metadata: authors: 0: author: Bougna Lonla,Theophile
1: author: Nguimkeu,Pierre
count: Cameroon
docna: 0: docna: Spatial and sectoral heterogeneity of
            occupational choice in Cameroon
docty: Policy Research Working Paper
subtopic: Marketing,Private Sector Development Law,Private Sector Economics,Labor Markets,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Common Carriers Industry,Construction Industry,Business Cycles and Stabilization Policies,Textiles, Apparel & Leather Industry,Plastics & Rubber Industry
teratopic: Macroeconomics and Economic Growth,Law and Development,Private Sector Development,Industry,Social Protections and Labor
abstracts: cdata!: This paper investigates the relationship
            between location, agglomeration, access to credit,
            informality, and productivity across cities and industries
            in Cameroon. Emphasizing the link between micro-foundations
            and the data, the paper develops and estimates a structural
            model of occupational choice in which heterogeneous agents
            choose between formal entrepreneurship, informal
            entrepreneurship, and non-entrepreneurial work. Their
            decision-making process is driven by institutional
            constraints such as entry costs, tax enforcement, and access
            to credit. The model predicts that agglomeration has a
            non-monotonic effect on formalization, and entrepreneurial
            profits increase with agglomeration effects. Estimating the
            model by the generalized method of moments, the paper finds
            that the returns to capital and labor are not uniform across
            sectors and cities. Manufacturing industries are highly
            constrained in capital and the elasticity of capital is
            higher in Yaoundé and Douala, whereas labor elasticity is
            higher in Kribi. Counterfactual simulations show that an
            increase in roads provision can have a substantial impact in
            terms of output, formalization, and productivity. A
            reduction in the current interest rate has a large and
            significant impact on formalization and no significant
            effect on business creation. Likewise, while the current tax
            rate is suboptimal for most cities, a tax reduction policy
            would have a much greater impact on formalization than on
            business creation. These effects differ substantially across
            cities and sectors, suggesting that those policy instruments
            could be implemented accordingly to support formalization
            and business creation.
geo_reg_key: 1927320,517191
repnme: repnme: Spatial and sectoral heterogeneity of
            occupational choice in Cameroon
display_title: Spatial and sectoral heterogeneity
            of occupational choice in Cameroon
listing_relative_url: /research/2018/07/30247853/spatial-sectoral-heterogeneity-occupational-choice-cameroon
topicv3: Private Sector Development m1326060 1921,Manufacturing Sectors m1326521 1281,Jobs m1503931 2670,Entrepreneurship m1582903 4424
publishtoextweb_dt: 2018-07-10T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Law and Development,Private Sector Development,Industry,Social Protections and Labor
no_of_pages: 57
author_keys: 0: author_key: 1057789
model_extractions: WPS8515 Policy Research Working Paper 8515 Spatial and Sectoral Heterogeneity of Occupational Choice in Cameroon _Théophile Bougna Pierre Nguimkeu_ Development Economics Development Research Group July 2018
Policy Research Working Paper 8515

# **Abstract**

This paper investigates the relationship between location, agglomeration, access to credit, informality, and productivity across cities and industries in Cameroon. Emphasizing the link between micro-foundations and the data, the paper develops and estimates a structural model of occupational choice in which heterogeneous agents choose between formal entrepreneurship, informal entrepreneurship, and non-entrepreneurial work. Their decision-making process is driven by institutional constraints such as entry costs, tax enforcement, and access to credit. The model predicts that agglomeration has a non-monotonic effect on formalization, and entrepreneurial profits increase with agglomeration effects. Estimating the model by the generalized method of moments, the paper finds that the returns to capital and labor are not uniform across sectors and cities.

Manufacturing industries are highly constrained in capital and the elasticity of capital is higher in Yaoundé and Douala, whereas labor elasticity is higher in Kribi. Counterfactual simulations show that an increase in roads provision can have a substantial impact in terms of output, formalization, and productivity. A reduction in the current interest rate has a large and significant impact on formalization and no significant effect on business creation. Likewise, while the current tax rate is suboptimal for most cities, a tax reduction policy would have a much greater impact on formalization than on business creation. These effects differ substantially across cities and sectors, suggesting that those policy instruments could be implemented accordingly to support formalization and business creation.

This paper is a product of the Development Research Group, Development Economics. 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 tbougna@worldbank.org and nnguimkeu@gsu.edu.

_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
# Spatial and Sectoral Heterogeneity of Occupational Choice in Cameroon<sup>*</sup>

Théophile Bougna<sup>†</sup> Pierre Nguimkeu<sup>‡</sup> _JEL Classification:_ O12, O17, C51, C52, C54, R23, R11.

> *We are grateful to Emma W. Hobson and two anonymous referees for very helpful comments and suggestions. Financial support from the Economic Performance of African Cities (nlta) is gratefully acknowledged. The content is solely the responsibility of the authors.

> †Development Research Group (decrg), The World Bank, 1818 H Street, NW Washington, DC 20433, USA; Email: tbougna@worldbank.org

> ‡Department of Economics, Andrew Young School of Policy Studies, Georgia State University,14 Mariette Street NW, Atlanta, GA 30303, USA; Email: nnguimkeu@gsu.edu.
# **1 Introduction**

The informal sector forms an integral part of developing economies, employing more than two-thirds of the urban workforce and accounting for about one-third of the economy-wide output in Cameroon (ins, 2010). An extensive body of the literature tries to explain the emergence of informality. For instance, Djankov and others (2002) document the existence of lengthy and costly registration procedures that hamper the formalization process of firms. Also, several papers have emphasized tax burden as a constraint to formalization (see, e.g., Fortin and others, 1997; Amaral and Quentin 2006; de Paula and Scheinkman, 2011; Prado, 2011; and Ordonez 2014). Nguimkeu (2014, 2015) showed that skills and access to credit play a significant role in the productivity and formalization of informal firms as well as the creation of new formal firms. However, the extent to which these constraints vary according to the sector of activity and the location of firms has received very little attention in the literature. The informal economy is getting progressively more heterogeneous in terms of demographic groups entering the sector, the types of linkages (including many cross linkages to the formal sector) and in the variety of economic enterprises. Each group brings different knowledge and skill sets leading to an increasing variety of enterprises in type and size. As noted by the u.k. 2017 Cities Alliance report “any future research agenda directed towards policy measures to enable better management of, and support to the informal sector will need to take on board this growing complexity and heterogeneity." Understanding the sectoral composition of the informal economy and micro-enterprises, spatial location, and the major constraints they face in their growth and formalization prospects, is critical in informing policy aiming at harnessing informality and the agglomeration potential within cities for greater productivity and job creation.

This paper examines the constraints hindering the creation of new formal firms, the formalization of informal firms and firm productivity in Cameroon, and how these constraints vary across sectors and spatial locations. We also analyze how these two dimensions (sector of activity and spatial location) interact with each other. Extending Nguimkeu (2014, 2015), we develop an occupational choice model in which individuals choose between formal entrepreneurship, informal entrepreneurship, and nonentrepreneurial work, and their decision-making process depends on entry costs, tax enforcement and access to credit all of which are heterogeneous in location and sector of activity. On the one hand, to join the formal sector, thereby enjoying better access to the capital market and a better technology, an entrepreneur must pay an upfront entry registration cost, and then pay taxes once in operation in the formal economy. On the other hand, the entrepreneur may choose informality, therefore saving the sunk cost, and avoiding taxes, unless caught and fined. However, the entrepreneur is bound 2
to remain credit-constrained and/or to operate an archaic technology in the informal economy. Our model allows, in particular, to trace various categories of informal firms, with respect to their productivity potentials and the level of constraints they face, a useful characterization in tailoring policy actions to address informality and promote entrepreneurship. The model predicts that agglomeration has a non-monotonic effect on formalization of informal firms, and entrepreneurial profits increase with agglomeration effects.

This paper also contributes to the literature looking at policies designed to bring informal businesses and individuals within the reach of the tax system. Evidence is nuanced and the growth benefits of formalization are positive but heterogeneous across firm types. The smallest firms usually benefit the least while mid-sized firms typically benefit the most. Much economic informality seems to be explained as rational sorting of firms and workers based on inherent productivity characteristics and so formalization _per se_ may not address the fundamental determinants of productivity (Oviedo and others, 2009). In addition, corruption or poor public services may also reduce workers’ and firms’ willingness to formalize and pay taxes. A World Bank (2007) report shows a strong and significant relationship between informal behavior of the firm (measured as tax evasion), and the incidence of corruption (measured by extortion from public officials). Torgler and Schneider (2007) also look at how institutions affect individuals’ decisions to stay informal or to join the formal economy. In particular, they look at how tax morale—which is a measure of how justifiable people think it is to avoid paying taxes—is associated with informality. They found that this measure is highly correlated with institutional measures such as rule-of-law, and corruption. Their result highlights the role of individual’s trust in the ability of the public sector to serve the public interest when they decide to participate in the established social and economic system, versus instead deciding to stay informal. In a recent paper, Benhassine et al. (2018) used a randomized experiment in Benin, to document how formalization rates can be enhanced by policies that aimed at providing information, training, and support in opening a bank account to business. However, they found no significant gains in sales, profits, or welfare. The international evidence is cautionary against expecting a quick growth dividend from policies to encourage formalization. As rightly pointed out by Oviedo and others (2009), strengthening enforcement will work best when the appropriate incentives to formalize are created, so that informal agents have a viable transition path from informality to formality. Strengthening enforcement only may not be growth-enhancing. Policy packages can comprise a series of carrots and sticks that need to be adapted to the nature of informality in the country. In some countries, regulatory reform might be more relevant, while in others it could be regulation enforcement, administrative reform or tax reform. In this paper, we provide evidence of how investing in local infrastructure, increasing access to finance, 3
and performing tax reforms can affect entrepreneurship, formalization rate, output, and productivity.

We first test the implications of the model using reduced form estimations techniques, based on a cross-section of micro-enterprises from the Business census and the National Survey on Employment and the Informal Sector. This allows us to test the theory by investigating empirically the importance of location and sector of activity on the extent of informality and firm performance. Our cross-sectional results show that firm profits increase with agglomeration and informal agricultural activities are the least profitable, whereas the least profitable formal activities are in the service sector. In addition, informal entrepreneurs operating in manufacturing, commerce and services are more likely to formalize than their counterpart in the agriculture sector. Second, we perform specification tests and provide structural estimates of the model using the Generalized Method of Moments and micro-data (firm-level and household-level) from Cameroon. We find that the return to capital and labor is not uniform across sectors and cities in Cameroon. Manufacturing industries are highly constrained in capital and the elasticity of capital is higher in Yaoundé and Douala, while labor elasticity is higher in Kribi, reflecting a low availability of workforce. One of the main advantages of using a structural econometric approach is that it allows one to generate counterfactual samples on the basis of a well-tested theoretical model that is consistent with the data. The counterfactual policy simulations that we perform based on the estimated model are then used to quantify the effects of several policy measures on informality and productivity. In particular, by identifying prominent sectors of activities (and/or strategic locations) and constraints faced by agents operating in these sectors (and/or locations), we provide a quantitative assessment of the impact that a targeted policy design such as improved infrastructure, access to finance or adjusted tax rates would have on aggregate productivity, job creation, formalization of informal firms, and government revenues. The results show that an increase in roads provision can have a substantial impact in terms of output, formalization of informal firms and productivity. A reduction in the current interest rate has no effect on business creation, but large and significant impact on the formalization of informal firms. The current tax rate could be adjusted to decentralized local optimal rates that would yield higher rates of formalization and business creation as well as higher amounts of government revenues. All these effects are heterogeneous across cities and/or sectors.

