*
Worker- and job-flows in Mexico
July 2003
David S. Kaplan
Departamento de Economía and
Centro de Investigación Económica
Instituto Tecnológico Autónomo de México (ITAM)
E-mail: [email protected]
Gabriel Martínez González
Dirección de Planeación y Finanzas
Instituto Mexicano del Seguro Social (IMSS)
E-mail: [email protected]
and
Raymond Robertson
Department of Economics
Macalester College
E-mail: [email protected]
* We gratefully acknowledge financial support from the Inter-American Development Bank for the project
“Market Institutions, Labor Market Dynamics, Growth and Productivity: An Analysis of Latin America and
the Caribbean.” We also acknowledge financial support from the Asociación Mexicana de Cultura.
Abstract
We present results from the first job- and worker-flows data set from Mexico. We
present annual statistics from 1994-2000 and detect no obvious trends over this period
despite multiple economic shocks. We then show that some established patterns in the
literature (large firms and older firms have lower job- and worker-turnover rates) apply to
Mexico. One difference we find is that net-growth rates are higher in older firms. We
note that large firms outperformed small firms during the economic crisis of 1995. We
then examine differences in job and worker flows between sexes. Men are more likely to
be employed in firm births. Women, however, have much higher net-growth rates in
young firms. Looking at differences across age of worker categories, we find that netgrowth rates decline monotonically with worker age mainly due to differences in
accession rates. Separation rates vary much less with worker age.
1. Introduction
In a recent survey article, Davis and Haltiwanger (1999) summarize the literature on job
creation and destruction. An important theme of this literature is that an economy is always
reallocating or “churning” labor. That is, although an economy might be expanding overall, a
look at microeconomic data would show many large employment contractions at the firm or
establishment level. This finding also holds within narrowly defined industries: although the
industry might be expanding overall, numerous firms or establishments would be contracting or
halting production completely.
Although the principal findings of the literature have proved robust across the numerous
countries studied, the only study that we are aware of for developing economies is Roberts
(1996).1 Recent economic history, however, makes the study of job creation and destruction
particularly valuable to the developing world. Since shocks are generally larger in developing
countries, the patterns and magnitudes of job flows may be more informative and significant than
in countries with smaller shocks. As barriers to trade are eliminated, for example, one might
expect a significant acceleration in the pace of job reallocation. Surprisingly, however, no
evidence currently exists regarding these expectations. In this paper, we present preliminary
results of a long-term project to analyze labor reallocations in the Mexico.
We show that some of the stylized facts of the literature hold true in Mexico. In
particular, older firms and larger firms exhibit lower rates of job turnover. One difference from
the common results of the literature is that net-growth rates in older firms are higher than in
1
That is, prior to the IDB project on “Market Institutions, Labor Market Dynamics, Growth and Productivity: An
Analysis of Latin America and the Caribbean” of which our project is a part.
younger firms. One interesting result specific to Mexico is that larger firms performed better (in
terms of net-growth rates) than did smaller firms during the economic crisis of 1995.
Hammermesh et al. (1996) demonstrate that employment expansions and contractions at
the firm level studied in works such as Davis and Haltiwanger (1990 and 1992) are only a part of
the employment-reallocation process. In particular, they find that half of all hiring occurs in
firms that are not expanding and that half of all separations occur in firms that are not
contracting. These results imply that when we focus on firm-level employment expansions and
contractions (which we will refer to as job flows) we miss a substantial amount of employeelevel turnover (which we will refer to as worker flows).2
Since the data for this paper come from person-level records, we can observe the
identities of the employees in a firm. This is a crucial advantage over many of the data sets used
to study employment reallocations and permits us to examine both job and worker flows. This
allows us to calculate more complete measures of labor reallocations, as in Hamermesh et al.
(1996), that incorporate within-firm employee turnover as well as labor reallocations across
firms. We find, for instance, that job flows represent roughly half of total worker flows.
The format of the rest of the paper is as follows. In the next section, we describe how we
created our job- and worker-flows data set from social-security records in Mexico. We also
present a comparison of our employment calculations with official statistics. In section 3, we
describe the methodology we use to calculate job- and worker-flows statistics. In section 4, we
present the statistics. We include results separated by firm age, firm size, gender, and employee
