- + h(eft, kft, lft, θft) + ft (F5) We follow Ackerberg et al. (2015) in approximating the right-hand-side of (F5) with a third-order polynomial in all its elements, except for the elements of θ, which we enter linearly.45 From the first stage, we obtain expected output qÌft and the residuals Ë ft.46 The next step is specifying a law of motion for productivity Ïft. We assume that Ïft follows a Markov process that can be shifted by plant managersâ action: Ïft = g Ïf,tâ1, Îf,tâ1 + ξft (F6) In (F6), ξft denotes the innovation to productivity and the vector Î includes variables controlled by plantsâ managers that influence the expected future value of productivity and state variables which determine differences in productivity dynamics across plants.
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Acemoglu, D. and P. Restrepo (2017). Robots and jobs: Evidence from us labor markets. NBER working paper (w23285).
- Acemoglu, D. and P. Restrepo (2018a). Demographics and automation. Technical report, National Bureau of Economic Research.
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Acemoglu, D., C. Lelarge, and P. Restrepo (2020). Competing with robots: Firm-level evidence from france. In AEA Papers and Proceedings, Volume 110, pp. 383â88.
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- As in Graetz and Michaels (2018), the construction of the stock of operational robots is obtained by assuming a yearly depreciation rate of 10% and applying the perpetual inventory method, using 1993 estimates of the existing stock by the IFR as initial values. The IFR does provide estimates of the stock, but it adopts a different assumption that robots fully depreciate after twelve years. The original IFR industry classification has been converted to obtain eighteen industries, roughly corresponding to 2 digit-level ISIC rev.4. These are: Agriculture, Food and tobacco, Textiles, Paper, Wood and furniture, Chemicals, Rubber and plastics, Nonmetallic mineral products, Basic metals, Metal products, Electronics, Machinery and equipment, Motor vehicles, Other transport equipment, Repair and installation of machinery, Construction, and Education and R&D, and Utilities.
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Autor, D. H., F. Levy, and R. J. Murnane (2003). The skill content of recent technological change: An empirical exploration. The Quarterly journal of economics 118(4), 1279â 1333.
- C Occupation Profiles The Occupation profiles are compiled by the World Bank in partnership with national governments. The Occupational profiles are used as key inputs of Critical Occupations Lists, which aim to identify shortages of certain occupations of strategic importance to the economy (e.g. World Bank (2020)).
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- Ë ft (F10) where the last term in (F11) is the residual obtained from the first stage estimation of (F5). As discussed in De Loecker and Warzynski (2012), including Ë ft is important, as it allows to purge the estimated markup for variation in output not due to changes in inputs. Finally, we recover marginal cost as mcft = Pft ft (F11) Prior to estimation, we drop the bottom and top one percent of the distribution of markup and marginal cost in order to avoid outliers driving the results. We obtain very similar coefficients if we do not trim our estimates.
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Eslava, M., J. Haltiwanger, A. Kugler, and M. Kugler (2004). The effects of structural reforms on productivity and profitability enhancing reallocation: evidence from colombia. Journal of development Economics 75(2), 333â371.
Faber, M. (2018). Robots and reshoring: Evidence from mexican local labor markets.
- Figure E1: Correlation between the 2006 shares of workers with secondary education and robots in use in 2015, by industry.
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Foster, L., J. Haltiwanger, and C. Syverson (2008). Reallocation, firm turnover, and efficiency: Selection on productivity or profitability? American Economic Review 98(1), 394â425.
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- λft WftLft Qft As in De Loecker and Warzynski (2012), we define the plantâs markup over the marginal cost of output λft as ft â¡ Pft λft where Pft is the price of output produced by the plant. The previous equation yields an expression of plantsâ markup depending on the elasticity of output with respect to the variable input, βl, and the inverse of the revenue share of expenditure on Llt: ft = âF(Kft, Lft) â¦ft âLft Lft Qft PjtQjt WjtLjt = βl PjtQjt WjtLjt In our empirical application, the markup is given by ft = βl PftQft WftLft 1
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Javorcik, B. and S. Poelhekke (2017). Former foreign affiliates: Cast out and outperformed ? Journal of the European Economic Association 15(3), 501â539.
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- On the horizontal axis there is the change between 2007 and 2015, of the OECD region industry-average number of robots per thousand employees. On the vertical axis, there is the change between 2007 and 2015, of the industry-level number of robots per thousand employees in Indonesia. Sources: IFR, STAN, SI. Figure A3: Correlation between Indonesian and OECD-average exposure to robots excluding Motor Vehicles and Rubber and plastics.
