metadata: authors: 0: author: Iimi,Atsushi
1: author: You,Liangzhi
2: author: Wood-Sichra,Ulrike
3: author: Humphrey,Richard Martin
count: Africa
docna: 0: docna: Agriculture production and transport
            infrastructure in east Africa : an application of spatial autoregression
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Ports & Waterways,Rural Transport,Food Security,Transport Services
teratopic: International Economics and Trade
abstracts: cdata!: Africa is estimated to have great
            potential for agricultural production, but there are a
            number of constraints inhibiting the development of that
            potential. Spatial data are increasingly important in the
            realization of potential as well as the associated
            constraints. With crop production data generated at 5-minute
            spatial resolution, the paper applies the spatial tobit
            regression model to estimate the possible impacts of
            improvements in transport accessibility in East Africa. It
            is found that rural accessibility and access to markets are
            important to increase agricultural production. In particular
            for export crops, such as coffee, tea, tobacco, and cotton,
            access to ports is crucial. The elasticities are estimated
            at 0.3–4.6. In addition, the estimation results show that
            spatial autocorrelation matters to the estimation results.
            While a random shock in a particular locality would likely
            affect its neighboring places, the spatial autoregressive
            term can be positive or negative, depending on how
            fragmented the current production areas are.
repnme: repnme: Agriculture production and transport
            infrastructure in east Africa : an application of spatial autoregression
display_title: Agriculture production and transport
            infrastructure in east Africa : an application of spatial autoregression
listing_relative_url: /research/2015/06/24574272/agriculture-production-transport-infrastructure-east-africa-application-spatial-autoregression
topicv3: Agriculture and Food Security m1326205 435,Crop Production m1326627 174,Transport m1326661 135,Ports m1327399 167,Rural Accessibility m1504911 4178
publishtoextweb_dt: 2015-11-05T00:00:00Z
historic_topic: International Economics and Trade
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 43
author_keys: 0: author_key: 914707
1: author_key: 1072043
model_extractions: WPS7281 Policy Research Working Paper 7281 Agriculture Production and Transport Infrastructure in East Africa An Application of Spatial Autoregression _Atsushi Iimi Liangzhi You Ulrike Wood-Sichra Richard Martin Humphrey_ Transport and ICT Global Practice Group June 2015
Policy Research Working Paper 7281

# **Abstract**

Africa is estimated to have great potential for agricultural production, but there are a number of constraints inhibiting the development of that potential. Spatial data are increasingly important in the realization of potential as well as the associated constraints. With crop production data generated at 5-minute spatial resolution, the paper applies the spatial tobit regression model to estimate the possible impacts of improvements in transport accessibility in East Africa. It is found that rural accessibility and access to markets are important to increase agricultural production. In particular for export crops, such as coffee, tea, tobacco, and cotton, access to ports is crucial. The elasticities are estimated at 0.3–4.6. In addition, the estimation results show that spatial autocorrelation matters to the estimation results. While a random shock in a particular locality would likely affect its neighboring places, the spatial autoregressive term can be positive or negative, depending on how fragmented the current production areas are.

This paper is a product of the Transport and ICT Global Practice Group. 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://econ.worldbank.org. 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
# **AGRICULTURE PRODUCTION AND TRANSPORT INFRASTRUCTURE IN EAST AFRICA: AN APPLICATION OF SPATIAL AUTOREGRESSION**

Atsushi Iimi,<sup>1 ¶</sup> Liangzhi You,<sup>2</sup> Ulrike Wood-Sichra,<sup>2</sup> Richard Martin Humphrey<sup>1</sup> 1 Sustainable Development Department, Africa Region The 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.
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# **I. INTRODUCTION**

Africa has great potential for agriculture. Together with agribusiness, it is estimated that agriculture currently generates $31 billion or nearly half of the GDP of the region. This is projected to continue growing to $1 trillion by 2030 (World Bank 2013). However, the potential has not been fully explored yet. For instance, the ratios of potential to actual agricultural outputs are estimated at 1.5 for cassava, 1.9 for rice, 2.7 for maize and 5 for wheat in West Africa (World Bank 2012). Importantly, from the agro-ecological point of view, Africa can feed itself if proper inputs, such as improved seeds and fertilizer, are used. But Africa is currently importing considerable quantities from outside the region.<sup>1</sup> Given rapid urbanization and strong population growth, Africa needs to produce more agricultural products.

