Place-Based Policies and the Geography of Corporate Investment ∗Cameron LaPoint†

Shogo Sakabe‡

Yale SOM

Columbia

June 2, 2022
Latest version here

Abstract
Growing spatial inequality has led policymakers to enact tax breaks to attract corporate
investment and jobs to economically peripheral regions. We demonstrate the importance ofmulti-plant firms’ physical capital structure for the efficacy of place-based policies by studying
a national bonus depreciation scheme in Japan which altered the relative cost of capital acrosslocations, offering high-tech manufacturers immediate cost deductions from their corporate
income tax bill. Combining corporate balance sheets with a registry containing investment
by plant location and asset type, we find the policy generated big gains in employment andinvestment in building construction and in machines at pre-existing production sites, with animplied fiscal cost per job created of $15,000. These responses are driven by more financiallyconstrained firms, firms which rely on costly but long-lived capital inputs, and firms with a larger
portion of their existing operations proximal to the policy catchment areas. The policy did not
generate positive local spillovers to ineligible plants or spillovers through inter-regional trade
networks. Plant-level hiring in ineligible areas outstripped that in eligible areas, suggesting
reallocation of resources within the internal capital and labor markets of multi-plant firms
attenuates the local benefits of corporate income tax breaks.

Keywords: place-based policies, spatial firms, bonus depreciation, internal capital markets,
long-lived assets, financing constraints
JEL classifications: E22, G31, H25, R12, R38∗
We thank Pat Akey (discussant), Costas Arkolakis, Taylor Begley (discussant), Dan Garrett (discussant), Song
Ma, Takashi Unayama, David Weinstein, Eric Zwick, and participants in the Yale International & Spatial Economics
Lunch, UEA North American Meeting, Colorado Finance Summit, Japan Economic Seminar at Columbia Business
School, Adam Smith Workshop, and RIDGE Forum Public Economics Workshop for detailed comments and helpfuladvice. We are grateful to the Research Institute of Economy, Trade and Industry (RIETI) for providing access tothe manufacturing firm Census microdata. Data from the Development Bank of Japan (DBJ) Financial Databaseand Nikkei NEEDS FinancialQUEST used in this paper are licensed through Columbia University. We thank staff at
the DBJ for answering our questions about the corporate balance sheet data. We also thank the Center on Japanese
Economy and Business (CJEB) at Columbia Business School for supporting this project by providing dissertation
fellowships to both authors. Additionally, Sakabe thanks the Nakajima Foundation for generous financial support.
We thank Sung Mo Koo and Melissa G. Li for their help with transcribing the corporate disclosure data used in thisproject, and Mingjun Sun for providing extraordinary research assistance. This research was funded in part by theInternational Center for Finance at Yale SOM. An earlier version of this paper circulated as RIETI Discussion Paper,
No. 21-E-059. First draft: July 12th, 2021.
†
Yale School of Management. 165 Whitney Avenue, New Haven, CT 06511. Email: cameron.lapoint@yale.edu;
Web: http://cameronlapoint.com‡

Department of Economics, Columbia University, 1022 International Affairs Building, 420 W 118th Street, New
York, NY 10027. Email: s.sakabe@columbia.edu; Web: http://shogosakabe.github.io

1

Introduction

In the summer of 2018, Governor Scott Walker and President Donald Trump brokered a deal
with Taiwanese electronics giant Foxconn which promised to bring 13,000 jobs and $10 billion in
investment to small town Mount Pleasant, Wisconsin, in exchange for a total tax subsidy packageof over $4 billion. By the end of 2019, Foxconn had employed 281 workers and fulfilled only 2.8% of
its investment pledge by building an empty showcase facility.1 How can policymakers offer targetedbusiness incentives for relocation while avoiding corporate reversals like the Foxconn case? And
how can such policies be designed to deliver long-lasting investment and increased opportunities
for residents of economically struggling areas?
To explore the tradeoff between targeting and local capital retention inherent in place-based
policies (PBPs), as illustrated by the Foxconn deal, we examine a bonus depreciation scheme inJapan which altered the relative cost of capital across locations. The Japanese government rolled
out the Technopolis program between 1984 and 1989 to promote regional industry clusters outside
the main metropolises, offering high-tech manufacturing firms immediate cost deductions fromtheir corporate income tax bill. These write-offs were granted as bonus depreciation, which allows
firms to deduct an additional fraction of physical capital costs in the first year of an asset’s tax
life, including deductions for purchases of buildings used towards business operations. A follow-up
policy enacted between 1989 and 1994, dubbed Intelligent Location, expanded the set of eligible
areas and offered bonus depreciation to firms in certain upstream, non-tradable industries.
Using a series of staggered difference-in-differences (DD) and triple differences specifications which
define treatment at the firm level based on industry and plant locations, we find Technopolis
was successful at generating investment in treated areas. The historical nature of the Japanesepolicy experiments and long time coverage of our data allow us to examine the long-run impactof local business tax incentives on regional economic development. In particular, we rule out “toedipping,” or firms making small reversible investments to capture tax benefits and then exiting
shortly thereafter. For listed firms, capital and employment shares within a firm’s internal network
are stable three decades after the bonus depreciation incentives expired. Corporate investment
responses were concentrated in construction projects on existing sites within the firm’s network; wefind granting firms Technopolis eligibility generated a 0.29 standard deviation increase in outlays
for construction, and a 0.40 standard deviation increase in non-real estate purchases.
Firms also increase their workforce by 5-7% (or, a 0.13-0.18 s.d. effect) within 10 years of
implementation of a local bonus depreciation regime. Given that Technopolis primarily subsidized
investment in industrial machinery, this suggests some complementarity between high-tech capitaland labor, contrary to recent concerns that machines may be displacing workers in certain sectors(Acemoglu & Restrepo 2020). Applying a partial equilibrium accounting approach which combines
1

The Verge, “Inside Foxconn’s Empty Buildings, Empty Factories, and Empty Promises in Wisconsin,” October
19, 2020. Accessed on May 28, 2021.

1

the observed stream of claimed tax write-offs with our DD estimates of the employment response,
we compute a fiscal cost from lost corporate income tax revenue of between $15,000 and $20,000 per
corporate job created. Our estimates are comparable to the $20,000 per job estimate of Garrett,Ohrn, & Suárez Serrato (2020) for all bonus claims offered in the U.S. between 2002 and 2012.
Overall, we conclude Technopolis was highly cost-effective on the labor market dimension relative
to similar manufacturing subsidies and bonus depreciation schemes enacted elsewhere.
To highlight the crucial role of multi-plant firms’ physical capital structure in the effectiveness of
spatially targeted tax incentives, we link a database containing balance sheets for all publicly listed
Japanese firms with a registry containing corporate investment by plant location and asset type.
Key to our research design is our ability to separate physical capital investment into six categories:
construction projects, machines, tools, vehicles, buildings, and land. This allows us to identify
firms, rather than coarse sectors, relying more on long-lived capital (buildings and machines) vs.
short-lived capital (tools and vehicles). Long-lived capital firms gain more in an immediate cash
flow sense from becoming eligible to claim spatial bonus depreciation, since normally the tax code
would require them to amortize costs over a much longer period.
Another major advantage to merging corporate balance sheets with plant-level data is that we
can move beyond intent-to-treat estimates towards average treatment effects by showing that firms
actually make use of the tax incentives offered by the policy. Technopolis-eligible firms are 9 p.p.
more likely to make bonus depreciation claims in the post-reform period (0.18 s.d. effect on thedollar value of claims). The observed effect on cash flows peaks after five years, which corresponds
to the first kink point in the depreciation schedule, implying firms promptly act to maximize theirdeductions. Our results are robust to choosing among non-OLS estimators designed to account for
the “negative weight problem” in aggregating heterogeneous treatment effects (Goodman-Bacon2021) and for anticipation effects (Borusyak, Jaravel, & Spiess 2021) in staggered DD contexts.
Determining the efficacy of PBPs is of central importance given the widely documented growthin spatial inequality coinciding with the decline of traditional manufacturing since the 1970s. In thelast three decades the U.S. has witnessed a stark decline in per capita income convergence (Ganong& Shoag 2017) and prime-age male employment rates (Austin, Glaeser, & Summers 2018), but
a convergence in poverty rates across locations (Gaubert et al. 2021). Figure 1 shows that Japan
has experienced an increase in directed migration and income divergence over the last few decades,
as population aging has exacerbated the depopulation of the countryside and economic activity
becomes increasingly concentrated around Tokyo.
Place-based policies are a catch-all term referring to transfers made conditional on economic
activity in a location, but such policies can take many forms. The vast majority of research on
PBPs covers targeted state and local tax subsidies and restricts attention to short-run effects due
to data limitations (Bartik 2020). An exception is Kline & Moretti (2014), who study the TennesseeValley Authority (TVA) over a century and conclude the TVA boosted national manufacturing
productivity but employment gains were reversed when subsidies ended. De Simone et al. (2019)2

FIGURE 1. Income Divergence across Japanese Municipalities

Income Convergence: 1995 – 2015
1995−2015 annual income growth

1975−1995 annual income growth

Income Convergence: 1975 – 1995
15

10

5

0
4

5

6

4

2

0

−2

−4

7

6

6.5

1975 log per capita income
β = −0.19
s.e. = 0.10
N = 1710
R−squared =β = 1.06
s.e. = 0.13
N = 1710
R−squared =

0.00

10

5

0

−5
5

6

8

0.06

7

4

2

0

−2

−4
6

6.5

1975 log per capita income
β = 1.69
s.e. = 0.07
N = 1710
R−squared =7.5

Directed Migration: 1995 – 2015
1995−2015 annual population growth

1975−1995 annual population growth

Directed Migration: 1975 – 1995

4

7

1995 log per capita income

7

7.5

8

1995 log per capita income
β = 1.95
s.e. = 0.09
N = 1710
R−squared =

0.20

0.29

Notes: The figure shows how Japan has transitioned from weak income convergence to strong income divergence (toppanel) and experienced an increase in directed migration (bottom panel) over the last 40 years. Population statistics
from the quinquennial Census. Income data from the Cabinet Office. We impose modern municipal boundaries using
the historical city code crosswalk available through RIETI (Kondo 2019), and exclude 9 municipalities which mergedwith another municipality during the last available Census year of 2015.

3

compile a database of firm-specific U.S. local tax subsidies and contend the most successful subsidies
are granted in jurisdictions where the initiatives receive little press coverage. Devereux, Griffith, &Simpson (2007) document that relocation grants in the U.K. were only effective at attracting plants
when the new location already had plants of the same industry, suggesting the industry targetingof PBPs like Technopolis and Intelligent Location is crucial to their success. Criscuolo et al. (2019)study the same setting in the U.K. and find large effects on manufacturing employment for small
firms, but larger firms “game the system” by accepting subsidies without increasing local activity.2
Much of the latest empirical literature on PBPs analyzes the Opportunity Zone (OZ) program
introduced by the 2017 U.S. Tax Cuts and Jobs Act (TCJA) to foster local job growth. The
program allows state governors to designate low-income Census tracts as OZs, subject to Treasury
Department approval. Investors can defer capital gains taxes on investment in OZs for at least five
years, or eliminate their tax liability entirely if they hold the assets for at least 10 years. Freedman,
Khanna, & Neumark (2021) conclude these tax incentives had no statistically significant impact
on resident employment, earnings, or poverty rates. Similarly, Chen, Glaeser, & Wessel (2019)
document minimal capitalization into single family home prices, suggesting that homebuyers do
not expect neighborhood change resulting from the OZ program in the near term. Arefeva et al.
(2021) instead find designated OZ Census tracts experienced increased employment growth of 2-4
p.p. between 2017 and 2019.
In recent work, Kennedy & Wheeler (2021) note using investors’ tax returns that the gains
from OZs are highly unequal, with relatively well-off and gentrifying Census tracts receiving thebulk of investment under the OZ program. This raises the question of what are the distributional
consequences of PBPs? We approach this question from three angles. First, motivated by evidenceof spillovers from a manufacturing investment subsidy program in Germany (Siegloch, Wehrhöfer,& Etzel 2021), we look at spillovers to a control group of firms located in eligible Technopolis sites
who are ineligible to claim bonus depreciation. We find no evidence of positive spillovers, but someevidence of crowd-out of non-real estate investment. Second, we show that indirect exposure to thepolicy through inter-regional trade networks had no effect on employment or investment beyond
direct eligibility. Third, we match our sample of listed firms to their establishments and show thatfirms’ hiring and intensive margin investment were concentrated in Technopolis ineligible areas.
Thus, while we do not find evidence of toe-dipping, it appears firms used the tax write-offs toredirect some cash flow benefits towards areas not targeted by policymakers.
Our paper is the first to evaluate bonus depreciation as a broad place-based policy. We emphasize
two main features which distinguish our policy setting from related local business incentive schemesin the U.S. First, our results point to the importance of providing immediate rather than deferredfinancial incentives for inducing firms to make irreversible investments in peripheral regions. Bonus
depreciation offers firms an opportunity to transfer cash flows from far future deduction claims to the2

Other prominent examples of PBPs include State Enterprise Zones (Neumark & Kolko 2010) which offer
state-specific income, property, and sales tax benefits, and Federal Empowerment Zones which distribute employment
subsidies and block grants to firms (Glaeser & Gottlieb 2008; Busso, Gregory, & Kline 2013).

4

present, operating much like the capital gains tax deferral incentives of OZs. Second, the Technopolis
and Intelligent Location policies we study are set at the national level, which limits the role of local
political economy concerns (Slattery & Zidar 2020), or tax competition between jurisdictions (Mast2020), in determining selection of treated regions and industries. In our case, eligible locations are
selected for their manufacturing capacity and proximity to research universities, with incentives
funded through national rather than local tax coffers.
Research on the economic impacts of PBPs has overwhelmingly examined wages and employmentoutcomes. In this paper we focus on how tax incentives can shift the spatial distribution of physicalasset expenditures within large firms, either by lowering the cost of capital at specific locations,or by mitigating frictions in capital markets. Such frictions might include financial constraints, asemphasized in a large corporate finance literature (e.g. Giroud & Mueller 2015, 2019), investment
adjustment costs or “time to build” (Cooper & Haltiwanger 2006), and the costs of transporting
tangible assets between locations (Ma, Murfin, & Pratt 2021). We find intra-firm transport costs
are a critical driver of local tax policy take-up. An eligible firm is 1% less likely to claim bonuses
for every 10 km increase in commuting distance between its network of existing plants and thenearest Technopolis area. In a closely related paper, Zwick & Mahon (2017) demonstrate sectors
using longer-lived assets like heavy industrial equipment exhibit larger investment responses to the2001 and 2008 U.S. bonus depreciation reforms, which is consistent with models featuring fixedadjustment costs or financing constraints.3
When we rank firms based on measures of external financing constraints popular in the empirical
corporate finance literature (e.g. Hadlock & Pierce 2010), we find that constrained firms completely
drive the take-up of bonus claims, investment, and hiring. We recover firms’ capital input sharesusing the perpetual inventory methods of Hayashi & Inoue (1991) to rank firms based on theirreliance on long-lived vs. short-lived assets. Assuming a constant returns to scale production
function, buildings account for 47% of the capital input share for the average listed firm in our
sample. This is particularly important because in the absence of bonus depreciation, commercial
use buildings have a depreciation life as long as 65 years, implying a tax deduction per annum of
only 1.54% of the acquisition cost under straight-line depreciation.4 The outsize share of properties
in firm production, combined with the maximum bonus depreciation claim of 15% for buildings
under the two PBPs, renders relocation and outright ownership of new plants (or expansions of
3
There is a voluminous empirical literature analyzing the investment response to corporate tax breaks, datingback to Hall & Jorgenson (1967). With the exception of Ohrn (2019), who studies state adoption of federal bonus
depreciation policies, this literature has largely ignored the spatial dimension of investment responses. Other notable
examples include Goolsbee (1998) and Chirinko, Fazzari, & Meyer (1999) on investment tax credits; Desai & Goolsbee(2004) and Yagan (2015) on the 2003 U.S. dividend tax cut; House & Shapiro (2008), Edgerton (2010) on bonus
depreciation. In contrast to Zwick & Mahon (2017), Maffini, Xing, Devereux (2019) argue that the investment effectof accelerated depreciation allowances arises from changes to the cost of capital, rather than through alleviating firms’
financial constraints.
4

Long depreciation lives for buildings are not unique to Japan. Income-generating properties in the U.S. havea depreciation life of 39 years, while owner-occupied housing has a depreciation life of 27.5 years, implying annual
straight-line deductions of 2.56% and 3.64% of acquisition cost, respectively.

5existing plants) in the treated regions substantially more attractive. Bonus depreciation is thus anespecially potent force towards fostering irreversible investment.
Finally, our paper lends empirical support to mechanisms introduced in a growing macro-trade
literature modeling the location decision of firms on the extensive margin (i.e. where to set upshop) and the intensive margin – that is, how many resources to allocate to a particular location.
Gaubert (2018) builds a model with agglomeration in which firms sort across cities on the extensive
margin and argues PBPs like Technopolis and Intelligent Location which subsidize smaller cities
have negative aggregate effects. In Fajgelbaum et al. (2018) firms sort into states which offer lower
corporate income tax rates, and tax competition between states diminishes aggregate welfare. Like
Jia (2008) and Holmes (2005, 2011), Oberfield et al. (2020) allow for sorting on both margins; theirframework adds cannibalization and span of control and transport costs, but does not allow thephysical size of plants to vary across locations. Importantly, none of these models directly includes
capital in production, even though Dougal, Parsons, & Titman (2015) document agglomeration
forces operating through capital rather than labor inputs.5 Giroud et al. (2021) introduce a form of
intangible capital, or local “knowledge” accumulation, to rationalize global productivity spillovers
through multi-plant firms. As emphasized in LaPoint (2021), incorporating physical capital andfinancing constraints into a spatial sorting model can generate huge output responses to policychanges. We view our work as a critical first step towards putting physical capital back into modelsof spatial firms to assess the aggregate effects of place-based policies.
The paper proceeds as follows. Section 2 offers background on the Technopolis and Intelligent
Location policies. Section 3 describes the plant-level Census data and corporate balance sheet data.
Section 4 discusses our staggered difference-in-differences empirical strategy. Section 5 summarizes
our findings on firm investment, hiring, and location choices in response to the place-based policies.
Section 6 presents fiscal cost-per-job calculations. Section 7 concludes.

2

Policy BackgroundWe study two place-based policies in 1980s and early 1990s Japan, dubbed the Technopolis policy
and the Intelligent Location policy, respectively. Between 1984 and 1989, the Japanese government
implemented the staggered rollout of the Technopolis policy targeting the manufacturing sector. The
Intelligent Location program was implemented between 1989 and 1994 and targeted services firms

5

Other papers in the theoretical spatial firms literature include Ziv (2019), who examines city density, and like
Gaubert (2018), studies an environment with firm sorting on the extensive margin. Kerr & Kominers (2015) study
the rise of industry clusters like Silicon Valley in a model where agglomeration forces decay with distance due tointeraction costs. Walsh (2019) allows for extensive margin firm sorting to show how new firm entry amplifies local
shocks by attracting high-wage workers. In some models, (e.g. Forslid & Okubo 2014) firms paying a fixed cost toenter a market is synonymous with purchasing a building, but capital investment dynamics are not specified. While
the spatial dimension is not explicitly modeled, Stein (1997) illustrates how headquarters allocate firm resources
across projects subject to span of control costs.

6

that provided support for manufacturing, such as equipment leasing, machine repairing, software,and information and communications.
For both policies, we obtain the schedule of bonus depreciation rates from Ministry ofInternational Trade and Industry (1995), which describes eligible asset classes and facilities atthe 4-digit Japan Standard Industry Classification (JSIC) level. We collect the list of treatedjurisdictions from the Japan Location Center (1999) history of the two policies. We provide in
Appendix A a full list of eligible JSIC industries and sites for Technopolis, and a full list of eligible
industries and sites for Intelligent Location in Appendix B.6 We now summarize the tax incentives
and eligibility criteria for each program.

2.1

The Technopolis Policy

The Japanese government conceived of the Technopolis policy in 1983 as a way to jump-startindustrial clusters in areas of the country geographically removed from the major metropolises ofTokyo, Osaka, and Nagoya. Another goal of the program was to diversify the economy away fromheavy industries towards high-tech industries following the oil price shocks of the 1970s. To thisend, the government chose sites satisfying three conditions: (i) possessing an already developed
manufacturing sector, (ii) being in the vicinity of a major research university with a strong
engineering department, and (iii) including a regional hub with a population of 200,000-300,000
residents (Ito 1995; Okubo & Tomiura 2012).
Panel A of Figure 2 maps by implementation year which municipalities were eligible sites forbonus depreciation claims under the Technopolis policy. While the law specified 26 Technopolis
clusters, the official designation was conducted at the city code level.7 In practice this meant thatwhile each cluster contained a large regional city after which the cluster was named, there were as
many as dozens of smaller towns and cities included in the cluster. For instance, the Hamamatsu
Technopolis created in 1984 included the main city of Hamamatsu, the two small satellite cities ofTenryu, Hamakita, and two neighboring townships. In total, 141 municipalities were included inTechnopolis sites: 62 became eligible in 1984, 27 in 1985, 11 in 1986, 19 in 1987, 17 in 1988, and 5
in 1989 as part of the Sapporo Technopolis.
Rather than featuring direct subsidies to either firms or local governments, Technopolis locations
offered businesses a bonus depreciation schedule, where the bonus percentage declined beginningfive years after the initial eligibility date specific to that location. Table 1 lists the rate schedulein percentages of asset acquisition cost for real estate and non-real estate assets. Buildings were6

We use the 1995 catalogue of bonus depreciation from MITI rather than earlier years because 1995 was the firstyear after the rollout of the last Intelligent Location sites (see the map in Panel B of Figure 2). Between 1993 and1994, the government added a new kink point to the bonus depreciation schedule in each policy, which extended theperiod of eligibility to firms investing in catchment areas. No further changes were made to either policy after 1994.
7

Each area in Japan is classified as a city (shi), town (machi) or village (mura), and receives an official Census citycode. Throughout the paper, we account for municipal mergers by imposing modern boundaries to define geographic
areas according to the 2015 list of city codes, and we refer to a city code as a “municipality.”

7

TABLE 1. Technopolis Bonus Depreciation Incentives
Time from start date

Non-RE Bonus Rate

RE Bonus Rate

Within 5 years

30%

15%

Between 5 and 7 years

25%

13%

Between 7 and 8 years

20%

10%

Between 8 and 10 years

15%

8%

Between 10 and 12 years

14%

7%

> 12 years

0%0%

Notes: The table gives the bonus depreciation schedule by investment timing relative to the policy implementation
date. The implementation date varies by Technopolis area. Non-RE Bonus Rate refers to the bonus depreciation asa percentage of acquisition cost for physical assets excluding buildings (e.g. tools and machinery), while RE Bonus
Rate refers to bonus depreciation as a percentage of acquisition cost for buildings. The kink point between 10 and 12
years was added in 1994. Source: Ministry of International Trade and Industry (1995).

eligible for half of the bonus depreciation percentage for which non-building depreciable assets were
eligible. However, due to the long depreciation life for commercial buildings – ranging from 23
years for cold storage warehouses to 65 years for concrete office buildings – the bonus incentives
for building purchases provided firms with substantial immediate cash flow benefits.
For instance, consider a firm purchasing a new concrete office building for $1 million plus $1
million in computers in 1990. If these investments were located in a Technopolis founded in 1985,
the maximum rate of 30% on the computers ($300,000) and 15% on the building purchase ($150,000)
could be deducted from corporate income tax liability. Assuming the firm faces a marginal tax rateof 40% – the statutory corporate income tax rate paid by firms in our data in 1990 – this implies
an immediate cash flow of $180,000 arising purely from bonus claims. In 1990, without bonus
depreciation, 25% of the computers (4-year depreciation life) and only 1.54% of the building cost
(65-year depreciable life) could be deducted under linear depreciation, resulting in a much lower
amount of $106,160 in immediate cash flow from tax savings.
While the Technopolis bonus depreciation claims expired 12 years after implementation (e.g. by
2001 for the Technopolis designated in 1989), businesses could still claim the usual depreciation rates
that applied to each asset class regardless of location. In Appendix C, we provide more detailed cash
flow projections for the major cost amortization strategies available under the corporate income
tax code. For the typical firm in our sample, we estimate a local CAPX subsidy of between 2% and
3% from claiming Technopolis bonuses within the first few years of the program. The subsidy rate
is linearly increasing in the share of investment in long-lived physical assets: a fact we exploit in
our empirical research designs.
The final dimension of Technopolis eligibility is the industry classification of the corporate tax

8TABLE 2. Intelligent Location Bonus Depreciation Incentives
Time from start date

Non-RE Bonus Rate

RE Bonus Rate

36%

18%

Within 2 years + Tokyo HQWithin 3 years

30%

15%

Between 3 and 5 years

24%

12%

Between 5 and 7 years

20%

10%

> 7 years

0%0%Notes: The table gives the bonus depreciation schedule by investment timing relative to the policy effective date.
The effective date varies by Intelligent Location area (see appendix for full list of start dates by area). Non-RE Bonus
Rate refers to the bonus depreciation as a percentage of acquisition cost for physical assets excluding buildings, while
RE Bonus Rate refers to bonus depreciation as a percentage of acquisition cost for buildings. Firms with a registeredheadquarters in the 23 central wards of Tokyo who relocate a portion of their operations to one of the treated areas
qualify for a higher bonus percentage if they take advantage within 2 years of the policy date. The kink point between
5 and 7 years was added in 1994. Source: Ministry of International Trade and Industry (1995).

unit.8 We create a crosswalk to convert the historical Japan Standard Industry Classification codes(JSICs) valid under Technopolis to the modern classification system and report the full list of eligible
industries in Appendix A. Of the 555 manufacturing industry codes, 66 JSICs (13%) are treatedby Technopolis, including firms producing textiles, chemicals, pottery and ceramics, non-ferrous
metals, machinery, precision tools, electronics, computers, and vehicles.

