Julien Grosjean

Lina F Soualmia

Khedidja Bouarech

Clément Jonquet
email: jonquet@lirmm.fr

Stefan J Darmoni
email: stefan.darmoni@chu-rouen.frbouarech

Comparing BioPortal and HeTOP: towards a unique biomedical ontology portal ?

The volume of data in the biomedical field constantly grows. The vast majority of information retrieval systems are based on controlled vocabularies and most specifically on terminologies or ontologies (T/O). These classification structures allow indexing, coding, annotating various types of documents. In Health, many T/O have been created for different purposes and it became a problem to find specific concepts in the multitude of nomenclatures. The NCBO (National Center for Biomedical Ontologies, Stanford University) BioPortal project 1 and the CISMeF (Catalogue et Index des Sites Médicaux de langue Franc ¸aise, Rouen University Hospital) HeTOP 2 portals have been developed to tackle this issue. While both portals are designed to store and make T/O available to the community, they are also very different mainly because of their basic purposes. The present work consists in comparing both portals and in answering the following question: is it possible to merge BioPortal and HeTOP into one unique solution to manage T/O ?

Introduction

The biomedical data constantly grows, specially with new technologies and the Internet media. Therefore, it becomes mandatory to index/annotate it with controlled and structured vocabularies in order to store it and to retrieve it, with intelligent methods. A key aspect in addressing semantic interoperability for life sciences data is the use of terminologies or ontologies (T/O) as a common denominator to structure data and make them interoperable [START_REF] Bodenreider | Bio-ontologies: Current Trends and Future Directions[END_REF] [START_REF] Rubin | Biomedical ontologies: a functional perspective[END_REF]. T/O are classifications that describe the knowledge of a specific domain with concepts and relations between them. Ontologies are more complex than terminologies because they can define rules and functions to infer and structure the knowledge. Since T/O are commonly used in Information Systems and specifically in Health, it is a real challenge to manage and consult them into a unique gateway. Indeed, many T/O have been created in the last decade for different purposes: indexing/annotating documents, organizing the knowledge, inferring facts, etc. Some T/O (e.g. MeSH 3 , ICD-104 , CCAM 5 ) are commonly used on a daily basis in hospitals or in research labs and are very useful for information retrieval. T/O are not always well structured or defined because a lack of standardization or formatting. Moreover, the semantics and syntactic interoperability between T/O is a great challenge to allow interconnection between systems and knowledge. Several tools have been created to store, search and use multiple T/O in the same time: among them, the UMLS (Unified Medical Language System) [START_REF] Bodenreider | The Unified Medical Language System (UMLS): integrating biomedical terminology[END_REF], the EBI Ontology Lookup Service [START_REF] Ôt É R | The Ontology Lookup Service: bigger and better[END_REF], the NCBO BioPortal [START_REF] Rubin | National Center for Biomedical Ontology: Advancing Biomedicine through Structured Organization of Scientific Knowledge[END_REF] or the CISMeF HeTOP [START_REF] Grosjean | Health Multi-Terminology Portal: a semantics added-value for patient safety[END_REF]. The NCBO project (National Center for Biomedical Ontology) of the university of Stanford and the CISMeF team (Catalogue et Index des Sites Médicaux de langue Franc ¸aise, Rouen University Hospital, France) have invested considerable effort in the development of tools and services based on T/O to assist health professionals to search documents on the Web and to use T/O. Both groups have developed web portals, respectively BioPortal and HeTOP that offer various services to find, index, browse, visualize and annotate T/O. Despite their specificities, the two portals share an overall common objective which is to provide to the biomedical community a unique interface to explore Health T/O and resources. Both portals can be very similar at first sight but many functionalities and specificities make them very different in terms of use, focus and technologies. Moreover, for the same T/O included in both portals, the content may differ: this can be an issue for the end users. This article aims to compare BioPortal and HeTOP defining objective comparison points based on their functionalities and features. Thus, it could be possible to define a strategy to merge both portals into one unique solution, offering the best services for end users and machines.

Material & Methods

2.1 BioPortal -http://bioportal.bioontology.org Developed by the NCBO, BioPortal is a repository of biomedical ontologies which hosts more than 350 ontologies in different formats [START_REF] Noy | BioPortal: ontologies and integrated data resources at the click of a mouse[END_REF], [START_REF] Whetzel | BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications[END_REF]. These ontologies are regularly updated by users and accessible via a web site for the humans or Web services for machines. BioPortal is a library of community ontologies [START_REF] Noy | Where to Publish and Find Ontologies? A Survey of Ontology Libraries[END_REF] designed as a "one-stop shop" repository. Users have access to the ontologies with or without restriction (depending on the level of restriction set by the editor) and can access to edition, comment, rating and content addition operations. The NCBO also develops tools and services to use and exploit ontologies in BioPortal: (i) the Annotator [START_REF] Jonquet | The Open Biomedical Annotator[END_REF] performs semantic annotation of biomedical documents, identifying ontology concepts present in the text, (ii) the Resource Index [START_REF] Jonquet | NCBO Resource Index: Ontology-Based Search and Mining of Biomedical Resources[END_REF] uses these annotations to index various biomedical resources and facilitate information retrieval and semantic search, (iii) the NCBO Recommender [START_REF] Jonquet | Building a Biomedical Ontology Recommender Web Service[END_REF] which helps to identify which ontology(ies) is mostly suitable for a specific data corpus.

2.2 HeTOP -http://www.hetop.org HeTOP (Health Terminology/Ontology Portal) [START_REF] Grosjean | Health Multi-Terminology Portal: a semantics added-value for patient safety[END_REF], is a T/O portal developed by the CISMeF team. It hosts more than 55 T/O in several languages. Most of the T/O are international or French national references such as MeSH, ICD-10, or CCAM. These T/O are regularly updated and are accessible via a web site and a Web service. HeTOP has been designed as reference multi-terminology and multi-lingual portal [START_REF] Grosjean | Multi-terminology cross-lingual model to create the Health Terminology/Ontology Portal[END_REF] to help librarians, translators, students and medical professionals to retrieve resources and knowledge across a high variety of complex medical fields. Like NCBO, CISMeF is developing additional tools and services to use and exploit T/O in HeTOP: (i) the ECMT6 performs document indexing, identifying T/O concepts present in a text [START_REF] Pereira | Using multi-terminology indexing for the assignment of MeSH descriptors to health resources in a French online catalogue[END_REF], (ii) InfoRoute7 , which is an info-button service allowing to access many portals from a simple query [START_REF] Darmoni | French Infobutton: an academic and business perspective[END_REF], performing semantic expansion based on the T/O, (iii) MT@HeTOP8 is a service also based on the HeTOP T/O to translate and map terms, (iv) Doc'CISMeF9 [START_REF] Darmoni | Doc'CISMEF: a search tool based on "encapsulated" MeSH thesaurus[END_REF] which is a search engine for quality web resources about Health manually or automatically indexed by CISMeF curators with the MeSH and other reference T/O.

