Experimentation in Federal Systems. June 2012
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1 Experimentation in Federal Systems Steven Callander Stanford Bård Harstad Kellogg/Oslo June 2012 Kellogg/Oslo) Experimentation June / 28
2 Motivation It is one of the happy incidents of the federal system that a single courageous state may, if its citizens choose, serve as a laboratory; and try novel social and economic experiments without risk to the rest of the country. Justice Brandeis, C Kellogg/Oslo) Experimentation June / 28
3 Motivation It is one of the happy incidents of the federal system that a single courageous state may, if its citizens choose, serve as a laboratory; and try novel social and economic experiments without risk to the rest of the country. Justice Brandeis, Two presumptions: free-riding doesn t undermine experimentation experiments are useful to all states preferences are similar Kellogg/Oslo) Experimentation June / 28
4 Motivation It is one of the happy incidents of the federal system that a single courageous state may, if its citizens choose, serve as a laboratory; and try novel social and economic experiments without risk to the rest of the country. Justice Brandeis, Two presumptions: free-riding doesn t undermine experimentation experiments are useful to all states preferences are similar E.g., California with environmental standards, Alabama with school vouchers. Kellogg/Oslo) Experimentation June / 28
5 Motivation It is one of the happy incidents of the federal system that a single courageous state may, if its citizens choose, serve as a laboratory; and try novel social and economic experiments without risk to the rest of the country. Justice Brandeis, Two presumptions: free-riding doesn t undermine experimentation experiments are useful to all states preferences are similar E.g., California with environmental standards, Alabama with school vouchers. Questions: Will states choose the right quantity of experiments? Will they choose the right type of experiments? Kellogg/Oslo) Experimentation June / 28
6 Our Contribution Part I Develop a model with two key features: 1 The choice whether to experiment (free-riding). 2 The policy to experiment with (preference heterogeneity). Kellogg/Oslo) Experimentation June / 28
7 Our Contribution Part I Develop a model with two key features: 1 The choice whether to experiment (free-riding). 2 The policy to experiment with (preference heterogeneity). Good News: Preference di erences mitigate free-riding. Kellogg/Oslo) Experimentation June / 28
8 Our Contribution Part I Develop a model with two key features: 1 The choice whether to experiment (free-riding). 2 The policy to experiment with (preference heterogeneity). Good News: Preference di erences mitigate free-riding. California experiments with liberal policies because no one else will. Kellogg/Oslo) Experimentation June / 28
9 Our Contribution Part I Develop a model with two key features: 1 The choice whether to experiment (free-riding). 2 The policy to experiment with (preference heterogeneity). Good News: Preference di erences mitigate free-riding. California experiments with liberal policies because no one else will. Bad News: Policy experiments are less socially bene cial. Kellogg/Oslo) Experimentation June / 28
10 Our Contribution Part I Develop a model with two key features: 1 The choice whether to experiment (free-riding). 2 The policy to experiment with (preference heterogeneity). Good News: Preference di erences mitigate free-riding. California experiments with liberal policies because no one else will. Bad News: Policy experiments are less socially bene cial. Really Bad News: Threat of free-riding induces Pareto dominated policy choices. Kellogg/Oslo) Experimentation June / 28
11 Our Contribution Part II Question: Can a better federalist system be designed? Kellogg/Oslo) Experimentation June / 28
