metadata: authors: 0: author: Iimi,Atsushi
count: Mozambique
docna: 0: docna: Firm Productivity and Locational Choice :
            Evidence from Mozambique
docty: Policy Research Working Paper
subtopic: Transport Services,Food & Beverage Industry,Common Carriers Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing,Plastics & Rubber Industry,Pulp & Paper Industry,Textiles, Apparel & Leather Industry,Energy Policies & Economics,Energy and Mining,Energy and Environment,Energy Demand
teratopic: Transport,Macroeconomics and Economic Growth,Industry,Energy
abstracts: cdata!: Transport infrastructure is an important
            driving force for economic growth. Africa has been lagging
            behind in the global manufacturing market. Among others,
            infrastructure is often an important constraint. The paper
            reexamines the impacts of transport infrastructure on firm
            productivity in Mozambique. A conventional cost function is
            estimated with two rounds of firm data collected in 2007 and
            2018. It is found that improved transport connectivity,
            particularly access to a port, has a positive impact on firm
            production. Firm inventory is also an important determinant
            of firm productivity. Over the long term, agglomeration
            economies are also found to be significant and associated
            with port accessibility. These findings are consistent with
            the fact that the Government of Mozambique heavily invested
            in the primary road network during the first half of the
            2010s. The improved transport network is considered to have
            contributed to the country’s robust economic growth in the
            industrial and service sectors until the mid-2010s.
geo_reg_key: 517195,517191
repnme: repnme: Firm Productivity and Locational Choice :
            Evidence from Mozambique
display_title: Firm Productivity and Locational
            Choice : Evidence from Mozambique
listing_relative_url: /research/2021/07/33246437/firm-productivity-locational-choice-evidence-mozambique
topicv3: Manufacturing Sectors m1326521 1281,Energy and Extractives m1326595 507,Transport m1326661 135,Power and Electricity Sector m1327094 515,Transport Infrastructure m1327632 1803
publishtoextweb_dt: 2021-07-02T00:00:00Z
dois: 10.1596/1813-9450-9727
historic_topic: Transport,Macroeconomics and Economic Growth,Industry,Energy
origu: ITRGK - Global Knowledge (ITRGK)
no_of_pages: 24
author_keys: 0: author_key: 914707
model_extractions: Policy Research Working Paper 9727 Firm Productivity and Locational Choice Evidence from Mozambique _Atsushi Iimi_ Transport Global Practice July 2021
Policy Research Working Paper 9727

# **Abstract**

Transport infrastructure is an important driving force for economic growth. Africa has been lagging behind in the global manufacturing market. Among others, infrastructure is often an important constraint. The paper reexamines the impacts of transport infrastructure on firm productivity in Mozambique. A conventional cost function is estimated with two rounds of firm data collected in 2007 and 2018. It is found that improved transport connectivity, particularly access to a port, has a positive impact on firm production.

Firm inventory is also an important determinant of firm productivity. Over the long term, agglomeration economies are also found to be significant and associated with port accessibility. These findings are consistent with the fact that the Government of Mozambique heavily invested in the primary road network during the first half of the 2010s. The improved transport network is considered to have contributed to the country’s robust economic growth in the industrial and service sectors until the mid-2010s.

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

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

Atsushi Iimi<sup>¶</sup> Transport Global Practice The World Bank Group Key words: Firm productivity, transport infrastructure, seemingly unrelated regression, stochastic frontier analysis.

JEL classification: H54; R42; D24; C31; C35.

> ¶ Corresponding author.
- 2 -

# **I. INTRODUCTION**

1. Infrastructure is an important driving force for economic growth. It reduces trade and transaction costs and stimulates productivity of the economy. The existing growth literature is generally supportive of this. With better transport infrastructure, firms can reduce inventory, increasing profitability. Shirley and Winston (2004) show that firm inventory decreases with highway investment and increases with highway congestion in the United States. International trade reduces with transportation costs, measured by the road density (Limão and Venables, 2001). Because of those effects, firms tend to be concentrated along major highways (e.g., Holl, 2004). Over the long run, agglomeration economies can help to strengthen competitiveness of the economy (Krugman, 1991; Fujita et al. 1999; Procher, 2011; Lee et al. 2012; Mare and Graham, 2013).