age. In section 5 we add some final concluding remarks.
2
Other studies that take into account both job flows and worker flows include Anderson et al. (1994) and Abowd et
al. (1999).
2
2. Creation of a Job- and Worker-Flows Data Set for Mexico
The raw data come from the Instituto Mexicano del Seguro Social (IMSS) which is the
agency that manages the social-security accounts for all private-sector tax-registered workers in
Mexico. In this sense, the data can be thought of as a census of formal-sector firms in the private
sector. Unfortunately, if an employee leaves the formal (tax-registered) sector, we are unable to
observe if the employee becomes unemployed or finds a job in the informal sector.
The raw data come in a somewhat unwieldy format. An individual record for a person
contains an identifying number for the person, an identifying code for the firm, the monthly
wage, the date when the information of this record became valid, and the date when the
information stopped being valid. If the worker leaves the firm, the old record is closed. If the
worker’s salary changes, the old record is closed and a new record is opened with the updated
wage information. Importantly, we have both a firm identifier and a person identifier that is
consistently coded over time.
Our first step was to convert this information into annual information. We chose
December 31 as the date for which we would extract the relevant information each year.3 Our
data span the 1993-2000 period, which encompasses the implementation of NAFTA beginning in
1994, the peso crisis in 1995, and the subsequent recovery. For each of those eight years, we
selected the records that were applicable to the date. If a person had two apparently applicable
records from the same firm, we chose the record with the later start date. If a person had two
applicable records from different firms, we assumed the person really was working in both firms.
We only selected workers with strictly positive wages. This restriction mainly excludes students
3
We chose December 31 because it often is used to represent annual statistics and because data from other countries
participating in the same IDB project used this date.
3
from the database, many of whom are insured by the IMSS although they are not really
employees.
The files mentioned above include wage and employment histories of all workers
registered with the IMSS. We then matched these data to a file that contained the gender of the
worker. The match rate for this process was about 95%, implying that we do not know the
gender of roughly 5% of our sample. We also merged in industry and location information of the
firm, this time with a nearly 100% match rate. 4
We tried to link the IMSS industry list to the 2-digit ISIC (Revision 2) system. We were
unable, however, to disaggregate beyond the 1-digit level for the industries of Wholesale and
Retail Trade, and Restaurants and Hotels; Financing, Insurance, Real Estate and Business
Services; and Community, Social and Personal Services. When we refer to industry in this paper,
we mean our modification of the 2-digit ISIC (Revision 2) system.
Since we are using a new data set, we believe it is useful to look at some simple statistics
and compare them to official statistics of the IMSS. Table 1 presents this comparison. The first
employment figures are official IMSS statistics on cotizantes on December 31 of each year. 5
Cotizantes are employees who pay social-security taxes or for whom social-security taxes are
paid. Of the official statistics we have found, these statistics use the definition that most closely
matches our definition of all individuals that receive positive salaries. The second set of
employment observations presents our calculations of employment on December 31 of each
year. The figures match up quite well. The third set of statistics give our counts of “jobs” which
will correspond to our worker- and job-flows statistics. The difference between the statistics on
jobs and employment is that one employee may have more than one job.
4
5
Although the match rate was essentially 100%, there were instances when the geographic codes were invalid.
These data can be accessed at http://www.imss.gob.mx/nr/IMSS/ventunica/memoria_2001/2/024000.htm.
4
Our data represent all sectors of the Mexican economy, but, as an additional check, we
also compared our 1993 average employment in manufacturing with the 1993 average total
employment in the 1993 Mexican Industrial Census. Our 1993 manufacturing employment is
2,989,740 and the 1993 Census manufacturing employment is 3,246,039, suggesting that our
data cover about 92.1% of total manufacturing employment. Although the distinction between
formal and informal labor markets is generally quite important in Mexico, it seems to be
relatively unimportant for the manufacturing sector.
Based on these comparisons, we believe that our data are reliable. We now turn to our
methodology in studying job and worker flows. Our definitions of both job flows and worker
flows are standard in the literature.
3. Methodology
We begin with the methodology for our worker-flows statistics. When a firm hires a new
employee, we refer to this event as an accession. For a given year, we define the accession rate
according to the following formula