12 Our Contribution Part II Question: Can a better federalist system be designed? Federalism is the sharing of power across levels of government. Brandeis view fully decentralized (= no federalism). Can power be shared in a more e ective way? Kellogg/Oslo) Experimentation June / 28
13 Our Contribution Part II Question: Can a better federalist system be designed? Federalism is the sharing of power across levels of government. Brandeis view fully decentralized (= no federalism). Can power be shared in a more e ective way? Our Answer: Progressive federalism. Dynamic power sharing: Begin decentralized and become centralized. C Kellogg/Oslo) Experimentation June / 28
14 Our Contribution Part II Question: Can a better federalist system be designed? Federalism is the sharing of power across levels of government. Brandeis view fully decentralized (= no federalism). Can power be shared in a more e ective way? Our Answer: Progressive federalism. Dynamic power sharing: Begin decentralized and become centralized. centralization implies policy harmonization. Kellogg/Oslo) Experimentation June / 28
15 Our Contribution Part II Question: Can a better federalist system be designed? Federalism is the sharing of power across levels of government. Brandeis view fully decentralized (= no federalism). Can power be shared in a more e ective way? Our Answer: Progressive federalism. Dynamic power sharing: Begin decentralized and become centralized. centralization implies policy harmonization. states compete for their policy to be implemented nationally. C Kellogg/Oslo) Experimentation June / 28
16 Our Contribution Part II Question: Can a better federalist system be designed? Federalism is the sharing of power across levels of government. Brandeis view fully decentralized (= no federalism). Can power be shared in a more e ective way? Our Answer: Progressive federalism. Dynamic power sharing: Begin decentralized and become centralized. centralization implies policy harmonization. states compete for their policy to be implemented nationally. Appropriate metaphor for federalism is a tournament, rather than a laboratory. C Kellogg/Oslo) Experimentation June / 28
17 Related Literature 1 Experimentation in federal systems surprisingly little. Kellogg/Oslo) Experimentation June / 28
18 Related Literature 1 Experimentation in federal systems surprisingly little. Free-riding: Rose-Ackerman 80, Cai and Treisman 09, Strumpf 02. Preference heterogeneity: Volden, Ting & Carpenter 09. Kellogg/Oslo) Experimentation June / 28
19 Related Literature 1 Experimentation in federal systems surprisingly little. Free-riding: Rose-Ackerman 80, Cai and Treisman 09, Strumpf 02. Preference heterogeneity: Volden, Ting & Carpenter Empirical work on policy di usion. Learning through similar states. Kellogg/Oslo) Experimentation June / 28
20 Related Literature 1 Experimentation in federal systems surprisingly little. Free-riding: Rose-Ackerman 80, Cai and Treisman 09, Strumpf 02. Preference heterogeneity: Volden, Ting & Carpenter Empirical work on policy di usion. Learning through similar states. Volden 06, Buera, Monge-Naranjo & Primiceri 11 Kellogg/Oslo) Experimentation June / 28
21 Related Literature 1 Experimentation in federal systems surprisingly little. Free-riding: Rose-Ackerman 80, Cai and Treisman 09, Strumpf 02. Preference heterogeneity: Volden, Ting & Carpenter Empirical work on policy di usion. Learning through similar states. Volden 06, Buera, Monge-Naranjo & Primiceri 11 3 Economic theory: Experimentation and bandit-problems Heavy on free-riding, not on preference heterogeneity. Bolton and Harris 99, Keller, Rady & Cripps 05, Keller and Rady 10, Rosenberg, Solan and Vieille 07 Kellogg/Oslo) Experimentation June / 28
22 The Model Policy has two components: 1 Ideology. 2 Quality (public good) Kellogg/Oslo) Experimentation June / 28