There are a number of constraints: Africa’s agriculture is mostly small-scale subsistence farming. Irrigation and fertilizer are among the most important missing inputs (e.g., GyimahBrempong, 1987; Bravo-Ortega and Lederman, 2004; Xu et al., 2009; Dillon, 2011). Access to markets is also important, for farmers to take advantage of advanced technologies and market opportunities. In Africa, rural accessibility is particularly limited.<sup>2</sup> Transport accessibility — regardless of mode — has a crucial role to play to reduce input prices and boost agricultural production (Khandker, Bakht and Koolwal, 2009; Donaldson, 2010). A growing literature also suggests the importance of access to information and communication technology to obtain market information (Kiiza and Pederson, 2012, Zanello 2012).

Spatial techniques are becoming an increasingly popular tool to identify potential economic opportunities and possible bottlenecks in unlocking them. On the agriculture side, there are several important international initiatives to examine current land use and potential crop

> 1 Africa imported $15 billion of cereals in 2008, out of which only 5 percent originated from the African region (World Bank 2012).

> 2 In Africa, rural accessibility measured by the proportion of the rural residents within a 2-km walking distance from an all-weather road is less than 30 percent (Gwilliam 2011).
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productivity in connection to climate and soil conditions (e.g., Global Agro-ecological Zones (GAEZ) system developed by the FAO and the International Institute for Applied Systems Analysis (IIASA)). These data provide information on crop suitability at the detailed spatial level. On the infrastructure side, the new economic geography literature is building a solid body of knowledge on regional distribution and disparity of infrastructure endowments, identifying missing links and bottlenecks (see, for instance, World Bank (2009) and (2010)).

There are however only a few empirical studies that statistically link these two different sources of spatial data on the agriculture and infrastructure sides, excepting a number of more recent works, such as Dorosh, Wang, You and Schmidt (2012). This paper examines the agricultural potential of East Africa, namely, Burundi, Kenya, Rwanda, Tanzania and Uganda, through an examination of the two different sources of spatial data. Despite the currently high international commodity prices, in particular in the traditional export crops, such as coffee and cotton, these East African countries are still struggling to improve agricultural productivity. This paper specifically aims at: (i) generating spatial agricultural production and potential data for the region; (ii) developing spatial data to show transport accessibility in each locality; and (iii) developing an empirical model to link these data and analyze the relationship between agriculture production and transport infrastructure investment.

The remaining sections are organized as follows: Section II describes our spatial agriculture data. Section III develops an empirical model and describes our infrastructure data. Section IV discusses our main estimation results and some policy implications. Section V concludes.

# **II. SPATIAL PRODUCTION ALLOCATION MODEL (SPAM) UPDATE**

The current paper relies on a spatial production allocation model (SPAM) developed by the International Food Policy Research Institute (IFPRI) for generating highly disaggregated crop-specific production data. The SPAM is a spatial model to allocate crop production
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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 9 km at the equator (normally referred to as 10km x10km pixel for simplicity). To infer likely production locations, the model uses a cross-entropy method (Shannon 1948). Given initial allocations, the crossentropy method minimizes the cross-entropy distance—entropy is referred to as a measurement of uncertainty of expected information—between different probability distributions of the variables in the analysis, under different spatial constraints. As a result, the SPAM allows the simulation of the most plausible agricultural production locations given all available data at different levels.