2. Africa has generally been lagging behind in the global manufacturing market. The regional economy is still dominated by traditional subsistence agriculture, and mining where resources exist and international commodity prices are high. The level of industrialization in Africa is less than half of that in Asia and has not been increased much in the last four decades (Dinh et al. 2012). A recent study indicates that without controls, Africa’s manufacturing sector performs significantly worse than firms in other regions. But after controlling for geography and the business environment, Africa performs as well as other similar developing countries (Harrison et al. 2011).

3. In Mozambique, the formal manufacturing and service sectors remain thin. About 58,000 firms were registered as of 2016. Most of them are located in the City of Maputo and Maputo Province, which account for more than one-third of the total enterprises. In Mozambique, the manufacturing sector has been experiencing losses of technical capacity and skills base (Warren-Rodrigues, 2010). Some technically capable firms established during the colonial period have closed since independence. Despite recent increases in foreign direct investment particularly in the mining sector, the accumulation of skills and technical capacity still seems slow, because large-scale investments are often capital intensive and made by
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# **II. RECENT DEVELOPMENTS IN ENTERPRISES AND TRANSPORT INFRASTRUCTURE**

6. In Mozambique, the manufacturing sector is generally thin, while some agribusinesses exist. As of 2016, there were about 58,000 firms registered ( **Figure 2** ). Since 2014, the formal business sector has been growing rapidly. Historically, the firm distribution is highly concentrated in major urban areas. The City of Maputo and Maputo Province account for about half of the total enterprises, followed by Gaza, Manica and Niassa ( **Figure 3** ). The northern provinces generally have a fewer enterprises, meaning that few job opportunities are available to people living in those areas, except for agriculture.

7. As far as “new firms” are concerned,<sup>1</sup> the 2014/15 national firm census data indicate that the traditional major cities experienced some reduction in the number of firms during the early 2010s. New firms seem to have been emerging in some nontraditional areas, such as Sofala, Nampula and Tete Provinces ( **Figure 4** ).<sup>2</sup> Although these changes are still marginal compared with the existing business base of the traditional urban areas, it can be interpreted to mean that some firms do not prefer to be located in traditional areas, such as Maputo, possibly because of increasing congestion and high business costs. Non-traditional areas may have become more attractive because of enhanced regional integration with neighboring countries.

8. Different industries are located in different places, depending on their nature and requirements. For instance, agribusiness, which is one of the promising industries in Mozambique, is concentrated in agricultural production areas ( **Figure 5** ). Manica and Sofala Provinces are considered to have the great advantage in packaging fruits and vegetables, meat and dairy products, and processing of seafood and rice (Ministry of Industry and

> 1 With available data, it is defined by a net increase (or decrease) in the number of firms over time. No information is available in gross terms.

> 2 There is a possibility that this may be caused by statistical errors in the database, which is by no means perfect, with many informal firms excluded.
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=f(w )
- 8 -

Four inputs are considered: labor ( _L_ ), electricity ( _E_ ), intermediate and/or final goods ( _F_ ) and others ( _M_ ). The firm’s productivity may also depend on the quality of available infrastructure services ( _Z_ ) and other firm characteristics ( _X_ ). Based on their locations, firm productivity is also affected by transport connectivity, _V_ .