∑j acc j ,t


accratet = 200 * 

 ∑ empl j ,t + ∑ empl j ,t −1 
j
 j

where acc j ,t is the number of employees in firm j in year t who were not working in firm j in
year t − 1 , empl j ,t is the number of employees of firm j in year t , and empl j ,t −1 is the number
of employees of firm j in year t − 1 . Similarly we define the separation rate as
5


∑j sep j ,t


sepratet = 200 * 

 ∑ empl j ,t + ∑ empl j ,t −1 
j
 j

where sep j ,t is the number of employees in firm j in year t − 1 who were not working in firm j
in year t . It is now natural to define the net-growth rate in employment which is simply
netratet = accratet − sepratet .
Our two statistics on worker flows, accratet and sepratet give us information of
reallocations of people within and across firms. As we mentioned in the introduction, however, it
is also common to examine reallocations of jobs across firms.6 Job flows statistics give us
information about firm-level changes in employment without taking into consideration the
identities of the employees. For example, consider a firm in which five employees have left since
the last year and were replaced by five new employees. We would say that this firm experienced
worker flows in the form of five accessions and five separations. Since total employment has not
changed, however, we would say that the firm neither created nor destroyed jobs.
More precisely, define net employment growth in firm j and period t as
net j ,t = empl j ,t − empl j ,t −1 .
Now denote job creation in firm j and period t as
pos j ,t = max (0, net j ,t )
and denote job destruction in firm j and period t as
neg j ,t = max(0,− net j ,t ) .
We can now define the job creation rate and job destruction rate in period t as
6
Most of the work in the literature focuses on job flows due to data constraints.
6