Criteria and comparison approach

The work of [START_REF] Noy | Where to Publish and Find Ontologies? A Survey of Ontology Libraries[END_REF] has inspired us for establishing a criteria list for comparing BioPortal and HeTOP properties (technologies, methodologies, policies, target users, final purposes, features,. . . ) but also from other similar portals . We categorized all the criteria in five groups: (i) Content, (ii) Policy and community, (iii) Functions & Tools, (iv) User Interface & usability, (v) Methods & Technologies.

Results

Content comparison

The comparison of content shown in the Table 1 reveals 3 important differences between BioPortal and HeTOP: (i) The volume of data is more important in BioPortal considering T/O numbers or concept numbers (ratio are respectively 6 and 3). However, since T/O have various number of concepts and terms (terms are preferred terms plus synonyms, in any language) and since HeTOP is dealing with multilingual content, T/O numbers are not good comparison indicators: the more relevant figures are the term number and the total relations number. Indeed, BioPortal and HeTOP have around the same number of terms (about 6,600,000). Unfortunately, it is not possible to easily calculate the total relation number in BioPortal. (ii) T/O formats and update frequencies are quite different in BioPortal compared to HeTOP. While BioPortal is focused on ontologies and takes advantage of standard formats and programs, HeTOP is also hosting heterogeneous representation formats such as Microsoft Excel files, XML files or database dumps. A special work has to be done for every new T/O source: this can not be executed automatically and it implies expertise and development. (iii) One major difference between BioPortal and HeTOP is the expertise brought to every T/O. While BioPortal is automatically importing ontologies and does not change anything except new automatic mappings and some manual users mappings, each HeTOP hosted T/O undergoes a series of process to leverage its content and meta-data. Those processes are automatic, semi-automatic or manual operations and aim to correct or add content: new translations, synonyms, definitions, relations, mappings, etc. Furthermore, since each T/O has a specific model (concept types, constraints, rules, etc.), additional work on models handling can help user to understand how to use T/O. In BioPortal, each concept is an instance of a unique class which does not respect the T/O initial model (specially for terminologies which are based on concept types specificities).

Policies & community aspects comparison

BioPortal and HeTOP have community members and strong policies about which T/O to host and how they are managed. The Table 2 shows several criteria to compare both approaches. Some points are about T/O policies and others about user interaction.

Functions & tools comparison

Despite the similarity of basic tools for both portals, some details and tools differ slightly (Table 3) but has a direct consequence for the end users. (i) The BioPortal search engine only searches exact terms in English (among preferred terms and synonyms) while HeTOP search engine is able to add wildcards to search terms containing the query, in two languages at the same time. This has a direct impact on how users can search terms; for example, if one searches "myopathy" (in English) in the NCIT 10 , Bio-Portal retrieves 5 terms while HeTOP matches 25 terms; because wildcard is managed, HeTOP search engine is actually querying "*myopathy*" and retrieves terms such as "Cardiomyopathy". (ii) Some tools are available in BioPortal but not in HeTOP and vice versa.

User Interface & usability comparison

To compare user experience using both portals, we compared two functions involving time responses (Table 4). First, a comparison have been made between search engines performances in terms of response time and result numbers; we picked and performed 10 random queries on both search engines and we measured the user experience time (with the FireBug Mozilla Firefox plug-in) and noted the result number (random queries are: "heart failure", "asthma", "lung disease", "pregnancy hypertension", "pneumonia", "childhood", "cell junction", "surgery", "gene methylation" and "egg allergy"). No options have been selected in both portals and the searches have been made among all T/O, in English and in French in HeTOP and only in English in BioPortal (no multilingual search). We also performed the same queries with a wildcard option (not available in BioPortal).

The third comparison of response times concerns concept view page access times. A random selection of 20 concept identifiers has been performed and we measured the time needed to open the given concept view with the same method that the previous comparison. About multilingualism, within BioPortal, T/O in another language than English are mostly available as "views" of the corresponding English T/O (e.g., the French MeSH is a view of the MeSH) but it is impossible to get the French term while browsing the English one and vice-versa. Within HeTOP, T/O are not language specific (e.g., the MeSH exists only once with translated terms available). Therefore it is easy from the English term to get to the French one and vice-versa. The whole user interface is internationalized and search can be performed per language. Switching from one language to another is context sensitive.

Methods & technologies comparison

In the Table 5, we compared the methods and technologies used in both portals using documentations and scientific productions. ) and maybe more adapted to lay people and students which means that it is more reliable and more friendly to use on a daily basis. Unfortunately, no evaluation on user's satisfaction has been performed on HeTOP nor BioPortal; this work is currently in preparation for HeTOP with a set of online questions. Despite those differences, we can assume that a merge is possible. The vast majority of policy points and functionalities could be kept in both approaches to create a single portal. The main difficulty would be the technical choices to merge a RDF data store and a relational database with many specificities. To tackle this, it would be possible to integrate the two data layers in a unique system coupled to a single API. This merge would have a great cost but it would be a considerable benefit for the biomedical community and T/O users.

Conclusion

In this study, BioPortal and HeTOP have been compared, based on many criteria. This helps to understand both portals philosophies and functionalities. Furthermore, it allows to point at BioPortal and HeTOP advantages and drawbacks. As a conclusion, a merge of those portals can be possible to create a better tool for end-users and machines. However, such developments would be a hard task to carry out and the inconsistency of T/O content (depending on the source and CISMeF experts capacity to leverage specific T/O) would be huge challenge to meet. This work is part of the SIFR (Semantic Indexing of French Biomedical Data Resources) research project 11 , in collaboration betweem LIRMM, CISMeF and NCBO.

Table 1 .

 1 Content comparison between BioPortal and HeTOP (dec. 2013)

		From the UMLS		From the UMLS
	T/O source	From the OBO Foundry	From different official sources
						(see HeTOP terminologies list
						for details)
		Directly from registered users From other research teams
	T/O formats	OWL, OBO, RDF, RRF	No automatic imports
	Handling T/O initial model	No				Yes
	T/O documentation	Documentation available on	Depending on the T/O, on a
		specific pages			single page
	T/O update	Automatic for OBO Foundry and UMLS.	Depending on the T/O and its update frequency, some are au-
						tomatic (4)
		Available form for custom T/O
	T/O versioning	Yes,	with	a	persistent	No, only the lastest version
		ontology version id	available
	T/O languages	Some "views" of English T/O n = 23, coverage depending on
						the T/O
	T/O added content	User annotations		Opened to the community, re-viewed by curators
		New automatic mappings	New content from auto-
						matic/manual tools
	T/O organization	T/O are categorized in groups	N/A
		and categories
	T/O quality leverage	No				Yes, on the vast majority of T/O
	T/O interoperability	Automatic and manual map-	Automatic and manual map-
		pings				pings

Table 2 .