2.2

The Intelligent Location Policy

In 1988, the Japanese government passed a second regional policy program, called Intelligent
Location (zunō ritti ), which offered similar bonus depreciation incentives to firms in industries
engaged in high-tech services such as software and telecommunications. The goal of this second
policy wave was to build up the intermediate goods network in the clusters created by Technopolis,
while also expanding the catchment areas for these clusters. Among the 26 Technopolis clusters,
15 regions were also designated Intelligent Locations. Figure 2 shows that the new Intelligent
Locations were adjacent to the existing Technopolis sites. In total, 319 municipalities were included
in Intelligent Locations, and of these, 244 were not previously eligible under Technopolis; 40 became
eligible in 1989, 132 in 1990, 45 in 1991, 64 in 1992, and 38 in 1994.
As Table 2 indicates, the bonus depreciation schedule under Intelligent Location shared many
features with the Technopolis tax incentives. Buildings could be deducted at half the percentage
of non-building investments, and the rates declined beginning three years after the local eligibility
date, with complete phase out after seven years. One notable difference was the special treatment
8

Since bonus incentives apply towards corporate income taxes, the cash flow benefit accrues at the level of thetax unit, rather than at the level of an individual plant or a parent subsidiary.

9

FIGURE 2. Map of Areas Eligible for Bonus Depreciation
A. Technopolis Policy

B. Intelligent Location Policy

Notes: Panel A displays the map of Technopolis catchment areas color-coded by the year the policy applied to thatarea. Panel B does the same for areas selected for the Intelligent Location policy. Source: Ministry of International
Trade and Industry (1995).

10

for firms headquartered in Central Tokyo; such firms could qualify for a 6 p.p. (3 p.p. for buildings)
top-up from the maximum 30% bonus claim for investments made within two years.
How economically distinct were the sites selected by the Technopolis and Intelligent Location
policies? Table 3 compares local macroeconomic characteristics of policy sites to non-policy sites in
1980, prior to the implementation of Technopolis. Policy sites have more manufacturing employment
and establishments, with a larger tangible capital stock than their ineligible counterparts. Whileeligible areas are more populated on average than the universe of ineligible locations, they have
similar per capita income and growth rates in plants and CRE prices. The main difference is interms of property values. The average median price per square meter for commercial land is roughly
one-third lower in eligible sites than in ineligible sites, and housing is also a slightly lower share of
total household expenditures. Our empirical strategy differences out these ex ante discrepancies inthe economic trajectory between eligible and ineligible sites by assigning treatment at the firm (orplant) level, which ultimately means comparing firms with otherwise similar balance sheets locatedin the same area but with different eligibility status due to the goods and services they produce.

3

Multi-plant Firm Data

This section describes the plant-level Census data and corporate balance sheet information we
combine to assess the short-run and long-run effects of the two spatial bonus depreciation schemes.

3.1

Census of Manufactures

The first layer of our dataset consists of plant-level microdata from the Census of Manufactures(COM, or kōgyō tōkei chōsa in Japanese) conducted by Ministry of Economy, Trade and Industry(METI) for each year from 1980 to 2000. In years ending in 0,3,5, and 8 (e.g. 1980, 1983, 1985,
1988) our data include all plants in the manufacturing sector regardless of size. However, in othersurvey years, METI only maintains microdata files for plants with four full-time employees or more,which excludes sole proprietorships. To form a balanced panel, we restrict our sample to all plants
with four or more employees for which we have continuous annual survey responses. The COM
data are valuable for studying responses to the Technopolis and Intelligent Policy initiatives given
previous findings that 1) immediate cash flows from bonus depreciation help offset the large fixed
costs of purchasing key production inputs (Zwick & Mahon 2017), and 2) financing constraints are
more prevalent for very small firms who tend to rely on pledging physical collateral to obtain bankloans (e.g. Berger & Udell 1995; Adelino, Schoar, & Severino 2015; Bahaj, Foulis, & Pinter 2020).
In terms of variable coverage, the COM survey asks plants to report a snapshot of their basic
operations within the survey year, including full-time and part-time employment, the total wage
bill, inventory, and cost of intermediate goods used in production. Key to our analysis are thevariables pertaining to physical capital investment such as the book value of properties, plants, and

11

12

370 (576)

Establishments w/> 4 employees

57.7 (40.1)
141[10.3, 203.0][−12.5, 72,7][−32.0, 136.6][0.027, 0.141][6.60, 180.00][568, 87,440][4,824, 1,401,757][292, 764]

[0, 5,961][10, 4,769][0.025, 0.516][136, 109,649][min,max]

69.8 (64.1)

6.4 (18.6)

6.3 (20.8)[196, 1,446][0, 7,570][1, 47,196][0.013, 0.875][0, 723,990][min,max][−37.1, 722.5][−72.7, 200.0][−100, 219.1][0.028, 0.241][6.35, 571.00][27, 686,436][225, 8,351,856]

1,568

0.096 (0.036)

100.91 (95.33)

5,783 (22,151)

64,110 (279,303)553 (158)

1,620 (4,605)

241 (1,389)

0.212 (0.150)

5,706 (23,648)

Mean (s.d.)Ineligible

62.9 (46.0)

6.1 (13.8)

6.8 (19.2)

0.084 (0.023)

66.22 (41.65)

7,339 (13,063)[229, 803]

[0, 7,570][3, 4,769][0.025, 0.516][34, 109,649][min,max][−9.2, 276.1][−66.7, 87.5][−100, 136.6][0.027, 0.141][6.60, 180.00][178, 167,476][1,360, 2,153,666]

319

75,536 (159,918)

536 (115)

2,334 (6,571)

246 (445)

0.178 (0.127)

6,466 (11,999)

Mean (s.d.)

Eligible

69.5 (64.7)

6.5 (19.0)

6.5 (21.2)[0, 723,990][196, 1,446][0, 5,687][1, 47,196][−37.1, 722.5][−72.7, 200.0][−100, 219.1][0.028, 0.241][6.35, 571.00][27, 686,436][225, 8,351,856]

1,390

0.100 (0.037)

103.16 (97.50)

5,997 (23,213)

67,122 (293,185)

557 (162)

1,650 (4,416)

252 (1,472)

0.214 (0.152)

5,919 (24,873)[min,max][0.013, 0.875]

Ineligible
Mean (s.d.)Intelligent Location

Notes: The table provides the mean, standard deviation, and min/max range for local economic conditions among eligible vs. ineligible municipalities underTechnopolis and Intelligent Location. All variables recorded in levels are as of the pre-reform period in 1980, except for the housing expenditure share which is
as of 1981. Heavy industry employment share is the share of manufacturing employment (mfg.) engaged in chemical, petroleum/coal, steel, vehicles, non-ferrous
metals, and metal refining 2-digit JSIC industries. Mfg. plant capital stock is the total PPE summed across local manufacturing plants in 10 millions of JPY.
Median price/m2 for CRE refers to the median price per square meter (in 1,000s of JPY) for commercial real estate in the central business district of the city.
The housing expenditure share is the share of housing costs (rent + mortgage payments + repairs) in total expenditures, computed from the Family Income
and Expenditure Survey. Manufacturing statistics from the METI Census of Manufactures, population counts from the Census, and CRE prices obtained fromcollapsing the MLIT appraisal surveys for commercial and industrial use properties. To obtain per capita income (in 1,000s of JPY), we use the Cabinet Officelocal statistics for taxable income and divide by total 1980 Census population. To compute these statistics, we impose modern municipal boundaries using thehistorical city code crosswalk available through RIETI (Kondo 2019).

# of municipalities%∆1980−83 CRE price/m2

0.091 (0.024)

Housing expenditure share
9.8 (20.7)

63.93 (35.83)

Median price/m2 for CRE

7.1 (12.0)

11,439 (14,653)

Population > 65 y.o.

%∆1980−83 establishments

119,885 (186,727)

Census population%∆1980−83 mfg. employment

556 (104)

Per capita income

3,527 (7,190)

0.175 (0.128)

Heavy industry employment share

Mfg. plant capital stock

9,524 (13,887)

Total mfg. employment

Mean (s.d.)

Eligible

Technopolis

TABLE 3. Economic Characteristics of Eligible vs. Ineligible Locations

equipment (PPE), which can be decomposed into three categories: machines, land, and buildings.
It is standard in the corporate finance literature to define investment as the year-on-year change innet book value of PPE plus accounting depreciation. Unfortunately depreciation is not separately
recorded for each major capital good category, while bonus depreciation incentives differ by the use
and type of asset. To isolate investment in each type of tangible asset, we instead rely on amounts
reported towards the acquisition of new buildings, machines, and non-machine goods.

3.2

DBJ Corporate Balance Sheet Data

While the COM data are comprehensive in their coverage of plants throughout the size distribution,
the Census survey does not ask plants or their parent firms to report on the liabilities side of thebalance sheet, or to provide detailed information on taxes and depreciation claims by type ofphysical capital good. The latter information is needed to compute measures of the cash flow gains
from bonus depreciation, conditional on making investments in treated areas. To assess the potential
role of financing constraints in the reallocation of resources across locations within the firm, we use
the non-consolidated firm-level balance sheet totals compiled by the Development Bank of Japan(DBJ). The DBJ data include all firms listed on the Tokyo Stock Exchange: 1,615 firms as of 1980.
We use years 1975 to 2000 as the sample period in our firm-level analysis.
Accounting for firm fixed effects is particularly important in our setting, because firms may differ
in their responses to the regional policies depending on whether they already operate a plant inor near a catchment area. Official firm panel id numbers in the COM survey are available startingin 1994, while plant panel id numbers are available starting in 1986.9 Moreover, while the COM
survey asks plant representatives to indicate whether the parent firm’s HQ is physically proximate,precise HQ addresses are unavailable prior to 1994.
Although location information is not directly available in the DBJ database, we obtain a snapshotof corporate geography in the pre-reform period by merging in the hand-collected data on listed
firms’ locations from LaPoint (2021). Registered and production HQ locations are reported by
the firm on the cover page of their annual securities filings – equivalent to the Form 10-K in the
U.S. (known as the yūhō in Japanese) – and firms are required to report the municipality of any
operating locations, regardless of whether the property is owned or rented.10 Firms also allocate
employees and book values of owned buildings and land to each facility reported in this section of

9

In future work, to track plants over the course of the early 1980s when Technopolis was first activated, we plan
to backfill the panel using the plant master database (kōgyō tōkei converter) prepared and disclosed by the ResearchInstitute of Economy, Trade and Industry (RIETI). Similarly, we use the Firm Master database (kigyō master)
prepared by METI and the “Basic Survey of Japanese Business Structure and Activity” to construct consistent firm
panel ids. This is the approach used in Bernard & Okubo (2016).
10

DBJ obtains the corporate balance sheet information from the annual yūhō filed with the Financial Services
Agency (FSA), so the locations are from the same regulated source as the rest of the data we use for listed firms.
The historical yūhō are on file at the Tokyo Stock Exchange (TSE), and we downloaded the PDFs for all firms listedon the TSE in 1980, for all available years, from the Pronexus eol Corporate Information Database.

13

their filings, which allows us to compare some plant-level outcomes before and after the reform.11
We match COM plants to their parent DBJ firms for the years 1986 – 2000 based on a fuzzy
merge on the Japanese name of the parent firm in 1997 (the first year for which the name string isavailable in COM). We make two sample restrictions to ensure that firms in the DBJ sample canbe matched to the COM data:
1. First, we require firms to have non-missing total assets for at least five consecutive years overthe period 1980-1987. In effect, this means firms in our sample must report business activities
for at least one year prior to and after the enactment of the Technopolis policy in 1984.
2. Second, for many Japanese firms (roughly 50% in 1980) the fiscal year runs from April inyear t − 1 to March in year t. To account for the fact that the COM survey responses refer tobeginning or end of the calendar year, we assign firm-fiscal year observations to the calendar
year in which the majority of their business activities occur. Thus, we assign a firm with afiscal year ending in March in calendar year t to values reported in COM for survey yeart − 1. To limit any measurement errors due to timing, we drop firm-year observations with
filing dates in May, June, or July, and any firm-year observations which change their fiscal
year start and end months during the sample period.12
After imposing these restrictions, but before matching DBJ to COM, we arrive at a sample of
1,508 firms. After merging to COM, we obtain 870 firms consisting of 2,765 plants in 1980 which
satisfy all sampling restrictions and for which we can compute the bonus depreciation variables
which are key to our analysis.13 The relatively small match rate between DBJ and COM arises
because COM only surveys firms engaged in manufacturing, while DBJ includes listed firms in all
non-FIRE sectors of the economy.
The pecking order theory of Myers & Majluf (1984) would imply that firms substitute debt andequity issuance with cash flows from bonus depreciation claims to finance their operations. We test
this implication by merging in from Nikkei NEEDS FinancialQUEST the monthly closing date stock
price and shares outstanding for each firm in our DBJ sample. For firms in our sample time period
which remain currently active, we merge between the two databases via the listed stock code. For
firms which are no longer active – for instance, if they were delisted or acquired by another firm
– we match on the standardized name string (e.g. by deleting suffixes like “CO” or “LTD”). We

11

We impose modern municipal boundaries using the city code crosswalk available through RIETI (Kondo 2019).
Crosswalking geographic boundaries is particularly important in the Japanese context due to a flurry of municipal
mergers driven by declining population in the countryside which has reduced the number of local jurisdictions from3,278 in 1980 to 1,741 as of 2015. Our main findings are qualitatively similar when we instead impose historical 1980
municipal boundaries to assign treatment status. We address differences in geography definitions in Appendix G.4.
12

We confirm that our results are robust to subsetting to firms with a fiscal year end date in March.

13

The matched DBJ-COM sample increases to 1,013 firms if we drop the requirement the first sample restrictionthat firms report non-missing total assets for five consecutive years.

14

hand match the stock series to balance sheets by tracking the company history for the remaining
159 inactive firms that cannot be directly merged between Nikkei and DBJ due to name changes.
Given the well-known skewness of firm-level outcomes, we winsorize all firm-level investment andemployment outcomes using as thresholds the median plus/minus five times the interquartile range,
as recommended by Chaney, Sraer, & Thesmar (2012). For variables which are close to mean zero,such as debt issuance, we winsorize at the 2nd/98th percentiles. In our preferred specifications for
non-zero outcomes, we take the log of the outcome variable.14 We also estimate some specifications
where we instead scale monetary outcomes by dividing by the firm’s total book asset value in theyear prior to the sample start date. The latter strategy accommodates cases where the variable canbe negative (e.g. cash flow), while also addressing the econometric critique of Welch (2020) thatscaling outcomes by lagged assets renders it difficult to disentangle the effect on the outcome ofinterest from the effect on the denominator.
Table 4 reports summary statistics using the full DBJ sample of 1,508 and the matched DBJ-COM
sample of 870 manufacturing firms. Our full sample of listed firms looks very similar to the matchedsample of manufacturing firms based on cash flows, employment, tangible asset composition, andinvestment (CAPX). The matched sample is slightly more likely to issue new debt or pay off
existing debt during the sample time period, and has more physical assets as a fraction of thebalance sheet. Firms in the matched sample are 7 p.p. more likely to derive positive net income
from bonus depreciation (1{bonus > 0}). This makes sense given that the full DBJ sample includes
non-manufacturing sector firms which were ineligible based on the Technopolis industry criteria.
Beyond the fact that only manufacturing plants are included in the COM data, we do not worry
about sample selection in moving from our overall full DBJ sample to the matched set of firms.

4

Empirical Strategy

Our empirical strategy is a staggered difference-in-differences (DD) which takes into accountthe spatial, industrial, and time-specific dimensions of eligibility for bonus depreciation underTechnopolis. The main firm-level specification we estimate takes the form:
yj,k,t = γj + δt + β · T reatmentj,k,t + η 0 · Xj,k,t + εj,k,t(4.1)

where yj,k,t is an outcome, such as employment or investment in new construction, γj are firm fixed
effects, δt are calendar year fixed effects, and Xj,k,t is a time-varying set of controls. T reatmentj,k,t
is a dummy equal to one if in calendar year t firm j operating in industry k is eligible to claim
bonus depreciation under the Technopolis schedule in Table 1.
As described in Section 2, plants in 66 4-digit JSICs within the manufacturing sector across14

We discuss in Appendix G.3 robustness to using transformations of outcome variables such as log(1 + x) andthe inverse hyperbolic sine function.

15

TABLE 4. Summary Statistics for Multi-plant Firms
Full DBJ Sample
MeanMedian10th pct.

Construction in progress

0.02

0.01

Non-real estate assets

0.83

0.44

Matched DBJ-COM Sample
90th pct.

Mean

Median10th pct.

90th pct.

0.00

0.11

0.03

0.01

0.00

0.11

0.02

2.26

1.07

0.74

0.07

2.76

Real estate assets

0.64

0.33

0.07

1.91

0.72

0.47

0.11

1.74

PPE

1.61

0.93

0.17

4.18

1.90

1.37

0.28

4.31

CAPX

0.11

0.06

−0.02

0.57

0.09

0.06

−0.05

0.40

Employment

2,572

991

240

5,559

2,516

950

262

5,144

Long-term debt issues

0.01

0.00

−0.10

0.15

0.01

0.00

−0.14

0.19

Cash flow

0.03

0.01

−0.02

0.16

0.03

0.01

−0.04

0.16

EBITDA

0.22

0.13

0.02

0.57

0.24

0.16

0.00

0.64

OCF

0.31

0.18

0.03

1.15

0.30

0.20

0.03

0.82

Bonus depreciation

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.01

1{bonus > 0}

0.23

0.00

0.00

1.00

0.30

0.00

0.00

1.00

# of firm-years

38,374

13,688# of 1980 plants

3,470

2,765

# of firms

1,508

870

Notes: The left-hand side of the table provides summary statistics for the full sample of listed DBJ firms we use
in our main firm-level analysis in Section 5, while the right-hand side provides statistics for the subset of DBJ firms
which can be matched to manufacturing plants in the manufacturing Census. Yen-denominated variables are scaled
by total book assets in the baseline year (1975). Variables are defined in a COMPUSTAT equivalent fashion. Realestate is the sum of the book value of buildings, land, and construction in progress, while non-real estate includes allother components of PPE, including machines, tools and precision instruments, and vehicles. CAPX is YOY change
in the net book value of PPE plus accounting depreciation, scaled by total book assets at baseline. Long-term debt
issues is defined as the YOY change in long-term loans payable, scaled by total book assets at baseline. Cash flowis net income less taxes paid. EBITDA is computed as operating income plus depreciation and amortization, andOCF is computed using the identity presented in Lian & Ma (2021). Bonus depreciation is net income from claiming
bonus depreciation. 1{bonus > 0} is a dummy equal to one in firm-years with strictly positive net income from bonus
depreciation. We tabulate the total number of manufacturing plants firms list on their 1980 securities filings (i.e. the“Condition of Facilities” section of their yūhō).

16141 municipalities were at some point eligible for these tax incentives, with implementation
dates spanning 1984 to 1989. This means there are several possible ways to define the dummy
T reatmentj,k,t . For our city-level analysis in Section 5.1 using data aggregated to the city ×
2-digit manufacturing sector in COM, we assign eligibility at the city level, so T reatmentc,t =T reatedc × P ostc,t , where T reatedc is equal to one if city c is an eligible city, and P ostc,t is equal
to unity if year t is after the implementation date specific to that city.
At the firm level the definition of T reatmentj,k,t is less obvious given the classic problem ofpinning down the “location of the firm.” For example, consider a firm which controls its HQlocated in a Technopolis ineligible municipality, and two additional plants: one which is located inan eligible municipality where bonus depreciation on investment can be claimed starting in 1984,
and another located in an eligible area where claims can be made starting in 1986. If we were toassign eligibility based on the location of the HQ (as is common in many corporate finance papers)
we would conclude the firm is ineligible. Looking beyond the HQ, how do we break ties wheremultiple locations might imply several different treatment timings?
In the end, we resolve this issue by setting T reatmentj,k,t equal to one if all three of the following
sequential criteria are satisfied:(i) Firm j level. Based on the facility locations reported in its 1980 yūhō the firm controls one
plant located in an eligible Technopolis area.15(ii) Industry k level. The parent firm operates in one of the eligible 4-digit JSIC industry codes.
We crosswalk by hand the 4-digit DBJ industry codes to the 2008 JSIC classification system
to determine eligibility under this criterion.
(iii) Timing t. If the firm fulfills the above two criteria, then we set T reatmentj,k,t equal to unity
in any year t equal to or greater than the minimum year of eligibility across all eligible plants
in the firm’s 1980 internal network.
These three criteria yield a decomposition of T reatmentj,k,t = T reatedj,k × P ostj,t . In cases suchas the above three-plant example where one plant is eligible in 1984 and another in 1986, we set
P ostj,t = 1 if t ≥ 1984, and T reatedj,k = 1 if the firm is in an eligible industry. Our DD model in(4.1) is a staggered DD where several potential within-firm treatments are stacked up via P ostj,t .16
15

We do not require the firm to own either the building or land to satisfy this criterion, but we do require themto report some strictly positive book value of physical assets at the location. However, given that CRE space inTechnopolis areas is far less expensive than in ineligible areas (see Table 3), the vast majority of firms own someproperty attached to plants in Technopolis areas. 99% of DBJ firms own some building or land among all the facilities
itemized in 1980.
16
We acknowledge this is not the only way to sort firms into eligibility. For instance, in a frictionless world withouttransport costs, if firms simply purchase physical capital through a plant in an eligible area and then move theresources to their HQ site, then only the industry determines eligibility, and we can write T reatmentk,t . Ultimately
this is an empirical question that gives rise to several placebo tests. Interestingly, we only find effects on firm-level
employment, investment, and bonus claims once we impose all three criteria (i)–(iii).

17

In the above empirical models, treatment is an absorbing state, so the P ostj,t dummy implicit
in T reatmentj,k,t never turns off. The Technopolis policy lasted into the early 2000s given that thelast catchment area was formed in 1989 and bonuses could be claimed up to 12 years after the
implementation date for an eligible area. Due to the strong overlap between Intelligent Location
and Technopolis, we argue that even the Technopolis areas formed earlier in the 1980s would havecontinued to be partially treated under Intelligent Location, even though the industry compositionof treated firms may have differed between the 1980s and 1990s. Further, in Section 5.4, we ruleout any direct effects of Intelligent Location on areas already treated by Technopolis, but use a
multiple treatment version of regression (4.1) to provide evidence that the two policies may haveamplified each other through local general equilibrium effects.
Identification of treatment effects in a staggered reform DD setting is challenging given thatthe composition of the treatment and control groups is changing over time, leading to potentiallynegative weights on average treatment effects (ATEs) for some group-time cells (Goodman-Bacon2021). To fix ideas, suppose we estimate the following event study version of (4.1):

yj,k,t = γj + δt +T
X

βt · T reatmentj,k,t + η 0 · Xj,k,t + εj,k,t(4.2)

t=1,t6=t0

where now the βt allow for dynamic effects of Technopolis eligibility which are measured relative
to period t0 . To interpret β as the average treatment effect on the treated (ATT), the parallel
trends assumption for potential outcomes without treatment must hold, and there must be no
anticipatory effects. To examine the validity of the parallel trends assumption, we apply the
imputation estimator of Borusyak, Jaravel, & Spiess (2021) [hereafter, BJS ], which is robust totreatment effect heterogeneity. Relative to other estimators designed to handle bias in staggeredrollout scenarios, BJS has the advantage of allowing us to explicitly model anticipatory leads.
Since new Technopolis sites were announced within the year prior to the implementation date,
we allow for anticipation effects of up to one year in our reported DD estimates. We accommodate
anticipation effects by shifting forward βt → βt+1 in event study specification (4.2).17 In Appendix
E, we assess the importance of anticipatory effects for our main results by comparing the BJS
estimator to the estimators of de Chaisemartin & D’Haultfœuille (2020), which uses not-yet treatedunits as controls, and of Sun & Abraham (2021), which only uses never-treated units as a control
group.18 BJS uses a two-step approach, which includes never-treated and not-yet treated units inthe first step, and then extrapolates the model to treated potential outcomes in the second step byimputing untreated potential outcomes.
Finally, our estimates are intent-to-treat (ITT) in the sense that the DD models yield the impact
17
As recommended by Borusyak, Jaravel, & Spiess (2021), we do not shift forward the βt for anticipation effects
when we test for parallel trends via separate regressions on untreated observations.
18

We exclude the Callaway & Sant’Anna (2021) estimator from our robustness checks since it produces identicalresults to the Sun & Abraham (2021) estimator for the baseline versions of (4.1) and (4.2) without covariates.

