metadata: authors: 0: author: Iimi,Atsushi
count: Madagascar
docna: 0: docna: Estimating the Demand for Informal Public
            Transport : Evidence from Antananarivo, Madagascar
docty: Policy Research Working Paper
abstracts: cdata!: Informal public transport has been
            growing rapidly in many developing countries. Because urban
            infrastructure development tends to lag rapid population
            growth, informal public transport often meets the growing
            gap between demand and supply in urban mobility. Despite the
            rich literature primarily focused on formal transport modes,
            the informal transport sector is relatively unknown. This
            paper analyzes the demand behavior in the “informal” minibus
            sector in Antananarivo, Madagascar, taking advantage of a
            recent user survey of thousands of people. It finds that the
            demand for informal public transport is generally inelastic.
            Essentially, people have no other choice. While the time
            elasticity is estimated at −0.02 to −0.05, the price
            elasticity is −0.05 to −0.06 for short-distance travelers,
            who may have alternative choices, such as motorcycle taxi or
            walking. Unlike formal public transportation, the demand
            also increases with income. Regardless of income level,
            everyone uses minibuses. The estimated demand functions
            indicate that people prefer safety and more flexibility in
            transit. The paper shows that combining these improvements
            and fare adjustments, the informal transport sector can
            contribute to increasing people’s mobility and reducing
            traffic congestion in the city.
display_title: Estimating the Demand for Informal
            Public Transport : Evidence from Antananarivo, Madagascar
listing_relative_url: /research/2022/04/33788531/estimating-demand-informal-public-transport-evidence-antananarivo-madagascar
topicv3: Public Transport Services m1583283 4234
publishtoextweb_dt: 2022-04-18T00:00:00Z
dois: 10.1596/1813-9450-10006
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 914707
model_extractions: Policy Research Working Paper 10006 Estimating the Demand for Informal Public Transport Evidence from Antananarivo, Madagascar _Atsushi Iimi_ Transport Global Practice April 2022
Policy Research Working Paper 10006

# **Abstract**

Informal public transport has been growing rapidly in many developing countries. Because urban infrastructure development tends to lag rapid population growth, informal public transport often meets the growing gap between demand and supply in urban mobility. Despite the rich literature primarily focused on formal transport modes, the informal transport sector is relatively unknown. This paper analyzes the demand behavior in the “informal” minibus sector in Antananarivo, Madagascar, taking advantage of a recent user survey of thousands of people. It finds that the demand for informal public transport is generally inelastic. Essentially, people have no other choice. While the time elasticity is estimated at −0.02 to −0.05, the price elasticity is −0.05 to −0.06 for short-distance travelers, who may have alternative choices, such as motorcycle taxi or walking. Unlike formal public transportation, the demand also increases with income. Regardless of income level, everyone uses minibuses. The estimated demand functions indicate that people prefer safety and more flexibility in transit. The paper shows that combining these improvements and fare adjustments, the informal transport sector can contribute to increasing people’s mobility and reducing traffic congestion in the city.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Estimating the Demand for Informal Public Transport: Evidence from Antananarivo, Madagascar**

Atsushi Iimi<sup>¶</sup> Transport Global Knowledge & Expertise World Bank Group Key words: Informal public transport, Urban transport, Demand analysis. JEL classification: R41 ; C21 ; E26

> ¶ Corresponding author. aiimi@worldbank.org
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# **I. INTRODUCTION**

In recent years, informal public transport or paratransit has been growing rapidly in many developing countries. It includes a wide variety of transportation services between formal transport normally characterized by mixed traffic with fixed routes and fixed schedule and private vehicles. Across countries there are different definitions and names of paratransit services (e.g., Tuk-tuk in Thailand, Jeepney in Manila, Matatus in Kenya, and Dala dala in Tanzania), but informal public transport is typically characterized by more flexible and cheaper mobility services, though less comfortable and sometimes unsafe, than formal public transport, meeting the more customized mobility demand without prefixed routes and schedules in a relatively loosely regulated environment.<sup>1</sup> It is estimated that more than half of the total public transport demand is met by paratransit modes in many large cities in developing Asia (Shimazaki and Rahman (1996)).

Informal public transport is becoming predominant in many African cities, as the region has been experiencing accelerated urbanization ( **Figure 1** ). Africa’s urban population was more than doubled from 200 million in 1990 to 548 million in 2018 (UN ESA 2018). It is expected to continue growing. Among others, urbanization is one of the most important driving forces for economic growth (UN ESA 2020). However, urban infrastructure development often lags behind rapid population growth, causing significant urban congestion and informality. Many African megacities, such as Abidjan, Lagos and Nairobi, have already been ranked among the most stressful cities in terms of traffic congestion ( **Figure 2** ). Because of the lack of wellprepared land use plans and sufficient infrastructure capacity, African cities are often found to be disconnected and overcrowded in small areas around the city centers (e.g., Lall et al. 2017). Informal public transport has been growing to meet the people’s growing demand for mobility toward urban centers.

> 1 Good literature reviews are provided by Shimazaki and Rahman (1996), Phun and Yai (2016), Behal et al. (2020), and IDB (2020).
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analyze the pattern of stop locations by Gramin Sewa operations to examine the role of informal public transport in New Delhi. According to Tiglao et al. (2020), in Metro Manila, informal public transport modes, such as Jeepneys, still tend to dominate public transportation because of operational speed, customer care and other key factors.

The current paper is focused on one of the fast-growing cities in Africa, Antananarivo, Madagascar, aimed at deepening our understanding of informal public transport in the region. It will contribute to filling the gap in the literature, providing useful policy recommendations in the region where paratransit services have been expanding. The paper casts particular light on demand behavior in the market. The existing literature is relatively rich on the supply side, describing physical or operational characteristics of paratransit services (e.g., Shimazaki and Rahman, 1996; CDIA, 2011; Tichagwa, 2016; Permana et al. 2018). However, the demand-side analysis has been carried out in a relatively limited number of studies thus far.

