Introduction Framework Recent Research Conclusion Internal-to-the-Firm Reasons ◮ The firm is not profit-maximizing. ◮ Leibenstein (1966): “X-inefficiency.” ◮ May be true, but we should be very careful about jumping to this conclusion.
Introduction Framework Recent Research Conclusion Internal-to-the-Firm Reasons ◮ The firm is not profit-maximizing. ◮ Leibenstein (1966): “X-inefficiency.” ◮ May be true, but we should be very careful about jumping to this conclusion. ◮ If firms appear not to be optimizing, perhaps we have not fully understood the problem they are solving.
Introduction Framework Recent Research Conclusion Internal-to-the-Firm Reasons ◮ The firm is not profit-maximizing. ◮ Leibenstein (1966): “X-inefficiency.” ◮ May be true, but we should be very careful about jumping to this conclusion. ◮ If firms appear not to be optimizing, perhaps we have not fully understood the problem they are solving. ◮ The firm does not know about k H . ◮ Even in the age of Google, firms are sometimes simply unaware of existing technologies. ◮ A firm may have uncertainty about how well a technology works, even if operated correctly.
Introduction Framework Recent Research Conclusion Internal-to-the-Firm Reasons ◮ The firm is not profit-maximizing. ◮ Leibenstein (1966): “X-inefficiency.” ◮ May be true, but we should be very careful about jumping to this conclusion. ◮ If firms appear not to be optimizing, perhaps we have not fully understood the problem they are solving. ◮ The firm does not know about k H . ◮ Even in the age of Google, firms are sometimes simply unaware of existing technologies. ◮ A firm may have uncertainty about how well a technology works, even if operated correctly. ◮ The firm lacks capability for implementing k H . ◮ It appears in many cases that capabilities have to be “home-grown”, and at a cost (Gibbons, 2010). ◮ If a firm lacks the required capabilities for technique k H , it may not be profitable to adopt it.
Introduction Framework Recent Research Conclusion Internal-to-the-Firm Reasons (cont.) ◮ First reason (X-inefficiency) points to increasing competition as a factor driving adoption. ◮ In some cases, it seems clear that competition raised productivity (Schmitz, 2005; Das et al., 2013). ◮ Little work on competition and technology adoption per se.
Introduction Framework Recent Research Conclusion Internal-to-the-Firm Reasons (cont.) ◮ First reason (X-inefficiency) points to increasing competition as a factor driving adoption. ◮ In some cases, it seems clear that competition raised productivity (Schmitz, 2005; Das et al., 2013). ◮ Little work on competition and technology adoption per se. ◮ Second and third reasons (lack of knowledge, capabilities) point to learning as a driver of adoption.
Introduction Framework Recent Research Conclusion Internal-to-the-Firm Reasons (cont.) ◮ First reason (X-inefficiency) points to increasing competition as a factor driving adoption. ◮ In some cases, it seems clear that competition raised productivity (Schmitz, 2005; Das et al., 2013). ◮ Little work on competition and technology adoption per se. ◮ Second and third reasons (lack of knowledge, capabilities) point to learning as a driver of adoption. ◮ Learning can take various forms: ◮ An increase in capabilities, { λ ijkt } ◮ An enlargement of set of techniques a firm knows about, K ijt . ◮ An enlargement of set of products a firm knows about, J it .
Introduction Framework Recent Research Conclusion Internal-to-the-Firm Reasons (cont.) ◮ First reason (X-inefficiency) points to increasing competition as a factor driving adoption. ◮ In some cases, it seems clear that competition raised productivity (Schmitz, 2005; Das et al., 2013). ◮ Little work on competition and technology adoption per se. ◮ Second and third reasons (lack of knowledge, capabilities) point to learning as a driver of adoption. ◮ Learning can take various forms: ◮ An increase in capabilities, { λ ijkt } ◮ An enlargement of set of techniques a firm knows about, K ijt . ◮ An enlargement of set of products a firm knows about, J it . ◮ And can happen through various channels: ◮ From peers (Cai and Szeidl, 2017; Hardy and McCasland, 2016) ◮ From customers or suppliers. ◮ Through worker flows. ◮ Consulting (Bloom et al., 2013; Bruhn et al., 2018) ◮ ...
