metadata: authors: 0: author: Iimi,Atsushi
1: author: You,Liangzhi
2: author: Wood-Sichra,Ulrike
count: Madagascar
docna: 0: docna: Crop production, transport infrastructure,
            and agrobusiness nexus : evidence from Madagascar
docty: Policy Research Working Paper
subtopic: Transport Services,Crops and Crop Management Systems,Climate Change and Agriculture,Inequality,Food Security
teratopic: Agriculture,Poverty Reduction,Transport
abstracts: cdata!: The literature suggests a wide range of
            impacts of improved transport connectivity on agricultural
            growth. Still, the infrastructure-growth nexus remains
            somewhat mysterious, particularly in the African context,
            because many rural farmers do not have their own transport
            means. Using data from Madagascar, the paper reexamines the
            important roles of agrobusinesses. By applying the spatial
            autoregressive model, it is shown that proximity to
            input-oriented agrobusinesses, such as input dealers and
            equipment suppliers, is particularly important to increase
            rice production. Fertilizer and irrigation use is also found
            important, indicating the needs for intensification in rice
            production. Market accessibility is always found as a
            significant determinant: transport infrastructure connecting
            farmers and markets, especially the capital city,
            Antananarivo, is therefore important to develop and maintain.
geo_reg_key: 517191
repnme: repnme: Crop production, transport infrastructure,
            and agrobusiness nexus : evidence from Madagascar
display_title: Crop production, transport
            infrastructure, and agrobusiness nexus : evidence from Madagascar
listing_relative_url: /research/2018/06/30184080/crop-production-transport-infrastructure-agrobusiness-nexus-evidence-madagascar
topicv3: Agriculture and Food Security m1326205 435,Crop Production m1326627 174,Transport m1326661 135,Poverty m1326687 695,Transport Infrastructure m1327632 1803
publishtoextweb_dt: 2018-06-21T00:00:00Z
historic_topic: Agriculture,Poverty Reduction,Transport
no_of_pages: 22
author_keys: 0: author_key: 914707
1: author_key: 1072043
model_extractions: WPS8486 Policy Research Working Paper 8486 Crop Production, Transport Infrastructure, and Agrobusiness Nexus Evidence from Madagascar _Atsushi Iimi Liangzhi You Ulrike Wood-Sichra_ Transport and Digital Development Practice June 2018
Policy Research Working Paper 8486

# **Abstract**

The literature suggests a wide range of impacts of improved transport connectivity on agricultural growth. Still, the infrastructure-growth nexus remains somewhat mysterious, particularly in the African context, because many rural farmers do not have their own transport means. Using data from Madagascar, the paper reexamines the important roles of agrobusinesses. By applying the spatial autoregressive model, it is shown that proximity to input-oriented agrobusinesses, such as input dealers and equipment suppliers, is particularly important to increase rice production. Fertilizer and irrigation use is also found important, indicating the needs for intensification in rice production. Market accessibility is always found as a significant determinant: transport infrastructure connecting farmers and markets, especially the capital city, Antananarivo, is therefore important to develop and maintain.

This paper is a product of the Transport and Digital Development 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/research. The authors 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
# **Crop Production, Transport Infrastructure, and Agrobusiness Nexus: Evidence from Madagascar**

Atsushi Iimi,<sup>1 ¶</sup> Liangzhi You,<sup>2</sup> Ulrike Wood-Sichra<sup>2</sup> 1 Transport & Digital Development Global Practice, World Bank 2 International Food Policy Research Institute (IFPRI) .

Key words: Spatial autoregressive model; agriculture production; transport infrastructure. JEL classification: H54; H41; Q12; C21; C34.