 2 Policies & community aspects comparison between BioPortal and HeTOP (dec. 2013)

	Property	BioPortal	HeTOP
	search engine	Exact match terms (concepts	Exact and partial match terms
		preferred terms and synonyms)	(concepts preferred terms,
		and advanced options	synonyms, ids, definitions,. . . ),
			and advanced options
	results display	In a dedicated page, organized	Integrated into a unique view,
		by T/O	limited to 500 results and orga-
			nized by T/O and concept types
	concept view	In a description tab	In a description tab
	hierarchy view	Always visible (if provided),	In a dedicated tab, with poly-
		no poly-hierarchy	hierarchy
	relations view	Flat, uncommented relation	Organized and understandable
		types	and multilingual relations types
	mappings view	In a dedicated tab	Embedded in the relations tab
	resources access	Not accessible from the con-	Dedicated tab to access the In-
		cept page	foRoute tool
	cross-lingual navigation	No	Yes by clicking on the flags

Table 3 .

 3 Functions and tools comparison between BioPortal and HeTOP (dec. 2013)

Table 4 .

 4 User Interface & usability comparison between BioPortal and HeTOP (dec. 2013)

	Property	BioPortal			HeTOP
	Multilingual display	No		Yes
	Contextual links to other por-	No		Yes, to several other portals
	tals/browsers			(including BioPortal, MeSH,
				LOINC, OMIM browsers)
	Navigation display	Hierarchy and concept links	Hierarchies, concept links and
				cross-lingual navigation
	Search engine results (execu-	5.57 sec. / 19.7		3.17 sec. / 359.4
	tion times/number)
	Search engine results with	Not applicable on search tab 4.46 sec / 501.3
	wildcard
	Concept page access time	4.1 sec.		less than 1 second
	Property	BioPortal			HeTOP
	Data model	RDF (ontologies are loaded in	Meta-model for T/O which en-
		a triple store)		capsulate specific T/O models
	Database implementation	4Store triple store		Oracle 11g r2, with partionning
				& domain indexes options
	Web services	Accessible in REST (http:	Accessible	in	SOAP
		//www.bioontology.	and	REST	(http://
		org/wiki/index.php/	cispro.chu-rouen.fr/
		Resource_Index_REST_	CISMeFhetopservice/)
		Web_Service_User_
		Guide)
	Other API	CTS2 (OMG Standard)	Yes but not accessible
	Technical documentation	NCBO wiki: full support doc-	Yes but not accessible
		umentation about technologies
		and developments
	Web site technologies	Ruby on Rails, Javascript,	Vaadin, JEE, Infinispan, CXF
		Spring/Hibernate,	Protégé,
		LexGrid, Rainbow
	License	open source		proprietary
	Reusability	NCBO Virtual appliance is	N/A
		available to install and run its
		own version of BioPortal lo-
		cally

Table 5 .

 5 Methods & technologies comparison between BioPortal and HeTOP (dec. 2013)4 DiscussionAs described in the result tables, BioPortal and HeTOP are sharing many features such as T/O browsing, tab representation or resources access tools. Both portals are valuable tools to support research projects. However, their policies and basic purposes are significantly different. While BioPortal is opened to the community T/O, HeTOP is focusing on reference T/O with experts interventions. Thus, BioPortal only accepts ontologies (at least ontology formats) whereas HeTOP provides access to non-ontological sources. BioPortal has been created as a "one-stop shop" repository and can be instantiate in other environments using the virtual appliance. On the contrary, HeTOP is a static server/web site acting like a platform to help various kinds of users for different goals. Moreover, HeTOP is focusing on T/O content, working hard with experts and curators to leverage lexicons, mappings and other knowledge resources. This has a direct consequence on data volume and restriction policies related to download, versioning or updates. Several criteria of this comparison study are focusing on usability: a portal dedicated to be used by experts or lay people has to be understandable and usable with less efforts and knowledge. A special work is made on HeTOP to deal with it: many T/O meta-data labels (attributes, relations, ...) are translated in several languages and well defined. On the another hand, BioPortal is more open than HeTOP: users can upload ontologies and annotate concepts and they have more users options. BioPortal proposes persistent concept URL thanks to the REST technology. However, HeTOP is more user-oriented with high quality and multilingual T/O content, it is also faster (search engine and navigation

http://bioportal.bioontology.org/

http://www.hetop.eu

Medical Subject Headings, edited by the National Library of Medicine: http://www.nlm. nih.gov/mesh/

International Classification of Diseases 10th revision, edited by the World Health Organization: http://www.who.int/classifications/icd/en/

[START_REF] Darmoni | Doc'CISMEF: a search tool based on "encapsulated" MeSH thesaurus[END_REF] Classification Commune des Actes Médicaux, edited by the French National Insurance system: http://www.ameli.fr/accueil-de-la-ccam/index.php

http://ecmt.chu-rouen.fr/

http://inforoute.chu-rouen.fr/

http://cispro.chu-rouen.fr/MT_EHTOP/

http://doccismef.chu-rouen.fr

National Cancer Institute thesaurus, edited by National Cancer Institute: http://ncit. nci.nih.gov/

http://www.lirmm.fr/sifr/

Acknowledgements

This work is issued from a collaboration between NCBO, CISMeF and the LIRMM. We used the knowledge of the partners, the scientific productions and the technical documentations of BioPortal and HeTOP to define comparison criteria. We thank the NCBO team for its cooperation on technical elements. This work was supported in part by the French National Research Agency under JCJC program, grant ANR-12-JS02-01001, as well as by University Montpellier 2, CNRS and IBC project.


David N Margolis
email: david.n.margolis@gmail.com

Keywords: JEL Classification: J21, L26, O14, O17 self-employment, entrepreneurship, development

By Choice and by Necessity: Entrepreneurship and Self-Employment in the Developing World

Over half of all workers in the developing world are self-employed. Although some selfemployment is chosen by entrepreneurs with well-defined projects and ambitions, roughly two thirds results from individuals having no better alternatives. The importance of selfemployment in the overall distribution of jobs is determined by many factors, including social protection systems, labor market frictions, the business environment, and labor market institutions. However, self-employment in the developing world tends to be low productivity employment, and as countries move up the development path, the availability of wage employment grows and the mix of jobs changes.

In the developing world, wage employment is often the exception rather than the norm. 1 In the least developed countries, agriculture, often based in family farms with unpaid labor, can make up the majority of employment. As households migrate to urban areas and countries develop, nonagricultural self-employment often picks up where agricultural employment left off. Only as countries move up the development scale does wage employment become a large share of total employment. Accordingly, the World Bank's 2013 World Development Report, which focused on jobs, paid a significant amount of attention to the question of self-employment and entrepreneurship in the developing world.

