metadata: authors: 0: author: Iimi,Atsushi
1: author: Rao,Kulwinder Singh
count: Liberia
docna: 0: docna: Firm location, transport connectivity, and
            agglomeration economies : Evidence from Liberia
docty: Policy Research Working Paper
subtopic: Transport Services,Inequality,Labor Markets,Industrial and Consumer Services and Products
teratopic: Industry,Social Protections and Labor,Poverty Reduction,Transport
abstracts: cdata!: Transport connectivity is among the most
            important factors in increasing firm productivity and
            accelerating economic development. The literature generally
            supports the idea of agglomeration economies, although there
            is little evidence of their effectiveness in Africa. There
            are often empirical challenges, such as spatial
            externalities and endogeneity of infrastructure development.
            Using firm registry data in Liberia, this study used the
            instrumental variable spatial autoregressive model to
            examine the effects of transport connectivity on firms'
            decisions on where to locate. The study found significant
            spatial autocorrelation and possible endogeneity related to
            transport infrastructure, and that firms are more likely to
            be located where market accessibility is better. The data
            indicated strong agglomeration economies, indicating that
            the primary city, Monrovia, is likely to continue to grow
            and attract more people and firms, and that secondary cities
            can also grow with greater transport connectivity between
            populated areas, such as district centers.
geo_reg_key: 517191,1927320
repnme: repnme: Firm location, transport connectivity, and
            agglomeration economies : Evidence from Liberia
display_title: Firm location, transport
            connectivity, and agglomeration economies : Evidence from Liberia
listing_relative_url: /research/2018/04/29815477/firm-location-transport-connectivity-agglomeration-economies-evidence-liberia
topicv3: Industrial Clusters and Value Chains
            m1326062 1226,Transport m1326661 135,Poverty m1326687 695,Transport Infrastructure m1327632 1803
publishtoextweb_dt: 2018-04-18T00:00:00Z
historic_topic: Industry,Social Protections and Labor,Poverty Reduction,Transport
no_of_pages: 26
author_keys: 0: author_key: 914707
1: author_key: 1110678
model_extractions: WPS8411 Policy Research Working Paper 8411 Firm Location, Transport Connectivity, and Agglomeration Economies Evidence from Liberia _Atsushi Iimi Kulwinder Rao_ Transport and Digital Development Practice April 2018
Policy Research Working Paper 8411

# **Abstract**

Transport connectivity is among the most important factors in increasing firm productivity and accelerating economic development. The literature generally supports the idea of agglomeration economies, although there is little evidence of their effectiveness in Africa. There are often empirical challenges, such as spatial externalities and endogeneity of infrastructure development. Using firm registry data in Liberia, this study used the instrumental variable spatial autoregressive model to examine the effects of transport connectivity on firms’ decisions on where to locate. The study found significant spatial autocorrelation and possible endogeneity related to transport infrastructure, and that firms are more likely to be located where market accessibility is better. The data indicated strong agglomeration economies, indicating that the primary city, Monrovia, is likely to continue to grow and attract more people and firms, and that secondary cities can also grow with greater transport connectivity between populated areas, such as district centers.

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
# **Firm Location, Transport Connectivity, and Agglomeration Economies: Evidence from Liberia**

Atsushi Iimi,<sup>¶</sup> Kulwinder Rao Transport & ICT Global Practice World Bank Group Key words: agglomeration economies; conditional logit model; instrumental variable spatial autocorrelation; infrastructure investment JEL classification: H54; H41; R32; C21; C26.

> ¶ Corresponding Author.
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# **I. INTRODUCTION**

Transport infrastructure is among the most important factors in increasing firm productivity and supporting local businesses. In particular, transport connectivity plays an important role in influencing firms’ decisions on where to locate. Proximity to markets is essential to firms, and transport infrastructure is its main determinant. In Hungary, road availability is considered to be a significant determinant of firm investment (Boudier-Bensebaa 2005). Access to good port facilities is also essential for private investors (Belderbos and Carree 2002; Deichmann et al. 2005). Foreign direct investment tends to be concentrated in countries with good transport infrastructure (Cieślik and Ryan 2004; Milner et al. 2006; Procher 2011; Lee et al. 2012; Mare and Graham 2013).

The traditional inventory model (e.g., Arrow et al. 1951) also suggests that firms can reduce inventory costs by lowering transportation costs. In general, it is financially costly to firms to carry inventory, restraining their operational flexibility. In the United States, the level of firm inventory declined by 7 cents for every additional dollar of investments in the highway capital (Shirley and Winston 2004). In China, each dollar of road spending has been estimated to reduce firms’ held inventories by 2 percent (Li and Li 2013). Using the Golden Quadrilateral project linking India’s four largest cities as a quasi-natural experiment, Datta (2012) found that firms in cities located along the improved highway reduced their inventory by 7 operating days’ supply on average.