12. Following the traditional industrial organization literature (e.g., Nerlove, 1963; Christensen and Greene, 1976), the translog cost function is considered: 1 1 0 2 y + 2 h h h + yy 1 kk wwkk kk kk wwkkww kk ln cc= ββ + ββ ln yy+ ββ ln yyln yy+ ∑ββ+ 2 h ln wwh ∑∑ββh + ln ww ln ww + ∑ββkk yywwh kk ln yyln wwh kk + ∑ββhkk+zzkk ln zzkk ∑∑ββkk zzkkzz ln zzkk ln zz + ∑ββkk yyzzkk ln yyln zzkk(2) kk wwkkzz kk kk xxkk kk kk vvkk kk ii ∑∑ββ ln ww ln zz ∑ββ ln xx + ∑ββ ln vv + ββ tt+ εε 13. Output is measured by total sales in Mozambique meticais. Ideally, this should be measured by some variable in real terms, but this is only the usable common proxy for output in the BEEPS data.<sup>3</sup> Along with firm-specific characteristics, the sector-specific dummy variables are also included to control for unobserved heterogeneity across sectors: agrobusiness, light manufacturing, metal and chemical, and services.

14. Three infrastructure services are examined: electricity, water supply and transport. For power, the level of service quality is measured by the average duration of power outages in hours ( _ZE_ ). The quality of water services is measured by the number of days without sufficient water ( _ZW_ ). The quality of transport infrastructure is not directly measured by available data in the data set, but it can be represented by the level of inventory that firms hold, i.e., the number of days measured by days of production or sales ( _ZT_ ). According to the conventional economic order quantity model, firms are required to have more inventory when transportation is costly and unreliable. The optimal firm inventory increases with the cost of purchasing goods, including transport and handling costs.

> 3 No physical output variable is available in the database, because firms in the sample engage in various businesses.
- 9 -

15. Transport connectivity, _V_ , is measured in three ways: Local market connectivity measured by the number of people who live within 30 minutes ( _LOCAL_ ), Market Access Index ( _MAI_ ), and transport costs to bring goods to the nearest port ( _PORT_ ). While _LOCAL_ is expected to capture local accessibility around each urban area, _MAI_ is a more general index representing proximity to all major domestic markets with population taken into account.<sup>4</sup> 16. These transport connectivity variables are time- and location-variant. In general, transport connectivity seems to have improved in Mozambique during the first half of the 2010s, because the government invested intensively and extensively in the road network. For instance, the local market accessibility at Nampula increased from 472,000 in 2010 to 567,000 in 2015. The transport costs to ports reduced from US$9.24 to US$8.80, and the MAI increased from 0.27 to 0.47 during the same period.

17. The cost function is estimated by seemingly unrelated regression (SUR) and stochastic frontier analysis (SFA). To have a well-behaved cost function, the following symmetry and homogeneity restrictions are imposed: For SUR, the following factor share equations can be derived from Equation (2) by Shephard’s lemma: 4 MAI is defined by the sum of purchasing power or market capacity, inversely weighted by the degree of impediment between two locations _i_ and _j_ : M � i M �/ max For MM _y_ ii, the city population is used as a proxy of the market capacity. = ��yyii ii dd kk MMkk _d_ is measured by the estimated transport _y d_ is measured by the estimated transport costs between locations _i_ and large city _j_ .
_ | (8(6 )s = (6 \/s

= (6
- 11 -

cross-sectional data at different points of time. Transport connectivity is also measured at the two points of time. All monetary variables are normalized to the 2018 prices.

21. The summary statistics are shown in **Table 1** . Firms are different in size. The operating cost ranges from less than 100,000 to 1.7 billion Mozambique metical (MZN), with a mean of about MZN25 million or US$0.4 million. Firms produced (or sold) on average MZN67 million or US$1 million. In the sample, the labor cost accounts for 36 percent of total costs on average. The average cost share of electricity is about 6 percent. Goods and material costs account for 36 percent on average.

