Deregulation and Safety: from the Industry1

Kuniyoshi Saito2

Abstract

We analyze how accident rates in the Japanese taxicab industry changed before and after the abolition of the entry regulation. A panel analysis of 47 prefectures produced the following findings: (1) Deregulation had no statistically significant impact on the number of accidents per kilometer. (2) Taxicab accidents surged during the depression years; we find that this is partly because, the taxicab supply was not reduced despite the sharp decline in demand, which resulted in an increase in the taxicab vacancy rate (vacant are more likely to have accidents than are occupied ones).

Key words: deregulation, safety, taxicab, entry regulation, taxi demand, vacancy rate.

1 The author thanks Fumio Akiyoshi, Shun-ichiro Bessho, Toru Fujiwara, Yoshitsugu Kanemoto, Minoru Kitahara, Naomi Miyazato, Ryoko Morozumi, Akihiro Nakamura, Hisashi Sawaki, Daisuke Shimizu, Harutaka Takahashi, Daisuke Tsuruta, and seminar participants at the Japanese Economic Association 2013 fall meeting, 2013 Annual Conference of the International Transportation Economics Association, and the Urban Economics Workshop at GRIPS for their valuable comments. 2 Department of Economics, Meiji Gakuin University. 1-2-37 Shirokanedai, Minato-ku, Tokyo 108-8636, Japan. Tel: 03-5421-5384, fax: 03-5421-5207, e-mail: [email protected] 1 / 33

1. Introduction

Economists have long proposed various pros and cons of taxi regulation, and the regulatory scheme in existence varies significantly depending on the region and era. A typical argument associated with deregulation is that competition harms safety. This line of reasoning is often advanced by interest groups in order to protect their vested interest, but formal theories do suggest a possible linkage between competition and safety.3 For example, Shapiro (1982) shows that a market in which reputation plays a relatively more important role provides better-quality goods. This implies, in the taxicab context, that with poor maintenance might be operated in a market where free entry and exit are allowed, because short-lived operators would be less concerned about their reputation. Another explanation would be the efficient wage hypothesis, which suggests that cab drivers who are paid higher wages might exert more effort to avoid accidents. More informally, deregulation might result in more accidents if harsh competition generates too exhausted workers or results in hiring inexperienced drivers at low wages. These informal arguments suggest a negative linkage between deregulation and safety, but a positive association can also be inferred. If deregulation enhances, rather than lowers, the importance of operators’ reputation, fewer accidents would occur. At any rate, whether such

3 For more comprehensive argument, refer to PANZAR, J. C. & SAVAGE, I. 1989. Regulation, Deregulation, and Safety: An Economic Analysis. Transportation Safety in an Age of Deregulation. Oxford: Oxford University Press. 2 / 33

linkages exist and how they interact are essentially empirical questions.

Empirical research of the impact of deregulation in the taxicab market has mainly focused on the effects on price, quantity, service quality, and operators’ productivity. For example, Teal and

Berglund (1987) report that taxi deregulation in several large US cities brought little advantage to providers or users in the sense that taxi rates increased and taxicab productivity declined after deregulation. More recently, Schaller (2007) assesses the effects of entry regulation based on the experiences of 43 communities in the U.S. and Canada and shows that, without entry restriction, the taxicab stand and cruising taxi market experienced an oversupply of taxis and resulted in deterioration of and driver quality.

Despite not a few empirical works on the impacts of deregulation in the taxi market, empirical studies on the interaction between deregulation and safety are scarce.4 Indeed, a survey article by

Elvik (2006) introduces 41 empirical studies that investigate the relationship between deregulation and safety in the transportation industry, but no study on the taxicab market is referred.5 To fill the gap between theory and practice, this study conducts an empirical research

4 Empirical studies on the taxicab market have mainly focused on firms’ productivity, workers’ environment, wage, and social welfare. VISCUSI, W. K., HARRINGTON, J. E., JR. & VERNON, J. M. 2005. Economics of Regulation and Antitrust, Fourth edition. Cambridge and London: MIT Press. and CARLTON, D. W. & PERLOFF, J. M. 1994. Modem industrial organization. Reading MA. provide some theoretical and empirical discussions about the taxicab market. 5 He analyzes 53 estimates (30 on the trucking industry, 19 on the airline industry, and 4 on the rail industry) in 25 papers and concludes that his summary estimates are not different from zero, suggesting that deregulation in the 3 / 33

on the Japanese taxicab industry’s experience following the abolition of the demand-supply adjustment regulation.

Our results are twofold and can be summarized as follows. First, the effect of deregulation on the number of accidents per 1 billon kilometers is not significant. We find, controlling for the factors that affect accident occurrences, that the coefficients of the regulatory change dummy variable are not significant at a reasonable statistical level. Second, we observe a clear escalating trend of accidents before the deregulation. The index of taxi accidents increased from 100 in

1990 to 177 in 2000, while the private-use index rose from 100 to 126 during the same period.

Investigating the reason behind this result, we find it is at least partly due to the inflexible operation of the demand-supply adjustment regulation. Taxicab demand decreased by about 30% to 37% during the 1990s, but the regulatory authority responded to this situation not by reducing the number of taxis but by raising fares. What followed was an excess supply situation, leading to an increasing number of vacant taxicabs and, in turn, to more accidents. We claim that these “side effects” of regulation would entail substantial costs when accidents involving damage are also considered.