When firms are located near one another, transport and transaction costs can be minimized. The new economic geography literature indicates that agglomeration economies can make an industrial cluster or a city attractive (e.g., Krugman 1991; Fujita et al. 1999). This creates a dynamic effect that continues to influence further investment decisions of firms. Therefore, although the cost of distance has been declining in recent years because of new technologies such as information and communications technology, firms still prefer to be located near one another to share the common input markets of labor and intermediate inputs and thus minimize trade and transaction costs.
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As a result, in many developed countries and rapidly growing emerging economies, firm agglomerations and industrial clusters have been established (e.g., Yusuf et al. 2008). In Africa, there are only a few examples of this occurring, such as the textile sector in East Africa (Otsuka and Sonobe 2011). Africa has lagged behind in the global manufacturing market since the 1960s. Among others, low labor productivity and lack of land access are significant constraints in Africa (Dinh et al. 2012). Poor infrastructure is another constraint (Harrison et al. 2011).

Empirically, there are methodological challenges to exploring the relationship between firm location, productivity, and transport infrastructure. First, infrastructure investment is often endogenous. Firms tend to be located where transport connectivity is better, but causality is less obvious than it would appear. Firms can be more productive because of better mobility of goods and people, but at the same time, governments may invest more in transport infrastructure where firms already exist. Thus, causality remains unclear. Recent publications propose different instrumental techniques to avoid this endogeneity problem (e.g., Chandra and Thompson 2000; Banerjee et al. 2012; Jedwab and Moradi 2012).

Another challenge is spatial autocorrelation. Transport infrastructure, by nature, forms a network. Thus, any improvement in transport connectivity has local and spillover effects. The extent to which firms would be affected is normally considered to depend on proximity to the improvement. Tobler’s (1970) first law of geography says that, “everything is related to everything else, but near things are more related than distant things.” Spatial autoregressive models address this problem. Wang, Kochelman, and Damien (2012) used the spatial autoregressive probit model to analyze land use in Austin, Texas. Other authors have incorporated possible spatial autocorrelation into the count data regression context (e.g., Bhat, Paleti, and Singh 2014).

The current study examined whether transport connectivity affects firms’ decisions on where to locate and how firms’ decisions affect those of other firms. Using the firm registry data in
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Liberia, the study used the instrumental variable spatial autoregressive model (Drukker et al. 2011), which allows the above-mentioned empirical issues to be addressed simultaneously. Policy implications will be developed from the estimation results: Where should investments be concentrated, in the primary city or other urban areas? What kind of transport connectivity affects firm location? The remaining sections are organized as follows: Section II provides an overview of recent developments of formal businesses and road networks in Liberia. Section III develops an empirical methodology and describes our data. Section IV presents main estimation results and discusses some policy implications. Section V concludes.

# **II. CURRENT ROAD NETWORK AND FIRM LOCATION IN LIBERIA**

Liberia is one of the poorest countries in the world and has a population of approximately 4.4 million. Gross domestic product per capita was US$457 in 2014.<sup>1</sup> Many people live below the poverty line, depending on subsistence farming without access to markets. According to the national census, the national poverty rate was estimated to be 54.1 percent in 2008 (LISGIS 2009). There is significant urban–rural inequality. Monrovia has a poverty rate of 31.6 percent, but poverty is generally more prevalent in the inland north central region (72 percent), such as Lofa County, which was most severely affected by the recent Ebola outbreak.

Agriculture is an important sector, employing approximately half of the labor force. A significant number of people work in the informal sector, which accounts for nearly 60 percent of the total labor force (table 1). The industry and service sectors, especially in the formal sector, are thin. A recent labor force survey showed that approximately 17,000 people are employed in the manufacturing sector and 343,000 in the service sector.

1 According to _World Development Indicators_ .
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# **<u>Table 1 Labor Statistics</u>**

|County|Population<br>(1,000)|Labor for<br> <br>(1,000)|ce<br>Informal e<br> <sup>% of</sup><br>total<br>(1,000)|mployment<br> <sup>% of labor</sup><br>force|
|---|---|---|---|---|
|Bomi|101|<br>31|<br>30.7<br>20|<br>64.5|
|Bong|379|<br>179|<br>47.2<br>108|<br>60.3|
|Grand Bassa|201|<br>75|<br>37.3<br>51|<br>68.0|
|Grand Cape Mount|120|<br>62|<br>51.7<br>39|<br>62.9|
|Grand Gedeh|79|<br>27|<br>34.2<br>21|<br>77.8|
|Grand Kru|54|<br>26|<br>48.1<br>16|<br>61.5|
|Lofa|234|<br>135|<br>57.7<br>86|<br>63.7|
|Margibi|219|<br>75|<br>34.2<br>41|<br>54.7|
|Maryland|96|<br>28|<br>29.2<br>17|<br>60.7|
|Montserrado|1,152|<br>362|<br>31.4<br>197|<br>54.4|
|Nimba|427|<br>164|<br>38.4<br>77|<br>47.0|
|Rivercess|70|<br>35|<br>50.0<br>20|<br>57.1|
|Sinoe|90|<br>18|<br>20.0<br>9|<br>50.0|
|River Gee|55|<br>22|<br>40.0<br>17|<br>77.3|
|Gbarpolu|61|<br>34|<br>55.7<br>23|<br>67.6|
|Total|3,338|<br>1,273|<br>38.1<br>742|<br>58.3|

Source: Liberia Institute of Statistics and Geo-Information Services.