Introduction Framework Recent Research Conclusion Input-Side Reasons ◮ Developing-country firms face different input-supply functions, ⇀ { W ijkt } . ◮ Newer, more advanced techniques often require highly skilled workers, high-quality inputs. ◮ These may be expensive, or unavailable.
Introduction Framework Recent Research Conclusion Input-Side Reasons ◮ Developing-country firms face different input-supply functions, ⇀ { W ijkt } . ◮ Newer, more advanced techniques often require highly skilled workers, high-quality inputs. ◮ These may be expensive, or unavailable. ◮ Points to changes in input prices/availability as a driver of adoption.
Introduction Framework Recent Research Conclusion Input-Side Reasons ◮ Developing-country firms face different input-supply functions, ⇀ { W ijkt } . ◮ Newer, more advanced techniques often require highly skilled workers, high-quality inputs. ◮ These may be expensive, or unavailable. ◮ Points to changes in input prices/availability as a driver of adoption. ◮ A number of studies show that reduction of import tariffs leads to product upgrading (Bas and Strauss-Kahn, 2015).
Introduction Framework Recent Research Conclusion Input-Side Reasons ◮ Developing-country firms face different input-supply functions, ⇀ { W ijkt } . ◮ Newer, more advanced techniques often require highly skilled workers, high-quality inputs. ◮ These may be expensive, or unavailable. ◮ Points to changes in input prices/availability as a driver of adoption. ◮ A number of studies show that reduction of import tariffs leads to product upgrading (Bas and Strauss-Kahn, 2015). ◮ Some work on labor supply and mechanization in agriculture (Hornbeck and Naidu, 2014).
Introduction Framework Recent Research Conclusion Input-Side Reasons ◮ Developing-country firms face different input-supply functions, ⇀ { W ijkt } . ◮ Newer, more advanced techniques often require highly skilled workers, high-quality inputs. ◮ These may be expensive, or unavailable. ◮ Points to changes in input prices/availability as a driver of adoption. ◮ A number of studies show that reduction of import tariffs leads to product upgrading (Bas and Strauss-Kahn, 2015). ◮ Some work on labor supply and mechanization in agriculture (Hornbeck and Naidu, 2014). ◮ Little evidence on input supply and technology adoption per se in LDC firms.
Introduction Framework Recent Research Conclusion Output-Side Reasons ◮ Developing-country firms face different product-demand functions, { P ijt } .
Introduction Framework Recent Research Conclusion Output-Side Reasons ◮ Developing-country firms face different product-demand functions, { P ijt } . ◮ Different techniques, e.g. k H and k L , may be applicable in the production of different (but possibly similar) products. ◮ e.g. different quality varieties.
Introduction Framework Recent Research Conclusion Output-Side Reasons ◮ Developing-country firms face different product-demand functions, { P ijt } . ◮ Different techniques, e.g. k H and k L , may be applicable in the production of different (but possibly similar) products. ◮ e.g. different quality varieties. ◮ Customer demand for different products may differ between developed/developing countries. ◮ e.g. Willingness to pay for quality greater in developed countries. ◮ Customers may have preferences over techniques used in production as well as product characteristics.
Introduction Framework Recent Research Conclusion Output-Side Reasons ◮ Developing-country firms face different product-demand functions, { P ijt } . ◮ Different techniques, e.g. k H and k L , may be applicable in the production of different (but possibly similar) products. ◮ e.g. different quality varieties. ◮ Customer demand for different products may differ between developed/developing countries. ◮ e.g. Willingness to pay for quality greater in developed countries. ◮ Customers may have preferences over techniques used in production as well as product characteristics. ◮ Market-size often smaller in developing countries. ◮ May be difficult to achieve scale to pay for fixed costs.
Introduction Framework Recent Research Conclusion Output-Side Reasons (cont.) ◮ Potential drivers:
Introduction Framework Recent Research Conclusion Output-Side Reasons (cont.) ◮ Potential drivers: ◮ Exports ↑ ⇒ quality ↑ (Verhoogen, 2008; Atkin et al., 2017a).