> ¶ Corresponding author. Atsushi Iimi, Senior Economist, World Bank Group. Tel: 202-473-4698; Email: aiimi@worldbank.org
- 2 -

# **I. INTRODUCTION**

There are a number of studies showing various impacts of improved transport connectivity on economic growth. For example, Lokshin and Yemtsov (2005) and Danida (2010) show that in the short term, people’s transport costs and travel time can be reduced by improved road conditions. In the medium term, agricultural productivity is likely be improved (Donaldson, 2010; Dorosh, et al., 2012), with input prices reduced (Khandker et al., 2009) and output prices increased (Bell and Van Dillen, 2012). Firms could also become more productive with more services offered in local economies (Mu and van de Walle, 2011). Then, poverty may be reduced over the long term (Dercon et al., 2008; Khandker and Koolwal, 2011).

Particularly in the African context, transport infrastructure has a lot of roles to contribute to agricultural growth. In the region, access to advanced inputs, such as fertilizer, improved seeds and irrigation, is often found to be a critical constraint (e.g., Gyimah-Brempong, 1987; Bravo-Ortega and Lederman, 2004; Xu et al., 2009). Damania et al. (2017) show that many farmers do not adopt new technologies because of limited transport connectivity. Good transport connectivity motivates farmers to participate in the market transaction (e.g., Rutto et al., 2012; Okay et al., 2016).

Still, the results chain from transport infrastructure to agricultural growth remains somewhat mysterious, particularly in Africa. One of the missing important elements may be agrobusinesses, such as input suppliers, collectors, processors and exporters. In Africa, the vast majority of rural farmers, who engage in subsistence farming, are poor and do not have their own transport means. In addition, affordable transport services are often not available (Teravaninthorn and Raballand, 2009). Therefore, transportation services must of necessity be provided by agribusinesses or trucking companies, for farmers to purchase advanced inputs, such as fertilizer and improved seeds, and access the output market to sell harvested crops. Rashid et al. (2013) show that in Ethiopia, transport costs account for 64-80 percent of fertilizer farmgate prices.
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The remaining sections are organized as follows: Section II develops our empirical strategy and describes our data, including key transport connectivity variables. Section III discusses the main estimation results and some policy implications. Then, Section IV concludes.

# **II. EMPIRICAL MODEL AND DATA**

Following the literature (e.g., Gyimah-Brempong, 1987; Bravo-Ortega and Lederman, 2004), a simple production function is considered with transport connectivity included as one of the production inputs:

# where _Y_ is the volume of rice produced in location _i_ .

Our primary source of data is the spatial production allocation model (SPAM) developed by the International Food Policy Research Institute (IFPRI) for generating geographically highly disaggregated crop production data.<sup>1</sup> The SPAM is a spatial model to allocate crop production derived from large statistics reporting units, such as country, province and district, to a raster grid at a spatial resolution of 5 minutes of arc (approximately, at a resolution of 10km x10km pixel). See You and Wood (2006) and You _et al._ (2009) for details.

While the model covers 42 crops, the current analysis is only focused on rice, which is the most common and important crop in Madagascar. The country’s total rice production amounts to about 4 million tons or US$1 billion per annum ( **Figure 2** ). About 85 percent of farmers engage in rice production. Using the spatial distribution of rice production based on the 2010 SPAM, the latest district-level rice production data for the period of 2013-15 are disaggregated into 3,198 locations or pixels. This is our dependent variable, _Y_ ( **Figure 3** ).

> 1 It is available on the Internet at MapSpaM.info.
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<!-- Start of picture text --> <q 250 = 2005 2010<br>a<br>&2.00<br>B<br># 1.50<br>=<br>% 1.00<br>3<br>t<br>3 0.50<br>=70,00<br>Bottom Second Middle Fourth Top<br>Quantile<br><!-- End of picture text --> <!-- Start of picture text --> =>?325¢TM 2005 TM 2010<br>@ 2.0<br>3<br>£15<br>8<br>2 10<br>£<br>gos-<br>0.0<br>Bottom Second Middle Fourth Top<br>Quantile<br><!-- End of picture text --> MAI; = (Spm dix)/max MAI;
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highly correlated with MAI even in a simple correlation diagram ( **Figure 8** ). Of course, there is considerable variation, which needs to be controlled by other factors.