Economic activity is highly concentrated in Monrovia, where more than 1.2 million people, or about 30 percent of the total population and 55 percent of the total urban population, live. The distribution of firm location is even more skewed (figure 1). The government’s business registry database indicates that more than 41,000 firms were officially registered in Liberia as of 2016, of which approximately 80 percent were located in Montserrado County, where the capital city, Monrovia, is (table 2). Thus, formal firms are almost exclusively concentrated in Monrovia, but even while Liberia has been experiencing rapid urbanization, many people live in rural and remote areas.

The skewness of firm location is higher than in other countries (figure 2). This does not mean that there are few businesses in other areas; there are many informal businesses, but they have not been registered. As discussed above, informality is significant in the Liberian economy, particularly in rural areas.
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where _x_ ' _j_  _j_ is the mean profit level at location (district) _j_ , and _ε ij_ is an idiosyncratic error including firm-specific unobservables; _x_ consists of location-specific observables, including a measurement of agglomeration economies and infrastructure variables.

Following Berry (1994), the set of firm-specific unobservables is defined by _Aj_   _i_ |  _ij_   _ik_ for  _k_  _j_  . Then the share of firms choosing to be located in district _j_ can be written implicitly as _s j_   _Aj f_ (  ; _x_ , <sup>)</sup> _d_  . When _ε_ is assumed to be independently and identically distributed, with the extreme-value distribution exp(  exp(  )) , the log of the probability of a firm choosing location _j_ is:

with the mean profit of the outside option normalized to zero; _s_ 0 is the share of firms that take an outside option, which is assumed to be employment in the informal sector in our context. In other words, people who are working in the informal sector can choose to formalize their businesses in a particular district. They do so if expected profits are positive. Otherwise, they continue to stay in the informal sector or unemployed. Recent labor force statistics were used to calculate the number of informal businesses that choose the outside option, and it is assumed that the average size of informal businesses is five employees.

Transport connectivity can be measured differently. In this analysis, six variables are examined: rural accessibility ( _RAI_ ) in district _j_ , road density ( _RDDEN_ ), share of roads in good condition ( _GDRD_ ), transport costs to a nearby large city ( _TCMK_ ), transport costs to the largest port (Freeport of Monrovia, _TCPT_ ), and market access index ( _MAI_ ) at location _j_ . The first variable is a global indicator of rural accessibility at the district level, as discussed above. _RDDEN_ and _GDRD_ are traditional transport indicators. It has long been argued whether they can measure actual connectivity accurately.
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The transport cost to market variable ( _TCMK_ ) is considered to be a more-accurate measurement than the above, representing connectivity with the road condition taken into account. Given the georeferenced road condition data, transport costs to bring one unit of a good to a major market are calculated using spatial software. In principle, if road conditions are bad, road user costs tend to be high and travel speed low, which increases time costs. From each location, the optimal route is selected to minimize total transport cost to reach a city with a population of more than 15,000. Similarly, transport costs to Freeport of Monrovia are calculated.

A more-general market accessibility measurement can be calculated. The above transport cost variables are still less general, because they reflect connectivity to a particular destination. In reality, people and firms need to be connected to different locations or potentially all cities and markets. The level of the importance of each market may depend on proximity to it. Thus, with the size of market capacity and distance to the market taken into account, the following simple market access index is considered, based on a conventional gravity framework:<sup>3</sup> This is the sum of purchasing power or market capacity, _Y_ , inversely weighted by the degree of impediment between two locations (e.g., distance, _d_ ). For _Y_ , city population is used as a proxy for market capacity; _d_ is measured according to transport costs from district _j_ and city _k_ .

To estimate Equation (2) in the logit framework, a critical empirical concern is that it assumes the independence of irrelevant alternatives, which requires that preferences between any pair of two choices be independent of the third option (e.g., Bhat, Paleti, and Singh 2014). In our context, this means that the relative attractiveness of one location ( _A_ ) over

> 3 The literature also uses negative exponential weights. See, e.g., Elbadawi, Mengistae, and Zeufack 2006; Lall and Mengistae 2005.
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another location ( _B_ ) is not changed even if the underlying characteristics of the third location ( _C_ ) are changed, but this is not likely to hold. For instance, suppose that some unanticipated investment is made in location _C_ . It is not in location _A_ or _B_ , but if _A_ is next to _C_ , _A_ may become more attractive than _B_ , because of the spillover effects from _C_ . In general, locational attractiveness is interdependent with all other locations, especially if transport connectivity is good. Thus, _Cov_ (  _j_ ,  _k_ )  0 in the equation.