The rest of this paper is organized as follows. In Section 2, we briefly introduce deregulation in

trucking, airline, and rail industries did not lower their safety levels though the estimates, especially for the airline industry, have large variances. 4 / 33

Japan and overview its impact on the taxicab market. In Section 3, we investigate how deregulation affected the number of taxicab accidents per kilometer, using two different empirical methods. In Section 4, we shift our focus to the 1990s, when taxicab accidents showed a significant increase, and provide our explanation for this finding. In Section 5, we conclude with some policy implications.

2. Deregulation in the Japanese Taxicab Industry: An Overview

In this chapter, we briefly introduce the regulatory scheme in the Japanese taxicab industry and provide some basic information on how the market was affected by deregulation.6

2.1. Regulation, Privatization, and Deregulation in Japan7

Japanese taxicab operators had been subject to entry and fare regulation since 1951. To operate a taxicab firm within a local district, the firm had to obtain a license from the minister of . The Transportation Law (Doro Unsou Hou) required that a license should be issued only if it does not cause an excess supply in the local region and that any subsequent

6 For the Japanese taxicab market prior to deregulation, see also FLATH, D. 2006. Taxicab regulation in Japan. Journal of the Japanese and International Economies, 20, 288-304. 7 This section is primarily based on information in the MLIT website (in Japanese) http://www.caa.go.jp/seikatsu/koukyou/data/19data/butu190419-shiryou1-1.pdf (accessed: August 15, 2012). 5 / 33

increase or decrease in the number of taxicabs should be approved by the authority. Fares were also subject to prior approval (ninka) by the authority, and, since 1955, the Road Transportation

Law required that fares should be the same for all cabs in the same operational district.

With the privatization wave, the Council for Transport Policy (Unyu Seisaku Shingikai) declared its intention to relax the regulatory policy, and the government decided, in December

1996, to abolish the demand-supply regulation for the entire transport industry. The primary purposed of the government decision was to encourage innovation by operators and to activate and enhance the efficiency of taxicab services through fair competition. In March 2000, the cabinet introduced a bill in the Diet, and the revised Road Transportation Law was passed in May

2000 and came into force from February 1, 2002.8 The most important aspect of the revised Road

Transportation Law, from the perspective of our analysis, was the abolition of the entry regulation.

More precisely, it stipulates the following:

(1) Entry of an operator is allowed by the authority as long as it satisfies several requirements

(kyoka) (it was previously necessary to obtain a certification for each business district

(menkyo)).

(2) An operator can increase or decrease the number of taxicabs subject to 7 days’ prior

notification (jizen todokede) (approval by the authority (jizen ninka)was earlier required).

8 For the contents of the law, refer to http://www.shugiin.go.jp/itdb_housei.nsf/html/housei/h147086.htm 6 / 33

(3) An operator can cease or close the business, submitting post-notification within 30 days

(jigo todokede) (an approval system (kyoka) existed previously).

(4) Fare regulation remains, but within a wider fare range (kyoka).

(5) The authority can stop new entry or increase the number of taxicabs temporarily if the

supply shows a “marked” increase or if the market is no longer able to ensure ’

safety and convenience.

In short, entering and exiting the market, and increasing and decreasing taxis became much easier than before, but fare regulation remained. Six years after the deregulation, however, taxicab industry participants claimed that confusion prevailed after deregulation and the taxi market was no longer sustainable. Therefore, the transport law was amended again, in October

2009, allowing 11 large cities to revive the demand-supply regulation if the market experiences considerable confusion. Our study, therefore, focuses on the years before 2009.

2.2. Effects on the Number of Operators and Taxicabs

First we will have a brief look at how deregulation affected the number of operators and taxicabs. Figure 1 shows the number of operators and taxicabs from FY1983 to FY2009. The number of corporate taxi operators was around 7,000 and quite stable in the pre-deregulation years. It then began to increase after deregulation and reached 12,786 in FY2007. Individual taxi

7 / 33

operators show a slightly different pattern. Their number decreased from around 47,000 before deregulation to less than 45,000 at the end of FY 2007. However, deregulation led to an increase in the total number of taxicabs, both corporate- and individual-operated, from 259,033 in 2001 to

273,740 in 2006 (a 5.68% increase). Thus, the deregulation introduced in February 2002 did have a significant impact on the number of operators and taxicabs.

2.3. Effects on the Accident Rate

Now we investigate how deregulation affected taxicab safety. We define “safety” as the number of accidents involving bodily injury per 1 billion kilometers. Specifically, we define

＝ accident . (1) annual number of accidents involving bodily injury annual mileage in billions of kilometers

Three caveats deserve mention here. First, we focus on accidents involving only bodily injury.9

This indicates that the number of accidents is undervalued. In Japan, accidents involving property damage are about five times more likely to occur than those involving only bodily injury, so our analysis understates the effect of the policy change (if any). Second, the number of accidents and

9 More precisely, accidents were defined as those “caused by vehicles or rails and result in the death or injury of victims” (Kotsujiko Tokei Nenpo). We restrict the study to accidents caused by the “party of the first part,” which means we focus on accidents caused by those who are primarily at fault. 8 / 33

the kilometerage figures are those of company taxi operators—those of individuals are excluded.

