Does Location Matter for Micro Shops Resilience? Evidence from Uganda

Abstract

This article analyzes the relationship between location and resilience of micro shops in Uganda. We use a unique city-level data set and consider the longevity of business as a proxy for resilience. After controlling for a set of relevant variables, our findings suggest positive correlation between longevity and proximity to Kampala, the capital city and vibrant commercial center of Uganda. We also find that entrepreneurship-related factors and natural events are further significant elements. We stress the importance to strengthen the integration between core and lagging areas as opposed to the current territorial development pattern. This calls for development models where the creation of satellite hubs and commercial platforms may positively influence the resilience of micro business.

Keywords

resilience micro entrepreneurship longevity location shops Uganda

Introduction

Rapid development of African economies has produced a diaspora of people in search for better quality of life from rural areas to cities (World Bank 2017). This has determined a new geography of production and employment of cities, leading to a sort of territorial trade-off within regions (Henderson and Wang 2007; Proost and Thisse 2019). Especially in those contexts where both political power and economic activities are highly polarized in specific areas, often lagging zones are not appropriately linked with the heart of their countries (McGranahan et al. 2009; United Nations Economic Commission for Africa 2017). For instance, this emerges if we compare employment rates, number of firms, or public expenditure for infrastructures in the main urban centers with those of remote areas (World Bank 2015; Tsiapa and Batsiolas 2018). Such reorganization is the result of adaptation strategies of cities to both global processes and endogenous forces, and it takes various forms. As claimed by Cruz et al. (2013), these different forms and dynamics impact on the resilience of cities.

This has stimulated recent studies on resilience that explore how territorial development patterns affect local entrepreneurship and its longevity. In particular, the research on firm resilience included either spatial dimension and/or features related to regional differences to test how these may influence the local economy, with a specific attention to remote areas (Starr, Newfrock, and Delurey 2003; Burnard and Bhamra 2011). Location of firms has been a key element in this strand of research (Boschma 2015; Tsiapa and Batsiolas 2018). Related to this, some studies found that the distance from a main hub affects the economy of secondary cities especially in Africa (Jedwab and Storeygard 2019). We base our work on findings like the latter to examine how the distance from the capital city is related to the resilience of businesses like micro shops in Uganda. In particular, we consider business longevity as a proxy of the capability to be resilient (Lall, Schroeder, and Schmidt 2014). The peculiar urbanization dynamics of the African continent is well-known and has been investigated by many contributors (Jedwab, Christiaensen, and Gindelsky 2017; Castells-Quintana 2017). In particular, Uganda is one of the fastest growing economies in sub-Saharan Africa. Its capital city has experienced rapid urbanization and economic growth, higher than other cities and villages (United Nations Economic Commission for Africa 2017). Uganda is a country that well represents the current trends and the main socioeconomic dynamics in Africa where some cities are expected to double their population in the next fifteen years as a result of loss of agriculture land productivity due climate change (Barrios, Bertinelli, and Strobl 2006). This last effect has clearly pushed people to move from rural areas and secondary cities.

We do not adopt a strict definition of resilience meant as a result of readjustment after one shock. Rather, we study how firms have reacted over the time to various social and economic shocks that are typical in a developing country. Therefore, in this article being still in business is the result of such an adaptive behavior (Pendall, Foster, and Cowell 2010; Faggian et al. 2018).

The main originality of our article stands in investigating the effects of location on resilience at a micro shop level in a developing country. As Proost and Thisse (2019) remark, city location is a key issue. Nonetheless, only few studies adopted this approach. Numerous studies discussed the sources of regional variations in new business creation (Reynolds 1994; Rosenthal and Strange 2003; Lee, Florida, and Acs 2004; Feser, Renski, and Goldstein 2008). Others adopted a survival analysis approach on different business categories and gave evidence on the capacity of firms to absorb and respond to external shocks over time (Williams and Vorley 2014). Few works analyzed the factors that encourage business longevity especially in Africa, and even fewer analyzed business resilience in association with location. Among these, some papers investigated spatial variation using area-based proxies for localization (Renski 2011), whereas some others tested the link between business duration and external economies using more direct measures (Brixy and Grotz 2007).

We make use of a unique sample from a direct survey on micro shops located in ten different cities of Uganda. These include both urbanized areas and remote ones. Despite primary data have become frequent in developing countries studies, the wide spatial distribution of the places of our survey is indeed a value added for the topic we analyze (Gambe 2018; Scuderi, Tesoriere, and Fasone 2019). This way, we want to provide complementary evidence to those studies based on data either from official sources or related to a more spatially limited context.