The ubiquity of self-employment in low-and middle-income countries raises several important questions that this paper seeks to answer. First, how much self-employment is there in the developing world? Second, is self-employment in these countries a choice or a constraint? Third, to what extent does self-employment in the developing world correspond to the idea of "entrepreneurship"? And finally, from a normative perspective, is so much self-employment a good thing for developing countries? This paper addresses each of these questions by summarizing the wide academic literature on self-employment and development and presents additional evidence to provide a more complete picture where empirical evidence is more limited. The approach adopted here allies theoretical arguments (in particular, with respect to the "choice or constraint" question) with empirical results so as to provide a more complete vision of each issue.

The structure of this paper follows the four questions above. Section 1 takes a detailed look at the data to describe the prevalence of self-employment, and characterizes the self-employed population relative to those in wage employment. Section 2 returns to the question of constrained versus chosen self-employment, while section 3 considers the sub-population of "entrepreneurs" among the self-employed. Section 4 addresses the normative question of whether there is "too much" selfemployment in the developing world, while section 5 concludes.

1 How much self-employment is there in the developing world?

Although it may seem straightforward, quantifying the importance of self-employment in the developing world requires making several decisions that can directly affect the estimates. 2 These include how one treats agriculture and household enterprises, notably the role of unpaid labor; the data source used to measure self-employment (individual, enterprise or expert surveys); the type of survey used (labor force survey, living standards measurement survey, census, specialized survey); efforts at harmonizing definitions and the use of imputation in the reporting of measurements.

The role of agriculture and household enterprises

The 2013 World Development report highlights the importance of agriculture in the least developed countries and the literature on the importance of non-agricultural household enterprises 3 has insisted on their role as employment drivers in developing economies. In the cases of family farms and non-agricultural household enterprises, many family members can work for the same family-owned activity without a standard employer-employee relation and without a clear decisional hierarchy. In accordance with ILO recommendations4 , statistical agencies will often code households in these situations as having a single own-account worker and multiple "contributing family workers" or "unpaid workers". However, the definition of a "contributing family worker" makes explicit reference to the self-employed nature of the person5 , implying the need to include these individuals in any count of the self employed to ensure consistency.

Including contributing household workers makes a big difference in the calculation of the number of self-employed. Using the ILO's LABORSTA data for each country's most recently available data year and for middle-and low-income countries that report own-account and contributing family worker data, Table 1 shows how including contributing family workers can change the estimate of the importance of self-employment. On average, contributing family workers comprise over 25 percent of all self-employed. The share is larger for women than for men, as statistical agencies may have instructions to code the male in the household as the own account worker by default, in which case the spouse (and potentially children) would be coded as contributing family workers. Moreover, as noted above, contributing family workers are much more present in the agricultural sector than elsewhere, in that over one third of all self employed are contributing family members, and over half of women are in this situation. Although it is less the case for men, the importance of female contributing family labor is also remarkable in the fishing sector, where (as in agriculture), there are more women classified as contributing family workers than as own-account workers. In sum, ignoring the importance of (unpaid) contributing family workers on family farms and non-agricultural household enterprises can lead to a significant underestimation of the importance of selfemployment in the developing world. Source: ILO-Laborsta and author's calculations.

Individual, enterprise or expert surveys?

The source of information on the amount of self-employment in developing countries can also have an important impact on estimates of its level. Most estimates are either drawn from individual data (Labor force surveys (LFS), Living standards measurement surveys (LSMS), Censuses, Global Entrepreneurship Monitor (GEM) individual data, specialized surveys, etc.), enterprise data (Investment climate surveys (ICA), Business environment and enterprise performance surveys (BEEPS), informal sector surveys) or responses of experts to questionnaires (Doing Business data, GEM expert data). Each of these sources has advantages and disadvantages.

With the exception of specialized surveys such as the GEM Adult Population Survey6 or Grimm, Knorringa and Lay's (2012) West African informal sector data, individual-level data is rarely collected with the explicit purpose of measuring entrepreneurship. As mentioned above, the coding of employment status can lead to underestimation of self-employment, and in some cases data about income from self-employment are not collected due to questionnaire flow, as earnings questions in LFS data, for example, are typically only asked of "employees". The lack of focus on entrepreneurship also does not allow the user to easily distinguish the motivation of self-employment, nor the productivity, employment generation or growth potential of the enterprise. Individual data do, however, often have the advantage of being relative large samples that are representative of the potentially self-employed population (or can be rendered representative by weighting).

Enterprise survey data can also be used to measure self-employment and entrepreneurship, but its advantages (detailed information on the enterprise in question and, in some cases, sufficient information to assess growth potential) tend to be limited relative to the drawbacks associated with using this data for the measurement and analysis of self-employment and entrepreneurship. First, in most cases, a minimum-size threshold is imposed on the sampling scheme 7 , excluding de facto the majority of self-employment enterprises in the developing world 8 . Second, these data often rely on firm registries to establish their sample frames, thereby ignoring the informal sector which is host to many self-employed enterprises. Lastly, several informal sector surveys adopt a "block enumeration" sampling strategy 9 , which generates non-representative samples of limited (usually urban) geographic areas.

The main expert surveys that are relevant for studying self-employment and entrepreneurship are the World Bank's Doing Business 10 surveys and the Global Entrepreneurship Monitor's National Expert Surveys 11 . The role of these surveys is primarily to characterize the environment in which the self-employed operate and take their decisions. As such, they are not directly useful for measuring the amount of self-employment or entrepreneurship, but they can be valuable resources for understanding cross-country differences in self-employment rates.

LFS? LSMS? Census? Specialized surveys?

As noted above, there are several types of individual data that can be used to quantify selfemployment, in particular LFS, LSMS, Censuses and specialized surveys. As with other data sources, each has its advantages and disadvantages, with the differences between sources corresponding broadly to a tradeoff between sample size/representativity and level of detailed information about self-employment and entrepreneurial activity. The censuses have, obviously, the broadest coverage and are the most representative data source, but not all censuses will code labor market status while available demographic and income information is usually very limited. Labor force surveys will often be of relatively large size and include a standard set of demographic characteristics and labor market status, but generally lack more detailed information on outcomes from self-employment and growth potential. Living standards measurement surveys (and similar instruments such as household income or expenditure surveys) will typically have smaller sample sizes than an LFS, and may have more restrictive geographic coverage. They will, however, often include income from self-employment and available household assets, and may include a more thorough characterization of the selfemployment activity. However, as the focus is not on entrepreneurship per se, evaluation of the growth potential of the entrepreneurial activity can require additional hypotheses or modeling. Lastly, specialized surveys have the distinct advantage of asking precisely the questions that are needed to assess productivity and entrepreneurial growth potential. However, as they tend to be narrowly focused and intensive questionnaires, the sample sizes are often small and representativity may be an issue.

A common risk to using most sources of individual data is measurement error due to proxy response. This occurs when the survey questions refer to a person different from the one who actually responds to the survey (the proxy). Although it seems unlikely that proxy response will significantly affect estimates of the number of self-employed12 , these data may be less reliable for measuring determinants of productivity or reasons for self-employment.