This shortcoming is due to non-availability of data for individuals, but it will not harm our analysis as long as the company operators and individuals behave in a similar way.10 Third, while accident data relate to the January to December year, kilometerage is indicated on a fiscal year

(April to March) basis, so that accident and kilometerage data have a three-month disparity. This is also related to data availability. With these three caveats in mind, let us look at taxi accident trends before and after deregulation.

Figure 2 shows the number of accidents per 1 billion kilometers for taxicabs and private-use .11 It shows that cab accidents escalated throughout the 1990s, but stopped increasing after deregulation. Private-use car accidents also show a similar pattern. Thus, whether the accident trend of taxicabs changed only because of deregulation is not clear from the figures.

3. Empirics

3.1. Difference in Differences

In this section, we investigate how the number of taxicab accidents per kilometerage changed after deregulation, using two simple econometric models. We first construct a

10 Kilometerage figures for individual operators are available only until 1991 in the “Hire Taxi Yearbook.” 11 Accident data on private-use cars include those involving small-size vehicles and private-use , but kilometerage Fs do not. 9 / 33

difference-in-differences (DID) model, in which the taxicab represents the treatment group and the private vehicle, the control group. Specifically, consider a model,

= + + 𝑗𝑗 𝑗𝑗 (2) 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖 𝛽𝛽0 𝛽𝛽1𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝛽𝛽2𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑡𝑡 + × + + 𝑗𝑗 𝑗𝑗 𝛽𝛽3�𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑡𝑡� 𝑦𝑦𝑦𝑦𝑦𝑦𝑟𝑟𝑡𝑡 𝜖𝜖𝑖𝑖𝑖𝑖

where subscripts , , indicate prefecture, type of vehicle (taxicab or private car), and year

𝑖𝑖 𝑗𝑗 𝑡𝑡 respectively, is the number of accidents per 1 billion kilometers driven defined as (1), 𝑗𝑗 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖 deregulation is a dummy variable, which takes 1 after deregulation and 0 before, is a 𝑗𝑗 t 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 dummy variable, which takes 1 if the vehicle is a taxicab and 0 otherwise, year is the year

t dummy, is the error term. 𝑗𝑗 ϵ𝑖𝑖𝑖𝑖 In this model, the effect of deregulation is measured as the difference in the accident rate of the treatment group (E( ) ( )) subtracted from the difference in the accident 1 1 �𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎�����������1 − 𝐸𝐸 �𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎�����������0 rate of the control group (E( ) ( )). Since this value corresponds to the 0 0 �𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎�����������1 − 𝐸𝐸 �𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎�����������0 coefficient of the cross term × , our interest is in the statistical 𝑗𝑗 �𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑡𝑡� significance of and its magnitude. In estimating the model, we use data on six prefectures

β3 (Tokyo, Kanagawa, Aichi, Osaka, Hyogo, and Fukuoka) and the nationwide data for the period

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1990 to 2008.12

Panel A of Table 1, shows the estimation results for the DID model based on data from 1990 to

2008. We estimate the model by OLS and calculate White’s robust standard errors. The coefficient of the cross term ( × ), , is positive and statistically significant 𝑗𝑗 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑑𝑑𝑒𝑒𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑡𝑡 β3 at the 1% level for all samples, indicating that the accident rate increased after deregulation. In addition, the estimated coefficients are large in size. In Tokyo, for example, total travel distances by taxicab are 11.7 billion kilometers per year, so the result of = 400 indicates that, on

𝛽𝛽�3 average, the number of taxicab accidents increased by 4,680 per year after deregulation.

These results, however, have a serious flaw. As we have seen in Figure 2, taxicabs and private-use cars show totally different trends in the 1990s. The DID method requires that the treatment and control groups have similar trends before the policy change, but the data do not appear to satisfy this condition. Indeed, when we estimate the same model using a shorter period,

1998 to 2003, we obtain completely different results. As shown in Panel B of Table 1, the coefficients on the cross term ( ) are no more statistically significant in all models, except for

β3 Osaka. For this reason, we conclude that the DID models do not provide reliable estimates.

12 We focus only on these six prefectures because they are the only ones for which vehicle-mileage data are available. Besides, data prior to 1990 are not available. 11 / 33

3.2. Dummy Variable Model

Now we consider an alternative model that includes a dummy variable for the policy change,

ln ( , ) = + ln ( , ) + , + + , (3)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖 𝑡𝑡 𝛼𝛼 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑖𝑖 𝑡𝑡−1 𝑿𝑿𝑖𝑖 𝑡𝑡𝜷𝜷 𝛾𝛾𝐷𝐷𝑡𝑡 𝑢𝑢𝑖𝑖 𝑡𝑡

where is the number of taxicab accidents per 1 billion kilometers driven defined as

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖 in (1), is a (sub)set of control variables listed in Table 2, is a dummy variable that takes

𝐗𝐗it 𝐷𝐷t 1 if the year falls after deregulation and zero otherwise.13 Considering the possibility that the effect of deregulation is realized after a time lag, we define = 1 in four different ways. We