Introduction Framework Recent Research Conclusion Output-Side Reasons (cont.) ◮ Potential drivers: ◮ Exports ↑ ⇒ quality ↑ (Verhoogen, 2008; Atkin et al., 2017a). ◮ Selling to local MNCs ↑ ⇒ quality ↑ (Alfaro-Urena et al., 2019)
Introduction Framework Recent Research Conclusion Output-Side Reasons (cont.) ◮ Potential drivers: ◮ Exports ↑ ⇒ quality ↑ (Verhoogen, 2008; Atkin et al., 2017a). ◮ Selling to local MNCs ↑ ⇒ quality ↑ (Alfaro-Urena et al., 2019) ◮ Exports, selling to local MNCs ↑ ⇒ working conditions ↑ (Tanaka, forthcoming; Boudreau, 2019).
Introduction Framework Recent Research Conclusion Output-Side Reasons (cont.) ◮ Potential drivers: ◮ Exports ↑ ⇒ quality ↑ (Verhoogen, 2008; Atkin et al., 2017a). ◮ Selling to local MNCs ↑ ⇒ quality ↑ (Alfaro-Urena et al., 2019) ◮ Exports, selling to local MNCs ↑ ⇒ working conditions ↑ (Tanaka, forthcoming; Boudreau, 2019). ◮ Exports ↑ ⇒ scale ↑ (Bustos, 2011; Lileeva and Trefler, 2010).
Introduction Framework Recent Research Conclusion Output-Side Reasons (cont.) ◮ Potential drivers: ◮ Exports ↑ ⇒ quality ↑ (Verhoogen, 2008; Atkin et al., 2017a). ◮ Selling to local MNCs ↑ ⇒ quality ↑ (Alfaro-Urena et al., 2019) ◮ Exports, selling to local MNCs ↑ ⇒ working conditions ↑ (Tanaka, forthcoming; Boudreau, 2019). ◮ Exports ↑ ⇒ scale ↑ (Bustos, 2011; Lileeva and Trefler, 2010). ◮ Competition ↑ ⇒ X-inefficiency ↓
Introduction Framework Recent Research Conclusion Output-Side Reasons (cont.) ◮ Potential drivers: ◮ Exports ↑ ⇒ quality ↑ (Verhoogen, 2008; Atkin et al., 2017a). ◮ Selling to local MNCs ↑ ⇒ quality ↑ (Alfaro-Urena et al., 2019) ◮ Exports, selling to local MNCs ↑ ⇒ working conditions ↑ (Tanaka, forthcoming; Boudreau, 2019). ◮ Exports ↑ ⇒ scale ↑ (Bustos, 2011; Lileeva and Trefler, 2010). ◮ Competition ↑ ⇒ X-inefficiency ↓ ◮ Again, not much direct evidence on technology adoption.
Introduction Framework Recent Research Conclusion Consulting Experiment ◮ Bloom, Eifert, Mahajan, McKenzie and Roberts (2013) ◮ Management practices can be thought of as a technology. ◮ “Modern management is a technology that diffuses slowly between firms.” (Bloom et al., 2011)
Introduction Framework Recent Research Conclusion Consulting Experiment ◮ Bloom, Eifert, Mahajan, McKenzie and Roberts (2013) ◮ Management practices can be thought of as a technology. ◮ “Modern management is a technology that diffuses slowly between firms.” (Bloom et al., 2011) ◮ Useful to study not only because they seem to matter a lot, but also because practices are used across a wide range of firms. ◮ Many other technologies need to be studied within narrow industries.
Introduction Framework Recent Research Conclusion Consulting Experiment ◮ Bloom, Eifert, Mahajan, McKenzie and Roberts (2013) ◮ Management practices can be thought of as a technology. ◮ “Modern management is a technology that diffuses slowly between firms.” (Bloom et al., 2011) ◮ Useful to study not only because they seem to matter a lot, but also because practices are used across a wide range of firms. ◮ Many other technologies need to be studied within narrow industries. ◮ This study randomly allocated intensive Accenture consulting services over 17 Indian textile firms. ◮ One-month diagnostic phase (all firms) ◮ Four-month implementation phase (treatment only) ◮ Market value of services ∼ $250k. ◮ Tracked 38 management practices (regular maintenance, tracking inventory, performance-based pay systems).
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ Firms had not implemented basic management practices (e.g. labelling inventory).
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) F IGURE V The Adoption of Key Textile Management Practices over Time ◮ Consulting was successful in getting firms to adopt management practices.