22. Firms suffer from power outage for about 2 hours when it happens. Firms also experience water disruption for on average 0.26 days per year. The level of inventory varies substantially from nearly zero to the equivalent amount to one year of production. The average inventory level is about 30 days.
- 12 -

**<u>Table 1. Summary statistics for firm cost function estimation</u>**

|Variable|Abb.|Obs. Mean St|d. Dev.|Min|Max|
|---|---|---|---|---|---|
|Total operating cost<sup>1</sup> <sup>2</sup>|C|903 25.13|138.60|0.00009|1686.80|
|Factor share: Labor|SL|903 0.36|0.28|<br>0|<br>1.00|
|Electricity|SE|903 0.06|0.09|<br>0|<br>0.71|
|Materials and goods<sup>3</sup>|SF|903 0.36|0.29|<br>0|<br>1.00|
|Wage (average per full time employee)<sup>1</sup>|WL|903 0.05|0.21|<br>0.00|<br>3.50|
|Electricity cost<sup>1</sup>|WE|903 0.54|6.67|<br>0.00|193.00|
|Material and goods cost<sup>1</sup>|WF|903 5.85|41.72|<br>0.00|587.88|
|Other input cost<sup>1</sup>|WM|903 4.12|58.60|<br>0.00|1650.00|
|Output (total sales)<sup>1 2</sup>|Y|903 67.49|331.65|<br>0.00|5079.49|
|Average duration of power outage (hours)|ZE|903 2.10|7.02|<br>0|<br>96.00|
|Number of days without water supply per year|ZW|903 0.26|1.71|<br>0|<br>30.00|
|Level of inventory measured by days of production|ZT|903 30.48|59.79|<br>0.00|365.00|
|Transport connectivity variables:||||||
|Local market areas measured by the number of people<br>|<br>LOCAL|903 0.14|0.16|<br>0.01|<br>0.57|
|who live within driving distance for 30 minutes<sup>2</sup>||||||
|<br>Market Access Index (Normalized to the max of|MAI|903 0.72|0.45|<br>0.27|<br>1.63|
|accessibility in 2007)||||||
|Transport costs to bring goods to the nearest port|PORT|903 7.49|6.57|<br>0.58|<br>30.72|
|($/ton)||||||
|Firm characteristics:||||||
|Number of firms that exist in the same province|N|903 25340|15785|<br>2104|40638|
|Number of years since the firm was established|YEAR|903 14.86|12.61|<br>1|<br>110|
|Dummy for firms having a female owner||903 0.25|0.43|<br>0|<br>1|
|Dummy for owner’s education level: Secondary||903 0.34|0.47|<br>0|<br>1|
|Tertiary||903 0.45|0.50|<br>0|<br>1|
|Share of exports in total sales (%)||903 5.89|18.68|<br>0|<br>100|
|Dummy for firms importing goods and materials||903 0.21|0.41|<br>0|<br>1|
|Dummy for firms owning a power generator||903 0.22|0.42|<br>0|<br>1|
|Sector dummy variable:||||||
|Agro business (food and wood products)||903 0.22|0.41|<br>0|<br>1|
|Chemical and metal||903 0.19|0.39|<br>0|<br>1|
|Light manufacturing||903 0.22|0.41|<br>0|<br>1|
|Service||903 0.24|0.43|<br>0|<br>1|
|Dummyfor 2018 data|_t_|903 0.44|0.50|<br>0|<br>1|
|1In constant 2018 metical.||||||

2 Divided by 1 million for presentation purposes.

3 Including expenditures on raw materials and intermediate goods, and finished goods.
- 13 -

_X_ includes location-specific variables representing agglomeration economies, size of market, infrastructure accessibility, and local labor costs.

The conditional logit approach has a variety of disadvantages, such as the property of independence of irrelevant alternatives (IIA). Unfortunately, however, it is difficult to overcome the problems with our data. No individual-specific data are available, except for sub-sector classification.

24. The main data come from the INE Estatisticas Territoriasis, 2014, in which 593 firms were “newly” registered in 75 districts over the 3-year period: 2009-2012 ( **Table 2** ). Agglomeration economies are expected to be captured by the number of firms that already existed in the same district. To mitigate the possible endogeneity issue, the 2008 data are used. Transport costs and road density are calculated based on geo-referenced road network data reflecting the condition in 2009. While road density is expected to capture local transport connectivity, transport costs to a port can explain access to the global market.