Various input data are taken into account in the cross-entropy procedure, such as sub-national crop production statistics, satellite data on land cover, maps of irrigated areas, biophysical crop suitability assessments, population density, secondary data on irrigation and rainfed production systems, cropping intensity, and crop prices. Specifically in the SPAM, we start with crop production statistics at the large administrative (geopolitical) units (Figure 1).<sup>3</sup> These are typically national or sub-national. Key information to determine where and how much agricultural land exists at the pixel level comes from the existing land cover imagery, which is divided into crop land and non-crop land. With this crop land combined with the crop suitability data based on local climate, terrain and soil conditions, the crop-specific land areas can be obtained (e.g., maize in the figure). Note that the SPAM model disaggregates crop areas and yields into four different management intensities: (i) irrigated; (ii) high-input rain-fed; (iii) low-input rain-fed; and (iv) subsistence. Together with all these data, the SPAM applies the cross entropy method to obtain the final estimation of each crop distribution.

For the current paper, the SPAM was updated for five member countries of the East Africa Community. The model was rerun for 42 crops around 2010 with the latest available crop statistics and other new spatial data included. In the previous SPAM, i.e., SPAM 2000, which

> 3 See You and Wood (2006), You _et al._ (2009), and You et al. (2014) for further details.
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# **III. EMPIRICAL MODEL AND DATA**

In the literature, the traditional approach to investigating the linkage between agricultural production and infrastructure investment is to estimate a production function (see for instance Gyimah-Brempong (1987) and Bravo-Ortega and Lederman (2004) for literature reviews). Besides infrastructure conditions (often represented by transport accessibility), at least four other inputs are considered essential: labor, land, fertilizer and irrigation. The literature suggests that the output elasticities are relatively high for labor, reflecting the fact that agriculture production is labor-intensive. The elasticities associated with land are often modest, reflecting the relative abundance of land in Africa.

Fertilizer and irrigation seem to be critical to improve production, but the statistical significance varies across studies (Bravo-Ortega and Lederman, 2004). A growing literature suggests their importance: In Zambia, timely availability of fertilizer could increase maize yields by 11 percent on average (Xu et al., 2009). Improved availability of irrigation could nearly double agricultural productivity in Mali (Dillon, 2011).

Transport infrastructure has multiple implications for agricultural production. Better market access can reduce input prices. Khandker, Bakht and Koolwal (2009) find that farm-gate fertilizer prices were lowered by rural road investment in Bangladesh. Better transport infrastructure also provided more opportunities for farmers to engage in cash crop production and market transactions. Agriculture output prices increased by 2 percent and the volume of production was boosted by 22 percent (ditto).

To examine the infrastructure impacts on agricultural production, the following simple production function is considered:
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where _y_ is the amount of a crop produced at location _i_ , which depends on an exogenous agroecological factor and the amounts of inputs used. _ui_ is an error term. _z_ is a measurement of crop suitability or productivity of land. _x_ ’s are production inputs: _k_ = { _L_ , _R_ , _I_ , _F_ , _T_ } . _L_ denotes labor. Land is divided into two types: rain-fed ( _R_ ) and irrigated ( _I_ ). _F_ and _T_ denote fertilizer and transport accessibility, respectively. The logarithms are taken for all dependent and independent variables.<sup>4</sup> As is often discussed, spatial autocorrelation is an important empirical issue for estimating the equation. Our primary data is spatial, and the unit of analysis is approximately10 x 10 km parcels of land for each crop. Therefore, one observation may not be independent of another, i.e., _Cov_ ( _ut_ ,<sup>_u_</sup> _s_ ) ≠ 0 . By nature, agricultural land is a continuum of various characteristics, such as soil fertility and water availability. Weather conditions are continuous across locations. On the infrastructure side, public infrastructure, such as transport networks, is a typical network industry, which also creates autocorrelation among neighboring areas.

Another issue is that our agricultural production data are censored. As in many empirical spatial studies, an actual value for the dependent variable—in our case, crop production _y_ —is only available for a subset of the observations. As discussed below, some crops are grown everywhere, and others are produced in specific areas. As a result, there are a large number of locations (i.e., 10 x 10 km of land parcels) with zero production of a given crop.