Spatial interdependence comes from two sources. First, there is often spatial interdependency between locations. Although infrastructure is a typical network industry (e.g., roads are connected to each other), agglomeration economies themselves imply spatial interdependency with the neighboring areas. Second, from an empirical point of view, some variables are likely to have been omitted from the model. Spatial autocorrelation lies in _ξj_ if there are any unobservable location-specific characteristics.

In the literature, there are two empirical solutions to address this problem. The first approach is the nested logit model, which can partly relax the independence of irrelevant alternatives assumption across different clusters. The potential disadvantage is that this is only a partial solution and that the nesting structure cannot be known ex ante. The second approach, which the current study relied on, is to allow correlated errors in _ξj_ and eliminate the independence of irrelevant alternatives assumption. This is more flexible, with few presumptions required, and computationally simpler. Particularly, taking the share equation (Berry 1994), as in Equation (2), is advantageous, because it is computationally heavy to incorporate spatial autocorrelation into the standard maximum likelihood procedure.

Letting our dependent variable, i.e., ln _s_  ln _s_ 0 , be **_s_** and explanatory variables _x_ be **_X_** in the matrix notation, the following general spatial autoregressive model is considered:
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where _λ_ is a spatial autoregressive dependence in district share **_s_** , _ρ_ represents possible autocorrelation in error term _ξ_ , and **_W_** and **_M_** are spatial weighting matrices. For both matrices, inverse distances between two locations ( _j_ and _k_ ) are used. This follows Tobler’s (1970) first law of geography: “everything is related to everything else, but near things are more related than distant things.” Two locations are more closely related to each other if they are located near each other. The distance is calculated according to the Euclidean distance between the two locations.

Another empirical challenge to estimating Equation (2) is that infrastructure placement may be endogenous. Firms are productive because of better transport connectivity. At the same time, the government may invest in particular places because firms are located there. For instance, some firms are technically more efficient and prefer to be located where transport access is good. Road authorities may invest more in the places where these productive firms are located. As a result, regardless of transport connectivity, good infrastructure and good firms are likely to coexist.

To address the possible endogeneity of infrastructure variables in **_X_** , the spatial instrumental variable estimator (e.g., Anselin 1988; Drukker et al. 2011) is used, in which (  ,  ) is first estimated using the instrumental variable technique in the untransformed model. Using the estimated residuals, _ρ_ is then estimated. Finally, using the results and transforming the equation, the generalized spatial two-stage least-squared estimator can be obtained.

To instrument the transport connectivity variables, three instruments in logarithm are considered: terrain slope at each district center ( _SLOP_ ), the elevation of each district center ( _ELEV_ ), and straight-line distance from each district center to the nearest historical port city ( _DIST_ ). The first two variables are considered to be fairly exogenous because they are geographic conditions. The third variable follows the recent literature focused on the evaluation of infrastructure investment. Chandra and Thompson (2000), examining the effect of U.S. interstate highways on earnings of firms, argue that the nonmetropolitan counties served by highways receive exogenous benefits, because the interstate highways first aimed
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at connecting metropolitan areas. Banerjee et al. (2012) apply the same concept to the case of Chinese railways, calculating the distance from counties to straight lines connecting historic cities and ports, which can be arguably treated as exogenous. The validity of our proposed instruments will be examined using conventional test statistics, such as the exogeneity and overidentifying restriction tests, in the following section.

The summary statistics are shown in table 3. There were an average of 52.6 firms registered per district in 2016. There is significant variation in this. To avoid complexity related to another type of endogeneity, the number of existing firms in each district was measured using the 2015 data. On average, there were 320 firms in each district, with a range from none to more than 25,000 firms. As shown above, the RAI varies considerably from district to district. Similarly, other transport variables also differ from district to district. The market access index was normalized to a range from zero to 100.

Population density was included in the model to control for differences between districts, which was expected to capture the size of the local market. Firms are normally expected to be located near their customers, holding everything else constant. Labor costs may also affect firms’ decisions on where to locate. In theory, firms prefer to locate where wages are low, everything else being equal. Because wage data were not available, poverty and unemployment were included. Presumably, wages tend to be low where poverty and unemployment are high. At the same time, firms may also consider the quality of labor. Firms may want to hire skilled labor regardless of cost. To show this opposite effect, the share of population that attained at least a junior high school education ( _EDU_ ) was included. As discussed, informality of the economy may also be an important factor in firms’ decisions about where to locate.

To control for other unobservable subregional characteristics, five region-specific dummy variables are also included (north western (Bomi, Gbarpolu, Grand Cape Mount), north central (Bong, Lofa, Nimba), south central (Grand Bassa, Margibi), south eastern A (Grand Gedeh, Rivercess, Sinoe), and south eastern B (Grand Kru, Maryland, River Gee)), and
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Montserrado is used as a baseline. This follows the classification in a recent poverty analysis (LISGIS 2009). Poverty data are not available at a more disaggregated level. Therefore, no poverty measurement can be included in _X_ .