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ Quality defects declined sharply.
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) TABLE II T HE I MPACT OF M ODERN M ANAGEMENT P RACTICES ON P LANT P ERFORMANCE (1) (2) (3) (4) (5) (6) (7) (8) Quality Quality Dependent variable defects Inventory Output TFP defects Inventory Output TFP Weeks of Weeks of Weeks of Weeks of Specification ITT ITT ITT ITT treatment treatment treatment treatment Intervention i,t � 0.564** � 0.245** 0.090** 0.154* (0.235) (0.117) (0.037) (0.084) During implementation i,t � 0.293** � 0.070 0.015 0.048 (0.137) (0.093) (0.031) (0.056) Cumulative treatment i,t � 0.032** � 0.015** 0.006*** 0.009** (0.013) (0.005) (0.002) (0.004) Small sample robustness Ibragimov-Mueller (95% CI) [ � 1.65,0.44] [ � 0.83,-0.02] [0.05,0.38] [ � 0.014,0.79] Permutation test ( p -value) .001 .060 .026 .061 Time FEs 127 127 127 127 127 127 127 127 Plant FEs 20 18 20 18 20 18 20 18 Observations 1,807 2,052 2,393 1,831 1,807 2,052 2,393 1,831 Notes . All regressions use a full set of plant and calendar week dummies. Standard errors are bootstrap clustered at the firm level. Intervention is a plant level dummy equal to 1 after the implementation phase at treatment plants and 0 otherwise. During implementation is a dummy variable equal to 1 from the beginning of the diagnostic phase to the end of the implementation phase for all treatment plants. Cumulative treatment is the cumulative weeks of treatment since the beginning of the implementation phase in each plant (0 in both the control group and prior to the implementation phase in the treatment group). Quality defects is the log of the quality defects index (QDI), which is a weighted average score of quality defects, so higher numbers imply worse quality products (more quality defects). Inventory is the log of the tons of yarn inventory in the plant. Output is the log of the weaving production picks. TFP is plant-level total factor productivity defined as log(output) measured in production picks less log(capital) times capital share of 0.42 less log(labor) times labor costs share of 0.58. ITT reports the intention to treat results from regressing the dependent variable directly on the intervention dummy. Time FEs report the number of calendar week time fixed effects. Plant FEs reports the number of plant-level fixed effects. Two plants do not have any inventory on site, so no inventory data are available. Small sample robustness implements two different procedures (described in greater detail in the Appendix and Online Appendix B) to address issues of plant hetero- geneity, within plant (and firm) correlation, and small sample concerns. Ibragimov-Mueller (95% CI) report 95% confidence interval estimates from firm-by-firm parameter estimates treating the estimates as draws from independent (but not identically distributed) normal distributions and conducts a two-sample t -test. Permutation test reports the p -values for testing the null hypothesis that the treatment has no effect for the ITT parameter by constructing a permutation distribution of the ITT estimate using the 12,376 possible permutations of treatment assignment. These tests have exact finite sample size. *** denotes 1%, ** denotes 5%, * denotes 10% significance.
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ Study shows convincingly that management consulting can raise firm performance. ◮ That’s already a significant achievement, rightfully influential.
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ Study shows convincingly that management consulting can raise firm performance. ◮ That’s already a significant achievement, rightfully influential. ◮ Study has been interpreted as showing: ◮ Firms are systematically making mistakes by not adopting the 38 practices. ◮ The 38 practices themselves unambiguously raise firm performance.
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ Study shows convincingly that management consulting can raise firm performance. ◮ That’s already a significant achievement, rightfully influential. ◮ Study has been interpreted as showing: ◮ Firms are systematically making mistakes by not adopting the 38 practices. ◮ The 38 practices themselves unambiguously raise firm performance. ◮ Some words of caution:
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ Study shows convincingly that management consulting can raise firm performance. ◮ That’s already a significant achievement, rightfully influential. ◮ Study has been interpreted as showing: ◮ Firms are systematically making mistakes by not adopting the 38 practices. ◮ The 38 practices themselves unambiguously raise firm performance. ◮ Some words of caution: ◮ Adoption of modern management may require capabilities that firms did not have and that are costly to acquire. ◮ $250k market value in consulting yielded about $300k increase in profit per year, noisily measured. ◮ Experiment paid cost for firms, but not clear they were making a mistake by not paying it on their own.