After controlling for a series of variables, as expected results show that higher distance from the capital city is correlated with lower resilience of the business. Our findings from value chain variables confirm the importance of the proximity to the main city: supplying products from Kampala, Uganda’s capital city, is positively associated with longevity, whereas other suppliers located across the country do not report significant effect. We also find that floods, one of the most impacting types of natural hazards (Scuderi, Tesoriere, and Fasone 2019), are negatively associated with resilience, and that the manager’s and business characteristics also matter.

This article proceeds as follows. The next section illustrates the conceptual background. The third section describes the research setting and the process of data collection. The fourth section presents our empirical model. Results and discussion are provided in the fifth section, and sixth section concludes.

Background

Uganda and Kampala: The Context

Recent analysis estimated that population in Africa is expanding at an average rate of 3.5 percent per year. Some cities are expected to double their population in the next fifteen years (United Nations Economic Commission for Africa 2017), whereas more than 50 percent of the African population would move to cities by 2040 (World Bank 2017). African cities have grown concentrated, unbalanced, and uncontrolled in most of the cases (United Nations Economic Commission for Africa 2017). This recalls the idea of Malthusian cities where larger proportion of population “reside under inadequate living conditions” and where “congestion effects of population growth” exceed the benefits of urbanization (Castells-Quintana 2017).

This is part of the debate on the different quality of different types urbanization and the related evolution of urban growth and total productivity (Krugman and Livas 1992; Ades and Glaeser 1995; Maurel and Tuccio 2015). The idea of urbanization without growth is particularly expressive of some peculiar developing countries’ dynamics (Castells-Quintana and Wenban-Smith 2020). Different studies have explored the nexus between urbanization trends and economic performance. As a result, they have fed the debate of how the growth of cities is not necessarily related with economic growth (Kojima 1996; Bloom, Canning, and Fink 2008; Vollrath 2009). Climate change effects, transportation infrastructure, and demographic dynamics are among the determinants that concur to explain the rise of megacities in Africa (Barrios, Bertinelli, and Strobl 2006; Castells-Quintana and Wenban-Smith 2020). Uganda is exemplar of this situation in the continent. The number of people living in urban areas has increased rapidly by an average of 300,000 people per year, most of them being located in specific areas (World Bank 2015). This is also the result of the loss of agriculture land productivity due to climate change (Barrios, Bertinelli, and Strobl 2006) which has pushed people from rural areas and secondary cities to the main ones (Jedwab, Christiaensen, and Gindelsky 2017). The central region, where the capital city Kampala is, has experienced the highest population growth rates (World Bank 2015). Kampala is the most populated city with an annual growth rate of 3.9 percent (United Nations 2015) and 1.5 million inhabitants. This corresponds to nearly 35 percent of the country’s urban population, with the remainder living in cities with less than 500,000 people (World Bank 2015). Greater Kampala Municipality Authority administers the whole metropolitan area hosting 3.5 million of people (World Bank 2015). This area of Uganda is the center of the country’s economic activity and its industrial sector (Lall, Schroeder, and Schmidt 2014). Nearly 70 percent of the country’s manufacturing plants are clustered in Greater Kampala (Lall, Schroeder, and Schmidt 2014). Currently, Kampala only accounts for one-third of the gross domestic product and hosts 46 percent of all formal employment in the country (Uganda Bureau of Statistics [UBOS] 2016), making secondary cities quite marginal in terms of their contributions to the domestic economy. Recent research stresses that Kampala is on the way to transitioning from a “market town” producing local services to “production center” (Lall, Schroeder, and Schmidt 2014), and this reinforces its importance as “center.” Kampala’s manufacturing share of gross domestic product, worth 31.1 percent, is greater than in 2008 when it was 27.7 percent (World Bank 2017). In parallel, transport, storage and ICT reported similar growth dynamics (World Bank 2017).

Oppositely to this, secondary cities like Soroti, Tororo, Masaka, and Moroto are focused on micro family activities and informal business (World Bank 2015; UBOS 2016)¹. Local economy in lagging areas is mainly anchored to the traditional retail trade of food, clothing, footwear, and household goods (World Bank 2017). These activities employ a significant share of workers and recorded a business turnover of less than UGX10 million (World Bank 2017). Expectedly, several factors constrained the expansion of this business and its daily operation. Limited access to capital market and transport network are the main influential factors to both running the business and its duration (World Bank 2013; World Bank 2017).

