Business Dynamics Review of Egypt

Annex C. Inclusion of Egypt in DynEmp

Set of variables used in DynEmp

The variables used in the DynEmp module for Egypt are summarised in Table C.1. , which shows, for each variable and its description (column 1 and 2) the original name in the Economic Census (column 3) and the name assigned in the micro-aggregated DynEmp dataset (column 4) after statistics (i.e. mean, median, standard deviations, etc.) have been computed and aggregated through the STATA code.

The DynEmp module uses the following variables on establishments’ information from the Economic Census:

• Economic activity at the level of macro sector (macrosect) and industry A-38 following STAN classification (ind_a38), retrieved from the two-digit ISIC Rev.4 information in the micro-data.

• Number of employees (emp) expressed as headcount.

• Performance variables: total revenue (trn), wages (wage).

• The establishment’s age, corresponding to the starting year of the activity.

• Female ownership (owned_F) and female share of total workers (Workers_sh_F). owned_F is equal to 1 if the number of female owners in the establishment is at least 50%.

  The firm-id, the sampling weights have been used to compute the aggregate statistics from the STATA code.

  Regarding the employment variable, the code implements an adjustment as total employment = employees + 1. This corresponds to assuming on average one proprietor/owner per unit. The code also replaces negative employment, turnover or wages to missing.

Table C.1. Variables exploited in DynEmp

Variable	Description	Variable in DynEmp data
Firm-id	Census-specific firm-id 2022/23 wave	(.) Note: missing, used to calculate statistics
Year	Fiscal year for public sector Calendar year for private sector	Year
Economic activity	ISIC Rev.4 code generated from the original string variable ACT2 available in the dataset	macrosect* Ind_a38*
Employees_total	Total number of employees	emp
Revenue	Total revenue (/turnover)	trn
Wage bill	Cash wage, insurance and benefits in-kind	wage
Activity starting date	Year of establishment	age = year - activity stating date ageclass
owned_F	Dummy for female ownership (female owners are ≥ 50% of the owners)	ownfem_group
Workers_sh_F	Share of female employment	meanemplshfem
Weights	Sampling weights	(.) Note: missing, used to calculate statistics

DynEmp outputs and metadata

The programme produces a set of statistics on employment dynamics, aggregating the micro-data by sector of activity, size class (based on employment), age class and gender of the owner or a combination of these measures. Six outputs are produced based on the different level of aggregation as presented in Table C.3. The detail of the classification of industry is reported in Table C.2).

For each level of aggregation, the code produces a set of variables summarised in Table C.4. , including total, mean and median employment; wage, turnover and age; number of units with positive employment, turnover and wage; Herfindahl-Hirschman (HH) concentration index of employment and turnover and other statistics.

Table C.2. Sectoral classification in DynEmp

A38 code		Intermediate SNA/ISIC A38 aggregation based on ISIC Rev.4 classification	A7 macro sector
A	1-3 Agriculture, forestry and fishing		1 - Agriculture, forestry and fishing
B	5-9 Mining and quarrying		2 - Mining and quarrying
CA	10-12 Food products, beverages and tobacco		3 - Manufacturing
CB	13-15 Textiles, wearing apparel, leather and related products
CC	16-18 Wood and paper products, and printing
CD	19 Coke and refined petroleum products
CE	20 Chemicals and chemical products
CF	21 Basic pharmaceutical products and pharmaceutical preparations
CG	22-23 Rubber and plastics products, and other non-metallic mineral products
CH	24-25 Basic metals and fabricated metal products, except machinery and equipment
CI	26 Computer, electronic and optical products
CJ	27 Electrical equipment
CK	28 Machinery and equipment n.e.c.
CL	29-30 Transport equipment
CM	31-33 Furniture; other manufacturing; repair and installation of machinery and equipment
D	35 Electricity, gas, steam and air conditioning supply		4 - Electricity, gas, water and waste
E	36-39 Water supply; sewerage, waste management and remediation activities
F	41-43 Construction		5 - Construction
G	45-47 Wholesale and retail trade, repair of motor vehicles and motorcycles		6 - Non-financial market services
H	49-53 Transportation and storage
I	55-56 Accommodation and food service activities
JA	58-60 Publishing, audiovisual and broadcasting activities
JB	61 Telecommunications
JC	62-63 IT and other information services
K	64-66 Financial and insurance activities		Excluded
L	68 Real estate activities		6 - Non-financial market services (continued)
MA	69-71 Legal and accounting activities; activities of head offices; management consultancy activities; architecture and engineering activities; technical testing and analysis
MB	72 Scientific research and development
MC	73-75 Other professional, scientific and technical activities
N	77-82 Administrative and support service activities
O	84 Public administration and defence		Excluded
P	85 Education		7 - Non-market services
Q	86-88 Human health and social work activities
R	90-93 Arts, entertainment and recreation
S	94-96 Other service activities
U	97-99 Households and extraterritorial activities		Excluded

Source: OECD (n.d.[1]).