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ Study shows convincingly that management consulting can raise firm performance. ◮ That’s already a significant achievement, rightfully influential. ◮ Study has been interpreted as showing: ◮ Firms are systematically making mistakes by not adopting the 38 practices. ◮ The 38 practices themselves unambiguously raise firm performance. ◮ Some words of caution: ◮ Adoption of modern management may require capabilities that firms did not have and that are costly to acquire. ◮ $250k market value in consulting yielded about $300k increase in profit per year, noisily measured. ◮ Experiment paid cost for firms, but not clear they were making a mistake by not paying it on their own. ◮ Question: Are effects due to 38 management practices themselves or to other effects of consulting?
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ My interpretation:
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ My interpretation: ◮ Experiment raised capabilities of firms, which allowed them to implement modern management practices.
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ My interpretation: ◮ Experiment raised capabilities of firms, which allowed them to implement modern management practices. ◮ Some of the practices do seem like “no-brainers” that would be advantageous for all firms in all contexts (e.g. tracking inventories). But others are less obvious (e.g. performance pay).
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ My interpretation: ◮ Experiment raised capabilities of firms, which allowed them to implement modern management practices. ◮ Some of the practices do seem like “no-brainers” that would be advantageous for all firms in all contexts (e.g. tracking inventories). But others are less obvious (e.g. performance pay). ◮ We need to evaluate setting by setting whether particular practices are indeed advantageous, worth promoting.
Introduction Framework Recent Research Conclusion Consulting Experiment (cont.) ◮ My interpretation: ◮ Experiment raised capabilities of firms, which allowed them to implement modern management practices. ◮ Some of the practices do seem like “no-brainers” that would be advantageous for all firms in all contexts (e.g. tracking inventories). But others are less obvious (e.g. performance pay). ◮ We need to evaluate setting by setting whether particular practices are indeed advantageous, worth promoting. ◮ The fundamental problem seems to be that firms lack capabilities, not that they have failed to adopt a particular set of practices.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment ◮ Atkin, Chaudhry, Chaudry, Khandelwal and Verhoogen (2017b). ◮ Through a series of fortuitous events, we came up with a technology that seemed to be as close to a no-brainer as one could hope for.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment ◮ Atkin, Chaudhry, Chaudry, Khandelwal and Verhoogen (2017b). ◮ Through a series of fortuitous events, we came up with a technology that seemed to be as close to a no-brainer as one could hope for. ◮ Randomly allocated it to 35 of 135 soccer-ball producers in Sialkot, Pakistan.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment ◮ Material (rexine) is by far the most expensive input.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) Standard “buckyball” design: 20 hexagons, 12 pentagons. For standard ball, almost all firms use 2-hexagon and 2- pentagon “flush” dies.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) Hexagons tessellate. ∼ 8% of rexine wasted.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) Pentagons don’t. ∼ 20-24% of rexine wasted.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) In a YouTube video of a Chinese factory producing the Adidas Jabulani ball, I noticed a different layout of pentagons.
Double-Lattice Packings of Convex Bodies in the Plane 393 inscribed in Ko, and the proof is complete. However, it can be noticed now that the minimality of the area of q implies that the length of one of the sides of q actually equals one-half of the length of Ko in the direction of that side. Therefore Ko actually touches a translate of itself, and Case II is not possible at all. [] Remark 1. If K is not strictly convex, the conclusion of the above theorem does not necessarily hold. However, in this case there exists a double-lattice packing with maximum density which is generated by a minimum-area extensive parallelogram inscribed in K. This can be obtained by approximating K with a sequence of strictly convex bodies K, and then selecting a convergent subsequence of double-lattice packings. Remark 2. Theorem 1 and the above remark yield an algorithm for finding a maximum density double-lattice packing with copies of K which goes as follows. For any diameter d of K, find a pair of chords parallel to d, each of length equal to one-half of the length of d. These two chords define a parallelogram q(d) Introduction Framework Recent Research Conclusion inscribed in K, which turns out to be extensive (see Lemma 1 of the following section). Now vary d and find a critical position of d = do such that q(do) is of minimum area. This minimum-area extensive parallelogram generates a maximum Soccer-ball Experiment (cont.) density double-lattice packing with copies of K. In general, locating the critical diameter do may be a problem, but in many special cases, as in the following examples, the diameter do is easy to find. I could also have gone to: G. Kuperberg and W. Kuperberg, “Double-Lattice Packings of Convex Bodies in the Plane,” Discrete Examples. An application of the algorithm described in Remark 2 to the case when K is a regular pentagon results in a double-lattice packing of density & Computational Geometry , 5: 389-397, 1990. (5-x/5)/3 =0.92131..., shown in Fig. 7. This packing may have the maximum Fig. 7. Maximum density double-lattice packing with regular pentagons.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) Or the Wikipedia Pentagons page:
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Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.)