To deal with these two problems, the spatial tobit (SPTobit) regression model is used. Denoting the latent variable of the output produced by _y*_ , the following censoring mechanism is considered:

> 4 A small positive number (one) is added if the amount of input used is zero to avoid taking the logarithm of zero. For instance, fertilizer is not used in many observations of our sample.
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where _w_ is an element of the spatial-weighting matrix. _λ_ and _ρ_ are spatial autoregressive parameters in the dependent variable and error term, respectively. _ε_ is an idiosyncratic error distributed independently and identically. Under the normality assumption, this can be estimated by the conventional maximum likelihood estimation procedure (e.g., Anselin, 1988; Amaral and Anselin, 2011).<sup>5</sup> For the spatial weighting matrix, inverse distances between two locations _s_ and _t_ are used. The distance is calculated using the Euclidean distance between the two locations. The intuition is that two locations are more closely related to each other, if they are located closely. This follows the Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things (Tobler 1970).” Our primary data source is the SPAM 2010 update. As discussed above, the SPAM is basically a model to disaggregate the national and subnational production data into the 10 x 10 km plots (pixels) using a number of different spatial data types. Therefore, it is critical to control for spatial autocorrelation in our analysis.

The analysis focuses on eight major crops that are produced in East Africa: maize, rice, bananas (and plantains), cassava, coffee, tea, cotton, and tobacco (Figure 2). The selection aims at examining both domestic food crops, such as maize and bananas, and export crops, such as tea and coffee. These eight crops account for about 40 percent of total agricultural commodities produced in the region.<sup>6</sup> The current productivity of these crops is generally low by regional standards, though it exceeds the African average in some cases (Figure 3).

> 5 For estimation we rely on a STATA command **_sptobitsac_** developed by Shehata and Michaiel (2013).

> 6 According to FAOSTAT, the East Africa Community member countries produced about $25 billion of agricultural commodities in 2011. The eight commodities examined amount to $9.3 billion in total.
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For production factors, various spatial and other data are used. For labor, the number of population engaged in crop _j_ production at location _i_ is calculated from the rural population estimate and the probability of household’s engagement in crop production. The latter comes from the national statistics of each country, and the level of aggregation differs across countries.<sup>7</sup> The pixel-level rural population data is taken from the Global Rural-Urban Mapping Project (GRUMP).<sup>8</sup> The land data comes from the same source as the output variable, i.e., SPAM. It provides an estimate of land use for each crop at each location. Land has two types: rain-fed ( _R_ ) and irrigated ( _I_ ). There is little irrigated area in East Africa (Figure 4). According to the SPAM data, there is no irrigated land in the region for bananas, for which the variable _I_ is omitted from the model.

The fertilizer variable represents the quantity of plant nutrients applied to the land area for each crop. This is calculated by multiplying the land area (ha) where fertilizer is used by the national average fertilizer consumption (kg per ha). The land area with fertilizer application is derived from SPAM, which generates the land area for each crop depending on 7 For Tanzania, the district-level share of households engaged in each crop production is calculated with the 2010 LSMS data. For Uganda, the LSMS 2010 is similarly used to calculate the shares at the first sub-national level. For Rwanda, the subnational statistics are available at the 30-district level. For Burundi, the national-level data are directly used because no subnational data are available. For Kenya, household-level agriculture production data are not available, either. In this paper it is assumed that the average plot size per household is the same across crops in each administrative unit. As in other countries in the region, the vast majority of farmers are small landholders with an average of 2.5 ha in Kenya. While some wealthy households sometimes own a significant amount of land, the variation of land per household is relatively small (Jayne et al. 2006; Salami et al. 2010). Thus, our assumption may not always hold but can generate a reasonable proxy.

8 The Global Rural-Urban Mapping Project (GRUMP) is a derivative product of the Gridded Population of the World (GPW) data, which is based on 2000 round census data and has actual 1990, 1995, and 2000 population grids and projected 2005, 2010, and 2015 grids at a 2.5 minute resolution (approximately 5km at the equator). GRUMP is at a 30 arc-second (1km) resolution (the same as Landscan) and includes an urban-rural reallocation.
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technologies, i.e., low-input, high-input and irrigated production systems. The last two figures are used for our calculation of areas with fertilizer application.