𝐷𝐷t assume that the error term , consists of the unobserved prefecture-specific effect and the

𝑢𝑢𝑖𝑖 𝑡𝑡 𝜇𝜇𝑖𝑖 observation-specific errors , , that is, , = + , with ~ (0, ) and 2 𝜈𝜈𝑖𝑖 𝑡𝑡 𝑢𝑢𝑖𝑖 𝑡𝑡 𝜇𝜇𝑖𝑖 𝜈𝜈𝑖𝑖 𝑡𝑡 𝜇𝜇𝑖𝑖 𝐼𝐼𝐼𝐼𝐼𝐼 𝜎𝜎𝜇𝜇 , ~ (0, ), independent of each other. The dependent variable is converted into its 2 ν𝑖𝑖 𝑡𝑡 𝐼𝐼𝐼𝐼𝐼𝐼 𝜎𝜎𝜈𝜈 logarithm because it has a skewed distribution.

Model (3) includes the lagged dependent variable, which may cause a correlation with the error term, so we use the difference GMM estimator and system GMM estimator proposed by Arellano

13 A subset of control variables listed in Table 2 is chosen so that the model satisfies two basic diagnostic statistics: the Sargan test for exogeneity and the AR(2) test. We also estimated the model without the lagged dependent variable by a fixed effects panel data method. The basic results are not altered except for a slight change in the size of the estimated coefficients. 12 / 33

and Bond (1991). We estimate model (3) using the sample for the period 1985 to 2008.

Table 3 shows the estimation results based on model (3) by one-step difference GMM and one-step system GMM.14 To save space, only the results on the dummy variables are reported.

Before we look at the policy effect, let us begin by checking two basic diagnostic statistics. The first is Sargan’s test of overidentifying restrictions, which tests the null hypothesis that “the instruments as a group are exogenous.” Sargan’s test statistics are not rejected at a reasonable level of statistical significance for all results based on the one-step difference GMM, implying that the null of exogeneity cannot be rejected. As for the results based on the one-step system

GMM, however, the null of exogeneity is rejected except for column (5). Thus, we basically rely on the one-step difference GMM results in columns (1) to (4). The other diagnostic statistic we need to check is the Arellano–Bond test for AR (2) in first differences, which will detect autocorrelation in levels at a reasonable statistical significance. For the results from the one-step difference GMM, we confirm that no autocorrelation is detected for columns (1) to (4) at a 5% statistical significance. From these, we confirm that we rely on the one-step difference GMM results (columns (1)–(4)) rather than the one-step system GMM (columns (5)–(8)).

Now that we have checked two basic diagnostic statistics, let us look at the main results. Our

14 We also estimated the model according to the fixed effect panel data method. The results of the dummy variable were not altered except for a slight change in the size of the coefficients. 13 / 33

interest is in the coefficient of the dummy variable . Columns (1) to (4) of Table 3 suggest that

𝐷𝐷t deregulation had no significant impact on the number of taxi accidents per 1 billion kilometers in the sense that none of the coefficients of the dummy variables is statistically significant. Hence, we conclude, controlling for a variety of factors that affect accident occurrence, that accidents neither increased nor decreased in the post-deregulation period.

One caveat deserves mention. The analysis so far is based on a panel of 47 prefectures, but deregulation effects might vary from district to district. Indeed, a simple look at the number of accidents per kilometerage for some selected prefectures shows the situation might be different.

Figure 3 illustrates the number of accidents per 1 billion kilometers for six selected prefectures:

Tokyo, Kanagawa, Aichi, Osaka, Hyogo, and Fukuoka. It indicates that four prefectures (Tokyo,

Kanagawa, Aichi, Fukuoka) have similar trends as the national average but the remaining prefectures, Osaka and Hyogo, have a different trend: cab accidents continued to rise even after deregulation. Therefore, we should remember that the analysis so far is about the national average and might not be applicable to some regions.

4. Why Accidents Increased Sharply during the 1990s?

So far, we have examined the effect of the deregulation on accident propensities. Referring backing to Figure 2, however, we clearly observe a continuous surge in taxi accidents throughout

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the 1990s. In this section, we explore the reason for this trend.

4.1. Vacancy Rate and Accidents

Since we have no prior idea about the reason for the increase in accidents, we regress taxi accidents on a variety of factors that might influence accident occurrences. More precisely, we construct a model

ln ( , ) = + ln ( , ) + , + , (4)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖 𝑡𝑡 𝛼𝛼 𝛽𝛽 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑖𝑖 𝑡𝑡−1 𝑿𝑿𝑖𝑖 𝑡𝑡𝜸𝜸 𝑢𝑢𝑖𝑖 𝑡𝑡

where all definitions are the same as those in model (3).

Table 4 shows the estimation results of model (4). Columns (1) and (2) are based on the pre-deregulation sample and columns (3) and (4) on the post-deregulation sample. We estimate the model using two different methods: the fixed effects model (columns (1) and (3)) and the dynamic panel data model (columns (2) and (4)). Results from the pre-deregulation period show that only two variables have a statistically significant association with taxicab accidents: the number of accidents of private-use cars and the vacancy rate. The former is quite easy to interpret because private-use car accidents represent a general traffic accident trend. The latter variable, the vacancy rate, is more difficult to interpret. So let us consider the following two questions in

15 / 33

turn. (A) Why is the vacancy rate positively correlated with taxicab accidents? (B) Why did the vacancy rate increase in the 1990s?