This territorial trade-off of Ugandan economy is exacerbated by the poor quality of transport linking the main cities and the rest of country with the ports and main commercial hubs of East Africa. In this regard, in 2009, the World Economic Forum pointed out how low the ratio of roads per square kilometer is, given also the scattered population all over the country and the long distances between urban areas. Moreover, many African countries like Uganda are landlocked, which is a natural obstacle to competitiveness (Schwab 2009; Adewole 2019).

Infrastructure quality limits rapid connection. Just to provide an example, the estimated travel time from Kampala to Lira (337 km) is more than six hours, whereas from Kampala to Mbale (225 km) it takes more than four hours (Lall, Schroeder, and Schmidt 2014). This situation persists despite the efforts of the Ministry of Works and Transport (MOWT) and Uganda National Road Authority, and the funds from donors (i.e., EU, World Bank, etc.) to support physical capital improvement (MOWT 2018). However, current infrastructure spending is extremely low in comparison, for instance, with Latin American countries at the end of the 1990s (Lall, Schroeder, and Schmidt 2014).

In addition to disconnected and costly transport pattern, the supply network is affected by traffic congestion and roads conditions within the Kampala boarder. The existing roads in Kampala were built in the 1960s for 100,000 vehicles per day; the latter number today has increased to 400,000. Nearly 73 percent of roads in Kampala is unpaved, which slows traffic and increases the likelihood of accidents (MOWT 2018). This is one of the major constraints affecting the connection of the capital city with the rest of the country (World Bank 2013). Kampala Capital City Authority calculated that currently 24,000 man-hours-day are lost by commuters due to traffic jams (World Bank 2017). Congestion is particularly troublesome for enterprises in the trade sector (World Bank 2017).

Resilience, Location, and Micro Entrepreneurship

In 1973, Holling was one of the first scholars to develop the notion of resilience. Afterward, the concept was applied by several disciplines as engineering, geography, psychology, and economics (Holling 1973; Martin 2011; Simmie and Martin 2010; Tóth 2015; Weichselgartner and Kelman 2015). The debate on resilience has had several contributions that have adopted different perspectives (Tsiapa and Batsiolas 2018). Gibson and Tarrant (2010) described resilience as positive attitude in making organizations robust to disturbances, recover from them, manage foreseeable volatilities, and adapt to extreme circumstances. In parallel, Gunasekaran, Rai, and Griffin (2011) view resilience as the ability to adapt, respond, being sustainable, and compete in a dynamic context.

Resilience is then a dynamic process of self-transformation and renewal within a specific context (Berkes 2007; Simmie and Martin 2010). It is influenced by both the impact of major shocks and the ongoing restlessness of structural economic changes (Bristow and Healy 2014; Kitsos, Carrascal Incera, and Ortega Argiles 2019). Recent studies pointed out how regional characteristics and their transformation may affect it. Boschma (2015) sees regional resilience as a comprehensive and evolutionary process, where the region is able not only to accommodate shocks but also to develop new long-term growth path. Reggiani, De Graaf, and Nijkamp (2002) assert that regional features and its transformation may also affect the resilience of entrepreneurship, and this is correlated with specific socioeconomic and institutional structure. According to some authors (Hindle 2010; Hundt and Sternberg 2016; Sohns and Diez 2018), firms and their correlation with region-level characteristics are key drivers to explain resilience (Moore 1993), as for instance their institutional and structural setting (Tsiapa and Batsiolas 2018).

Some studies investigate the influence of regional-level elements on the resilience of firms through spatial analysis, especially with reference to either location or proximity to specific areas (Szczepaniak 2012; Andres and Round 2015; Tsiapa and Batsiolas 2018; Herbane 2018). Kitsos and Bishop (2018) stress how firm location in Great Britain may have a strong influence on resilience, emphasizing a sort of “geographically diverse” resilience. Similar conclusions emerged also in a study on Ireland (Power, Doran, and Ryan 2019), where spatial dependence implies that bordering economic activities experience different patterns of longevity and that location of firms strongly impacts their duration. This recalls Audretsch and Dohse (2007), for which firm-specific characteristics are not the only highly influential factors to business resilience but also the “business environment” of the place where an enterprise operates matters. Geographical proximity may lead to positive externalities and reduce risk, thus making local firms more resilient. This is a mainstream of the research on this field (Kitsos, Carrascal Incera, and Ortega Argiles 2019). From a test on Vietnam (Sohns and Diez 2018), regional economic prosperity is found to considerably differ between urban and rural provinces, with proximity to main markets, population density, and economic structure being explanatory of micro enterprises development.

