business demography in mexico progress and perspectives€¦ · deaths and births by economic...

Session I

Statistical production on entrepreneurship combining statistical

business registers with other data sources

Expert Group Meeting on Business Registers

21-23 September 2015, Brussels

Business Demography in Mexico Progress and Perspectives

I. Initial measurements

Deaths and births of businesses in Mexico in the last five

years

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

5000000

2010 DIRECTORY DEATHS BIRTHS 2015 DIRECTORY

4'331,202

1'630,415

2'225,274

4'926,061

37.6

%

51

.4%

(from 2009 to 2014)

Deaths and births by economic sector

(from 2009 to 2014)

0.0

5.0

10.0

15.0

20.0

25.0

17.4

14.4

3.5 2.3

21.7 21.5

5.3

2.8

TRADE SERVICES MANUFACTURING OTHER SECTORS

DEATHS BIRTHS

Sector

Percentage

Business deaths by state

Percentage of deaths

2009-2014

40.1 – 52.9

35.1 – 40.0

32.1 – 35.0

(from 2009 to 2014)

Percentage of births

2009-2014

46.0 – 55.6

44.3 – 45.9

36.7 – 44.2

Business births by state

(from 2009 to 2014)

II. Methodology

Initial questions

On the activity it

performs?

Does this probability

depend on the size of

the business?

What is the life expectancy of a

business in Mexico?

On its geographical

location?

What is the probability of business

survival or death in Mexico?

Technical steps

I. Category definition

National

32 states

Geographic

area

Manufacturing

Trade

Services

Sector

0 to 2

3 to 5

6 to 10

11 to 15

16 to 20

21 to 30

31 to 50

51 to 100

Business

size

Studies on

economic

demography

II. Generational Monitoring

Economic Censuses

Monitoring

16 Business

Generations (from 1983 to 1998)

14 Million Questionnaires

Example: following the 1988

generation

SURVIVORS

DEA

D

Survivors (x+5)

Survivors (x) Prob(sup x+5) =

Dead (x+5)

Survivors (x) Prob(death x+5) =

LEXIS DIAGRAM

25

20

15

10

5

0 1989 1994 1999 2004 2009

29,328

(0.82)

CENSUSES

207,888

11

4,0

45 93,843

41

,64

8 52,195

16

,44

8 35,747

6,4

19

(0.55)

(0.44)

(0.32)

(0.18)

(0.56)

(0.45)

(0.68)

Survival probability Death probability

6,6

74

Source: 1989-2009 Economic Censuses

AG

E

III. Quinquennial probability calculation

32 states

16 generations

3 sectors

8 Business

sizes

National concentrate

12,672 tables

Example:

Manufacturing

sector,

1983 generation.

1993 MANUFACTURING GENERATION

Age Quinquennial

probabilities

p(x) annual

probabilities q(x) S(x) d(x)

0 0.8837 0.1163 100,000 11,633

1 0.7809 0.2191 88,367 10,280

2 0.6900 0.3100 78,087 9,084

3 0.6098 0.3902 69,002 8,027

4 0.5388 0.4612 60,975 7,093

5 0.5388 0.4856 0.5144 53,882 5,322

6 0.4376 0.5624 48,560 4,796

7 0.3944 0.6056 43,764 4,323

8 0.3555 0.6445 39,441 3,896

9 0.3203 0.6797 35,545 3,511

10 0.5945 0.2944 0.7056 32,035 2,590

11 0.2706 0.7294 29,444 2,381

12 0.2488 0.7512 27,064 2,188

13 0.2286 0.7714 24,875 2,011

14 0.2102 0.7898 22,864 1,849

15 0.6560 0.1970 0.8030 21,015 1,315

16 0.1847 0.8153 19,700 1,233

17 0.1731 0.8269 18,467 1,156

18 0.1623 0.8377 17,311 1,083

19 0.1521 0.8479 16,228 1,016

20 0.7239 0.1460 0.8540 15,212 612

21 0.1401 0.8599 14,600 587

22 0.1345 0.8655 14,013 564

23 0.1291 0.8709 13,449 541

24 0.1239 ** 0.8761 12,908 519

25 0.8144 0.1217 0.8783 12,389 215

26 0.1196 0.8804 12,174 211

27 0.1176 0.8824 11,963 207

28 0.1155 0.8845 11,756 204

29 0.1135 0.8865 11,552 200

30 0.9163 0.1126 0.8874 11,352 92

IV. Calculation of Annual Probabilities

Example:

Manufacturing

sector,

1983 generation.