One popular approach to examine the paratransit demand is based on case studies and qualitative methods. For instance, Tichagwa (2016) carried out user interviews with a relatively limited sample of 43 paratransit users in Harare and Masvingo, Zimbabwe, to assesses the difference in their sociodemographic profiles among different routes. Based on user survey data at 48 informal public transport stops in New Delhi, Behal et al. (2020) show that paratransit users are concerned about safety, while formal bus users are more concerned about reliability. Similarly, based on interview data from users, Tiglao et al. (2020) found five key determinants of paratransit use: vehicle condition, service availability, reliability and convenience, and access to information in the Philippines.

There are only a few studies which adopt more rigorous quantitative methods to analyze the demand behavior for informal public transportation. Applying the discrete choice model, Golub et al. (2009) estimate the welfare impacts of different policy interventions in Rio de Janeiro. It is shown that the demand for “vans,” an informal transport service using 10-15 passenger minibuses, as opposed to train and bus transport, is elastic to household income and travel time. Gadepalli et al. (2020) examine socioeconomic characteristics of paratransit
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users in Visakhapatnam, a port city in Andhra Pradesh situated on the eastern coast of India. Their estimated discrete choice model between formal buses and paratransit modes, including shared auto-rickshaw and three-wheeler services, indicates that females, the youth and the poor have higher preferences for paratransit. The demand for informal transport is also found to be crucially dependent on efficiency, frequency and transitability.

Still, unlike formal public transport,<sup>2</sup> it remains largely unknown in many places how the paratransit demand behaves and how the public transport sector involving informal modes could be improved or should be regulated, if needed. Taking advantage of a recent large user survey carried out in Antananarivo, the current paper attempts to examine the demand for paratransit minibus services, called “taxi-be,” within and around the city. The sample data comprise about 3,300 paratransit users. Taxi-be has the potential to contribute to reducing urban congestion and ameliorating people’s mobility if the system is better organized. On the other hand, the current chaotic taxi-be operations may even worsen the city’s congestion if proper policy measures are not taken. To better manage the growing paratransit market, it is essential to understand the demand behavior, including basic economic parameters, such as price and income elasticities.

The remaining sections are organized as follows: Section II provides a brief overview of the informal public transport sector in Antananarivo. Section III elaborates on our methodology and Section IV explains our data. Section V presents the main estimation results. Section VI examines different model specifications and discusses policy implications as well as robustness of the estimates. Then, Section VII concludes.

> 2 There are a number of studies examining the demand for induvial car use and public transportation, including rail, bus, tram, taxi and even walk (e.g., Hensher and Rose, 2007; Habibi, 2012; Deb and Filippini, 2013; Brands et al. 2014; Chakrabarti 2017).
<!-- Start of picture text --> 50<br>rs<br>32 4540<br>a ~2° —— Zambia<br>9 35 = -<br>= ——— Mozambique<br>3 30 =s=F<br>5 25 =-- --- — Tanzaniai<br>X20 — Kenya<br>s 15 —— Ethiopia<br>id<br>2 10 = == Madagascar<br>as=<br>a)<br>0<br>1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>‘6& 35 Sek —— Zambia<br>z= 30 Sow aoee ~ ——Tanzania<br>2s<br>% 825 ——— Kenya<br>bo<br>22xS —— Uganda<br>E = 15 —— Ethiopia<br>5 = —— Mozambique<br>% 10<br>= === Madagascar<br>aS 5<br>ie)<br>1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->
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In Antananarivo, currently, there is no formal public transportation that is operational. The vast majority of passenger traffic is carried by “informal” public transport, mostly, minibus, called taxi-be. There are no official statistics of minibus ridership, however, based on the 2016 household survey in Antananarivo, it is estimated that about 38 percent of the residents use minibus services (World Bank 2019). The total ridership in the sector is estimated at about 1.3 million rides per day.

Minibuses are loosely regulated by the central and local governments, depending on their service areas. While within-city services are registered at the Antananarivo Urban Commune (CUA), the Ministry of Transport is responsible for licensing other suburban operations that operate across districts and communes ( **Table 1** ). Although all operating routes, stops and schedules are in theory supposed to be registered, their actual implementation is not necessarily supervised. Operators only pay a license fee of Ar35,000 (US$10) and a parking fee of Ar120,000 (US$35) per year.

**<u>Table 1. Minibus Market Structure in Greater Antananarivo</u>**

||Suburban|Within city|
|---|---|---|
|Operating area|Across communes|CUA|
|Regulating authority|Ministry of Transport|CUA|
|Operating entity|31 cooperatives|52 cooperatives|
|Bus routes|64|73|
|Average distance of routes (km)|15.9|12.3|
|No. of buses registered|1,472|5,578|
|Average fleet age (year)|23.6|...|
|Average rotation (times per bus-day)|3 to 5|4|
|Average capacity (seats)|20|20|
|Average fares (Ar.)|888|500|

Source: Updated based on World Bank (2019).