18

of Technopolis eligibility at the firm and/or plant level on investment and employment. The “firststage” effect of Technopolis eligibility on overall bonus depreciation claiming behavior is informative
for scaling up this reduced form effect to an ATE. While we do not observe the precise provisionin the tax code that allows firms to make their depreciation claims, it is difficult to imagine ascenario through which Technopolis lowers the cost of claiming bonuses available under rules fromthe pre-existing tax code. We demonstrate in the next section that bonus claiming substantially
increases on the extensive margin (by around 9 p.p. in most specifications), which validates our
proposed mechanism, and suggests we are identifying treatment effects of the policy.

5

Firm Employment & Investment Responses

In this section, we report our main results from estimating the staggered DD models described in
Section 4. We find in response to Technopolis eligibility firms become more likely to claim bonus
depreciation, leading to higher cash flow which peaks several years after the reform. Firms also
increase their employment and outlays towards construction projects and non-real estate assets,
while substituting away from land which does not depreciate. These effects are driven by (i) firms
relying on relatively long-lived assets as production inputs, (ii) ex ante financially constrained firms,
and (iii) firms with existing operations proximal to a policy area.

5.1

City-level Evidence of Extensive Margin Responses

We begin by aggregating the Census of Manufactures to the city × 2-digit industry level andestimating versions of (4.1) at the city level, where T reatmentc,t = T reatmentc × P ostc,t . Figure
3 plots the dynamic effects of βbt on log city-level manufacturing employment (Panel A) and thelog number manufacturing establishments (Panel B) from estimating equation (4.2). We allow for
one-year anticipation of Technopolis eligibility and apply the BJS estimator for staggered DD
designs. We obtain a balanced panel of 1,699 municipalities which continuously supply information
on employment and establishments.19
The event study analysis reveals a clear, but slow-moving gap in the evolution of employment andplant creation between Technopolis eligible cities and ineligible cities. For employment, this gap
widens starting four years after the introduction of tax incentives (βb5 ). Ten years after the reform,
employment is 13% higher in eligible sites, while the number of establishments is 6% higher. The
fact that Technopolis was associated with growth in new plants points to the success of the policy
at generating long-lasting investment in the targeted regions.20
19

To construct this city × 2-digit industry panel, we use modern industry classifications and crosswalk the 2-digit
manufacturing codes across the historical systems instituted in 1980, 1985, 2002, and 2008.
20

While we observe PPE at the plant level in COM, we cannot aggregate up PPE to the city level due to changes
across survey waves in the composition of plants which are required to report this information. In some years, plants
with 10 or more employees are required to report PPE, while in other years only plants with 20 or more employees
are required to report PPE.

19

FIGURE 3. Dynamic City-level Responses to Technopolis Eligibility
B. Number of Establishments

A. Employment

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD modelin equation (4.2) using the imputation estimator method of Borusyak, Jaravel, & Spiess (2021). Panel A examines
log of total employment among all manufacturing plants within the city, and Panel B examines the total number ofmanufacturing plants within the city. The point estimates allow for anticipatory effects one year in advance of thereform, so the coefficient at 0 years represents the one-year anticipatory effect. Our estimation sample is 1981 – 2000.
Shaded regions contain 95% confidence intervals obtained from standard errors clustered at the municipality level.
We impose modern municipal boundaries using the historical city code crosswalk available through RIETI (Kondo2019). See text for details on the definition of each outcome.

Given the summary statistics in Section 2, it is clear that locations selected for the Technopolis
and Intelligent Location programs have a distinct local economic profile which is reflected in thestrong pre-trend in the event study for employment. Technopolis was enacted in the backgroundof one of the largest real estate booms in modern history, and eligible areas both started withlower commercial real estate (CRE) price levels and experienced more muted price growth during
the 1980s. However, within-region, Technopolis sites were selected based on proximity to major
research universities, which means they were more economically dynamic than neighboring cities.
We attempt to control for trends related to the real estate boom by computing median price persquare meter for CRE as of 1980. We find qualitatively similar effects on employment and extensive
margin investment when we do so, but the standard errors blow up because our sample drops down
to only 375 cities for which we have CRE appraisal data.21 The ability to more precisely measureeligibility at the 4-digit industry × location level and difference out some of these local macro trends
motivates our firm-level analysis in the next subsection.

5.2

Firm-level Analysis

In this subsection we present our main analysis which explores the effects of Technopolis eligibility
at the firm level on cash flow, employment, investment, and debt issuance.

21

See LaPoint (2021) for details on the appraisal data.

20

5.2.1Baseline Results

We start our firm-level analysis by presenting event study evidence from estimating equation (4.2),
allowing for one-year anticipation of Technopolis eligibility, and again applying the BJS estimatorfor staggered DD designs. Figure 4 plots the dynamic effects βbt of Technopolis eligbility for our six
main outcomes of interest: the probability a firm claims bonus depreciation, cash flow (defined as
net income before depreciation, after taxes paid), employment, construction in progress, the grossbook value of new non-real estate assets (including precision tools + machinery + vehicles), andlong-term debt issuance (the YOY increase in long-term bank loans payable + bonds outstanding).
All event studies feature one-year leads on the βt coefficients to capture one-year anticipatory
effects, although we do not lead the coefficients to conduct our pre-trends testing in what follows.
We focus on bonus depreciation claiming on the extensive margin given that 77% of firm-years
feature zero net income from bonus depreciation. We deflate monetary variables by the value forthat firm in the filing year before our sample starts (1975). Hence, the effects are scaled so thatβbt captures the growth in a monetary variable relative to the pre-sample baseline that can be
attributed to the firm becoming eligible for Technopolis bonus claims.22
The first panel in Figure 4 shows the first stage of our research design by plotting how take-up
of bonus depreciation incentives varies with respect to Technopolis eligibility. The propensity of
eligible firms to increase their bonus claims steadily rises after the implementation date, with theeffect peaking at 6.7 p.p. five years after enactment. Five years corresponds to a kink point in the
tax schedule (Table 1), since firms can maximize their bonus rate if they invest within five yearsof the designated Technopolis area. While there is visual evidence of a pre-trend in our first stage(with the one-year anticipation), when we test for pre-trends by running a separate regression using
untreated observations, we obtain a p-value of 0.694 on the hypothesis of joint significance of theloadings on the six lags.23 The second panel shows that income from bonus claims begins to show up
in firm cash flows several years into the Technopolis period, also spiking five years after eligibility.
Overall employment rises at treated firms by 5% (a 0.13 s.d. effect) relative to the level at the
sample start date about 5 years into the reform, and the effect plateaus thereafter.24 We also22

As mentioned in Section 3.2, scaling by baseline assets accounts for skewness in the distribution of firm balance
sheet variables. This scaling also has an advantage over taking logs for variables like debt issuance and cash flowwhich can be zero or negative.
23

We augment the staggered DD model with linear firm time trends in Appendix E and show estimates under
OLS and Sun & Abraham (2021). With the exception of employment, the estimated effects are stronger for our othermain outcomes with linear trends, although the trends are not well-identified under the imputation method of BJS.
24

Our results are qualitatively similar, but larger and more precisely estimated, when we use male full-time
employment rather than overall firm employment as the outcome variable. Restricting to male employment is common
in studies of the labor market, since female employment is highly cyclical and more closely related to fertility decisions
than economic fundamentals. We estimate a dynamic effect on male employment that starts out at 4% and grows to 9%
after 10 years of the policy regime. We do not adopt male employment as our main measure of job creation to maintaincomparability of our cost-per-job estimates in Section 6 to those computed in other studies. Male employment is also
missing as a line item for around 14% of our sample.

21

FIGURE 4. Dynamic Firm Responses to Technopolis Eligibility
A. Bonus depreciation probability

B. Operating cash flow

C. Employment

D. Construction in progress

E. Non-real estate purchases

F. Land acquisition

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD model inequation (4.2) using the imputation estimator method of Borusyak, Jaravel, & Spiess (2021). Each regression includes
HQ Census region × year fixed effects. With the exception of the bonus depreciation dummy and employment, eachvariable is deflated by the firm’s book assets in 1975 before our estimation sample start date. Firm employment isscaled by its value in 1975. The point estimates allow for anticipatory effects one year in advance of the reform, so
the coefficient at 0 years represents the one-year anticipatory effect. Shaded regions contain 95% confidence intervals
obtained from standard errors clustered at the firm level. See text for details on the definition of each outcome.

22

find a clear upward trend in outlays for construction in progress, although due to the lumpinessof investment and frequent revision of construction costs for projects, these dynamic effects arevolatile. Recall that while the Technopolis bonus rates for real estate investment are half those for
non-real estate tangible investment, buildings are much longer-lived assets, and therefore offer alarger immediate cash flow benefit. The acquisition of non-real estate assets explodes and continues
to grow until 9 years into the program. While part of this effect could be due to an inflationary
component to new acquisitions rather than a real response, our models include both time and region
× year fixed effects, which differences out national and semi-local pricing trends. Firms substituteaway from investment in land (a 0.11 s.d. decline), which does not depreciate and thus becomes
more expensive relative to other types of capital after the reform.25
The investment responses in Figure 4 are economically sizeable. The peak effect of Technopolis
eligibility on purchases of non-real estate assets is 0.29 which is 40% of the standard deviation of
new gross book non-real estate assets. Similarly, for construction, the effect peaks at 0.009 which
is 29% of the standard deviation of construction in progress. Importantly, while purchases of many
types of non-real estate assets are reversible local investments, to the extent that the parent firm cansell or easily transport machines and other equipment away from the treated plant, constructionof new structures or adding onto existing ones is not a reversible expense (at least not in the
short-run). Technopolis was therefore successful relative to place-based tax breaks like the recent
Foxconn-Wisconsin case study in our Introduction at incentivizing firms not to “toe dip.”
Table 5 establishes the robustness of our results to the inclusion of a battery of controls fortime-invariant firm characteristics interacted with year fixed effects, other common cash flow
measures such as EBITDA and operating cash flow (OCF), and Tobin’s Q. To render the effect sizes
easier to interpret, we present results using log outcomes for employment and monetary variables.
Overall, our first stage effect of eligibility on bonus claiming (Panel A) is stable across estimatorsand the inclusion of financial controls and region, size, and age-specific trends.
We do not include industry fixed effects in our baseline specifications. Industry fixed effects would
be too fine of a control in the sense that many treated Technopolis 4-digit industry codes fall under
the same 2-digit category (e.g. the 2-digit non-ferrous metals industry contains the copper smelting
and electric wire 4-digit industries, both of which are eligible). Including a 2-digit fixed effect inthis instance would thus mean differencing out the impact of Technopolis on two similar treatedunits, leading to an estimated null effect. Our results are qualitatively similar, albeit with smaller
point estimates, when we include 1-digit industry code × year fixed effects.26
Comparing the point estimates in Panel B of Table 5 from estimating model (4.1) by OLS vs.
25

The p-values on the pre-trends tests for the other outcomes we consider in Figure 4 are 0.757 for operating
cash flow, 0.288 for employment, 0.193 for construction, 0.169 for non-real estate investment, and 0.001 for landacquisition. Hence, with the exception of land investment, we find the parallel trends assumption to be valid.
26

The agriculture, real estate/construction, transportation, services/tradables sectors all contain zero 4-digit
industries eligible for Technopolis bonuses. Eligibility is instead highly concentrated among the light manufacturing(27% eligible), electronics (65% eligible), and heavy manufacturing (42% eligible) sectors.

23the BJS estimator demonstrates the role that treatment effect heterogeneity plays in our setting.
We find a 16.6 log points effect on construction outlays when we use OLS to estimate the staggered
DD model (column 1), but a 21.2 log points effect when we estimate the same model via BJS.
We present in Appendix E our results using other popular staggered DD estimators which addressconcerns about treatment effect heterogeneity. We consider the estimators of de Chaisemartin &D’Haultfœuille (2020) and Sun & Abraham (2021) and compare the dynamic treatment effects to
those obtained under OLS and BJS. Among these non-OLS estimators, our results are qualitatively
and quantitatively similar.
Although it weakens our results, in Table 5 we still uncover positive responses of bonus claiming,
construction, and non-real estate purchases after including time-varying financial controls. Whilecommon in the empirical corporate finance literature, controls like EBITDA, OCF, and Q are “badcontrols” in our setting because they are outcomes that may be directly influenced by Technopolis
eligibility. In particular, OCF includes cash flow from bonus claims, so it is mechanically related
to the take-up behavior induced by Technopolis.27 We discuss results for other firm outcomes inAppendix G.1, and compare first stage effects of bonus depreciation eligibility across different
measures of firm cash flow in Appendix G.2.

5.2.2

Local Spillovers of Technopolis

Did Technopolis generate local spillovers to untreated firms? Answering this question is important
for assessing the local general equilibrium consequences of place-based tax incentives. One might
imagine that by stimulating investment among high-tech intermediate goods firms in these areas,
local firms in downstream industries might benefit from cheaper inputs or productivity gains frominnovation. Our original specification in (4.1) is silent on this question, so we instead run anaugmented model which includes an additional term to isolate the effect of being located in an
eligible area but not satisfying the industry criteria for bonus claims:
yj,c,k,t = γj + δt + β1 · T reatmentj,k,t + β2 · T reatedCityj,c,t + η 0 · Xj,k,t + εj,c,k,t(5.1)

where T reatmentj,k,t is defined as in Section 4 (i.e. it is equal to unity if all three eligibility criteria
apply). The new dummy T reatedCityj,c,t is equal to unity if firm j controls a plant located in a
Technopolis eligible area and t is greater than the minimum eligibility year across all eligible cities
represented within the firm’s 1980 internal network. That is, T reatedCityj,c,t is equal to one if thefirm satisfies the first and last criteria, but not the second criterion listed Section 4.
Table 6 provides results from estimating this spillover regression for our four main outcomes27
See Lian & Ma (2021) for a discussion on how to construct operating cash flow (OCF) and how it differs from
EBITDA. For our purposes, the main distinction between the two cash flow measures is that net income from bonus
depreciation write-offs will be reflected in OCF but not in EBITDA. Indeed, when we estimate (4.2) with OCF as theoutcome variable (Panel B of Figure 4), we find cash flows peak at years 5 and 7 after the reform, which corresponds
to the first two kink points in the bonus depreciation schedule in Table 1.

24

TABLE 5. Bonus Claiming, Investment, and Employment Responses to Technopolis
A. First stage: extensive margin bonus depreciation claims
(1)

(2)

(3)

(4)

(5)

(6)

0.101∗∗∗

0.087∗∗∗

0.089∗∗∗

0.094∗∗∗

0.070∗∗

0.090∗∗∗(0.032)(0.034)(0.032)(0.028)(0.030)(0.028)

OLS

OLS

OLS

BJS

BJS

BJS

N

38,37434,57838,36038,37434,57838,360# Firms

1,508

1,408

1,507

1,508

1,408

1,507

Adj. R2

0.535

0.547

0.551

0.535

0.547

0.551

T reatment

Estimator
Firm FEs
Financial controls
Controls × year FEs

B. Investment and employment responses
Construction

T reatment

Estimator

Non-RE purchases

Employment

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

0.166∗∗

0.111∗

0.221∗∗∗

0.184∗∗∗

0.145∗∗∗

0.189∗∗∗

0.070∗∗

0.035

0.074∗∗(0.072)(0.067)(0.077)(0.046)(0.039)(0.046)(0.030)(0.028)(0.032)

OLS

BJS

BJS

OLS

BJS

BJS

OLS

BJS

BJS

26,996

24,40826,98536,39632,82936,38338,34034,57838,326

Firm FEs
Financial controls
Controls × year FEs
N# Firms

1,416

1,318

1,415

1,499

1,399

1,498

1,508

1,408

1,507

Adj. R2

0.702

0.723

0.702

0.948

0.957

0.949

0.954

0.964

0.955

Notes: The table shows results from estimating our staggered DD model in equation (4.1) at the firm level for our
main outcomes of interest, pooling all years (1975–2000). The outcome in Panel A is a dummy equal to one if thefirm receives net income from bonus depreciation in a given year. In Panel B, construction is the log book value of
construction in progress, non-RE purchases is the log gross book value of new PPE excluding buildings, land, andstructures, and employment is the log number of employees. Controls include static factors such as the size quintile(by total assets), quintile of age measured from the Tokyo Stock Exchange listing date, and Census region of the HQ,
all interacted with a full set of year dummies. Specifications with financial controls include EBITDA, OCF, and the Q
ratio as time-varying controls. EBITDA and OCF are defined using standard accounting principles. The Q ratio is theratio of the market value of the firm (total assets + market equity − common equity − deferred tax payments relative
to book assets). Standard errors clustered at the firm level are in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1

25

TABLE 6. Local Spillovers of Technopolis via Untreated Firms

T reatment

Bonus claim

Construction

(1)

(2)

(3)

(4)

Non-RE purchases
(5)

(6)

Employment
(7)

(8)

0.100∗∗∗

0.084∗∗∗

0.139∗

0.145∗

0.151∗∗∗

0.136∗∗∗

0.080∗∗

0.076∗∗(0.028)(0.028)(0.074)(0.074)(0.047)(0.047)(0.031)(0.030)−0.129∗∗∗0.029−0.004−0.087−0.083−0.105∗∗∗

0.029

0.014

(0.016)(0.017)(0.065)(0.068)(0.033)(0.036)(0.021)(0.022)

N

38,37438,36026,99626,98536,39636,38338,34038,326# Firms

1,508

1,507

1,416

1,415

1,499

1,498

1,508

1,507

Adj. R2

0.535

0.551

0.702

0.702

0.948

0.949

0.954

0.955

T reatedCity

Firm FEs
Controls × year FEs

Notes: The table shows results from estimating the spillover model in equation (5.1) at the firm level for our main
outcomes of interest. Bonus claim is a dummy equal to one if the firm receives net income from bonus depreciation
in a given year, construction is the log book value of construction in progress, non-RE purchases is the log grossbook value of new PPE excluding buildings, land, and structures, and employment is the log number of employees.
Controls include static factors such as the size quintile (by total assets), quintile of age measured from the Tokyo
Stock Exchange listing date, and Census region of the HQ, all interacted with a full set of year dummies. Standard
errors clustered at the firm level are in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1

of interest: extensive margin bonus claiming, and the logs of construction investment, non-realestate purchases, and employment.28 The first two columns using the bonus claim dummy as
the outcome act as a placebo test: firms for which T reatedCityj,c,t = 1 are not eligible toclaim the bonus write-off, even though they have a presence at a Technopolis site. Reassuringly,
we find no significant uptick in bonus claims among local untreated firms. We find evidence ofnegative spillovers for non-real estate investment; firms in ineligible industries located in an active
Technopolis site experienced a reduction in their non-real estate PPE of between 10% and 12%. The
negative spillover to untreated firms is of a similar magnitude with the full set of controls, meaningthat it exists even when comparing two firms with an HQ in the same region of the country andwithin the same size and age quintiles. Given that wholesale price indices for non-real estate assets
vary minimally across regions during this time period, our finding is unlikely to be a mechanicalconsequence of the late 1980s boom-bust cycle. This suggests Technopolis may have crowded out
non-real estate physical investment among ineligible incumbent firms.

5.2.3

Trade (Non-)Spillovers of Technopolis

While we found no evidence of local spillovers to ineligible firms, it is possible that both eligible andineligible firms’ hiring and investment decisions were influenced by inter-regional trade linkages to28

We plot the dynamic effects of the T reatment and T reatedCity dummies in Appendix F.

26

firms in eligible sectors operating in Technopolis areas. For example, manufacturers of woodworking
machines, an industry eligible for Technopolis, may pass along reduced capital costs to ineligible
furniture makers, leading the latter to increase hiring or CAPX. This would be an example
of indirect trade spillovers through imports, or a supply chain channel. Conversely, for the
woodworking machine manufacturers there could be amplification of the direct effects of bonus
eligibility because their Technopolis eligible customers are looking to expand, and therefore will
demand more machines as an intermediate input. This would be an example of indirect trade
spillovers through an export demand channel.
To test for the presence of such trade spillovers, we augment our baseline event study specificationin equation (4.2) to include leads and lags of a firm’s exposure to inter-regional trade. We adapt
the approach of Siegloch, Wehrhöfer, & Etzel (2021) to our setting, which takes imports originatingfrom prefecture q (alternatively, exports to q) by each sector k located in prefecture p, and dividethat number by total imports (exports) of the prefecture pair × sector cell. We then interact these
prefecture pair × sector import and export shares with a treatment dummy equal to one if prefecture
q contains one of the 26 regional Technopolises.29 Finally, after summing up all the interaction terms,
we convert the resulting regional measure of trade exposure to a firm-level measure by taking aweighted average across all prefectures where the firm operates an establishment. We use as weights
the share of 1980 firm book PPE located at prefecture p; hence, the weights will be zero if the firm
does not have a 1980 presence in p.30
Our procedures can be summarized by the following sequence of equations:yj,k,t = γj + δt +

T
X

T
X

β1,t · T reatmentj,k,t +

t=1,t6=t0

with T radeExposurej,k,t =β2,t · T radeExposurej,k,t + εj,k,t(5.2)

t=1,t6=t0

Xj· T radeExposurekp,t
ωp,1980

for J = {j1 , j2 , . . . , jn }p∈Jjwhere ωp,1980=Pj
P P Ep,1980jp∈J P P Ep,1980

and TradeExposurekp,t =X Exportskp,qImportskp,q
× T reatmentq,t +× T reatmentq,tk
T otalImportsp
T otalExportskp
q6=p
q6=p
|
{z
} |
{z
}
X

supply(5.3)

demandwhere equation (5.2) describes our augmented event study model, which uses firm-level trade29

We use the R-JIP database from 2005 which provides the prefectural input-output matrix denominated in yen for
26 industrial sectors. In constructing the prefecture pair × industry T radeExposure measures in (5.3), we sort these
sectors on the basis of whether they contain 4-digit JSICs which are eligible for bonus claims through Technopolis.
The trade matrices are can be downloaded at https://www.rieti.go.jp/jp/database/R-JIP2005/index.html.
30

Our results are unchanged if we instead weight by employment across plant locations in 1980, or if we define thetreatment dummy in the trade exposure measure at the prefecture × sector cell.

27

exposure obtained from aggregating the prefecture × sector exposure measure in (5.3) by taking
a PPE share-weighted average across all prefectures in the firm’s network of locations spanned by
the set J . We also estimate separate versions of (5.2) where we use only the “supply” or only the“demand” components which link a firm to other firms across the country through trade.
Figure 5 plots the estimated direct effects represented by βb1,t and the indirect effects βb2,t for theimport and export exposure measures from (5.2) and (5.3). For each of our main outcomes, theevolution of the direct effects of Technopolis eligibility (red) are virtually identical in magnitude
to the baseline effects reported in Figure 4. At the same time, we find no effects of indirect
exposure through trade linkages regardless of whether we examine import/supply (Panel A) or
export/demand shocks (Panel B); the confidence intervals are quite large for the loadings on bothtypes of shocks, and there is no clear trend in the point estimates.31 We conclude that while targetedbonus incentives may have helped stimulate local labor and capital markets, these responses did
not propagate through inter-regional trade networks for corporations.

5.3

Heterogeneous Firm Responses

We now examine heterogeneous responses to the Technopolis policy based on (i) firms’ pre-existing
physical capital structure, (ii) the extent to which bonus claims have the potential to relieve
financing constraints, and (iii) the role of intra-firm transport costs in policy take-up.

5.3.1

Long vs. Short-lived Capital Shares

Recall the example from Section 2.1 of a firm purchasing a new office building and computers tostaff a site in a Technopolis-eligible area. Since the typical office site can be depreciated over 50
years, while computers can only be depreciated over 4 years, a firm relying more on long-lived
assets like buildings will be better able to extract cash flow from the future to the present throughbonus claims. That is, we expect take-up, investment, and hiring responses to be more pronounced
among firms which have a more long-lived physical capital structure. We test this hypothesis byconstructing a measure – informed by the Q-theory of investment – to rank firms based on theirreliance on short-lived vs. long-lived assets.
Following the methods in Hayashi (1990) and Hayashi & Inoue (1991), we recover physicalproduction input shares for each firm. We apply this method to the DBJ data on listed firms
to sort firms based on their reliance on long-lived vs. short-lived capital.32 The complete algorithm
steps are described in LaPoint (2021), but we provide an outline in Appendix D. The economic
31

Summing up the import and export exposure measures, as we do in (5.3), also produces very imprecise estimatesand no clear monotonicity following policy implementation.
32

The plant-level Census only decomposes tangible assets into land, buildings, machinery, and a residual othercategory. At the same time, we cannot compute other parameters such as the weighted average cost of capital (WACC)
and corporate income tax bill which are necessary for the calculations.