**Figure 8. Correlation between rice production and MAI**

<!-- Start of picture text --> 0 .2 .4 .6 .8<br>Market Access Index<br>40000<br>30000<br>20000<br>10000<br>Rice production (ton/100km2)<br>0<br><!-- End of picture text --> Source: World Bank estimate.

The AGAI is defined in the same way, with the market capacity replaced with the number of agrobusinesses, _s_ , that exist in each location (commune). _d_ is measured by estimating transportation costs from pixel _i_ and location _h_ .

According to the official firm registry database, as of 2017, there existed 1,309 agrobusinesses in the country. Output-related agrobusinesses, including large plantations, collectors, processing companies, and exporters, are dominant, which amount to 902 companies in the database. On the other hand, 399 input-related agrobusinesses, also exist, such as fertilizer and other input dealers and equipment suppliers.<sup>5</sup> The spatial distribution of agrobusinesses is highly skewed ( **Figure 9** ). 569 firms or nearly half of the total agrobusinesses are located in three districts: Antananarivo, Fianarantsoa and Ambatondrazaka. While the first two are the largest two urban areas in the middle of the

> 5 This classification may be ambiguous. In the database, each agrobusiness is categorized as either an input- or output-related firm based on the description of its primary business activity. In practice, however, some of them are likely to engage in both activities.
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<!-- Start of picture text --> —— (inputAG businesses rotated)<br>AG businessead Network ».:<br>Access index a<br>HMGi 0.0070.034-- 0.0580.033 ree<br>om 0.0460.039- - 0.045 0.054 ee) ue<br>(i 0.055 - 0.066 * —-4<br>[MM 0.0670.095 -- 1.000 0.094 eesee i ' iris<br>4" rea we,/ Boe f<br>yet<br>a Ub4 i<br>/<br>; 0 50100 200 300<br><!-- End of picture text --> <!-- Start of picture text --> AG(Outputbusinesses retated)<br>AG—— businessead Network > 3<br>Access index ti<br>HM(i 0.0040.039-0.048 - 0.038 ~~ei “ill*<br>oh 00052-0.045 - 0.0510.063 he :a :<br>0.064 - 0.079 oF a<br>(MMMy 0.080-0.1170-118 - 1.000 atte: ray.i ae<br>oe= a ee eM53<br><a :<br>an es j<br>a 0 50100200 300<br><!-- End of picture text -->
Ye Belo Xgi = Ve Bel Xj + Uz Ok Dui Dei = if Xp > 0 Xj = Max(Xy;,xi)
- 12 -

**<u>Table 1. Summary statistics</u>**

|<br>Variable|Abb.|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Rice produced (ton)|_Q_|3,198|1161.2|<br>3472.4|<br>0.09|120197|
|Agro-climatic rice productivity (kg/ha)|_A_|3,198|691.6|<br>415.3|<br>0.10|2016|
|Population who engage in agricultural activities|_L_|3,198|2756.7|<br>6251.8|<br>55.49|210146|
|Rain-fed land area harvested (ha)|_R_|1,039|179.3|<br>227.7|<br>0.10|1052|
|Irrigated land area harvested (ha)|_I_|2,527|411.9|<br>565.2|<br>0.90|4908|
|Fertilizer used (kg)|_F_|3,127|1591.6|<br>2372.3|<br>0.45|22084|
|Distance to the road network (km)|_KM_|3,198|10.4|<br>9.9|<br>0.00|62|
|Market Access Index (0 to 1)|_MAI_|3,198|0.061|<br>0.055|<br>0.01|1|
|Agrobusiness Access Index (0 to 1)|||||||
|All agrobusinesses|_AGAI_|3,198|0.085|<br>0.076|<br>0.01|1|
|Input dealers and suppliers|_AGAI_i_|3,198|0.076|<br>0.077|<br>0.01|1|
|Output collectors, processing firms, exporters|_AGAI_o_|3,198|0.089|<br>0.085|<br>0.01|1|

# **III. ESTIMATION RESULTS AND POLICY IMPLICATIONS**

First of all, the ordinary least squares (OLS) regression is performed as a basis for discussion ( **Table 2** ). As discussed above, the results may be biased because of possible autocorrelation in spatial data and excessive zeros in input variables. A small positive number is here used for _R_ , _I_ and _F_ in case they are zero. The results are broadly consistent with economic theory: All inputs are productive. While disconnectedness from the road network has a negative effect, proximity to the market and agrobusinesses look important to increase rice production. Though, one unexpected result is that crop suitability, _A_ , has a negative elasticity. It means that farmers might be growing crops in less fertile land, which is not realistic in general.