Over 6,000 minibuses are currently operating along about 130 routes. Since there is no formal supply and demand matching mechanism in Antananarivo, many operators choose to offer their services where the passenger demand is concentrated, i.e., along the major national roads (e.g., RN1, RN2, RN3, RN4 and RN7). Other less populated areas or remote suburban areas are left unserved. Because of the lack of coordination between the two regulatory authorities, within-city and suburban bus services are duplicated geographically,
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<!-- Start of picture text --> ee<br>04 Ee<br>=§ 3 ea<br>a<br>|<br>——— eee<br>0% 20% 40% 60% 80% 100%<br>m Walk @ Bicycle = Microbus/Matatu<br>®@ Taxi-be @ Shared Taxi @ Taxi, individual<br>@ Own vehicle<br><!-- End of picture text -->
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The above equation follows a traditional Marshallian demand curve (e.g., Friedman 1949; Deaton and Muellbauer, 1980). It is simplistic and a special case of consumer theory under certain restrictions but still useful for empirical analysis of consumer demand for a given good or service, especially with fairly limited data. One of the well-known challenges in analyzing demand is the endogeneity of prices. Whenever some of the determinants of prices are correlated with unobserved demand characteristics, treating prices as exogenous leads to biased estimates (e.g., Dhar et al. 2003; Berry and Haile, 2021). In our case, the minibus market in Antananarivo is competitive. Thus, consumers are considered to behave as price takers. Note that this does not eliminate the possibility of price endogeneity. One approach to control for it is to use instrumental variables representing supply shifters, such as production costs. Unfortunately, however, no data are available to identify which bus or company individuals used.

The price elasticity _β_ is of particular interest because there is little evidence on this in the literature. In the formal public transport sector, there are a number of studies and literature reviews on transport elasticities (see, for example, Goodwin et al. 2004; Litman 2013; Wardman et al. 2018; Litman 2021). In the literature, the price elasticities for public bus transport are estimated at -0.2 to -0.3 in the short run. The long-term elasticity exceeds 0.5 in absolute terms. There is little evidence on the informal transport side.

For informal transport services, it is often difficult to observe price data by nature. Fares are not necessarily determined systematically. Customers can negotiate prices. The actual prices may vary depending on subjective judgement of trip distance, time and traffic congestion. On the other hand, service operators may simply collect a uniform “normal” price (e.g., MGA500 per ride) regardless of distance traveled. In the informal transport sector, there is no formal fare collection system on board. In the case of Antananarivo, the average cost is MGA840 per trip, with a wide variation from nearly zero to MGA24,000 or about US$6 ( **Figure 8** ).
<!-- Start of picture text --> 700 TM@ Spending on transport (Ar1,000) 7<br>2 @ Share<br>5 _ 600 in totalpexpenditure (%)(% 6<x<br>2s 5<br>2 == 8a 400500 4g52<br>me}5 5 300 3x<br>oo £<br>= § 200 22<br>3 § £<br><a<br>=~ 1 0 0 | | 051<br>1 2 3 4 5<br>Quantile by HH expenditure<br><!-- End of picture text -->
<!-- Start of picture text --> Female<br>Male<br>0% 20% 40% 60% 80% 100%<br>M|Taxibe only mSuburbanbusonly Both<br><!-- End of picture text -->

<!-- Start of picture text --> v Female<br>€6<br>eo3 Male<br>2ry Female<br>A<br>Male<br>0% 20% 40% 60% 80% 100%<br>lm Unsafe/Uncomfortable m Safe/Comfortable TM Very safe/Very comfort<br><!-- End of picture text -->
<!-- Start of picture text --> Female<br>Male<br>0% 20% 40% 60% 80% 100%<br>mWork School Shopping mVisitto family Other<br><!-- End of picture text --> <!-- Start of picture text --> Female 62<br>Male 8 2<br>0% 20% 40% 60% 80% 100%<br>Once @2 ©3 ©4 B5 B6 B7 B8ormore<br><!-- End of picture text -->
<!-- Start of picture text --> Female 4<br>Male 4<br>0% 20% 40% 60% 80% 100%<br>@ < 200,000 § 200,000 - 300,000 ® 300,000 - 400,000<br>= 400,000 - 500,000 500,000 - 750,000 = 750,000 - 1 mil<br>m>1 million<br><!-- End of picture text --> <!-- Start of picture text --> 1400 53<br>i& 1200 1162 514 52 z<br>< 1000 927 515<br>~39 800 5S0££<br>a]£~600 48.9 49 &v<br>8&2 400200 48a78ig<br>0 46<br>Cost (Ar) Time (minutes)<br>m= Male m Female<br><!-- End of picture text --> <!-- Start of picture text --> Female<br>Male<br>0% 20% 40% 60% 80% 100%<br>m@ Self-employed = Employee<br>TM Unemployed TM Other (students, etc.)<br><!-- End of picture text -->
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coefficients can be differentiated between the two groups, for instance, male and female riders:

# **IV. DATA**

Our data come from a minibus user survey carried out around Antananarivo in March 2021. The survey covers 10 zones in 8 Antananarivo districts<sup>7</sup> and 2 adjoining districts<sup>8</sup> where about 3.2 million people live according to the latest 2018 census ( **Figure 17** ). For each zone, about 400 minibus users were randomly surveyed around major bus stops. In total, 4,124 responses were collected, including partial responses. With observations with missing values and outliers excluded, the following analysis uses 3,133 observations.

Despite the recognition of disparities in population, our survey attempted to interview the same number of minibus users in each district. To take this into account, each observation will be weighted by the inverse of its probability of being sampled according to the respondent’s origin. The sampling weights are calculated based on the total census population.