28

FIGURE 5. Non-evidence of Exposure to Technopolis through Inter-regional Trade
A. Import exposure (supply)

Estimated ATTs

Bonus claim probability

Employment

.4

.4

.2

.2

0

0

−.2

−.2

−.4

−.4
−6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10

−6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10

Construction in progress

Non−RE purchases

.04

.6
.4

.02

.2
0

0

−.2
−.4

−.02
−6 −5 −4 −3 −2 −1 0

1

2

3

4

5

6

7

8

9 10

−6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10

Years since Technopolis
Direct treatment effectTrade exposure effect

B. Export exposure (demand)
Bonus claim probability

Employment
.4

.4

.2

Estimated ATTs

.2
0

0

−.2

−.2

−.4

−.4
−6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10

−6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10

Construction in progress

Non−RE purchases

.04

.5

.02
0
0
−.5

−.02
−.04

−1
−6 −5 −4 −3 −2 −1 0

1

2

3

4

5

6

7

8

9 10

−6 −5 −4 −3 −2 −1 0

1

2

3

4

5

6

7

8

9 10

Years since Technopolis
Direct treatment effectTrade exposure effectNotes: Each panel shows the dynamic response of an outcome of interest estimated from the staggered DD model of(5.2) via OLS, with eligibility dummies (blue) and leads/lags of the indirect trade exposure measure (red). We report
separate indirect trade effects for imports (Panel A) and exports (Panel B). Each regression includes HQ Census
region × year fixed effects. Construction in progress and non-real estate assets are deflated by the firm’s book assets
in 1975 before our estimation sample start date. Firm employment is scaled by its value in 1975. We bin the dummies
at the end of the effect windows for t = −6 and t = 10. All dynamic effects are relative to one year before Technopolis
eligibility begins. The bars show 95% confidence intervals obtained from standard errors clustered at the firm level.

29

intuition underlying the approach is that a profit-maximizing firm will set the marginal rate of
substitution between any two capital goods equal to the ratio of the goods’ user costs. In addition
to profit maximization, recovering the capital input shares relies on two technical assumptions:(i) The profit function is homogeneous of degree one in the capital inputs ki , where here i = 1, . . . 6
and the capital goods categories are buildings, land, structures, machines, precision tools,
and transportation vehicles. We exclude inventories from the decomposition since our data
are not itemized to the extent that we can separate inventories into inputs and outputs. Even
though land does not depreciate, we include it in the capital aggregator because land is acomplementary good to buildings and outdoor structures (e.g. wells, sheds, encampments).
(ii) There is a capital aggregator f (Kj ) for each firm j, which is homogeneous of degree one ineach of the goods ki,j . For tractability, we make the additional assumption that the aggregatoris Cobb-Douglas, or:f (Kj ) =6Yωki i,j

i=1

s.t.

6
X

ωi,j = 1, ∀j

(5.4)

i=1

Armed with these two assumptions, for each firm we compute the input shares ωi,j by iteratingon the system of equations consisting of the full set of tangency conditions implied by profit
maximization together with equation (5.4). Implicitly we are assuming the functional form f (·) to
be exogenous and fixed. Since it is possible that offering tax incentives for investment in long-lived
assets might induce firms to alter their mix of inputs, we compute the shares ωi,j,t for each year
and then take the average shares over the pre-reform period 1975 – 1983.33
This structural method based on firm profit maximization generates input share distributions
which are broadly in line with the mix of intermediate goods used by each 2-digit industrial sector.
For instance, heavy manufacturing firms have an average machine input share of 0.24, while thisis only 0.18 for agricultural and 0.17 for services firms. Although this approach has the advantage
of being motivated by theory, one downside is that it requires firms to have non-missing values
for corporate income tax payments to identify user costs in the first-order conditions of the firm’s
problem. As such, we can only directly recover input shares for roughly one-third of DBJ firms; thissubsample spans all 2-digit industry codes in the full sample. To overcome this issue, we apply anearest-neighbor match where we assign firms with missing input shares the input shares of a donor
firm with the smallest distance in propensity scores. We provide more details on the imputation
procedure and statistics of input shares for each capital good in Appendix D.
We then run the following regression which tests for differential effects of the Technopolis policy

33

The input shares for long-lived assets decline in the 1990s. This is reflected in the fact that while we find growth inboth the stock of new construction and non-real estate assets – which are complementary inputs under the aggregatorin (5.4) – we find that YOY investment in long-lived assets falls after the early 1990s crash.

30

depending on whether the firm relies on a larger share of long-lived capital inputs to production:
yj,t = γj + δt + β1 · T reatmentj,t × LL − F irmj

(5.5)

+ β2 · T reatmentj,t × SL − F irmj + η 0 · Xj,t + εj,twhere T reatmentj,t is defined at the firm level based on whether the firm is in an eligible industry
and has a presence in a Technopolis area after the minimum possible implementation date. Here
we suppress the k industry subscript for ease of exposition. We define the dummy LL − F irmj(“long-lived”) as equal to unity if firm j has an ex ante share of building inputs ωbuild above themedian value across all firms. Similarly, SL − F irmj (“short-lived”) is equal to one if the firm
has an ex ante value for ωbuild below the median. In some specifications, we include the usual setof time-invariant firm characteristics interacted with year dummies in Xj,t , so the comparison is
between firms with HQs in the same Census region, and operating within the same size bin, agebin, and main bank cell, which differ on city × industry eligibility to participate in Technopolis.
We define LL − F irmj and SL − F irmj according to the share of building inputs due to theincredibly long-lived nature of commercial buildings in the tax code. An alternative would be tocategorize the six capital goods we observe in the DBJ data by their average linear depreciation
rate, assuming firms use a straight-line depreciation accounting method. This can be accomplishedby comparing accumulated depreciation for each PPE category to gross book value to back out
average asset age for goods type. This exercise yields a depreciation life of 25 years for buildings,
15 years for machines, 11 years for tools, and 10 years for transportation.34 Hence, we could thenlump buildings and machines into one category of long-lived assets, and group the remaining CAPX
categories together as short-lived assets. We do not take this approach because non-real estate assets
are very heterogeneous in the tax code in terms of their depreciation lives. For example, within
the machines category depreciation lives vary from 3 years for electricity boards used in the textile
dyeing industry to 25 years for starch processing machines used in the agricultural industry.
Table 7 provides evidence in favor of the notion that long-lived asset firms were more likely
to claim and use bonus cash flows under the Technopolis regime. The first column shows bonus
claim probability increased by 9.6 p.p. for long-lived asset firms, but not at all for short-lived asset
firms. Firms relying more on properties also employed more workers in response to Technopolis
eligibility. On the other hand, the difference between βb1 and βb2 in equation (5.5) is never statisticallysignificant; this is driven by the large standard errors on the point estimates for the effect oftreatment on short-lived asset firms.35 One possibility is that long-lived asset firms stand to gain
less from bonus depreciation because they already rely on declining balance accounting, which allows
34This 4% linear rate of depreciation is about half of what Yoshida (2020) finds via a hedonic model approach
using CRE transactions, suggesting that bonus claims among listed firms are disproportionately applied towards
investment in buildings. A 2% rate is consistent with the Japanese tax code wherein CRE buildings typically havedepreciation lives between 50 and 60 years.
35

We plot the dynamic effects of the LL − F irm and SL − F irm dummies in Appendix F.

31

TABLE 7. Firm-level Results by Long-lived Asset Share

T reatment × LL − F irm

Bonus claim

Construction

(1)

(2)

(3)

(4)

Non-RE purchases
(5)

(6)

Employment
(7)

(8)

0.096∗∗∗

0.089∗∗∗

0.166∗∗

0.170∗∗

0.180∗∗∗

0.171∗∗∗

0.077∗∗

0.076∗∗(0.029)(0.028)(0.074)(0.074)(0.048)(0.049)(0.031)(0.031)0.265∗∗∗−0.011

0.028

0.169

0.160

0.245∗∗−0.037−0.004(0.104)(0.109)(0.261)(0.273)(0.097)(0.094)(0.111)(0.106)

0.319

0.586

0.991

0.971

0.542

0.367

0.323

0.465

N

38,37438,36026,99626,98536,39636,38338,34038,326# Firms

1,508

1,507

1,416

1,415

1,499

1,498

1,508

1,507

Adj. R2

0.535

0.551

0.702

0.702

0.948

0.949

0.954

0.955

T reatment × SL − F irm

p-value on difference
Firm FEs
Controls × year FEs

Notes: The table shows results from estimating equation (5.5) at the firm level for our main outcomes of interest.
Bonus claim is a dummy equal to one if the firm receives net income from bonus depreciation in a given year,
construction is the log book value of construction in progress, non-RE purchases is the log gross book value of new
PPE excluding buildings, land, and structures, and employment is the log number of employees. Controls include
static factors such as the size quintile (by total assets), quintile of age measured from the Tokyo Stock Exchange
listing date, and Census region of the HQ, all interacted with a full set of year dummies. We use the pre-Technopolis
share of buildings in the firm’s constant returns to scale production function as the basis for classifying firms as using
primarily long-lived or short-lived assets. See text and Appendix D for details on how we construct capital input
shares. Standard errors clustered at the firm level are in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1

firms to extract more cash flow earlier in the asset’s life, in exchange for small tax write-offs lateron. Yet, when we compare firms who rely entirely on declining balance vs. straight-line depreciation
methods we find they have statistically identical ωbuild , with an average of 0.47 in each subgroup.36

5.3.2

The Role of Financing Constraints

Previous work in spatial corporate finance has argued that multi-plant firms are more likely to rely
on internal capital markets to smooth out shocks if they are financially constrained (e.g. Giroud &Mueller 2015). In our context, a natural question is whether the real responses to the Technopolis
bonus depreciation scheme documented in this section are driven by ex ante constrained firms forwhich the immediate cash flow benefits may have a higher marginal value. We find that the answer
to this question is yes – both in terms of the firms who claim the benefit and those which engage
in more new construction and hiring within treated industry-location cells.

36
We also checked whether a simple above/below median split inherent in equation (5.5) is masking non-linear
effects across the distribution of ωbuild . We uncover a U-shaped pattern when we re-estimate versions of (5.5) where
we interact T reatmentj,t with dummies indicating the quintile of ωbuild . For example, bonus claiming probability
increases by 16 p.p. for firms in the bottom quintile with ωbuild < 0.27, and by 15 p.p. for firms in the top quintile,
with no statistically significant response in the middle of the building share distribution.

32

We use several indexes popular in the corporate finance literature to rank firms from least
constrained to most constrained as of the last year prior to the first implementation of a Technopolis
area (1983). Our main measure, and the one most commonly cited in recent work, is the size-age
index of Hadlock & Pierce (2010) [HP] which ranks firms according to:−0.737 · Size + 0.043 · Size 2 − 0.040 · Age
where Size refers to the log of inflation-adjusted total assets, and Age is the number of years
the firm has been listed as of 1983.37 In addition to the Hadlock-Pierce index, we also consider
the Kaplan-Zingales [KZ] index and the Whited-Wu [WW] index. The KZ index is virtuallyuncorrelated with WW and HP, while the WW index is highly negatively correlated (−69%) withHP in the cross-section of firms. Given the evidence in LaPoint (2021) that the HP index is a robust
predictor of debt issuance sensitivity to collateral values, we are confident that the HP index is anappropriate proxy for the external financing access of Japanese firms.
Similar to the specification in (5.5) comparing firms with long-lived vs. short-lived capital inputs,
we estimate the following equation which allows for differential effects of the Technopolis policy
depending on financing constraints:
yj,t = γj + δt + β1 · T reatmentj,t × F Cj + β2 · T reatmentj,t × N F Cj + η 0 · Xj,t + εj,t(5.6)

where T reatmentj,t is defined analogously to the previous specifications (i.e. based on whether thefirm is in an eligible industry and has a presence in a Technopolis area after the implementationdate). We suppress the industry subscript for simplicity. We define the dummy F Cj (“financiallyconstrained”) as equal to unity if firm j has an ex ante HP index value above the median value
across all firms. Similarly, N F Cj (“non-financially constrained”) is equal to one if the firm has
an ex ante HP index value below the median. We include the usual set of baseline characteristics
interacted with year dummies in the vector Xj,t .
The results in Table 8 show that our findings of economically significant investment andemployment responses are indeed driven by financially constrained firms and not by unconstrained
firms. Bonus depreciation claim probability increased by 12.2 p.p. for constrained firms after
Technopolis eligibility kicked in, with a 21.4% increase in construction outlays, a 34.6% increasein non-real estate investment, and 13.7% increase in employment. In contrast, the loading on
T reatment × N F C is never statisticallly significant across all four outcomes, regardless of whether
we saturate the model with non-parametric trends. While we cannot reject the null that βb1 andβb2 are equivalent for construction, we can reject the null of equivalent non-real estate acquisition37

In the original HP index, Size and Age are capped at 4.5 billion USD and 37 years, respectively. Given thatfirms in the DBJ sample are older than the typical sample of COMPUSTAT firms, we also test additional calibrations
where we do not censor the Age and Size variables and using age measured from the time of establishment rather thanthe listing date. We find our results virtually unchanged for these alternative versions of the index, which supports
the argument in Hadlock & Pierce (2010) that for the largest and oldest firms there ceases to be any relation between
financing constraints and balance sheet size or age.

33

TABLE 8. Firm-level Results by Ex Ante Financing Constraints
Bonus claim

Construction

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.122∗∗∗

0.096∗∗∗

0.194∗∗

0.195∗∗

0.297∗∗∗

0.256∗∗∗

0.128∗∗∗

0.087∗∗(0.038)(0.037)(0.089)(0.093)(0.049)(0.051)(0.036)(0.036)

0.050

0.054

0.120

0.122

0.038

0.044

−0.0040.024(0.040)(0.040)(0.109)(0.116)(0.085)(0.084)(0.048)(0.050)

0.186

0.441

0.583

0.616

0.003

0.016

0.024

0.299

N

38,37437,84526,996

26,52936,39635,88538,34037,811# Firms

1,508

1,507

1,416

1,411

1,499

1,498

1,508

1,507

Adj. R2

0.535

0.555

0.702

0.702

0.948

0.950

0.954

0.956

T reatment × F C
T reatment × N F C

p-value on difference

Non-RE purchases

Employment

Firm FEs
Controls × year FEs

Notes: The table shows results from estimating equation (5.6) at the firm level for our main outcomes of interest.
Bonus claim is a dummy equal to one if the firm receives net income from bonus depreciation in a given year,
construction is the log book value of construction in progress, non-RE purchases is the log gross book value of new
PPE excluding buildings, land, and structures, and employment is the log number of employees. Controls include
static factors such as the size quintile (by total assets), quintile of age measured from the Tokyo Stock Exchange
listing date, Census region of the HQ, and the main bank identifier, all interacted with a full set of year dummies.
We use an uncensored HP index to classify firms as financially (un)constrained. Standard errors clustered at the firmlevel are in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1(p-value = 0.003) and employment responses across the two groups (p-value = 0.024). Overall, Table
8 suggests financially constrained firms were more likely to claim the cash flow benefit provided by
the Technopolis policy.38 Financially constrained firms then used the funds to finance construction
and non-real estate purchases and hire more employees.

5.3.3

Intra-firm Transportation Costs & Policy Take-up

So far we have focused on corporate responses on the intensive margin of policy take-up, meaning
conditioning on firms having operations within catchment areas prior to policy implementation. We
now relax that assumption by considering how the decision to deploy capital and labor to a location
might depend on notions of physical distance to the geographic boundaries of place-based policy
eligibility. If the costs of transporting goods or span of control costs associated with employees andmanagers commuting between locations are substantial, then firms which are more geographicallyconcentrated around major cities rather than the peripheral ones targeted by Technopolis will beless likely to participate.
Examining this question involves extending our baseline difference-in-differences specification in38

We plot the dynamic effects of the F C and N F C dummies in Appendix F.

34

equation (4.1) to a triple differences design like the following:
yj,k,t = γj + δt + Distancej × P ostt + T reatedk × P ostt

(5.7)

+Distancej × T reatedk × P ostt + η 0 · Xj,k,t + εj,k,twhere now we have decomposed the T reatmentj,k,t dummy previously used into three components.
The first component of treatment status, Distancej , is a continuous measure of a firm’s physicaldistance to the Technopolis regions, based on its pre-existing network of plant locations. We
offer several ways to compute Distancej and precisely describe our procedures in Appendix G.5.
Essentially, Distancej captures how close in driving distance the “average” plant of firm j is tothe nearest area where deployed capital would be subsidized through special bonus claims. We also
explore a semi-parametric version of (5.7) where Distancej is discretized into bins, rather thanassuming that the cost of policy take-up is linear in our notion of distance.
The second component of treatment is whether firm j is classified into one of the 55 4-digit JSIC
industries k eligible for Technopolis bonus claims. The final component is an unstaggered policy
timing dummy P ostt equal to one in all years after the enactment of the first wave of Technopolissites in 1984. We adopt a simultaneously absorbing treatment for this model since once we extend
the treatment status to the entire firm network it is unclear which Technopolis becomes the pivotal
one for determining treatment timing. In other words, there is no well-defined concept of “staggeredrollout” with multi-plant firms which can move resources to an entirely new location.39
Figure 6 demonstrates how the dynamic treatment effects for our main outcomes in Figure 4 varyby corporate distance to the policy areas. The first stage effect on bonus claim probability (topleft panel) is declining with average driving distance to the nearest Technopolis, as shown by theestimates plotted in red. A firm with an average distance 100 km further from Technopolis areas
is 10% less likely to claim bonus depreciation. However, there is no appreciable effect of distanceon employment or non-real estate CAPX, although the sign of the estimates is always persistently
negative. Instead, the T reatedk × P ostt estimates in green are of a similar trend and magnitude
to our main estimates in Figure 4 which define treatment based on existing operations within aTechnopolis, while the loadings on Distancej × P ostt in blue are roughly constant across time andinsignificant for all outcomes.
These patterns point to distance as an important determinant of policy take-up. Yet, among thefirms which extract cash flow from the policy, there is no observable difference in the employment
or non-real estate investment responses with respect to distance between catchment areas and theinternal resource network. This suggests the decision of large firms to respond to local capital
investment subsidies depends on threshold fixed costs of opening a new plant which is potentiallyon the outskirts of the existing network of locations. Hence, local capital lock-in provisions, like
39

Still, we try staggered versions of (5.7) with P ostj,t set to unity in years after the rollout of the Technopolis site
closest to all plants within the firm’s network and find virtually identical results.

35

FIGURE 6. Triple Differences: Policy Take-up as a Function of Driving Distance
Bonus claim probability

Employment

.4

.2
.1

.2

0

Estimated ATTs

0
−.1
−.2

−.2
−6 −5 −4 −3 −2 −1 0

1

2

3

4

5

6

7

8

9 10

−6 −5 −4 −3 −2 −1 0

Construction in progress
.6

.01

.4

0

.2

−.01

0

−.02

−.2
1

2

3

4

5

6

2

3

4

5

6

7

8

9 10

Non−RE purchases.02−6 −5 −4 −3 −2 −1 0

1

7

8

9 10

−6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10

Years since Technopolis
Distance x Treated Industry x Post
Treated Industry x Post

Distance x Post

Notes: Each panel shows the response of an outcome of interest estimated from a dynamic version of the unstaggered
triple differences model of (5.7) via OLS. We plot the loadings on the triple difference Distancej × T reatedk × P ostt
in red, those on Distancej × P ostt in blue, and T reatedk × P ostt in green. Each regression includes HQ Census
region × year fixed effects. Construction in progress and non-real estate assets are deflated by the firm’s book assets
in 1975 before our estimation sample start date. Firm employment is scaled by its value in 1975. All dynamic effects
are relative to one year before Technopolis eligibility begins. We use an equal-weighted average measure of distanceto the nearest Technopolis across plants within each firm (see Appendix G.5 for details). We rescaled estimates withDistancej so that the effect is in terms of a 100 km increase in distance. The bars show 95% confidence intervals
obtained from standard errors clustered at the firm level.

those of U.S. Opportunity Zones, are a double-edged sword. Tying the subsidy to local deployment
helps bring resources to peripheral economies, but it also particularly increases the cost of take-up
for firms in high-productivity areas with which those economies are struggling to compete.

5.4

Separating Multiple Policy Treatments

Our main results center on the effects of the Technopolis policy, which targeted the physicalcapital-intensive manufacturing firms that are more likely to benefit from extracting immediate cash
flow from long-lived assets. The Japanese government introduced the Intelligent Location policy
between 1989 and 1994 in an attempt to accelerate the industry clusters of Technopolis by expanding
the catchment areas and extending bonus incentives to firms engaged in high-tech services (e.g.
software) and precision instruments manufacturing. Due to the spatial overlap of Technopolis andIntelligent Location (cf. Figure 2), and the fact that Intelligent Location industries were in manycases downstream of the industries eligible under Technopolis (cf. Appendix B), the two policies
36

TABLE 9. Firm-level Results with Separate Policy Treatments

T reatmentT

Bonus claim

Construction

Non-RE purchases

Employment

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.093∗∗∗

0.087∗∗∗

0.163∗∗

0.167∗∗

0.172∗∗∗

0.165∗∗∗

0.060∗

0.062∗∗(0.028)(0.028)(0.072)(0.072)(0.046)(0.047)(0.031)(0.030)0.138∗∗

0.125∗∗∗

0.119∗∗∗−0.023−0.018

0.044

0.042

0.143∗∗(0.024)(0.023)(0.108)(0.109)(0.059)(0.059)(0.039)(0.039)N

38,37438,36026,99626,98536,39636,38338,34038,326# Firms

1,508

1,507

1,416

1,415

1,499

1,498

1,508

1,507

Adj. R2

0.535

0.551

0.702

0.702

0.948

0.949

0.954

0.956

T reatmentIL

Firm FEs
Controls × year FEs

Notes: The table shows results from estimating equation (5.8) at the firm level for our main outcomes of interest.
T reatmentT refers to point estimates for the loading on Technopolis eligibility, and T reatmentIL refers to point
estimates for the loading on Intelligent Location eligibility. Bonus claim is a dummy equal to one if the firm receives
net income from bonus depreciation in a given year, construction is the log book value of construction in progress,
non-RE purchases is the log gross book value of new PPE excluding buildings, land, and structures, and employment
is the log number of employees. Controls include static factors such as the size quintile (by total assets), quintile ofage measured from the Tokyo Stock Exchange listing date, Census region of the HQ, all interacted with a full set ofyear dummies. Standard errors clustered at the firm level are in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1

may have cross-pollinated each other. For instance, manufacturing companies who invested undera qualifying Technopolis may in turn realize productivity gains from software companies expanding
their operations in an overlapping Intelligent Location. Such productivity gains would then bereflected in the employment and investment outcomes we consider in estimating regressions likeequation (4.1), as firms set the marginal products of capital and labor equal to real input costs.
To assess the extent to which the dynamic effects exhibited in Figure 4 may reflect such local
general equilibrium effects, we augment our baseline staggered DD specification to account forfirms’ eligibility under the Intelligent Location criteria:
0yj,k,t = γj + δt + β1 · T reatmentTj,k,t + β2 · T reatmentIL
j,k,t + η · Xj,k,t + εj,k,t(5.8)

where T reatmentTj,k,t is equal to one for firms eligible for bonus incentives under Technopolis, andT reatmentIL
j,k,t is equal to one for firms eligible for the bonuses provided by Intelligent Location.
Both treatment dummies are defined using the three-step procedure described in Section 4. The
sets of firms eligible according to each policy are not disjoint; in our sample of 1,508 DBJ firms,
31% (N = 457) are in a Technopolis treated industry, 18% (N = 276) are in a Intelligent Location
treated industry, and 8% are in both (N = 121).
Table 9 provides results for our main outcomes of interest from estimating the multiple treatment

37

regression in equation (5.8).40 First, we observe that the estimates for β1 are quantitatively similar
to those reported in Panel B of Table 5. Thus, conditioning on receiving eligibility for Intelligent
Location bonuses between 1989 and 1994 does not affect our estimates of the average effect of using
Technopolis bonuses. We do not observe any significant effect of Intelligent Location eligibility on
bonus claims or construction after conditioning on Technopolis eligibility. Yet, there is a positiveand significant loading on Intelligent Location treatment for non-real estate acquisitions (14.3 log
points in column 5) and employment (12.5 log points in column 7). This makes intuitive sense,
since Intelligent Location targeted specialized service sector firms which rely less on physical spaceand more on high-skilled labor and advanced technology. Even if IL-eligible firms did not increase
their bonus claims (column 1), they may have increased their employment and output in catchment
areas to service proximal downstream firms who expanded under Technopolis.
Directly interpreting βb2 in Table 9 as a treatment effect of the Intelligent Location policy iscomplicated by the cross-contamination of treatment and control groups in regressions with multiple
treatment dummies. de Chaisemartin & D’Haultfœuille (2021) analyze regressions like our equation(5.8) and formally decompose the coefficient on one treatment dummy as the sum of two terms:(i) the weighted average effect of moving the first treatment from 0 to 1 while keeping the second
treatment at its observed value, and (ii) the weighted average effect of moving the second treatment
from 0 to 1 while keeping the first treatment at 0 across all group-time cells that receive the second
treatment. That is, in our setting, both β1 and β2 are inclusive of treatment effects of the otherpolicy for some subgroup of firms.
We adopt the approach recommended by de Chaisemartin & D’Haultfœuille (2021) for extending
the estimator introduced in de Chaisemartin & D’Haultfœuille (2020) to isolate an unbiased
estimate of the average treatment effect of moving T reatmentIL from 0 to 1. Constructing thisestimator involves running the following event study specification:
yj,k,t = γj + δt +TX

T
β2,t · T reatmentIL
j,k,t + Fj,t + εj,k,t(5.9)

t=1,t6=t0

which mirrors our previous event study equation from Section 4, but with two key differences.
First, we only estimate (5.9) on the set of firm-time observations such that T reatmentTj,k,t = 1.
T with respect to the first year in which each firm
Second, we include non-parametric trends Fj,tbecomes eligible to claim bonuses under Technopolis (i.e. under the first treatment). The resulting
event study coefficients β2,t compare outcomes between firms that do vs. those that do not become

40

We plot the dynamic effects of the T reatmentT and T reatmentIL dummies in Appendix F.