(A). Why is the vacancy rate positively correlated with taxicab accidents?

First, it should be mentioned that a positive linkage between accidents and the vacancy rate is reported by other sources, and is not specific to our data. A report from Ministry of Land,

Infrastructure, Transport and Tourism (MLIT), for example, writes that “vacant taxis are approximately twice as likely to have accidents as occupied ones” (MLIT, 2009, p. 7). We do not, however, have any decisive answer for the observation. The driver’s psychological factor would be playing an important role, but we do not have enough evidence for this claim. Instead, we provide information on the types of accidents that vacant taxis are likely to have. According to figures in Table 5, vacant taxis are more likely to have accidents than are occupied ones when they start or change lanes. This simple observation implies that taxi drivers take less care when their cabs are not occupied.15 However, further examination is needed to solve this puzzling observation.

(B). Why did the vacancy rate increase in the 1990s?

15 Another report by MLIT says that empty taxicabs are more likely to have accidents because of driver’s careless behaviors due to stress, sleepiness, and daydreaming. Additionally, it reports that empty taxies are more likely to have accidents involving two wheels. As of 2007, occupied taxies accounted for 35.6% and empty taxis 50% of such accidents. 16 / 33

Our next question is why the vacancy rate increased during the 1990s—from 50.15% in 1990 to 56% in 2000. Our explanation is that operators had little incentives to reduce taxis under the regulatory scheme at the time, despite the sharp decline in the demand.

Let us first look at how the demand changed in the depression years. Figure 4 illustrates the transition in demand measured by number of passengers and travel distance with passengers, which decreased by 37.5% and 30.9%, respectively, from 1985 to 2010. Facing this situation, operators had to either reduce the number of taxis or raise the fares. What happened was the latter.

The number of taxis decreased only by 1.25%, from 259,600 in 1990 to 256,300 in 2000, while per-kilometer fares with passengers continued to increase from about 0.27 yen in 1990 to about

0.35 yen in 2010, as shown in Figure 5.

The political economy of this situation goes as follows. At that time, the MLIT adopted the full cost pricing as a fare regulation, which means that the authority always admits increasing fares if a certain percentage of operators file a request for price increase and if the “middle-efficient” operators incur a loss. Under this regulation, the taxi industry can always raise the fare to the point where it can cover the total cost. Anticipating this price increase in the future, each operator has little incentive to reduce taxis unless its productivity is far below the average. In addition, the authority had no effective way to force operators reduce taxis.16 Thus, the number of taxis was

16 The medallion system might be superior to the approval system as a method of controlling the 17 / 33

not reduced and fares were increased during the depression years.

4.2. A Simple Policy Simulation

Finally, we conduct a simple policy simulation to investigate how the accident trend would behave if the regulator operated the demand-supply adjustment regulation in a different way. We assume that each regional authority controlled demand and supply throughout the 1990s, keeping the vacancy rate at the same level as in 1990.

Consider a model

ln ( , ) = + ln ( , ) + , . (5)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖 𝑡𝑡 𝛼𝛼� 𝛽𝛽̂ 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑖𝑖 𝑡𝑡−1 𝑿𝑿𝑖𝑖 𝑡𝑡𝜸𝜸�

where all the estimates are from column (2) of Table 4. To predict the number of accidents, we consider only statistically significant variables in Table 4: vacancy rate, number of accidents of private-use cars, and age. The simulation result in Figure 6 clearly shows that accidents would have been much fewer had the regulator controlled demand and supply, keeping the vacancy rate constant. In 2000, for example, the number of accidents would have been 14.6% fewer (20,274

number of taxicabs, especially when the demand is declining. Reducing cabs is much easier under the medallion system because all the regulators need to do is to simply purchase as many medallions as they want to reduce. The approval system, on the other hand, requires political negotiations with specific operators, which would be difficult to implement as can be seen in the case of Japan. 18 / 33

instead of 23,753) if the vacancy rate was kept constant.

5. Conclusion

Using a prefecture-level panel dataset for the period 1985–2008, we set out to investigate how the incidence of taxicab accidents in Japan was affected by the abolition of the entry regulation.

While we found no statistically significant evidence that taxicab accidents increased or decreased in the post-deregulation period, we found that taxicab accidents per kilometer increased substantially before deregulation. Our explanation for this observation is as follows. In the 1990s, the demand for taxicabs decreased by approximately 30% to 37%. Because the full cost pricing regulation enabled operators to ask for increasing fares to cover their total cost, operators had little incentive to reduce taxis. The result was a significant excess supply that led to a sharp increase in the number of accidents through a rising vacancy rate. Of course, this would not be the only factor that affected the accident rate, but our regression results suggest a strong correlation between vacancy rate and accident rate.