> 7 Antananarivo Renivohitra (comprised of 6 districts), Antananarivo Avaradrano, Antananarivo Atsimondrano. 8 Arivanimamo and Ambohidratrimo.
<!-- Start of picture text --> @ Lac Anosy (CBD)<br>—— Suburban bus<br>—— City bus<br>Survey districts<br>_ [__}1er Arrondissement<br>f |__| 2e Arrondissement<br>J {| 3e Arrondissement<br>US {| 4e Arrondissement<br>‘\ 5e Arrondissement<br>Zo {| 6e Arrondissement<br>{/a {|{| AmbohidratrimoAntananarivo Atsimondrano<br>7} } {| Antananarivo Avaradrano<br>C C {| Arivonimamo<br>acoe}3qy)_6b :a nt<br>2s7=><br>Ww tel io? Rly<br>2 XH<br>iS<br>\ My =<br>{4;(wt<br>N 7{ \<br>w E at —~<br>0 5 10 20 \<br>s KM /<br><!-- End of picture text -->
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However, having too many informal buses in limited areas may be causing massive traffic congestion in the city. Minibus users spend on average 50 minutes on board of the vehicle ( _TIME_ ) ( **Figure 19** ). This is comparable or greater than the average commute duration in developed countries, for instance, 26 minutes in the United State. The average commuting time is 53 minutes in the United Kingdom and 42 minutes in other European countries (Chatterjee et al., 2020). For some people, it takes more than 4 hours to get their destinations. The average user transfers minibuses 1.3 times per trip ( _TRS_ ). Thus, at least one transfer is needed to get to a destination, which means that route coordination is important. In the sample, about 5 percent of travelers have no transfer. To take the logarithm of the variable with zero values unbiasedly, the Battese’s (1997) specification is used: While about 75 percent use only within-city taxi-be, 20 percent use only suburban minibuses ( _dSUBU_ ). About 5 percent use both ( _dBOTH_ ). The within-city bus only users are set as a baseline. Informal public transport users are not confident of the quality of the services. About 19 percent feel that minibuses are not safe. Similarly, 21 percent responded that minibuses are not comfortable.

For individual characteristics, the income level is categorized into 7 groups. Half of the respondents earn less than MGA300,000, approximately, US$75 per month. Minibuses are an important means of transportation in Antananarivo or in Madagascar in general. Slightly more than half of the surveyed minibus users are female ( _dFEMALE_ ). About 40 percent are considered household heads ( _dHHH_ ).

In Madagascar, formal job opportunities are limited. About 32 percent responded that they are employees. While 39 percent are considered self-employed, 22 percent of minibus riders
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are not employed. Self-employees are used as a baseline in the analysis. For household asset ownership, only 6 percent in the sample own private cars. Motorcycles and bicycles are more prevalent: 17 percent for the former and 13 percent for the latter. These modes of transport can potentially be an alternative to informal public transport.

**<u>Table 2. Summary Statistics</u>**

|Variable|Abb.|Obs<br>|Mean|Std. Dev. Min<br>|Max|
|---|---|---|---|---|---|
|Number of trips made per week|_Q_|3,133|4.477|<br>1.687<br>1|15|
|Total travel costs paid per trip (MGA)|_P_|3,133|837.612|966.144<br>2|24000|
|Trip purpose (dummy variable):||||||
|Go to work||3,133|0.459|<br>0.498<br>0|1|
|Business||3,133|0.023|<br>0.149<br>0|1|
|School||3,133|0.129|<br>0.335<br>0|1|
|Shopping||3,133|0.245|<br>0.430<br>0|1|
|Personal reasons||3,133|0.064|<br>0.244<br>0|1|
|Family visit||<br>3,133|0.063|<br>0.242<br>0|1|
|Hobby||3,133|0.010|<br>0.101<br>0|1|
|Escort for others||3,133|0.003|<br>0.054<br>0|1|
|In-vehicle time (minute)|TIME|<br>3,133|50.167|<br>36.471<br>1|280|
|<br>Access time (minute)|ACC|3,133|9.444|<br>9.375<br>1|180|
|<br>Egress time (minute)|EGR|3,133|15.738|<br>19.535<br>1|180|
|Number of transfers made|TRS|3,133|1.271|<br>0.659<br>0|7|
|Transport mode: Only within city bus||3,133|0.753|<br>0.431<br>0|1|
|Only suburban bus|dSUBU|3,133|0.191|<br>0.393<br>0|1|
|<br>Both|dBOTH|3,133|0.048|<br>0.214<br>0|1|
|Dummy variable for users within CUA|dCUA|3,133|0.564|<br>0.496<br>0|1|
|<br>Users perception: Unsafe|dUNSAFE|3,133|0.191|<br>0.393<br>0|1|
|Uncomfortable|dUNCOMF|<br>3,133|0.210|<br>0.407<br>0|1|
|Individual effects:||||||
|Income: < 200k||3,133|0.243|<br>0.429<br>0|1|
|200-300k||3,133|0.304|<br>0.460<br>0|1|
|300-400k||<br>3,133|0.185|<br>0.388<br>0|1|
|400-500k||3,133|0.119|<br>0.324<br>0|1|
|500-750k||3,133|0.079|<br>0.270<br>0|1|
|750k-1 mil||3,133|0.036|<br>0.187<br>0|1|
|> 1 mil||3,133|0.034|<br>0.181<br>0|1|
|Dummy variable for female users|dFEMALE|3,133|0.527|<br>0.499<br>0|1|
|Age: Users above 56 years old|dAGE56|3,133|0.071|<br>0.256<br>0|1|
|No. of household members|SIZE|3,133|4.425|<br>1.723<br>1|22|
|Dummy variable for household head|dHHH|<br>3,133|0.413|<br>0.492<br>0|1|
|<br>Occupation: Self employed||3,133|0.389|<br>0.488<br>0|1|
|<br>Employed||3,133|0.318|<br>0.466<br>0|1|
|<br>Unemployed||3,133|0.226|<br>0.418<br>0|1|
|Other||3,133|0.068|<br>0.251<br>0|1|
|Asset ownership: Car||3,133|0.063|<br>0.243<br>0|1|
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|Motorcycle|3,133|0.169|0.375<br>0<br>1|
|---|---|---|---|
|Bike|3,133|0.129|0.335<br>0<br>1|