25. Economic theory tells that local labor costs are an important factor determining firm location and agglomeration economies. Local wage data come from one of the household surveys that are regularly collected by the Ministry of Agriculture. Using the 2008 Agriculture Sample Survey, the average monthly wage is calculated for each district. In case there is no data available in a given district, data at the closest district are used as a proxy. The average monthly wage is about 2,700 metical or US$110. The current minimum wages
- 14 -

in Mozambique are MZN3,000 to 7,000, depending on industry. Labor quality may have to be taken into account, when local labor costs are considered. Primary school enrolment rates in 2007 are used to control for this.

|**Table 2. Summary statistics of variables used for firm locational model estimation**|
|---|

|Variable|Obs|Mean|Std. Dev.|Min|<br>Max|
|---|---|---|---|---|---|
|No. of existing firms in 2008|43808|275.5|577.1|3.0|<br>3423.0|
|Road density (km per 100 km<sup>2</sup>)|43808|11.9|14.4|0.0|<br>72.7|
|Transport cost to a port (US$ per ton)|43808|25.3|16.5|0.1|<br>74.9|
|District population in 2007|43808|163391.5|115413.8|9506.4|682691.4|
|Population density (per km<sup>2</sup>)|43808|343.7|780.6|0.8|<br>3203.6|
|Local monthly wage (Metical)<sup>1</sup>|43808|2705.9|1656.8|150.0|<br>7883.0|
|Primaryschool enrolment(%, gross)|43808|117.1|24.3|56.4|<br>239.7|

1 Average monthly wage at the district level, based on the 2008 Agriculture Sample Survey. In case no data are available in a given district, the closest district data is used as a proxy.

# **IV. MAIN ESTIMATION RESULTS**

26. To estimate the translog cost function, both SUR and SFA are performed. The results are found broadly consistent with economic theory ( **Table 3** ). The implied price elasticities of production factor demand calculated based on Equation (6) range from -0.5 to -1.0 ( **Table 4** ). The elasticity of labor demand is estimated at about -0.6, the largest in absolute terms, implying that a small change in wages would result in a large change in employment. This is relatively large compared with the literature (e.g., Lichter et al. 2014) and may reflect the fact that the level of industrialization remains low in Mozambique and the demand for low-skilled labor is normally more elastic than the demand for high-skilled labor. The elasticity of electricity demand is estimated at -0.3, which is consistent with the literature, though it is statistically insignificant.<sup>6</sup>

> 6 The price elasticity of manufacturing energy demand is estimated at -0.28 to -0.49 in absolute terms in the United States (Anderson, 1981). As per Pindyck and Rotemberg (1983), the elasticity of U.S. manufacturing is - 0.36 in the short run, -0.58 in the medium run, and -0.99 in the long run. Energy demand elasticities also differ across industries (Denny _et al_ ., 1981). The short-run elasticity varies from -0.61 in the paper industry to nearly zero in the tobacco, metal fabricating, and machinery electrical industries. The long-run energy elasticities are also different between -0.01 to -0.73.
- 15 -

27. Regarding output elasticities, the results indicate that labor issues are most important to increase firm production ( **Table 5** ). The elasticity is estimated at about -0.4 to -0.6, depending on model. A 10 percent reduction in labor costs could increase output by about 4 to 6 percent.

28. Among the infrastructure quality variables, only inventory has a significant elasticity to output ( **Table 6** ). Neither power outage nor water interruption affects firm production significantly. However, firms’ held inventory is clearly costly, reducing firm production: The elasticity is estimated at -0.16 to -0.17. A 10 percent increase in inventory would reduce firm production by 1.6 to 1.7 percent, reconfirming the importance of reliable and efficient transport and logistic infrastructure.