Harmonized definitions and imputation

An obvious concern with estimating the prevalence of self-employment in the developing world is ensuring that common definitions are used in all contributing countries. International repositories such as the ILO's ILOSTAT and KILM databases, or the World Bank's International Income Distribution Database (I2D2) archive devote significant resources to ensuring comparability across countries in variable definitions, but not all differences are correctable and extra information may be needed. The ILO has promulgated a standard for defining labor market status, called the International Classification by Status in Employment (ICSE) 13 , but not all countries adopt it and it has been revised (slightly) over time. In the ILO's LABORSTA database (currently being phased out in favor of the ILOSTAT database), labor market status in tables 1C and 1D was coded according to 2 different nomenclatures under the same variable name, and a second variable was needed to identify the information to which the variable refers. For example, if the country used ICSE-1993, one variable measures the number of employees, whereas if the country used ICSE-58, the same variable refers to Employers and own-account workers. Not accounting for differences in the nomenclature used in each country can thus generate highly misleading estimates of self-employment rates.

A more subtle problem resides in the imputation of missing labor market data. Developing countries often do not produce labor market data on a regular basis14 (see Figure 1), although major international repositories (such as the ILO's Key Indicators of the Labor Market (KILM)) present figures for countries, even when the underlying data does not exist, by using imputation models. The assumptions and techniques used in these models can be debated 15 and can have major impacts on estimated shares of self-employed, often by drawing particular country shares toward average rates in the same sub-region for similar types of workers. This approach can have the effect of giving excessive importance to the (sometimes) few countries that actually collect data regularly, and tend to have more sophisticated statistical administrations. Perhaps unsurprisingly, these countries (especially in the Sub-Saharan African region) are among the most developed16 , which could bias the imputations if the country missing the data is significantly poorer and there is a link between the share of self-employment and the level of development. Such a link has indeed been found in several studies 17 , and suggests that one should be particularly attentive to imputation techniques if one absolutely needs annual data; otherwise, a safer approach is to restrict attention to years when actual data is available.

Several "Best guesses"

Perhaps the most detailed presentation of self-employment in the developing world comes from Gindling and Newhouse (2012), who exploit the harmonized individual data on developing countries collected in the World Bank's I2D2 data archive. They find that among all low and middle income countries in their sample18 combined, the share of wage employment is 49.3 percent, whereas in high income countries (over $12,275 in GDP per capita) the share of wage employment is 85.9 percent. 32.7 percent of workers in developing countries are "own account" self-employed, 15.4 percent are unpaid and 2.7 percent are employers, highlighting the importance of defining selfemployment precisely.

Gindling and Newhouse (2012) also break down self-employment along several dimensions. For example, non-agricultural self-employment plus agriculture accounts for the majority of male employment in all regions except Europe and Central Asia (28 percent) and the Middle East and North Africa (46 percent), going as high as 82 percent of male employment in Sub-Saharan Africa (see Table 2). On the other hand, self-employment is higher than wage employment for working women in MENA, although wage employment is more important in the Latin America and Caribbean region. In South Asia, East Asia and the Pacific and Sub-Saharan Africa, self-employment outnumbers wage employment, especially for women. The Global Entrepreneurship Monitor presents another perspective on the question of entrepreneurship and self-employment, through their measures of "Total Early-Stage Entrepreneurial Activity" (TEA) 19 and established businesses 20 . Although the average estimates are unweighted and the country groupings differ somewhat from those of Gindling and Newhouse (2012), the results are qualitatively similar. TEA is highest in Sub-Saharan Africa and men outnumber women, with the smallest difference being in Sub-Saharan Africa.

19 TEA is defined (Xavier et. al. (2013), p. 13) as the sum of nascent entrepreneurs (those starting new enterprises less than three months old) and new business owners (former nascent entrepreneurs who have been in business for more than three months, but less than three and a half years). 20 Established businesses are those that have been in existence for more than three and a half years (Xavier et. al. (2013), p.13). There are important differences between the GEM and I2D2 measures of self-employment that can affect one's perception of self-employment and entrepreneurship in the developing world. In the GEM data, the focus is on entrepreneurial activity and contributing family labor is ignored, thereby excluding a large share of self-employed workers, particularly women. On the other hand, the I2D2 data presented here do not distinguish by the age of the enterprise and do not separate out agricultural wage employment from the rest of agricultural employment. Nevertheless, the sum of TEA and established business (plus 8 to 17 percent for contributing family workers) should generate figures comparable to the I2D2 numbers. Some of the quantitative differences between the data underlying Figure 2 and Table 2 are likely due to the way in which the data was collected 21 , the countries for which data is available in each study (the largest differences appear in MENA and EAP & SA) and whether or not the averages give larger countries more weight than smaller countries 22 .

2 Self-employment: by choice, by necessity or both?

Although much of the research on entrepreneurship in the developed world treats self-employment as a decision, this question is very much debated in the developing world context. 23 In many 21 Most surveys in the GEM data use telephone interviewing, which will tend to under-sample poor households who cannot afford telephones and may be more likely to be subsistence entrepreneurs. 22 The Gindling and Newhouse (2012) results weight the averages by population (larger countries get more weight), while Xavier et.al. (2013) equally weight countries in their tables. 23 See, for example, Fields (2012) or Poschke (2013a) for the "necessity" perspective and some of the work of David McKenzie and coauthors (such as de Mel, McKenzie and Woodruff ( 2008)) for the "opportunity" perspective. Perry et. al. (2007) presents both sides in a study for Latin America, while Bosch and Maloney (2011) present an empirical analysis of transitions into and out of self-employment.

developing countries, formal safety nets do not exist and not working can mean starvation. As a result, ILO-definition unemployment (not working but looking for work and available to start a job) is often very low in developing countries where it can be calculated, as individuals in need who do not have access to subsistence agriculture will start their own microenterprises to earn at least the minimum necessary to survive.

Although the different types of self-employment go by various names (Opportunity or necessity? Exit or exclusion? Choice or constraint? De Soto or Tokman?), both will typically exist simultaneously in any given country. 24 A variety of factors determine the share of self-employed in a country and how many are there by choice versus by constraint, from supply-side factors (that operate on the individual) to market and demand factors (that affect the opportunities an individual has available). 25 What follows is a discussion of the main determinants of total self-employment and the share of necessity versus choice-driven self-employment in a given environment.

Formal and informal social protection

When workers have access to sources of income other than labor income, they can afford to search for the best available wage job26 or, if they prefer, choose self-employment. When workers lack access to alternative income sources, they must live off of savings, transfers and household production until they can find paying work. As savings diminish, reservation utility falls and any sort of work becomes preferable to the alternative which, in the limit, can be starvation. In these cases, an individual may be constrained to start his or her own business, even a very low productivity one, when funds run out. In developing countries, where many people are poor and have limited savings, circumstances can quickly turn dire, especially when a health or other shock occurs.