**Figure 18. Number of trips per week in the sample**

<!-- Start of picture text --> 0 5 10 15<br>Number of trips per week<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text -->

**Figure 19. Travel time in the sample (minutes)**

<!-- Start of picture text --> 0 50 100 150 200<br>Travel time (minutes)<br>.025<br>.02<br>.015<br>Density<br>.01<br>.005<br>0<br><!-- End of picture text -->

# **V. MAIN ESTIMATION RESULTS**

The ordinary least squares (OLS) regression is performed ( **Table 3** ). Regardless of the inclusion of the OD pair dummy variables, the estimated results are broadly consistent. From
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the statistical point of view, the specification with the OD dummy variables may be more preferable. According to the Ramsey RESET test, the hypothesis that there is no omitted variable can be rejected in the specification without the OD pair dummy variables. The test statistic is estimated at 3.92, which is significant at the 1 percent confidence level. With the OD dummy variables included, the omitted variable test cannot be rejected. The test statistics is 1.93, statistically insignificant. This suggests that in the market that we are analyzing, there are many important unobserved route- or trip-specific characteristics affecting the people’s informal public transport demand, such as local environment around origins and/or destinations and traffic conditions.

The estimated results are broadly consistent with our prior expectation, however, there are important differences from the literature mostly focused on the formal sector. First, the price elasticity is found to be relatively modest at -0.02 and statistically insignificant under these model specifications. The _p_ -value of the coefficient of ln _P_ is 0.21. It can be concluded that the demand is relatively inelastic to fares in the informal public transport sector. As will be discussed the next section, however, the price elasticity seems to vary across different types of passengers. It can be statistically significant in some cases, but the elasticity is small in any case. This is considered to be particularly characteristic of informal public transport in developing countries: There are few alternatives regardless of fares.

Time elasticity is significant but its magnitude is relatively modest ( **Figure 20** ). Under the specification with the OD pair dummy variables included, the coefficient of in-vehicle time ( _TIME_ ) is estimated at -0.033. Holding everything else constant, people are discouraged to take a trip by minibus when in-vehicle time gets longer. The estimated elasticity is more modest than those found in the literature that often examines the formal transport sector, ranging from -0.3 to -0.5 (TRL 2004). Again, there are few alternative modes of transport in Antananarivo. Even though the operational efficiency of minibuses is low, people cannot help but use them.
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The estimation results indicate that the minibus demand is particularly strong for those who commute to workplace or school. The coefficients of trip purposes being to work or to go to school are statistically significant and numerically large. It implies that commuting workers and students are crucially relying on this informal transport sector for their everyday mobility.

Access and egress times are less relevant in the informal transport sector. In the formal sector, it is often shown that efficiency in multimodal connection among different modes is important to stimulate the demand for public transportation (e.g., Hensher and Rose 2007). For instance, Brands et al. (2014) find that about 40 percent of public transport users use bicycle as access mode. In the case of Antananarivo, there is little evidence supporting such a view. Both coefficients of access and regress times are insignificant. In fact, the current minibus network is already comprehensive. Many users may have fairly good access to minibus stops from their home, offices or schools. As shown above, average access time to boarding point is on average 10 minutes, and regress time, 15 minutes.

Regarding transit, first, suburban bus to within-city bus transfer is not preferable. The coefficient of _dBOTH_ is found to be significantly negative. This indicates that the sector reform is needed to improve the interface between the two networks by enhancing coordination to make transfers easier. The duplicated regulatory frameworks under the two different authorities should be improved to make the routes and schedules better synchronized.

Second, however, transit itself looks important for minibus users. _dTRF_ is negative and ln _TRF_ has a positive coefficient. Thus, the demand increases when people transit more frequently. The result may be understood as the indication that having more transfers does not mean inconvenience but flexibility for users. Unlike traditional formal public transport systems, the current minibus network is not optimally designed to meet the people’s real demand. Thus, transfers are unavoidable and inevitable. Under the circumstances with high route density, it can be more efficient to have a number of short routes. There is some similar
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discussion in the literature. Deb and Filippini (2013) found that the service quality, defined by density of transport routes, has the highest demand elasticity. Shorter waiting time can also increase the demand for public transportation (e.g., Hensher and Rose 2007).

Safety matters. As discussed, there is a concern about safety of minibus operations in Antananarivo. Over 6,500 people died in road crashes in the city in 2016, of which more than 20 percent involved minibuses (World Bank 2019). The coefficient of _dUNSAFE_ is negative and significant. When minibus operations are unsafe (though judgements are fundamentally subjective), the demand declines. This is consistent with increasing concern among users. On the other hand, there is no significant result for comfortability of minibus operations ( _dUNCOMF_ ).

For the individual effects, it is found that informal minibus transportation in Antananarivo is a “normal good” (as opposed to inferior good). The estimated coefficients of the income group dummy variables tend to increase with income level ( **Figure 21** ). This is contradictory to earlier studies in the literature, which generally show that the income elasticity is negative (e.g., Deb and Filippini, 2003; TRL 2004; Paulley et al. 2006). In developed countries, the demand for public transportation could decrease with household income because people with high income can own and use their private cars. However, in developing countries, such as Madagascar, buying a car is not an immediate, viable option. Private vehicles are still expensive for most people. As shown above, the private vehicle ownership is only 6 percent in Antananarivo. Therefore, people still tend to continue using minibuses even if their income increases. A policy implication is straightforward: It continues to be important for the government to make increased efforts toward improving efficiency in this public transport sector.