29. The estimation results indicate that port access is of particular importance to stimulate firm production. In both SUR and SFA, the output elasticity is significantly positive at about -0.09, meaning that a reduction of port access costs would result in increasing firm production. Local market accessibility is only found to be significant in the SUR model. If this is the case, improving local access around cities could also contribute to augmenting firm production. The market access index does not have a significant impact.
- 16 -

**<u>Table 3. Estimated production cost function: SUR</u>**

||SUR|||SFA|||
|---|---|---|---|---|---|---|
||Coef.|Std. Err.||Coef.|Std. Err.||
|βY|0.5616|(0.0478)|***|0.7264|(0.0663)|***|
|βYY|0.0048|(0.0033)||-0.0056|(0.0079)||
|βWl|0.2474|(0.0269)|***|0.4895|(0.1032)|***|
|βWe|0.2440|(0.0242)|***|0.4085|(0.0969)|***|
|βWf|0.4719|(0.0303)|***|0.0275|(0.0666)||
|βWlWl|0.0203|(0.0021)|***|0.0081|(0.0147)||
|βWlWe|0.0139|(0.0019)|***|0.0042|(0.0133)||
|βWlWf|-0.0344|(0.0020)|***|-0.0420|(0.0137)|***|
|βWlWw|-0.0253|(0.0023)|***|0.0046|(0.0162)||
|βWlWe|0.0317|(0.0022)|***|0.0621|(0.0129)|***|
|βWeWf|-0.0138|(0.0017)|***|-0.0057|(0.0070)||
|βWeWw|0.0075|(0.0020)|***|0.0128|(0.0079)|*|
|βWfWf|0.0553|(0.0026)|***|-0.0015|(0.0127)||
|βWfWw|-0.0198|(0.0023)|***|-0.0136|(0.0051)|***|
|βYWl|-0.0076|(0.0017)|***|-0.0086|(0.0077)||
|βYWe|-0.0163|(0.0016)|***|-0.0162|(0.0082)|**|
|βYWf|0.0004|(0.0019)||0.0207|(0.0088)|**|
|βZe|0.0338|(0.0384)||0.0162|(0.0398)||
|βZw|-0.1082|(0.0860)||-0.0781|(0.0600)||
|βZt|0.0417|(0.0369)||0.0432|(0.0375)||
|βZeZe|0.0098|(0.0075)||0.0076|(0.0072)||
|βZeZw|-0.0007|(0.0039)||-0.0019|(0.0029)||
|βZeZt|0.0003|(0.0016)||-0.0003|(0.0018)||
|βZwZw|-0.0502|(0.0258)|*|-0.0365|(0.0177)|**|
|βZwZt|-0.0036|(0.0039)||-0.0029|(0.0028)||
|βZtZt|0.0231|(0.0045)|***|0.0217|(0.0057)|***|
|βYZe|-0.0016|(0.0018)||-0.0033|(0.0032)||
|βYZw|0.0028|(0.0034)||0.0018|(0.0043)||
|βYZt|-0.0009|(0.0018)||-0.0008|(0.0040)||
|βWlZe|0.0012|(0.0015)||0.0035|(0.0035)||
|βWeZe|-0.0021|(0.0012)|*|-0.0046|(0.0034)||
|βWfZe|-0.0015|(0.0017)||-0.0044|(0.0038)||
|βWlZw|-0.0026|(0.0030)||-0.0019|(0.0051)||
|βWeZw|0.0048|(0.0026)|*|0.0071|(0.0061)||
|βWfZw|-0.0035|(0.0034)||-0.0069|(0.0057)||
|βWlZt|-0.0014|(0.0013)||-0.0008|(0.0046)||
|βWeZt|-0.0005|(0.0011)||-0.0015|(0.0038)||
|βWfZt|-0.0009|(0.0014)||-0.0019|(0.0028)||
- 17 -