The remainder of the paper is structured as follows. Section 2 documents our data, and briefly presents the stylized facts about informality, location and business constraints. It also provides a first look at the geographic concentration of industries, the specialization of cities, and the formal/informal entrepreneurship in Cameroon. In Section 3, we develop a structural model of occupational choice in which individuals choose between wagework, informal entrepreneurship and formal entrepreneurship.

4
In Section 4 we perform the estimation of the model by presenting an econometric strategy that uses differences in location entrepreneurship rates and earnings in Cameroon to identify the structural parameters of the theoretical model. We also present the structural estimates by cities and by sectors. In Section 5, we perform a set of counterfactual simulations to evaluate the impact of public policies on entrepreneurship and informality across cities and industries in Cameroon. Section 6 concludes while additional results and technical details are relegated to the appendix.

# **2 Facts about Firm Informality, Location and Constraints**

Our structural and empirical analysis requires three key pieces of industry-level and city-level information: (i) measures of absolute geographic concentration; (ii) measure of specialization of cities (places), and (iii) measures of formal, and informal entrepreneurship. We discuss our data and these measures, and take a first look at Cameroon’s geographical concentration of industries, specialization of cities, firm productivity, the constraints hindering the creation of new firms (formal and informal), and how these constraints vary across sectors and spatial locations. This will allow us to uncover important features of the data used for this study and assess the empirical relevance of the model predictions for Cameroon’s cities and industrial sectors.

## **2.1 Data**

Our primary data sources are the Cameroon National Survey of Employment and Informal Sector (henceforth eesi) and the General Enterprise Census (henceforth gec). The eesi is at the household level and covers the years 2005 and 2010. This dataset will be used to measure and characterize formal and informal entrepreneurship. The General Enterprise Census is at the firm level and is only for the year 2009. We are using the gec dataset to measure the absolute geographic concentration of industries, and the specialization of cities.

### **2.1.1 General Enterprise Census data**

The gec provides detailed micro-geographic information on all formal and informal establishments with fixed location, including the 4-digit nace (Statistical classification of economic activities) classification, the number of employees of the establishment, turnover, as well as information on the business environment. Table 1 presents the 2009 distribution of formal and informal establishments and jobs in Cameroon (by region and by industries). As can be seen, the bulk of jobs (68 percent) and firms (85 percent) are in the commerce and services sectors. Manufacturing industries represents roughly 13 percent in terms of firm counts and 18 percent in terms of jobs. Economic activities 5
are strongly concentrated (in terms of employment) in Yaoundé and Douala and, more generally, along the region surrounding these economic and political capital cities, respectively. These two cities which represent less than 0.2 percent of Cameroon’s total surface account for more than 60 percent of employment. One of the key reasons for that strong overall clustering is plausibly the uneven distribution of the population arising from colonial settlement patterns. Geographical variations in industrialization are the primary factor affecting the spatial variations in incomes. We use the 2009 General Enterprise Census data to get a more precise picture of the extent and the strength of industrial localization of industries as well as the specialization of cities in Cameroon.

Table 1: Sectoral distribution of formal firms, informal firms and jobs in Cameroon

||**Establi**|**shments**|**Jo**|**bs**|
|---|---|---|---|---|
|**Industries**|**Freq.**|**Percent**|**Freq.**|**Percent**|
|Agriculture|145|0.16|26,530|6.87|
|Breeding|86|0.10|549|0.14|
|Forestry|47|0.05|6,787|1.76|
|Fishingand fsh farming|6|0.01|39|0.01|
|**Sub-total Primary**|**284**|**0.32**|**33,905**|**8.78**|
|Mining|25|0.03|1,162|0.30|
|Food industry|695|0.79|19,447|5.03|
|Other Manufacturing|10,227|11.60|49,993|12.94|
|Electricity water and gas|74|0.08|8,530|2.21|
|Construction|664|0.75|8,757|2.27|
|**Sub-total Secondary**|**11,685**|**13.26**|**87,889**|**22.75**|
|Commerce|46,788|53.08|105,458|27.30|
|Transport|518|0.59|15,041|3.89|
|Bank and insurance|596|0.68|12,584|3.26|
|Other tertiary|26,963|30.59|128,844|33.36|
|**Sub-total Tertiary**|**74,865**|**84.93**|**261,927**|**67.81**|
|Unknown|1,310|1.49|2542|0.66|
|**Total**|**88,144**|**100.00**|**386,263**|**100.0**|

# **2.1.2 Cameroon National Surveys on Employment and Informal Sector**

We use data from the Cameroon National Surveys on Employment and Informal Sector (eesi) to empirically investigate the characteristics of the Cameroon informal sector and its most salient features. The eesi surveys were conducted in 2005 and 2010 by the 6
National Institute of Statistics of Cameroon in partnership with the World Bank. These are nationwide cross-sectional and representative surveys that collect a comprehensive set of information about household characteristics and the characteristics of their economic activities. The surveys identify thousands of active household members aged 15 and above operating in the informal sector defined as “activities and production units that do not have written formal accounts and/or are not registered with the tax authorities.” We present the most salient features of the Cameroon informal sector using both these surveys.

The Cameroon labor market is characterized by a large informal sector. The informal sector employs around 90% of the occupied workforce in Cameroon. In 2010, there were approximately 2.5 million (90.5%) of Informal Production Units (upi) in Cameroon. The regional distribution of households with informal businesses revealed that the highest number of informal businesses were in the Littoral region, including Douala (19.0%), followed by the Centre region, including Yaoundé (17.4%), the FarNorth (14.4%), and the West (12.1%). The regions with the least number of informal businesses are respectively the South (2.1%), the Adamawa (4.3%), and the East (4.3%). The informal sector is largely concentrated in agricultural, and non-tradable services (e.g. retail trade, transportation, hotels or restaurants). Figure 1 shows the distribution of informal and formal firm by sector of activity in Cameroon. As can be seen, most informal businesses are primarily engaged in agricultural (48.5%) followed by services sector (23.1%), while formal firms operate mainly in services (74.2%). The sectoral distribution of informal businesses is heterogeneous across regions in Cameroon. While predominant activities in Douala and Yaoundé are related to services (about 50%), agricultural businesses are dominant in other regions. Manufacturing (26%) and hotels and restaurant (38%) businesses are more predominant in Douala (26%), while food processing (26.5%) is more common in the Far-North.

The Cameroon informal sector is a great contributor to its economy. This sector accounts for more than 30 percent of the gdp. During the first decade of the 2000s, there was an increase in the contribution of the informal sector to Cameroon’s gdp—from 37.3 percent in 2005 to 38.4 percent in 2010 (see Figure 2). Given its large contribution to the gdp, any diagnostic on Cameroon productivity and growth should include policy recommendations for the informal sector. The most important challenges are therefore how to alleviate constraints to business environment and improve productivity in the informal sector and incentivize them to transit into the formal sector. Indeed, despite this substantial contribution to the gdp and the labor market, the Cameroon informal sector consists of micro-units, often with one worker, very low-paid jobs and low labor productivity. The average size of informal units decreased from 1.5 employee in 2005 to 1.3 employee in 2010. This is driven by an increase in the share of 7
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# **2.2 Geographical Concentration, Specialization, and Entrepreneurship**

Here, we discuss measures of geographical concentration, specialization and entrepreneurial activity in Cameroon.

## **2.2.1 Geographical Concentration of industries in Cameroon**

The importance of agglomeration varies across industries, hence different industries have been found to exhibit surprisingly different levels of agglomeration (see Ellison and Glaeser, 1997). As pointed out by Kerr and Kominers (2015) “a complete understanding of the pathways by which agglomeration increases firms’ returns requires an effective agglomeration index, a measurement which identifies the concentration of industry and explains the relationship between firm location choice and industry concentration". To get a precise measure and picture of the extent and strength of industrial localization in Cameroon, we compute the well-known Ellison and Glaeser (1997) discrete measure of localization using micro-geographic data from the gec; see Appendix A.1 for details. This allows us to identifies the industries for which localization is potentially the strongest and to examine how this trend varies across different types of industries (formal vs informal, manufacturing, and new entrepreneur).

Table 2 reports our results, and several comments are in order. First, the mean of the eg index is 0.007 and the median is 0.012.<sup>2</sup> Out of the 99 four-digit industries in Cameroon, 60 industries (60%) exhibit a positive value for the eg index. In other words, 60 percent of industries are weakly or strongly concentrated. Within those industries, roughly half of them are considered as strongly agglomerated. Second, formal firms are on average more geographically concentrated than informal firms. Third, new entrants tend to exhibit more geographical concentration than existing firms. Fourth, manufacturing industries are less concentrated than other industries in general. Fifth, few manufacturing and natural-resources related industries are among the most concentrated industries in Cameroon. Sixth, informal firms are on average less concentrated than formal firm in Cameroon. Our results show that around 50 percent of formal firm are geographically concentrated while less than 30% of informal industries are clustered in space. This suggests that informal firms do not tend to cluster in the same locations as formal firms and we should expect a very weak buyersupplier and technology linkages between firms in these two sectors of the economy. Using data from India, Mukim (2015) show that buyer-seller and technology linkages explain much of formal-informal coagglomeration in India manufacturing. Our results

> 2A comparison between our results and the different studies is not reasonable since the eg index should be sensitive to the size and shape of the underlying zoning system as pointed out by Briant et al. (2010).

10
Table 2: Summary statistics for the geographic concentration measures

||||**Arrondi**|**ssement**|**Level**|
|---|---|---|---|---|---|
|**Statistics**|**Region Level**|All|Informal|Formal|New Entrants|
||**Al**|**l industr**|**ies**|||
|Mean|0.091|0.007|-0.020|-0.042|-0.124|
|Median|0.010|0.012|0.007|0.000|0.029|
|Minimum|-4.363|-2.081|-2.002|-1.647|-3.536|
|Maximum|6.903|1.757|1.146|0.418|1.975|
|Standard deviation|1.200|0.358|0.311|0.299|0.755|
|Share_<_0|42.424|39.394|41.414|39.190|28.790|
|Share_∈_(0, 0.05]|24.242|31.313|32.323|12.160|13.630|
|Share_>_0.05|33.333|29.293|26.263|48.650|57.580|
||**Manufac**|**turing i**|**ndustries**|||
|Mean|-0.138|-0.078|-0.075|-0.162|-0.120|
|Median|-0.015|-0.001|-0.016|-0.044|-0.006|
|Minimum|-4.927|-1.272|-1.225|-1.201|-0.941|
|Maximum|4.481|0.387|0.387|0.143|0.182|
|Standard deviation|1.211|0.292|0.286|0.300|0.284|
|Share_<_0|53.490|51.170|53.490|44.830|36.000|
|Share_∈_(0, 0.05]|13.950|39.530|32.560|13.790|16.000|
|Share_>_0.05|32.560|9.300|13.950|41.380|48.000|

_Notes:_ Authors’ computations using gec data. Mean and median values are for 99 fourdigit nace classification of industries (43 manufacturing, 74 formal, and 66 new entrants). Share _<_ 0 means ‘not clustered’; Share _∈_ (0, 0.05] means ‘weakly clustered’; Share _>_ 0.05 means ‘strongly clustered’. See Ellison and Glaeser (1997) for details.

show that almost 60 percent of new entrants are strongly concentrated. This result is consistent with Glaeser, Kerr, and Ponzetto (2010).