# **<u>Table 3 Summary Statistics</u>**

|<br>Variable|Abb.|Obs.|Mean|Std.<br>Dev.|Min|<br>Max|
|---|---|---|---|---|---|---|
|Number of firms newly registered at each<br>district in 2016|_dN_|136|52.62|407.03|0.00|4264.00|
|Number of firms registered as of 2015|_N15_|136|320.36|2463.31|0.00|25819.00|
|Rural access index at each district<br>|_RAI_|136|18.38|20.83|0.00|<br>94.60|
|Road density (km/100 km<sup>2</sup>)|_RDDEN_|136|15.84|17.23|0.00|<br>143.25|
|Share of roads in good condition (%)|_GDRD_|136|6.82|9.65|0.00|<br>58.33|
|Transport cost to bring one unit of goods to<br>|_TCMK_|134|5.78|3.88|0.00|<br>19.98|
|large city with population > 15,000 ($/ton)|||||||
|Transport cost to bring one unit of goods to<br>|_TCPT_|134|27.11|14.71|0.63|<br>51.39|
|Freeport of Monrovia ($/ton)|||||||
|Market access index from each district center<br>|_MAI_|134|0.87|8.65|0.01|<br>100.00|
|Population density (1,000 per km<sup>2</sup>)|_POPDEN_|136|0.10|0.52|0.004|<br>5.92|
|Share of informal employment (%)|_INFO_|136|47.36|15.45|20.93|<br>69.70|
|Unemployment rate (%)|_UNEMP_|136|40.11|20.38|12.12|<br>91.95|
|Share of population who attained at least|_EDU_|136|17.79|7.11|7.00|<br>48.00|
|junior high school<br>Instruments:|||||||
|Slope at each district center (degree)|_SLOP_|136|0.45|0.53|0.03|<br>3.62|
|Elevation (m)|_ELEV_|136|153.86|126.46|5.00|<br>563.00|
|Distance to nearest historic port (km)|_DIST_|136|93.60|55.38|0.02|<br>214.39|

# **IV. MAIN ESTIMATION RESULTS**

First of all, ordinary least squares (OLS) regression is performed. The results are shown in table 4. Although the results may be biased because endogeneity and spatial autocorrelation are not addressed, they are largely consistent with economic theory. Population density, which is an indication of the size of the local market, has a significant effect. Agglomeration economies are also always strongly positive; firms establish themselves where other firms already exist. For transport connectivity, the coefficients are largely consistent with prior expectations, except for road density, which has a significant negative coefficient. Market access seems particularly important; the coefficients of _TCMK_ and _MAI_ are statistically significant, although the results are not conclusive, as discussed above.
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# **<u>Table 4 Ordinary Least Squares Estimation</u>**

||<br>(1)||<br>(2)||<br>(3)||(4)|(5)||(6)||
|---|---|---|---|---|---|---|---|---|---|---|---|
|ln_RAI_|0.023<br>(0.114)|<br>||||||||||
|ln_RDDEN_|||–0.183<br>(0.088)|**||||||||
|ln_GDRD_|||||0.022<br>(0.130)|<br>||||||
|ln_TCMK_|||||||–0.341<br>(0.097)|***||||
|ln_TCPT_||||||||–0.544<br>(0.651)|<br>|<br>||
|ln_MAI_||||||||||0.643<br>(0.140)|***<br>|
|ln_N15_|0.818|***|0.870|***|0.820|***|0.751|***<br>0.804||0.726|***|
||(0.073)||(0.076)||(0.074)||(0.074)|(0.072)||(0.074)||
|_POPDEN_|0.2490|*|0.2680|*|0.2490|*|0.3280|***<br>0.2740|*|0.2800|***|
||(0.1370)|<br>|<sup>(0.1520)</sup>||<sup>(0.1380)</sup>|<br>|<sup>(0.1130)</sup>|<sup>(0.1530)</sup>||<sup>(0.0929)</sup>||
|_INFO_|–0.006||–0.010||–0.006||0.003|0.000||0.006||
||(0.027)||(0.027)||(0.027)||(0.026)|(0.027)||(0.026)||
|_UNEMP_|–0.004||–0.005||–0.004||–0.009|–0.001||–0.005||
||(0.015)||(0.014)||(0.015)||(0.014)|(0.015)||(0.014)||
|_EDU_|0.020||0.010||0.019||0.015|0.025||0.012||
||(0.098)||(0.098)||(0.098)||(0.095)|(0.099)||(0.095)||
|Constant|–13.370|**|–12.280|**|–13.340|**|–13.090|***<br>–13.440|**|–12.010|**|
||(5.214)||(5.132)||(5.280)||(4.983)|<br>(5.198)||(5.032)||
|Observations|136||136||136||134|<br>134||134||
|R-squared|0.64||0.643||0.64||0.654|0.643||0.66||
|<br>F-statistic|103.5||110||103.9||157.04|102.5||186.86||
|Number of<br>region||||||||||||
|dummy||||||||||||
|<br>variables|5||5||5||5|<br>5||5||