With the Battese’s specification used, the results are broadly similar to the above ( **Table 3** ). However, the coefficients of some production inputs have been changed, especially for rainfed land, _R_ , which has a number of zeros in our data. The coefficients of irrigation _I_ and fertilizer _F_ also became greater than before, indicating the possible downward bias caused by the small positive number specification. In any case, the OLS estimates are potentially biased without spatial autocorrelation controlled.
- 13 -

**<u>Table 2. OLS regression results with the small positive number specification</u>**

||<br>OLS<br>Coef.|<br>Std.Err.||<br>OLS<br>Coef.|<br>Std.Err.||<br>OLS<br>Coef.|<br>Std.Err.||
|---|---|---|---|---|---|---|---|---|---|
|ln_A_|-0.029|(0.014)|**|-0.032|(0.014)|**|-0.034|(0.014)|**|
|ln_L_|0.305|(0.026)|***|0.354|(0.025)|***|0.344|(0.025)|***|
|ln_R_|0.099|(0.010)|***|0.099|(0.010)|***|0.100|(0.010)|***|
|ln_I_|0.142|(0.012)|***|0.150|(0.012)|***|0.148|(0.012)|***|
|ln_F_|0.482|(0.020)|***|0.471|(0.020)|***|0.473|(0.020)|***|
|ln_KM_|-0.046|(0.014)|***|-0.069|(0.014)|***|-0.067|(0.014)|***|
|ln_MAI_|0.583|(0.038)|***|||||||
|ln_AGAI_||||0.417|(0.032)|***||||
|ln_AGAI_i_|||||||0.210|(0.063)|***|
|ln_AGAI_o_|||||||0.248|(0.061)|***|
|constant|1.948|(0.299)|***|1.075|(0.277)|***|1.273|(0.280)|***|
|Obs|3,198|||3,198|||3,198|||
|F statistic|742.2|||724.4|||644.87|||
|R-squared|0.703|||0.696|||0.698|||

Note: The dependent variable is ln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

**<u>Table 3. OLS regression results with the Battese’s specification</u>**

||<br>OLS<br>Coef.|<br>Std.Err.||<br>OLS<br>Coef.|Std.Err.||OLS<br>Coef.|Std.Err.||
|---|---|---|---|---|---|---|---|---|---|
|ln_A_|-0.039|(0.015)|***|-0.043|(0.015)|***|-0.044|(0.015)|***|
|ln_L_|0.289|(0.026)|***|0.334|(0.025)|***|0.325|(0.025)|***|
|ln_R_|-0.013|(0.030)||-0.034|(0.030)||-0.026|(0.030)||
|ln_I_|0.307|(0.033)|***|0.310|(0.034)|***|0.311|(0.034)|***|
|ln_F_|0.534|(0.031)|***|0.531|(0.032)|***|0.529|(0.032)|***|
|ln_KM_|-0.053|(0.013)|***|-0.074|(0.013)|***|-0.071|(0.013)|***|
|ln_MAI_|0.500|(0.038)|***|||||||
|ln_AGAI_||||0.344|(0.030)|***||||
|ln_AGAI_i_|||||||0.206|(0.056)|***|
|ln_AGAI_o_|||||||0.174|(0.053)|***|
|d_R_|-0.455|(0.107)|***|-0.544|(0.109)|***|-0.517|(0.109)|***|
|d_I_|1.035|(0.116)|***|1.027|(0.118)|***|1.032|(0.119)|***|
|d_F_|-0.087|(0.218)||-0.102|(0.225)||-0.104|(0.224)||
|constant|0.884|(0.290)|***|0.133|(0.272)||0.294|(0.275)||
|Obs|3,198|||3,198|||3,198|||
|F statistic|801.6|||803.5|||736.96|||
|R-squared|0.738|||0.733|||0.734|||