|Agro-industry|0.4032|(0.0912)|***|0.2519|(0.1158)|**|
|---|---|---|---|---|---|---|
|Chemicals|0.2640|(0.0929)|***|0.1439|(0.1057)||
|Light manufacturing|0.2090|(0.0938)|**|0.1296|(0.1020)||
|Service|-0.7168|(0.0881)|***|-0.2735|(0.1033)|***|
|Generator|0.1139|(0.0709)||0.1785|(0.0820)|**|
|ln_YEAR_|0.0621|(0.0294)|**|0.0687|(0.0330)|**|
|Female|0.1235|(0.0563)|**|0.1363|(0.0537)|*|
|Export share|0.0031|(0.0014)|**|0.0030|(0.0019)||
|Importing|0.2094|(0.0702)|***|0.1453|(0.0835)|*|
|Secondary education|0.2179|(0.0710)|***|0.1309|(0.0596)|**|
|Tertiary education|0.4072|(0.0779)|***|0.2353|(0.0725)|***|
|ln_LOCAL_|-0.0823|(0.0442)|*|-0.0465|(0.0583)||
|ln_MAI_|0.0408|(0.1766)||0.1810|(0.2369)||
|ln_PORT_|0.0565|(0.0304)|*|0.0546|(0.0297)|*|
|ln_N_|-0.0622|(0.0974)||-0.1381|(0.1148)||
|_t_|-0.0692|(0.2932)||-1.2878|(0.3938)|***|
|Constant|4.2699|(1.1817)|***|2.2652|(1.3859)||
|Obs.|903|||903|||
|R squared|0.9654||||||
|Wald chi 2|49023.7|||44707.2|||

**<u>Table 4. Implied own price elasticities</u>**

||SUR<br>Elasticity|Std. Err.||SFA<br>Elasticity|Std. Err.||
|---|---|---|---|---|---|---|
|Labor|-0.662|(0.015)|***|-0.629|(0.057)|***|
|Electricity|-0.282|(0.229)||-0.252|(0.331)||
|Materials and goods|-0.527|(0.012)|***|-1.028|(0.184)|***|

**<u>Table 5. Output elasticities with respect to input prices</u>**

||SUR<br>Elasticity|Std. Err.||SFA<br>Elasticity|Std. Err.||
|---|---|---|---|---|---|---|
|Labor|-0.433|(0.046)|***|-0.604|(0.162)|***|
|Electricity|-0.079|(0.030)|***|-0.165|(0.116)||
|Materials and goods|-0.353|(0.036)|***|0.097|(0.099)||
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**<u>Table 6. Output elasticities with respect to transport variables, infrastructure quality and agglomeration</u>**

||SUR<br>Elasticity|Std. Err.|SFA<br>Elasticity|Std. Err.|
|---|---|---|---|---|
|Transport connectivity:|||||
|Local access|0.137|<br>(0.074) *|0.081|<br>(0.101)|
|MAI|-0.068|<br>(0.294)|-0.314|<br>(0.417)|
|Cost to access port|-0.094|<br>(0.051) *|-0.095|<br>(0.055) *|
|Infrastructure quality:|||||
|Power outage|-0.049|<br>(0.059)|-0.010|<br>(0.059)|
|Water interruption|0.063|<br>(0.077)|0.073|<br>(0.067)|
|Inventory|-0.161|<br>(0.050) ***|-0.175|<br>(0.059) ***|
|Agglomeration economies:|||||
|Number of firms in theprovince|0.103|<br>(0.162)|0.240|<br>(0.204)|

30. Another unexpected result may be that the firm production does not exhibit agglomeration economies. The estimated elasticity is positive but statistically insignificant. There are several possible reasons for this. First, the variation in firm location is very limited in the BEEP data set, leading to very little variation in the number of firms in the same province as a firm is located. Second, our agglomeration variable may be too broad. Each province is very large in Mozambique. The potential intangible impacts of neighboring firms may not be so clear at the provincial level. Unfortunately, however, more granular data are not available.