The lesson from this study is quite simple: controlling both price and quantity in an appropriate way is a difficult task. The taxicab market is probably one of the least complicated markets with little product differentiation and technological innovation, but identifying an ideal price and quantity is quite difficult or perhaps almost impossible given our finding that regulation caused a

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serious “side-effect” that the regulator had not expected.

References

ARELLANO, M. & BOND, S. 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58, 277-297. CARLTON, D. W. & PERLOFF, J. M. 1994. Modem industrial organization. Reading MA. ELVIK, R. 2006. Economic deregulation and transport safety: A synthesis of evidence from evaluation studies. Accident Analysis & Prevention, 38, 678-686. FLATH, D. 2006. Taxicab regulation in Japan. Journal of the Japanese and International Economies, 20, 288-304. PANZAR, J. C. & SAVAGE, I. 1989. Regulation, Deregulation, and Safety: An Economic Analysis. Transportation Safety in an Age of Deregulation. Oxford: Oxford University Press. SCHALLER, B. 2007. Entry controls in taxi regulation: Implications of US and Canadian experience for taxi regulation and deregulation. Transport Policy, 14, 490-506. SHAPIRO, C. 1982. Consumer Information, Product Quality, and Seller Reputation. The Bell Journal of Economics, 13, 20-35. TEAL, R. F. & BERGLUND, M. 1987. The impacts of taxicab deregulation in the USA. Journal of Transport Economics and Policy, 37-56. VISCUSI, W. K., HARRINGTON, J. E., JR. & VERNON, J. M. 2005. Economics of Regulation and Antitrust, Fourth edition. Cambridge and London: MIT Press.

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Table 1: Estimation Results of Difference-in-Differences Model

Panel A: Full Sample (from 1990 to 2008)

(1) (2) (3) (4) (5) (6) (7) Nationwide Tokyo Kanagawa Aichi Osaka Hyogo Fukuoka

267.1*** 331.0*** 388.9*** 172.6*** 70.17 196.5* 343.4*** 𝑗𝑗 𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡 (4.15) (3.69) (4.84) (3.44) (0.68) (1.87) (4.21) deregulation 366.0** 997.8*** 499.6** 382.2*** 386.4 188.4 391.1* t (2.31) (4.51) (2.52) (3.08) (1.52) (0.73) (1.94)

× 585.4*** 400.4*** 465.2*** 484.9*** 1224.1*** 1027.8*** 550.7*** 𝑗𝑗 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑡𝑡 (5.91) (2.90) (3.76) (6.26) (7.69) (6.35) (4.38) 763.1*** 769.8*** 1302.4*** 678.5*** 966.4*** 1001.5*** 1079.1***

𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 (6.85) (4.96) (9.36) (7.80) (5.41) (5.50) (7.64) Year dummy yes yes yes yes yes yes yes

N 38 38 38 38 38 38 38 adj. R2 0.862 0.825 0.809 0.895 0.878 0.811 0.835

Panel B: Subsample (from 1999 to 2003)

(1) (2) (3) (4) (5) (6) (7)

Nationwide Tokyo Kanagawa Aichi Osaka Hyogo Fukuoka

713.9*** 918.5*** 816.8** 456.1** 741.5*** 750.0*** 847.1*** 𝑗𝑗 𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡 (14.21) (15.84) (5.08) (3.98) (14.01) (9.65) (17.85) deregulation 144.8* 236.0** 86.50 251.8 325.8** 289.6* -36.31

t (2.42) (3.42) (0.45) (1.85) (5.17) (3.13) (-0.64)

× 109.0 -152.2 -95.16 200.0 248.7** 105.9 55.88 𝑗𝑗 𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑡𝑡 (1.68) (-2.03) (-0.46) (1.35) (3.64) (1.06) (0.91) 1045.5*** 1431.3*** 1852.7*** 943.8*** 1390.2*** 1099.5*** 1598.8***

𝑎𝑎𝑑𝑑𝑎𝑎𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 (24.03) (28.51) (13.32) (9.51) (30.34) (16.34) (38.89) Year dummy yes yes yes yes yes yes yes

N 10 10 10 10 10 10 10

Adj. R2 0.986 0.984 0.856 0.885 0.989 0.970 0.990 Notes: Estimation results were obtained by ordinary least squares. White’s robust standard errors are in parentheses. * p < .1, ** p < .05, *** p < .01

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Table 2: Summary Statistics for Control Variables ( )

𝐗𝐗𝐢𝐢𝐢𝐢 Variable Definition Unit Mean Std. Dev. Min. Max. taxi_accident Number of taxicab Accidents 1102.41 483.85 236 3,100 accidents car_accident Number of private-use car Accidents 10,925.28 10,432.89 1,070 53,485 accidents operator Number of operators Operators 150.56 81.33 27 463 (corporations) taxicab Number of taxicabs Taxicabs 4,489.09 5,338.95 728 37,671 vacancy_rate Vacancy rate