38

eligible under Intelligent Location, but that became eligible for Technopolis at some prior date.41
Figure 7 plots the dynamic treatment effects of the Intelligent Location policy obtained fromestimating the model in (5.9) for the subsample of firm-time observations which were directly treatedby Technopolis. The figure tells a very different story than the regression results in Table 9. In
particular, there is a clear, but imprecisely estimated, uptick in bonus claiming behavior of roughly
10 p.p. in the first few years of the policy implementation; the drop off in bonus claims after year
2 corresponds to the first kink point in the depreciation schedule for Intelligent Location in Table
2. However, these tax write-offs do not translate into any noticeable employment, construction, orother investment responses. We note that the confidence intervals are quite wide, as we lose a lotof statistical power by restricting to observations with T reatmentTj,k,t = 1.
The results of this subsection suggest that while some firms in Technopolis areas may havemade bonus claims under the Intelligent Location policy, the second policy implementation had
no additional direct effects on firm hiring and investment. This is perhaps unsurprising given that
Intelligent Policy offered the same bonus claims against physical capital investment, but unlike
Technopolis, it offered these incentives to firms in industries which rely more on intangible capital.
Contrasting the two policies reveals the importance of the firm’s capital lifespan in determining the
success of bonus depreciation initiatives aimed at spurring local economic growth.

5.5

Matched Plant-Parent Firm Analysis

We have so far conducted the analysis at the level of the parent firm. We now turn to the
distributional consequences of the investment and employment responses to the Technopolis policy.
In this subsection we match the listed firms in the DBJ database to their manufacturing plants in
the COM data and address whether the cash flows extracted under Technopolis actually arrived at
economically peripheral areas as policymakers intended.
We lack credible within-firm plant identifiers that would allow us to track plants between the1980 manufacturing facilities reported in the firm’s yūhō and the manufacturing plants surveyed inCOM. However, we know the location of each plant up to the municipality and its 4-digit industry
code, and so we can sort plants within the firm on the basis of Technopolis eligibility. Much like ourfirm-level empirical strategy in Section 4, we set the treatment status of plant i attached to firm j
in industry k at time t, T reatmenti,j,k,t , equal to one if all three of the following criteria are met:(i) Plant i level. The plant is located in an eligible Technopolis municipality.
41

Goldsmith-Pinkham, Hull, & Kolesár (2021) also consider multiple treatment regressions. They derive efficientestimators for separating treatment effects under the assumption of conditional independence (i.e. under a set ofcontrols), while the de Chaisemartin & D’Haultfœuille (2021) approach relies on the parallel trends assumption.
Additionally, the unbiasedness of the estimator requires no anticipation and a balanced panel of firms within theestimation sample. The latter condition is required in our context because we are estimating a “fuzzy” DD, where
policy eligibility criteria are set at the city × industry level, but locations are specific to the firm. We confirm thatleading the β2,t → β2,t+1 by one year – as we did with the BJS estimator – and restricting to a balanced panel foreach outcome does not materially change our results.

39

FIGURE 7. Dynamic Firm Responses to Intelligent Location Eligibility
A. Bonus depreciation probability

B. Cash flow

.3

.05

Estimated ATTs

Estimated ATTs

.2

.1

0

0

−.05

−.1

−.2

−.1
−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

−4

−3

−2

−1

0

Years since Technopolis

1

2

3

4

5

6

7

8

6

7

8

6

7

8

Years since Technopolis

C. Employment

D. Construction in progress

.1
.03

.05

Estimated ATTs

Estimated ATTs

.02

0

.01

−.05

0

−.01

−.1
−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

−4

−3

−2

−1

0

Years since Technopolis

1

2

3

4

5

Years since Technopolis

E. Non-real estate purchases

F. Long-term debt issuance

.2

.2

.1

Estimated ATTs

Estimated ATTs

.1

0

−.1

0

−.1
−.2

−.2

−.3
−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

−4

Years since Technopolis

−3

−2

−1

0

1

2

3

4

5

Years since Technopolis

Notes: Each panel shows the dynamic response to Intelligent Location eligibility, conditional on already being treatedby Technopolis, of an outcome of interest estimated via the staggered DD model in equation (5.9) using the estimator
proposed by de Chaisemartin & D’Haultfœuille (2021). With the exception of the bonus depreciation dummy andemployment, each variable is deflated by the firm’s book assets in 1975 before our estimation sample start date. Firm
employment is scaled by its value in 1975. We bin the dummies at the end of the effect window for t = −4 and t = 8.
All dynamic effects are relative to one year before a firm becomes eligible for Intelligent Location. The bars show
95% confidence intervals obtained from standard errors clustered at the firm level, with 1,000 bootstrap iterations.
See text for details on the definition of each outcome.

40

(ii) Industry k level. The firm is operating in one of the eligible 4-digit JSIC industry codes.
(iii) Timing t. If the plant fulfills the above two criteria, then we set T reatmenti,j,k,t equal to one
for year t equal to or greater than the first eligibility year for the municipality-industry pair.
Under this approach, we find that roughly 13% of the plant-year observations in our matchedsample covering 1980 and 1986–2000 are located in a Technopolis eligible area.42 The number ofmanufacturing plants in our sample grows from 3,470 in 1980 (from the yūhō) to 5,639 in 2000, andpeaks at 6,339 plants surveyed in 1997.
We use the building input share ωbuild constructed in Section 5.3 to sort parent firms basedon the attractiveness of the tax incentives offered by Technopolis. As already shown in Table 7,
the responses we document in our staggered DD models are driven by firms with a larger shareof long-lived assets in production. Hence, we should expect to see a positive gradient between
employment growth and investment with respect to ωbuild . The question is whether this gradient islarger for Technopolis eligible areas. If the gradient is larger for ineligble areas this would indicatethat the cash flows firms are extracting from their eligible investments are being used to financeinvestments in areas not targeted by policymakers.
Figure 8 and Figure 9 provide visual evidence in favor of the narrative that ineligible areas
captured much of the investment intended for plants in eligible areas. Figure 8 separately computes
the change in the total number of eligible (blue) and ineligible plants (red) for each firm and plots
these changes in plants against ωbuild , which can vary between 0 and 1. In cases where a firm has
both eligible and ineligible plants, then it will appear twice on the plot. Figure 9 conducts the same
exercise except the y-axis is the total employment growth rate (Panel A) or the real book land
asset growth rate across all plants in each bucket. To compute real book land asset growth, wedeflate the book value of land reported by each plant by the city-level repeat appraisal index for
CRE properties in that year compiled by LaPoint (2021). Technopolis overlapped with a dramaticrise in land values, especially commercial land in the central business district, so deflating by the
local price index helps isolate the real investment response from the mechanical effects of spatialdifferences in land price inflation. We compute growth over 1980 and 1995 to allow all Technopolis
locations to become eligible – recall the last one was enacted in 1989 – and to allow construction
projects begun during the initial Technopolis period to be completed.43
At the extensive margin of investment in Figure 8 we observe that there is a negligible, positive
gradient (slope = 0.119) in the change in the number of plants and ωbuild for eligible areas, anda negligible but slightly positive gradient for ineligible areas (slope = 0.004). The lack of any
42

Under a more stringent definition of T reatmenti,j,k,t where in step (ii) we consider the plant treated at theindustry level based on the 4-digit industry code attached to the plant rather than the parent firm, we find only 3.4%of plants in the COM sample are eligible. Since the depreciation claims are made at the level of the parent firm, weview it more appropriate to assign the industry eligibility status at the firm level.
43

Based on construction itemizations hand-collected from the 1980 yūhō corresponding to our sample of DBJ firms,
the average projected time to completion for construction projects is 1.5 years, with a maximum duration of 5 years.

41

FIGURE 8. 1980-1995 Growth in Number of Plants by ωbuild

1980−1995 change in # of plants

40

Technopolis eligible
Ineligible

20

0

−20
0

.2

.4

.6

.8

Pre−Technopolis building input share

Notes: Each point on the graph corresponds to a DBJ firm matched to the set of manufacturing plants it reported
in the COM survey in 1995 and the manufacturing plants it reported in its securities filings in 1980. Points in red
represent the 1980–1995 change in the number of plants located in a city not eligible for Technopolis. Points in blue
represent the same statistic except computed over plants within the firm’s network which are located in cities eligible
for Technopolis. Therefore the same firm can appear twice on the plot. The x-axis variable is the firm-level building
input share ωbuild computed via the methods outlined in Section 5.3.

discernible relationship between new plant creation and the desirability of bonus claims in bothtypes of areas suggests the bulk of the construction response we document in Section 4 comes fromexpansions of existing plants in Technopolis areas. In contrast, when we examine employment andreal land asset growth in Figure 9, there is a clear divergence between eligible and ineligible areas.
For employment growth rates the gradient is 1.918 in eligible areas, and 9.056 in ineligible areas;
for real land value growth rates the gradient is −8.560 in eligible areas, but the gradient flips sign
to 3.584 in ineligible areas.
This evidence points to much of the gains in firm-level hiring and investment we documented
in our main results in Section 5.2 arising from firms allocating resources to ineligible sites. Taken
together, it appears our relatively large listed firms expanded existing plants in Technopolis eligible
areas to capture the immediate cash flow benefits of bonus depreciation, and then funneled theresources towards pre-existing plants in ineligible areas.44 The possibility of this “leakage” of publicfunds was noted by Broadbent (1989), who conducted onsite qualitative surveys of newly establishedrobotized facilities – including those of several companies in our dataset (e.g. Sony and Canon) –during the early years of the Technopolis program. Hence, while the place-based tax incentives
44

While we observe other variables at the plant-level in COM such as acquisition of buildings and machines, at the
moment we have no way to link these measures to the information reported at the plant level in the securities filingsof our DBJ firms. In future work, we plan to expand the set of outcomes in this matched setting and run plant-level
regressions by backfilling plant identifiers to before the Technopolis policy.

42

FIGURE 9. Employment Growth and Land Acquisitions by ωbuild
B. 1980-1995 real land asset growthA. 1980-1995 employment growth

400

Technopolis eligible
Ineligible

1980−1995 % real land asset growth

1980−1995 % employment growth

400

300

200

100

0

−100

Technopolis eligible
Ineligible

300

200

100

0

−100
0

.2

.4

.6

.8

0

Pre−Technopolis building input share

.2

.4

.6

.8

Pre−Technopolis building input share

Notes: In both panels, each point on the graph corresponds to a DBJ firm matched to the set of manufacturing
plants it reported in the COM survey in 1995 and the manufacturing plants it reported in its securities filings in 1980.
Points in red represent the 1980–1995 percentage growth rate in either the number of employees (Panel A) or thereal acquisition value of land (Panel B) summing across all plants within the same firm located in a city not eligible
for Technopolis. Points in blue represent the same statistics except computed over plants within the firm’s network
which are located in cities eligible for Technopolis. Therefore the same firm can appear twice on the plot. The x-axis
variable is the firm-level building input share ωbuild computed via the methods outlined in Section 5.3. To obtainreal land values, we use the set of commercial real estate price indices constructed in LaPoint (2021). We winsorize
growth rates at the 99th percentile.

promoted irreversible investment in areas outside the major metros, it is unclear whether theseinvestments were to the direct benefit of local residents.

6

Fiscal Cost Calculations

In

this

section,we

assessthe

cost-effectivenessofTechnopolis

byconvertingthedifference-in-differences estimates of Section 5 to a cost-per-job measure. Following Garrett, Ohrn,& Suárez Serrato (2020), the fiscal cost per dollar of CAPX is the present discounted value ofcorporate income tax revenue forgone from offering bonus depreciation during the policy regime.
In our setting, that implies the accounting identity:
Fiscal cost =1995
X
t=1984



τt
bonus
normal×D−
D× Take-upttt(1 + r)t(6.1)

where we compute the fiscal cost from 1984 through 1995, the final year that firms could claim
bonus depreciation through investing in the first wave of Technopolis clusters. We feed in thehistorical corporate income tax rates τt and assume at baseline a constant real discount rate of 7%
to render our estimates directly comparable to those in the literature on accelerated depreciation.45
45

A real discount rate of 7% turns out to be roughly equal to the average observed daily rate on the 1-year JGB
of 6.4% during the first year of the policy.

43

Corporate income tax rates vary between 43.3% and 37.5% during the policy period, and for agiven tax year the rate is the same for all corporations with above $80,000 in taxable earnings.46
The gap between Dtbonus and Dtnormal represents the difference in benefits per dollar of investment
accruing to firms under the bonus regime relative to under the benchmark accounting methods
available in the tax code. Since firms can normally elect to amortize costs via declining balance[DB] or straight-line [SL] depreciation, we can write Dtnormal as:
Dtnormal = ξ · DtDB + (1 − ξ) · DtSL

(6.2)

where ξ is the share of firms who choose declining balance.47 93% of firms in our sample use the more
accelerated declining balance method for at least some of their investments, and the remaining 7%
use a combination of either straight-line or other methods allowed for certain niche asset classes.48
The last component of the accounting identity is the take-up rate, or the share of firms which
are both classified within a Technopolis eligible 4-digit JSIC and claim bonus depreciation. 41%
of corporations claim bonuses at least once during the policy period, with an average take-up rateof 11.3%. It is necessary to scale by eligibility because some bonus claims are allowed outside theTechnopolis regime – for example, airline companies purchasing aircraft – and we cannot separate
Technopolis bonus claims from other non-Technopolis bonus claims. Putting everything together,
we calculate a fiscal cost of 1.5% per dollar of qualifying capital investment from (6.1).
A final step is needed to produce the fiscal cost in dollars. We compute aggregate qualifying
CAPX among our firms over the policy period by summing up the firm-level changes in the grossbook value of physical capital excluding land between 1984 and 1995. We remove land from CAPX
in computing the Dt benefit flows and aggregate qualifying CAPX because land does not depreciate.
This results in $1.42 trillion in corporate CAPX (141.9 trillion JPY), but not all of this investment
satisfied the industry and location eligibility criteria for bonuses. We thus scale total CAPX down bycomputing the share of eligible investment among manufacturing firms for which we have investment
itemized by plant location:
PT

t=0

P

i ∆P P Ei,t × T reatmenti,k,t
PT P
t=0
i ∆P P Ei,t

(6.3)

where T reatmenti,k,t is equal to one if plant i is located in a Technopolis area and attached to aparent firm in industry k that is one of the treated 4-digit JSICs. ∆P P Ei,t refers to the YOY change
in the net book value of non-land physical assets, plus accounting depreciation, or investment in46

We obtained the historical corporate income tax rate series from an official Ministry of Finance memo: https://www.mof.go.jp/tax_policy/summary/corporation/c01.htm. A lower, flat rate applies to firms with taxable earnings
below the $80,000 (8 million JPY) threshold. None of the publicly listed firms qualifies for the lower rate in any yearof our sample.
47

We describe these accounting methods in detail through several cash flow simulation exercises in Appendix C.

48

With the exception of bonus claims, we cannot separate depreciation claims by normal accounting method. We
only observe the list of methods a firm uses in a given fiscal year.

44

non-land assets. The investment eligibility rate implied by (6.3) is 6.6%, resulting in $93.65 billion
in eligible corporate CAPX conducted under Technopolis. The fiscal cost in dollars amounts to$93.65 billion × 1.5% = $1.40 billion.
It is far more straightforward to compute a measure of jobs generated by Technopolis. Ourpreferred DD estimate of the employment response from Figure 4 is 5% relative to 1975 firm
employment. Scaling up total 1975 employment among listed firms in eligible industries by 5%
implies 65,717 corporate jobs created, leading to a cost per job of $1.40 billion/65,717 ≈ $21,065.
If we instead use our pooled OLS estimate (Table 5) of a 7% bump in employment, then 92,004
jobs were created, and the cost per job falls to $15,046.
How reliable are these cost-per-job estimates? One advantage to using balance sheet data is that
we can compute the lost corporate income tax revenues using the observed stream of bonus andnon-bonus depreciation claims. This means our measure calculated via (6.1) is an ex post fiscal cost,whereas estimates in the literature generally assume a change in the average benefit rate implied bya simulated cost amortization schedule, and then apply that percentage to aggregate CAPX eligible
for the tax break to produce an overall dollar value cost. Using time-variation in Dtbonus − Dtnormal
is more appropriate in our setting, given the event study evidence in Figure 4 that bonus claiming
activity under the policy was concentrated soon after enactment, implying higher fiscal costs in a
PDV sense than if we assumed a constant gap Dbonus − Dnormal . On the other hand, our access tobalance sheets is predicated on a firm being publicly listed, so our measure only recovers the fiscal
cost per large corporate job.49
Our cost per job numbers likely overestimate the true fiscal cost, because the factor we apply
from (6.3) to pare down total CAPX to eligible CAPX is calculated across manufacturing plants,
which we know were already more concentrated in areas designated as a Technopolis cluster. Hence,our 6.6% scale factor is biased upward by selection. Finally, we emphasize that due to the natureof the DD estimates we use to calibrate the overall employment gains, our estimate does not take
into account general equilibrium forces through which employment might increase. Yet, we find noevidence of trade network or local spillover effects on firm-level employment, suggesting generalequilibrium forces play a limited role in our setting. In Appendix G.6 we provide alternative fiscal
cost estimates based on ex ante measures which use alternative definitions of the counterfactual
Dtnormal , simulated subsidy rates, and estimates of firm-level and city × industry-level employment
responses from Section 5. Our fiscal cost estimates fall between 2%-3% per dollar of qualifying
capital investment, which accords with the cash flow simulations we perform in Appendix C toestimate subsidy rates for different firm investment profiles.

49

We are not aware of any extant reports confirming the value of aggregate depreciation claims (including both
corporate and non-corporate entities) eligible under Technopolis. Therefore, we cannot perform the simpler calculationof scaling down aggregate depreciation claims by a measure of the average increase in the present value of deductions
relative to the identity in (6.2) for Dtnormal we can obtain from cash flow simulations.

45

7Conclusion

We investigate the effects of spatial bonus depreciation incentives on local investment, employment,
and corporate (re)location decisions using a series of bonus depreciation schemes in 1980s and 1990s
Japan that altered the relative cost of capital across locations. Our results highlight the critical rolefirms’ physical capital structure – which consists of both the geographic distribution of corporate
resources and the composition of inputs used in production – plays in the targeting vs. retention
trade-off within spatially targeted tax incentives. We find that multi-plant firms exercised these tax
write-offs to increase their current cash flow by engaging in construction projects at locations within
their internal network and investing in complementary non-real estate assets such as machinery. Ourestimated effects are economically large. A firm which became eligible to claim bonus depreciation
on investments at one of its plant locations increased its outlays for construction by 0.29 standard
deviations and increased its non-real estate acquisitions by 0.40 standard deviations.
Much like the U.S. experience with Opportunity Zones enacted in 2017, which grant capitalgains tax deferrals in exchange for a five-year investment in distressed neighborhoods, our setting
features immediate financial incentives, targeting firms in high-tech manufacturing industries using
long-lived capital inputs. Another important distinction of the bonus depreciation schedules offeredby Japan’s Technopolis policy is that they applied to investment in buildings, an exceptionallylong-lived asset class which was ineligible for bonus depreciation episodes in the U.S. in 2001 and2008. Bonus depreciation as a place-based policy promotes retention of local capital in contexts
where eligible firms rely heavily on long-lived assets in their production function, and bonus claims
are attractive relative to existing cost amortization methods allowed under the tax code.
At the same time, we uncover mixed evidence that place-based incentives extended to large
multi-plant firms can stimulate peripheral labor markets. We find no evidence of positive local
spillovers to firms operating in eligible areas but which were ineligible for bonus claims due totheir industry classification, or spillovers via indirect policy exposure through inter-regional trade.
While firm-level employment increased by around 7% after 10 years of the new policy regime, this
response was apparently driven by firms hiring at sites where physical investment was not eligible
for bonus claims. The policy was effective from a cost-per-job accounting perspective, but firms
redirected the cash flows from the tax breaks to higher marginal productivity areas within their
network. Future work will take our reduced-form estimates of firm responses to local tax incentives
and examine the distributional consequences of place-based policy instruments through the lens ofa structural model of multi-location firms using long-lived and short-lived capital.

46

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51

Online Appendix toPlace-Based Policies and the Geography of Corporate Investment
by Cameron LaPoint (Yale SOM) and Shogo Sakabe (Columbia)

Contents
A Eligible Technopolis Industries & Areas

54

A.1 List of Eligible Technopolis Industries . . . . . . . . . . . . . . . . . . . . . . . . . . 54
A.2 List of Eligible Technopolis Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
B Eligible Intelligent Location Industries & Areas
B.1 List of Eligible Intelligent Location Industries & Assets

57. . . . . . . . . . . . . . . . 57

B.2 List of Eligible Intelligent Location Areas . . . . . . . . . . . . . . . . . . . . . . . . 59
C Depreciation Accounting Methods

60

D Details on Capital Input Share Calculations

65

E Main Results Using Other Staggered DD Estimators

71

F Dynamic & Heterogeneous Effects

73

G Additional Results & Robustness Checks

77

G.1 Results for Other Firm-level Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . 77
G.2 Comparing Cash Flow Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
G.3 Main Results Using Transformed Outcomes . . . . . . . . . . . . . . . . . . . . . . . 79
G.4 Robustness to Applying Historical Municipal Boundaries . . . . . . . . . . . . . . . . 82
G.5 Measuring Corporate Distance to Policy Areas . . . . . . . . . . . . . . . . . . . . . 82
G.6 Alternative Fiscal Cost Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

List of Figures
C.1 Tax Benefits over the Lifespan of a Typical Investment . . . . . . . . . . . . . . . . . 63
C.2 Simulated Tax Benefit PDVs as a Percentage of Investment Cost . . . . . . . . . . . 64
D.1 Distribution of Physical Capital Input Shares . . . . . . . . . . . . . . . . . . . . . . 70
E.1 Dynamic Effects of Technopolis by Staggered DD Estimator . . . . . . . . . . . . . . 71
E.2 Robustness to Including Linear Firm-Time Trends . . . . . . . . . . . . . . . . . . . 72
F.1 Evolution of Local Spillovers to Untreated Firms . . . . . . . . . . . . . . . . . . . . 73
52

F.2 Dynamic Effects by Reliance on Long-lived Assets . . . . . . . . . . . . . . . . . . . 74
F.3 Dynamic Effects by Financial Constrainedness (Size-Age) . . . . . . . . . . . . . . . 75
F.4 Dynamic Effects of Multiple Policy Treatments . . . . . . . . . . . . . . . . . . . . . 76
G.1 Event Study Results for Other Outcomes . . . . . . . . . . . . . . . . . . . . . . . . 78
G.2 Comparing Dynamic Responses of Cash Flow Measures . . . . . . . . . . . . . . . . 80
G.3 Main Event Study Results Using Transformed Outcomes . . . . . . . . . . . . . . . . 81
G.5 Distribution of Firm-level Distance to Policy Areas . . . . . . . . . . . . . . . . . . . 87
G.6 Corporate Income Tax Rate Time Series . . . . . . . . . . . . . . . . . . . . . . . . . 88

53

A

Eligible Technopolis Industries & Areas

Here we report the lists of industries and areas where firms could claim bonus depreciation incentives
under the Technopolis policy. We hand-collected information in the industry tables from the
Ministry of International Trade and Industry (1995) depreciation catalogue, and information in
the area tables from the Japan Location Center (1999) history of the two policies.