To explore how location affects the resilience of firms, recent studies consider the supply chain as a proxy of the network among cities and their level of dependence (Ponomarov and Holcomb 2009; Chen, Hsieh, and Wee 2014). In this research context, there are different and opposite views. Among others, Pereira, Christopher, and Lago Da Silva (2014) remark that “flexibility in sourcing” reduces the dependence from one source and increases firms’ resilience. This point is criticized by Güller and Henke (2019) who claim that an increasing number of suppliers increase the complexity of a firm’s network at the same time negatively impacting on resilience. This point needs to be taken into account accurately, especially in regions where political instability, poor physical capital, and natural barriers may vanish the effort to improve the resilience of those that want to diversify their suppliers (Güller and Henke 2019) and also their risk (Scuderi, Tesoriere, and Fasone 2019).

From these aforementioned studies, one key lesson emerges. Regions and entrepreneurship are strictly interconnected, especially with regard to resilience. The way entrepreneurship grows is shaped by factors external to the firm but internal to regions. They all should also be taken into account to explore the dynamics behind the complexity of firms’ resilience (Tsiapa and Batsiolas 2018). This aspect is more critical in developing countries where resilience policies may not keep the pace of the rapid development and transformation process occurring within the regions. This imposes a reflection on the strength and weaknesses of the growth model.

Setting and Data Collection

Data were collected under the auspices of Ministry of Lands Housing and Urban Development in charge of the Uganda Support for Municipal Development (USMID) Programme financed by the World Bank. The Programme was launched in 2013 with the aim to support physical capital across the country and thus make cities more resilient. Data collection took place during the preliminary stage of the drainage infrastructures design and master plan for each city of the Programme. One of the project’s tasks was to conduct a socioeconomic survey in order to fully understand the local milieu and its economy with a main focus on micro entrepreneurship. Based on previous studies in the field, we used a community-based approach to prepare and conduct the survey (Doocy et al. 2013; Gunathilaka 2018; Sohns and Diez 2018). The main inputs emerged from project workshops, on-site visits, and consultation with relevant stakeholders from each of the involved municipalities. This allowed to identify the most vulnerable areas. After classifying the shops into categories according to the type of traded good or service, the same number of shops within each category was selected via a convenience procedure (Scuderi, Tesoriere, and Fasone 2019). Then, face-to-face data collection was done by trained pollsters.

Given the project budget and its timing, the survey was cross sectional and involved only active shops. For the design and definition of the questionnaire, several sources were consulted. Besides the studies we already surveyed, we accounted for United Nations Development Programme (UNDP 2013) recommendations to explore micro entrepreneurship features in order to study resilience, where gender, business size, location, supply chain, age of business owners, and market capital access are stressed as key variables. This is also tested in other studies.² The questionnaire was rearranged several times, following also cultural mediators’ recommendations to make the understanding of the text easy because of the low education level of shops managers, and the possibility for pollsters to easily translate questions in local dialects. This is the reason why we mainly inserted qualitative items. The final questionnaire surveyed demographic data of the shop manager, general business information, influence of floods, and business specific factors. Similar variables were used also in recent studies exploring resilience of micro and small business in emerging economies (Marks and Thomalla 2017). Also, the longevity of the business as a measure of resilience is not novel in the literature (Sohns and Diez 2018). We collected 519 questionnaires from shops in cities and villages with no more than five employees, where the interviewed manager was also the owner. The initial number of 519 questionnaires reduced to 352 because of missing data.

Empirical Approach

Model and Statistics

The following analysis considers data from micro shops of the trade retail sector located in ten municipalities of Uganda. In Figure 1 we display the location of our sample’s cities, the percentage of shops buying goods from Kampala, and distance from Kampala.

Figure 1.

Distribution of sample, percentage of shops supplying from Kampala, and distance.

The model to test has the following specification:

y_{i} = f (l i n k a g e_{i}, l o c a t i o n_{i}, s h o p_{i}, m a n a g e r_{i}, h a z a r d_{i}),

where i is the single shop. Dependent variable y_i is the longevity of the business (number of years of activity) as a measure of resilience. Target regressors linkage_i and location_i account for, respectively, the type of the shop’s suppliers, and the distance of the village/city from the capital city. Specifically, linkage_i considers two dummy variables, that is, if the shop’s supplier is, respectively, either in the same city (Sourcesamecity) or in Kampala (SourceKampala). In addition, a count variable measures the number of suppliers not included in those previously mentioned (Diversification). As to location _i , we consider the travel hours from our samples to Kampala (Traveltime) based on Lall, Schroeder, and Schmidt (2014).