% 54.78 5.03 41 72 (= )

empty kilometerage total kilometerage Number of passengers 1,000 47,956.55 69,158.73 4,588 497,500 carried Passengers driver_age Average age of taxicab Age 51.46 4.52 41 66 drivers service_year Average service years Years 10.06 2.07 4 20 wage Wages for taxicab drivers (=monthly fixed wage + 1,000 yen 574.13 218.10 129 1613 yearly bonus) employee Number of taxicab Employees 632.44 971.73 10 7,740 employees work_hour Work hours (=official Hours 214.47 20.33 163 284 working hours + overtime) rain Number of rainy days Days 117.18 27.17 59 202 snow Number of snow days Days 31.64 32.30 0 145 insurance Voluntary auto insurance penetration rate (bodily % 65.03 8.95 38 83 injury) young Percentage of people aged % 6.40 1.17 4 10 from 15 to 19 N = 1,222

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Table 3: Arellano–Bond Dynamic Panel Data Estimation Results for Dummy Variable Model (3) (1) (2) (3) (4)

1 2001 1 2002 1 2003 1 2004 Definition of = = = = 0 0 0 0 𝑖𝑖𝑖𝑖 𝑡𝑡 ≥ 𝐹𝐹𝐹𝐹 𝑖𝑖𝑖𝑖 𝑡𝑡 ≥ 𝐹𝐹𝐹𝐹 𝑖𝑖𝑖𝑖 𝑡𝑡 ≥ 𝐹𝐹𝐹𝐹 𝑖𝑖𝑖𝑖 𝑡𝑡 ≥ 𝐹𝐹𝐹𝐹 𝐷𝐷t � � � � Panel A: Estimation Results by the One𝑜𝑜𝑜𝑜-stepℎ𝑒𝑒𝑒𝑒 𝑒𝑒Difference𝑒𝑒𝑒𝑒𝑒𝑒 GMM 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 VARIABLES ln(accdnt) ln(accdnt) ln(accdnt) ln(accdnt) L.lnaccdnt 0.547 0.240 -0.391 -0.395

(0.403) (0.298) (0.223) (0.266)

-0.244 -0.437 0.014 0.027

𝐷𝐷t (1.073) (0.650) (0.057) (0.046) Year dummy yes yes yes yes Observations 1,034 1,034 1,034 1,034 Num. of regions 47 47 47 47

Num. of instruments 45 46 24 24 Sargan’s test of 2.34 5.47 10.00 9.59 overid. restrictions (0.939) (0.707) (0.189) (0.213)

Arellano–Bond test -2.02 -1.91 -1.78 -1.76 for AR(1) (0.044) (0.057) (0.075) (0.079)

Arellano–Bond test -1.15 -1.55 -1.87 -1.80 for AR(2) (0.251) (0.121) (0.061) (0.071)

Panel B: Estimation Results by the One-step System GMM

(5) (6) (7) (8)

1 2001 1 2002 1 2003 1 2004 Definition of = = = = 0 0 0 0 𝑖𝑖𝑖𝑖 𝑡𝑡 ≥ 𝐹𝐹𝐹𝐹 𝑖𝑖𝑖𝑖 𝑡𝑡 ≥ 𝐹𝐹𝐹𝐹 𝑖𝑖𝑖𝑖 𝑡𝑡 ≥ 𝐹𝐹𝐹𝐹 𝑖𝑖𝑖𝑖 𝑡𝑡 ≥ 𝐹𝐹𝐹𝐹 𝐷𝐷t � � � � VARIABLES lnaccdnt 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 lnaccdnt 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 lnaccdnt 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 lnaccdnt 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 L.lnaccdnt 0.155 0.178* 0.276*** 0.277*** (0.110) (0.096) (0.068) (0.067)

-0.567* -0.287 0.026 -0.021

𝐷𝐷t (0.284) (0.204) (0.032) (0.033) Observations 1,081 1,081 1,081 1,081 Num. of regions 47 47 47 47

Num. of instruments 68 69 67 67 Sargan’s test of 31.46 45.05 134.37 140.10

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overid. restrictions (0.344) (0.038) (0.000) (0.000)

Arellano–Bond test -3.45 -3.90 -4.65 -4.67 for AR(1) (0.001) (0.000) (0.000) (0.000)

Arellano–Bond test -0.43 0.05 1.64 1.75 for AR(2) (0.669) (0.959) (0.102) (0.080)

Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1

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Table 4: Estimation Results for Model (4)

(1) (2) (3) (4)

Before deregulation After deregulation

1985–2000 2001–2008

Fixed Effect Dynamic Panel Fixed Effect Dynamic Panel

lnaccdnt lnaccdnt lnaccdnt lnaccdnt

ln (taxi_accident ) --- -0.071 --- -0.204 t−1 (0.152) (0.263)

operator 0.001 -0.002 0.001 0.026 (0.002) (0.006) (0.001) (0.014)

ln(taxicab) -0.000*** -3.214 0.000* -6.499 (0.000) (1.681) (0.000) (4.634) vacancy_rate 0.024*** 0.080*** 0.000 0.196** (0.007) (0.022) (0.008) (0.077) ln(passenger) -0.000 0.182 -0.000*** 2.319 (0.000) (0.402) (0.000) (1.751) ln(car_accident) 0.847*** 0.695*** 0.830*** 1.772** (0.076) (0.134) (0.089) (0.807) driver_age -0.014* -0.051** -0.003 -0.029 (0.007) (0.023) (0.006) (0.111) service_year -0.008* 0.021 -0.002 0.001* (0.005) (0.028) (0.003) (0.097) work_hour 0.002* 0.002 -0.000 -0.016 (0.001) (0.005) (0.001) (0.016) rain -0.001*** -0.000 (0.000) (0.001) snow 0.003*** -0.000 (0.001) (0.001) insure -0.035*** 0.002 0.054*** (0.005) (0.010) (0.011) young -0.052*** 0.005 -0.072*** (0.017) (0.044) (0.023) constant 1.099 -3.580** (0.731) (1.432)