Social protection is designed to palliate this risk, by providing individuals with a "safety net" to allow them to survive, ideally until they can provide for themselves. Although formal safety nets in developing countries are often limited27 , family solidarity can play a similar role, at least in the short term. However, countries with weaker social protection systems and cultures that are less oriented toward providing others in the family or community with support when needed are more likely to have a higher share of necessity, as opposed to choice-driven, self-employed.

Labor market frictions

Even when individuals have the resources necessary to support unemployed job search, they must still search. Nobody knows all of the job opportunities for which he or she is qualified that are potentially available at any point in time and can costlessly get to the jobs. Labor market frictions make information about where jobs are available and how much they pay costly to obtain (in time, energy and sometimes financial resources), while transportation costs and particularly low wages can make some jobs not worthwhile even when information is available. In developing countries where infrastructure can be deficient, information can circulate slowly, workers often lack appropriate skills and labor markets are full of frictions that provide employers with the power to suppress 24 Schoar (2010) uses an alternative distinction between subsistence and transformational entrepreneurs, but her categories often resemble those found elsewhere in the literature. Papers such as de Mel et. al. (2010a) address this distinction directly. 25 Poschke (2013b, 2013c) and Margolis and Robalino (2013) present decision-theoretic frameworks for modeling entry into self-employment, and subsequent success, in the developing world.

competition and drive wages down (effective monopsony power), the perfectly competitive labor market model is inapplicable and it becomes necessary to search for sufficiently remunerated work. As in the case with social protection, countries with worse infrastructure and more information or labor market frictions will tend to have more individuals unable to find a wage job quickly, and thus a higher share of constrained self employed.

Business environment

In order to be able to choose between wage and self-employment, enough wage jobs have to exist (and have to be findable). The ability of employers to create wage jobs depends heavily on the business environment. Multiple data sources, typically based on surveys of national experts, exist to assess the institutional constraints and costs of doing business, as well as the ease of investment and employment growth28 . There are also firm-based surveys 29 in which current firms are asked to assess the importance of particular constraints to growth and job creation. While the expert surveys tend provide a broader view of the business environment and the various constraints existing and potential entrepreneurs might face, the enterprise surveys are more able to provide quantitative assessments of the importance of various constraints and subjective rankings from the perspective of the employer.

The business environment affects the share of workers in self-employment overall as well as the share of the self-employed who are there by choice versus necessity. In countries where potential entrepreneurs have only a limited access to capital, 30 there will be fewer "choice" entrepreneurs (necessity entrepreneurs will still start their businesses, even if undercapitalized) and the share of the population in chosen self-employment will be lower. 31 Moreover, existing firms may find it difficult to grow and hire additional workers, thereby reducing the availability of wage jobs and tilting the balance further toward constrained self-employment. The literature has demonstrated, in some cases, that the returns to capital for microenterprises in developing countries can be much higher than market interest rates, 32 suggesting that constrained access to capital may not only restrict entry into self-employment, but may also limit the creation of wage jobs in developing countries. 33 Another dimension of the business environment that affects the self-employment rate, especially chosen self-employment, is a country's taxation, 34 business registration and licensing procedures. 35 When business registration is a costly and/or time consuming procedure, fewer potential choice entrepreneurs may consider registration worthwhile 36 . Likewise, if obtaining permits and business licenses is costly and time consuming, entry into self-employment by choice may be lower. 37 Of course, many potential entrepreneurs do not pay taxes and open their business without registration or licenses in developing countries, but when there is a risk of being caught and having to pay a cost, 38 even imperfectly enforced procedures can have a chilling effect on new business creation when they become overly costly to potential entrepreneurs.

A final dimension of the business environment that can have an impact on self-employment and its composition is corruption 39 and the effectiveness of the legal system. 40 As self-employed workers are directly responsible (and in cases where damages are concerned, liable) for outcomes resulting from their business activity, potential choice entrepreneurs will likely factor in the risk associated with the various transactions they will need to make before deciding to start or grow a business. 41 When the contracting environment is reliable and justice is relatively efficient and non-corrupt, this can significantly reduce the expected risk of entering self-employment. Conversely, when an individual fears that the assets or income from his or her enterprise can be arbitrarily seized, this can increase the expected risk of starting a business to the point where the individual chooses not to do so.

Labor market regulations

In addition to overall business environment characteristics, labor market regulations and practices that affect the willingness of employers to create wage jobs will also increase the time that an individual will need to spend to find an available wage job. [START_REF] Djankov | [END_REF] For example, minimum wages [START_REF] Margolis | of the implementation of minimum wages in the developing world and Gindling (2014) for an overview of their effects. 44 For a recent assessment[END_REF] and health and safety standards, although providing direct benefits to wage workers who benefit from them, can also impose costs on employers and lead them to create fewer wage jobs. As mentioned above, despite the omnipresence of informality in developing countries, 44 the simple risk of being caught and having to pay the additional costs can reduce job creation. When fewer wage jobs are created, more workers are pushed into constrained self-employment.

Another type of factor that can indirectly affect self-employment is payroll taxes and, when they exist, employment subsidies. Although payroll taxes are often (at least partially) avoided in many developing countries by a large share of informal firms, a higher payroll tax rate can reduce the incentives to hire wage workers. When this occurs, the number of wage jobs falls and necessity selfemployment increases. To counter this potential negative effect on wage job creation, employment subsidies are a frequently used tool in developing countries. Insofar as these subsidies have an effect on wage employment [START_REF] Betcherman | survey the impact of employment programs, including wage subsidies[END_REF] , they can reduce the share of workers forced into constrained selfemployment.

3 How many self-employed are "entrepreneurs"?

In order to consider the question of whether self-employment in the developing world corresponds to a developed-world preconception of entrepreneurship [START_REF] Bögenhold | this distinction. 47 The issue of measurement of entrepreneurship relative to self-employment in the developed world has been addressed by Bjuggren[END_REF] , an appropriate definition of entrepreneurship is needed 47 . Typical dictionary definitions of entrepreneur include 48 "a person who organizes and manages any enterprise, especially a business, usually with considerable initiative and risk" or [START_REF]Collins English Dictionary -Complete & Unabridged 10[END_REF] "the owner or manager of a business enterprise who, by risk and initiative, attempts to make profits." Both of these definitions emphasize two main dimensions, initiative and risk. The latter definition also stresses the profit objective, whereas the former focuses more on the actions undertaken. It is worth noting that neither of these definitions corresponds cleanly to the choice versus necessity distinction, since in both cases the self-employment enterprise faces risk, and both types of self-employment require initiative to start the enterprise. The definition that focuses on the profit objective, however, can find a counterpart in the choice-based type of self-employment in the developing world. [START_REF] Schoar | definition of "transformational" entrepreneurship corresponds more closely to this distinction, although one might further disaggregate her categories into truly transformational entrepreneurs (with revolutionary ideas and high growth potential) and more traditional[END_REF] The most direct source of information on "standard definition" entrepreneurs can be found in the GEM entrepreneurial attitudes questions. Individuals are asked about their motives for pursuing selfemployment opportunities, and nearly half of all respondents in the 2009 survey replied that their main objective was to increase personal income (see Figure 3). This corresponds more to the profitdriven dictionary definition of entrepreneurship, although its links to choice versus necessity selfemployment are less clear, as even necessity-driven self-employed attempt to increase their income, typically starting from zero. Other questions about attitudes are also available, including questions on expected hiring and additional variables that allow for the measurement of necessity-driven TEA and improvement-driven opportunity TEA. These latter variables seem closest to the literature's definition of constrained versus chosen self-employment, but are only available for new enterprises. 51 Gindling and Newhouse (2012) also try to understand the distinction between choice and necessity entrepreneurs, using a breakdown of successful and potentially successful versus unsuccessful self employed. Their definition of success is either a) having employees; or b) living in a household with per capita consumption over the $2/day poverty line. The latter definition is based on the idea that successful entrepreneurs earn enough to pull their families out of poverty, while the former supposes that a minimum level of success corresponds to earning enough revenue to be able to afford to hire outside labor. In both definitions, one would expect choice entrepreneurs, especially those motivated by profit, to be among the successful (or potentially successful) self-employed.