The average fertilizer consumption is calculated based on IFA/IFDC/IPI/PPI/FAO (2002) and Liu et al. (2010). Our fertilizer variable is the sum of three major elements of fertilizer: nitrogenous (N), phosphate (P2O5) and potash (K2O), because the actual mix and content of N, P and K varies across fertilizers. They are simply the sum of all the three elements for each crop.

Transport connectivity is also a crucial input for agricultural production. Farmers need to go to the markets to purchase necessary inputs and sell their products. Transport connectivity can be measured at different levels. First, the distance to the nearest road—denoted by _T_ 0—is fundamental regardless of the type of crop, because farmers need to get to the road network no matter which crop is produced and where the products are transported to. In East Africa, rural accessibility is still a challenge and the accessibility indices are estimated at about 2535 percent (Figure 5). Using the spatial data of road networks, the distance to the nearest road is calculated from the centroid of each location _i_ . This varies from less than 1km to over 100 km, depending on location (Figure 6).

Beyond the local accessibility, required transport connectivity may differ depending on the destination of the products. For domestically consumed food crops, such as maize and bananas, the destination is assumed to be a large town/ city, which is defined by the nearest populated area with more than 50,000 inhabitants.<sup>9</sup> Under the cost minimization assumption, the transport network cost is calculated by summing up the transport unit costs of road, railway and port along the optimal path from the production area to the city (Figure 7). This is denoted by _T_ 1. Note that the unit road transport costs vary, depending on type and class of road, and road conditions. In addition, not only road user costs and transport tariffs, but also

> 9 For small countries, such as Burundi, some other cities below this threshold are also included, because there would otherwise be only one domestic market in the country, i.e., the capital city, Bujumbura.
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## **Table 1. Regional crop** **<u>prices of selected commodities</u>**

|Food crop|$/ton|Export crop|$/ton|
|---|---|---|---|
|Maize|260|Coffee|3,192|
|Rice|856|Tea|1,506|
|Bananas|536|Cotton|1,429|
|Plantain|240|Tobacco|1,365|
|Cassava|294|||

Source: Calculated based on FAOSTAT.

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

The spatial tobit (SPTobit) regression is performed for each crop and each geographic group. Since the amount of data points is significant from the computational point of view, the entire region is separated into 6 areas for food crop calculations and 3 areas for export crop calculations (Figure 9).<sup>10</sup> The three landlocked countries comprise the first group. Kenya is the second group, but the analysis is focused only on the western half of the country, because there is little variation in agricultural production in the other half.

Tanzania is a large country, which is composed of over 9,000 10 x 10 km pixels. This would cause significant computational difficulties without disaggregation of the country. Food crops are produced in many places. Therefore, the sample data is divided into four areas: “Tabora”, “Mbeya”, “Arusha”, and “Coastal.” For export crops, the production takes place in very limited places (compared to the size of the country). Thus, the sample data are trimmed down to focus on the areas where major production takes place.

All the estimation results are shown in Annex Tables. The ordinary least squares (OLS) estimates are also included as references. The results are broadly consistent with _a priori_ expectations. First of all, regarding the autocorrelation parameters, i.e., _λ_ and _ρ_ , they indicate

> 10 Major national parks are also excluded from our analysis, because these areas are environmentally protected and not supposed to be used for agriculture production purposes.
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that the OLS estimation is likely biased. The hypothesis that the spatial autocorrelation parameters are zero can easily be rejected. Therefore, not surprisingly, spatial autocorrelation matters to our data. Because of this autocorrelation, some estimated coefficients are significantly different from the SPTobit results, although the coefficients tend to be broadly similar (see Annex Tables).

An important finding is that the spatial autoregressive term _λ_ is almost always significant but can be positive or negative. This implies that spatial concentration varies across crops and areas. For tobacco production, for instance, the estimated spatial autoregressive term is positive at 0.502 in Kenya. It means that if tobacco is produced in one place, the neighboring places are also likely to grow tobacco in Kenya. On the other hand, the same parameter is estimated at -0.246 in Tanzania, which is also statistically significant. It means that if tobacco production takes place in one locality, the neighboring places are less likely to grow tobacco in Tanzania. The evidence indicates certain fragmentation or agglomeration diseconomies in tobacco production in Tanzania. This is consistent with the maps depicting production areas at the same scale: Production areas are more dispersed in Tanzania than in Kenya (Figure 10).

