model owes fi rst to its parsimony; the growth process is described by only two equations: (1) a production equation that expresses the current fl ow of output goods as a function of the current stocks of capital and labor:
The fi rst version of endogenous growth theory is the so-called AK theory. AK models do not make an explicit distinction between capital accumulation and tech-nological progress. In effect they just lump together the physical and human capital whose accumulation is studied by neoclassical theory with the intellectual capital that is accumulated whe...
= with A a constant. If capital accumulates according to the same equation
as before, then the economy’s long-run (and short-run) growth rate is simply K g = = sA −δ K which is increasing in the saving rate s. AK theory presents a “one-size-fi ts-all” view of the growth process. It applies equally to advanced countries that have already accumulated capital and to coun-tries that are far behind. Like the neoclassical model...
in which there are Nt different varieties of intermediate product, each produced using Kit units of capital. By symmetry, the aggregate capital stock Kt will be divided up evenly among the N existing varieties equally, with the result that we can reexpress the production function as 1 −α α t N = t K t According to this function, the degree of produ...
< α < 1 where it A is a productivity parameter attached to the most recent technology used in industry i at time t. In this equation, it K represents the fl ow of a unique inter-mediate product used in this sector, each unit of which is produced one-for-one by fi nal output or, in the most complete version of the model, by capital. Aggre-gate outpu...
t t where the labor-augmenting productivity factor A t is just the unweighted sum of the sector-specifi c Ait’s. As in neoclassical theory, the economy’s long-run growth rate is given by the growth rate of At, which here depends endogenously on the economy-wide rate of innovation. There are two main inputs to innovation, namely, the private expendi...
is an inverse measure of “distance to the frontier.” Thus, by taking into account that innovations can interact with each other in different ways in different countries, Schumpeterian theory provides a framework in which the growth effects of various policies are highly context-dependent. In particular, the Schumpeterian apparatus is well suited to...
1. Of course, K is an aggregate index of the different capital goods and should be interpreted broadly so as to include human as well as physical capital. of output Y that can be produced depends on K according to an aggregate produc-tion function Y F K ( ) We assume that all capital and labor are fully and effi ciently employed, so F(K) is not onl...
= in fi gure 1.1, the capital stock will be increasing. Moreover, it will continue to increase monotonically, and it will converge in the long run to K*, the capital stock at which the two schedules intersect. Thus K* is a unique, stable, stationary state of the economy.5 The economic logic of this dynamic analysis is straightforward. When capital ...
φ = AL = κ The rate at which new saving raises k is the rate of saving per effi ciency unit sy. The rate at which depreciation causes k to fall is the amount of depreciation per person dk. In addition, growth in the number of effi ciency units, at the rate g, causes k to fall at the annual rate (n g)k. As before, the net rate of increase + in k is ...
= + ακ κ = + ακ κ In the long run, when k approaches k*, the time derivative κ approaches zero, so the growth rate G approaches the exogenous rate of technological change g:
But in the short run, as before, the growth rate can rise above g temporarily as a result of an increase in the saving rate s, which raises the rate of increase in the capital stock per effi ciency unit k according to the fundamental differential equa-tion (1.9). Intuitively, the growth rate of output per person does not fall to zero because as cap...
One of the main questions that growth economics addresses is whether poor countries are likely to catch up with rich ones. If so, we say there is cross-country convergence. The neoclassical model answers this question with a qualifi ed yes. That is, suppose country 1 has a lower per capita GDP than country 2. Then the theory predicts that this diff...
Sometimes economists speak of cross-country convergence in terms of the growth rate of per capita GDP rather than its level. We say there is convergence in growth rates if there is a tendency for cross-country differences in growth rates to vanish over time. This form of convergence does not imply that the standard of living of poor countries will ...
t = t dt Dividing through by u ′(ct), we can rewrite the preceding equation as
Whenever we shall apply this Euler formula in the next chapter, we will do it in the special isoelastic case:
The main lesson to take from the neoclassical model is that, in the long run, economic growth (that is, growth in per capita GDP) is driven by technological change. Without technological change an economy can perhaps grow for a while by accumulating capital, but eventually that growth will be choked off by the diminishing marginal product of capita...
Consider a representative infi nitely lived individual whose lifetime utility func-tion is pt W ∞
where c(t) is the time path of consumption per person, u(.) is an instantaneous utility function exhibiting positive but diminishing marginal utility, and r a posi-tive rate of time preference. To simplify the analysis we abstract once again from population growth by assuming a constant labor force: L = 1. Then, with continu-ous market clearing, pe...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
In other words, the fi xed effects create a different intercept for the regression line corresponding to each state, which captures the omitted variables that are present in each state and that are constant over time. For state j, the intercept will be b0 + aj. The slope coeffi cient will be the same for all states. We can also control for fi xed e...
I.1 The Importance of Growth 1 I.2 The World Income Distribution 6 I.3 Empirical Regularities about Economic Growth 12 I.4 A Brief History of Modern Growth Theory 16 I.5 Some Highlights of the Second Edition 21 1Growth Models with Exogenous Saving Rates (the Solow–Swan Model) 23 1.1 The Basic Structure 23 1.2 The Neoclassical Model of Solow ...
- 3MB
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4.4.4 Inequality, Multiple Equilibria, and Growth 185 4.5 Michael Kremer’s O-Ring Theory of Economic Development 186 4.5.1 The O-Ring Model 186 4.5.2 Implications of the O-Ring Theory 189 4.6 Economic Development as Self-Discovery 191 4.7 The Hausmann-Rodrik-Velasco Growth Diagnostics Framework 192 4.8 Conclusions 199
Mar 4, 2022 · Reflections on growth theory / Robert M. Solow -- Neoclassical models of endogenous growth : the effects of fiscal policy, innovation and fluctuations / Larry E. Jones and Rodolfo E. Manuelli -- Growth with quality-improving innovations : an integrated framework / Philippe Aghion and Peter Howitt -- Horizontal innovation in the theory of growth and development / Gino Gancia and Fabrizio ...
Nov 9, 2022 · Pdf_module_version 0.0.20 Ppi 360 Rcs_key 24143 Republisher_date 20221109173004 Republisher_operator associate-glennblair-beduya@archive.org Republisher_time 164 Scandate 20221107043639 Scanner station10.cebu.archive.org Scanningcenter
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a primer on growth theory • In the Solow model, growth is exogenous since it is driven by a rate of technical progress that is assumed to be constant. • In the 1980s, economists became interested in models where growth was endogenous, that is, was explained from within the system. • To do this, it is necessary to explicitly solve the consumer