A.1

List of Eligible Technopolis Industries

Broad Sector

Industry Description

Light Manufacturing

Rayon-acetate
Synthetic fiber
Cyclic intermediates, synthetic dyes and organic pigments
Plastic
Medical material preparations
Medical product preparations
Biological preparations
Natural drugs and Chinese medicines style medicines
Medical products for animals
Porcelain electrical supplies
Ceramic, stone and clay products, n.e.c
Food processing machinery and equipment
Woodworking machinery
Printing, bookbinding and paper converting machinery

Heavy Manufacturing

Carbonaceous electrodes
Miscellaneous carbon and graphite products
Miscellaneous primary smelting and refining of non-ferrous metals
Rolling and drawing of copper and copper alloys
Rolling of aluminum and aluminum alloys, including drawing andextrudingMiscellaneous rolling of non-ferrous metals and alloys, including
drawing and extrudingElectric wire and cable, except optical fiber cableNon-ferrous metal products, n.e.c.
Mechanical power transmission equipment, except ball and roller
bearings
Valves and fittings
Ball and roller bearings
Foundry equipment
Machinery for fabrication of plastic and its equipment
Metal machine tools
Metalworking machinery and its equipment, except metal machine
tools
Parts and accessories for metal working machines and machine tools,
except machinists’ precision tools, molds and dyes
Machinists’ precision tools, except powder metallurgy products
54

Molds and dyes, parts and accessories for metal products
RobotsTransportation

Logistics and conveying equipment
Motor vehicles, including motorcycles
Motor vehicles parts and accessories
Aircraft
Aircraft engines
Miscellaneous aircraft parts and auxiliary equipment

Electronics

Office machinery and equipment
Manometers, flow meters and quantity gauges
Precision measuring machines and instruments
Analytical instruments
Testing machines
Miscellaneous measuring instruments, analytical instruments,
testing machines, surveying instruments and physical and chemical
instruments
Medical instruments and apparatus
Microscopes and telescopes
Cameras, motion picture equipment and their parts
Movie machines and their pats
Optical lenses and prisms
Electron tubes
Semiconductor element
Integrated circuits
Miscellaneous electronic components
Generators, motors and other rotating electrical machinery
Electrical relay switches
Auxiliary equipment for internal combustion engines
X-ray equipment
Miscellaneous electronic equipment
Electric measuring instruments, except otherwise classified
Industrial process controlling instruments
Miscellaneous electrical machinery equipment and supplies
Communication equipment wired
Communication equipment wirelessVideo equipment
Computer, except personal computer

Notes: The table lists the 4-digit JSIC industries eligible to claim bonus depreciation under the Technopolis policy,
obtained from Ministry of International Trade and Industry (1995). We crosswalk historical JSICs to the modern
classification system. See Section 2 for more details on the policy, including the bonus rate schedule.

55

A.2

List of Eligible Technopolis Areas

The table below reports the list of Technopolis-eligible areas, which include 26 named“Technopolises,” each of which forms a cluster around a large regional city. In total, there are
141 municipalities (according to modern Census city codes) included within these 26 sites. For
conciseness, we list each Technopolis site, the regional hub it corresponds to, the number of cities(shi ) and towns (machi or mura) included in the catchment area, and the policy rollout date.

Technopolis Name

Policy Date

Regional City

# Cities# Towns# Unique City Codes

Central Hiroshima

3/24/1984

Kure

3

2

3

Hamamatsu

3/24/1984

Hamamatsu

3

3

2

Kumamoto

3/24/1984

Kumamoto2

11

9

Miyazaki

3/24/1984

Miyazaki

1

6

3

Northeastern Kyushu

3/24/1984

Oita

6

15

8

Shinanogawa

3/24/1984

Nagaoka87

9Southern Kyushu

3/24/1984

Kagoshima

2

12

4

Toyama3/24/1984

Toyama

2

4

3

Ube

3/24/1984

Ube

4

4

4

Akita

5/21/1984

Akita

1

2

1

Utsunomiya

5/21/1984

Utsunomiya

2

2

4

Hakodate

7/14/1984

Hakodate

1

3

3

Yoshino Plateau

8/3/1984

Okayama3

5

4

Kurume-Tosu

9/17/1984

Kurume2

5

4Nagasaki

3/12/1985

Sasebo3

3

6

Aomori8/14/1985

Aomori526Western Suma

9/18/1985

Himeji4

98

Kagawa12/6/1985

Takamatsu

5

7

8

Koriyama

12/3/1986

Koriyama

2

4

6

Northern Sendai

12/3/1986

Sendai1

4

5

Kitakami River Basin

9/24/1987

Morioka5

1

5

Yamagata

9/24/1987

Yamagata

5

1

6

Asama

12/25/1987

Nagano

4

7

8

Kofu

2/12/1988

Kofu

2

19

10

Ehime

4/26/1988

Matsuyama

6

6

7

Central Hokkaido

2/14/1989

Sapporo

4

1

5

–

–

86

145

141

Total

Notes: Technopolis sites are listed in chronological order based on policy implementation date. In some cases (e.g. the“Northeastern Kyushu” Technopolis) we translated portmanteaus to reflect the region of Japan where the catchment
area is located. The number of cities and towns refers to the number of historical jurisdictions in those two official area
categories. In the final column, the number of unique Census city codes is weakly less than the sum of the number ofdistinct cities and towns due to municipal mergers. We impose modern municipal boundaries using the historical citycode crosswalk available through RIETI (Kondo 2019). Policy dates obtained from Ministry of International Tradeand Industry (1995). Eligible sites obtained from Japan Location Center (1999).

56

B

Eligible Intelligent Location Industries & Areas

Here we report the lists of industries and areas where firms could claim bonus depreciation incentives
under the Intelligent Location policy. We hand-collected information in the industry tables from
the Ministry of International Trade and Industry (1995) depreciation catalogue, and informationin the area tables from the Japan Location Center (1999) history of the two policies.

B.1

List of Eligible Intelligent Location Industries & Assets

In contrast to Technopolis, which explicitly listed 4-digit JSICs eligible for bonus depreciation
incentives, the Intelligent Location policy instead targeted four broad descriptions of activities thatwould render firms eligible. Each of these descriptions is attached to a set of “targeted assets,”
which we map to 4-digit JSICs.

Broad Industry Description

2-digit Category

4-digit Category

Industrial Machinery & Equipment Leasing

Goods Rental & Leasing

Leasing management
General goods leasing
Industrial equipment
rentalOffice machinery rental
Automobile rental
Sports and hobby goods
rentalAudio and visual
recording rental
Theatrical goods rental

Machinery Repair

Machine Repair Services

Repair management
Machine repair shops,
except electricalappliances
Electrical machine andappliance repair

SoftwareInformation Services

Information Processing/Provision

Communication Electronics

Information
management
Computer programmingand software services
Data processing
Information services,
except marketing or
surveys
Miscellaneous dataprocessing
Equipment management
Communicationequipment57

Industrial Design

Technical Services

Industrial Installation

Equipment Installation Work

Natural Sciences R&D

Scientific Research Institutes

Chemical Research Instruments

Equipment wholesale trade

Image and audio
equipment
Electronic dataprocessing machines
Mechanical design
services
Installation
management
Electric work
Telecommunication andsignal work
Piping work, except
water-well drilling
Machine and equipment
installation
Miscellaneous
equipment installation
R&D management
Research institutes fornatural sciences
Measuring instruments,
analytical instruments,
testing machines,
chemical instruments
Medical instruments
Optical instruments
Miscellaneousmachinery andequipment

Notes: The table lists the 4-digit JSIC industries eligible to claim bonus depreciation under the Technopolis policy,
obtained from Ministry of International Trade and Industry (1995). We crosswalk historical JSICs to the modern
classification system. Unlike Technopolis, the Intelligent Location policy does not list specific 4-digit industry codes
which are eligible. Rather, it offers descriptions of eligible production activities which we map to 4-digit JSICs. See
Section 2 for more details on the policy, including the bonus rate schedule.

58

B.2

List of Eligible Intelligent Location Areas

The table below reports the list of Intelligent Location-eligible areas, which include 26 named“Intelligent Cities.” In total, there are 319 municipalities (according to modern Census city codes)
included within these 26 sites. Of these, 75 municipalities were previously eligible for bonus
incentives under Technopolis. We list each policy site, the regional hub it corresponds to, the numberof cities (shi ) and towns (machi or mura) included in the catchment area, and the enactment date.

Intelligent Location

Policy Date

Regional City

# Cities# Towns

Hachinohe

3/15/1989

Toyama3/15/1989

Hamamatsu
Tokushima
Ishikawa
Kagoshima
Kofu
Okayama# Unique City Codes

Hachinohe

2

8

10

Toyama6

7

13

3/15/1989

Hamamatsu

10

1

3/15/1989

Tokushima

3

13

16

2/23/1990

Kanazawa

3912

2/23/1990

Kagoshima

22

4

2/23/1990

Kofu

1

12

13

2/23/1990

Okayama

4

2

6

Central Hiroshima

3/15/1990

Kure

3

6

9

Kita-Kyushu

3/15/1990

Kita-Kyushu

7

12

19

Tottori

3/15/1990

Tottori1

10

11

Wakayama

3/15/1990

Wakayama

3

13

16

Mito-Hitachi

8/28/1990

Mito

4

5

9

Oita

8/28/1990

Oita

5

6

11

Okinawa

8/28/1990

Naha

7

15

22

Koriyama

3/29/1991

Koriyama

2

4

6Asahikawa

9/20/1991

Asahikawa

8

17

25

Gunma

9/20/1991

Maebashi

5

9

14

Yamagata

4/10/1992

Yamagata

9

4

13

Kagawa6/17/1992

Takamatsu

5

9

14

Nagasaki

6/17/1992

Sasebo3

7

10

Yamaguchi

6/17/1992

Ube

3

3

6

Gifu

11/26/1992

Gifu

6

15

21

Miyazaki

1/31/1994

Miyazaki

3

12

15

Morioka

1/31/1994

Morioka

3

8

11

Utsunomiya

1/31/1994

Utsunomiya

0

12

12

–

–

99

220

319

Total

Notes: Intelligent Location sites are listed in chronological order based on policy implementation date. The number ofcities and towns refers to the number of historical jurisdictions in those two official area categories. Unlike Technopolis,
the regional hub that lends its name to the industry cluster may not actually be treated itself (e.g. firms in the historicaljurisdiction of Hachinohe are not eligible). In the final column, the number of unique Census city codes is equal to
the sum of distinct cities and towns after accounting for municipal mergers. We impose modern municipal boundaries
using the historical city code crosswalk available through RIETI (Kondo 2019). Policy dates obtained from Ministryof International Trade and Industry (1995). Eligible sites obtained from Japan Location Center (1999).

59

C

Depreciation Accounting Methods

In this appendix we provide additional context on the depreciation accounting methods allowed
under the corporate income tax code and present a detailed example of tax benefits a typicalqualifying investment in tangible assets would receive in our setting. When a firm is incorporated, it
can decide whether to change its depreciation accounting from the default declining balance method
to straight-line accounting. If the firm does not specify an accounting method, any depreciation
claims made within the tax year must use the declining balance method. In only 7% of firm-years,
firms use a combination of declining balance and straight-line methods. This combination is dictatedby input composition and changes to the tax code which require the use of straight-line amortization
for certain very long-lived assets (e.g. an industrial storage freezer).1
Declining balance is an accelerated depreciation method which results in larger tax write-offs
early on in the lifespan of an investment, in exchange for lower tax write-offs later; in that sense,
declining balance operates similarly to bonus depreciation but is not as generous in terms of theinitial rate. Firms in certain industries can claim bonus depreciation in normal times towards
particularly large inputs such as aircraft – a feature which is present in the U.S. tax code as well.
Still, 80% of firms rely exclusively on declining balance accounting. As we will show in the example
below, the prevalence of declining balance accounting is due to the fact that for most investments
it strictly dominates linear cost accounting from a PDV perspective.
These standard accounting methods can be mathematically summarized as follows. Let θt denotethe depreciation rate in year t of the asset’s lifespan. For straight-line (linear) depreciation, thisrate is simply equal to θt = 1/x, ∀t, where x is the lifespan of the asset. For declining balance, theformula is given recursively by:
Pt = P0 −tXθt−k · Pt−k ,given θ0

(C.1)

k=1

where Pt refers to the cost basis, and P0 is the initial cost basis. For all methods, the initial costbasis is set to 90% of the actual investment cost, which corresponds to the concept of a 10% “salvage
value” in the U.S.2 For declining balance, the tax authority calibrates θ0 such that at t = x only
the salvage value remains undepreciated.3 Across all methods, when x = 1, such as with certain
kinds of goods inventories, the entire cost can be deducted in the investment year, and there is(mechanically) no difference in rates across methods because θ0 = 1.
Depreciation rates θ and lifespan x allowed under each method are dictated by the National Tax
Agency in Japan. Historically, the rates differed not just by the asset class (e.g. real estate) but
by a combination of industry of the taxable parent firm and the use of the asset (e.g. a concrete1

For instance, as of the 2017 tax year, building improvements and structures can no longer be deducted using
the declining balance approach.
2

In the U.S. tax code declining balance is defined in terms of a multiple of the straight-line depreciation rate
(e.g. “100% or 200% Modified Accelerated Cost Recovery System” [MACRS] in IRS Publication 946). In practice,
the rates in our setting are close to the rates obtained under a 200% MACRS rule in the U.S. See Zwick & Mahon(2017) for an example of the 200% declining balance method with bonuses in the U.S.
3

Importantly, these cost accounting relationships hold even if for some reason a firm does not claim depreciation
in a given year. This can happen if a firm is particularly aggressive in its claiming behavior and reaches the limit(with carryovers) in a given filing year.

60office building used as an administrative site for a manufacturing company).4 This schedule ismore detailed than tax codes in the U.S., where assets are lumped together into large categories oflifespans, ranging from 3 years for tractors and livestock to 39 years for commercial use properties.
Yet, internationally, tax codes share common principles with respect to how lifespans are set, withbuildings and industrial machines being among the longest-lived, and office fixtures being among
the shortest-lived assets.
Adding bonus depreciation to this system results in the following overall depreciation rates:θbonus + (1 − θbonus ) · θtnormal if t = 0
θt =
(C.2)
(1 − θbonus ) · θnormal
if
0
<
t
≤
x
t
where θbonus is the bonus depreciation rate (e.g. maximum of 30% for Technopolis), and θnormalrefers to the allowed rate under the normal accounting method chosen by firms. Both rates willvary over time depending on tax reforms, and across firms depending on their election of normal
depreciation method, their location decisions, and whether they operate in an eligible industry.
The corporate income tax (CIT) bill for income I, asset cost basis P , and depreciation rate θ is:τ CIT · (I − θ · P )(C.3)

Combining (C.2) and (C.3) the immediate cash flow benefit of Technopolis shows up clearly as:


bonus
bonus
τ CIT · Pi,0 × θi,c
+ (1 − θi,c
) · θ0normal(C.4)
bonus to emphasize that bonus rates depend on the location c of the investment andwhere we write θi,c
whether the capital good is real estate or non-real estate. National corporate income tax rates cantake one of two values: a standard rate for firms earning above 8 million JPY (or roughly 80,000
USD in taxable earnings), or a lower rate for small firms below this earnings threshold. During oursample period, national corporate income tax rates varied between 28% − 31% for small firms, and
40% − 43.3% for large firms.5

To further illustrate, we now return to the example referenced in Section 2.1 of the main text,
which is typical of the corporate investment responses to Technopolis we observe in the data.
Suppose a firm invests $1 million in construction of a new site in a Technopolis area, plus $1
million in computers to be installed at the new plant when it is finished in 2 years. For reference,
the average duration of construction projects in our dataset is 15 months (median of 11 months).
The firm faces a corporate income tax rate of τ CIT = 40%, and can claim the Technopolis bonus
rates of θbonus = 30% against the cost of the PCs and and θbonus = 15% against the new building
upon its completion.6 Assume the lifespan of the PCs is four years, while the lifespan of the new
office building is 65 years.

4

A major overhaul of Japan’s depreciation schedule in 2008 reduced complexity by stipulating rates that depend
only on industrial sector and asset type without any dependence on the use.
5

The effective corporate income tax rate depends on both the national rate and the accumulation of any local
enterprise tax rates set in the local jurisdictions where a firm operates. Technopolis bonuses apply only towards
national corporate income tax liability, which generates the bulk of the tax bill for our large multi-plant firms.
6

Construction unrelated to improvements of existing structures is not a depreciable expense. Instead, allowable
depreciation claims must occur after the construction is completed and the asset appears on the balance sheet.

61

Table C.1. Default and Bonus Depreciation Schedules for Short and Long-lived Items
Year

1

2

3

4

5

...

Total

PDV (r = 7%)

90
0

90
0

90
5.5

90
5.5

0
...
5.5 ...

360
360

326
73

175
0

98.5
0

55.5
14

31
13.5

0 ...
13 ...

360
360

341
124.5

242.5
0

69
0

39
72

10
11.5

0 ...
11 ...

360
360

349
158

Straight-line (linear)
Cash flow (PCs)
Cash flow (CRE)
Declining balance (default)
Cash flow (PCs)
Cash flow (CRE)
Bonus (Technopolis) + default
Cash flow (PCs)
Cash flow (CRE)

Notes: The table displays year-by-year cash flows from tax benefits of claiming depreciation for the example describedin the main text consisting of a $1 million investment in computers (PCs, lifepspan of 4 years) and a $1 million
investment in constructing a new office building (CRE, lifespan of 65 years + 2 year construction horizon). Cash flows
in thousands. In normal times, firms have the option of choosing between straight-line (linear) and declining balance(the default) accounting methods. See text for precise formulas underlying these cost amortization schedules. We
assume τ CIT = 40%, θbonus = 30% for PCs and θbonus = 15% for CRE, and for the PDV calculations a 7% annual real
discount rate to match the analogous exercise conducted in Zwick & Mahon (2017) for the U.S. To make things simple,
we assume the assets are deployed in the first month of the tax year (April), so there is no pro-rating by months withina tax year. The initial basis Pi,0 is set to 90% of acquisition cost as in the tax code. We sourced the historical decliningbalance rates from the official depreciation catalogue (MITI 1995). See National Tax Agency, Publication No. 12013 foran overview of the depreciation system in Japan: https://www.nta.go.jp/english/taxes/individual/12013.htm

Table C.1 summarizes the stream of tax benefit flows for these parameters under the mostcommon accounting methods. While all three methods result in the same amount of total deductions($720,000 = 40% × [$2 million outlay − 10% salvage value]), the PDV implications are starkly
different: $507,000 with bonuses vs. $465,500 under declining balance, and $399,000 under linear
accounting. The shifting of cash flows to the very first few years of the capital life-cycle can be seenin Figure C.1, where we plot the full sequence of cash flows for four methods over the full 67-year
investment horizon (65 years of CRE + 2-year construction period), including the three methods
in Table C.1 and a hypothetical fourth method (“DB + SL”) in which we assume the firm uses
linear depreciation for the computers, but declining balance for the buildings.7
Finally, in Figure C.2 we consider a generalization of the simple two-asset example in which we
project how the PDV of the overall tax benefit of bonuses – benchmarked to the outside option
without bonuses – varies with the key accounting parameters: the real discount rate, the costshare of the long-lived asset, and the lifespan of the long-lived asset (holding fixed the lifespanof the other asset). The return to bonus claims, or the CAPX subsidy rate, is concave in theinterest rate and asset lifespan, since the incremental gains are smaller as discounting becomes astronger force. Crucially, the return to bonuses is linear in the share of the long-lived asset, butinvariant to the dollar amount of the total initial investment, conditional on this share. This simple
7

We assume all assets are deployed (or construction begins) in April of year 1, so there is no pro-rating ofdepreciation claims across tax years. Japan does not have the half-year convention as in the U.S. tax code. Hence, if
the construction horizon is h, the first year when claims can be made against the newly made asset is t = h + 1.

62

FIGURE C.1. Tax Benefits over the Lifespan of a Typical Investment
250000

BONUS+DB
DB
DB+SL
SL

Tax Benefit Cash Flow

200000150000100000

50000

0
0

20

40

60

Years

Notes: The figure plots undiscounted tax benefit cash flows over the lifespan of the investment strategy described in
the text. We consider four accounting methods: “BONUS + DB” refers to a firm which claims Technopolis bonuses at
the maximum possible rate and uses declining balance as it outside option, “DB” refers to declining balance withoutbonuses, “DB + SL” refers to declining balance claimed against the CRE investment, but straight-line claimed against
the PCs, and “SL” refers to linear depreciation against both asset types. By law, under bonus depreciation the total
deductions over the asset’s lifespan can never exceed the total deductions claimed under the alternative method
without the bonus. This truncates the “BONUS + DB” series at $0 in the final years of the lifespan.

accounting result underlines our use of the production input share of buildings as a measure offirm treatment intensity in Section 5. At the extreme, a firm investing only in a new building but
no computers receives a 4% subsidy (Panel B). Panel C shows that if we applied the 39-year tax
lifespan of commercial buildings in the U.S. to the Japanese tax code, the construction subsidy
under Technopolis would have been between 2% to 3%.

63

FIGURE C.2. Simulated Tax Benefit PDVs as a Percentage of Investment Cost
A. Varying the real interest rate3

% of investment cost

% of investment cost

4

2

2

1

tax

tax
τ = 36.5%

τ = 36.5%

τ = 43.4%

τ = 43.4%

τ = 50.3%

τ = 50.3%

0

0
0.00

0.05

0.10

0.15

0.20

0.00

0.05

0.10

real interest rate

0.15

0.20

real interest rate

B. Varying the cost share of the long-lived asset
4.5

6% of investment cost

% of investment cost

3.5

2.5

4

2

1.5
tax

tax
τ = 36.5%

τ = 36.5%

τ = 43.4%

τ = 43.4%

τ = 50.3%

τ = 50.3%

0.5

0
0.00

0.25

0.50

0.75

1.00

0.00

share of investment in long−lived asset

0.25

0.50

0.75

1.00

share of investment in long−lived asset

C. Varying the lifespan of the long-lived asset
3

4% of investment cost

% of investment cost

3
2

1

2

1
tax

tax
τ = 36.5%

τ = 36.5%

τ = 43.4%

τ = 43.4%

τ = 50.3%

τ = 50.3%

0

0
0

20

40

60

0

lifespan of the long−lived asset

20

40

60

lifespan of the long−lived asset

Notes: Panels in the left-hand column compute the PDV of the total tax benefit flows from bonuses as a percentageof the initial investment cost (i.e. the subsidy rate) benchmarked to declining balance without bonuses; the right-handcolumn does the same but benchmarked to straight-line depreciation as the normal option. Because θ0 for the decliningbalance method is rounded to the third decimal place in the tax code, the relationship between lifespan and returns
in the bottom left-hand panel is not strictly monotonic. In each panel, we plot the returns with “confidence intervals”
which reflect the range of minimum and maximum effective tax rates (incorporating local and national income tax
rates) during our sample period.