Instrumental variable regression is performed to address the endogeneity related to transport infrastructure investment (table 5). The results are broadly similar; the data exhibit significant economies of agglomeration and show that market access is important to attracting firms. More firms are located where transport costs to markets are lower and the market access index is higher. The results indicate that transport connectivity seems largely exogenous, although in the model in which _MAI_ is regressed, the exogeneity test can be rejected at the 10 percent significance level. In this case, our instruments are found to be
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largely valid; the overidentifying restriction test cannot be rejected no matter which transport connectivity variables are used.

|**Table 5 Instru**|**mental Varia**|**ble Regressi**|**on Estim**|**ation**|||||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)||(4)||(5)||(6)||
|ln_RAI_|0.028<br>(0.402)||||||||||
|ln_RDDEN_||0.163<br>(1.277)|||||||||
|ln_GDRD_|||–0.214<br>(0.370)|<br>|||||||
|ln_TCMK_|||||–0.240<br>(0.141)|*<br>|||||
|ln_TCPT_|||||||–0.028<br>(1.678)||<br>||
|ln_MAI_|||||||||0.405<br>(0.194)|**<br>|
|ln_N15_|0.817 ***|0.783 **|0.870|***|0.773|***|0.824|***|0.763|***|
||(0.135)|(0.323)|(0.101)||(0.076)||(0.096)||(0.077)||
|_POPDEN_|0.2500|0.2270|0.2160||0.3050|***|0.2500||0.2680|***|
||(0.1400)|(0.1890)|(0.1310)||(0.1080)||(0.1590)||(0.0891)||
|_INFO_|–0.006|–0.003|–0.014||0.001||–0.004||0.003||
||(0.027)|(0.035)|(0.029)||(0.025)||(0.029)||(0.026)||
|_UNEMP_|–0.004|–0.005|–0.015||–0.008||–0.005||–0.005||
||(0.022)|(0.014)|(0.024)||(0.014)||(0.020)||(0.014)||
|_EDU_|0.021|0.025|0.004||0.019||0.027||0.017||
||(0.096)|(0.114)|(0.094)||(0.091)||(0.095)||(0.091)||
|Constant|–13.420 **|–13.940 *|–11.390|**|–13.250|***|–13.630|***|–12.610|***|
||(5.964)|(8.199)|(5.605)||(4.776)||(5.008)||(4.803)||
|Observations|136|136|136||134||134||134||
|R-squared|0.64|0.631|0.627||0.653||0.641||0.658||
|Wald chi<sup>2</sup>|1247.7|1184.7|1160.7||1669.1||1191.9||1774.8||
|Number of<br>|||||||||||
|region dummy|||||||||||
|variables|5|5|5||5||5||5||
|Exogeneity test<sup>1</sup>|||||||||||
|<br>Chi<sup>2</sup>test|||||||||||
|statistic|0.000|0.071|0.321||1.152||0.106||2.775|*|
|P-value|(0.990)|(0.790)|(0.571)||(0.283)||(0.744)||(0.096)||
|Overidentifying r|<br>estriction test<sup>1</sup>||||||||||
|Chi<sup>2</sup>test|||||||||||
|statistic|3.999|3.943|3.695||3.096||4.065||2.909||
|P-value|(0.135)|(0.139)|(0.158)||(0.213)||(0.131)||(0.234)||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

1 The instrumental variable estimation is performed using three instruments in logarithm: _SLOP_ , _ELEV_ , and _DIST_ .
- 17 -

The instrumental variable spatial autoregressive model is used to address the possible spatial autocorrelation. The results are shown in table 6, showing that agglomeration economies are significant and market accessibility is important for firms in deciding where to locate. Both coefficients of _TCMK_ and _MAI_ are statistically significant, but transport costs to Freeport of Monrovia have a negative but insignificant coefficient. Thus, port access per se may not be particularly important to attracting firm investment, possibly because port accessibility is generally poor except for in the Monrovia area. The significance of the coefficient of _MAI_ can be interpreted to mean that connectivity to Monrovia is still important both because it is a populated market and because it has the primary port.

The instrumental variable spatial autoregressive results also indicate that spatial autocorrelation affects firms’ decisions on where to locate. The spatial autoregressive term _λ_ is estimated to be –0.33 to –0.37. Contrary to prior expectations, an unanticipated shock of a firm locating in a particular district had negative externalities for neighboring areas. On the other hand, the spatial error term ρ is found to be positive but statistically insignificant. This means that an exogenous shock in a particular district is not likely to affect its neighboring districts. For instance, if an extreme weather event, such as flood, unexpectedly happens in a particular district, the event may affect firm investment behavior in that district but not in neighboring districts.

