1. Preparation of Labor Input Matrices: the case of Cameroon Deffo Achille Carlos National Accounts Division National Institute of Statistics, Cameroon EGM on Statistics for SDGs: Accounting for Informal Sector in National AccountsECA, Addis Ababa, Ethiopia 11 - 14 January 2016

2. 1. Definition and background 2. Data sources 3. Methods and assumptions 4. Informal employment 5. Results 6. Conclusion 0.Outline of the presentation 2

3. I.Definition and background (1) Definition: The labor input matrix is a presentation of the estimations of the work force used to produce goods and services that are included within the production boundary of the SNA. Measure: The labor input matrix estimates can be measured by the number of persons employed, the number of full-time equivalents persons employed and the total actually worked hours (in increasing order of accuracy and statistical complexity). These measures can be adjusted for the quality of employment. Presentation: The labor input matrix can be presented by industry, by status in employment (employees, self-employed persons and family workers), by sector or any other relevant disaggregation. 3

4. I.Definition and background (2) Usefulness: The labor input matrix is necessary to analyze productivity, especially labor productivity, and is therefore useful to show how much the average worker contribute to output; Also, changes in productivity over time can be used to assess the efficiency of economic production in a country or compared to other country of comparable size. Context of Cameroon: This paper references Cameroon’s experiences and practices for the years from 2005 to 2010. In fact, Cameroon has recently adopted the year 2005 as the benchmark year for its national accounts and has taken into account some of the changes introduced by the 2008 SNA. 4

5. II.Data Sources (1) Various data sources are used to prepare labor matrix input in Cameroon. They can be classified into two groups: timely surveys and regular annual data sources. Timely surveys: they provide baseline estimates. Among them:  The employment and informal sector surveys (EISS 1 & 2): conducted in 2005 and 2010, are 1-2 household surveys on employment in the 1 st phase and informal sector in the 2 nd phase. They provide information on the supply of labor by individuals in all the sectors of the economy;  The General Census of enterprises (GSE 2009), conducted in 2009, it has been used to estimate labor used by non-profit institutions;  Third General Census of Population and Housing (3 rd GSPH 2005), conducted in 2005, that was used to benchmark the estimations of the 2005 employment survey. 5

6. II.Data Sources (2) regular annual data sources: they provide yearly changes. Among them:  The company financial statements: they provide information on labor used by formal financial and non-financial companies;  The Division of salary payment and pensions of the Ministry of finances: it provides information on labor used by the central government;  The annual survey on other government agencies: it provides information on labor used by other specialized agencies of the government;  The annual collection of local government statements: it provides information on labor used by the local governments. 6

7. III.Methods and assumptions (1) The preparation of labor input matrix in Cameroon follows the “philosophy” of compilation of national accounts with the ERETES module. The framework used to assess, analyze and validate employment data in the national accounts of Cameroon is the industry accounts broken down into production modes that include: 1. Individual entrepreneur in the formal sector, 2. Enterprises having returned their fiscal documents, 3. Enterprises that haven’t returned their fiscal documents, 4. Under declaration in formal enterprises, 5. General government and social security, 6. Informal Sector, 7. Households, 8. Non-profit institutions serving household (NPISH). 7

8. III.Methods and assumptions (2) Two major indicators are used in that framework to fine tune labor estimates by industry and production mode: productivity: production per employee, and salary rate: compensation of employee per employee. Compilation methods vary slightly according to whether we are compiling base (benchmark) year accounts or the current year accounts. In fact in the current year accounts campaigns timely survey data may not be available, and assumptions are made for the projection (extrapolation) of the labor input matrix estimates, especially in the informal sector. 8

9. III.Methods and assumptions (3) In the benchmark year accounts campaign, the procedure can be summarized in four steps: 1. Each data source is treated separately and its internal coherence is analyzed; 2. All the data are loaded and put together into the ERETES module and analyzed within the industry accounts framework broken down into modes of production in relation with production and compensation of employees; At this step, one important assumption that we make is that even though LFS provide information on labor for all activities (formal or informal), we give privilege to formal activity sources, when they provide the same information. 3. Tradeoffs are made to fill the gaps, wherever necessary, using productivity and salary rates in similar activities or complementary sources. 4. All the data are analyzed for their global coherence in relation to production (productivity ratios) and compensation employees. 9

10. III.Methods and assumptions (4) In the current (non-benchmark) year accounts campaign, the procedure is almost the same. The main differences are that: in the 3 rd step, annual changes and previous year estimates are used, in addition, to fill the gaps; Since timely survey data are not available, we make the assumption that employment in the informal sector grows at a rate equal to the average demographic growth rate, which is 2.6% in Cameroon in the period from 2005 to 2010, to derive labor input matrix estimates for the informal sector. 10