Age 4.


Age Quinquennial

probabilities

p(x) annual


0 0.8837 0.1163 100,000 11,633

1 0.7809 0.2191 88,367 10,280

2 0.6900 0.3100 78,087 9,084

3 0.6098 0.3902 69,002 8,027

4 0.5388 0.4612 60,975 7,093

5 0.5388 0.4856 0.5144 53,882 5,322

6 0.4376 0.5624 48,560 4,796

7 0.3944 0.6056 43,764 4,323

8 0.3555 0.6445 39,441 3,896

9 0.3203 0.6797 35,545 3,511

10 0.5945 0.2944 0.7056 32,035 2,590

11 0.2706 0.7294 29,444 2,381

12 0.2488 0.7512 27,064 2,188

13 0.2286 0.7714 24,875 2,011

14 0.2102 0.7898 22,864 1,849

15 0.6560 0.1970 0.8030 21,015 1,315

16 0.1847 0.8153 19,700 1,233

17 0.1731 0.8269 18,467 1,156

18 0.1623 0.8377 17,311 1,083

19 0.1521 0.8479 16,228 1,016

20 0.7239 0.1460 0.8540 15,212 612

21 0.1401 0.8599 14,600 587

22 0.1345 0.8655 14,013 564

23 0.1291 0.8709 13,449 541

24 0.1239 ** 0.8761 12,908 519

25 0.8144 0.1217 0.8783 12,389 215

26 0.1196 0.8804 12,174 211

27 0.1176 0.8824 11,963 207

28 0.1155 0.8845 11,756 204

29 0.1135 0.8865 11,552 200

30 0.9163 0.1126 0.8874 11,352 92

4 0.5388 0.4612 60,975 7,093

Probability of a business from the Manufacturing

sector to survive at age 4.


Example:

Manufacturing

sector,

1983 generation.

Age 4.


Age Quinquennial

probabilities

p(x) annual


0 0.8837 0.1163 100,000 11,633

1 0.7809 0.2191 88,367 10,280

2 0.6900 0.3100 78,087 9,084

3 0.6098 0.3902 69,002 8,027

4 0.5388 0.4612 60,975 7,093

5 0.5388 0.4856 0.5144 53,882 5,322

6 0.4376 0.5624 48,560 4,796

7 0.3944 0.6056 43,764 4,323

8 0.3555 0.6445 39,441 3,896

9 0.3203 0.6797 35,545 3,511

10 0.5945 0.2944 0.7056 32,035 2,590

11 0.2706 0.7294 29,444 2,381

12 0.2488 0.7512 27,064 2,188

13 0.2286 0.7714 24,875 2,011

14 0.2102 0.7898 22,864 1,849

15 0.6560 0.1970 0.8030 21,015 1,315

16 0.1847 0.8153 19,700 1,233

17 0.1731 0.8269 18,467 1,156

18 0.1623 0.8377 17,311 1,083

19 0.1521 0.8479 16,228 1,016

20 0.7239 0.1460 0.8540 15,212 612

21 0.1401 0.8599 14,600 587

22 0.1345 0.8655 14,013 564

23 0.1291 0.8709 13,449 541

24 0.1239 ** 0.8761 12,908 519

25 0.8144 0.1217 0.8783 12,389 215

26 0.1196 0.8804 12,174 211

27 0.1176 0.8824 11,963 207

28 0.1155 0.8845 11,756 204

29 0.1135 0.8865 11,552 200

30 0.9163 0.1126 0.8874 11,352 92

4 0.5388 0.4612 60,975 7,093

Probability of a business from the Manufacturing

sector of dying before age 4.