Note: The dependent variable is ln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
- 14 -

With spatial autocorrelation taken into account, the spatial autoregressive model is estimated. Again, the model is estimated with both small positive number specification ( **Table 4** ) and Battese’s specification ( **Table 5** ). The results are broadly similar; however, the latter is our preferred specification for the reasons mentioned above. First, the land potential variable, _A_ , now has a small but positive coefficient, which is consistent with our expectation: Farmers are generally cultivating relatively fertile land areas from the agro-climatic perspective.

Second, advanced inputs, such as irrigation and fertilizer, are productive. The elasticities are estimated at about 0.3 and 0.5, respectively. Thus, fertilizer use is important to increase productivity of rice production. On the other hand, the coefficient of labor is still positive but small, indicating that the marginal productivity impact of labor is limited. These are consistent with the view that labor is abundant in rural Madagascar, and further intensification is needed.

Regarding transport connectivity, as expected, proximity to the road network is found to be important. The coefficient of _KM_ is consistently negative: The more distant are the areas, the less is production. Note that even though a farming area is not connected to the official road network, there are at least unofficial roads connecting it to the official network. They have not been unclassified yet and are likely to be in very poor condition, because there is no one who is officially responsible for those roads. Madagascar has a low road density of 5.4 km per 100 km<sup>2</sup> (c.f., 8.6 km in Ethiopia and 10.6 km in Tanzania). The estimation result confirms that this last-mile distance is still a constraint on rice production in the country.

Market accessibility is also important. The coefficient of _MAI_ is estimated at 0.31, which is statistically significant and numerically relatively large. A policy implication is that transport infrastructure connecting farmers and major markets, especially the capital city, Antananarivo, is important to be developed and maintained. Without improving access to Antananarivo, agricultural growth cannot be stimulated.
- 15 -

The estimation results show that proximity to agrobusinesses has a positive impact on rice production: The elasticity with respect to AGAI is 0.16. When disaggregating into input- and output-oriented firms, this impact is mainly explained by the effect of input-oriented agrobusinesses. While _AGAI_i_ has a positive and significant coefficient of 0.22, the coefficient of _AGAI_o_ is negative and insignificant. This is consistent with the above finding that the advanced input variables, fertilizer and irrigation, have large elasticities. It can be interpreted to mean that input-related agrobusinesses, such as input dealers and equipment suppliers, are important to stimulate rice production further in Madagascar. Since they are mainly located to major urban areas, transport infrastructure is critical to connect them to local farmers.

Of course, the possible impact of output-oriented agrobusinesses may have been captured by market accessibility ( _MAI_ ) to a certain extent. When all accessibility indices are included, the impact of MAI dominates all other effects. However, the relative importance of input-related agrobusinesses remains unchanged. Therefore, it can be concluded that in Madagascar, access to input-related agricultural services is more critical for rice production growth.

**<u>Table 4. Spatial autoregressive model with the small positive number specification</u>**

||<br>Spatial r|<br>egression||<br>Spatial r|<br>egression||<br>Spatial r|<br>egression||
|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||
|ln_A_|0.038|(0.011)|***|0.047|(0.011)|***|0.045|(0.011)|***|
|ln_L_|0.097|(0.020)|***|0.094|(0.020)|***|0.095|(0.020)|***|
|ln_R_|0.100|(0.009)|***|0.101|(0.009)|***|0.101|(0.009)|***|
|ln_I_|0.133|(0.010)|***|0.136|(0.010)|***|0.136|(0.010)|***|
|ln_F_|0.518|(0.011)|***|0.511|(0.011)|***|0.512|(0.011)|***|
|ln_KM_|-0.055|(0.011)|***|-0.065|(0.011)|***|-0.064|(0.011)|***|
|ln_MAI_|0.247|(0.028)|***|||||||
|ln_AGAI_||||0.144|(0.025)|***||||
|ln_AGAI_i_|||||||0.172|(0.051)|***|
|ln_AGAI_o_|||||||-0.012|(0.051)||
|constant|-7.090|(0.393)|***|-7.992|(0.348)|***|-7.920|(0.357)|***|
|Obs|3,198|||3,198|||3,198|||
|Wald||||||||||
|chi2|7343.2|||7263.4|||7288.1|||
|Spatial para|meters:|||||||||
|_λ_|1.554|(0.056)||1.652|(0.047)||1.650|(0.049)||
- 16 -