Looking at the geographical concentration of industries within cities, the following facts emerge. Douala and Bamenda exhibit significant geographical localization compared to the overall distribution of industries in Cameroon. Out of our 99 four-digit types of industries in Cameroon, 95 are operating in Douala, and 56 in Bamenda. The mean eg index of the overall industries in Cameroon is only 4.3 and 1.2 percent of that in Yaoundé and Bamenda respectively. Sugar manufacture, fabrics and stitches, and the growth of other industrial or export products are among the most localized industries in Douala. In Bamenda, the most localized industries are related to soap factories, perfume, detergents and cleaning products, the raising of other animals, and other wholesale trade. As expected, general public administration activities are the 11
second most concentrated sector in Yaoundé which is the country’s political capital. However, it is important to note that seven out of the ten most concentrated industries in Yaoundé are related to manufacturing activities (Manufacture of dairy products, pulp, paper, paperboard and paper products, cereal flour, soap, perfume, detergents and cleaning products, footwear, and other non-metallic products). The most concentrated industries in Kribi are related to the retail trade services, hotels and camps, restaurants and bars. This result is not surprising given that Kribi is one of the country’s loveliest cities endowed with natural touristic attribute.<sup>3</sup>

# **2.2.2 Regional Specialization in Cameroon**

The previous analysis helps understand whether economic activities are localized or clustered in particular places in Cameroon. In this section, we ask a different but related question, which is whether cities or arrondissements specialized in particular activities. To answer this question, we need a relative specialization index that will help understand whether specific industries tend to account for a larger share in the economic activity of a city relative to their average share in all other cities. As its well known in the urban economic literature, cities manufacture different types of goods and, as such, production patterns differ across the urban hierarchy. Specialization and diversification are important for different types of entrepreneurial activities, and diversification is linked to more early-stage entrepreneurial activities (Fritsch and Storey, 2014). Using data on 23 urban areas in 12 European Union countries, they show that urban regions with high levels of economic growth and diversity of economic activities exhibit higher levels of opportunity-motivated entrepreneurial activity than their counterparts. Henderson, Lee and Lee (2001) showed that larger cities tend to have a more diverse industrial base, with providers of niche products and services who can find a market in a large agglomeration. On the other hand, smaller and medium size cities tend to be fairly specialized in food and beverage production, textiles, shoes, or pulp and paper products.

To get an accurate picture of the extent and the strength of cities specialization in Cameroon, we computed the Krugman’s specialization index at the 4-digit nace level (see Appendix A.1 for details). A high value of the index (higher than 0.75) represents a highly specialized city, and a lower value of the index (below 0.35) represents a diverse one. As can be seen from Table 3 and Figure 4, localities are specialized on average (37 percent of cities are specialized in 2009). In other words, compared

> 3Kribi is a town located at the coastal lowlands of the southern region of Cameroon. In addition to its various touristic services such as hotels, bars, sporting complex, and cultural evenings for entertainment, the Chad-Cameroon pipeline project along with the seaport construction are expected to boost the industrial sector there in the coming years.

12
to the overall distribution of economic activities across cities, few industries tend to account for a large share in the economic activity of a city. Our results also show that specialization decreases with city size. In 2009, the average specialization index in large (0.271) and medium (0.379) agglomerations were respectively 61 and 86 percent of that in the small agglomerations (0.442).<sup>4</sup> Figure 5 shows a significant negative relationship between locality specialization and population size. The corresponding regression also shows a negative and statistically significant relationship between agglomeration specialization and city size. We ran a simple corresponding regression for large cities and found statistically significantly negative relationship between specialization and city size. The slope is -0.31 with an _R_ -square of 0.03. 5 This result is consistent with Da Mata et. al (2005) who found that the specialization index in the largest agglomerations in Brazil was just 28 percent of that in the bottom agglomerations. As for Brazil’s large cities, large cities in Cameroon are on average more diversified than smaller ones. As can be seen from Figure 5, most arrondissements in Yaoundé and Douala are diversified, except Yaoundé II, while a small city like Kribi is specialized.

Natural-resources based localities are among the most specialized cities in Cameroon. In 2009, the most specialized localities were Niete and Dizangue–which are agroindustrial cities (see Table 12 in Appendix A.2).<sup>6</sup> As can bee seen from Figure 4 and Table 12 in Appendix A.2, most of the least specialized cities are located in the East, Far-North, and Adamawa regions—80% of the twenty least specialized localities are in these regions. The most specialized cities are in the South (Kribi, and Niete), the Centre, and the Littoral (Dizangue, Dibamba, and Melong) regions.

# **2.2.3 Entrepreneurship in Cameroon**

There is an increasing interest on entrepreneurship especially at its regional dimension. Empirical research provides evidence that ‘space matter’ in entrepreneurship. In words, there are significant spatial differences in entrepreneurial activities across sub-national spatial units within a country. See, e.g., Reynolds (2004) and Armington and acs (2002) for the u.s.; Fritsch and Mueler (2004) for Germany, and Keeble and Walker (1995) for the u.k. However, there is little consensus about the most appropriate measure of entrepreneurship. Two main approaches to measure entrepreneurship

> 4We find similar results by using population quantiles to differentiate small and large cities. The average specialization index in the top quintile (largest agglomerations) is 66 percent of that in the bottom quintile (small agglomerations).

> 5We include only large cities i.e., cities with population above the median population. We also run similar regressions with all cities and found similar results i.e., a significantly negative relationship between specialization and city size, with a slope of -0.16 and an _R_ -square of 0.03.

> 6 Niete hosts the industrial rubber company _Hevea du Cameroun_ (hevecam).

13
<!-- Start of picture text --> ee<br>a<br>Specialization Index<br>'.<br>o5<br>e At<br>Fr: a<br><!-- End of picture text -->
Table 3: Summary statistics the Krugman specialization measure

|**Statistics**|**All**|Small|**City size**<br>Medium|Large|
|---|---|---|---|---|
|Mean|0.408|0.442|0.379|0.271|
|Median|0.232|0.249|0.265|0.138|
|Minimum|0.000|0.000|0.000|0.020|
|Maximum|1.958|1.958|1.347|1.021|
|Standard deviation|0.426|0.461|0.359|0.285|
|Number of cities|300|199|66|35|
|**Share of speci**|**alized ci**|**ties**|||
|Status|All|Small|Medium|Large|
|Highly specialized (ksi_>_0.75)|21.00|25.13|15.15|8.57|
|Weakly specialized (ksi_∈_(0.35, 0.75])|20.33|18.09|28.79|17.14|
|Not specialized(ksi_<_0.35)|58.67|56.78|56.06|74.29|
|Total|100.00|100.00|100.00|100.00|

_Notes:_ Authors’ computations using gec data. Mean and median values of the Krugman Specialization Index (ksi) for 300 arrondissements in Cameroon. Small, medium, and large cities are respectively cities with less than 50,000, between 50,000 and less than 100,000, and more than 100,000 inhabitants in 2011. Population data are from Cameroon Census Bureau (bucrep), and the National Institute of Statistics (ins).

have been extensively used in the literature (see Glaeser and Kerr, 2009). The first approach consisted of using (i) the share of startups within a single industry, (ii) the new product introductions, (iii) the venture capital placement, and (iv) the creation of new firms. All these metrics allow to capture the dynamic nature of entrepreneurship. However, obtaining these data and information is difficult, especially in the developing context and for confidentiality reasons. The second approach consists in associating entrepreneurship with the number of people leading independent enterprises. To this end, relevant metrics are (i) self-employment, and (i) average firm size. Hence, in this paper, we define as an informal entrepreneur a self-employed in the informal sector who owns a business that has at least one employee. The definition of formal entrepreneur is the traditional one (that is a self-employed operating in the formal sector) Despite significant improvement in the government willingness to improve the business climate in Cameroon, more than 50% of entrepreneurs are not satisfied with the overall business environment. Table 4 reports, for each 2-digit industry the weighted share of firms that identify a given factor as one of the top five most challenging con15
<!-- Start of picture text --> Kribi II<br>Yaoundé VII<br>Garoua I<br>Bafoussam III Yaoundé V Yaoundé IV<br>Yaoundé III<br>Douala IV<br>Douala III<br>Yaoundé II<br>Yaoundé VI<br>Bamenda IIIGaroua II Bamenda II Douala I Douala V<br>Bafoussam II<br>Douala IIYaoundé I<br>Bafoussam I<br>10 11 12 13 14<br>ln(population in 2011)<br>95% CI Fitted values<br>lncity_empl_ksi<br>0<br>−1<br>−2<br>ln(City Specialization)<br>−3<br>−4<br><!-- End of picture text --> Figure 5: Cameroon City Specialization and population size in 2009.

straints for doing business in Cameroon. As can be seen from this table, corruption, high taxes and complex tax regime, credit constraints, excessive bureaucracy, unfair competition, financing cost, infrastructure, and insufficient public-private dialogue to promote collective action are the most challenging obstacles. Overall, these eight obstacles represent around 85 percent of the reported complaints. Table 4 and Figure 6 also show how the reported obstacles vary across sector of activities. Taxation, corruption, and access to credit are the major challenges in almost all industries, though it is important to note some disparities. For example, administrative delays and the lack of skills and training are major constraints in sectors such as energy, transport, and banking and insurance. Meanwhile, high financing costs are reported to be more important in the food, and construction industries, compared to other industries. These obstacles plague the business environment in Cameroon and undermine its ability to compete in global markets. A promising avenue to improve the business environment is to promote local economic policies that will help alleviate these obstacles. For example, a strong local level public-private dialogue can complement the existing dialogue taking place at the national level. The main advantage of such approach is that government 16
can better address or respond to the constraints that local prominent industries are facing.

Table 4: Entrepreneurship obstacles by sub-sector of activities in 2009

|Obstacles|Overall|Food|Other Mfg|Utilities|Construction|Commerce|Transport|Banking|Other tertiary|
|---|---|---|---|---|---|---|---|---|---|
|Tax system|**24.6%**|24.9%|24.2%|22.2%|21.0%|24.8%|22.5%|21.8%|24.8%|
|Corruption|**16.5%**|17.6%|16.8%|14.4%|14.4%|16.8%|15.8%|15.9%|16.1%|
|Access to credit|**13.1%**|9.8%|12.7%|5.6%|12.6%|13.1%|18.0%|16.1%|13.2%|
|Bureaucracy|**9.1%**|8.8%|9.4%|12.2%|10.1%|9.0%|11.1%|9.9%|9.2%|
|Unfair competition|**6.7%**|5.4%|6.2%|8.9%|6.5%|6.5%|5.3%|4.3%|7.2%|
|Costs of fnancing|**5.3%**|6.2%|5.7%|4.4%|7.1%|5.2%|5.3%|4.4%|5.3%|
|Infrastructure|**5.0%**|4.9%|5.7%|3.3%|5.6%|4.9%|2.0%|6.6%|4.9%|
|Insuffcient public-private Dialogue|**3.6%**|6.0%|3.9%|4.4%|5.1%|3.6%|5.1%|3.9%|3.3%|
|Water and Energy|**2.9%**|4.1%|2.8%|2.2%|2.8%|2.9%|1.8%|1.7%|3.0%|
|Transportation|**2.6%**|4.1%|2.2%|0.0%|2.8%|2.8%|0.4%|2.8%|2.4%|
|Skills and training|**2.5%**|0.8%|2.8%|8.9%|3.1%|2.4%|3.3%|4.8%|2.3%|
|Justice system|**2.3%**|2.8%|2.4%|5.6%|3.9%|2.3%|1.8%|1.7%|2.3%|
|Labor legislation|0.3%|1.9%|0.0%|0.7%|1.7%|2.0%|1.4%|1.6%|**1.7%**|
|Supply of raw materials|**1.6%**|0.0%|1.4%|2.2%|0.8%|1.6%|2.2%|2.1%|1.6%|
|Market opportunities|**1.2%**|2.1%|1.0%|3.3%|2.5%|1.2%|2.2%|1.7%|1.3%|
|Preferential schemes|**0.3%**|0.0%|0.2%|2.2%|0.3%|0.3%|0.0%|0.4%|0.3%|
|No obstacles|**0.2%**|0.0%|0.3%|0.0%|0.0%|0.1%|0.0%|0.0%|0.2%|
|Others|**0.8%**|2.1%|0.6%|0.0%|0.8%|0.8%|0.9%|0.7%|0.9%|
|**Obsevations**|**39**|**742**|**9**|**75**|**3592**|**45**|**73**|**2066**|**6660**|

_Notes:_ Firms were asked to report the five (in decreasing order) most important obstacles to entrepreneurship. We used these rankings to compute a weighted average score by allowing a decreasing weighting scheme (0.5 for the top and 0.1 for the bottom). The percentage represents the average score of the weighted sum. Other Mfg gathers all non-food manufacturing activities. Utilities include Gas, electricity, and Water.