31. To examine a more dynamic aspect of improved transport infrastructure, the firms’ locational choice is estimated by the conditional logit model. The estimated results show that firms prefer to locate themselves where (i) other firms exist (i.e., agglomeration economies), (ii) local connectivity, measured by road density, is higher, (iii) transport costs to port are low, and (iv) the local market size is large ( **Table 7** ).

32. The policy implications are straightforward: First, the existing clusters and cities will continue to grow because of agglomeration economies. This calls for efforts toward improving efficiency and sustainability in urban transportation and mitigating congestion. Second, port connectivity is essential to keep competitiveness of firms. The share of Mozambican firms involved in international trade to purchase inputs or sell outputs is still
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relatively low.<sup>7</sup> There must be significant economic potential to explore business opportunities in the regional and global markets. To support such potential, port accessibility is critical.

33. One unexpected result may be that the coefficient of labor costs turned out positive, meaning that firms prefer to be located where labor costs are high, not low. There seems to be high correlation between wages and city population. Local wages tend to be higher in more urban areas where more people live, partly because they may be more educated and have higher skills. To avoid this problem, the secondary school enrollment rate is used to control for possible heterogeneity in labor quality. It is found that firms prefer to have access to the quality labor market, regardless of potentially high wages. The coefficient is estimated at 0.012. When both factors are incorporated, the last column model shows that higher school enrollment has a positive impact to attract firms, while the coefficient of local wages is still positive. It can be interpreted to mean that labor quality is more important than wages. Firms may prefer to pay high wages if the quality of labor is also high.

**<u>Table 7. Conditional logit estimation: Firm location</u>**

||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|
|ln No. of existing firms|0.451|(0.060) ***|0.137|(0.052) ***|0.091|(0.051) *|
|Road density|0.045|(0.003) ***|0.063|(0.004) ***|0.063|(0.004) ***|
|Transport cost to port|-0.005|(0.003) *|-0.014|(0.003) ***|-0.016|(0.003) ***|
|ln Population|-0.225|(0.084) ***|0.556|(0.093) ***|0.604|(0.092) ***|
|ln Population density|-0.417|(0.048) ***|-0.826|(0.076) ***|-0.856|(0.074) ***|
|ln Local wage|0.300|(0.081) ***|||0.305|(0.078) ***|
|Secondary school enrollment|||0.012|(0.001)***|0.011|(0.001)***|
|Obs.|43808||43808||43808||
|Wald chi2|426.15||643.33||676.14||
|Pseudo R-squared|0.066||0.095||0.099||

7 According to the 2007 BEEP data, about 17 percent of the firms surveyed import materials and equipment from abroad. About 5 percent of the firms export their products.
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# **V. CONCLUSION**

34. Infrastructure is an important driving force for economic growth. It reduces trade and transaction costs and stimulates productivity of the economy. Africa has been lagging behind in the global manufacturing market, despite its accelerated growth in recent years. Among others, the poor quality of infrastructure services, including transport connectivity, is an important constraint to many countries in the region.

35. The paper reexamined the short- and long-term impacts of transport connectivity on firm productivity in Mozambique. Different data sources and methods were used. The paper found that in the short run, firms can increase their sales if transport connectivity, particularly accessibility to a port, is improved. In addition, firm inventory is found to be highly costly for them. These findings are consistent with economic theory. Not only transport but also other institutional issues are found to be important. The estimation results suggest that labor costs are among the most important determinants of firm production.

36. Over the long run, transport connectivity is found to affect firms’ locational choice, fostering agglomeration economies, which could determine competitiveness of the overall economy. To attract more investment by firms, local connectivity and accessibility to a port are found to be important. All the indications are that port accessibility is essential to increase firms’ production and stimulate their competitiveness. Since agglomeration economies are significantly positive, the currently populous cities and urban areas are likely to continue growing in the future. Therefore, it is also important for the government to address increasing urban congestion and ensure efficient mobility of goods and people. Otherwise, firms would lose their competitiveness.