� � � � � � � � ������ ������ �� ��������� � � � � � � � � � � � � � � � � � � � � � � � � � � � � ���� ���������� � � � � � � � � � � � � ������ ������ �� ��������� � � � � � � � � � � � � � � � � � � � � � � � � � �� � � � � ���� � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � ������������ � � � � ��������������������������� � � � � � � � � � � � � � � � � � � � � � � � � � � ���� � � � � � � � � � � � � � � � � � � � � � � � � � � �������� ������� ������ �� �������� � � � � � � � � � � � � � � � � �������� ���������� � � � � � � � � � � � � � � � � � � � ������ ������ �� ��������� � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � � � � � � � � � � ��� � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � � � ��� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Blueprint Annalisa Guzzini (an architect, my wife) and I developed a blueprint for a 4-pentagon die to implement the optimal packing. ◮ 44mm-edge pentagons: ∼ 250 with old die vs. 272 with ours. ◮ 43.5mm-edge pentagons: ∼ 258 vs. 280. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen � �
� � � � ����� � � � � � � � � � � � � � � � � � � ���� � � � � � � � � � � � �� � � � � � ����� �� � � � � � � ��� �� � �� � � � � � � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � �� � � � � ���� � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Blueprint (cont.) Blueprint includes instructions for modifying size of die. � � ◮ Sides of adjacent pentagons are “offset,” not flush. ◮ 4-pentagon pattern can be replicated by two 2-pentagon cuts. ◮ We will also consider a two-piece offset die as our technology Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion The “Shamyla” Die Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Net Variable Cost Reduction from Offset Die ◮ Values for median firm (see Table 2 for distribution). ◮ Cost reduction from reduced rexine wastage: Cost reduction per pentagon: 7.9% Pentagons as share of rexine sheet cost: 33% Rexine sheet cost as share of total cost: 44.7% Estimated total cost reduction (7.9% × 33% × 44.7%): 1.12% ◮ Additional labor costs: Increase in pentagon cutting time (conservative): 50% Pentagons as share of cutting time: 33% Cutting as share of total costs .45% Estimated total cost increase (50% × 33% × .45%): ∼ .1% ◮ Net variable cost reduction: ∼ 1%. ◮ Small, but 12.3% of profits Details Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Net Benefits of Adoption (Table 3) 10 th 25 th 50 th 75 th 90 th mean net variable cost reduction (%) 0.52 0.72 1.02 1.29 1.87 1.09 (0.05) (0.03) (0.03) (0.06) (0.17) (0.04) % net variable cost/avg % profit rate 5.27 8.10 12.34 19.86 28.98 15.45 (0.42) (0.50) (0.73) (1.21) (2.26) (0.71) total cost savings per month (Rs 000s) 4.46 12.19 49.38 165.21 475.01 174.12 (0.60) (1.24) (5.43) (18.42) (79.89) (18.54) days to recover fixed costs 8.48 15.98 36.61 80.34 193.92 136.94 (0.97) (1.68) (2.93) (6.51) (15.64) (44.54) days to recover fixed costs (no die) 4.40 8.30 19.01 41.71 100.69 71.10 (0.51) (0.87) (1.52) (3.38) (8.12) (23.13) ◮ We estimate that 50% of tech drop firms would recover fixed costs in 19 days or less, 75% in 42 days or less. ◮ Possibility of unobserved fixed costs considered in paper. Full table Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Tech Drop Experiment: Design 3 groups: 1. Tech drop: ◮ Die + blueprint. ◮ 30 min. demonstration, including comparison to existing die. ◮ Offer to trade in die for different size at no cost. ◮ Panel sizes vary, even for a given size ball. ◮ To be usable, pentagon die has to be exactly same size as hexagon die. 2. Cash drop: ◮ 30,000 Rs cash ( ∼ US$300) — the amount we paid for each die. 3. “No drop” ◮ No intervention. Dropped technology in May-June 2012. Surveys approx. every 3 months since then. Timeline Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Adoption as of Aug. 2013 (Table 7) Tech Cash Drop Drop No Drop Total Full sample # ever active firms 35 18 79 132 # ever responded 35 17 64 116 # currently active and ever responded 32 15 59 106 # traded in 19 0 0 19 # ordered new die (beyond trade-in) 1 0 6 7 # received new die (beyond trade-in) 1 0 4 5 # ever used new die ( > 1000 balls) 5 0 1 6 # currently using new die ( > 1000 balls) 5 0 1 6 Full table Characteristics of exiters Adoption as of March 2014 Hypotheses for non-adoption Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Die Purchases by Firm Z 40 30 cumulative number of dies 20 10 0 Apr 12 Oct 12 Apr 13 Oct 13 Apr 14 date ◮ Second-largest by employment in Sialkot ( ∼ 2,200 employees). ◮ No-drop group, late responder. ◮ As of March 2014, using offset die for ∼ 100% of production. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Table 10: Reasons for Non-Adoption waiting for waiting for other no orders doubt others to others to cutters printing production firm to try on too busy profitable prove value iron out kinks unwilling problems issues other 1 2 3 1 2 2 1 3 2 1 4 2 1 5 2 1 6 4 3 1 2 7 3 2 1 8 3 1 2 9 3 2 1 10 1 11 1 12 1 13 3 1 2 14 3 1 2 15 2 1 3 16 1 17 5 3 1 2 4 18 2 3 1 3 ◮ Numbers indicate order of importance indicated by respondent. ◮ Sample is round-4 respondents who have had die in their factory but are not currently using it. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Die Purchases by Firm Z Redux 40 30 cumulative number of dies 20 10 0 Apr 12 Oct 12 Apr 13 Oct 13 Apr 14 date ◮ Second-largest by employment in Sialkot ( ∼ 2,200 employees). ◮ No drop group, late responder. ◮ As of March 2014, using offset die for ∼ 100% of production. ◮ Pays monthly salary to cutters, not piece rate. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Brief Summary of Model ◮ Principal-agent model with non-contractible effort, limited liability for agent. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Brief Summary of Model ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Brief Summary of Model ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal ◮ Technology may be of 3 types, one of which resembles ours (material-saving, labor-using). Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Brief Summary of Model ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal ◮ Technology may be of 3 types, one of which resembles ours (material-saving, labor-using). ◮ We show that there exist conditions under which: Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Brief Summary of Model ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal ◮ Technology may be of 3 types, one of which resembles ours (material-saving, labor-using). ◮ We show that there exist conditions under which: ◮ If only standard linear contract (that cannot be conditioned on ex-post-revealed marginal cost) available, agent misinforms principal and a beneficial technology is not adopted. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Brief Summary of Model ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal ◮ Technology may be of 3 types, one of which resembles ours (material-saving, labor-using). ◮ We show that there exist conditions under which: ◮ If only standard linear contract (that cannot be conditioned on ex-post-revealed marginal cost) available, agent misinforms principal and a beneficial technology is not adopted. ◮ If contract can be conditioned on marginal cost at transaction cost G , and G is sufficiently low relative to expected gains (which depend on principal’s prior), adoption occurs. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Incentive-Payment Experiment: Design Randomly assign still-active tech-drop firms to: A. Incentive group: ◮ Refresher about technology. Offer repeat of demonstration. Mention 2-pentagon die. ◮ Incentive treatment: ◮ Explain misaligned incentives to owner. ◮ Offer incentive payment to one cutter, one printer (US$150 or US$120, roughly monthly income) if they can demonstrate competence using new technology. ◮ Pay 1/3 up front, 2/3 conditional on satisfactory performance (272 pentagons in 3 min. for cutter, 48 2-pentagon swipes in 3 min. for printer) in 4-6 weeks. ◮ 20 rexine sheets to practice with. US$50 to owner to defray overhead costs (electricity, additional practice rexine). B. No-incentive group: ◮ Refresher about technology, offer repeat of demonstration, mention 2-pentagon die. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Covariate Balance, Incentive Payment Experiment Group B Group A No Incentive Incentive Payment Payment log avg output/month 9.86 9.31 (0.41) (0.29) log avg employment 3.35 3.23 (0.38) (0.25) log avg price, size 5 promo 5.40 5.45 (0.02) (0.07) log avg price, size 5 training 6.00 5.93 (0.06) (0.06) avg % promotional (of size 5) 34.90 32.04 (6.20) (7.26) avg Rs/ball, head cutter 1.45 1.63 (0.10) (0.15) CEO university indicator 0.56 0.36 (0.18) (0.15) CEO experience 15.50 16.50 (3.60) (3.60) age of firm 24.53 20.60 (2.83) (2.28) N 15 16 ◮ No differences significant at 5% level. ◮ As of Aug. 2013, we believed there were 34 active tech drop firms, and randomized 17 firms each into groups A and B. Three were subsequently revealed to have closed and/or stopped producing soccer balls. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Incentive Payment Experiment: Summary ◮ Of the 15 active Group A (Incentive Payment) firms: ◮ 5 refused intervention ◮ 10 accepted (2 were already adopters) ◮ All passed, average time 2:52 (2:15 Traditional) “Test” results Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Incentive Payment Experiment: Summary ◮ Of the 15 active Group A (Incentive Payment) firms: ◮ 5 refused intervention ◮ 10 accepted (2 were already adopters) ◮ All passed, average time 2:52 (2:15 Traditional) ◮ Of the 8 accepters who had not yet adopted: “Test” results Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Incentive Payment Experiment: Summary ◮ Of the 15 active Group A (Incentive Payment) firms: ◮ 5 refused intervention ◮ 10 accepted (2 were already adopters) ◮ All passed, average time 2:52 (2:15 Traditional) ◮ Of the 8 accepters who had not yet adopted: ◮ 5 have adopted ( > 1000 balls). “Test” results Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Incentive Payment Experiment: Summary ◮ Of the 15 active Group A (Incentive Payment) firms: ◮ 5 refused intervention ◮ 10 accepted (2 were already adopters) ◮ All passed, average time 2:52 (2:15 Traditional) ◮ Of the 8 accepters who had not yet adopted: ◮ 5 have adopted ( > 1000 balls). ◮ 3 (not strict subset) have purchased their first offset die (beyond trade-in). “Test” results Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Incentive Payment Experiment: Summary ◮ Of the 15 active Group A (Incentive Payment) firms: ◮ 5 refused intervention ◮ 10 accepted (2 were already adopters) ◮ All passed, average time 2:52 (2:15 Traditional) ◮ Of the 8 accepters who had not yet adopted: ◮ 5 have adopted ( > 1000 balls). ◮ 3 (not strict subset) have purchased their first offset die (beyond trade-in). ◮ Of the 16 active Group B (No Incentive) firms: ◮ 3 were already adopters. ◮ Of 13 non-adopters, none have adopted or purchased die. “Test” results Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Table 13: Current Use as Outcome Dep. var.: currently using offset die (have produced > 1000 balls with it) All Strata Initial Non-Adopters Reduced Reduced First Form IV First Form IV Stage OLS (ITT) (TOT) Stage OLS (ITT) (TOT) (1) (2) (3) (4) (5) (6) (7) (8) rec’d treatment 0.48*** 0.48*** 0.59*** 0.63*** (0.15) (0.15) (0.18) (0.18) assigned to group A 0.68*** 0.32** 0.62*** 0.38*** (0.12) (0.12) (0.14) (0.13) stratum dummies Y Y Y Y Y Y Y Y R-squared 0.57 0.66 0.59 0.66 0.50 0.43 0.26 0.43 N 31 31 31 31 26 26 26 26 ◮ We also conduct small-sample-robust two-tailed permutation test. ◮ Calculate ITT estimate for all 25.8 million treatment assignments. ◮ Reject null if our estimate lies outside 2.5 th , 97.5 th percentile. ◮ p-value (corresponding to Column (3)) = .03 Permutation test coeefficient distribution Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Conclusion ◮ In paper we argue against alternative stories: ◮ We just paid fixed costs of adoption for firms. ◮ Second experiment just increased salience of technology. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Conclusion ◮ In paper we argue against alternative stories: ◮ We just paid fixed costs of adoption for firms. ◮ Second experiment just increased salience of technology. ◮ Results still preliminary. We are finishing up additional survey round. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Conclusion ◮ In paper we argue against alternative stories: ◮ We just paid fixed costs of adoption for firms. ◮ Second experiment just increased salience of technology. ◮ Results still preliminary. We are finishing up additional survey round. ◮ But results suggest that piece-rate-induced worker resistance is an important barrier to adoption. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Conclusion ◮ In paper we argue against alternative stories: ◮ We just paid fixed costs of adoption for firms. ◮ Second experiment just increased salience of technology. ◮ Results still preliminary. We are finishing up additional survey round. ◮ But results suggest that piece-rate-induced worker resistance is an important barrier to adoption. ◮ A relatively small intervention in monetary terms has had a reasonably large impact on adoption. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Conclusion ◮ In paper we argue against alternative stories: ◮ We just paid fixed costs of adoption for firms. ◮ Second experiment just increased salience of technology. ◮ Results still preliminary. We are finishing up additional survey round. ◮ But results suggest that piece-rate-induced worker resistance is an important barrier to adoption. ◮ A relatively small intervention in monetary terms has had a reasonably large impact on adoption. ◮ Consistent with explanation that workers were misinforming owners, and that intervention worked by inducing truthful revelation. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Conclusion (cont.) ◮ Big picture: Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Conclusion (cont.) ◮ Big picture: ◮ Inertia in labor contracts can hinder technological change. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Conclusion (cont.) ◮ Big picture: ◮ Inertia in labor contracts can hinder technological change. ◮ Workers need to expect to share in gains to adoption in order for adoption to be successful. ◮ One answer to why “inclusiveness” matters for growth. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion References I Kuperberg, G. and W. Kuperberg, “Double-Lattice Packings of Convex Bodies in the Plane,” Discrete & Computational Geometry, 1990, 5, 389–397. Wright, Tom, “Pakistan Defends Its Soccer Industry,” Wall Street Journal, 2010. April 26, 2010. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Setting: Soccer-Ball Cluster in Sialkot, Pakistan ◮ 70% of world production of hand-stitched balls (WSJ, 2010). ◮ 40% of total world production. ◮ 135 active firms ◮ ∼ 30 million balls/year. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Industry Structure ◮ 5-10 large firms (250+ employees): ◮ Produce own brands or high-quality name-brand balls for Adidas (including 2014 World Cup ball), Nike etc. ◮ Tend to be the innovators in sector. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Industry Structure ◮ 5-10 large firms (250+ employees): ◮ Produce own brands or high-quality name-brand balls for Adidas (including 2014 World Cup ball), Nike etc. ◮ Tend to be the innovators in sector. ◮ Fringe of small- and medium-sized firms: ◮ Find clients at large industry expos, or subcontract for local firms. ◮ Produce low-quality “promotional” balls, often with advertiser logo, or medium-quality “training” balls, often with club logo. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Industry Structure ◮ 5-10 large firms (250+ employees): ◮ Produce own brands or high-quality name-brand balls for Adidas (including 2014 World Cup ball), Nike etc. ◮ Tend to be the innovators in sector. ◮ Fringe of small- and medium-sized firms: ◮ Find clients at large industry expos, or subcontract for local firms. ◮ Produce low-quality “promotional” balls, often with advertiser logo, or medium-quality “training” balls, often with club logo. ◮ Almost all balls exported. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Fig. 2: U.S. Imports of Soccer Balls 50 Pakistan China Other total imports (f.o.b.) into US (mil. 2000 US$) 40 30 20 10 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 year ◮ 10-digit HS category 9506.62.40.80 (inflatable soccer balls). Return Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Table 1: Pentagons/sheet traditional die offset die owner direct owner direct report obs. report obs. (1) (2) (3) (4) size 43.5 257.4 257.7 273.5 277.5 (10.4) (6.7) (4.4) (5.3) size 43.75 256.3 254.4 269.0 272.0 (6.2) (9.4) (1.4) (0.0) size 44 253.8 248.4 280.0 272.5 (8.4) (18.7) (0.7) size 44.25 246.1 262.0 272.0 (8.3) rescaled (to size 44) 253.6 248.3 280.0 272.9 (8.5) (11.0) (3.0) (3.9) N (after rescaling) 274 39 8 10 ◮ Pentagons/sheet rescaled using means for each size in each column. N in final row is pooled # observations for all die sizes. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Table 2: Production Costs Share of Production Input Cost Input Costs (%) (in Rs) rexine 19.79 39.68 (5.37) (13.87) cotton/poly cloth 12.32 23.27 (4.56) (8.27) latex 13.94 38.71 (10.73) (90.71) bladder 21.07 42.02 (4.87) (14.09) labor for cutting 0.76 1.47 (0.21) (0.30) labor for stitching 19.67 39.24 (5.25) (12.82) other labor (laminating, washing, packing, matching) 7.32 15.59 (4.55) (13.21) overhead 5.14 10.84 (2.05) (6.10) total 100.00 210.83 N 38 38 ◮ For promotional (lower-quality) ball. Data from baseline survey. Exchange rate: 100 Rs/US$1. Return Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Table 3: Net Benefits of Adoption 10 th 25 th 50 th 75 th 90 th mean A. Variable cost reduction laminated rexine waste reduction (%) 4.39 5.19 7.93 8.31 13.43 7.69 (0.46) (0.41) (0.30) (0.05) (1.18) (0.22) laminated rexine as share of cost (%) 34.85 39.87 44.72 51.22 55.44 45.94 (1.14) (0.71) (0.58) (0.44) (0.95) (0.66) variable cost reduction (%) 0.60 0.80 1.10 1.37 1.94 1.17 (0.05) (0.04) (0.03) (0.06) (0.17) (0.04) B. Variable cost increase cutter wage as share of cost (%) 0.29 0.36 0.45 0.60 0.70 0.48 (0.01) (0.00) (0.01) (0.01) (0.02) (0.01) variable cost increase (%) 0.05 0.06 0.07 0.10 0.12 0.08 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) C. Net benefits net variable cost reduction (%) 0.52 0.72 1.02 1.29 1.87 1.09 (0.05) (0.03) (0.03) (0.06) (0.17) (0.04) % net variable cost/avg % profit rate 5.27 8.10 12.34 19.86 28.98 15.45 (0.42) (0.50) (0.73) (1.21) (2.26) (0.71) total cost savings per month (Rs 000s) 4.46 12.19 49.38 165.21 475.01 174.12 (0.60) (1.24) (5.43) (18.42) (79.89) (18.54) days to recover fixed costs 8.48 15.98 36.61 80.34 193.92 136.94 (0.97) (1.68) (2.93) (6.51) (15.64) (44.54) days to recover fixed costs (no die) 4.40 8.30 19.01 41.71 100.69 71.10 (0.51) (0.87) (1.52) (3.38) (8.12) (23.13) ◮ Uses hot-deck imputation procedure. Standard deviation of 1,000 replications of hot-deck procedure in parentheses. Return Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Table 5: Treatment Assignment, Tech-Drop Experiment # Firms Tech Drop Cash Drop No Drop Total A. Initial responders smallest 5 3 12 20 medium-small 6 3 13 22 medium-large 6 3 13 22 largest 6 3 12 21 total 23 12 50 85 B. Late responders active, late response 12 5 14 31 active, refused all surveys 0 1 15 16 inactive 7 3 12 22 total 19 9 41 69 Return Baseline balance Summary statistics, initial-responder sample Summary statistics, full sample Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Table 7: Adoption as of Aug. 2013 Tech Drop Cash Drop No Drop Total A. Initial-responder sample # ever active firms 23 12 50 85 # ever responded 23 12 50 85 # currently active and ever responded 22 11 46 79 # traded in 15 0 0 15 # ordered new die (beyond trade-in) 1 0 4 5 # received new die (beyond trade-in) 1 0 2 3 # ever used new die ( > 1000 balls) 4 0 0 4 # currently using new die ( > 1000 balls) 4 0 0 4 B. Full sample # ever active firms 35 18 79 132 # ever responded 35 17 64 116 # currently active and ever responded 32 15 59 106 # traded in 19 0 0 19 # ordered new die (beyond trade-in) 1 0 6 7 # received new die (beyond trade-in) 1 0 4 5 # ever used new die ( > 1000 balls) 5 0 1 6 # currently using new die ( > 1000 balls) 5 0 1 6 Return Characteristics of exiters Adoption as of March 2014 Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Adoption Regressions, Scale & Quality Variables Dep. var.: indicator for currently using offset die (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) tech drop group 0.18** 0.18** 0.59 0.17** (0.08) (0.08) (0.51) (0.07) cash drop group -0.00 (0.02) log avg output/month 0.03 0.04* 0.03 0.06 (0.03) (0.02) (0.03) (0.04) log avg output*tech drop -0.04 (0.05) share standard (of size 5) -0.39 -0.38 -0.44 (0.32) (0.33) (0.27) log avg price, size 5 training -0.06 -0.19* (0.05) (0.11) avg share promotional (of size 5) -0.11 -0.16 (0.07) (0.10) avg profit rate, size 5 training 0.54 0.37 (0.64) (0.62) constant 0.02 0.02 -0.22 -0.29 0.41 0.14 0.40 0.11 0.02 1.14* (0.05) (0.05) (0.22) (0.18) (0.32) (0.45) (0.31) (0.07) (0.05) (0.67) stratum dummies Y Y Y Y Y Y Y Y Y Y R-squared 0.22 0.22 0.10 0.25 0.16 0.18 0.09 0.10 0.10 0.37 N 79 79 79 79 74 74 68 79 66 63 ◮ Initial-responder sample. Linear probability model. Return Full sample Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Adoption Regressions, Manager & Cutter Chars. Dep. var.: indicator for using offset die ( > 1000 balls) as of Aug. 2013 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) tech drop group 0.18** 0.22** (0.08) (0.09) CEO university indicator 0.04 0.01 (0.07) (0.07) CEO experience (/100) -0.24 -0.21 (0.17) (0.28) age of firm (/100) -0.06 0.03 (0.09) (0.16) cutters paid piece rate 0.02 -0.04 (0.03) (0.05) Rs/ball, head cutter 0.11 (0.15) head cutter experience (/100) -0.03 (0.09) head cutter tenure (/100) -0.19 (0.23) cutter raven’s score -0.01 (0.03) avg pent/sheet, rescaled (/100) 0.65* -0.04 (0.37) (0.45) log avg output/month 0.05 (0.04) constant 0.02 0.05 0.11 0.07 0.04 -0.10 0.00 0.03 0.03 -1.58* -0.24 (0.05) (0.05) (0.07) (0.05) (0.04) (0.19) (0.01) (0.03) (0.07) (0.90) (0.99) stratum dummies Y Y Y Y Y Y Y Y Y Y Y R-squared 0.22 0.09 0.09 0.08 0.09 0.11 0.12 0.12 0.18 0.12 0.31 N 79 70 77 78 75 74 33 32 37 70 56 ◮ Initial-responder sample. Linear probability model. Return Full sample Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion A Model of Organizational Barriers to Adoption ◮ Goal: simplest possible model that can capture the sort of strategic communication illustrated by the anecdotes. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion A Model of Organizational Barriers to Adoption ◮ Goal: simplest possible model that can capture the sort of strategic communication illustrated by the anecdotes. ◮ Elements: Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion A Model of Organizational Barriers to Adoption ◮ Goal: simplest possible model that can capture the sort of strategic communication illustrated by the anecdotes. ◮ Elements: ◮ Principal-agent model with non-contractible effort, limited liability for agent. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion A Model of Organizational Barriers to Adoption ◮ Goal: simplest possible model that can capture the sort of strategic communication illustrated by the anecdotes. ◮ Elements: ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion A Model of Organizational Barriers to Adoption ◮ Goal: simplest possible model that can capture the sort of strategic communication illustrated by the anecdotes. ◮ Elements: ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal ◮ We show that there exist conditions under which: Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion A Model of Organizational Barriers to Adoption ◮ Goal: simplest possible model that can capture the sort of strategic communication illustrated by the anecdotes. ◮ Elements: ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal ◮ We show that there exist conditions under which: ◮ If only standard linear contract (that cannot be conditioned on technology type) available, a beneficial technology is not adopted. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion A Model of Organizational Barriers to Adoption ◮ Goal: simplest possible model that can capture the sort of strategic communication illustrated by the anecdotes. ◮ Elements: ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal ◮ We show that there exist conditions under which: ◮ If only standard linear contract (that cannot be conditioned on technology type) available, a beneficial technology is not adopted. ◮ Truthful revelation is not an equilibrium. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion A Model of Organizational Barriers to Adoption ◮ Goal: simplest possible model that can capture the sort of strategic communication illustrated by the anecdotes. ◮ Elements: ◮ Principal-agent model with non-contractible effort, limited liability for agent. ◮ Strategic communication within firm ◮ Fully informed agent ◮ Imperfectly informed principal ◮ We show that there exist conditions under which: ◮ If only standard linear contract (that cannot be conditioned on technology type) available, a beneficial technology is not adopted. ◮ Truthful revelation is not an equilibrium. ◮ If contract can be conditioned on technology type (with some transaction cost) and transaction cost is sufficiently low, adoption occurs. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). ◮ Agent faces effort cost: e ( a ) = a 2 2 . Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). ◮ Agent faces effort cost: e ( a ) = a 2 2 . ◮ Output sells at price p . Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). ◮ Agent faces effort cost: e ( a ) = a 2 2 . ◮ Output sells at price p . ◮ Materials cost: C ( q ) = cq Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). ◮ Agent faces effort cost: e ( a ) = a 2 2 . ◮ Output sells at price p . ◮ Materials cost: C ( q ) = cq ◮ Wage w ( q ) Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). ◮ Agent faces effort cost: e ( a ) = a 2 2 . ◮ Output sells at price p . ◮ Materials cost: C ( q ) = cq ◮ Wage w ( q ) ◮ Principal’s payoff: pq − w ( q ) − cq Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). ◮ Agent faces effort cost: e ( a ) = a 2 2 . ◮ Output sells at price p . ◮ Materials cost: C ( q ) = cq ◮ Wage w ( q ) ◮ Principal’s payoff: pq − w ( q ) − cq ◮ Wage contracts: Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). ◮ Agent faces effort cost: e ( a ) = a 2 2 . ◮ Output sells at price p . ◮ Materials cost: C ( q ) = cq ◮ Wage w ( q ) ◮ Principal’s payoff: pq − w ( q ) − cq ◮ Wage contracts: ◮ Assumed initially to be of the form: w ( q ) = α + β q . Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). ◮ Agent faces effort cost: e ( a ) = a 2 2 . ◮ Output sells at price p . ◮ Materials cost: C ( q ) = cq ◮ Wage w ( q ) ◮ Principal’s payoff: pq − w ( q ) − cq ◮ Wage contracts: ◮ Assumed initially to be of the form: w ( q ) = α + β q . ◮ Limited liability: α ≥ 0. Agent can’t be made to pay for job. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up ◮ Basics: ◮ Agent produces output q = sa , where s is speed of technology and a is effort (non-contractible). ◮ Agent faces effort cost: e ( a ) = a 2 2 . ◮ Output sells at price p . ◮ Materials cost: C ( q ) = cq ◮ Wage w ( q ) ◮ Principal’s payoff: pq − w ( q ) − cq ◮ Wage contracts: ◮ Assumed initially to be of the form: w ( q ) = α + β q . ◮ Limited liability: α ≥ 0. Agent can’t be made to pay for job. ◮ Below we will consider cases in which principal can/cannot condition contract on ex-post-revealed characteristics of technology. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up (cont.) ◮ Technology: ◮ Existing technology has speed s 0 , cost per unit c 0 . Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up (cont.) ◮ Technology: ◮ Existing technology has speed s 0 , cost per unit c 0 . ◮ New technology is one of 3 types: 1. c 1 = c 0 , s 1 < s o : Dominated by existing technology. 2. c 2 < c 0 , s 2 < s 0 : Our technology. 3. c 3 = c 0 , s 3 > s 0 : Faster than existing technology, same cost. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up (cont.) ◮ Technology: ◮ Existing technology has speed s 0 , cost per unit c 0 . ◮ New technology is one of 3 types: 1. c 1 = c 0 , s 1 < s o : Dominated by existing technology. 2. c 2 < c 0 , s 2 < s 0 : Our technology. 3. c 3 = c 0 , s 3 > s 0 : Faster than existing technology, same cost. ◮ Agent knows type. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up (cont.) ◮ Technology: ◮ Existing technology has speed s 0 , cost per unit c 0 . ◮ New technology is one of 3 types: 1. c 1 = c 0 , s 1 < s o : Dominated by existing technology. 2. c 2 < c 0 , s 2 < s 0 : Our technology. 3. c 3 = c 0 , s 3 > s 0 : Faster than existing technology, same cost. ◮ Agent knows type. ◮ Principal has priors: ρ 1 , ρ 2 , ρ 3 , with � 3 i =1 ρ i = 1 Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up (cont.) ◮ Technology: ◮ Existing technology has speed s 0 , cost per unit c 0 . ◮ New technology is one of 3 types: 1. c 1 = c 0 , s 1 < s o : Dominated by existing technology. 2. c 2 < c 0 , s 2 < s 0 : Our technology. 3. c 3 = c 0 , s 3 > s 0 : Faster than existing technology, same cost. ◮ Agent knows type. ◮ Principal has priors: ρ 1 , ρ 2 , ρ 3 , with � 3 i =1 ρ i = 1 ◮ Fixed cost of adoption of new technology: F Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Set-up (cont.) ◮ Technology: ◮ Existing technology has speed s 0 , cost per unit c 0 . ◮ New technology is one of 3 types: 1. c 1 = c 0 , s 1 < s o : Dominated by existing technology. 2. c 2 < c 0 , s 2 < s 0 : Our technology. 3. c 3 = c 0 , s 3 > s 0 : Faster than existing technology, same cost. ◮ Agent knows type. ◮ Principal has priors: ρ 1 , ρ 2 , ρ 3 , with � 3 i =1 ρ i = 1 ◮ Fixed cost of adoption of new technology: F ◮ Timing: ◮ Stage 1: Principal chooses wage contract. ◮ Stage 2: Agent can send one of three costless messages, { m 1 , m 2 , m 3 } , about type of technology. ◮ Stage 3: Principal adopts or not. ◮ Stage 4: Payoffs realized. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Benchmark 1: Fully Informed Principal ◮ Principal’s problem: max ps i a − ( α + β s i a ) − c i s i a s.t. a ,β α + β s i a − a 2 ≥ ¯ u (PC) 2 arg max a α + β s i a − a 2 = a (ICC) 2 α ≥ 0 (LLC) Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Benchmark 1: Fully Informed Principal ◮ Principal’s problem: max ps i a − ( α + β s i a ) − c i s i a s.t. a ,β α + β s i a − a 2 ≥ ¯ u (PC) 2 arg max a α + β s i a − a 2 = a (ICC) 2 α ≥ 0 (LLC) ◮ Optimal contract: α i = 0 , β i = p − c i 2 ◮ Agent receives rent, because of limited liability. ◮ Bargaining model would have similar flavor. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Benchmark 2: No Signal from Agent ◮ Principal bases decision solely on priors. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Benchmark 2: No Signal from Agent ◮ Principal bases decision solely on priors. ◮ Turns out optimal contract is weighted average of optimal piece rates from full-information case: 3 � α = 0 , β ′ = λ i β i i =1 where ρ i s 2 i λ i = � 3 1 ρ i s 2 i Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Benchmark 2: No Signal from Agent ◮ Principal bases decision solely on priors. ◮ Turns out optimal contract is weighted average of optimal piece rates from full-information case: 3 � α = 0 , β ′ = λ i β i i =1 where ρ i s 2 i λ i = � 3 1 ρ i s 2 i ◮ Expected profit is: � 3 � � � β ′ � 2 − F π ′ = ρ i s 2 i i =1 Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Parameter Restrictions ◮ Notation: π i ( β ) = s 2 i β ( p − β − c i ) − F · ✶ ( i ∈ { 1 , 2 , 3 } ) ◮ β not necessarily optimal. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Parameter Restrictions ◮ Notation: π i ( β ) = s 2 i β ( p − β − c i ) − F · ✶ ( i ∈ { 1 , 2 , 3 } ) ◮ β not necessarily optimal. ◮ To focus on interesting case, we impose 3 restrictions: Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Parameter Restrictions ◮ Notation: π i ( β ) = s 2 i β ( p − β − c i ) − F · ✶ ( i ∈ { 1 , 2 , 3 } ) ◮ β not necessarily optimal. ◮ To focus on interesting case, we impose 3 restrictions: ◮ Technology 2 more profitable than existing technology, even under optimal piece rate for existing technology: π 2 ( β 0 ) > π 0 ( β 0 ) (1) Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Parameter Restrictions ◮ Notation: π i ( β ) = s 2 i β ( p − β − c i ) − F · ✶ ( i ∈ { 1 , 2 , 3 } ) ◮ β not necessarily optimal. ◮ To focus on interesting case, we impose 3 restrictions: ◮ Technology 2 more profitable than existing technology, even under optimal piece rate for existing technology: π 2 ( β 0 ) > π 0 ( β 0 ) (1) ◮ Technology 3 more profitable than existing technology, even under optimal piece rate for type 2: π 3 ( β 2 ) > π 0 ( β 2 ) (2) Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Parameter Restrictions ◮ Notation: π i ( β ) = s 2 i β ( p − β − c i ) − F · ✶ ( i ∈ { 1 , 2 , 3 } ) ◮ β not necessarily optimal. ◮ To focus on interesting case, we impose 3 restrictions: ◮ Technology 2 more profitable than existing technology, even under optimal piece rate for existing technology: π 2 ( β 0 ) > π 0 ( β 0 ) (1) ◮ Technology 3 more profitable than existing technology, even under optimal piece rate for type 2: π 3 ( β 2 ) > π 0 ( β 2 ) (2) ◮ If principal bases decision only on priors, new technology not adopted: π 0 ( β 0 ) > π ′ (3) Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Imperfectly Informed Principal, No Conditional Contract ◮ Proposition 1: Under (1)-(3), without conditional contracts, the following set of strategies is part of a perfect Bayesian equilibrium (PBE). Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Imperfectly Informed Principal, No Conditional Contract ◮ Proposition 1: Under (1)-(3), without conditional contracts, the following set of strategies is part of a perfect Bayesian equilibrium (PBE). ◮ Agent’s strategy: ◮ If the technology is of type 1 or type 2, signal m 1 ◮ If the technology is of type 3, signal m 3 . Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
Introduction Technology Experiment I: Tech Drop Model Experiment II: Incentive Payment Conclusion Imperfectly Informed Principal, No Conditional Contract ◮ Proposition 1: Under (1)-(3), without conditional contracts, the following set of strategies is part of a perfect Bayesian equilibrium (PBE). ◮ Agent’s strategy: ◮ If the technology is of type 1 or type 2, signal m 1 ◮ If the technology is of type 3, signal m 3 . ◮ Principal’s strategy: α ∗ = 0 , β ∗ = p − c 0 � � ◮ Offer wage contract 2 ◮ If agent signals m 2 or m 3 , adopt. ◮ If agent signals m 1 , do not adopt. Organizational Barriers to Technology Adoption Atkin, Chaudhry, Chaudry, Khandelwal & Verhoogen
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