Table C.3. Datasets of DynEmp

Level	Dataset	Sector	Size	Age	Gender of the owner
1	dynemp_EGY_plant_lev1.dta	Macro sector classification (see Table C.2); all	0-1; 2-9; 10-19; 20-49; 50-99; 100-249; 250-499; 500+; all (excl. 0-1); missing	All	All
2	dynemp_EGY_plant_lev2.dta	Macro sector classification (see Table C.2); all	0-1; 2-9; 10-19; 20-49; 50-99; 100-249; 250+; all (excl. 0-1); missing	0-2; 3-5; 6+; all; missing age	All
3	dynemp_EGY_plant_lev3.dta	Macro sector classification (see Table C.2); all	0-1; 2+; missing	0; 1-2; 3-4; 5-9; 10-14; 15-19; 20-24; 25-29; 30-34; 35-39; 40+; missing age	All
4	dynemp_EGY_plant_lev4.dta	Macro sector classification (see Table C.2); all	0-1; 2-9; 10-19; 20-49; 50-99; 100-249; 250+; all (excl. 0-1); missing	All	Male ownership; female ownership; missing information
5	dynemp_EGY_plant_lev5.dta	27 STAN A38 (~2-digit ISIC Rev.4/NACE Rev.2)	0-1; 2-9; 10-19; 20-49; 50-99; 100-249; 250+; all (excl. 0-1); missing	All	All
6	dynemp_EGY_plant_lev6.dta	Macro sector classification (see Table C.2); all	0-1; 2+; missing	0; 1-2; 3-4; 5-9; 10-14; 15-19; 20-24; 25-29; 30-34; 35-39; 40+; missing age	Male ownership; female ownership; missing information

Source: OECD (n.d.[1]).

In addition to the data file, the code produces up to two Excel files containing the output of distributed regressions and tabulations of the firm size distribution, namely:

• dynemp_EGY_plant_pareto.xml: estimates a Pareto coefficient focusing on different parts of the employment distribution (estimates are computed for all firms with employment higher than 1; for firms that have employment larger than the median employment; larger than the 90th percentile; larger than the 95th percentile). The estimation technique used is based on an Ordinary Least Square regression (OLS) log rank corrected methodology. Estimates are computed at different points in time (for years available in the database).

• dynemp_EGY_plant_sizedist.xls: contains a series of tabulations of the firm size (employment) distribution, focusing on units with employment lower or equal to 100. For each macro sector of activity (ms) and year, these tabulations report the number of units in each employment category (employment = 0, 1, 2, …, 99, 100). Macro sector codes are the same codes used in the other output databases (see also Table C.2).

Table C.4. Variables included in DynEmp datasets

Variable name	Description
ageclass*	age class (note: different age classifications, see Table C.3).
employment_dom*	share of employment of * leading units
empltype	employment is headcount (HC) or full-time equivalent (FTE)
empltypenr	employment is persons engaged (PERSENG) or employees (EMPLOYEES)
hhemp	HH concentration index of employment (sum of shares^2)
hhtrn	HH concentration index of turnover (sum of shares^2)
ind_a38	2-digit sector STAN A38 classification (see Table C.2)
macrosect	macro sector classification (see Table C.2)
meanage	average age of firms
meancensoring	mean of year of left censoring in the cell
meanavwage	mean of average wage
meanemp	average employment at time t for firms in the group
meanemplshfem	mean of share of employment by females
meantrn	average turnover
meanturnovemp	mean turnover/employment ratio
meanwage	average of wages
meanwageovemp	mean wages/employment ratio
medianage	median age of firms
medianavwage	median of average wage
medianemp	median employment of firms in the group
mediantrn	median turnover
mediantrnovemp	median turnover/employment ratio
medianwage	median of wages
medianwageovemp	median wages/employment ratio
nremplshfem	number of non-missing share of employment by females
nrunit	number of units in the cell
nrunit_posemp	number of units with positive employment
nrunit_postrn	number of units with positive turnover
nrunit_poswage	number of units with positive wages
ownfem_group	gender of owner
p*avwage	percentile p* of average wage (with * = 10; 25; 50; 75; 90)
p*trnovemp	percentile p* of turnover/employment ratio (with * = 10; 25; 50; 75; 90)
p*wageovemp	percentile p* of wages/employment ratio (with * = 10; 25; 50; 75; 90)
p90p10avwage	difference between p90 and p10 of average wage
p90p10trnovemp	difference between p90 and p10 of turnover/employment ratio
p90p10wageovemp	difference between p90 and p10 of wages/employment ratio
sdage	standard deviation of age of firms
sdavwage	standard deviation of average wage
sdcensoring	standard deviation of year of left censoring in the cell
sdemp	standard deviation of employment
sdtrn	standard deviation of turnover
sdtrnovemp	standard deviation of turnover/employment ratio
sdwage	standard deviation of wages
sdwageovemp	standard deviation of wages/employment ratio
sizeclass*	size class (note: different size classifications, see Table C.3).
totemp	total employment
tottrn	total turnover
totwage	total wages
turnover_dom*	share of turnover of * leading units
wage_dom*	share of wages of * leading units

Source: OECD (n.d.[1]).

References

[1] OECD (n.d.), DynEmp (database), OECD, Paris, https://www.oecd.org/en/about/projects/measuring-job-creation-by-start-ups-and-young-firms.html.