Using the methodology of Grimm, Knorringa and Lay (2012), Gindling and Newhouse (2012) find that, according to both definitions, just over 35 percent of self employed are "high potential", and that there is significant variability across regions (see Table 3). Moreover, and as might be suspected, the share of "entrepreneurs" among the self employed is highest in high income countries, and is roughly increasing by income level. Notes: Numbers in parentheses reflect the number of countries in the category for which the estimates were calculated. "Employer" definition uses the presence of employees as the measure of success, while "$2/day" definition uses per capita consumption of the household of at least $2/day as the measure of success.

Perhaps the most direct assessment of the importance of "entrepreneurs" among the self employed, albeit limited to West Africa, comes from the original paper by Grimm, Knorringa and Lay (2012). Using a set of "1-2-3" surveys, their objective is to identify three groups: the top performers (defined as the top 40% of self-employed, based on value added per unit of physical capital, among the 25% largest enterprises defined the amount of capital), the "constrained gazelles" (entrepreneurs who resemble the top performers but are not yet successful) and "survival entrepreneurs with fundamentally different characteristics". They propose an econometric methodology based on the rich set of characteristics of the self-employed and their enterprises available in their data, which allows them to identify those self-employed among those not classified as "top performers" whose characteristics are the most similar. As seen in Table 4 below, they arrive at figures that are quite similar to Gindling and Newhouse (2012); roughly one third of self-employed are true "entrepreneurs" and two thirds constrained in their surveyed countries. 4 Is there too much self-employment in the developing world?

The previous sections have established that self-employment comprises the majority of jobs in the developing world, and that only a third of self-employed are what are normally thought of as "entrepreneurs". However, beyond the simple acknowledgement of the fact that many selfemployed do not occupy their current status by choice (and thus welfare could potentially be improved by shifting them out of self-employment and into wage employment, if it exists), excessively high self-employment may actually inhibit economic development 52 .

The productivity of firms, and the microenterprises created by the self-employed in particular, is lower in the developing world than in developed countries (see Table 5). When productivity is low, less income is available for redistribution, either to workers (through wages) or to the entrepreneur (as the residual claimant). The lower level of income can be associated with more poverty and fewer resources available for reinvesting in enterprises, thereby stunting growth. In this manner, low productivity can lead to lower welfare in the current situation and a lower chance of a country's being able to grow into higher levels of income through investment and development. 53 As noted in section 2, many factors can limit the creation of wage jobs and drive people into selfemployment, but even chosen self-employment can be of low productivity. Bloom et. al. (2010) mention several causes for low productivity in developing country enterprises, including poor infrastructure, informality, overly-restrictive regulations and trade policies, low human capital, poor management practices and limited access to finance. In fact, de Mel, McKenzie and Woodruff (2008) show that access to finance constraints in particular can be so binding that projects with rates of return far above market rates can go unfinanced and growth opportunities are wasted. In a follow-up paper (de Mel, McKenzie and Woodruff ( 2012)), they show that providing credit to microentrepreneurs, especially male-owned subsistence firms, can have positive and long-lasting effects on profits and firm survival.

These results suggest that reforms to ease the constraints that limit productivity can have effects along several dimensions. First, they can improve the productivity and income of those currently self-52 See Gries and Naudé (2010) for a model along these lines. 53 This mechanism is a common feature of endogenous growth models; see the October 1990 issue of the Journal of Political Economy for a series of papers all adopting a similar framework.

employed. Second, they can allow the choice entrepreneurs to grow and wage employers in general to create more wage jobs. This allows subsistence entrepreneurs to move into (more productive) wage jobs. As a result of the higher productivity and fewer wasted resources, the country can grow and develop, and the share of wage employment can gradually increase.

Figure 4 shows that this is actually the typical profile traced by countries at various stages of development. 54 Non-agricultural, low productivity self-employment initially replaces agricultural employment as the least developed countries start to grow. However, at a level of GDP per capita of roughly 600 2005 PPP US Dollars, workers begin to shift out of low productivity self employment and into wage work. However, the real structural changes appear to occur as countries cross the threshold from lower-middle to upper-middle income status ($1006 in 2010 US Dollars). At this point, successful self employment starts to grow and agricultural employment starts shifting into nonagricultural wage employment, while low-productivity self-employment and unpaid labor continues to fall. Thus although developing countries may have a majority of their jobs outside of the wage sector, economic growth appears to come with a steady reduction in self employment and an increase in wage employment.

Conclusion

This paper has examined self employment and entrepreneurship in the developing world from four angles. First, it discussed how to quantify the amount of self-employment using available data 54 Development processes such as those described in Figure 4 have been modeled extensively in the macroeconomic dynamics literature. See, for example, Alvarez-Cuadrado and Poschke (2011), Gollin et. al. (2002, 2004) and McMillan and Rodrik (2011).

sources. Second, it addressed the issue of chosen versus constrained self-employment. Third, it considered the extent to which self-employment in the developing world can be considered "true" (transformational or vocational, as opposed to necessity) entrepreneurship. Lastly, it discussed the links between self employment, productivity and economic growth.

In response to the first question, best estimates appear to suggest that over half of jobs in the developing world are in self employment, although the measure can differ broadly based on several factors. First, family farms and household enterprises, insofar as they involve unpaid contributing family workers, need to be counted among the self employed. Second, individual-based data sources should be favored over firm-side data sources and expert surveys. Third, depending on the level of detail required, different individual-based data sets can be used, but proxy response and nonrepresentativity can be important issues. Lastly, it is important to ensure common definitions are used when comparing self-employment rates across countries, and one should be wary of using imputed data, especially for measuring subjects like self-employment in data-poor regions such as Sub-Saharan Africa.