Right-hand side controls include three groups. The first one takes into account the following information on shops (shop_i ):

a dummy variable UpFront that equals 1 if the upfront capital to start the activity was greater than 10 million of Shillings (Tsiapa and Batsiolas 2018);

the dummy Emp3more for the business size, equal to 1 in case the business has three or more (Cull et al. 2006).

The second one (manager_i ) reports information on the shop manager, and specifically:

dummy variable for gender (Female);

manager’s median value of the age class indicated in the questionnaire (Medianage) (Williams and Vorley 2014).

The third group accounts for the influence of natural hazards (hazard _i) and specifically if the shop closed at least once in the last year due to floods (Stopflood; Scuderi, Tesoriere, and Fasone 2019). To further test location effects, we also interacted Stopflood with travel time (Stopflood × Traveltime), which measures the way infrastructure quality in lagging areas influences longevity. Good infrastructures may prevent shops to close because of flood. Then, our hypothesis is that being located progressively far from Kampala may influence business longevity also because of bad infrastructures.

Table 1 reports descriptive statistics and the correlation with longevity, our dependent variable. In the Appendix, we also report descriptive statistics by city. Figure 2 shows the relationship between longevity and travel hours from Kampala. Lines represent the simple linear regression for the whole sample (black line); firms whose suppliers are in Kampala (hatched line); firms whose suppliers are not in Kampala (gray line). Longevity for firms supplying from Kampala stands above the full sample line, whereas the one of firms not buying goods in Kampala is below.

Table 1.

Descriptive Statistics.

Variables	Measurement unit	Mean	Standard Deviation	Minimum	Maximum	Correlation
Longevity	Years	5.49	5.153	1	39	1.00
Sourcesamecity	1 if yes, 0 otherwise	0.471	0.499	0	1	–.0985
Diversification	Number of cities	1.067	0.273	1	3	.0405
SourceKampala	1 if yes, 0 otherwise	0.409	0.492	0	1	.1901*
Traveltime	Number of hours	4.91	1.66	2	8	–.1073*
UpFront	1 if yes, 0 otherwise	0.409	0.492	0	1	.0428
Emp3more	1 if yes, 0 otherwise	0.370	0.483	0	1	0.2686*
Female	1 if yes, 0 otherwise	0.446	0.497	0	1	.2188*
Medianage	Number of years	33.231	7.731	21.5	47.5	.3965*
Stopflood	1 if yes, 0 otherwise	0.232	0.4233	0	1	−.1679*

Note: We report correlation of each variable with longevity; asterisk (*) indicates that the correlation coefficient is 5 percent significant.

Figure 2.

Longevity and travel time.

Econometric Analysis

Our analysis of the longevity as dependent variable, measured in number of years, adopted two estimation models. We first performed nonlinear regression through count data model (Cameron and Trivedi 1985, 2005). After appropriately testing for overdispersion, among the classic alternatives for this class of models, we selected negative binomial regression (results not reported).

In addition, we tested longevity in a way that accounts for its characteristic of duration over time. Although survival analysis models can be seen as appropriate to this end, we applied tobit regression. Given that all our observations were right-censored (Miller 1981; Amemiya 1984), tobit model is preferable as all “patients” were “alive” at the last time, that is the time of the interview. We reported robust clustered estimates of standard errors in all our models in order to account for potentially non independent and identically distributed observations within the same city (Cameron and Miller 2015).

In addition to the covariates we listed above, we tested further ones to check the robustness of our findings. We then estimated models that included other characteristics related to shop, manager, and the region. We tested squared age of the shop manager to see whether its effect was nonlinear. We also checked whether longevity is associated with the type of shop classified according to the traded good or service. In particular, we evaluated those related to food and textile as proxies of sectors relying on, respectively, primary goods and imported goods (see UBOS 2016).

As to regional features, we also considered alternative specifications that included the distance from two other important cities than Kampala, namely Entebbe (belonging also to Greater Kampala Authority) and Jinja (an industrial city), plus dummies indicating whether the supplier was located in one of these two cities (World Bank 2017). This was done in order to test whether there would have been some evidence of a multicentric regional development model in Uganda. For all the additional checks, our main results were not altered nor tested regressors turned out to be significant.