23 The Model Policy has two components: 1 Ideology. 2 Quality (public good) We assume ideology is perfectly controlled & quality is unknown. Volden, Ting & Carpenter 09. Kellogg/Oslo) Experimentation June / 28
24 The Model Policy has two components: 1 Ideology. 2 Quality (public good) We assume ideology is perfectly controlled & quality is unknown. Volden, Ting & Carpenter 09. Experiment is binary: succeeds with probability p, at cost k. Kellogg/Oslo) Experimentation June / 28
25 The Model Policy has two components: 1 Ideology. 2 Quality (public good) We assume ideology is perfectly controlled & quality is unknown. Volden, Ting & Carpenter 09. Experiment is binary: succeeds with probability p, at cost k. Two districts (/states) with ideal points t i 2 R, i 2 fa, Bg Heterogeneity h = t B t A. C Kellogg/Oslo) Experimentation June / 28
26 Timing Decentralized System 1 Choose policy to explore: x i 2 R, i 2 fa, Bg. 2 Play safe or experiment e i 2 f0, 1g. outcomes observed s xi 2 f0, 1g 3 Final policy chosen: y i 2 fx A, x B g, i 2 fa, Bg. payo s: u i = s yi c (t i y i ) k e i. c (.) is concave, c 0 (0) = 0. C Kellogg/Oslo) Experimentation June / 28
27 The First-Best Kellogg/Oslo) Experimentation June / 28
28 The First-Best Kellogg/Oslo) Experimentation June / 28
29 The First-Best Kellogg/Oslo) Experimentation June / 28
30 The First-Best Kellogg/Oslo) Experimentation June / 28
31 The First-Best C Kellogg/Oslo) Experimentation June / 28
32 The First-Best Proposition Convergence from ideal points, t A < x A < 0 < x B < t B, is e cient i h 2 [h 0, h 00 ]. Each district should accomodate, a i = jx i t i j, satisfying c 0 (a i ) c 0 (h a i ) + c 0 = p (1 p), i 2 fa,bg (a i ) Kellogg/Oslo) Experimentation June / 28
33 Decentralization C Kellogg/Oslo) Experimentation June / 28
34 Decentralization - given locations C Kellogg/Oslo) Experimentation June / 28
35 Decentralization - given locations If locations are identical, i experiments even when j does if: p (1 p) k 0. 0 k p (1 p). Kellogg/Oslo) Experimentation June / 28
36 Decentralization - given locations If locations are identical, i experiments even when j does if: p (1 p) k 0. 0 k p (1 p). Given di erent locations, h a j > a i, i experiments if: [c (h a j ) c (a i )] p 2 k p (1 p) Kellogg/Oslo) Experimentation June / 28
37 Decentralization - given locations If locations are identical, i experiments even when j does if: p (1 p) k 0. 0 k p (1 p). Given di erent locations, h a j > a i, i experiments if: [c (h a j ) c (a i )] p 2 k p (1 p) If a i = a j = a, then c (h a) c (a) increases in h, decreases in a, and is 0 if h = 0 or a = h/2 Kellogg/Oslo) Experimentation June / 28
38 Decentralization - given locations If locations are identical, i experiments even when j does if: p (1 p) k 0. 0 k p (1 p). Given di erent locations, h a j > a i, i experiments if: [c (h a j ) c (a i )] p 2 k p (1 p) If a i = a j = a, then c (h a) c (a) increases in h, decreases in a, and is 0 if h = 0 or a = h/2 If k p (1 p) > 0, inducing both districts to experiment requires: C Kellogg/Oslo) Experimentation June / 28
39 Decentralization - given locations If locations are identical, i experiments even when j does if: p (1 p) k 0. 0 k p (1 p). Given di erent locations, h a j > a i, i experiments if: [c (h a j ) c (a i )] p 2 k p (1 p) If a i = a j = a, then c (h a) c (a) increases in h, decreases in a, and is 0 if h = 0 or a = h/2 If k p (1 p) > 0, inducing both districts to experiment requires: Heterogeneity h > 0 C Kellogg/Oslo) Experimentation June / 28
40 Decentralization - given locations If locations are identical, i experiments even when j does if: p (1 p) k 0. 0 k p (1 p). Given di erent locations, h a j > a i, i experiments if: [c (h a j ) c (a i )] p 2 k p (1 p) If a i = a j = a, then c (h a) c (a) increases in h, decreases in a, and is 0 if h = 0 or a = h/2 If k p (1 p) > 0, inducing both districts to experiment requires: Heterogeneity h > 0 Su ciently di erent policies x A 6= x B, a < h/2 C Kellogg/Oslo) Experimentation June / 28