_<u>ρ</u>_ <u>5.331 5.317 (0.100) 5.319 (0.075)</u> Note: The dependent variable is ln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

|**Table 5. Spa**|**tial autor**<br>|**egressive**<br>|**model**|**with the**<br>|**Battese’s s**<br>|**pecific**|**ation**<br>|||
|---|---|---|---|---|---|---|---|---|---|
||Spatial r|egression||Spatial r|egression||Spatial r|egression||
||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||
|ln_A_|0.030|(0.010)|***|0.048|(0.010)|***|0.045|(0.010)|***|
|ln_L_|0.040|(0.018)|**|0.048|(0.019)|**|0.044|(0.019)|**|
|ln_R_|0.167|(0.020)|***|0.157|(0.020)|***|0.161|(0.020)|***|
|ln_I_|0.316|(0.024)|***|0.311|(0.024)|***|0.313|(0.024)|***|
|ln_F_|0.527|(0.022)|***|0.525|(0.023)|***|0.523|(0.023)|***|
|ln_KM_|-0.053|(0.010)|***|-0.062|(0.010)|***|-0.061|(0.010)|***|
|ln_MAI_|0.310|(0.024)|***|||||||
|ln_AGAI_||||0.166|(0.024)|***||||
|ln_AGAI_i_|||||||0.224|(0.046)|***|
|ln_AGAI_o_|||||||-0.041|(0.048)||
|d_R_|0.268|(0.085)|***|0.260|(0.088)|***|0.266|(0.087)|***|
|d_I_|1.056|(0.092)|***|1.022|(0.093)|***|1.031|(0.093)|***|
|d_F_|0.073|(0.114)||0.117|(0.116)||0.103|(0.116)||
|constant|-6.719|(0.362)|***|-8.243|(0.387)|***|-8.168|(0.390)|***|
|Obs|3,198|||3,198|||3,198|||
|Wald||||||||||
|chi2|9426.3|||9189.5|||9230.7|||
|Spatial para<br>|meters:<br>|||||||||
|_λ_|1.332|(0.051)||1.497|(0.056)||1.503|(0.056)||
|_ρ_|5.963|(0.087)||5.404|(0.122)||5.540|(0.046)||

Note: The dependent variable is ln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

# **IV. CONCLUSION**

In the literature, there are a number of empirical studies showing a wide range of impacts of improved transport connectivity on agricultural growth. Still, the assumed nexus between transport infrastructure and agricultural growth remains somewhat mysterious, particularly in the African context. One of the missing important elements is agrobusiness. Because many rural farmers do not have their own transport means, someone has to deliver advanced inputs, and go and pick up their produce. Agrobusinesses, such as input suppliers, collectors, processors and exporters, have a crucial role to play in this regard.
- 17 -

The paper reexamined such a nexus among crop production, transport infrastructure and agrobusinesses. The results show the particular importance of intensification in Malagasy rice production. Fertilizer use has the highest elasticity, followed by irrigation and market accessibility. Consistently, the results indicate the importance of agrobusinesses. Particularly, proximity to input-oriented agrobusinesses, such as input dealers and equipment suppliers, is found to be important: They can facilitate farmers’ adoption of new inputs and technologies.

Methodologically, the paper also attempted to apply newly available data and techniques. Spatial data are generally rich data sources, which allow to generate various information that would be unavailable or very costly to collect otherwise. The paper combined highly disaggregated crop production data and detailed geo-referenced road network data. Various transport accessibility measurements were developed, which are clearly useful to consider many other policy issues.