Example:

Manufacturing

sector,

1983 generation.

Age 4.


Age Quinquennial

probabilities

p(x) annual


0 0.8837 0.1163 100,000 11,633

1 0.7809 0.2191 88,367 10,280

2 0.6900 0.3100 78,087 9,084

3 0.6098 0.3902 69,002 8,027

4 0.5388 0.4612 60,975 7,093

5 0.5388 0.4856 0.5144 53,882 5,322

6 0.4376 0.5624 48,560 4,796

7 0.3944 0.6056 43,764 4,323

8 0.3555 0.6445 39,441 3,896

9 0.3203 0.6797 35,545 3,511

10 0.5945 0.2944 0.7056 32,035 2,590

11 0.2706 0.7294 29,444 2,381

12 0.2488 0.7512 27,064 2,188

13 0.2286 0.7714 24,875 2,011

14 0.2102 0.7898 22,864 1,849

15 0.6560 0.1970 0.8030 21,015 1,315

16 0.1847 0.8153 19,700 1,233

17 0.1731 0.8269 18,467 1,156

18 0.1623 0.8377 17,311 1,083

19 0.1521 0.8479 16,228 1,016

20 0.7239 0.1460 0.8540 15,212 612

21 0.1401 0.8599 14,600 587

22 0.1345 0.8655 14,013 564

23 0.1291 0.8709 13,449 541

24 0.1239 ** 0.8761 12,908 519

25 0.8144 0.1217 0.8783 12,389 215

26 0.1196 0.8804 12,174 211

27 0.1176 0.8824 11,963 207

28 0.1155 0.8845 11,756 204

29 0.1135 0.8865 11,552 200

30 0.9163 0.1126 0.8874 11,352 92

4 0.5388 0.4612 60,975 7,093

Number of businesses from the Manufacturing

sector that survived age 4.


Example:

Manufacturing

sector,

1983 generation.

Age 4.


Age Quinquennial

probabilities

p(x) annual


0 0.8837 0.1163 100,000 11,633

1 0.7809 0.2191 88,367 10,280

2 0.6900 0.3100 78,087 9,084

3 0.6098 0.3902 69,002 8,027

4 0.5388 0.4612 60,975 7,093

5 0.5388 0.4856 0.5144 53,882 5,322

6 0.4376 0.5624 48,560 4,796

7 0.3944 0.6056 43,764 4,323

8 0.3555 0.6445 39,441 3,896

9 0.3203 0.6797 35,545 3,511

10 0.5945 0.2944 0.7056 32,035 2,590

11 0.2706 0.7294 29,444 2,381

12 0.2488 0.7512 27,064 2,188

13 0.2286 0.7714 24,875 2,011

14 0.2102 0.7898 22,864 1,849

15 0.6560 0.1970 0.8030 21,015 1,315

16 0.1847 0.8153 19,700 1,233

17 0.1731 0.8269 18,467 1,156

18 0.1623 0.8377 17,311 1,083

19 0.1521 0.8479 16,228 1,016

20 0.7239 0.1460 0.8540 15,212 612

21 0.1401 0.8599 14,600 587

22 0.1345 0.8655 14,013 564

23 0.1291 0.8709 13,449 541

24 0.1239 ** 0.8761 12,908 519

25 0.8144 0.1217 0.8783 12,389 215

26 0.1196 0.8804 12,174 211

27 0.1176 0.8824 11,963 207

28 0.1155 0.8845 11,756 204

29 0.1135 0.8865 11,552 200

30 0.9163 0.1126 0.8874 11,352 92

4 0.5388 0.4612 60,975 7,093

Number of businesses from the

Manufacturing sector that died before

age 4.