Introduction Framework Recent Research Conclusion Net Benefits of Adoption 10 th 25 th 50 th 75 th 90 th mean net variable cost reduction (%) 0.42 0.61 0.82 1.09 1.47 0.89 (0.11) (0.10) (0.09) (0.19) (0.27) (0.11) % net variable cost/avg % profit rate 4.55 6.82 10.63 16.56 24.42 13.07 (1.05) (1.13) (1.60) (2.35) (4.15) (1.79) total cost savings per month (Rs 000s) 3.66 9.82 41.35 135.92 397.95 137.77 (0.99) (2.33) (9.43) (36.39) (130.62) (31.68) total cost savings per cutter per month (Rs 000s) 2.75 6.47 14.91 33.83 63.61 27.31 (0.83) (1.33) (2.43) (6.28) (14.02) (5.04) days to recover fixed costs 10.28 19.11 43.03 100.86 247.53 168.80 (2.23) (3.66) (7.37) (21.74) (76.42) (84.72) days to recover fixed costs (no die) 5.34 9.92 22.34 52.37 128.53 87.64 (1.16) (1.90) (3.83) (11.29) (39.68) (43.99) ◮ We estimate that 50% of tech drop firms would recover fixed costs in 23 days or less, 75% in 53 days or less. Full table Pentagons per sheet
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary:
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not. ◮ Cutters didn’t like the die.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not. ◮ Cutters didn’t like the die. ◮ They are paid piece rates, with no incentive to reduce waste, and the new die was slowing them down.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not. ◮ Cutters didn’t like the die. ◮ They are paid piece rates, with no incentive to reduce waste, and the new die was slowing them down. ◮ They told owners it didn’t work.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not. ◮ Cutters didn’t like the die. ◮ They are paid piece rates, with no incentive to reduce waste, and the new die was slowing them down. ◮ They told owners it didn’t work. ◮ We did a second experiment.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not. ◮ Cutters didn’t like the die. ◮ They are paid piece rates, with no incentive to reduce waste, and the new die was slowing them down. ◮ They told owners it didn’t work. ◮ We did a second experiment. ◮ Offered bonus of one month’s pay to show owners that dies work.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not. ◮ Cutters didn’t like the die. ◮ They are paid piece rates, with no incentive to reduce waste, and the new die was slowing them down. ◮ They told owners it didn’t work. ◮ We did a second experiment. ◮ Offered bonus of one month’s pay to show owners that dies work. ◮ Workers accepted and about 50% of affected firms adopted.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not. ◮ Cutters didn’t like the die. ◮ They are paid piece rates, with no incentive to reduce waste, and the new die was slowing them down. ◮ They told owners it didn’t work. ◮ We did a second experiment. ◮ Offered bonus of one month’s pay to show owners that dies work. ◮ Workers accepted and about 50% of affected firms adopted. ◮ We argue that the results are not due simply to us subsidizing the fixed costs of adoption.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not. ◮ Cutters didn’t like the die. ◮ They are paid piece rates, with no incentive to reduce waste, and the new die was slowing them down. ◮ They told owners it didn’t work. ◮ We did a second experiment. ◮ Offered bonus of one month’s pay to show owners that dies work. ◮ Workers accepted and about 50% of affected firms adopted. ◮ We argue that the results are not due simply to us subsidizing the fixed costs of adoption. ◮ Hard to reconcile with both initial non-adoption and adoption in response to small incentives.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Quick summary: ◮ We gave out the new dies to a random subset of firms. ◮ A few adopted, most did not. ◮ Cutters didn’t like the die. ◮ They are paid piece rates, with no incentive to reduce waste, and the new die was slowing them down. ◮ They told owners it didn’t work. ◮ We did a second experiment. ◮ Offered bonus of one month’s pay to show owners that dies work. ◮ Workers accepted and about 50% of affected firms adopted. ◮ We argue that the results are not due simply to us subsidizing the fixed costs of adoption. ◮ Hard to reconcile with both initial non-adoption and adoption in response to small incentives. ◮ It appears that second experiment induced information flow in firm.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Interpretation:
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Interpretation: ◮ Conflict of interest within the firm can prevent adoption of a “no-brainer” technology.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Interpretation: ◮ Conflict of interest within the firm can prevent adoption of a “no-brainer” technology. ◮ The conflict of interest can be thought of as a lack of organizational capability.