Unlike the spatial autoregressive term, the autocorrelation coefficient in the errors, _ρ_ , is almost always positive at 0.8-0.95 and highly significant. This can be interpreted to mean that an exogenous shock—for example, drought and flood—in a given location has a substantial spillover effect on its neighboring areas. Obviously, this is plausible because there must be various shocks that affect agricultural production in a wider area than our unit of analysis (10 x 10 km). Because of this, it is important to control spatial autocorrelation in the analysis.
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Regarding the impacts of transport accessibility on agricultural production, the main results from the SPTobit models are shown in Table 2. Despite the autocorrelation discussed above, these are considered unbiased and consistent. The estimated elasticities vary across areas and crops but are broadly consistent with our prior expectation: Both rural accessibility and market access are important factors in stimulating agriculture production. Figures 11 and 12 depict the predicted changes in each crop production under the assumption that each element of transport accessibility is improved by 10 percent.<sup>11</sup> Recall that while our rural accessibility is measured by the distance to the nearest road (km), market accessibility is measured by the economic transport cost ($ per ton) to the nearest city or port, depending on the type of crop.

Our estimation results suggest that market accessibility is generally more important than rural accessibility. Particularly, for export crops, access to maritime port is found to be crucial: In some cases (e.g., coffee in the landlocked countries, tea in Kenya and tobacco in Tanzania), a 10-percent reduction in transport costs to the maritime port could boost export crop production by more than 10 percent. These are considered as high-yield crops from the infrastructure investment point of view.

Notably, however, this does not underplay the importance of rural accessibility. For instance, there is no evidence showing that banana production is constrained by market access in Burundi, Kenya, Rwanda and Uganda. But improving rural accessibility would likely result in some increases in banana production. While a 10-percent rural accessibility improvement could increase coffee production by 0.5 percent in Tanzania, the same amount of improvement in port access could increase the production by 4 percent. Thus, both are important to improve, and the relative importance varies across countries.

> 11 The predicted changes are shown only in case the estimated elasticity is statistically significant. In several cases, the predicted impacts are counter-intuitive. For instance, the evidence implies that an improvement in rural accessibility would result in less production of tobacco. This may be interpreted to mean that rural roads are well developed in the tobacco producing area of Kenya. At the same time, there may be a statistical issue that over- or under-estimate the impact, particularly when a large number of observations are censored, as in the case of tobacco in Kenya.
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For the landlocked countries, access to a port is found to be most important. The expected impact is largest on coffee production, followed by tobacco and cotton. For domestic food crops, access to a large city is also a significant determinant of production, particularly for rice. The result indicates that rice production areas are not well connected to major consumption areas.

For Kenya, cotton and tobacco production could benefit greatly from transport cost reductions to the port. Expected benefits to tea and coffee production are also likely significant. To stimulate coffee production, rural accessibility is also found to be important. For food crops, there would be some gains by improving market access to cassava, maize and rice producers.

The expected impacts of improved connectivity to the port look relatively modest in Tanzania. The obvious reason is that the country has the port of Dar es Salaam and major export crop production areas seem to be well connected to the port by the trunk road network. Of course, cotton and tobacco production, which takes place largely in inland areas, such as Tabora and Mwanza, could still be stimulated by improved transport access to the port. But rural accessibility and access to domestic markets are expected to be relatively important in the case of Tanzania. Expected benefits are large for rice production in Mbeya Area, maize in Arusha, and cassava in Tabora and Coastal Areas.

Regarding other production inputs, it is commonly found that land, regardless of whether rain-fed or irrigated, is among the most important production factors. It can be interpreted to mean that agricultural production in East Africa is and still can be dependent on extensification (more land), rather than improving technological level (intensification). For instance, the land elasticity is estimated at 0.74 to 0.98 for food crops, such as rice, maize and cassava in Burundi, Rwanda and Uganda. For cash crops, such as tea and cotton, the land elasticity is also large at 0.85 to nearly 1 (see Annex Tables).
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and the level of production. The expected benefits range from $10 million to $170 million, which account for 0.2-0.6 percent of current GDP (Table 3).