Still, relatively speaking, private car owners are less likely to use minibuses than those who do not own a car. After all other characteristics, including household income, are controlled for, the coefficient of _dCAR_ is significantly negative. This is consistent with the literature.
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Naturally, if people own a car, they will likely use it. Bicycle may also be able to be an alternative, but the statistical significance of _dBIKE_ is weaker than car ownership.

For other individual characteristics, our results show females are less likely to use informal minibuses than males. This is not consistent with earlier findings in the formal public transport sectors. For instance, Hensher and Rose (2007) show that public transport modes, such as rail and bus, are more often selected by women and the youth. The negative coefficient associated with female users in our case may be relevant to the above-mentioned safety concern but also affected by the employment status of the surveyed bus users. As shown in **figure 16** , more males are formally employed than females. Related to the above discussion about the difference in trip objective, the transport demand is significantly lower for those who are not employed than self-employees (baseline). All these variables are interacted with one another.
- 24 -

**<u>Table 3. Estimated demand function: Baseline</u>**

||Withou|t OD dummies|With O|D dummies|
|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|
|lnP|-0.026|(0.016)|-0.021|(0.017)|
|Trip purpose: Work|0.322|(0.061) ***|0.265|(0.060) ***|
|Business|0.158|(0.086) *|0.115|(0.083)|
|School|0.348|(0.061) ***|0.298|(0.060) ***|
|Shopping|0.117|(0.060) *|0.063|(0.060)|
|Personal|0.037|(0.062)|-0.011|(0.061)|
|Family visit|-0.017|(0.062)|-0.064|(0.062)|
|Hobby|0.097|(0.084)|0.042|(0.085)|
|lnTIME|-0.031|(0.010) ***|-0.033|(0.011) ***|
|lnACC|0.008|(0.010)|0.011|(0.011)|
|lnEGR|-0.008|(0.008)|-0.007|(0.008)|
|dTRF|-0.059|(0.039)|-0.072|(0.041) *|
|lnTRF|0.066|(0.025) ***|0.065|(0.025) ***|
|dSUBU|0.008|(0.018)|0.016|(0.021)|
|dBOTH|-0.084|(0.032) ***|-0.067|(0.035) **|
|dCUA|-0.022|(0.015)|0.008|(0.062)|
|dUNSAFE|-0.035|(0.019) *|-0.035|(0.020) *|
|dUNCOMF|0.007|(0.018)|0.007|(0.018)|
|Individual effects:|||||
|Income: 200-300k|0.065|(0.019) ***|0.066|(0.020) ***|
|300-400k|0.089|(0.020) ***|0.090|(0.021) ***|
|400-500k|0.051|(0.026) **|0.058|(0.026) **|
|500-750k|0.109|(0.028) ***|0.110|(0.029) ***|
|750k-1 mil|0.099|(0.037) ***|0.106|(0.038) ***|
|> 1 mil|0.162|(0.040) ***|0.168|(0.040) ***|
|dFEMALE|-0.029|(0.016) *|-0.028|(0.016) *|
|dAGE56|-0.074|(0.028) ***|-0.087|(0.029) ***|
|lnSIZE|-0.019|(0.016)|-0.021|(0.017)|
|dHHH|-0.055|(0.019) ***|-0.047|(0.019) **|
|Occupation: Employed|0.031|(0.018) *|0.027|(0.018)|
|Unemployed|-0.043|(0.020) **|-0.045|(0.020) **|
|Other|-0.012|(0.030)|-0.021|(0.030)|
|dCAR|-0.071|(0.031) **|-0.064|(0.032) **|
|dMOTO|0.014|(0.017)|0.013|(0.018)|
|dBIKE|-0.033|(0.020) *|-0.031|(0.020)|
|constant|1.510|(0.133)***|1.490|(0.139)***|
|Obs|3,133||3,133||
|F-statistics|24.19||57.05||
|R squared|0.180||0.210||
|No. of OD dummy variables|0||96||
|Ramsey RESET test|3.92|***|1.93||

Note: The dependent variable is ln _Q_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
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entities, although it may not be straightforward because the service areas of the two networks are partially duplicated.

To examine whether the demand behavior is different between short- and long-distance travelers, the interaction term between ln _P_ and _dCUA_ is added. This allows us to differentiate the price elasticity between the two groups. The selected results are shown in **Table 4** . The full results are presented in Appendix. The coefficients of ln _P_ are significantly different depending on _dCUA_ : For short distance travelers (i.e., d d = 1), the price elasticity is estimated at -0.054, which is significant. For longer distance travelers (d d = 0), the elasticity remains insignificant. Thus, the demand-side management through fare adjustments is only effective for within-city taxi-be: A 10 percent increase in fares would reduce the demand by about 5 percent. This is consistent with the literature: Within the center of the city, people may have alternative modes of transport, i.e., taxi, motorcycle taxi or even walking, because trip distances are likely to be relatively short.

According to the Chow test, the demand structures are considered to be different between the two groups ( **Figure 22** ). The estimated test statistic is 1.57, which is statistically significant. Again, the price elasticity is only statistically significant in the estimation with data from within-city bus users. The estimated elasticity is -0.063, still relatively modest. Other coefficients are broadly similar, except for a few. For instance, the negative demand impact by gender is only found in short-distance travels. The potential impact of private car ownership is found to be particularly significant for long-distance minibus users.

The number of transfers is only significant when d d = 1. Flexible connectivity matters only to taxi-be users. However, the time elasticity is significant for both taxi-be and suburban buses. The elasticity is larger for the former. This is because traffic congestion is severer within CUA. It is critical to improve operational efficiency of taxi-be for stimulating the people’s mobility through the public transportation system.
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The number of transfers, which can be interpreted as a proxy of the connectivity among bus routes, is particularly important for travelers within CUA where more bus routes are available. Outside of the CUA, the current suburban bus routes are lineal. The transferability does not matter too much.<sup>10</sup> Travelers within CUA are more worried about safety. Females have lower demand particularly within CUA. Suburban residents who own a car have particularly low demand for informal public buses, presumably because they can drive. On the other hand, vehicle ownership does not seem to affect the demand of city residents for public transport significantly. Vehicle ownership is minimal anyway, and people may not use their vehicles within CUA because of the current traffic congestion.