As discussed above, the spatial autoregressive model is more efficient if the transport connectivity variables are exogenous. The results are unchanged (table 7). Firms are more likely to be located where transport costs to market are lower and general market accessibility measured according to _MAI_ is higher. The coefficient of _N15_ is estimated to be approximately 0.75 to 0.8, which is also similar to the above, indicating strong agglomeration economies. Monrovia is likely to continue growing because that is where (formal) enterprises are currently located, although this does not exclude the possibility that other cities will grow. The coefficient of _TCPT_ , which essentially measures the effect of proximity to Monrovia, is always insignificant, but _MAI_ has a significant coefficient, which measures
- 18 -

more-general connectivity, including not only Monrovia, but also secondary cities. Therefore, other secondary cities can also grow when their intercity connectivity is improved.

# **<u>Table 6 Instrumental Variable Spatial Autoregressive Model</u>**

||<br>(1)||<br>(2)||<br>(3)||<br>(4)||(5)||(6)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|ln_RAI_|<br>0.115<br>(0.192)|<br>|||||||||||
|ln_RDDEN_|||–0.035<br>(0.385)|<br>|||||||||
|ln_GDRD_|||||–0.123<br>(0.216)|<br>|||||||
|ln_TCMK_|||||||–0.312<br>(0.132)|**<br>|||||
|ln_TCPT_|||||||||–0.775<br>(1.087)|<br> <br>|||
|ln_MAI_||||||||||<br>|<br>0.503<br>(0.189)|***<br>|
|ln_N15_|0.788|***|0.828|***|0.845|***|0.752|***|0.788|***|<br>0.741|<br>***|
||(0.088)||(0.114)||(0.083)||(0.074)||(0.075)||(0.075)||
|_POPDEN_|0.1660||0.1780||0.1840||0.2390||0.1880||0.1860||
||(0.1190)||(0.1440)||(0.1260)||(0.1140)||(0.1390)||(0.0877)||
|_INFO_|–0.003||–0.006||–0.009||0.001||0.000||0.003||
||(0.025)||(0.026)||(0.027)||(0.024)||(0.024)||(0.024)||
|_UNEMP_|0.002||–0.003||–0.009||–0.006||0.003||–0.003||
||(0.014)||(0.013)||(0.017)||(0.013)||(0.016)||(0.013)||
|_EDU_|0.021||0.010||0.006||0.003||0.007||0.001||
||(0.091)||(0.091)||(0.091)||(0.089)||(0.092)||(0.089)||
|Constant|<br>–15.960|<br>***|<br>–14.630|<br>***|<br>–13.820|<br>***|<br>–14.260|<br>***|<br>–14.540|<br>***|<br>–13.560|<br>***|
||(4.991)||(5.260)||(5.124)||(4.662)||(4.778)||(4.703)||
|Observations|134||134||134||134||134||134||
|Number of|||||||||||||
|region dummy|||||||||||||
|<br>variables|5||5||5||5||5||5||
|Autoregressive c|oefficients||||||||||||
|λ|–0.372|**|–0.349|*|–0.335|*|–0.330|**|–0.376|***|–0.353|**|
||(0.152)||(0.193)||(0.200)||(0.160)||(0.136)||(0.157)||
|ρ|<br>0.526|<br>|<br>0.759|<br>|<br>1.057|<br>|<br>0.711|<br>|<br>0.390|<br>|<br>0.702|<br>|
||(1.181)||(1.160)||(1.103)||(1.118)||(1.183)||(1.120)||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

1 The instrumental variable estimation is performed with three instruments in logarithm: _SLOP_ , _ELEV_ , and _DIST_ .
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**<u>Table 7 Spatial Autoregressive Model without Instruments</u>**

||<br>(1)||<br>(2)||<br>(3)||<br>(4)||(5)|(6)||
|---|---|---|---|---|---|---|---|---|---|---|---|
|ln_RAI_|<br>0.046<br>(0.107)|<br>||||||||||
|ln_RDDEN_|||–0.132<br>(0.092)|<br>||||||||
|ln_GDRD_|||||0.055<br>(0.122)|<br>||||||
|ln_TCMK_|||||||–0.324<br>(0.091)|***<br>||||
|ln_TCPT_|||||||||–0.745<br> <br>(0.634)|||
|ln_MAI_|||||||||<br>|<br>0.653<br>(0.124)|***<br>|
|ln_N15_|0.806|***|0.853|***|0.808|***|0.749|***|0.789 ***|<br>0.718|<br>***|
||(0.069)||(0.072)||(0.070)||(0.068)||(0.066)|(0.067)||
|_POPDEN_|0.1680||0.2000||0.1660||0.2420||0.1880|0.1910||
||(0.1200)|<br>|<sup>(0.1360)</sup>|<br>|<sup>(0.1210)</sup>|<br>|<sup>(0.1120)</sup>|<br>|<sup>(0.1300)</sup><br>|<sup>(0.0969)</sup>||
|_INFO_|–0.004||–0.008||–0.004||0.001||0.000|0.005||
||(0.024)||(0.025)||(0.025)||(0.024)||(0.024)|(0.024)||
|_UNEMP_|<br>–0.001|<br>|<br>–0.003|<br>|<br>0.000|<br>|<br>–0.007|<br>|<br>0.003|<br>–0.003|<br>|
||(0.013)||(0.013)||(0.014)||(0.013)||(0.014)|(0.013)||
|_EDU_|<br>0.016|<br>|<br>0.007|<br>|<br>0.015|<br>|<br>0.002|<br>|<br>0.007|<br>–0.003|<br>|
||(0.091)||(0.090)||(0.091)||(0.089)||(0.091)|(0.089)||
|Constant|<br>–15.260|<br>***|<br>–14.120|<br>***|<br>–15.240|<br>***|<br>–14.200|<br>***|<br>–14.540 ***|<br>–13.140|<br>***|
||(4.831)||(4.744)||(4.908)||(4.644)||(4.750)|(4.667)||
|Observations|134||134||134||134||134|134||
|Number of||||||||||||
|region dummy||||||||||||
|<br>variables|5||5||5||5||5|5||
|Autoregressive c|oefficients|||||||||||
|λ|–0.357|**|–0.330|*|–0.358|**|–0.324|**|–0.370 **|–0.348|**|
||(0.160)||(0.181)||(0.160)||(0.159)||(0.147)|(0.157)||
|ρ|0.622||0.899||0.556||0.700||0.408|0.674||
||(1.111)||(1.060)||(1.151)||(1.118)||(1.126)|(1.148)||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