1983 MANUFACTURING

GENERATION

Age S(x)

0 100 000

1 88 367

2 78 087

3 69 002

4 60 975

5 53 882

6 48 560

7 43 764

8 39 441

9 35 545

10 32 035

11 29 444

12 27 064

1984 MANUFACTURING

GENERATION

Age S(x)

0 100 000

1 86 291

2 74 462

3 64 254

4 55 446

5 51 107

6 47 107

7 43 421

8 40 023

9 36 890

10 35 266

11 33 713

12 32 229

1985 MANUFACTURING

GENERATION

Age S(x)

0 100 000

1 91 333

2 83 416

3 76 186

4 72 521

5 69 031

6 65 710

7 62 548

8 59 539

9 57 092

10 54 746

11 52 497

12 50 339

…

1998 MANUFACTURING

GENERATION

Age S(x)

0 100 000

1 79 113

2 62 589

3 49 516

4 39 174

5 30 992

6 28 907

7 26 962

8 25 148

9 23 457

10 21 879

11 21 673

12 21 469

MANUFACTURING BUSINESSES

x S(x) average

0 100,000

1 67,599

2 54,246

3 46,407

4 40,726

5 36,427

6 33,083

7 30,196

8 27,710

9 25,604

10 23,937

11 22,622

12 21,450

=

V. Estimating survival function

0

20000

40000

60000

80000

100000

120000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61

Age

Survivors Manufacturing

V. Estimating survival function (linear adjustment of average

survival)

S(x) average S(x) adjusted

VI. Obtaining definite tables of survival and mortality (for

every category)