**<u>Table 4. Demand functions differentiated by distance travelled</u>**

||Pooled model:<br>|||Separat<br> <br>|e regressi<br>|on:<br>||
|---|---|---|---|---|---|---|---|
||Without OD dummies|With O|D dumm|ies<br>dCUA=|0|dCUA|=1|
||Coef. Std.Err.|Coef.|Std.Err.|<br>Coef.|Std.Err.|Coef.|Std.Err.|
|lnP*(dCUA=0)|-0.006 (0.020)|0.002|(0.020)|0.007|(0.022)|||
|lnP*(dCUA=1)|-0.055 (0.022) **|-0.054|(0.022)|**||-0.063|(0.026) **|
|Trip purpose: Work|0.326 (0.060) ***|0.267|(0.059)|***<br>0.286|(0.072)|*** 0.231|(0.106) **|
|lnTIME|-0.032 (0.010) ***|-0.034|(0.011)|***<br>-0.024|(0.015)|*<br>-0.047|(0.015) ***|
|dTRF|-0.060 (0.039)|-0.072|(0.041)|*<br>-0.013|(0.061)|<br>-0.158|(0.052) ***|
|lnTRF|0.067 (0.025) ***|0.066|(0.025)|***<br>0.032|(0.033)|<br>0.113|(0.039) ***|
|dSUBU|0.008 (0.018)|0.015|(0.021)|0.015|(0.024)|<br>-0.015|(0.039)|
|dBOTH|-0.093 (0.033) ***|-0.078|(0.035)|**<br>-0.068|(0.037)|*<br>0.028|(0.141)|
|dCUA|0.297 (0.170) *|0.374|(0.177)|**||||
|dUNSAFE|-0.037 (0.020) *|-0.037|(0.020)|*<br>-0.009|(0.031)|<br>-0.066|(0.025) ***|
|dUNCOMF|0.008 (0.018)|0.007|(0.018)|-0.052|(0.027)|*<br>0.078|(0.023) ***|
|dFEMALE|-0.030 (0.016) *|-0.028|(0.016)|*<br>-0.008|(0.022)|<br>-0.045|(0.022) **|
|dCAR|-0.071 (0.031) **|-0.064|(0.032)|**<br>-0.094|(0.046)|**<br>-0.024|(0.042)|
|dMOTO|0.013 (0.017)|0.011|(0.018)|0.014|(0.026)|<br>0.002|(0.023)|
|dBIKE|-0.033 (0.020)*|-0.031|(0.020)|-0.032|(0.027)|<br>-0.017|(0.030)|
|Obs|3,133|3,133||1,367||1,766||
|F-statistics|25.58|35.01||27.25||9.33||
|R squared|0.181|0.211||0.238||0.211||
|No. of OD dummy var.|<br>0|96||61||35||
|Ramsey RESET test|3.19**|1.81||1.56||3.19|**|

Note: Only selected coefficients are presented. See the full result in Appendix. The dependent variable is ln _Q_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

10 The average number of transfers made is the same between the two groups. It is 1.2 times for travelers within CUA and 1.3 times for those who travel from and to the outside of the CUA.
<!-- Start of picture text --> 0.25 wm Suburban<br>0.20 mCUA<br>5 0.15 = +1.64°S.E.<br>2 010 = -1.64*S.E. ;<br>Oo '<br>2°ooO 0.050.00 ~- a ;<br>% -0.10<br>Ww<br>-0.15<br>-0.20<br>Price Time Transfer BOTH Unsafe Uncom-<br>fortable<br><!-- End of picture text -->
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Finally, the potential difference in demand between male and female minibus users is examined. The Chow test statistic is estimated at 1.48, which is significant at the 5 percent level. Thus, the demand structure is systematically different between male and female passengers ( **Figure 24** ). The main results are shown in **Table 6** . Male passengers seem to be more sensitive to prices. The price elasticity is only significant for male minibus users. Male passengers are also particularly sensitive to travel time. The time elasticity ( _TIME_ ) is only significant for males. These may be related to the difference in trip objectives. The male demand is more influenced by their trip purposes, e.g., work, business and schooling.

For female users, the demand is more related to nonessential trip purposes, such as personal trips and family visits. Despite our prior expectation, there is no gender-specific impact related to safety or comfortability. The household ownership of vehicle particularly matters to females. If a vehicle is owned, the demand for public buses is reduced significantly.