41 Decentralization - given (symmetric) locations No convergence. Kellogg/Oslo) Experimentation June / 28
42 Decentralization - given (symmetric) locations No convergence. Possible divergence. Kellogg/Oslo) Experimentation June / 28
43 Decentralization - equilibrium locations C Kellogg/Oslo) Experimentation June / 28
44 Decentralization - equilibrium locations C The local optimum h is global if k 2p 1 p 2 p Kellogg/Oslo) Experimentation June / 28
45 Decentralization - equilibrium locations Proposition If h 2 [hd 0, h d ), experiments diverge: x A < t A < t B < x B, a i = a > 0 : k p (1 p) c (h a) c (a) = p 2 Divergence increases in k but decreases in p The smaller is h, the larger is divergence: jx B h x A j < 0. Kellogg/Oslo) Experimentation June / 28
46 Centralization - Model C Kellogg/Oslo) Experimentation June / 28
47 Centralization - Model Stage 3: A median voter decides on y A = y B 2 fx A, x B g, implying: C Kellogg/Oslo) Experimentation June / 28
48 Centralization - Model Stage 3: A median voter decides on y A = y B 2 fx A, x B g, implying: If both fail/succeed, the smallest jx i j is chosen C Kellogg/Oslo) Experimentation June / 28
49 Centralization - Model Stage 3: A median voter decides on y A = y B 2 fx A, x B g, implying: If both fail/succeed, the smallest jx i j is chosen If both equally close: fair draw Kellogg/Oslo) Experimentation June / 28
50 Centralization - Model Stage 3: A median voter decides on y A = y B 2 fx A, x B g, implying: If both fail/succeed, the smallest jx i j is chosen If both equally close: fair draw Ex post, the uniform policy is ine cient Kellogg/Oslo) Experimentation June / 28
51 Centralization - Model Stage 3: A median voter decides on y A = y B 2 fx A, x B g, implying: If both fail/succeed, the smallest jx i j is chosen If both equally close: fair draw Ex post, the uniform policy is ine cient Otherwise, the game is as before Kellogg/Oslo) Experimentation June / 28
52 Centralization - Given Locations C Kellogg/Oslo) Experimentation June / 28
53 Centralization - Given Locations Proposition If j experiments, i does too i c (h a j ) c (a i ) k p (1 p) p/2 Kellogg/Oslo) Experimentation June / 28
54 Centralization - Given Locations Proposition If j experiments, i does too i c (h a j ) c (a i ) k p (1 p) p/2 Compared to decentralization: Larger incentives if p < 1/2 Kellogg/Oslo) Experimentation June / 28
55 Centralization - Given Locations Proposition If j experiments, i does too i c (h a j ) c (a i ) k p (1 p) p/2 Compared to decentralization: Larger incentives if p < 1/2 When choosing locations, inequality will bind Kellogg/Oslo) Experimentation June / 28
56 Centralization - Given Locations Proposition If j experiments, i does too i c (h a j ) c (a i ) k p (1 p) p/2 Compared to decentralization: Larger incentives if p < 1/2 When choosing locations, inequality will bind Convergence is possible: accomodate median voter ) a > 0 Kellogg/Oslo) Experimentation June / 28
57 Centralization - Equilibrium Locations The optimal heterogeneity is h c > 0 C Kellogg/Oslo) Experimentation June / 28
58 Centralization or Decentralization? Centralization is always ine cient ex post Proposition Kellogg/Oslo) Experimentation June / 28
59 Centralization or Decentralization? Centralization is always ine cient ex post Proposition If p > 1/2, incentives to experiment is lower, so centralization worse Kellogg/Oslo) Experimentation June / 28
60 Centralization or Decentralization? Centralization is always ine cient ex post Proposition If p > 1/2, incentives to experiment is lower, so centralization worse If p < 1/2 is small, centralization can be better Kellogg/Oslo) Experimentation June / 28