# **2.3 Regression analysis: Entrepreneurial activities and Infrastructure**

In this subsection, we use simple ols regression analysis to test the relationship between entrepreneurial profits (from formal and informal activity) with population density and regional infrastructure in Cameroon. Specifically, we implement an ols model where we regress entrepreneurs net income as a function of their individual characteristics such as education, age, wealth and city common factors such as population density.<sup>7</sup> We include location dummies for the following six cities: Douala, Yaoundé, Garoua, Bamenda, Bafoussam, and Kribi. Transport infrastructure is an important factor for the development of economic activity, especially in developing countries, where it plays an essential role by reducing the trade and transportation costs, providing economic and social opportunities and benefits that result in accessibility to markets and employment opportunities. The size or density of local population also captures an important aspect of agglomeration. This is standard in the literature since the pioneering work by Ciccone and Hall (1996).

> 7We use principal component analysis to compute the wealth index using household belonging. The description of the procedure as well as variables taken from the questionnaire are given in the appendix.

17
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which is reasonable, given that these cities are also the most expensive in the country. Interestingly, our wealth index shows that wageworkers in Yaoundé are wealthier than those in Douala, although those in Douala have higher wages.

Table 6 reports the results of the regression equation given by (1). Columns (1) and (2) provide profit regression results for informal entrepreneurs and Columns (3) and (4) give results for formal entrepreneurs. In specifications (2) and (4), we add industries and city dummies as controls. These results show that firm profits increase with agglomeration, captured here by the population density. Moreover, in the informal sector, agriculture is the least profitable activity whereas the least profitable formal activities are in the services sector. As expected, age has a nonlinear effect on profits for all our specification. As entrepreneurs earn more profit as they get older, plausibly due to more experience. However, at a certain age threshold, aging becomes an handicap to profit. Education is also positively associated with informal firms’ profits, while wealth is insignificant. These effects are similar for formal entrepreneurs except that wealth is an important factor of productivity in the formal sector, essentially because it is used as a collateral credit borrowing. Finally, Column (5) and (6) report the probability of formalizing. Although most of the coefficients are insignificant, their sign seem consistent with our theory, as we discuss below. Results also show that informal entrepreneurs operating in manufacturing, commerce, and services are more likely to formalize than their agriculture counterpart. This could be explained by the fact that agricultural firms are usually located in the suburbs making it difficult for the tax authority to reach them.

Table 5: Summary statistics of the variables by specific cities

|Variables|Douala|Yaoundé|Garoua|Bamenda|Bafoussam|Kribi|Average|
|---|---|---|---|---|---|---|---|
|Share of worker in agriculture|0.03|0.05|0.36|0.23|0.32|0.02|0.11|
|Share of worker in manufacture|0.28|0.17|0.21|0.24|0.20|0.34|0.23|
|Share of worker in commerce|0.24|0.21|0.13|0.17|0.22|0.18|0.21|
|Share of worker in service|0.54|0.66|0.37|0.49|0.44|0.57|0.55|
|Share of formal worker|0.31|0.36|0.23|0.19|0.22|0.66|0.31|
|Share of non-entrepreneur|0.78|0.84|0.80|0.74|0.77|0.85|0.80|
|Share of formal entrepreneur|0.03|0.02|0.02|0.02|0.02|0.07|0.02|
|Share of informal entrepreneur|0.19|0.14|0.17|0.25|0.22|0.08|0.18|
|Average monthly income|141.12|132.14|86.60|91.40|82.72|104.72|124.31|
|Average monthly wages|117.26|108.30|77.55|80.87|74.25|94.96|104.18|
|Education|8.75|8.99|5.10|7.93|7.14|7.75|8.15|
|Age|29.43|28.44|28.17|28.89|26.61|29.16|28.79|
|Wealth|149.12|131.44|120.92|126.84|21.87|7.85|117.38|
|Paved road density|0.08|0.15|0.10|0.12|0.13|0.07|0.11|

19
Table 6: Regression analysis of firm profits and formalization

|||**Firm**|**Profts**||**Formalization**|
|---|---|---|---|---|---|
|Variables|(1)|<br>(2)|<br>(3)|(4)|(5)<br>(6)|
|Education|6.517***|6.279***|5.930**|5.310*|0.127***<br>0.142***|
||(0.723)|(0.726)|(2.891)|(2.960)|(0.019)<br>(0.021)|
|Age|3.133**|3.502***|6.328|5.022|0.012<br>0.046|
||(1.019)|(1.026)|5.436|(5.403)|(0.033)<br>(0.034)|
|Age<sup>2</sup>|-0.027**|-0.028**|-0.032|-0.014|0.000<br>0.000|
||(0.012)|(0.012)|0.069|(0.069)|(0.000)<br>(0.000)|
|Wealth|-0.015|-0.018|14.40***|14.73***|-0.001<br>-0.002|
||(0.049)|(0.028)|(4.408)|(4.352)|(-0.004)<br>(0.004)|
|PopDensity|1.251**|0.843***|1.561|2.388|-0.018<br>-0.070|
||(0.615)|(0.626)|(2.944)|(2.923)|(0.017)<br>(0.062)|
|PopDensity<sup>2</sup>|||||0.002<br>0.002|
||||||(0.004)<br>(0.004)|
|Manufacturing||18.59***||51.85**|1.669***|
|||(7.087)||(26.22)|(0.181)|
|Commerce||22.50***||-0.944|1.331***|
|||(6.829)||(29.63)|(0.189)|
|Services||119.8***||-45.77|2.191***|
|||(6.591)||(31.96)|(0.191)|
|City Dummies|NO|YES|NO|YES|NO<br>NO|
|<br>R<sup>2 </sup>/Pseudo R<sup>2</sup>|0.062|0.055|0.136|0.118|0.039<br>0.153|
|Obs|1897|1897|247|247|2143<br>2143|

_Notes:_ Our unit of observation is individual. Standard errors are in parentheses. Column (1) and (2) and profit regressions for informal firms; Column (3) and (4) are profit regression for formal firms; Column (5) and (6) are probit regressions for the probability of formalizing. Significance codes: ‘*’ 10%, ‘**’ 5%, and ‘***’ 1%.

# **3 Model Economy**

The economy comprises _J_ cities (or locations) and a mobile population of size _Nj_ in each city _j_ . Individuals choose between wagework, informal entrepreneurship and entrepreneurship. We assume as in Evans and Jovanovic (1989), Paulson et al (2006) and others, that each individual is endowed with an initial wealth _z_ and an intrinsic level of entrepreneurial ability _θ ∈_ [0, +∞), drawn from a fixed, continuously differentiable, city distribution _Gj_ ( _θ_ ). There is a large number of industries (or sectors), indexed by _i_ . Each sector produces a homogeneous final consumption good. Production of each good requires labor and capital, both of which are freely mobile across cities. Workers are hired locally and paid city-specific wages, whereas capital is owned globally and is rented at the same price everywhere. We assume that the total output _yij_ of an entrepreneur with ability _θij_ , operating in industry _i_ within city _j_ is given by: 20
where _kij_ and _lij_ denote the capital and labor inputs, with economy-wide capital and labor shares _αi_ , _βi ∈_ (0, 1), respectively; and _Aij_ is a location and sector specific agglomeration externality (that includes both road infrastructure and firm/household agglomeration). We also have _γi_ = _αi_ + _βi <_ 1, implying diminishing returns to scale in variable factors at the establishment level, as in Lucas (1978). As suggested by Rosenthal and Strange (2003) we allow the agglomeration externality _Aij_ to act as a multiplier on the production function.

# **3.1 Entrepreneurship**

In addition to renting capital at rate _rj_ and paying wages at rate _wj_ , there are two regulation costs that formal firms pay. In order to formalize, they need to pay a registration cost, _cj_ , that represents administrative costs such as license fees as well as bribes and the opportunity cost of the days spent for the registration procedure. They also pay a profit tax _τ_ at the end of each period. Moreover, at the end of the period, the government levies a tax _τ_ on the profit of formal enterprises. Their formal status however provides them with a better access to credit from financial institutions so that capital is assumed to be freely optimized (see Bruhn 2011, Laporta and Shleifer 2014).

Denoting by _πij_<sup>_F_the formal firm’s one period profit in location</sup><sup>_j_and industry</sup><sup>_i_, the</sup> (static) profit maximization problem of the firm is:<sup>9</sup> The interior solutions of the entrepreneurs maximization problem are : The optimal formal entrepreneur’s profit can therefore be expressed as follows: Unlike the formal entrepreneur, the informal entrepreneur pays no taxes, but faces a capital cost of _λrj_ , where _λ ≥_ 1. This means that informal entrepreneurs have less access to formal credit, since their cost of capital is higher than that of the formal

> 9Notice that while the agent pays the one-time entry cost _cj_ to formalize his business, his periodic formal profit accounts for an amount of _rc_ instead. This is because _c_ can be seen as the present value of a sum of periodic payments of _rj cj_ across the lifetime of the firm, i.e. _cj_ = ∑ _t_<sup>∞</sup> =1 (1 _r_ + _<u>j</u> crj_ )<sup>_t_,assuming</sup> payments begin at the end of the current period.

21
entrepreneurs. Moreover, the informal entrepreneur also faces a probability of being detected by the authorities in which case all profit is forfeited. We assume that the detection probability increases with the “visibility” of the firm. It is therefore denoted _p_ ( _lij_ ) and depends monotonically on the size of the firm measured by its labor force, as follows:<sup>10</sup> In other words, an informal firm in sector _i_ and location _j_ cannot employ more than<sup> ̄</sup> _lj_ units of labor without being caught by the authorities, but will not suffer any penalty when _l ≤_<sup> ̄</sup> _lj_ .

The informal entrepreneur’s optimal investment capital and labor then solves the expected profit maximization problem: The optimal choices for the informal entrepreneur are given by: This solution is feasible only when _lij_<sup>_I∗≤_ ̄</sup><sup>_lij_,which,withthemonotonicityofthede-</sup> mand function with respect to entrepreneurial ability, ensures the existence of a unique ability threshold _θij_<sup>_c_(</sup><sup>_Aij_) above which the informal entrepreneur is constrained on the</sup> labor market. This means all informal entrepreneurs with _θij ≤ θij_<sup>_c_operateuncon-</sup> strained with labor _lij_<sup>_I_=</sup><sup>_l_</sup> _ij_<sup>_I∗≤_ ̄</sup><sup>_lj_,whileallthosewith</sup><sup>_θij>θ_</sup> _ij_<sup>_c_operateconstrained</sup> with _lij_<sup>_I_= ̄</sup><sup>_lij_.Intheformercase,theoperationalcapitalis</sup><sup>_k_</sup> _ij_<sup>_I_=</sup><sup>_k_</sup> _ij_<sup>_I∗_whereasinthe</sup> latter it is given by : The optimal informal entrepreneur’s profits can therefore be expressed as follows:

> 10Note that this functional form is adopted for simplicity. A more general form for the function _p_ ( _·_ ) can be assumed and our qualitative results would be unchanged.