One may wonder to examine whether the agglomeration variable _N15_ is also endogenous. More-lagged values are used to test this. The numbers of firms registered by 2013 and 2014 are denoted as _N13_ and _N14_ , respectively. It is less likely that past decisions directly affect a firm’s decision on where to locate. The results confirm robustness of the main estimation results shown above (table 8). Market accessibility is an important determinant of firm
- 20 -

location, and agglomeration economies were still found to be significant. Autocorrelation was also significant.

**Table 8 Instrumental Variable Spatial Autoregressive Model with Different** **<u>Agglomeration Variables</u>**

||<br>(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|ln_TCMK_|–0.322 **<br>(0.137)||–0.307 **<br>(0.124)<br>||
|ln_MAI_||0.512 ***||0.468 ***|
|||(0.195)||(0.181)|
|ln_N14_|0.694 ***|0.683 ***|||
||(0.092)|(0.093)|||
|ln_N13_|||0.757 ***|0.746 ***|
||||(0.089)|(0.091)|
|_POPDEN_|0.2910 **|0.2390 **|0.1700|0.1280|
||(0.1370)|(0.1110)|(0.1320)|(0.1070)|
|_INFO_|0.002|0.004|0.005|0.007|
||(0.026)|(0.026)|(0.024)|(0.024)|
|_UNEMP_|–0.011|–0.007|–0.009|–0.006|
||(0.014)|(0.014)|(0.014)|(0.013)|
|_EDU_|0.018|0.016|0.044|0.042|
||(0.095)|(0.095)|(0.089)|(0.089)|
|Constant|–14.100 ***|–13.400 ***|–15.210 ***|–14.550 ***|
||(4.913)|(4.967)|(4.628)|(4.672)|
|Observations|134|134|134|134|
|Number of|||||
|region dummy|||||
|variables|5|5|5|5|
|Autoregressive co|efficients||||
|λ|–0.303 *|–0.327 **|–0.294 ***|–0.315 ***|
||(0.156)|(0.152)|(0.104)|(0.105)|
|ρ|0.432|0.372|–1.091|–1.020|
||(1.330)|(1.318)|(1.249)|(1.250)|

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively. 1 The instrumental variable estimation is performed with three instruments in logarithm: _SLOP_ , _ELEV_ , and _DIST_ .

# **V. CONCLUSION**

Agglomeration economies are an important factor in increasing firm productivity and promoting economic growth. The evidence shows that firms prefer to be located close to one
- 21 -

another, but unlike in other developing areas, Africa has only a few industrial clusters, such as textiles in East Africa. There is little evidence, although many African countries are currently experiencing rapid urbanization.

This study explored the relationship between firm location and transport connectivity. Using the recent firm registry database in Liberia, firms’ decisions on where to locate were examined with respect to various indicators that measure transport connectivity. The instrumental variable spatial autoregressive model was used to address the potential endogeneity that infrastructure placement causes spatial autocorrelation between neighboring locations. The results indicate significant agglomeration economies. It is likely that Monrovia continues to grow because that is where (formal) enterprises are located. Other cities will also grow when their intercity transport connectivity is improved.

It was also found that market accessibility is a main driver of firm agglomeration; it is important to connect firms to large markets. Other connectivity measures do not have significant coefficients. Traditional indicators such as road density and the share of good roads have no power to explain firms’ decisions on where to locate. These measurements may not be important to consider firm location and promote firm investment.

Finally, from a methodological point of view, the study found the importance of addressing spatial autocorrelation in the firm location literature. Spatial autocorrelation is always significant. There are externalities of firm location choice around neighboring areas.