11. IV.Informal employment (1) The labor input matrix estimates do not fully cover all the components of the informal employment as defined by the 17th International Conference of Labor Statisticians (ICLS) and the 2008 SNA; It only includes informal jobs in the informal sector enterprises and in the households producing mainly for their final uses. Informal jobs in the formal sector enterprises and formal jobs in the informal sector enterprises are not identified. 11

12. IV.Informal employment (2) 12

13. V.Results (1) We present the final results of the preparation of labor input matrix for the years 2005 to 2010 by: Industry status in employment: declared employees, not declared employees, employers, own-account workers, and family workers; institutional sector; with a subsequent decomposition by type of production unit: formal sector enterprises, informal sector enterprises, and households producing mainly for their own final use. 13

14. V.Results (2) 14 Labor input matrix by institutional sector

15. V.Results (3) 15 Labor input matrix by institutional sector (cont’d)

16. V.Results (3) 16 Labor input matrix by status in employment

17. V.Results (4) 17 Labor input matrix by industry IndustryType of unit200520062007200820092010 1. Agriculture, forestry and fishery products4,391.74,601.64,726.44,878.55,006.25,467.3 Formal sector enterprises 33.335.435.944.946.450.3 Informal sector enterprises 2,043.12,124.72,182.92,252.62,304.82,389.2 Households mainly own-account producer 2,315.32,441.42,507.62,580.92,655.03,027.9 2. Mining and quarrying19.519.920.621.121.621.8 Formal sector enterprises 3.2 3.43.5 3.3 Informal sector enterprises 16.316.717.217.618.118.6 3. Manufacturing1,508.81,549.21,681.01,713.11,999.22,048.9 Formal sector enterprises 122.4139.9138.3170.0169.9173.7 Informal sector enterprises 1,370.31,392.61,527.51,524.71,808.41,852.5 Households mainly own-account producer 16.116.715.318.420.922.7 4. Electricity, gas and water1,892.91,944.21,997.22,068.32,124.32,179.4 Formal sector enterprises 5.15.48.09.79.3 Informal sector enterprises 2.52.6 5.65.7 Households mainly own-account producer 1,885.31,936.21,986.62,056.02,109.52,164.3 5. Construction464.7479.2468.2477.5481.9475.2 Formal sector enterprises 31.734.832.032.133.456.2 Informal sector enterprises 196.7201.8207.4216.7219.3183.7 Households mainly own-account producer 236.4242.6228.8 229.3235.2 6. Trade, accommodation, food & beverages1,704.71,779.61,830.41,933.42,019.02,191.0 Formal sector enterprises 75.5108.0115.4133.0144.7153.2 Informal sector enterprises 1,629.21,671.61,715.01,800.41,874.32,037.9 7. Transport, storage and communication344.5357.5357.1364.2387.8405.1 Formal sector enterprises 54.159.558.459.570.880.8 Informal sector enterprises 290.4298.1298.7304.7317.0324.3 8. Finance and Insurance10.610.510.911.711.512.8 Formal sector enterprises 10.610.510.911.711.512.8 9. Real estate services; and rental and leasing services2,970.03,050.53,172.33,308.03,374.53,380.4 Formal sector enterprises 3.74.34.411.111.939.6 Informal sector enterprises 252.2258.7267.6323.9313.7227.8 Households mainly own-account producer 2,714.12,787.42,900.32,973.03,048.93,113.0 10. Business and production services128.2134.6147.9137.2152.7132.8 Formal sector enterprises 46.951.958.152.854.645.4 Informal sector enterprises 81.482.889.784.498.187.4 11. Public administration124.2108.6111.9 119.7124.6 Formal sector enterprises 124.2108.6111.9 119.7124.6 12. Community and social services240.1254.1258.9275.3280.7297.1 Formal sector enterprises 201.8211.6215.3231.2234.8246.5 Informal sector enterprises 38.342.543.644.145.950.6 13. Other services545.7519.0530.0366.9392.6394.5 Formal sector enterprises 43.843.644.343.843.418.4 Informal sector enterprises 368.0378.5386.3225.4248.6275.5 Households mainly own-account producer 133.996.999.497.7100.5 General total 14,345.714,808.615,312.815,667.216,371.717,131.0

18. VI.Conclusion Due to lack of information we were not able to apply all the recommendations of the SNA: We used number of jobs whereas hours worked is the preferred estimate; We made crude assumptions to derive labor inputs in the informal sector for the years where a LFS is not conducted. So, to improve labor input matrix estimates, we recommend that: small scale LFS should be undertaken every year between two large scale surveys for a better estimation of annual changes of labor, particularly in the informal sector; the module of hours worked in LFS should be improve for a better integration in national accounts. 18

19. Thank you for your kind attention 19