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- On the horizontal axis there is the change between 2007 and 2015, of the OECD region industry-average number of robots per thousand employees. On the vertical axis, there is the change between 2007 and 2015, of the industry-level number of robots per thousand employees in Indonesia. The figure excludes two high-exposure industries in Indonesia, Motor vehicles and Rubber and Plastics. Sources: IFR, STAN, SI.
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Rodrik, D. (2016). Premature deindustrialization. Journal of economic growth 21(1), 1â33.
- Secondary is the 2006 share of plantsâ employment with secondary education. Other technologies are capture by an index of innovation activities in 2006, interacted with year fixed effects. Standard errors are clustered at the 2-digit industry- and year-level. The coefficients with ??? are significant at the 1% level, with ?? are significant at the 5% level, and with ? are significant at the 10% level. Table A7: ETR and Plant level productivity: ETR based on PC analysis. (1) (2) VARIABLES TFPQ TFPQ ETR (PC) -0.003 0.774** (0.005) (0.250) ETR (PC) Ã robot-intensive industry-0.780** (0.249) Observations 36,517 36,517 R-squared 0.994 0.994 Plant FE yes yes Industry-year FE yes yes Other technologies yes yes The table presents OLS estimates of the relationship between plantsâ exposure to robots, productivity, markup, value added and revenue. The dependent variables are TFPQ (1) the log of plant level real marginal cost (2), markup (3) real value added (4) and real revenue (5).
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- Studying the behavior of the stock within plants reveals that in some circumstances plants reported values in different units. The phenomenon is somewhat more frequent in 1996 and 2006, when the BPS conducted a wider economic census that collected information in units rather than in thousand Rupiah. For instance, in 2006 the number of surveyed firms increased by 40%. The increase in coverage required hiring inexperienced enumerators that were more likely to make mistakes, which contributed to increase measurement errors.
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- The production function (F1) is a structural value added specification De Loecker and Scott (2016) in which capital and labor are allowed to be characterised by some degree of substitution and energy use is a perfect complement to the combination of the other inputs. We use energy inputs rather than materials as the former is less likely to be affected by adjustment costs.44 Given (F1), a profit maximising plant sets Qft = γeEft = F(Kft, Lft) â¦ft (F2) To estimate production function parameters, we take the logged version of (F2): qft = f kft, lft; β + Ïft + ft (F3) The variable Ïft represents the log of Hicks-neutral productivity, which is known by plantsâ managers but not by us. The variable ft is an i.i.d. error term that captures factors such as measurement errors.
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- Verhoogen, E. (2020). Firm-level upgrading in developing countries. CDEP-CGEG Working Paper 83. Online Appendix (not for publication) A Figures and Tables Appendix Figure A1: Penetration of robots in the Indonesian manufacturing sector, by industry. The figure shows the number of industrial robots per thousand employees used in selected industries over the years of the sample. Source: IFR, SI. Figure A2: Correlation between Indonesian and OECD-average exposure to robots (logscale) .
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- We follow De Loecker and Warzynski (2012) to obtain a measure of plant level markup from the plantsâ first order conditions. Cost minimisation with respect to labor, which we consider a static input, implies the following first order condition: 48 It should be noticed that labor and electricity consumption are both significantly correlated within a plant over time, which justify their inclusion in (F9) as instruments. âLjt âLft = Wft â λft âF(Kft, Lft) â¦ft âLft = 0 where L is plantâs f Lagrangian, Wft wages and λft the Lagrangian multiplier. Rearranging terms and multiplying both sides of the previous equation by Lft Qft , we obtain âF(Kft, Lft) â¦ft âLft Lft Qft = 1
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- World Bank (2020). Indonesiaâs critical occupations list 2018 : Occupation profiles.
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- Y p=1 Ppjt Ppj,tâ1 .5(spjt+spj,tâ1) Ïj,tâ1 where Ppjt is the price of good p and spjt is the share of this good in total product market sales of plant j in period t. Therefore, the growth of Ïjt is the product of each plantâs price growth, each weighted with the average share of sales in t and tâ1. Wee set Ïjt = 100 in 2006. For plants entering after 2006, we follow Eslava et al. (2004) and Mertens (2019) and use the 5-digit industry average of the plant price indices as a starting value. When price growth data are missing, we replace it with an average of product or inputs price changes within the same 5-digit industry.
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