22
Although this paper has a similar broad objective of investigating occupational choice in the Cameroon context as in Nguimkeu (2015), this framework substantially differs from the latter in at least three distinct ways. First, it extends the model in Nguimkeu (2015) to allow for agglomeration externalities and examine how they affect the sorting of talents across cities and occupations. Second, unlike in Nguimkeu (2016) where the detection probability in the informal economy was constant and exogenously fixed, we endogeneize it in the current paper so that it monotonically vary with the size of the firm. This better fits the reality since in real economies, the likelihood of getting in trouble with the tax authorities often increases with the degree of visibility of the enterprise (see De Paula and Sheinkman 2011). Finally, we allow for the cost of capital to differ with the formality status of the firms in order to reflect the perception that informal firms face a higher cost of capital compare to their formal counterparts. These differences allow to obtain additional insights and to uncover potentially subtle empirical patterns that may help design more refined and targeted policies aiming at enhancing the economic performance of Cameroon cities in particular, and African cities in general.

# **3.2 Occupational Choice and Agglomeration**

Each agent in location _j_ also has the choice of becoming a worker (or non-entrepreneur) and receiving the local market wage _wj_ . In this class of models, usual arguments (continuity, monotonicity, differentiability) guarantee the existence of unique ability cutoff points _θij_<sup>_W_(</sup><sup>_Aij_)and</sup><sup>_θ_</sup> _ij_<sup>_F_(</sup><sup>_Aij_)such that :</sup>

- If _θij < θij_<sup>_W_,</sup> the agent is a worker,

- if _θij_<sup>_W≤θij< θ_</sup> _ij_<sup>_F_,</sup> the agent is an informal entrepreneur,

- if _θij ≥ θij_<sup>_F_,</sup> the agent is a formal entrepreneur.

These cutoff points are implicitly defined by: and The expressions of _θij_<sup>_c_(</sup><sup>_Aij_),</sup><sup>_θ_</sup> _ij_<sup>_W_(</sup><sup>_Aij_)and</sup><sup>_θ_</sup> _ij_<sup>_F_(</sup><sup>_Aij_)aregivenintheAppendix.Notice</sup> 1 _αi_ /1 _−γi_ that if 1 _− τ <_ , that is, taxes are too high compared to the capital cost ( _λ_ ) wedge provided by the formal sector, then unconstrained informal entrepreneurs in 23
~~—————— SS ns~~ <!-- Start of picture text --> ar F 7/<br>/<br>/<br>Z<br>a<br>n_¢<br>aao**Pe*raoo?“<br>0 ——_———<br>Agglomeration Externalities Ai<br><!-- End of picture text -->
Dumais, Ellison, and Glaeser (2002) examine the effect of firm dynamics (entry, exit, expansion, and contraction) on the concentration of economic activity. Therefore, agglomeration plays a fundamental role in shaping occupational choice and associated payoffs, and all the key qualities derived above—ability cutoffs, labor demands, capital demands, and the equilibrium wages—depend on the specific location and sector in which the agent operates. Note that these key qualities can also depend other factors like natural advantages.However, Ellison and Glaeser (1999) find that natural advantage account for only a small fraction of industrial concentration in the United States, and Combes, Duranton, and Gobillon (2008) conclude that sorting and agglomeration account for the bulk of spatial wage disparities in France.

As explain in the previous sections, agglomeration externalities regroup interactions within and between sectors that give rise to various sorts of complementarities and indivisibilities, such as matching, sharing, and learning externalities (Duranton and Puga, 2004). Many studies (e.g. Behrens, Duranton, and Robert-Nicoud 2014) find that stronger agglomeration effects lead to larger cities and firms are more productive, on average, in larger cities (see Combes et al, 2012.). We therefore have the following results.

**Proposition 1.** _We have the following results, ceteris paribus._

_1. Entrepreneurial profits increase with agglomeration effects._ _2. Stronger agglomeration effects increase the demand for labor._ _3. Agglomeration has a non-monotonic (U-shaped) effect on the formalization of informal firms._ Item (1) of the proposition implies that more talented workers, who stand a higher chance of becoming entrepreneur, sort themselves to larger cities. However, it is also possible that individuals living in larger cities have the opportunity to obtain more and better education, making them more talented. The result given by Item (2) implies that stronger agglomeration effects may translate into higher wages, because labor demand might increase while labor supply is unchanged. Item (3) means that the likelihood of formalizing decreases with agglomeration effects up to a certain agglomeration threshold after which it becomes increasing beneficial for firms to formalize. Essentially, Firms decide to formalize only when their expected profits as formal entrepreneurs net of their expected profits as informal entrepreneurs is non-negative. In cities with low levels of agglomeration, the gains from formality are low so for the same level of talent it is more profitable to remain informal; This is especially true for unconstrained informal entrepreneurs who have no incentive to formalize. However, above a certain agglomeration threshold formality becomes increasingly attractive. This is because agglomeration is a scaling factor on entrepreneurial talent and hence entrepreneurs with 25
<!-- Start of picture text --> 1<br>Formalization of informal firms Creation of new formal firmsos0 +”:4<br>o :<br>,x :<br>Creation of new informal firms o 2<br>oe 2<br>g==3--7“o~ :“<br>a°2 7ea o”<br>a o ~<br>roenos aro<br>0<br>Agglomeration Externalities Ai<br><!-- End of picture text -->
where _xij_ are years of education, and sector and location fixed effects are included to capture productivity shocks shared by all firms in a sector and all firms in a location, respectively. In other words, we specify the covariance matrix of _ε_ = ( _εij_ ) to allow for spatial correlation between locations _j_ and across sectors _i_ .

Hence, Note that assuming log-normality for the talent distribution is not restrictive; other distributions such as the Pareto distribution can be used. However, as shown by Combes et al. (2012), the productivity distribution is usually better approximated by a log-normal than by a Pareto.

The model estimation also requires to determine both the agglomeration variable _Aij_ , and the minimum visible size of informal firms<sup> ̄</sup> _lj_ . For the agglomeration, we assume that _Aij_ = _Zij_<sup>_κjR_</sup> _ij_<sup>_ρj_,where</sup><sup>_Zij_isanagglomerationindex,</sup><sup>_Rij_isinfrastructure</sup> index, and _κj_ , _ρj ≥_ 0 are elasticity parameters to be estimated. This construction of these indices are describec in the appendix.

For the estimation procedure, we have a total sample of _N_ households, _i_ = 1 _. . . N_ and _J_ cities, _j_ = 1, _. . ._ , _J_ , with data on their initial wealth, _zij_ , years of schooling of the principal earner, _xij_ and occupational status _Eij_ (with _Eij_ = 1 if the household runs a formal business and zero otherwise), _Fij_ (with _Fij_ = 1 if the household runs an informal business and zero otherwise), _Wij_ (with _Wij_ = 1 if the household does not run a business and zero otherwise). Institutional parameters such as the borrowing interest rate and the tax rate on formal firms income are set to the national averages taken from the 2010 Doing Business report for Cameroon.

# **4.1 Estimation Approach:** gmm

Denote by _ψ_ the parameter vector of the structural model to be estimated. These structural parameters are related to technology, credit and labor market access as well as the distributional parameters of entrepreneurial ability. This parameter vector is given 27
by _ψ_ = ( _αj_ , _βj_ , _δ_ 0, _δj_ , _κj_ , _ρj_ , _σs_ , _j_ )<sup>_′_</sup> , where _αj_ , _βj_ , _κj_ , _ρj_ and _δj_ , _j_ = 1, _. . ._ , _M_ , are the elasticities of capital, labor, agglomeration factors and fixed-effects for each location, and _σs_ , _j_ , _s_ = 1, _. . ._ , _S_ , is the spatial correlation between locations and sectors.

We adopt a generalized method of moments (gmm) approach to estimate the parameters of our model that treats factor inputs as a latent variables. The estimation strategy relies on the idea that the structural model should replicate the observed joint empirical distribution function of location choice, earnings, informality status, and employment conditional on occupational choice. The gmm method therefore allows us to match a list of entrepreneurship probabilities and income moments predicted by the model to their data counterparts, for the observed personal characteristics _xij_ and agglomeration index _Zij_ and infrastructure index _Rij_ .

In our baseline specification we match a list of moments corresponding to the probabilities (proportions) of formal business ownership and informal business ownership in different cities and subsamples defined based on the terciles of years of schooling and initial wealth. These set of moments includes static and conditional moments on occupational outcomes, location, formality status, education, earnings, and labour market outcomes. Tables 7 lists all moments which are used to identify the model. The parameters in the first column are identified by one or several corresponding moments given in the second column, and the number of moments is given in column 3.

If we combine all moments used in the estimation procedure into one vector _mN_ and denote by _m_ ( _ψ_ ) their theoretical counterparts in the model, then the orthogonality conditions are given by Following Hansen (1982), _ψ_ can be estimated using the following moments estimator: for some positive semi definite matrix _Ω_<sup>�</sup> which converges in probability to _Ω_ . The estimator _ψ_<sup>�</sup> is consistent and where _D_ = **E** [ _∂h_ ( _ψ_ )/ _∂ψ_ ], and _V_ is the asymptotic covariance matrix of the vector of sample moments, and denotes the parameter of the data generating process. The asymptotically efficient estimator is obtained by setting _Ω_<sup>�</sup> = _V_<sup>�</sup><sup>_−_1</sup> , so that 28
Moreover, standard _JN_ -statistics can be used for hypothesis and specification tests. The statistic _JN_ has an asymptotic _χ_ 2 distribution with degrees of freedom equal to the number of moments minus the number of parameters.