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Observations 752 658 470 376

R-squared 0.599 0.228 Number of regions 47 47 47 47

Num. of instruments 29 17

Sargan’s test of 27.80 5.95 overid. restrictions (0.065) (0.546)

Arellano–Bond test for -0.54 -1.29

AR(2) (0.588) (0.198) Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1

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Table 5: Incidence of Accidents When Taxicabs Are Vacant and Not Vacant

2007

Start Straight Overtaking Lane change Left turn Right turn Turnaround

Vacant 2,920 7,695 77 1,368 1,955 3,210 523

14.8% 39.1% 0.4% 6.9% 9.9% 16.3% 2.7%

Not Vacant 682 2,850 31 251 410 970 120

10.5% 43.7% 0.5% 3.8% 6.3% 14.9% 1.8% Parking Back Get across Weave Quick stop Stop Other Total (with driver) Vacant 909 70 1 51 714 30 170 19,693 4.6% 0.4% 0.0% 0.3% 3.6% 0.2% 0.9% 100%

Not Vacant 297 25 2 227 567 15 79 6,526 4.6% 0.4% 0.0% 3.5% 8.7% 0.2% 1.2% 100% Notes: Figures include both company and individual taxis and limousine cabs. The italicized figures indicate the type of accidents that are more likely to occur when taxicabs are vacant than when they are not vacant. Source: ITARDA (2007), “ Jigyo-yo Jidosya no Kotsu Jiko Tokei” (Traffic Accident Statistics of Commercial Automobiles)

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Figure 1: Operator and Taxicab Population before and after Deregulation (FY1983–

FY2009) 50000 275000 270000 40000 265000 30000 260000 Number of Operators 20000 255000 10000 Number of Taxicabs (Company&Individual)

1980 1990 2000 2010 250000 Year

Corporate Operators Individual Operators Taxicabs

Note: 1) The red vertical line indicates the deregulation year. Because the horizontal axis represents fiscal years (April to March), the line is located in 2001. 2) This figure shows that the number of taxicabs increased significantly in the post-deregulation years. Source: “Haiyaa Takushii Nenkann” (Limousine and Taxicab Yearbook), Tokyo Kotsu Shimbun Sya (Tokyo Transport Newspaper).

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Figure 2: Number of Accidents per 1 Billion Kilometers 2000 1500 Num. of Accidents 1 Billion of Drive Num. Kilo per 1000

1990 1995 2000 2005 2010 Year

Taxicab Private Cars

Notes: 1) Private cars include both standard-size vehicles and light cars. 2) Figures for private-use cars are available only for the period 1990–2009.

Source: Accidents data are from ITARDA “Kotsu Jiko Tokei Nenpo” (Traffic Accidents Annual Statistics).

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Figure 3: Number of Accidents per 1 Billion Kilometers (Taxis and Private-use Cars) for six

Prefectures

Tokyo Kanagawa Aichi 3000 2000 1000

Osaka Hyogo Fukuoka 3000 2000 1000

Num. of Accidents per 1 billion kilometrage Num. of Accidents per 1990 1995 2000 2005 20101990 1995 2000 2005 20101990 1995 2000 2005 2010 Fiscal Year Taxicab Private-use Car (Standard&Light Vehicle)

Notes: These figures indicate that deregulation’s effect on taxi accidents might differ from region to region. The red vertical lines indicate the deregulation year, February 1, 2002.

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Figure 4: Decline in Taxi Demand during the Depression Years: 1985–2010 7.00e+07 2.00e+08 6.00e+07 1.50e+08 5.00e+07 Travel Distance (km) 4.00e+07 Number of Passengers Carried (Persons) Number of Passengers 1.00e+08

3.00e+07 1985 1990 1995 2000 2005 2010 year

Passenger Carried Travel Distance

Notes: We provide both number of passengers and travel distance with passengers as measures of taxi demand. The former decreased by 37.5% and the latter by 30.9% from 1990 to 2000. The red vertical line indicates the deregulation year, February 1, 2002.

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Figure 5: Operators’ Revenue per Drive with Passengers (1985–2010) .36 .34 .32 .3 .28 Income per Kilometrage with Passengers Income per Kilometrage .26 1985 1990 1995 2000 2005 2010 Year

Notes: This figure shows a surge in taxi fares with the increase in excess supply during the depression years.

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Figure 6: Simulation Results for the Taxi Accident Trend with a Constant Vacancy Rate 1400 1200 1000 Num. of Accidents per 1B Kilometrage Num. of Accidents per 800

1985 1990 1995 2000 year

Simulation Result Actual

Note: The figure shows the predicted number of cab accidents under the hypothesis that regulators controlled the supply so that the vacancy rate was held at the 1990 level.

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