INDICATORS

x = Business age

p(x) = Probability of a business surviving at age x

q(x) = Probability of a business dying during age x

S(x) = Number of survivors at age x

d(x) = Number of businesses that die before reaching age x

e(x) = Life expectancy of businesses at age x

III. Results

Some general results

Life expectancy of businesses in Mexico, according to age

0

5

10

15

20

25

7.7 8.1 9.9

12.7

16.3

21.0

Years of

life*

Business

age AT BIRTH 1 5 10 15 20

* Years a bussiness is expected to live additional to the ones already lived

Business life expectancy by state

8.0 to 9.1 years

7.2 to 7.9 years

6.7 to 7.1 years

3.8 to 6.6 years

0.0 2.0 4.0 6.0 8.0 10.0

OAXACAGUERRERO

MICHOACÁN DE OCAMPOMORELOSTABASCO

SAN LUIS POTOSÍTAMAULIPAS

QUINTANA ROOVERACRUZ DE IGNACIO DE LA…

CHIHUAHUATLAXCALADURANGO

AGUASCALIENTESCOAHUILA DE ZARAGOZA

NUEVO LEÓNSINALOACHIAPAS

HIDALGOBAJA CALIFORNIA

NAYARITGUANAJUATO

SONORACAMPECHEZACATECAS

BAJA CALIFORNIA SURCOLIMAJALISCO

QUERÉTAROPUEBLAMÉXICO

DISTRITO FEDERALYUCATÁN

Years

0.0 5.0 10.0 15.0 20.0 25.0

OAXACA

GUERRERO

MICHOACÁN DE OCAMPO

MORELOS

TABASCO

SAN LUIS POTOSÍ

TAMAULIPAS

QUINTANA ROO

VERACRUZ DE IGNACIO DE LA LLAVE

CHIHUAHUA

TLAXCALA

DURANGO

AGUASCALIENTES

COAHUILA DE ZARAGOZA

NUEVO LEÓN

SINALOA

CHIAPAS

HIDALGO

BAJA CALIFORNIA

NAYARIT

GUANAJUATO

SONORA

CAMPECHE

ZACATECAS

BAJA CALIFORNIA SUR

COLIMA

JALISCO

QUERÉTARO

PUEBLA

MÉXICO

DISTRITO FEDERAL

YUCATÁN

Business life expectancy by state, according to age At birth

State Years of life

0.0 5.0 10.0 15.0 20.0 25.0

OAXACA

GUERRERO


MORELOS

TABASCO

SAN LUIS POTOSÍ

TAMAULIPAS

QUINTANA ROO


CHIHUAHUA

TLAXCALA

DURANGO

AGUASCALIENTES


NUEVO LEÓN

SINALOA

CHIAPAS

HIDALGO

BAJA CALIFORNIA

NAYARIT

GUANAJUATO

SONORA

CAMPECHE

ZACATECAS

BAJA CALIFORNIA SUR

COLIMA

JALISCO

QUERÉTARO

PUEBLA

MÉXICO

DISTRITO FEDERAL

YUCATÁN

Age 1 Business life expectancy by state, according to age

State Years of life

0.0 5.0 10.0 15.0 20.0 25.0

OAXACA

GUERRERO


MORELOS

TABASCO

SAN LUIS POTOSÍ

TAMAULIPAS

QUINTANA ROO


CHIHUAHUA

TLAXCALA

DURANGO

AGUASCALIENTES


NUEVO LEÓN

SINALOA

CHIAPAS

HIDALGO

BAJA CALIFORNIA

NAYARIT

GUANAJUATO

SONORA

CAMPECHE

ZACATECAS

BAJA CALIFORNIA SUR

COLIMA

JALISCO

QUERÉTARO

PUEBLA

MÉXICO

DISTRITO FEDERAL

YUCATÁN


State Years of life

Cumulative survival and mortality of businesses in Mexico,

according to age

0

10

20

30

40

50

60

70

80

90

100

0 1 5 10 15 20

100

64

30 22

16 11

36

70

78 84

89

Reached age

SURVIVAL MORTALITY

Some results per sector

Business life expectancy in Mexico by economic sector,

according to age

0

5

10

15

20

25

AT BIRTH 10 15 20

9.5

14.2

17.3

21.0

6.6

11.7

15.7

21.0

8.0

13.0

16.5

21.0

Years of life*

Business age

MANUFACTURING TRADE SERVICES


Survivors from every 100 businesses that entered the

economic activity by sector, according to age in Mexico

0

10

20

30

40

50

60

70

80

1 10 15 20

68

27 20

15

62

19 13

9

64

23 17

12

Survivors

Business age

MANUFACTURING TRADE SERVICES

Some results according to size

Life expectancy of businesses at birth according to size in

Mexico

0

5

10

15

20

25

(0-2) (3-5) (6-10) (11-15) (16-20) (21-30) (31-50) (51-100)

6.9 8.4

15.0

20.7

23.3 22.0 22.0 22.0

Size

determined by

the number of

persons

employed

Years

of

life *


0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

GUERRERO

MORELOS

OAXACA


TABASCO

CHIHUAHUA

CAMPECHE


HIDALGO

NAYARIT

SINALOA

DURANGO

QUERÉTARO

TAMAULIPAS

TLAXCALA

GUANAJUATO

ZACATECAS

JALISCO

BAJA CALIFORNIA

BAJA CALIFORNIA SUR

COLIMA

NUEVO LEÓN

QUINTANA ROO


SONORA

PUEBLA

DISTRITO FEDERAL

AGUASCALIENTES

SAN LUIS POTOSÍ

MÉXICO

CHIAPAS

YUCATÁN

State Years of life

10.1 National average

Life expectancy at business birth, from 0 to 10 persons employed, by state

Cumulative mortality of businesses from every 100 that enter

the economic activity in Mexico, according to their size

Business size (persons employed)

38

74

81 86

91

32

66

75

82 86

17

44

52

59

66

11

27 33

39 43

7

18 23

27 33

0

10

20

30

40

50

60

70

80

90

100

AT BIRTH 1 5 10 15 20

Deaths

Years elapsed

0 to 2

3 to 5

6 to 20

21 to 50

51 and more

IV. Conclusions and future plans

Conclusions

Business survival and life expectancy indicators have different behavior by sector, size, age

and the region or state they belong to.

At national level, manufacturing businesses are the least volatile, followed by those in the

private non-financial services, while those in the trade sector are the most volatile.

In general, the smallest businesses are the most volatile.

Conclusions

Business life expectancy has a growing trend as the business age increases.

These indicators are a powerful tool for policy planning and decision-making for the public,

private and social sectors.

Future plans

Measuring the business survival, mortality and life expectancy indicators for more detailed

geographic and economical coverage.

Studying the demographic phenomena of survival, mortality and life expectancy at enterprise

level.

business demography in mexico progress and perspectives€¦ · deaths and births by economic...

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