Table 7: Model Moments and Sample Analogs

||Moment|Model<br>_Ml_(_Xi_, _ψ_)|Sample<br>_ml_|
|---|---|---|---|
|1.<br>2.|Prob. formal<br>Prob. formal,_x < xm_<sup>_∗_</sup>|1<br>_nj_ <sup>∑</sup><br>_nj_<br>_i_=1 <sup>Pr[</sup><sup>_Fij_ = 1</sup><sup>_|·_]</sup><br>∑<br>_nj_<br>_i_=1 <sup>Pr[</sup><sup>_Fij_ = 1</sup><sup>_|·_]</sup><sup>**1**[</sup><sup>_zij < zm_]</sup><br>∑<br>_nj_<br>_i_=1 <sup>**1**[</sup><sup>_zij < zm_]</sup>|1<br>_nj_ <sup>∑</sup><br>_nj_<br>_i_=1 <sup>_Fij_</sup><br>∑<br>_nj_<br>_i_=1 <sup>_Fij_</sup><sup>**1**[</sup><sup>_zij < zm_]</sup><br>∑<br>_nj_<br>_i_=1 <sup>**1**[</sup><sup>_zij < zm_]</sup>|
|3.|Prob. informal|1<br>_nj_ <sup>∑</sup><br>_nj_<br>_i_=1 <sup>Pr[</sup><sup>_Iij_ = 1</sup><sup>_|·_]</sup><br>|1<br>_nj_ <sup>∑</sup><br>_nj_<br>_i_=1 <sup>_Iij_</sup><br>|
||Pbifl|∑<br>_nj_<br>_i_=1 <sup>Pr[</sup><sup>_Iij_ = 1</sup><sup>_|·_]</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|∑<br>_nj_<br>_i_=1 <sup>_Iij_</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|
|4.|ro. norma,_x < xm_|∑<br>_nj_<br>_i_=1 <sup>**1**[</sup><sup>_xij < xm_]</sup>|∑<br>_nj_<br>_i_=1 <sup>**1**[</sup><sup>_xij < xm_]</sup>|
|5.|Income formal|∑<br>_nj_<br>_i_=1 <sup>**E**[</sup><sup>_yij|Fij_ = 1] Pr[</sup><sup>_Fij_ = 1</sup><sup>_|·_]</sup><br>∑<br>_nj_<br>_i_=1 <sup>Pr[</sup><sup>_Fij_ = 1</sup><sup>_|·_]</sup>|∑<br>_nj_<br>_i_=1 <sup>_yijFij_</sup><br>∑<br>_nj_<br>_i_=1 <sup>_Fij_</sup>|
|6|I fl_<_|∑<br>_nj_<br>_i_=1 <sup>**E**[</sup><sup>_yi|Fij_ = 1] Pr[</sup><sup>_Fij_ = 1</sup><sup>_|·_]</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|∑<br>_nj_<br>_i_=1 <sup>_yijFij_</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|
|.|ncome orma,_x xm_|∑<br>_nj_<br>_i_=1 <sup>Pr[</sup><sup>_Fij_ = 1</sup><sup>_|·_]</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|∑<br>_nj_<br>_i_=1 <sup>_Fij_</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|
|7.|Income informal|∑<br>_nj_<br>_i_=1 <sup>**E**[</sup><sup>_yij|Iij_ = 1] Pr[</sup><sup>_Iij_ = 1</sup><sup>_|·_]</sup><br>∑<sup>_n_</sup><br>_i_=1 <sup>Pr[</sup><sup>_Iij_ = 1</sup><sup>_|·_]</sup>|∑<br>_nj_<br>_i_=1 <sup>_yijIij_</sup><br>∑<br>_nj_<br>_i_=1 <sup>_Iij_</sup>|
|8|I ifl|∑<br>_nj_<br>_i_=1 <sup>**E**[</sup><sup>_yij|Iij_ = 1] Pr[</sup><sup>_Iij_ = 1</sup><sup>_|·_]</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|∑<br>_nj_<br>_i_=1 <sup>_yijIij_</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|
|.|ncome norma,_x < xm_|∑<br>_nj_<br>_i_=1 <sup>Pr[</sup><sup>_Iij_ = 1</sup><sup>_|·_]</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|∑<br>_nj_<br>_i_=1 <sup>_Iij_</sup><sup>**1**[</sup><sup>_xij < xm_]</sup>|
|9.|Income non-entrep|∑<br>_nj_<br>_i_=1 <sup>**E**[</sup><sup>_yij|Wij_ = 1] Pr[</sup><sup>_Wij_ = 1</sup><sup>_|·_]</sup><br>∑<br>_nj_<br>_i_=1 <sup>Pr[</sup><sup>_Wij_ = 1</sup><sup>_|·_]</sup>|∑<br>_nj_<br>_i_=1 <sup>_yijWij_</sup><br>∑<br>_nj_<br>_i_=1 <sup>_Wij_</sup>|

_Notes._ The table reports averages of the described quantities. The derivation of the predicted model moments are given in Nguimkeu (2015) _∗ xm_ is the median years of schooling.

# **4.2 Estimation results**

The results of the gmm parameter estimates are reported in Tables 8 and 9. Table 8 presents the structural estimates according to sectors. These estimates are obtained for Agriculture, Manufacturing, Commerce and Services. Pairwise residual correlations among these industries are not reported in the Table but are available from the authors. The results show that average expected monthly wages are not significantly different across sector and are roughly the same as the national average of 104, 000 fcfa. The estimates of _δ_ 1 show that education has a differential effect on productivity, depending on the sector of activity in which one operates. At the national level, a 29
10% increase in years of education is associated with a 5% increase in Yaoundé entrepreneurial productivity on average, whereas this effect is larger in Agriculture and lower in Manufacturing. The elasticities of capital and labor, _α_ and _β_ , are 0.23 and 0.49 respectively at the national level. It means that a 10 percent increase in the capital devoted to a business leads to a 2.3% increase in output, while a 10 percent increase in hired labor increases output by 4.9%, respectively. However, when we look at the sectoral level, we see that the elasticity of capital is higher in Agriculture and lower in Manufacturing. Since returns to capital are usually high in the informal sector (e.g. Udry and Anagol 2006, De Mel et al. 2008) and low in the formal sector in Africa (e.g., Alby, Auriol and Nguimkeu 2015), this results may reflect the fact that compared to the manufacturing sector, the agricultural sector is highly informal (roughly 95% in 2010) so that the capital elasticity estimate is driven by firms from the informal sector. The borrowing constraints is measured by _λ_ and is estimated at 2.27 for the whole sample, 2.69 for Agriculture, 2.86 for Manufacturing, 1.61 for Commerce and 1.92 for Services. This implies that entrepreneurs from the Commerce and Services industries are less constrained than those from the Agriculture and Manufacturing industries. This is presumably because the capital needed to start a business in the former is usually lower than in the later, especially in the informal sector.

Table 9 presents the structural estimates according to the main cities of Cameroon. These cities include Douala, Yaoundé, Bamenda, Kribi, Bafoussam and Garoua. Unlike above stratification where wages are uniform across industries, in this case wages significantly vary by city. The average expected monthly wages are higher in Douala and Yaoundé, estimated at 117, 300 fcfa and 108, 300 fcfa respectively. This is consistent with the fact that these two metropolitan cities gather more than 70% of the countries economic activities so that workers outside options are higher. The elasticity of capital is higher in Douala and Yaoundé and lower in Bafoussam and Garoua. In contrast, the elasticity of labor is the highest in Kribi, reflecting a low availability of workforce in this city as given by the summary statistics.

Except for Bafoussam, borrowing constraints, captured by the parameter _λ_ are also lower in these two cities, compared to the rest of the country (since their estimates of _λ_ are 2.37 and 2.51, which are relatively lower). The only exception is Bafoussam where _λ_ is estimated at 1.14 and is the lowest in the country. This is presumably because many entrepreneurs in this part of the country rely more on informal finance such as kinship networks and rotating saving and credit associations (rosca) to finance their business (see, Baland et al 2017), rather than collateral-based lending as in large cities. At the city level, we also provide structural estimates for output elasticities with respect to agglomeration (captured by population density index) and infrastructure (captured by paved road density index), _κ_ and _ρ_ , respectively. We estimated _κ_ at 0.163 30
Table 8: Structural Estimates - Industries

|**Parameter**||||**Esti**|**mate**||
|---|---|---|---|---|---|---|
|Parameter|Name|Whole|Agri|Manuf|Com|Serv|
|Average wage|_w_|104.2|103.64|104.63|104.7|104.6|
|||(15.9)|(16.1)|(15.4)|(15.8)|(15.7)|
|Ability - educ|_δ_1|0.51|0.62|0.35|0.52|0.45|
|||(0.079)|(0.118)|(0.042)|(0.076)|(0.17)|
|Capital share|_α_|0.230|0.321|0.186|0.292|0.213|
|||(0.057)|(0.023)|(0.073)|(0.019)|(0.021)|
|Labor share|_β_|0.499|0.519|0.617|0.696|0.561|
|||(0.059)|(0.023)|(0.074)|(0.019)|(0.024)|
|Borrow. constr.|_λ_|2.27|2.69|2.86|1.61|1.92|
|||(0.314)|(0.320)|(1.164)|(0.569)|(0.245)|
|Number of Obs.||10709|1150|2454|2268|5911|

Asymptotic standard errors in parenthesis Parameters _κ_ and _ρ_ are not included because geographical factors may not vary across industries.

for the whole sample, implying that a 10 percent increase in the population density increases firm productivity by 1.63%. For Douala and Yaoundé, these values are 0.065 and 0.91 which are lower than the national average for the obvious reason that the population density in these cities is already quite high. In other less populated cities such as Kribi and Bamenda, this elasticity is higher, estimated at 0.28 and 0.26 respectively. We estimated _ρ_ at 0.27 for the whole sample, implying that a 10 percent increase in paved road density increases firm productivity by 2.7%. This elasticity is higher in Garoua and Bamenda compared to the rest of the cities.

Before we perform counterfactual simulations, it is important to assess how well the model fits the data. We do this by comparing the model predicted moments and their sample analogs. Table 10 reports the model fit for the nine chosen moments, of which five were targeted in the gmm estimation as defined in Table 7 above, and four were not targeted in the gmm estimation. Apart from the proportion of formal entrepreneurs which is matched within a 20% discrepancy, all the remaining moments are matched within 10% or lower of their counterpart values in the data. A good fit in these moments can be seen as a validation of the model with data that have or have not been used directly in the estimation.

31
Table 9: Structural Estimates - Cities

|**Parameter**|||||**Estimate**||||
|---|---|---|---|---|---|---|---|---|
|Parameter|Name|All|Dla|Yde|Bda|Krb|Baf|Gar|
|Avg wage|_w_|104.2|117.3|108.3|80.97|94.96|74.25|77.55|
|||(15.9)|(12.1)|(11.2)|(9.27)|(9.35)|(7.12)|(7.36)|
|Ability - educ|_δ_1|0.51|0.48|0.49|0.52|0.56|0.41|0.63|
|||(0.04)|(0.02)|(0.03)|(0.01)|(0.01)|(0.13)|(0.03)|
|Capital share|_α_|0.23|0.28|0.27|0.20|0.21|0.18|0.17|
|||(0.05)|(0.02)|(0.07)|(0.01)|(0.03)|(0.08)|(0.06)|
|Labor share|_β_|0.499|0.523|0.531|0.471|0.601|0.432|0.425|
|||(0.06)|(0.03)|(0.07)|(0.02)|(0.01)|(0.08)|(0.06)|
|Borrow. constr.|_λ_|2.27|2.37|2.51|2.83|2.79|1.14|2.60|
|||(0.31)|(0.51)|(0.45)|(0.79)|(1.33)|(0.32)|(0.98)|
|Elast - agglo|_κ_|0.163|0.065|0.091|0.257|0.275|0.211|0.195|
|||(0.031)|(0.055)|(0.044)|(0.069)|(0.032)|(0.027)|(0.05)|
|Elast - infrast|_ρ_|0.266|0.201|0.214|0.292|0.197|0.258|0.299|
|||(0.107)|(0.093)|(0.094)|(0.096)|(0.085)|(0.102)|(0.121)|
|Number of Obs.||10709|4334|3647|653|87|850|1138|

Asymptotic standard errors in parenthesis.

# **5 Policy Simulations**

The informal sector can generate negative externalities (Loayza, 2007). For example, by using and congesting public infrastructure without paying a contribution, and by taking formal business market shares. The latter externality is however theoretically ambiguous and there is little empirical evidence that informal firms unfairly take market share from law-abiding formal firms. However, while informality helps firms avoid certain costs, it may also preclude access to certain opportunities, including greater access to cheaper credit, increased opportunities to engage with larger firms and government contracts, reduced harassment by police, and access to broader training and support programmes. In this section, we perform a set of counterfactual simulations to evaluate the impact of public policies on entrepreneurship and informality across cities and industries in Cameroon. The estimated model represents the current state and the purpose is to evaluate how this equilibrium would change as a result of possible changes in some quantities in the model. For each policy change, an equilibrium wage rate is computed assuming the same distribution of wealth and the same exoge32
Table 10: Matched and Unmatched Moments at the gmm estimates

||Description|Model|Data|Discrepancy (%)|
|---|---|---|---|---|
|1.|Prob. of formal entrep (%)|2.78|2.31|20.1|
|2.|Prob. of formal entrep,_z < zm_ (%)|0.65|0.55|18.2|
|3.|Prob. of informal entrep (%)|18.5|17.71|10.2|
|4.|Prob. of informal entrep,_z < zm_ (%)|12.8|11.70|9.40|
|5.|Income of formal entrep|179.8|189.12|-10.4|
|6.|Income of formal entrep,_z < zm_|116.8|106.13|10.1|
|7.|Income of informal entrep|80.7|85.60|-5.72|
|8.|Income of informal entrep,_z < zm_|59.8|66.93|-10.6|
|9.|Income of non-entrep|104.2|104.2|0.00|

_Notes._ The table reports averages. Income is in thousands of fcfa ($1 _∼_ 500 fcfa) _zm_ is the median initial wealth in the data nously fixed interest rate, and agents make their choices based on these factors. Our focus is on the impact of factors such as infrastructure and borrowing constraints. We provide graphical representations that focus on how various policy options lead to differences within cities or industries.