64

D

Details on Capital Input Share Calculations

In this appendix we offer some additional details on the perpetual inventory approach andnearest-neighbor matching algorithm outlined in Section 5.3 of the main text. Although a moredetailed treatment of the perpetual inventory approach applied to the DBJ data can be found in
LaPoint (2021), we emphasize aspects of the procedure that are specific to this paper.
The basic idea behind this approach is that the input shares for each profit-maximizing firm area function of the user costs, since the marginal rate of substitution in the capital aggregate between
any two inputs will be equal to the ratio of the user costs. The key component to this approach isiterating on the investment law of motion to recover real capital inputs:
P ki,t · ki,t+1 = (1 − δi ) · P ki,t ki,t + N OM Ii,t(D.1)

where nominal investment N OM Ii,t is the change in net book value of assets of type i plus
accounting depreciation. To start the recursion, we convert assets from book to market value
using the wholesale price index for each capital good for non-real estate assets, and using the
local commercial property price indices constructed in LaPoint (2021) to inflate book values of thereal estate components of PPE (buildings + land). We then set P ki,t ki,t to this market value in thebenchmark year of 1975; we truncate the investment series by setting N OM Ii,t equal to the bookvalue of assets i as of the end of the year prior to the benchmark year.
From the FOC of the firm’s profit maximization problem, the ki,t in the investment law of motion
are functions of the user costs of capital, which are in turn a function of observable parameters:hi (1 − z ) · P k
i,t
i,t
R
ci,t = 1 − (1 − δi ) · Et βi,t,t+1·(1 − τt ) · Pt
Rβi,t,t+1= βt,t+1 ·(1 − zi,t+1 ) · P ki,t+1(1 − zi,t ) · P ki,t

(D.2)
(D.3)

Equation (D.3) refers to the asset-specific real discount factor from t to t + 1, which is obtained byadjusting the nominal overall discount factor βt,t+1 for asset-specific inflation (P ki ) and changes
to depreciation allowances for that asset type (zi ). We compute the firm’s weighted average cost ofR
capital (WACC) and set βt,t+1 = 1/(1 + W ACCt ). We take Et (βi,t,t+1) to be the average value ofR
βi,t,t+1 over the panel.
User costs in equation (D.2) reflect output prices net of the corporate income tax rate (τt ). The
effective corporate income tax rate τt reflects the combination of a national income tax rate ut anda local enterprise tax rate vt which varies by firm location. Since local enterprise taxes paid in t are
deductible from income in t + 1, the effective corporate income tax rate isτt =(ut + vt )(1 + rt )(1 + rt + vt )(D.4)

where rt is a short-term nominal rate proxied by the 1-year Bank of Japan prime lending rate.
Unfortunately, many firms in our sample do not separately report national and local taxes paid.
This leads to many missing values for the user cost. The other issue that cuts down the sample offirms for which we can directly compute the input shares in production ωi described in Section 5.3
is that we do not have an adequate empirical proxy for output price Pt for certain types of firms in
the real estate, construction, and transportation, and services sectors. In the end, we can directly65back out ωi for about one-third of our sample of DBJ firms.
To impute the ωi for the firms which lack all the necessary variables to identify the user costs in(D.2), we use a simple nearest-neighbor matching approach. We create a dummy Tj equal to one if
firm j has a directly observed ωi , and then estimate the following logit model with the dummy as
the probabilistic outcome:
exp h(Xj
P Tj = 1|Xj =(D.5)
1 + exp h(Xj )where we include in the function h(Xj ) the following variables: dummies for three broad industrial
sectors, total assets, and a quadratic in age. We select this parsimonious set of variables to predictthe probability of having non-missing user costs because the missing values arise for firms inparticular sub-industries, and for firms which may pay more or less taxes on each of the tax
bases depending on their age and balance sheet size. We then take the fitted probability value from
(D.5) as the propensity score, and compute for each firm j with missing ωi the squared difference
between its propensity score and the propensity score of all firms with non-missing ωi . The firm −jthat has the smallest squared difference in propensity scores then becomes the donor. We donate
all of the ωi from firm −j to firm j.
Table D.1 displays the log odds ratio from estimating our preferred logit model (column 2)
and other variations including more or fewer covariates to perform the nearest-neighbor match.
Manufacturing is a persistently positive and significant predictor of a firm reporting all variables
needed to back out the capital input shares from iterating on the investment law of motion in(D.1), and there is no relationship between balance sheet size or age and reporting completeness.
Although this requires us to drop many firms, we also check whether more sophisticated firms –proxied by their pre-reform use of declining balance depreciation accounting – more productive
firms (Q ratio), or firms with more cash flow (EBITDA) keep more detailed records. Of thesevariables, only EBITDA has any significant correlation on our ability to compute capital input
shares, implying that firms with higher initial earnings are more fastidious in their bookkeeping.
Table D.2 tabulates the average and standard deviation for each of the six capital input
shares for firms sorted into one of eight industrial sectors, including: light manufacturing, heavy
manufacturing, real estate, construction, transportation, electronics, non-transportation services,
and agriculture. There are intuitive differences in the capital structure across sectors, which provides
a sanity check on our nearest-neighbor matching and perpetual inventory approaches to recovering
the input shares. For example, heavy manufacturing firms make the most use of machinery in theirproduction, while electronics firms have the highest input share for tools and precision instruments.
Unsurprisingly, the transportation sector has the highest input share for vehicles.
In inspecting the differences in physical capital structure for firms in distinct sectors, weunderscore that these capital input shares are based on asset ownership, rather than renting. While areal estate and construction firm may have a lot of properties listed on its portfolio, many of theseproperties are partially leased from third parties. In additional most of the profits from leasing
companies come from rental income and management of properties. In contrast, manufacturingfirms are more likely to fully own their facilities, and so the building share is therefore highest forthat subset of firms.
Figure D.1 plots the distribution of input shares for each capital type, after applying thenearest-neighbor matching. Dashed red lines indicate the average input share reported in Table

66

D.1. Buildings account for an outsize share of production inputs for the majority of firms in our
sample, with an average share of 0.47. At the same time, for all other capital types there is asizeable mass of firms which have an input share of approximately zero; 9% of DBJ firms do notuse machines and 8% of firms do not use vehicles in their operations. The land share of production
is low compared to buildings. This reflects, in part, that the listed firms in our sample are more
likely to be located in very urban areas where land is scarce and owned office space takes the formof several floors within a larger high-rise.

67

Table D.1. Nearest-neighbor Matching Logit Model

AssetsAge
Age2

Mfg dummy

(1)

(2)

(3)

(4)

−0.022−0.022−0.011−0.012(0.033)(0.033)(0.033)(0.035)−0.001−0.001−0.0040.008(0.019)(0.019)(0.021)(0.022)

0.000

0.000

0.000

−0.000(0.000)(0.000)(0.000)(0.000)0.836∗∗∗

0.825∗∗∗

0.826∗∗∗

0.709∗∗∗(0.170)(0.190)(0.200)(0.207)

0.047

0.086

0.418

(0.342)(0.360)(0.370)−0.224−0.207−0.991(0.490)(0.544)(0.642)

0.461

0.346

(0.329)(0.344)Retail dummyServices dummy

DB method dummy−0.163Q ratio(0.114)7.715∗∗∗EBITDA(1.504)−2.519∗∗∗−2.505∗∗∗−2.871∗∗∗−3.774∗∗∗(0.526)(0.536)(0.664)(0.721)

N

1,507

1,507

1,360

1,334

Pseudo-R2

0.025

0.025

0.027

0.079

Constant

Notes: The table shows the estimated log odds ratios for firm-level characteristics obtained from estimating versionsof the logit model in equation (D.5) with the outcome equal to 1 if the firm has all non-missing capital input
shares from imposing the perpetual inventory equations. Assets measured as average pre-Technopolis (pre-1984) total
assets in millions of yen. Age measured from the Tokyo Stock Exchange listing date. We group firms into coarse
manufacturing, retail, and services categories based on their one-digit JSIC. DB method dummy is equal to unity if
the firm uses declining balance depreciation accounting methods in the pre-Technopolis period. EBITDA is definedusing standard accounting principles. The Q ratio is the ratio of the market value of the firm (total assets + market
equity − common equity − deferred tax payments relative to book assets). Both EBITDA and the Q ratio are deflated
by total assets in the first year before the sample start date. ***p < 0.01, **p < 0.05, *p < 0.1

68

Table D.2. Capital Input Shares by Type and Industrial Sector
N
Light manufacturing
Heavy manufacturing
Real estate
Construction
Transportation
Electronics
Non-transportation services
Agriculture

Overall

237
525
30
121
88
259
82
13

1,507

ωbuildωmachineωlandωstructureωtoolsωvehicle

0.468

0.222

0.243

0.042

0.036

0.037

(0.131)(0.153)(0.163)(0.027)(0.035)(0.031)

0.472

0.240

0.224

0.041

0.038

0.035

(0.133)(0.146)(0.161)(0.027)(0.038)(0.031)

0.429

0.214

0.286

0.055

0.024

0.036

(0.173)(0.183)(0.193)(0.035)(0.026)(0.032)

0.448

0.224

0.259

0.050

0.022

0.041

(0.153)(0.174)(0.181)(0.030)(0.024)(0.034)

0.512

0.195

0.210

0.046

0.027

0.049

(0.160)(0.160)(0.160)(0.031)(0.024)(0.035)

0.467

0.229

0.239

0.033

0.055

0.030

(0.111)(0.120)(0.147)(0.019)(0.048)(0.026)

0.470

0.196

0.266

0.051

0.024

0.042

(0.180)(0.167)(0.199)(0.036)(0.024)(0.036)

0.532

0.177

0.217

0.046

0.029

0.044

(0.129)(0.136)(0.120)(0.013)(0.024)(0.036)

0.469

0.222

0.240

0.042

0.036

0.037

(0.144)(0.150)(0.168)(0.029)(0.037)(0.032)

Notes: The table displays the average input shares (ωi ), with standard errors in parentheses, for the six types ofcapital reported by firms in the DBJ database: buildings, machines, land, structures, precision tools, and vehicles.
We sort firms into eight broad industrial sectors based on their 2-digit industry code. Light manufacturing includes
handicrafts, food, textile, lumber/wood, paper/pulp, and printing firms. Heavy manufacturing includes those in themetal refining, smelting, and chemical production. Real estate includes leasing and rental companies. Construction
includes construction, engineering, and dredging companies. Transportation includes automobile manufacturers,
trucking, and railway companies. Electronics includes household appliances, software, and precision instruments
producers. Non-transportation services includes wholesale/retailers and services firms outside shipping and transport.
Agriculture includes fisheries, livestock, and farming.

69

FIGURE D.1. Distribution of Physical Capital Input Shares
B. Machines

.1

.1

.08

.08

.06

.06

Fraction

Fraction

A. Buildings

.04

.02

.04

.02

0

0
0

.2

.4

.6

.8

0

Building input share of production

.2

.4

.6

Machinery input share of production

C. LandD. Structures
.08

.06

.06

Fraction

Fraction

.04

.04

.02
.02

0

0
0

.2

.4

.6

0

.8

.05

.1

.15

Structure input share of production

Land input share of production

E. Precision Tools & Instruments

F. Vehicles.15.1

Fraction

Fraction

.1

.05

.05

0

0
0

.05

.1

.15

.2

0

Precision tool input share of production

.05

.1

.15

Vehicle input share of production

Notes: Each panel plots the distribution of capital input shares obtained from assuming a Cobb-Douglas physicalcapital aggregator in firm production and adapting the perpetual inventory method of Hayashi & Inoue (1991) to the
DBJ data. Dashed red vertical lines indicate the average share. Our classification of long-lived asset firms is based onshare of buildings used in production. Structures here refers to small buildings detached from the main plant site or
non-enclosed spaces (such as a shed or outdoor well with roof). In cases where a firm is missing variables needed toconstruct the user costs underlying this method, we assign to that firm the input share of its nearest neighbor using
a logit propensity score matching procedure based on firm size, age, and industrial sector. See text for details.

70

E

Main Results Using Other Staggered DD Estimators
FIGURE E.1. Dynamic Effects of Technopolis by Staggered DD Estimator
B. Cash flow

.2

.03

.1

.02

Estimated ATTs

Estimated ATTs

A. Bonus depreciation probability

0

.01

0

−.1

−.01

−.2
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

−1

Years since Technopolis
Baseline (OLS)de Chaisemartin & D’Haultfoeuille (2020)

Sun & Abraham (2020)

1

2

3

4

Baseline (OLS)de Chaisemartin & D’Haultfoeuille (2020)

C. Employment

5

6

7

8

9

10

Sun & Abraham (2020)

D. Construction in progress

.1

.015

.01

Estimated ATTs

.05

Estimated ATTs

0

Years since Technopolis

0

.005

0

−.05
−.005

−.1

−.01
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

−1

Years since Technopolis
Baseline (OLS)de Chaisemartin & D’Haultfoeuille (2020)

0

1

2

3

4

5

6

7

8

9

10

Years since Technopolis
Sun & Abraham (2020)

Baseline (OLS)de Chaisemartin & D’Haultfoeuille (2020)

E. Non-real estate purchases

Sun & Abraham (2020)

F. Long-term debt issuance.1.2

Estimated ATTs

Estimated ATTs

.3

.1

.05

0

0

−.05

−.1
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

Years since Technopolis
Baseline (OLS)de Chaisemartin & D’Haultfoeuille (2020)

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

Years since Technopolis
Sun & Abraham (2020)

Baseline (OLS)de Chaisemartin & D’Haultfoeuille (2020)

Sun & Abraham (2020)

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD model inequation (4.2) using different estimators, including OLS (baseline), de Chaisemartin & D’Haultfœuille (2020), and
Sun & Abraham (2021). Each regression includes HQ Census region × year fixed effects. With the exception of thebonus depreciation dummy and employment, each variable is deflated by the firm’s book assets in 1975 before ourestimation sample start date. Firm employment is scaled by its value in 1975. We bin the dummies at the end of theeffect windows for t = −6 and t = 10. All dynamic effects are relative to one year before a firm becomes eligible forTechnopolis. The bars show 95% confidence intervals obtained from standard errors clustered at the firm level. For
the de Chaisemartin & D’Haultfœuille estimator we obtain standard errors from 1,000 bootstrap iterations. See text
for details on the definition of each outcome.

71

FIGURE E.2. Robustness to Including Linear Firm-Time Trends
B. Cash flow

A. Bonus depreciation probability

.2

.04

Estimated ATTs

.06

Estimated ATTs

.4

0

.02

0

−.2

−.02

−.4
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

−1

Years since Technopolis
OLS w/linear trends
OLS w/o linear trends

0

1

2

3

4

5

6

7

8

9

10

9

10

9

10

Years since Technopolis

Sun & Abraham w/linear trends

OLS w/linear trends
OLS w/o linear trends

C. Employment

Sun & Abraham w/linear trends

D. Construction in progress.03.1

Estimated ATTs

Estimated ATTs

.02
.05

0

.01

0

−.05

−.01
−.1
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

−1

Years since Technopolis
OLS w/linear trends
OLS w/o linear trends

0

1

2

3

4

5

6

7

8

Years since Technopolis

Sun & Abraham w/linear trends

OLS w/linear trends
OLS w/o linear trends

E. Non-real estate purchases

Sun & Abraham w/linear trends

F. Long-term debt issuance
.15

.8

.1

Estimated ATTs

Estimated ATTs

.6

.4

.2

.05

0

0

−.05

−.2

−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

Years since Technopolis
OLS w/linear trends
OLS w/o linear trends

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

Years since Technopolis

Sun & Abraham w/linear trends

OLS w/linear trends
OLS w/o linear trends

Sun & Abraham w/linear trends

Notes: Each panel shows the dynamic response of an outcome of interest estimated via a version of the staggered
DD model in equation (4.2) using either OLS or Sun & Abraham (2021). Each regression includes HQ Census region
× year fixed effects. The blue and red lines plot estimates obtained from including linear firm time trends, while thegreen line shows our baseline estimates without including linear trends. With the exception of the bonus depreciation
dummy and employment, each variable is deflated by the firm’s book assets in 1975 before our estimation sample
start date. Firm employment is scaled by its value in 1975. We bin the dummies at the end of the effect windows fort = −6 and t = 10. All dynamic effects are relative to one year before a firm becomes eligible for Technopolis. The
bars show 95% confidence intervals obtained from standard errors clustered at the firm level. See text for details on
the definition of each outcome. Note we do not provide estimates via de Chaisemartin & D’Haultfœuille (2020) or
Borusyak, Jaravel, & Spiess (2021), because these estimators use not-yet treated firms as a control group for which
the trends are not identified.

72

F

Dynamic & Heterogeneous Effects
FIGURE F.1. Evolution of Local Spillovers to Untreated Firms
B. Operating cash flow

.2

.1

.1

.05

Estimated ATTs

Estimated ATTs

A. Bonus depreciation probability

0

−.1

0

−.05

−.2

−.1
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

Years since Technopolis designation
Eligible JSIC firms

−1

0

1

2

3

4

5

6

7

8

9

10

7

8

9

10

8

9

10

Years since Technopolis designation

Ineligible JSIC firms

Eligible JSIC firms

C. Employment

Ineligible JSIC firms

D. Construction in progress

.1

.015

.01

Estimated ATTs

Estimated ATTs

.05

0

.005

0

−.05
−.005

−.1

−.01
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

Years since Technopolis designation
Eligible JSIC firms

−1

0

1

2

3

4

5

6

Years since Technopolis designation

Ineligible JSIC firms

Eligible JSIC firms

E. Non-real estate purchases

Ineligible JSIC firms

F. Long-term debt issuance.1.3

Estimated ATTs

Estimated ATTs

.2

.1

.05

0

0

−.05

−.1
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

Years since Technopolis designation
Eligible JSIC firms

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

Years since Technopolis designation

Ineligible JSIC firms

Eligible JSIC firms

Ineligible JSIC firms

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD model inequation (5.1) using OLS. We report separate dynamic effects depending on whether the firm is eligible to claim bonus
depreciation under Technopolis (T reatment), or if the firm is located in a Technopolis area but does not operate within
an eligible JSIC (T reatedCity). We define firm-level eligibility according to the three criteria outlined in Section 4.
Each regression includes HQ Census region × year fixed effects. With the exception of the bonus depreciation dummy
and employment, each variable is deflated by the firm’s book assets in 1975 before our estimation sample start date.
Firm employment is scaled by its value in 1975. We bin the dummies at the end of the effect windows for t = −6 andt = 10. All dynamic effects are relative to one year before a firm becomes . The bars show 95% confidence intervals
obtained from standard errors clustered at the firm level.

73

FIGURE F.2. Dynamic Effects by Reliance on Long-lived Assets
B. Operating cash flow

A. Bonus depreciation probability
.15

.2

.1

Estimated ATTs

Estimated ATTs

0

−.2

.05

0

−.4

−.05

−.6
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

−1

Years since Technopolis
Long−lived asset firm (LL)

0

1

2

3

4

5

6

7

8

9

10

9

10

9

10

Years since Technopolis

Short−lived asset firm (SL)Long−lived asset firm (LL)

C. Employment

Short−lived asset firm (SL)D. Construction in progress
.06

.2
.04

Estimated ATTs

Estimated ATTs

.1

0

−.1

.02

0

−.02

−.2

−.04

−.3
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

−1

Years since Technopolis
Long−lived asset firm (LL)

0

1

2

3

4

5

6

7

8

Years since Technopolis

Short−lived asset firm (SL)Long−lived asset firm (LL)

E.-real estate purchases

Short−lived asset firm (SL)F. Long-term debt issuance
.4

.6

.2

Estimated ATTs

Estimated ATTs

.4

.2

0

0

−.2
−.2
−.4
−.4
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

Years since Technopolis
Long−lived asset firm (LL)

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

Years since Technopolis

Short−lived asset firm (SL)Long−lived asset firm (LL)

Short−lived asset firm (SL)

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD modelin equation (5.5) using OLS. We report separate dynamic effects depending on whether the firm primarily relies onlong-lived (LL) or short-lived (SL) assets. LL and SL firms are defined as in Section 5.3.1 and Appendix D. Eachregression includes HQ Census region × year fixed effects. With the exception of the bonus depreciation dummy andemployment, each variable is deflated by the firm’s book assets in 1975 before our estimation sample start date. Firm
employment is scaled by its value in 1975. We bin the dummies at the end of the effect windows for t = −6 andt = 10. All dynamic effects are relative to one year before a firm becomes eligible for Technopolis. The bars show
95% confidence intervals obtained from standard errors clustered at the firm level.

74

FIGURE F.3. Dynamic Effects by Financial Constrainedness (Size-Age)B. Cash flow

A. Bonus depreciation probability.04.1

Estimated ATTs

Estimated ATTs

.2

0

.02

0

−.1

−.2

−.02

−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

−1

Years since Technopolis
Financially constrained (FC)

0

1

2

3

4

5

6

7

8

9

10

Years since Technopolis

Non−financially constrained (NFC)

Financially constrained (FC)

C. Employment

Non−financially constrained (NFC)

D. Construction in progress.02.1

Estimated ATTs

Estimated ATTs

.05

0

−.05

.01

0

−.1

−.15

−.01
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

−5

−4

−3

−2

−1

Years since Technopolis
Financially constrained (FC)

0

1

2

3

4

5

6

7

8

9

10

Years since Technopolis

Non−financially constrained (NFC)

Financially constrained (FC)E. Non-real estate purchases

Non−financially constrained (NFC)

F. Long-term debt issuance.15.4

Estimated ATTs

Estimated ATTs

.1

.2

0

.05

0

−.05

−.2

−.1
−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

−6

Years since Technopolis
Financially constrained (FC)

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

9

10

Years since Technopolis

Non−financially constrained (NFC)

Financially constrained (FC)Non−financially constrained (NFC)

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD model inequation (5.6) using OLS. We report separate dynamic effects depending on whether the firm is financially constrained(FC) or non-financially constrained (NFC) according to the size-age index of Hadlock & Pierce (2010) describedin Section 5.3.2. Each regression includes HQ Census region × year fixed effects. With the exception of the bonus
depreciation dummy and employment, each variable is deflated by the firm’s book assets in 1975 before our estimation
sample start date. Firm employment is scaled by its value in 1975. We bin the dummies at the end of the effect windows
for t = −6 and t = 10. All dynamic effects are relative to one year before a firm becomes eligible for Technopolis.
The bars show 95% confidence intervals obtained from standard errors clustered at the firm level.

75

FIGURE F.4. Dynamic Effects of Multiple Policy Treatments
B. Operating cash flow

A. Bonus depreciation probability.2.1

Estimated ATTs

Estimated ATTs

.1

0

−.1

.05

0

−.05

−.2

−.1
−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

−4

−3

−2

−1

0

Years since policy eligibility
Technopolis

1

2

3

4

5

6

7

8

6

7

8

6

7

8

Years since policy eligibility

Intelligent Location

Technopolis

C. Employment

Intelligent Location

D. Construction in progress

.1

.015

Estimated ATTs

Estimated ATTs

.01
0

−.1

.005

0

−.005
−.2
−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

−4

−3

−2

−1

Years since policy eligibility
Technopolis

0

1

2

3

4

5

Years since policy eligibility

Intelligent Location

Technopolis

E. Non-real estate purchases

Intelligent Location

F. Long-term debt issuance

.4

.1

.05

Estimated ATTs

Estimated ATTs

.2

0

0

−.05

−.2
−.1

−.4

−.15
−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

−4

Years since policy eligibility
Technopolis

−3

−2

−1

0

1

2

3

4

5

Years since policy eligibility

Intelligent Location

Technopolis

Intelligent Location

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD model withmultiple policy treatments in equation (5.8) using OLS. We report separate dynamic effects Each regression includes
HQ Census region × year fixed effects. With the exception of the bonus depreciation dummy and employment, eachvariable is deflated by the firm’s book assets in 1975 before our estimation sample start date. Firm employment isscaled by its value in 1975. We bin the dummies at the end of the effect windows for t = −4 and t = 8. All dynamic
effects are relative to one year before a firm becomes eligible for either Technopolis (red) or Intelligent Location(blue). The bars show 95% confidence intervals obtained from standard errors clustered at the firm level.

76

G

Additional Results & Robustness Checks

G.1

Results for Other Firm-level Outcomes

Here we present event study results for other outcomes beyond those we focused on in the main
text, including overall CAPX, net building purchases, bond issuance, long-term debt issuance,
leverage, and equity issuance. We define overall CAPX as the YOY change in the net book valueof PPE plus accounting depreciation. Bond issuance is the YOY change in the book value of totalbonds outstanding, including straight, convertible, and subscription bonds. For now, we follow theprocedure adopted in the main analysis of deflating monetary values by total firm assets in the year
before our sample starts. The exception is book leverage, which we define in a standard fashion as
total book debt scaled by total (contemporaneous) assets. We also considered measures of market
leverage, such as total book debt scaled by market assets (total book assets + shares outstanding− common equity), but these ratios deliver nearly identical results. Equity issuance is the YOYchange in the value of outstanding shares, where the share price is taken as of year-end close.
We refer to building investment as “net” building purchases to emphasize that this CAPX measureincludes two competing effects: the increase in the value of buildings on the firm’s balance sheet
when newly constructed buildings become capitalized, and the decrease in the value of buildings
from the purchase of existing buildings. As Figure G.1 indicates, these two effects net out to a zero
effect on overall building investment. Bonus depreciation incentives subsidize the construction of
new buildings given that older buildings have already substantially depreciated.
Figure G.1 shows that overall CAPX exhibits muted bumps, as acquisitions of non-real estate
assets are partially offset by substitution away from land (see Figure 4). While we do not see any
clear uptick in issuance of bank loans (Panel D), overall firm leverage spikes around policy year
3 (Panel E), and this is driven by the issuance of bonds (Panel C). Bond issuance following theTechnopolis tax break is contemporaneously offset by a reduction in equity issuance (Panel F).
These responses are large in magnitude and translate to a 0.25 s.d. decline in equity issuance inyear 3 of the policy regime, and a 0.42 s.d. spike in leverage in the same year.

G.2

Comparing Cash Flow Measures

Here we document how various measures of cash flow evolve in response to firms’ becoming eligible toclaim bonus depreciation under the Technopolis policy. In addition to our main first-stage outcomeof bonus depreciation claims, we consider three other popular cash flow measures in the accounting
and corporate finance literatures: EBITDA, operating cash flow (OCF), and cash flow from net
income. The precise accounting definitions of these measures are:(i) Bonus claims: In the DBJ database bonus claims are included as a line item called, “reserve
for special depreciation,” separate from accounting depreciation, where the latter includes
bonus and non-bonus depreciation costs. Bonus are not available as a separate line item in
COMPUSTAT, and our ability to directly observe bonus claims allows us to identify first-stage
effects of the Technopolis reform.
(ii) EBITDA: Earnings before interest, taxes, depreciation, or amortization, or operating income+ depreciation & amortization. In COMPUSTAT mnemonics this can be computed either
simply as EBITDA or OIBD + DP.