Concerning the choice versus constraints issue, several factors were highlighted that determine overall self-employment rates and the share of self-employment that is chosen. Social protection systems were identified as allowing individuals to avoid subsistence self-employment, while labor market frictions were a source of higher self-employment rates. The business environment, in particular business registration and licensing, access to finance and corruption and judicial systems, were noted as key determinants of the share of chosen self-employment and the overall level of wage employment. Labor market regulations and payroll taxes/wage subsidies were also identified as potential factors that could determine the availability of wage employment and the share of subsistence self-employed.

The discussion of the share of "true" entrepreneurship focused on definitions and measurement issues, and concluded that roughly one third of self-employed in the developing world are entrepreneurs in the standard sense. Standard definitions of entrepreneurship refer to initiative and risk, as well as profit-seeking behavior, as characteristics of the entrepreneur. Different data sources allow for different measures that approach this definition, with the cleanest measures being those that rely on individual and enterprise characteristics associated with high performance.

Finally, the paper concluded by remarking that governments should not be content with the high level of self-employment in the developing world, although it does serve the purpose of providing subsistence income, since the current situation represents an inefficient utilization of resources. Selfemployment in the developing world tends to be relatively unproductive, and higher levels of productivity can lead to faster growth and higher levels of income. In fact, as countries develop and their institutions evolve, self-employment rates appear to fall naturally and wage employment takes over as the main source of jobs.

Figure 1 :

 1 Figure 1: Years of available data, 2000-2008

Figure 2 :

 2 Figure 2: Total Early-Stage Entrepreneurship and Established Business Ownership in Developing Countries by Region

Figure

  Figure 3: Reasons for Entering Self-Employment

Figure 4 :

 4 Figure 4: Evolution of Employment Status with Per Capita GDP

Table 1 :

 1 Share of employment by status

			All sectors		Agriculture, hunting and forestry		Fishing
		Both Sexes	Men	Women Both Sexes	Men	Women Both Sexes	Men	Women
	Paid employees	54.9%	55.6%	53.1%	26.8%	27.6%	19.8%	32.8%	32.5%	29.6%
	Self Employed	41.9%	41.3%	43.5%	69.0%	68.6%	74.3%	67.1%	67.3%	70.2%
	Employers	6.5%	7.5%	5.0%	7.4%	9.7%	4.3%	7.5%	7.4%	5.7%
	Own account	24.7%	25.9%	21.7%	36.7%	41.6%	27.7%	44.5%	47.1%	27.5%
	Contributing Family	10.7%	7.8%	16.8%	25.0%	17.3%	42.3%	15.1%	12.8%	37.0%

Table 2 : Self-Employment (and Agricultural Employment) Shares by Region and Sex

 2

		ECA	LAC	MENA	SA	EAP	SSA
		Working-Age Population (15-65)
	Men 18%	34%	33%	55%	51%	59%
	Women	9%	17%	21%	23%	34%	55%
			All Employment
	Men 28%	59%	46%	68%	64%	82%
	Women 21%	35%	66%	82%	67%	92%
			Non-Agricultural Employment
	Men 12%	29%	25%	42%	37%	52%
	Women	6%	29%	21%	44%	47%	76%

Source: Gindling and Newhouse (2012) Figure

3

, author's calculations.

Table 3 : Share of "High Potential" Self-Employed by Region and Income Level

 3

	Employer	$2 / Day

Table 4 : Share of Each Type of Self Employed Worker in the Informal Sector

 4

		Benin	Burkina Faso Côte d'Ivoire	Mali	Niger	Senegal	Togo
	Top-performers	10%	10%	10%	10%	10%	10%	10%
	Constrained gazelles	24%	22%	34%	27%	25%	31%	19%
	Survivalists	66%	68%	56%	63%	65%	59%	71%
	Source: Grimm, Knorringa and Lay (2012), table 8.
	Notes: By definition, the share of "top performers" in each country is 10%.

Table 5 : Income Level and Productivity

 5

		GDP per	Sales per employee,
	Country	capita, Dollars	Dollars
	US	42736	433884
	UK	37886	457674
	Japan	35699	428336
	France	35100	393024
	Germany	33838	379341
	Greece	22410	320859
	Poland	7967	178525
	Brazil	4787	144831
	Colombia	3170	150198
	Ecuador	2814	71263
	Morocco	1952	105271
	China	1761	66885
	Indonesia	1249	80203
	Philippines	1090	102975
	India	741	120656

Source

: Bloom et. al. (2010)

, table 1.

See Gindling and Newhouse(2012).

See Desai (2009) for a discussion of some of these issues.

See for example,[START_REF] Cho | Labor markets in low and middle income countries: trends and implications for social protection and labor policies[END_REF],[START_REF] Chuhan-Pole | Africa Pulse: An analysis of issues shaping Africa's economic future[END_REF][START_REF] Fields | Working Hard, Working Poor: A Global Journey[END_REF].

http://www.ilo.org/global/statistics-and-databases/statistics-overview-and-topics/status-in-employment/ current-guidelines/lang--en/index.htm.

Contributing family workers are defined as people "…who hold self-employment jobs in an establishment operated by a related person, with a too limited degree of involvement in its operation to be considered a partner".

See Bergmann, Mueller and Schrettle (2013) for a specific discussion of the advantages and disadvantages of the GEM data for academic research. Relative to other individual databases, some of its primary disadvantages relate to sample size, sampling strategies and transferability of the developed-country questionnaire to the developing-country context.

[START_REF] Dillon | Explaining variation in child labor statistics[END_REF] show, for example, that proxy response does not generate significant errors in the much more sensitive case of child labor reports in Tanzania.

See http://laborsta.ilo.org/applv8/data/icsee.html.

See Margolis, Newhouse and Weber (2010a).

See Weber and Denk (2011) for a detailed discussion of imputation techniques for labor market data.

Mauritius and South Africa were the only two countries in the SSA region that produced data for LABORSTA every year between 2000 and 2008.

See, for example,[START_REF] Gindling | Minimum Wages and Povert in Developing Countries[END_REF] Newhouse (2012), Xavier et. al. (2013).

It is important to note that the I2D2 sample used by[START_REF] Gindling | Self-employment in the developing world[END_REF] covers an estimated 63 percent of the population of low and middle income countries, and 60 percent of all countries worldwide. The main loss of worldwide population comes from the lack of data on China (coverage of East Asia and Pacific developing countries is only 21%) and some larger Middle East and North Africa countries, such as Algeria, Iran, Iraq and Lebanon (regional coverage among MENA developing countries is 46 percent).

See[START_REF] Meyer | Unemployment Insurance and Unemployment Spells[END_REF], for example.

See the World Bank's ASPIRE database for details (http://datatopics.worldbank.org/aspire/).

See, for example, the IFS-World Bank Doing Business database (http://doingbusiness.org) or the Global Entrepreneurship Monitor's National Expert Surveys (http://www.gemconsortium.org).