Given the cross-sectional nature of our data and the variables we used, our estimates report correlation and as such our results cannot be interpreted in terms of causal linkages. In our econometric analysis we tried to make the best use of our cross-sectional data. Indeed, gathering information in a single time is a main limitation to analyze a process taking place over the time like the one under investigation. In this sense, longitudinal data would have been more suitable to detect which of the shops surveyed at an initial time would have been still open at a subsequent point of time, and then test a concept of longevity based on the survival of firms. Similar limits have been recognized in other studies using primary data in developing countries (Sohns and Diez 2018). However, this was not possible in our survey given the limited time span over which the project took place.

Results and Discussion

In Table 2, we report estimates from negative binomial (models 1 and 2) and tobit (models 3 and 4) regressions, along with clustered standard errors and marginal effects. We started from a baseline model including only target variables (models 1 and 3), and then, we show full models that included controls (models 2 and 4).

Table 2.

Empirical Results.

	(1)	(2)	(3)	(4)
SourceSameCity	–0.0612 (0.104)	0.0581 (0.106)	–0.376 (0.587)	0.239 (0.541)
SourceSameCity	–.329	.284	–.236	.160
Diversification	−0.0138 (0.181)	0.0155 (0.276)	−0.176 (1.096)	−0.349 (1.622)
Diversification	−.074	.055	−.110	−.234
SourceKampala	0.326** (0.101)	0.259* (0.104)	1.990*** (0.548)	1.475** (0.560)
SourceKampala	1.754	1.270	1.252	.990
Traveltime	−0.0377 (0.0273)	−0.0728** (0.0275)	−0.258 (0.147)	−0.465** (0.165)
Traveltime	−.203	−.357	−.162	−.312
UpFront		−0.260** (0.0921)		−1.546*** (0.429)
UpFront		−1.275		−1.038
Emp3more		0.348** (0.129)		2.083* (0.824)
Emp3more		1.708		1.399
Female		−0.127 (0.0866)		−0.760 (0.564)
Female		−.621		−.510
Medianage		0.0390*** (0.00431)		0.237*** (0.0439)
Medianage		.191		.159
Stopflood		−0.730*** (0.0832)		−4.306*** (0.602)
Stopflood		−3.580		−2.891
Stopflood × Traveltime		0.0467** (0.0171)		0.303* (0.124)
Stopflood × Traveltime		.229		.203
Intercept	1.838*** (0.266)	0.760 (0.390)	6.518*** (1.622)	0.886 (2.666)
lnalpha	−0.596*** (0.159)	−1.031*** (0.207)
Sigma			5.300*** (0.542)	4.644*** (0.468)
Akaike information criterion	1,940.13	1,833.384	2,099.039	2,002.057

Note: Negative binomial (1–2) and tobit (3–4) regression for the declared longevity of business (longevity) as dependent variable. Marginal effects at means in italics and clustered standard errors at city level in parenthesis.

*p < .05.

**p < .01.

***p < .001.

Starting from our target variables, in all models we found that SourceKampala is positively correlated with business longevity: shops buying goods from suppliers located in the main city of Uganda are those that have been open for longer. This happens as opposed to Sourcesamecity, which indicates that the most resilient shops do not necessarily purchase their goods from suppliers in the same city.

Full models show that Traveltime is likely to significantly contribute in explaining the resilience variability in our sample, in the sense that firms located further from the main city exhibited negative correlation with business longevity. Of course, this evidence deserves further investigation in the light of the limitations of our empirical analysis. However, it encourages further in-depth analysis to see how being closer to the capital city is likely to be a factor that positively influences longevity.

Unexpectedly, Diversification is not a significant covariate, thus suggesting that the most lasting businesses are not necessarily those that merely have a higher number of suppliers. The number of suppliers can be taken as a measure of the willingness to reduce risk because of diversification. This matters, for instance, when shops need to recover fast from natural hazard (Scuderi, Tesoriere, and Fasone 2019). Our evidence suggests that this may not be the case of our sample.

As to controls related to shop characteristics, we note that greater upfront capital (Upfront) is proper of those shops lasting less. This interesting evidence deserves further analysis. These results can be an indication of how costly gaining credit in developing countries is, and how the credit-related burden may harm the longevity of micro shops (Bridges and Guariglia 2008). Specific tools such as bank guarantee, credit guarantee consortium, job security, and so on, should be introduced in order to sustain the business. Policy makers should support the diffusion of appropriate means to support the financial business sustainability.

Looking at the number of employees (Emp3more), we find that size is likely to matter in the sense that increasing the business size can make micro shops more resilient (Cull et al. 2006). This is a paramount of a strand of literature on firm’s resilience (Salavou, Baltas, and Lioukas 2004).