# **5.1 Impact of Infrastructure: Paved road density**

This counterfactual experiment consists in examining how the equilibrium would change should there be a change in the country infrastructure level. Our measure of infrastructure index is the paved road density. This policy could be a project of road construction, a bridge construction or the paving of an existing road. We assume a change in the infrastructure index _Rij_ defined by where _η_ represents the increment in infrastructure quality implied by the policy.

## **Impact on Industries**

The impact of this infrastructure policy with the Cameroon data within the context of the model is quantified for a range of relative quantity increments _η_ , starting from _η_ = 0, the current state as produced by the structural estimates, to _η_ = 5 _R_<sup> ̄</sup> , where _R_<sup> ̄</sup> is 33
<!-- Start of picture text --> 0.3 ><br>aa ° of °<br>. ¢ -*<br>0.2 .?<br>¢<br>-<br>¢<br>“a ’<br>¢<br>0.1+ 7<br>— Creation of new firms<br>= = =Formalization of informal firms<br>0<br>00.1 0.2 0.3 0.4 0.5 0.6<br><!-- End of picture text --> <!-- Start of picture text --> (0.02<br>—Creation of new firms<br>= = =Formalization of informal firms y<br>> a?<br>--- ¢<br>—— ¢<br>‘<br>0.01 J<br>¢<br>-<br>o<br>ae<br>¢<br>lied<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>.<br>0.8 went ~<br>0.6 aa a - -7 -<br>a<br>o<br>0.4 rae<br>uf “<br>02 , = = =Creation of new firms<br>—Formalization of informal firms<br>0<br>00.1 0.2 0.3 0.4 0.5 0.6<br><!-- End of picture text --> <!-- Start of picture text --> 0.12<br>=— Creation of new firms<br>0.1; === =Formalization of informal firms o?<br>0.08 aa .? ¢ , o<br>0.06 o<br>¢<br>o? .<br>0.04 4<br>¢<br>ae<br>0.02 a<br>0<br>) 0.1 0.2 0.3 0.4 0.5 0.6<br><!-- End of picture text -->
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relatively constant pace across the four sectors (right vertical axis). On average, a 20% increase in infrastructure more than double the initial productivity in all four sectors of activities.

# **Impact on Cities**

We now look at the impact of infrastructure on cities. Figure 11 shows the variation in the fraction of new enterprise creation and on the fraction of informal firms that become formal in six major cities of Cameroon. While Douala and Yaoundé present similar patterns of formalization for increasing quantities of paved road density, the patterns of creation of new firms are different between these two metropolitan cities. While a small increase in paved road density has an immediate impact on entrepreneurship in Yaoundé , it is only when this density increases above 15% that entrepreneurship kicks off in Douala. This may appear quite surprising because paved road density is initially lower in Douala than in Yaoundé (as shown by the summary statistics given in Table 5). Moreover, above an increment threshold of about 30%, additional paved roads do not seem to generate any additional new firm creation in Douala. In contrast, the relationship between paved road density and firm creation seems to be steadily increasing in Yaoundé . This suggests that unlike in Yaoundé , paved roads are not necessarily a crucial ingredient for business creation in Douala. The reason may be found in the difference in the nature of landscape between these two cities. While Yaoundé has a hilly and mountainous landscape which may be a serious obstacle to entrepreneurship (e.g., for commerce and transportation businesses) if roads are unpaved, Douala, on the other hand, has a low-lying plain landscape, making it more practicable even when unpaved.

Another striking pattern of business creation depicted in Figure 11 is the counterfactual impact for the city of Kribi. Perharps for similar reasons as in Douala (that is, low-lying plain landscape), the relationship between paved road density and business creation appear to be piecewise increasing (or piecewise horizontal). The remaining cities, that is, Bamenda, Bafoussam and Garoua, exhibit similar and regular upward sloping patterns of formalization for increasing availability of paved roads. Likewise, the patterns of business creation are also upward sloping in these three cities, although these patterns seem to be steeper in Bamenda and Garoua compared to Bafoussam.

That the patterns of formalization are, in general, upward sloping across all cities for increasing quantities of paved roads suggests that the availability of paved roads encourages informal firms to formalize. This could be either because paved roads make it easier to do business by increasing productivity (as we discuss below) so that 36
informal entrepreneurs quickly grow and graduate to the formal sector,<sup>11</sup> , or because paved roads provide more access to tax inspectors to otherwise remote areas, which discourages informality as informal entrepreneurs become easily detectable.

Figure 12 depicts variations in enterprise output (left vertical axis) and productivity (right vertical axis) across the six major Cameroon cities resulting from an increase in the density of paved roads. The patterns of productivity are all similar across the cities: for increasing availability of paved roads, firms are more productive. This suggests that paved roads make it easier to do business in general. As for the industry, there are stark differences in productivity effects between cities.This important role of infrastructures on firm performance is consistent with earlier work on Cameroon in the context retail firms (see Nguimkeu 2016). Likewise, output appear to be similarly increasing across the cities for increasing availability of paved roads. Interestingly, these patterns seem to be pairwise comparable among cities. For example, Yaoundé is quite similar to Kribi, whereas Bafoussam is similar to Garoua, and Douala is similar to Bamenda. In addition, these patterns are all convex, meaning that the impact of paved roads on enterprise output increases at an increasing rate in the major Cameroon cities considered.

In summary, a policy that improves the level of infrastructure has heterogeneous impacts across industries and cities. Depending on their comparative advantage in terms of having more or less hospitable landscape, economic activities in some cities would be more or less sensitive to road pavement than in others. Likewise, some industries for which road infrastructure are key ingredient for their well-functioning would benefit more from a road improvement policy than others. We now turn to the counterfactual impact of a policy aiming at relaxing borrowing constraints for potential entrepreneurs.

# **5.2 Impact of Borrowing constraint: Interest rate**

We analyze a credit access policy by evaluating the counterfactual impact of a reduction of the borrowing interest rate.<sup>12</sup> We assume an exogenous change in interest rate defined by

> 11Indeed, according to ilo (2002), the informal sector is an incubator for business potential and a stepping stone for accessibility and graduation to the informal sector

> 12Nguimkeu (2014) simulated the impact of an exogenous policy change in collateral requirement through microfinance in Cameroon and found a positive impact on entrepreneurship and earnings. Here, we take another route by looking at an exogenous policy change in the price of capital

37
<!-- Start of picture text --> 0.03 —Creation of new firms ,! a ==<br>~ = =Formalizationae of informal: firms. | ;<br>’<br>e ¢<br>0.02 ’<br>7<br>o<br>¢ “<br>o<br>-* ‘<br>0.01 ’ ¢ rae<br>¢<br>-* °<br>ie<br>0<br>00.1 0.2 0.3 0.4<br><!-- End of picture text -->

<!-- Start of picture text --> 0.08 — Creationi of new firms _ ¢ ><br>= = =Formalization of informal firms . -<br>0.06 ue<br>¢<br>rae<br>e<br>¢<br>0.04. ae -<br>a c oe<br>0.02 -*<br>¢<br>- °<br>0<br>0 0.2 0.4 0.6 0.8<br><!-- End of picture text --> <!-- Start of picture text --> 0.35<br>—— Creation of new firms<br>0.3 } = = =Formalization of informal firms -7 - oc?<br>0.25 oc” -<br>02 . os<br>, ae<br>¢<br>0.15 ao<br>ae<br>0.1 o<br>/<br>0.05, -*<br>fe)<br>ie) 0.1 0.2 0.3 0.4 0.5 0.6<br><!-- End of picture text --> <!-- Start of picture text --> 0.1 cote<br>morc -<br>!<br>0.08<br>0.06 ' —— Creation of new firms<br>, 7— = =Formalization of informal firms<br>i<br>0.04 /<br>sae<br>0.02<br>0<br>0 0.1 0.2 0.3 0.4<br><!-- End of picture text --> <!-- Start of picture text --> 0.2<br>Creation. of new firms.<br>= = =Formalization of informal firms<br>0.15 orre .<br>-?<br>e - ¢<br>O17 ue ¢<br>a<br>on<br>0.05 ’<br>- aa<br>-"<br>ie) -<br>0 0.1 0.2 03 04 O05 06<br><!-- End of picture text --> <!-- Start of picture text --> 0.14<br>—— Creation of new firms<br>0.12 = = =Formalization of informal firms ae .-~<br>one<br>0.1 ¢<br>, ¢<br>0.08 ae i. ¢<br>0.06 oe<br>e ¢<br>cA<br>0.04 oc” ¢<br>0.0247"<br>(e)<br>0 0.1 0.2 0.3 0.4 0.5<br><!-- End of picture text -->
<!-- Start of picture text --> 4 1<br>3.57 —— Output<br>8 = Productivity . x<br>3 ae 9<br>yaaa<br>2.5 ae 8<br>o*<br>2 * ¢ 7<br>¢<br>“<br>1.5 aa 6<br>a4<br>1 ¢ o 5<br>z<br>0.1 0.2 0.3 04<br><!-- End of picture text --> <!-- Start of picture text --> 40 20<br>—_——— Output<br>=== ==) Productivity<br>won"<br>20 . 15,<br>—<br>o”<br>- 7<br>1 ad 10<br>+?<br>ae<br>Poe<br>O 0.2 0.4 0.6 0.8<br><!-- End of picture text --> <!-- Start of picture text --> 400 ——5 Output .? oad 50<br>- - - Productivity o*<br>aa<br>300 ae 40<br>ae<br>o”<br>200 rae 30<br>oo ¢<br>¢<br>¢<br>¢<br>100 o<br>¢<br>o?<br>0 10<br>0 01 #02 O03 O4 O05 06<br><!-- End of picture text --> <!-- Start of picture text --> 60 30<br>Output _.<br>= = = Productivity<br>40 A 1%<br>ry<br>o” -<br>20 7° 20<br>o”<br>¢ rae<br>7<br>> 15<br>¢<br>a<br>¢<br>¢<br>10<br>0 005 O01 O15 O02 025 0.3<br><!-- End of picture text --> <!-- Start of picture text --> 20 30<br>—— Output<br>= = = Productivity Ce<br>10 Le 20<br>uct<br>o -<br>oc<br>o<br>o”<br>va 10<br>1<br>o<br>0 01 #O2 03 04 O05 0.6<br><!-- End of picture text --> <!-- Start of picture text --> 60 30<br>—— Output a aa<br>~~ = Productivity va<br>40 ae 20<br>ae<br>of<br>¢<br>o<br>20 o* 10<br>O 0<br>0 0.1 0.2 0.3 0.4 0.5<br><!-- End of picture text -->
## where _b_ represents the decrement in the gross interest rate implied by the policy.

The impact of this policy with the Cameroon data within the context of the model is depicted in Figure 13. The impact is quantified for a range of interest rate decrements, starting from 0, the current state as produced by the structural estimates, to 20 percentage points (that is, increasing values on the horizontal axis in the graphs represent corresponding reductions in interest rates). The effects of these decrements on the creation of new firms, formalization of informal firms and enterprise profits are evaluated across the different industries and pictured in the Figure 13 (The left vertical axis are transition probabilities and the right vertical axis are profits). The results show that reducing the current interest rate has no effect on business creation in the Manufacturing and Services sectors, and has a moderate effect on commerce when the reduction is above 15 percentage point. In the Agriculture sector, however, the impact on business creation is significant although moderate for low values of the decrements and larger when these decrements increase by 15 percentage point or above.

