sources

{
    "link": "https://unstats.un.org/sdgs/indicators/database/",
    "retrievedDate": "15-November-19",
    "additionalInfo": " \n\nLast updated: 02 April 2018 \n\nGoal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster \ninnovation \nTarget 9.3: Increase the access of small-scale industrial and other enterprises, in particular in developing \ncountries, to financial services, including affordable credit, and their integration into value chains and \nmarkets \nIndicator 9.3.2: Proportion of small-scale industries with a loan or line of credit \n \nInstitutional information \n\n \nOrganization(s): \nUnited Nations Industrial Development Organization (UNIDO) \n  \nConcepts and definitions1 \n\n \nDefinition: \n \nSmall-scale industrial enterprises, in the SDG framework also called \u201csmall-scale industries\u201d, defined here \nfor the purpose of statistical data collection and compilation refer to statistical units, generally enterprises, \nengaged in production of goods and services for market below a designated size class. \n \nThis indicator shows the number of \u201csmall-scale industries\u201d with an active line of credit or a loan from a \nfinancial institution in the reference year in percentage to the total number of such enterprises. \n \nRationale: \n \nIndustrial enterprises are classified to small compared to large or medium for their distinct nature of \neconomic organization, production capability, scale of investment and other economic characteristics. \n\u201cSmall-scale industries\u201d can be run with a small amount of capital, relatively unskilled labor and using local \nmaterials. Despite their small contribution to total industrial output, their role in job creation, especially in \ndeveloping countries is recognized to be significant where the scope of absorbing surplus labor force from \ntraditional sectors such as agriculture or fishery is very high. \u201cSmall-scale industries\u201d are capable of meeting \ndomestic demand of basic consumer goods such as food, clothes, furniture, etc.  \n \nThus \u201csmall-scale industries\u201d play an important role in the economy. However, it has quite limited access to \nfinancial services, especially in developing countries. In order to improve the skill of workers and technology \nfor production, small-scale industrial enterprises require financial support in the form of preferential loan, \ncredit etc. This indicator shows how widely financial institutions are serving the \u201csmall-scale industries\u201d. \nTogether with the indicator SDG 9.3.1, this indicator reflects the main message of the target 9.3 which \npromotes to increase the access of \u201csmall-scale industries\u201d to financial services. \n \n\n                                                           \n1 Some of the text on concepts and definition may be identical to Metadata submitted for Indicators 9.3.1. \n\n\fLast updated: 02 April 2018 \n\n \n\n \n\nConcepts: \n \nInternational recommendations for industrial statistics 2008 (IRIS 2008) (United Nations, 2011) define an \nenterprise as the smallest legal unit that constitutes an organizational unit producing goods or services. The \nenterprise is the basic statistical unit at which all information relating to its production activities and \ntransactions, including financial and balance-sheet accounts, are maintained. It is also used for institutional \nsector classification in the 2008 System of National Accounts. \n \nAn establishment is defined as an enterprise or part of an enterprise that is situated in a single location and \nin which only a single productive activity is carried out or in which the principal productive activity accounts \nfor most of the value added. An establishment can be defined ideally as an economic unit that engages, \nunder single ownership or control, that is, under a single legal entity, in one, or predominantly one, kind of \neconomic activity at a single physical location. Mines, factories and workshops are examples. This ideal \nconcept of an establishment is applicable to many of the situations encountered in industrial inquiries, \nparticularly in manufacturing. \n \nAlthough the definition of an establishment allows for the possibility that there may be one or more \nsecondary activities carried out in it, their magnitude should be small compared with that of the principal \nactivity. If a secondary activity within an establishment is as important, or nearly as important, as the \nprincipal activity, then the unit is more like a local unit. It should be subdivided so that the secondary activity \nis treated as taking place within an establishment separate from the establishment in which the principal \nactivity takes place. \n \nIn the case of most small-sized businesses, the enterprise and the establishment will be identical. Some \nenterprises are large and complex with different kinds of economic activities undertaken at different \nlocations. Such enterprises should be broken down into one or more establishments, provided that smaller \nand more homogeneous production units can be identified for which production data may be meaningfully \ncompiled.  \n \nAs introduced in IRIS 2008 (United Nations, 2011), an economic activity is understood as referring to a \nprocess, that is to say, to the combination of actions carried out by a certain entity that uses labor, capital, \ngoods and services to produce specific products (goods and services). In general, industrial statistics reflect \nthe characteristics and economic activities of units engaged in a class of industrial activities that are defined \nin terms of the International Standard Industrial Classification of All Economic Activities, Revision 4 (ISIC \nRev.4) (United Nations, 2008) or International Standard Industrial Classification of All Economic Activities, \nRevision 3.1 (ISIC Rev. 3) (United Nations, 2002). \n \nTotal numbers of persons employed is defined as the total number of persons who work in or for the \nstatistical unit, whether full-time or part-time, including: \n\n\u2022  Working proprietors \n\u2022  Active business partners \n\u2022  Unpaid family workers \n\u2022  Paid employees (for more details see United Nations, 2011). \n\n\fLast updated: 02 April 2018 \n\n \n\n \n\nThe size of a statistical unit based on employment should be defined primarily in terms of the average \nnumber of persons employed in that unit during the reference period. If the average number of persons \nemployed is not available, the total number of persons employed in a single period may be used as the size \ncriterion. The size classification should consist of the following classes of the average number of persons \nemployed: 1-9, 10-19, 20-49, 50-249, 250 and more. This should be considered a minimum division of the \noverall range; more detailed classifications, where required, should be developed within this framework. \nA loan is a financial instrument that is created when a creditor lends funds directly to a debtor and receives a \nnonnegotiable document as evidence of the asset. This category includes overdrafts, mortgage loans, loans \nto finance trade credit and advances, repurchase agreements, financial assets and liabilities created by \nfinancial leases, and claims on or liabilities to the International Monetary Fund (IMF) in the form of loans. \nTrade credit and advances and similar accounts payable/receivable are not loans. Loans that have become \nmarketable in secondary markets should be reclassified under debt securities. However, if only traded \noccasionally, the loan is not reclassified under debt securities (IMF, 2011). \n \nLines of credit and loan commitments provide a guarantee that undrawn funds will be available in the future, \nbut no financial liability/asset exists until such funds are actually provided. Undrawn lines of credit and \nundisbursed loan commitments are contingent liabilities of the issuing institutions\u2014 generally, banks (IMF, \n2011). A loan or line of credit refers to regulated financial institutions only. \n \nComments and limitations: \n \nThe main limitation of existing national data is varying size classes by country indicating that data are \nobtained from different target populations. Data of one country are not comparable to another.  \n \nThe definition of size class in many countries is tied up with the legal and policy framework of the country. It \nhas implications on registration procedure, taxation and different waivers aimed to promote \u201csmall-scale \nindustries\u201d. Therefore, countries may agree on a common size class for compilation purposes. In this context, \nUNIDO proposes that all countries compile the data by a size class of \u201csmall-scale industries\u201d as with less than \n20 persons employed. From such data, an internationally comparable data on the share of \u201csmall-scale \nindustries\u201d in total could be derived.  \n  \nMethodology \n\n \nComputation Method: \n \nThe proportion of \u201csmall-scale industries\u201d with a loan or line of credit is calculated as the number of \u201csmall-\nscale industries\u201d with an active line of credit or a loan from a financial institution in the reference year in \npercentage to the total number of such enterprises: \n \n\n\ud835\udc61\u210e\ud835\udc52 \ud835\udc5b\ud835\udc62\ud835\udc5a\ud835\udc4f\ud835\udc52\ud835\udc5f \ud835\udc5c\ud835\udc53 \"\ud835\udc60\ud835\udc5a\ud835\udc4e\ud835\udc59\ud835\udc59 \u2212 \ud835\udc60\ud835\udc50\ud835\udc4e\ud835\udc59\ud835\udc52 \ud835\udc56\ud835\udc5b\ud835\udc51\ud835\udc62\ud835\udc60\ud835\udc61\ud835\udc5f\ud835\udc56\ud835\udc52\ud835\udc60\" \ud835\udc64\ud835\udc56\ud835\udc61\u210e \ud835\udc59\ud835\udc5c\ud835\udc4e\ud835\udc5b \ud835\udc5c\ud835\udc5f \ud835\udc59\ud835\udc56\ud835\udc5b\ud835\udc52 \ud835\udc5c\ud835\udc53 \ud835\udc50\ud835\udc5f\ud835\udc52\ud835\udc51\ud835\udc56\ud835\udc61\n\n\ud835\udc47\ud835\udc5c\ud835\udc61\ud835\udc4e\ud835\udc59 \ud835\udc5b\ud835\udc62\ud835\udc5a\ud835\udc4f\ud835\udc52\ud835\udc5f \ud835\udc5c\ud835\udc53 \"\ud835\udc60\ud835\udc5a\ud835\udc4e\ud835\udc59\ud835\udc59 \u2212 \ud835\udc60\ud835\udc50\ud835\udc4e\ud835\udc59\ud835\udc52 \ud835\udc56\ud835\udc5b\ud835\udc51\ud835\udc62\ud835\udc60\ud835\udc61\ud835\udc5f\ud835\udc56\ud835\udc52\ud835\udc60\"\n\n\u2217 100 \n\n\f \n\nData Sources \n\nLast updated: 02 April 2018 \n\n \nDescription: \n \nData were collected from the World Bank Enterprise Surveys as a pilot study on this indicator, however the \npreferable source of data are national statistical offices. \n  \nCollection process: \n \nOne of the main sources of data for this indicator currently available is the Enterprise Survey conducted by \nthe World Bank which covers the formal sector and contains data for small and medium enterprises only \n(with 5 or more employees).  In some countries, additional surveys, including Informal Surveys of \nunregistered enterprises and/or Micro Surveys for registered firms with less than five employees, are \nconducted and available at country level.  \n \nThe Enterprise Survey is based on a representative sample of enterprises run by the private sector. The \nsurveys cover a broad range of business environment topics including access to finance, corruption, \ninfrastructure, crime, competition, and performance measures. Since 2002, the World Bank has collected \nthese data from face-to-face interviews with top managers and business owners in over 130,000 companies \nin 135 economies. \n \nThe surveys have been conducted since 2002 by different units within the World Bank. Since 2005-06, most \ndata collection efforts have been centralized within the Enterprise Analysis Unit. Data from 2006 onward is \ncomparable across countries. The raw individual country datasets, aggregated datasets (across countries and \nyears), panel datasets, and all relevant survey documentation are publicly available on the Enterprise Surveys \nweb site. \n \nThe indicator uses a simple weighted percentage formula, where the weights are the sampling weights. The \nstrata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Enterprise \nSurveys provide indicators covering manufacturing and services activities. Proportion of \u201csmall-scale \nindustries\u201d with a loan or line of credit for manufacturing only can be extracted from the micro data. \nEnterprises are classified as small, medium or large based on the number of employees as follows:  \n \n\nSize of enterprise \nSmall \nMedium \nLarge \n\nNumber of employees \n5 to 19  \n20 to 99  \nmore than 99 \n\n \nThe survey also defines an enterprise with female ownership as an enterprise having at least one female \nowner, and female-managed is measured by whether the top manager is a woman. \n \nData Availability \n\n\f \n\nLast updated: 02 April 2018 \n\n \nDescription: \nData for around 130 economies were collected. \n  \nTime series: \n \nSurveys are implemented every year in around 20 countries. Data frequency for each country is around 4 \nyears. \n   \nData providers \n\n \nWorld Bank Enterprise Surveys \n \nData compilers \n\n \nUnited Nations Industrial Development Organization (UNIDO) \n  \nReferences \n\n \nInternational Monetary Fund. (2011). Public Sector Debt Statistics: Guide for Compilers and Users. \nWashington, DC: International Monetary Fund.         http://www.tffs.org/pdf/method/2013/psds2013.pdf \n \nUnited Nations. (2002). International Standard Industrial Classification of All Economic Activities (ISIC Revision \n4). New York : United Nations.  https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf \n \nUnited Nations. (2008). International Standard Industrial Classification of All Economic Activities (ISIC Revision \n3.1). New York : United Nations. https://unstats.un.org/unsd/publication/SeriesM/seriesm_4rev3_1e.pdf \n \nUnited Nations. (2011). International Recommendations for Industrial Statistics 2008 (IRIS 2008), New York: \nUnited Nations. http://dx.doi.org/10.18356/677c08dd-en \n \nWorld Bank Enterprise Surveys. 2017. Methodology. http://www.enterprisesurveys.org/methodology \n \n\n\f",
    "dataPublishedBy": "United Nations Statistics Division",
    "dataPublisherSource": null
}