Expectedly, manager-related variables show that older shops are run by older managers (Medianage), with gender being not significant (Female). The age of the business managers is a well-studied socioeconomic factor in the literature on enterprises longevity; as pointed out in Storey and Wynarczyk (1996: 220): they have already gathered valuable life experience, but still have enough energy to run an enterprise successfully. Furthermore, this is in line with other relevant research, which finds that every additional year of the business head’s age, the failure rate decreases significantly in the case of micro-enterprises (Holtz-Eakin, Joulfaian, and Rosen 1994: 59). As to the nonsignificant gender effect, this is not unexpected. Uganda has made progress in supporting gender equality policies and campaigns to improve the access to equal opportunities for women, especially in the private sector. In this instance, Gender Equality Seal Certification Programme for Public and Private Enterprises is a tangible example (UNDP 2016). However, in general, we have to say that empirical studies have not come to the same conclusions as to the direction of the gender effect (Mead and Liedholm 1998; Vijverberg and Haughton 2002).

Finally, stops because of natural hazard (Stopflood) are less frequent for those shops lasting longer. This remarks how influential these natural events are to the local economy, as already studies by Scuderi, Tesoriere, and Fasone (2019). In addition, the result of the interaction between floods and travel time (Stopflood × Traveltime) is positive and significant, which can support the idea that further places from the capital city are those where infrastructure quality is worse, and all this is correlated with longevity.

Given the caveats we already pointed out, we have some evidence that resilience of micro entrepreneurship is associated with the proximity with Kampala, the main commercial hub of Uganda. This can be interpreted in the light of Hassink (2010) who uses the term “path-dependency effect” to explain regional development policies. The extremely concentrated dependence of development from few cities may create the basis to make business environment less favorable for entrepreneurship especially in lagging areas (Estrin, Meyer, and Bytchkova 2005). This is more evident in Africa due to the poor quality of transportation system, negatively impacting the access to the main markets from small and remote cities (Jedwab and Storeygard 2019). The adoption of policies for resilience should consider the complexity behind regional development and micro entrepreneurship. This topic needs further investigation, especially for those contexts where policy has not kept pace with the rapid transformation of regions and their local economies.

Conclusions

Based on a unique data set from a survey across ten cities in Uganda, this research explored the resilience of micro shops, trying to depict how location may be associated with it. Uganda is a country whose socioeconomic dynamics well represent the ones that are happening in Africa. To the best of our knowledge, this is one of the few studies focused on micro enterprises’ resilience across several cities of a developing country. Despite our analysis has several limitations and a longitudinal survey would be preferred, our findings give evidence on how location matters to explain the resilience of micro shops. The main finding suggests that both proximity to Kampala, the main city, and supplying from it are positively correlated with the longevity of a micro firm. Furthermore, manager individual characteristics like age is strongly associated with longer life of the shops. This positive effect is found also for shop size. On the contrary, more up-front capital to start the business and exposure to flooding events as natural hazard characterize activities with less longevity. In line with recent academic debate, our results suggest that the approach to firm resilience should be based on a deeper linkage between location and entrepreneurship. Lack of territorial policies and firm-level actions are key priorities. This stresses the urgent issue to regulate territorial policies by improving the network inside and outside the counties, as well as firm-specific actions to favor the access to spatially diversified supply sources.

Footnotes

Appendix

Table A1.

Descriptive Statistics by City.