Finally, there exist many other investment options: Because of the expected spillover effects and the significance of investment requirements, strategic prioritization and coordination are called for not only within a country but also in the East African region. For instance, consider an alternative investment option that would halve the waiting time at the port of Mombasa from 260 hours to 130 hours.<sup>12</sup> The total port costs would decline from $78 to $49 per ton. Kenya would greatly benefit from this investment, but benefits would also be accrued to Uganda and the northern part of Tanzania (Table 4). Burundi and Rwanda could also benefit to a certain extent. From the regional point of view, this investment choice would be more cost-effective than the above-mentioned port improvement project at Dar es Salaam, “if” everything else, including investment costs, remained the same.

> 12 Kenya embarked upon a significant expansion project at the port of Mombasa in 2013. The first phase of the terminal is expected to be completed by 2016 and the whole project will be completed by 2020, increasing the port capacity from 750,000 TEU to 1.2 million TEU. The project cost is estimated at $366 million.
# **Table 2. Estimated** **<u>output elasticities with respect to transport accessibility</u>**

||Burundi,|Rwanda &|Ken|a|Tanzania||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Uga|nda|||Tabora||Mbeya||Arusha||Coastal||
||_T_0|_T_1|_T_0|_T_1|_T_0|_T_1|_T_0|_T_1|_T_0|_T_1|_T_0|_T_1|
|Rice|-0.010|-0.117 ***|0.019|-0.091 *|-0.013|-0.100 ***|0.000|-0.244 ***|0.005|-0.125 ***|-0.025 *|-0.037|
||(0.017)|(0.024)|(0.032)|(0.053)|(0.010)|(0.021)|(0.020)|(0.035)|(0.021)|(0.044)|(0.014)|(0.028)|
|Maize|0.011|0.028 *|0.020|-0.142 ***|-0.019 **|-0.048 ***|-0.021 **|-0.082 ***|-0.008|-0.203 ***|-0.043 ***|-0.068 **|
||(0.011)|(0.017)|(0.013)|(0.023)|(0.008)|(0.016)|(0.010)|(0.019)|(0.012)|(0.027)|(0.012)|(0.028)|
|Cassava|-0.005|-0.039 **|0.022|-0.169 ***|-0.002|-0.207 ***|-0.134 ***|<br>0.047|-0.001|-0.109 ***|-0.024|-0.252 ***|
||(0.013)|(0.019)|(0.017)|(0.028)|(0.016)|(0.037)|(0.018)|(0.029)|(0.013)|(0.030)|(0.020)|(0.038)|
|Bananas|-0.020 **|-0.013|-0.049 **|0.060 *|-0.011|0.005|-0.040|-0.042|0.008|0.041|0.010|-0.109 ***|
||(0.009)|(0.013)|(0.019)|(0.032)|(0.025)|(0.041)|(0.037)|(0.071)|(0.015)|(0.028)|(0.022)|(0.034)|
||Burundi,|Rwanda &|||Tanza|nia|||||||
||Uga|nda|Keny|a|(all regi|ons)|||||||
||_T_0|_T_2|_T_0|_T_2|_T_0|_T_2|||||||
|Coffee|-0.003|-1.644 ***|-0.045 **|-0.804 ***|-0.055 **|-0.437 ***|||||||
||(0.007)|(0.097)|(0.018)|(0.094)|(0.023)|(0.076)|||||||
|Tea|-0.013 **|-0.254 ***|0.029|-1.270 *|-0.005|-0.218 ***|||||||
||(0.007)|(0.030)|(0.024)|(0.767)|(0.032)|(0.060)|||||||
|Cotton|0.004|-0.647 ***|-0.020|-4.623 ***|-0.009|-0.853 ***|||||||
||(0.009)|(0.027)|(0.021)|(0.340)|(0.012)|(0.077)|||||||
|Tobacco|-0.014 **|-0.831 ***|0.090 ***|-3.312 ***|0.004|-1.157 ***|||||||
||(0.007)|(0.262)|(0.030)|(0.634)|(0.008)|(0.039)|||||||