Introduction Framework Recent Research Conclusion Soccer-ball Experiment (cont.) ◮ Interpretation: ◮ Conflict of interest within the firm can prevent adoption of a “no-brainer” technology. ◮ The conflict of interest can be thought of as a lack of organizational capability. ◮ Reinforces view of Bloom et al. (2013) that “information failures” explain lack of adoption.
Introduction Framework Recent Research Conclusion Conclusion ◮ The mere fact that Π it ( k H ) > Π it ( k L ) in developed countries does not imply that Π it ( k H ) > Π it ( k L ) in developing countries.
Introduction Framework Recent Research Conclusion Conclusion ◮ The mere fact that Π it ( k H ) > Π it ( k L ) in developed countries does not imply that Π it ( k H ) > Π it ( k L ) in developing countries. ◮ Developing-country firms have different capabilities, { λ ijkt } , and face different product-demand and input-supply functions ⇀ { P ijt } , { W ijkt } .
Introduction Framework Recent Research Conclusion Conclusion ◮ The mere fact that Π it ( k H ) > Π it ( k L ) in developed countries does not imply that Π it ( k H ) > Π it ( k L ) in developing countries. ◮ Developing-country firms have different capabilities, { λ ijkt } , and face different product-demand and input-supply functions ⇀ { P ijt } , { W ijkt } . ◮ Statement applies to management practices as well as to machines.
Introduction Framework Recent Research Conclusion Conclusion ◮ The mere fact that Π it ( k H ) > Π it ( k L ) in developed countries does not imply that Π it ( k H ) > Π it ( k L ) in developing countries. ◮ Developing-country firms have different capabilities, { λ ijkt } , and face different product-demand and input-supply functions ⇀ { P ijt } , { W ijkt } . ◮ Statement applies to management practices as well as to machines. ◮ That said, it does appear that there are cases where more technically efficient technologies are not adopted, e.g.: ◮ Keeping track of inventories. ◮ Offset die.
Introduction Framework Recent Research Conclusion Conclusion ◮ The mere fact that Π it ( k H ) > Π it ( k L ) in developed countries does not imply that Π it ( k H ) > Π it ( k L ) in developing countries. ◮ Developing-country firms have different capabilities, { λ ijkt } , and face different product-demand and input-supply functions ⇀ { P ijt } , { W ijkt } . ◮ Statement applies to management practices as well as to machines. ◮ That said, it does appear that there are cases where more technically efficient technologies are not adopted, e.g.: ◮ Keeping track of inventories. ◮ Offset die. ◮ We have some results on what is getting in the way, e.g.: ◮ Lack of manager know-how. ◮ Organizational barriers to information flows.
Introduction Framework Recent Research Conclusion Conclusion (cont.) ◮ But the literature on technology adoption in non-agricultural firms in developing countries (with direct observation of adoption) is still pretty wide open. ◮ Little evidence on input-side drivers of adoption. ◮ Little evidence on output-side drivers of adoption. ◮ More to do on learning, capabilities, internal-to-the-firm drivers.
Introduction Framework Recent Research Conclusion Conclusion (cont.) ◮ But the literature on technology adoption in non-agricultural firms in developing countries (with direct observation of adoption) is still pretty wide open. ◮ Little evidence on input-side drivers of adoption. ◮ Little evidence on output-side drivers of adoption. ◮ More to do on learning, capabilities, internal-to-the-firm drivers. ◮ We’ve got out work cut out for us!
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