Variables	Mean	Standard Deviation	Minimum	Maximum
Arua (N = 27)
Longevity	4.925	7.141	0	26
SourceSameCity	0.740	0.446	0	1
Diversification	1	0	1	1
SourceKampala	0.222	0.423	0	1
Traveltime	7	0	7	7
UpFront	0.111	0.320	0	1
Emp3more	0.481	0.509	0	1
Female	0.444	0.506	0	1
Medianage	31.944	8.54	21.5	47.5
Stopflood	0.111	0.320	0	1
Moroto (N = 35)
Longevity	2.457	1.836	0	8
SourceSameCity	0.171	0.382	0	1
Diversification	1.085	0.373	1	3
SourceKampala	0.257	0.443	0	1
Traveltime	8	0	8	8
UpFront	0.5428	0.505	0	1
Emp3more	0.257	0.443	0	1
Female	0.4	0.497	0	1
Medianage	31.557	8.422	21.5	47.5
Stopflood	0.0571	0.235	0	1
Lira (N = 33)
Longevity	6.939	7.697	0	39
SourceSameCity	0.575	0.501	0	1
Diversification	1	0	1	1
SourceKampala	0.484	0.507	0	1
Traveltime	6	0	6	6
UpFront	0.545	0.505	0	1
Emp3more	0.393	0.496	0	1
Female	0.484	0.507	0	1
Medianage	34.045	8.979	21.5	47.5
Stopflood	0.090	0.291	0	1
Gulu (N = 27)
Longevity	5.629	6.108	0	27
SourceSameCity	0.629	0.492	0	1
Diversification	1	0	1	1
SourceKampala	0.370	0.492	0	1
Traveltime	6	0	6	6
UpFront	0.185	0.395	0	1
Emp3more	0.296	0.465	0	1
Female	0.259	0.446	0	1
Medianage	34.981	8.312	21.5	47.5
Stopflood	0.407	0.500	0	1
Soroti (N = 28)
Longevity	3.571	2.11	1	8
SourceSameCity	0.642	0.487	0	1
Diversification	1	0	1	1
SourceKampala	0.25	0.440	0	1
Traveltime	5	0	5	5
UpFront	0.428	0.503	0	1
Emp3more	0.357	0.487	0	1
Female	0.607	0.497	0	1
Medianage	33.285	8.211	21.5	47.5
Stopflood	0.407	0.500	0	1
Fort Portal (N = 40)
Longevity	4.825	4.137	0	16
SourceSameCity	0.4	0.496	0	1
Diversification	1	0	1	1
SourceKampala	0.5	0.506	0	1
Traveltime	5	0	5	5
UpFront	0.15	0.361	0	1
Emp3more	0.25	0.438	0	1
Female	0.525	0.505	0	1
Medianage	30.725	6.149	21.5	47.5
Stopflood	0.35	0.483	0	1
Mbale (N = 40)
Longevity	4.675	3.165	0	13
SourceSameCity	0.35	0.483	0	1
Diversification	1	0	1	1
SourceKampala	0.45	0.503	0	1
Traveltime	4	0	4	4
UpFront	0.5	0.506	0	1
Emp3more	0.55	0.503	0	1
Female	0.35	0.483	0	1
Medianage	35.425	7.546	21.5	47.5
Stopflood	0.333	0.477	0	1
Tororo (N = 41)
Longevity	7.585	5.630	0	20
SourceSameCity	0.292	0.460	0	1
Diversification	1	0	1	1
SourceKampala	0.219	0.419	0	1
Traveltime	4	0	4	4
UpFront	0.487	0.506	0	1
Emp3more	0.341	0.480	0	1
Female	0.536	0.504	0	1
Medianage	33.475	6.965	21.5	47.5
Stopflood	0.146	0.357	0	1
Hoima (N = 41)
Longevity	6.829	4.609	0	19
SourceSameCity	0.487	0.506	0	1
Diversification	1.268	0.448	1	2
SourceKampala	0.682	0.471	0	1
Traveltime	4	0	4	4
UpFront	0.219	0.419	0	1
Emp3more	0.317	0.471	0	1
Female	0.487	0.506	0	1
Medianage	34.085	7.668	21.5	47.5
Stopflood	0.0243	0.156	0	1
Masaka (N = 42)
Longevity	6.523	4.748	0	18
SourceSameCity	0.595	0.496	0	1
Diversification	1.238	0.484	1	3
SourceKampala	0.523	0.505	0	1
Traveltime	2	0	2	2
UpFront	0.785	0.415	0	1
Emp3more	0.452	0.503	0	1
Female	0.357	0.484	0	1
Medianage	32.880	6.767	21.5	47.5
Stopflood	0.4285	0.500	0	1

Authors’ Note

The data used in this research were surveyed during the consultancy services for comprehensive planning of storm water drainage needs study and preparation of drainage master plans for thirteen municipalities in Uganda. This project is part of Uganda Support for Municipal Development Programme of Ministry of Lands Housing and Urban Development of Uganda.

Acknowledgments

The authors, and in particular Giuseppe Tesoriere, would like to thank to all professors and scholars, who attended to the Second Annual Innovation, Economic Complexity and Economic Geography Workshop, held at University Utrecht in September 2019, for their insightful contribution, making this research more robust and coherent.

In addition, the authors would like to thank the Ministry of Lands Housing and Urban Development and USMID in Kampala, as well as Town clerks and Municipal officials of the cities involved in the surveys, and Mr. Sergio di Maio, Mr Gabriele Speciale, Mr. Gaetano Beninati and Ms. Patricia Mugala for their valuable support.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Raffaele Scuderi

Notes

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