On the other hand, reducing the interest rate has a much larger and significant impact on the formalization of informal firms across sectors. The patterns of formalization in the Agriculture and Commerce sectors are similar and upward sloping in a smooth and steady way. In the Manufacturing and Service sectors, formalization is inelastic to interest rate for low levels of decrements. It is only for interest rates reductions under a certain threshold (7 percentage point for Manufacturing and 12 percentage points for Services) that entrepreneurs in these sectors find it beneficial to formalize.

A reduction of interest rates has, as expected, a positive impact on firm profits in general. The Manufacturing and Service sectors exhibit similar increasing and smooth patterns of profit when the interest rates go down. The pattern of profits in Agriculture is also increasing, although the impact is quite moderate for decrements lower than 10 percentage points. The most striking case is in the Commerce sector. Reductions of less than 18 percentage points have no impact on firm profits. However, for interest rate reductions above this threshold, firm profits increase dramatically.

# **5.3 Impact of Taxation**

In this counterfactual experiment, we investigate the impact of tax reforms on entrepreneurship and informality across cities. Formally, we assume reductions in tax rates of magnitude _d_ , such that 40
<!-- Start of picture text --> 104<br>Oe<br>4<br>—— Creation of new firms “3<br>= = =Formalization of informal firms i<br>~ Enterprise profits a ‘ ;<br>a o iA<br>-* -<br>ww—<br>sunuveseseesesneneesonse<br>0<br>00.05 0.1 0.15 0.2<br><!-- End of picture text --> <!-- Start of picture text --> {<br>—— Creation of new firms |<br>= = = Formalization of informal firms a<br>smn Enterprise profits on<br>0.005 ww / 4<br>anenennnene® -- s<br>e o” -"<br>0.001<br>.<br>0 0.05 01 0.15 0.2<br><!-- End of picture text -->

<!-- Start of picture text --> 0.4 1011!<br>;<br>i<br>i<br>_e--” or" Hi<br>0.2 errr i 40<br>a -_--" al i “<br>i<br>—— Creation of new Firms i<br>= = =Formalization of Informal firms H<br>“ww Enterprise profit i<br>0 0.05 0.1 0.15 0.2<br><!-- End of picture text --> <!-- Start of picture text --> 51019°73<br>—— Creation of new firms |<br>is, 7 7 7 Formalization of informal firms 1<br>“ms Enterprise profits ue<br>voat<br>10 er, 4<br>’<br>’<br>os i<br>5 —_, / |<br>senssesannsseeseen ,<br>0 0.05 0.1 0.15 0.2<br><!-- End of picture text -->
<!-- Start of picture text --> 0.014<br>4<br>0.012 —— Creation of new firms na<br>= = = Formalization of informal firms oa<br>o<br>0.01 _/<br>-<br>ra ¢<br>0.008 if<br>ad 4<br>0.006 rae<br>: o<br>0.004 a <7” .<br>0.002 -- ,<br>0 — —<br>0 0.1 0.2 0.3 0.4<br><!-- End of picture text --> <!-- Start of picture text --> 0.035<br>°<br>0.03 c<br>o<br>¢<br>0.025 ra<br>¢<br>0.02 ea ¢ _- --”<br>0.015 oe<br>- --7 -<br>0.01 —— Creation of new firms<br>0.005 = = =Formalization of informal firms<br>er<br>0 0.05 O01 O15 O02 0.25 0.3<br><!-- End of picture text --> <!-- Start of picture text --> 0.12<br>0.1 ——Creation= = -Formalizationof newof informalfirms firms - - ~~ _<br>ca 7”<br>-<br>0.08 ¢ - ¢ oa<br>0.06 <<br>ueoc -?<br>7”<br>0.04 F ~~<br>;<br>fe} 0.02 0.04 0.06 0.08 0.1<br><!-- End of picture text --> <!-- Start of picture text --> 0.1<br>Creation| of new firms: . '.<br>= - -Formalization of Informal firms '<br>0.08 t1<br>prt tts.<br>0.06 4 _.'<br>’<br>a ,<br>0.04 ‘<br>sonal<br>; fo<br>fe) 0.05 0.1 0.15 0.2 0.25<br><!-- End of picture text -->

<!-- Start of picture text --> 0.12<br>0.1} ——Creation of new firms vad<br>= = =Formalization of informal firms , ¢ °<br>¢<br>0.08 -*<br>¢<br>Pon va<br>0.06 -*<br>o”<br>¢<br>¢<br>0.04 o”<br>--7<br>oc<br>0.02, -*<br>fe)<br>0 0.05 0.1 0.15<br><!-- End of picture text --> <!-- Start of picture text --> 0.12 77<br>——Creation of new firms o?<br>0.1 | = = =Formalization of informal firms | +”<br>oc<br>¢<br>0.08<br>4<br>v7 -7<br>0.06 ae<br>- ¢<br>¢<br>¢<br>0.04 --"*<br>-?<br>wad “=<br>0.02<br>0 es<br>0 0.05 0.1 0.15 0.2<br><!-- End of picture text -->
consistent with those found earlier where counterfactual policy only have piecewise effects.

On the other hand, a reduction of taxes only has minor effect on the creation of new firms. For example, in all the major cities, the incentive to create new firms as a reaction to a decrease in taxes does not take up unless the tax decrement is at least 7% of firm profits (Bamenda, Yaoundé , and Bafoussam). In other cities such as Douala, kribi and Garoua, it is quite high and takes a 24%, 13% and 14% decrements in taxes, respectively, to see any incentive to start a formal firm in those cities. This might be due to fact that wages are relatively higher in these cities, or the cost of registration (such as the number of days for the registration procedure and the foregone income incurred during the days spent in the registration office for these procedures) are relatively higher.

Figure 15 depicts the effects of variations in tax rates on firm profits and tax revenues across cities. Note that the tax revenues reported here include both government revenues in terms of taxes income collected on firm profits as well as registration fees collected from firms in the formalization produres. As one would obviously expect, a decrease in taxes increases the aggregate profit of firms, since it increases the profit of formal firms and has no effect on informal firms profits. The more interesting pattern, however, is the impact of taxes on tax revenues across cities. The counterfactual results show that taxation rates have the well-known Laffer’s inverted-U pattern on government revenues; there exists an optimal tax rate for the government that generates a maximum tax revenue gains. Interestingly, this optimal tax rate is not the same across cities. In Yaoundé and Bafoussam, the optimal tax decrement is around 15%, implying an optimal tax rate of about 33%, lower than the current rate of 48%. In Bamenda and Garoua, the optimal tax decrement is about 8%, implying an optimal government tax rate of 40%. The two extreme cases are Douala and Kribi. In Douala, the optimal tax decrement is the highest in the country, evaluated at 40%, implying an optimal government tax rate of about 8%. In Kribi, the optimal tax decrement is the lowest in the country, evaluated at 0%, implying that the current tax rate is optimal for the government.

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<!-- Start of picture text -->
1.4 0.02<br>——Firm profit<br>= = =Tax revenue<br>1.3 4 0.015<br>a “7<br>¢ ¢<br>4<br>1.2 , 0.01<br>a<br>¢<br>¢<br>¢ ¢<br>1.1 w-cr7” 0.005<br>’<br>-—=—7<br>1 0<br>Oo 0.1 0.2 0.3 0.4<br><!-- End of picture text -->

<!-- Start of picture text --> 3 0.<br>—— Firm profit<br>prot = Tax revenue<br>yn" “TS.<br>2 Pan ‘ ae ‘ 0.<br>- on”<br>1 0.<br>0 °<br>oO 0.05 0.1 0.15 0.2 0.25 0.3<br><!-- End of picture text --> <!-- Start of picture text --> 12 0.25<br>11 0.2<br>‘ s\<br>10 ‘\ 04s<br>‘<br>s<br>\<br>‘<br>9 . * Jo<br>—Firm profit ‘<br>= = -Tax revenue y<br>8 0.05<br>ie} 0.02 0.04 0.06 0.08 0.1<br><!-- End of picture text --> <!-- Start of picture text --> 4 0.1<br>Tate ~.<br>2+ ——Firm- profitre aan ‘. ie)<br>Tax revenue x<br>-0.1<br>1<br>-0.2<br>0 0.15 0.2 0.25 0.3 0.35<br><!-- End of picture text --> <!-- Start of picture text --> 2 01<br>‘<br>‘ 005<br>1<br>——— Firm profitre ‘\<br>== =Tax revenue<br>0<br>0 0.05 0.1 0.15 0.2<br><!-- End of picture text --> <!-- Start of picture text --> 3 01<br>——Firm Profit<br>--=-=Taxrevenue o — __ ~.<br>o” . .<br>¢ x<br>¢ x<br>o ¢<br>o*<br>2 e“7 0.05<br>a<br>-<br>‘<br>‘<br>1 ()<br>0 0.05 0.1 0.15<br><!-- End of picture text -->
# **6 Conclusion**

We use data from the Cameroon National Surveys on Employment and Informal Sector (eesi), and the General Enterprises Census to empirically investigate the characteristics of the informal sector in Cameroon and its most salient features. Cameroon’s labor market is characterized by a large informal sector that employs around 90% of the occupied workforce, is largely concentrated in agricultural, and non-tradable services (e.g. retail trade, transportation, hotels or restaurants), and accounts for more than 30% of the gdp. Despite this substantial contribution to the gdp and the labor market the informal sector is made of micro-units, often with one worker, very low-paid jobs and low labor productivity. We document that economic activities are strongly concentrated in Yaoundé and Douala and 60% of Cameroon’s industries are geographically concentrated. Informal firms are on average less concentrated than formal firms, and cities are less specialized. Our results show that only 16 percent of these firms were highly specialized in 2009, and the most specialized cities are natural-resources based cities.

To further investigate the relationship that exists between location, agglomeration, access to credit, informality and productivity across cities and industries, we develop an occupational choice model where individuals decide between formal entrepreneurship, informal entrepreneurship and non-entrepreneurial work. Our model predicts that while entrepreneurial profits increase with agglomeration effects, agglomeration has a non-monotonic (U-shaped) effect on formalization of informal firms. Using data from eesi, we provide structural estimates of the model test some of its salient implications using the generalized method of moments, disaggregated along the main cities and industries of the country. We found that certain sectors such as Manufacturing are highly constrained in capital, compared to other sectors such as Commerce. We also found that the return to capital is not uniform across sectors since some of them are more populated by informal firms compared to others.

The estimated model is used in counterfactual policy experiments to simulate the effect of infrastructure on entrepreneurship, informality and productivity. We found that increased availability of paved road, better access to finance, and tax reforms can have a substantial impact on the economy in terms of entrepreneurship, output, formalization of informal firms and productivity. This is particularly true in sectors such as Agriculture and Services which are likely to beenefit the most from such policy (e.g., improving roads has an obvious positive impact on the transportation of goods and production factors). As countries and regions strive to remain competitive in the face of globalization, local and national governments seek increasingly to support competitive cities and industries. Three key determinants of cities’ wealth are related 45
to human capital, location, and size. These advantages are the outcome of long term investments in human capital, infrastructure and institutions. Our findings highlight three implications for policy makers interested in supporting formalization, business creation and productivity. These policy instruments include : (i) increase infrastructure provision by investing in local infrastructure; (ii) increase access to finance, (iii) local tax reforms, and (iv) increase the public-private dialogue at local level to better understand the other heterogenous constraints that firms face.

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