**<u>Table 6. Demand functions differentiated by gender</u>**

|||Pooled model:||Separate|regression:||
|---|---|---|---|---|---|---|
|||Without OD dummies<br>Coef. Std.Err.|With OD dummies<br>Coef. Std.Err.|dFEMA<br>Coef.<br>|LE=1<br>Std.Err.|dFEMALE=0<br>Coef. Std.Err.|
|lnP*(dFEMA|LE=0)|-0.057 (0.023) **|-0.054 (0.024) **|||-0.049 (0.027) *|
|lnP*(dFEMA|LE=1)|0.003 (0.019)|0.008 (0.019)|-0.005|(0.023)||
|Trip purpose:|Work|0.319 (0.060) ***|0.262 (0.059) ***|0.044|(0.119)|0.407 (0.058) ***|
||Business|0.158 (0.086) *|0.115 (0.083)|-0.078|(0.153)|0.258 (0.093) ***|
||School|0.345 (0.060) ***|0.294 (0.060) ***|0.091|(0.121)|0.406 (0.062) ***|
||Shopping|0.114 (0.060) *|0.060 (0.059)|-0.165|(0.118)|0.216 (0.060) ***|
||Personal|0.033 (0.061)|-0.016 (0.061)|-0.270|(0.121) **|0.173 (0.066) ***|
||Family visit|<br>-0.023 (0.062)|<br>-0.070 (0.061)|<br>-0.272|<br>(0.119) **|<br>0.032 (0.070)|
||Hobby|0.092 (0.084)|0.037 (0.085)|-0.383|(0.127) ***|<br>0.239 (0.098) **|
|lnTIME||-0.031 (0.010) ***|-0.034 (0.011) ***|-0.020|(0.015)|-0.049 (0.016) ***|
|dTRF||-0.056 (0.039)|-0.070 (0.041) *|-0.124|(0.059) **|-0.038 (0.056)|
|lnTRF||<br>0.066 (0.025) ***|<br>0.065 (0.025) ***|<br>0.050|<br>(0.034)|<br>0.080 (0.039) **|
|dSUBU||<br>0.008 (0.018)|<br>0.015 (0.021)|<br>0.049|<br>(0.028) *|<br>-0.036 (0.032)|
|dBOTH||-0.079 (0.032) **|-0.063 (0.034) *|-0.026|(0.043)|-0.107 (0.057) *|
|dCUA||-0.021 (0.015)|0.005 (0.062)|0.027|(0.091)|-0.053 (0.089)|
|dUNSAFE||<br>-0.035 (0.019) *|<br>-0.034 (0.020) *|<br>-0.036|<br>(0.027)|<br>-0.040 (0.031)|
|dUNCOMF||0.006 (0.018)|0.005 (0.018)|0.011|(0.026)|-0.003 (0.026)|
|dFEMALE||-0.423 (0.165) ***|-0.434 (0.170) **||||
|dCAR||-0.069 (0.031) **|-0.062 (0.032) *|-0.102|(0.051) **|-0.015 (0.043)|
|dMOTO||<br>0.015 (0.017)|<br>0.013 (0.018)|<br>-0.015|<br>(0.028)|<br>0.029 (0.025)|
|dBIKE||-0.033 (0.020)*|-0.031 (0.020)|-0.045|(0.032)|-0.014 (0.028)|
|Obs||3,133|3,133|1,651||1,482|
<!-- Start of picture text --> 0.20 m= Male<br>0.15 m Female<br>€ = +1.64*S.E.<br>5 010 = -1.64*S.E.<br>5 0.05<br>°<br>2<br>G 0.00 +t<br>%i= -0.05 a TH =<br>3s -0.10<br>rr<br>-0.15<br>-0.20<br>Price Time Transfer BOTH Unsafe Uncom-<br>fortable<br><!-- End of picture text -->
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A number of new findings were discovered, though they cannot be overgeneralized. First, the demand for informal public transport is generally relatively inelastic. The price elasticity was estimated at 0.05 to 0.06 in absolute terms, which is significantly lower than the norm in the formal sector (0.3 to 0.5). This is attributed to the fundamental characteristic of the informal sector: While users do not have an alternative, operators offer their services to directly respond to users’ needs under the loosely regulated circumstances.

Consistently, it is found that the demand is price-elastic only for short-distance travelers, who may have alternative choices, such as motorcycle taxi or walking. The demand is also inelastic for everyday users for commuting purposes (employees and students), though their demand is strong.

Still, the price elasticity is found to be significant particularly within the central city where alternative transport modes are available. Thus, it is possible to manage the demand through fare policies. For instance, strategic price discrimination, such as peak pricing and student discount, can be adopted for demand side management, though it may require proper regulatory frameworks as well as supporting ticketing systems (e.g.,smartcard) and infrastructure.

Second, the time elasticity is also found to be very small at about -0.02 to -0.05, probably, one-tenth of the elasticities observed in the formal transport sector in the literature. The demand is rigid no matter how the operational efficiency changes and no matter how much is charged. Essentially, users have no other choice. This is particularly relevant to developing countries. However, it is important to remind us of the significance of the market size. In Antananarivo, for instance, the minibus sector carries about 600,000 passengers every day. Even small changes in efficiency (i.e., within-vehicle time) or fares could result in a significant change in travel demand as a whole.

Third, unlike formal public transportation, the demand increases with income. Regardless of income level, everyone uses minibuses, though some modal shift may be expected for those
- 33 -

who own a private car. It is well known that private car ownership would pick up when per capita GDP exceeds US$5,000-US$10,000. But at this stage of development, it continues to be essential for the government to make increased efforts toward improving efficiency in the public transport sector, even if it is somewhat “informal.” In Madagascar, GDP per capita is still about US$520, and private car ownership remains minimal.

On the institutional side, finally, the analysis shows that several other features are important to influence the demand for informal public transport. It is essential to improve safety. Users also demand more flexibility of transit. Perhaps the current network design may not be optimal for them, calling for further investigation into the need for bus route restructuring. Especially, using both within-city and suburban buses is found to be a particular burden for users. Their connectivity is particularly poor because of the lack of coordination between the two systems. The duplicated regulatory frameworks need to be ameliorated.

By improving these features, the demand can be stimulated even if fares are increased. The current fares seem to be too low, creating overcompetition in the market, overusing the old and unsafe fleet, and aggravating traffic congestion on the road. By organizing the network better and imposing stricter safety regulations, the operational efficiency can be improved with passengers’ total trip times reduced. The improved benefits can be sufficient to compensate for potential changes in bus fares, potentially allowing proper fleet maintenance and timely fleet renewal.