61 Centralization or Decentralization? Centralization is always ine cient ex post Proposition If p > 1/2, incentives to experiment is lower, so centralization worse If p < 1/2 is small, centralization can be better If c (a) = qa 2, centralization is better for small h, q, p and large k: qh 2 < [k p (1 p)] 1/4p 2 1 1/2 p (1 p) Kellogg/Oslo) Experimentation June / 28
62 Alternative Applications Political parties developing new ideas Each tries to prevent the other from copying a success An explanation for polarization (or gay-marriage support) Kellogg/Oslo) Experimentation June / 28
63 Alternative Applications Political parties developing new ideas Each tries to prevent the other from copying a success An explanation for polarization (or gay-marriage support) Firms investing in R&D Firm-speci c tech to reduce free-riding, or accomodate to sell? Less rm-speci c general technology with intellectual property rights. Kellogg/Oslo) Experimentation June / 28
64 Alternative Applications Political parties developing new ideas Each tries to prevent the other from copying a success An explanation for polarization (or gay-marriage support) Firms investing in R&D Firm-speci c tech to reduce free-riding, or accomodate to sell? Less rm-speci c general technology with intellectual property rights. Co ee-brewing C Kellogg/Oslo) Experimentation June / 28
65 Progressive Federalism in the Wild Riker (1964). Centralized vs. Peripheralized federalism. Describes dynanics in all federal systems since founding of U.S. Kellogg/Oslo) Experimentation June / 28
66 Progressive Federalism in the Wild Riker (1964). Centralized vs. Peripheralized federalism. Describes dynanics in all federal systems since founding of U.S. Conclusion: Increasingly centralized systems succeed, increasingly peripheralized systems fail. Kellogg/Oslo) Experimentation June / 28
67 Progressive Federalism in the Wild Riker (1964). Centralized vs. Peripheralized federalism. Describes dynanics in all federal systems since founding of U.S. Conclusion: Increasingly centralized systems succeed, increasingly peripheralized systems fail. Increasing size of US and EU governments. Kellogg/Oslo) Experimentation June / 28
68 Progressive Federalism in the Wild Riker (1964). Centralized vs. Peripheralized federalism. Describes dynanics in all federal systems since founding of U.S. Conclusion: Increasingly centralized systems succeed, increasingly peripheralized systems fail. Increasing size of US and EU governments. Extensive policy harmonization in EU. Kellogg/Oslo) Experimentation June / 28
69 Progressive Federalism in the Wild Riker (1964). Centralized vs. Peripheralized federalism. Describes dynanics in all federal systems since founding of U.S. Conclusion: Increasingly centralized systems succeed, increasingly peripheralized systems fail. Increasing size of US and EU governments. Extensive policy harmonization in EU. Federal government learns from states, e.g., Rabe (2004). Kellogg/Oslo) Experimentation June / 28
70 Progressive Federalism in the Wild Riker (1964). Centralized vs. Peripheralized federalism. Describes dynanics in all federal systems since founding of U.S. Conclusion: Increasingly centralized systems succeed, increasingly peripheralized systems fail. Increasing size of US and EU governments. Extensive policy harmonization in EU. Federal government learns from states, e.g., Rabe (2004). Welfare policy in U.S.: Authority devolved to the states. Kellogg/Oslo) Experimentation June / 28
71 Progressive Federalism in the Wild Riker (1964). Centralized vs. Peripheralized federalism. Describes dynanics in all federal systems since founding of U.S. Conclusion: Increasingly centralized systems succeed, increasingly peripheralized systems fail. Increasing size of US and EU governments. Extensive policy harmonization in EU. Federal government learns from states, e.g., Rabe (2004). Welfare policy in U.S.: Authority devolved to the states. Prescriptive theory: Constitutions should do it! Kellogg/Oslo) Experimentation June / 28
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