Source: Based on the SPTobit estimation results (shown in Annex Tables).
**Table** **<u>3. Predicted production increases by halving the port waiting time at Dar es Salaams</u>**

||Increase i<br>Coffee|n prod<br>Tea|uction va<br>Cotton|lue ($ milli<br>Tobacco|on)<br>Total|% of GDP<br>(2010)<br>|
|---|---|---|---|---|---|---|
|Burundi|17.1|0.5|0.4|0.3|18.3|<br>0.90|
|Kenya|0.0|0.0|0.0|0.0|0.0|<br>0.00|
|Rwanda|12.4|1.4|0.0|1.0|14.8|<br>0.26|
|Tanzania|13.1|2.7|72.2|29.8|117.7|<br>0.51|
|Uganda|0.9|0.2|0.2|0.2|1.4|<br>0.01|
|Total|43.4|4.8|72.8|31.2|152.2|<br>0.19|

Source: Authors’ calculation based on the SPTobit estimation results.

|**Table 4. Pr**|**edicted p**<br>Increase<br>|**roducti**<br>in prod<br>|**on incre**<br>uction va<br>|**ases by ha**<br>lue ($ milli<br>|**lving th**<br>on)<br>|**e port waiting time at Dar es Salaams**<br>% of GDP<br><br>|
|---|---|---|---|---|---|---|
||Coffee|Tea|Cotton|Tobacco|Total|(2010)<br>|
|Burundi|14.3|0.4|0.3|0.2|15.3|<br>0.75|
|Kenya|109.8|181.9|128.9|19.1|439.6|<br>1.37|
|Rwanda|10.9|1.3|0.0|0.9|13.0|<br>0.23|
|Tanzania|12.4|1.7|69.7|20.3|104.1|<br>0.45|
|Uganda|167.4|2.9|13.4|4.8|188.4|<br>1.10|
|Total|314.8|188.1|212.3|45.3|760.4|<br>0.95|

Source: Authors’ calculation based on the SPTobit estimation results.

# **V. CONCLUSION**

Africa has great potential for agriculture. However, the potential has not been fully explored yet. It is important to accelerate agricultural growth further, because Africa is currently importing significant amounts of food from abroad, though Africa can feed itself from the agro-ecological point of view.

The literature suggests a lot of constraints, from fertilizer use to public infrastructure availability. The current paper focuses on the relationship between major crop production and transport connectivity in the East Africa Community. Unlike the existing literature, the paper generates new agricultural production data for each crop at the detailed spatial resolution. In addition, spatial data is also developed to measure transport connectivity from each production location to the destination, either domestic market or port to export crops.
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To deal with autocorrelation and censoring in data, the spatial tobit regression model was used in the paper. The results are consistent with prior expectation. Spatial autocorrelation matters, presumably because our agricultural production data are spatially connected to neighboring areas. In addition, transport infrastructure is a typical network industry. Therefore, it is methodologically important to take autocorrelations into account: OLS estimation is likely biased.

It is also found that the impacts of transport accessibility on agriculture production vary across areas and crops. However, the results suggest that both rural accessibility and market access are important to stimulate agriculture production. Particularly, for export crops, access to port is found to be crucial: In some cases (e.g., coffee in the landlocked countries, tea in Kenya and tobacco in Tanzania), the estimated elasticities exceed one, implying that public investments to improve transport connectivity for these crops are highly profitable from the economic point of view.

There are many possible investment options to improve transport connectivity in the region. For illustration purposes, the paper examined two particular scenarios under the assumption that the waiting time costs at the two regional ports, Mombasa and Dar-es-Salaam, would be halved. The expected impacts are different, depending on estimated elasticity and the current level of production. But the results indicate that infrastructure investment tends to have a spillover effect in the region. Therefore, strategic prioritization and coordination may be important not only within a country but also in the whole East African region.