77

FIGURE G.1. Event Study Results for Other Outcomes
A. Overall CAPX

B. Net building purchases

C. Bond issuance

D. Long-term loan issuance

E. Book leverage

F. Equity issuance

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD model inequation (4.2) using the imputation estimator method of Borusyak, Jaravel, & Spiess (2021). Each regression includes
HQ Census region × year fixed effects. With the exception of book leverage, each variable is deflated by the firm’sbook assets in 1975 before our estimation sample start date. Book leverage is the ratio of total outstanding debt tototal assets. Equity issuance is the YOY change in the value of outstanding shares, where the share price is as of
year-end close. We winsorize bond issuance, long-term loan issuance, and book leverage at the 2nd/98th percentiles.
The point estimates allow for anticipatory effects one year in advance of the reform, so the coefficient at 0 years
represents the one-year anticipatory effect. Shaded regions contain 95% confidence intervals obtained from standard
errors clustered at the firm level.

78

(iii) OCF (following Lian & Ma 2021): EBITDA + non-operating income + special items + saleof PPE − income taxes + deferred taxes or refunds + ∆ taxes payable + ∆ accounts payable− ∆ accounts receivable − ∆ inventory + ∆ unearned revenue − ∆ prepaid expenses. In
COMPUSTAT mnemonics this can computed as either: OANCF+XINT, or EBITDA + NOPI+ SPI + SPPE − TAX − DTAX − ∆ATAX + ∆AP − ∆INV + ∆UR − ∆PX + OCFO.
(iv) Cash flow from net income (à la Zwick & Mahon 2017): A common definition of cash flow inthe bonus depreciation literature is net income before depreciation, after taxes paid. Or, in
COMPUSTAT mnemonics: IBC + DPC.
Figure G.2 shows how all four cash flow measures increase after firms are allowed to claim bonuses
through Technopolis, with no discernible pre-trends. As in the main text, we use the BJS estimatorwhich is robust to treatment effect heterogeneity and include a one-year anticipatory lead. There
are some subtle differences between the responses of each measure. Bonus claims (first stage) peakaround policy year 5 (a 0.18 s.d. effect) before flat-lining, which corresponds to the final year
in which firms can lock in the highest bonus depreciation rate for their physical expenditures.
EBITDA does not include receipts from bonus write-offs, but rises steadily, as firms increasing
their capital inputs led to increased production capacity. OCF does include receipts from bonus
write-offs, and consequently we see a more lumpy response corresponding to the kink points in thebonus depreciation rate schedule in Table 1 and a typical time to build horizon for construction
projects of two years. The net income measure of cash flow in panel (iv) does include income frombonuses, since it is net of the tax bill.

G.3

Main Results Using Transformed Outcomes

Previously we either scaled outcome variables by total book assets in the year before the start ofour sample, or took logs. Specifications in which we used log outcomes (e.g. Panel B of Table 5)
can thus be interpreted as intensive margin investment responses. Here we discuss our results when
we use the log(1 + x) or inverse hyperbolic sine (IHS) transform IHS(x) to accommodate potential
zero values for a variable x. Relative to results presented using log outcomes, these transformed
variables allow for extensive margin investment responses to the bonus tax write-offs.
Our empirical design is a difference-in-differences, meaning that identification of averagetreatment effects of bonus depreciation on firm outcomes relies on the validity of the parallel
trends assumption. Hence, we must also check for pre-trends in transformed variables. To thisend, using our preferred estimator [BJS ] which is robust to treatment effect heterogeneity, weplot in Figure G.3 the event studies for bonus claims, construction spending, and non-real estatepurchases, transformed using either log(1 + x) (left panels) or the IHS transform (right panels).
Our results are qualitatively similar to those in Figure 4 for bonus claiming and non-real estate
investment, with the former increasing by approximately 20% and the latter increasing by 35% after
Technopolis takes effect. In contrast, construction spending evolves differently when we consider theextensive × intensive margin response. There is a 0.10 s.d. spike within the first policy year, before
construction spending quickly drops back down to trend. The stark divergence relative to the log
and asset-deflated specifications suggests the overall response of construction outlays is dominatedby firms who were influenced by the bonus write-offs to break ground on new projects.

79

FIGURE G.2. Comparing Dynamic Responses of Cash Flow Measures(i) Bonus claims(ii) EBITDA(iii) Operating cash flow (OCF)(iv) Cash flow (ZM definition)

Notes: Each panel shows the dynamic response of a cash flow measure estimated via the staggered DD model inequation (4.2) using the imputation estimator method of Borusyak, Jaravel, & Spiess (2021). Each regression includes
HQ Census region × year fixed effects. Each cash flow measure is deflated by the firm’s book assets in 1975 before ourestimation sample start date. The point estimates allow for anticipatory effects one year in advance of the reform, so
the coefficient at 0 years represents the one-year anticipatory effect. Shaded regions contain 95% confidence intervals
obtained from standard errors clustered at the firm level. Operating cash flow (OCF) in panel (iii) includes income
from bonus claims, while EBITDA in panel (ii) does not. The panel (iv) cash flow measure follows Zwick & Mahon(2017) in defining cash flow as net income before depreciation, after taxes paid. See Appendix G.2 text for the precise
accounting definition of each cash flow measure.

80

FIGURE G.3. Main Event Study Results Using Transformed Outcomes
A. Bonus claims

B. Construction

C. Non-real estate asset purchases

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD model inequation (4.2) using the imputation estimator method of Borusyak, Jaravel, & Spiess (2021). The left-hand panels plot
estimated treatment effects for variables x transformed using log(1 + x), while the right-hand panels plot treatment
effects under the inverse hyperbolic sine function. Each regression includes HQ Census region × year fixed effects.
Shaded regions contain 95% confidence intervals obtained from standard errors clustered at the firm level. See text
for details on the definition of each outcome.

81

G.4

Robustness to Applying Historical Municipal Boundaries

We impose modern municipal boundaries throughout our main analysis to remove the effect ofmunicipal mergers in Japan, which have resulted in a reduction in the number of local jurisdictions
from 3,278 in 1980 to 1,741 as of 2015. Using modern boundaries will have the effect of increasing
the size of the Technopolis catchment areas, since small towns were absorbed by an untreatedregional hub city during the wave of municipal mergers beginning in the 1990s. Historical municipal
boundaries offer a stricter definition of treatment; 43 out of 141 contemporaneously treated city
codes between 1980 and 2015 become integrated into a generally larger city with a shared border.
Although our results are virtually unchanged for bonus claiming, construction spending, andnon-real estate CAPX, Table G.1 and Figure G.4 show that imposing historical boundaries todefine firms’ treatment status attenuates the measured effect on employment. The point estimates
only approach statistical significance at the 10% level for some specifications. This finding is
consistent with the “leakage” argument from the plant-level results in Section 5.5. Why? By
imposing modern geography, we include some larger city codes which are not part of the official
Technopolis boundaries, but are adjacent to treated areas where firms could claim bonuses if they
deployed assets to that area. The 2 p.p. reduction in the employment response relative to thosein Table 5 and Figure 4 suggests part of the employment response originates from firms claiming
bonuses through qualifying investment in an allowed Technopolis area, but staffing treated sites byhiring from a thicker, neighboring labor market.

G.5

Measuring Corporate Distance to Policy Areas

Consider a generalized version of the triple differences specification in (5.7), where the Distancevariable is a function f (·) mapping individual pairwise distances between the set of plant locations
J = {j1 , j2 , . . . , jn } to a corporate-level measure of physical distance to the set of Technopolis
policy areas T = {1, 2, . . . , n}.
yj,k,t = γj + δt + f (dist(J , T )) × P ostt + T reatedk × P ostt

(G.1)

+f (dist(J , T )) × T reatedk × P ostt + η 0 · Xj,k,t + εj,k,twhere dist(·, ·) is a distance metric which takes as inputs a location in the firm-specific set of
locations J and the set of treated policy locations T common to all firms. We consider four mainweighting schemes implicit in f (·):
nof (dist(J , T )) = min min dist(p, τ ) (minimum distance)
(G.2)
p∈J

f (dist(J , T )) =

τ ∈T

o
1 Xn
min dist(p, τ ) (average distance)
τ ∈T
|J |

(G.3)

p∈J

f (dist(J , T )) =

X
p∈J

j
ωp,1980·n

min dist(j, τ )

o

τ ∈T

(share-weighted average distance)(G.4)

In each case, for each plant location p within the firm’s network, we compute the minimum distance
between p and all possible Technopolis city codes in the set T . We then require a way to aggregate
across all plants p, which is how the distance measures differ. Equation (G.2) computes a minimum
distance between the firm and all policy regions in that it represents the shortest possible distance82

Table G.1. Bonus Claiming, Investment, and Employment Responses (Old Geography)
A. First stage: extensive margin bonus depreciation claims
(1)

(2)

(3)

(4)

(5)

(6)

0.091∗∗∗

0.072∗∗

0.086∗∗∗

0.093∗∗∗

0.079∗∗

0.080∗∗(0.028)(0.029)(0.027)(0.033)(0.035)(0.032)

OLS

OLS

OLS

BJS

BJS

BJS

N

38,37434,57838,36038,37434,57838,360# Firms

1,508

1,408

1,507

1,508

1,408

1,507

Adj. R2

0.535

0.547

0.551

0.535

0.547

0.551

T reatment

Estimator
Firm FEs
Financial controls
Controls × year FEs

B. Investment and employment responses
Construction

Non-RE purchases

Employment

(1)

(2)

(3)

(4)

(5)

(6)

0.193∗∗

0.137∗

0.225∗∗∗

0.157∗∗∗

0.118∗∗

0.161∗∗∗

0.052

0.019

0.050

(0.080)(0.072)(0.081)(0.055)(0.048)(0.056)(0.035)(0.031)(0.036)

OLS

BJS

BJS

OLS

BJS

BJS

OLS

BJS

BJS

N

26,99624,441

27,02736,39632,82936,38338,34034,57838,326# Firms

1,416

1,318

1,415

1,499

1,399

1,498

1,508

1,408

1,507

Adj. R2

0.702

0.723

0.702

0.948

0.957

0.949

0.954

0.964

0.955

T reatment

Estimator

(7)

(8)

(9)

Firm FEs
Financial controls
Controls × year FEs

Notes: The table shows results from estimating our staggered DD model in equation (4.1) at the firm level forour main outcomes of interest, pooling all years (1975–2000). The table is analogous to Table 5, except we impose
historical municipal boundaries in assigning treatment status. The outcome in Panel A is a dummy equal to one if
the firm receives net income from bonus depreciation in a given year. In Panel B, construction is the log book valueof construction in progress, non-RE purchases is the log gross book value of new PPE excluding buildings, land, andstructures, and employment is the log number of employees. Controls include static factors such as the size quintile(by total assets), quintile of age measured from the Tokyo Stock Exchange listing date, and Census region of the HQ,
all interacted with a full set of year dummies. Specifications with financial controls include EBITDA, OCF, and the Q
ratio as time-varying controls. EBITDA and OCF are defined using standard accounting principles. The Q ratio is theratio of the market value of the firm (total assets + market equity − common equity − deferred tax payments relative
to book assets). Standard errors clustered at the firm level are in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1

83

FIGURE G.4. Dynamic Firm Responses to Technopolis Eligibility (Old Geography)
A. Bonus claims

B. Operating cash flow

C. Employment

D. Construction in progress

E. Non-real estate purchases

F. Land acquisition

Notes: Each panel shows the dynamic response of an outcome of interest estimated via the staggered DD model inequation (4.2) using the imputation estimator method of Borusyak, Jaravel, & Spiess (2021). This figure is analogous
to Figure 4, except we impose historical municipal boundaries in assigning treatment status. Each regression includes
HQ Census region × year fixed effects. With the exception of employment, each variable is deflated by the firm’s bookassets in 1975 before our estimation sample start date. Firm employment is scaled by its value in 1975. The point
estimates allow for anticipatory effects one year in advance of the reform, so the coefficient at 0 years represents the
one-year anticipatory effect. Shaded regions contain 95% confidence intervals obtained from standard errors clusteredat the firm level. See text for details on the definition of each outcome.

84

Table G.2. Policy Take-up Response to Distance Measures

Distance × T reated × P ost

Min. distance

Avg. distanceRE-wgt. distanceEmp-wgt. distance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

−0.045−0.056∗−0.099∗∗−0.117∗∗∗−0.084∗∗−0.075∗∗−0.100∗∗−0.104∗∗∗(0.035)(0.034)(0.040)(0.040)(0.033)(0.034)(0.037)(0.036)
0.069∗∗∗−0.062∗∗−0.007

0.000

0.022

0.010

0.022

0.045∗∗(0.020)(0.020)(0.019)(0.021)(0.013)(0.016)(0.020)(0.021)0.118∗∗∗

0.132∗∗∗

0.176∗∗∗

0.209∗∗∗

0.162∗∗∗

0.169∗∗∗

0.167∗∗∗

0.184∗∗∗(0.027)(0.027)(0.042)(0.042)(0.035)(0.035)(0.036)(0.035)

N

34,52034,52034,773

34,773

34,69534,69534,32634,326# Firms

1,355

1,355

1,365

1,365

1,362

1,362

1,347

1,347

Adj. R2

0.515

0.534

0.514

0.535

0.514

0.535

0.513

0.536

Distance × P ost
T reated × P ost

Firm FEs
Controls × year FEs

Notes: Each column in the table presents results from estimating equation (G.1) via pooled OLS, with a dummy forbonus depreciation claiming 1{bonus > 0} as the outcome variable. We rescaled estimates with Distancej so that theeffect is the change in probability of claiming given a 100 km increase in distance. The columns differ in the functionf (·) used to map distances between plant locations to a corporate-level measure of distance to the Technopolis policy
areas. See text and equations (G.1)–(G.4) for complete definitions. Controls include static factors such as the size
quintile (by total assets), quintile of age measured from the Tokyo Stock Exchange listing date, and Census region of
the HQ, all interacted with a full set of year dummies. Standard errors clustered at the firm level are in parentheses.
***p < 0.01, **p < 0.05, *p < 0.1

between firm operations and an area where CAPX is eligible for bonus claims. Equation (G.3) takes
an equal-weighted average distance between plants and each plant’s nearest Technopolis, and (G.4)
instead takes a share-weighted average. We compute share-weighted average distances in (G.4)
using either initial capital shares or employment shares across locations p ∈ J . For instance, we
compute capital shares as:jωp,1980=Pj
P P Ep,1980jp∈J P P Ep,1980

using 1980 as the base year since we have detailed coverage in the corporate filings for that year
which allow us to observe itemized book P P E for each firm j and location p.
To empirically implement (G.1), we use the georoute package combined with the Here API tocreate a matrix of all truck driving distances between the center of each pair of modern city code
locations in Japan. We retrieved latitude/longitude of each city code center based on its city halllocation.8 The georoute package adopts modern routes and thus ignores the non-viability of certain
routes during our historical policy period. We then match each firm to a vector of distances between
its pre-existing set of plant locations and the policy areas to compute f (·). Our results are alsonearly identical if we drop from the sets J any locations on the island of Shikoku, which was more
8

The PDFs containing the city hall locations can be found here: https://www.gsi.go.jp/KOKUJYOHO/center.htm.

85

geographically isolated from the rest of Japan prior to the completion of the Great Seto Bridge in1988. Our results are qualitatively similar, albeit much less precisely estimated, if we instead use
Haversine distance. However, given how mountainous Japan is, geodesic distance is a weak proxy
for the costs of transporting inputs and workers between firm locations.
Table G.2 shows how policy take-up declines with measures of firm distance according to themodel in (G.1) and the metrics defined by (G.2)–(G.4). The coefficient of interest is the tripleinteraction term Distance × T reated × P ost which captures how the propensity of firms in eligible
industries to claim bonus depreciation changes with notions of physical distance to the policy
areas. Consistent with intra-firm transport costs, a firm with plants at an average distance of 100
km from the nearest Technopolis area is 10% less likely to make bonus claims during the policy
regime. The results are much weaker using the minimum distance measure in (G.2). We conjecture
this is because – as the distributions of each firm-level distance measure in Figure G.5 show – mostlarge firms already have a plant operating either within a Technopolis site or within 10 km of one.
Indeed, 43% of firms have a plant within a qualifying area as of 1980.

G.6

Alternative Fiscal Cost Calculations

Key to producing the fiscal cost calculations in Section 6 is assuming a proxy for counterfactual
depreciation claims firms would have made in the absence of the place-based incentives ofTechnopolis. To recap, the fiscal cost as a percentage of eligible CAPX of offering bonus depreciation
through Technopolis is:
Fiscal cost =1995
X
t=1984



τt
bonus
normal×D−
D× Take-upttt(1 + r)t(G.5)

where Dbonus is total depreciation claimed under Technopolis as a fraction of eligible CAPX, andDnormal is total depreciation as a fraction of eligible CAPX that would have been claimed under
the normal corporate income tax regime. The accounting identity says that the fiscal cost is the
PDV of tax revenues lost as a result of offering Technopolis bonus depreciation. Corporate income
tax rates τt are flat across large firms but vary over time according to the standard rate series(black line) pictured in Figure G.4. As a baseline, we take Dbonus to be total depreciation claims
divided by total CAPX less land investment among bonus-claiming firms, and take Dnormal to be
the same ratio except computed over the set of firm-years who do not claim bonuses.
Our baseline exercise implicitly assumes that firms only claim bonuses through Technopolis,
and not through other provisions in the tax code. While this assumption largely holds true,bonus depreciation was allowed for certain special asset classes (e.g. airplanes) even whenTechnopolis was not applicable. Unfortunately, we do not observe a separate line item for bonus
depreciation specifically claimed under Technopolis or its companion policy of Intelligent Location.
For shorthand, define Dgap , as the dollar value of additional claims due to Technopolis bonus andDtotal as the dollar value of all depreciation claims. We consider several alternative counterfactual
definitions that may be used to back out the fiscal cost, summarized as follows:• Version A (baseline): Compare Dttotal /It of bonus claiming to non-claiming firms. Thismethod includes some policy ineligible firms who still can claim bonuses under the normal
tax regime.

86

FIGURE G.5. Distribution of Firm-level Distance to Policy Areas

A. Minimum distance

B. Equal-weighted average distance.25.3

Fraction of firms

Fraction of firms.2.2

Mean = 44
St. dev. = 47

.15

Mean = 91
St. dev. = 49
.1

.1

.05

0

0
0

20

40

60

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400

0

50

100

Distance to nearest Technopolis (km)C. Capital share-weighted average distance

200

250

300

350

400

450

500

550

600

D. Labor share-weighted average distance

.15

.15

.1

.1

Fraction of firms

Fraction of firms

150

Avg. distance to nearest Technopolis (km)Mean = 82
St. dev. = 53

.05

Mean = 78
St. dev. = 49

.05

0

0
0

20

40

60

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400

0Weighted avg. distance to nearest Technopolis (km)

20

40

60

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400

Weighted avg. distance to nearest Technopolis (km)Notes: Panel A shows the distribution of firm-level policy area distances computed via (G.2), Panel B via (G.3), andjPanels C and D via (G.4) for different definitions of ωp,1980. Panel C uses initial net book PPE shares at each plant
location, while Panel D uses employment shares. Vertical red lines indicate the average distance in kilometers.

87

FIGURE G.6. Corporate Income Tax Rate Time Series
50Standard CIT rate

Corporate income tax rate (%)

SME rate (< 8 million JPY)
Special SME rate
40

30

20

10
1980

1984

1988

1992

1996

2000

2004

2008

2012

2016

2020

Year
Notes: Historical corporate income tax (CIT) rates plotted from the Ministry of Finance series available here:
https://www.mof.go.jp/tax_policy/summary/corporation/c01.htm. The standard CIT rate (black line) applies tofirms with nominal earnings greater than 8 million JPY (≈ 80,000 USD) within the tax year, while the SME rate
(blue line) applies to firms earning below this threshold. The special SME rate (dashed red line) is a transitional ratethat applies in a newly formed company’s first tax year if earnings are below the threshold. The standard CIT rateapplies to all firms in our main analysis sample for which we have corporate balance sheet information.

gap/Ipre accruing only to the eligible• Version B (first differences): Compute Dtgap /It − Dpre
firms. This is a first differences calculation comparing the average fraction of bonus claims
among Technopolis-eligible firms in each policy year to the average fraction of bonus claims
gap
Dpre/Ipre among Technopolis-eligible firms in the pre-reform period (1975 – 1983).

• Version C (difference-in-differences): Calculate the difference in means represented by

 

gap
gap
Dtgap /It − Dpre
/Ipre | eligible − Dtgap /It − Dpre
/Ipre | ineligible
This is a difference-in-differences calculation where we take the Version B estimate andsubtract off the change in the average fraction of bonus claims between the pre and post-reformperiod for the Technopolis-ineligible firms.
• Version D (regression DD): The method we adopt in Section 6 uses a residualized versionof the Version C difference-in-differences estimate in which we run the following regression:
gap
Dj,k,t/Ij,k,t = γj +

1995
X




βt · T reatedj,k,t × P ostt + Controls × δt + εj,k,t(G.6)

t=1975Here we define T reatedj,k,t as equal to unity if firm j is in an industry k eligible for bonus
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Table G.3. Sensitivity Analysis of Fiscal Cost-per-job Estimatesr = 5%; βbemp = 5%
r = 7%; βbemp = 5%
r = 5%; βbemp = 7%
r = 7%; βbemp = 7%

A: Baseline

B: 1st diff in means

C: DD in means

D: Residualized DD$52,543$49,928$35,084$25,270[3.69%][3.50%][2.46%][1.77%]$41,288$43,072$28,935$21,065[2.90%][3.02%][2.03%][1.48%]$37,474$32,490$20,668$16,068[3.69%][3.50%][2.46%][1.77%]$29,491$30,766$18,019$15,046[2.90%][3.02%][2.03%][1.48%]

Notes: The table shows cost-per-job estimates (USD) for different parameter estimates using the accounting identityin (6.1) for lost cash flows from offering bonus depreciation. Brackets indicate the fiscal cost as a percentage of adollar of capital investment that qualifies for bonuses. In each method we compute the denominator of Dt /It using
the YOY change in the net book value of PPE excluding land, plus accounting depreciation. To calculate aggregate
CAPX eligible for Technopolis bonus claims, we scale down total corporate CAPX using the eligibility ratio computedvia (6.3). See Appendix G.6 text for complete descriptions of the counterfactual definitions underlying each method.

claims under Technopolis. P ostt is an indicator for the post-1984 period. For this exercise, weshut down time variation in the policy rollout across locations to account for bonus claimingon the extensive margin of firm entry into an eligible catchment area. The vector Controls
includes fixed effects for 1980 HQ Census region, 1980 balance sheet size quintile, and age
quintile. By interacting these time-invariant controls with a complete set of fixed effects, βbt
represent the estimated average percentage cash flow benefit of Technopolis bonus claims,
comparing two firms in the same part of the size and age distribution and with an HQ in thesame region. We then feed the βbt for 1984 ≤ t ≤ 1995 into equation (G.5) as our measure of
the benefit per dollar of eligible CAPX derived from Technopolis, Dtbonus − Dtnormal .

Table G.3 summarizes the cost-per-job amounts and fiscal cost percentages (in brackets) obtained
under each of the four counterfactual definitions described above. For methods B,C, and D, wegap
winsorize Dj,k,t
at the 2nd/98th percentiles, given the extreme skewness of the distribution of
bonus claims and a median Dgap of zero. For r = 7% and our more conservative DD estimate of
βbemp = 5% for the employment bump generated by Technopolis, the estimates from the first three
versions range from $28,935 to $41,288 per corporate job, or 2%-3% in terms of the net subsidy
rate. These fiscal cost percentages match the cash flow simulation exercises we perform in Appendix
C, wherein a typical firm with a long-lived asset share around 50% recoups 2% to 3% of investment
costs through Technopolis bonus allowances. Version D produces the lowest cost estimates because
it is based on a more local policy treatment, comparing two firms in the same HQ region × size ×
age cell, where one receives bonus allowances and the other does not. At the same time, Version
D produces fiscal cost estimates which are closer in spirit to the average treatment effects on thetreated (ATT) represented by βbemp .

89

Appendix References
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LaPoint, C. (2021): “You Only Lend Twice: Corporate Borrowing and Land Values in Real
Estate Cycles,” mimeo, Yale.
Lian, C. & Y. Ma (2021): “Anatomy of Corporate Borrowing Constraints,” Quarterly Journalof Economics, 136(1): 229-291.
Ministry of International Trade and Industry (1995): “The Catalogue of Equipment andFacilities Eligible for Bonus Depreciation,” MITI Kinki Office.
Sun, L. & S. Abraham (2021): “Estimating Dynamic Treatment Effects in Event Studies with
Heterogeneous Treatment Effects,” Journal of Econometrics, 225(2): 175-199.
Zwick, E. & J. Mahon (2017): “Tax Policy and Heterogeneous Investment Behavior,” American
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