∑j pos j ,t
∑j neg j ,t




posratet = 200 * 
 and negratet = 200 * 

 ∑ empl j ,t + ∑ empl j ,t −1 
 ∑ empl j ,t + ∑ empl j ,t −1 
j
j
 j
 j


respectively.
It should be clear that statistics on job flows and statistics on worker flows are related. If
a firm increases its total employment by one, at least one current employee must be new. If a
firm reduces its total employment by one, at least one employee must have left. In this sense,
statistics of job flows give us a lower bound on our worker-flows statistics.
Along these lines, we will now explain our decomposition of worker flows into two
components: the component explained by job flows and the “excess” component.
First, we will define
sumwf t = accratet + sepratet
as our summary measure of worker flows. Similarly, we will define our summary measure of job
flows as
sumjf t = posratet + negratet .
As we mentioned earlier, the sum of job flows ( sumjf t ) can be thought of as a component of
worker flows ( sumwf t ). Our definition of “excess” worker flows will simply be
excwf t = sumwf t − sumjf t .
In words, excess worker flows are the worker flows not accounted for by job flows.
7
4. Results on Job and Worker Flows
Finally we are ready to see some results. Table 2 presents the results of the above
decomposition using annual data from 1994-2000. The date of observation is December 31 of
each year. The average net-growth rate of 6 percent comes from an average accession rate of
36.3 percent and an average separation rate of 30.0 percent. Job flows account for roughly half of
worker flows. We observe no obvious changes in the worker- or job-reallocation process over
time, but the time period might be too short to pick up on these changes.
Job flows in Mexico appear not very different from job flows in the US. Spletzer (2000),
for example, finds an annual job creation rate of 15.82% (slightly lower than ours) and an annual
job destruction rate of 14.42% (slightly larger than ours) using data from 1990-1994. Other than
the important difference in net growth rates, job flows appear fairly consistent between the two
countries. When we restrict our analysis to the manufacturing sector, we reach a similar
conclusion comparing our job flows to those in Davis and Haltiwanger (1992).
One common practice in the literature on job flows is to separate jobs created by births
(firms that had zero employment in the previous year) from jobs created by expansions (firms
that had positive employment in the previous year and expanded). Similarly, it is common to
distinguish jobs destroyed by deaths (firms whose employment fell to zero) from jobs destroyed
by contractions (firms that reduced employment but continue to employ at least one employee).
Our data are particularly well suited for studying births and deaths because we observe all firms,
no matter how small they are. We do not, for example, only observe firms only when they cross
some employment-size threshold.
It is also common to decompose the sum of job creation and destruction ( sumjf t ) into an
8
aggregate component, an industry component, and an idiosyncratic component. The aggregate
component is simply the absolute value of the net-growth rate. If the economy-wide employment
increases (decreases) by five percent, we know the job creation rate (job destruction rate) must
be at least five percent.
The industry component of the decomposition is the component of job creation and
destruction that can be explained by industries simultaneously expanding and contracting.
Specifically it is the average across industries of the absolute value of each industry’s net-growth
rate, weighted by the industry’s average employment, minus the absolute value of the economywide net-growth rate. If all industries are expanding or if all industries are contracting, this
component is equal to zero. This happened in our data in the year 1997. In several other years,
the industry component was almost zero.
The final component of the decomposition, the idiosyncratic component, is the
component of job creation and destruction that arises from firms simultaneously expanding and
contracting within the same industry. It can be defined as the sum of job flows minus the
aggregate component minus the industry component. In our data this is always the largest
component of the job-flows decomposition.
Table 3 separates the job creation and destruction figures from table 1 into births,
expansions, deaths, and contractions. Table 3 also presents the results of the decomposition of
the sum of worker flows into aggregate, industry, and idiosyncratic components using annual
data from 1994-2000. Once again, the date of observation is December 31 of each year.
We observe that firm births and deaths account for slightly less than one third of job
creation and job destruction respectively.7 We also see that the idiosyncratic component of job
7
The extent to which births and deaths are important components of job flows depends crucially on the frequency at
which the job flows are measured. See for example Spletzer (2000).
9
creation and job destruction usually dwarfs both the aggregate component and the industry
component, although in certain years the aggregate component of job flows is substantial.
Before turning to some of the more novel results of our paper, we first show that many of
the stylized facts of job and worker flows also appear to be true in Mexico. Table 4 presents
averages of job- and worker-flows statistics for the period 1998-2000 for several firm-age
categories beginning with firms that are at least one year old but less than two years old. The
other categories are: firms that are at least two years old but less than three years old, at least
three years old but less than four years old, at least four years old but less than five years old, and
at least five years old. Since our data begin in 1993 we have to wait until the year 1998 before
we can observe whether a firm first appeared in the data five or more years earlier.
Table 4 shows many of the results one would expect. The accession rate, separation rate,
job-creation rate, and the job destruction rate all decrease with firm age. Excess worker flows
show this same general trend with the exception of the youngest age category. Table 4, however,
does yield two results that may be surprising. First, the net-growth rates are negative for all age
categories (except, of course, for new firms whose net-growth rates are 200%). Since the average
net-growth rate in our data for this period is 5.6%, table 4 highlights the importance of new firms
in the labor market. We also observe that net-growth rates are lowest for the youngest firms,
which is in direct contrast with the results in Davis and Haltiwanger (1992).
Table 5 shows the average of job- and worker-flows statistics for the period 1994-2000
separated by size category of the firm. We define four categories based and the average of
current- and previous-year employment of the firm. The categories are: less than 50 employees,
at least 50 employees but less than 100, at least 100 employees but less than 250, and at least 250
employees. We see here that the accession rate, separation rate, job creation rate, and job
10
destruction rate all decline with firm size. That is, small firms exhibit higher rates of both job and
worker flows. This is consistent with the stylized facts of the literature.
We now turn to some of the more novel results of our study. Probably the most important
economic event in Mexico over the period studied was the economic crisis of 1995. Indeed, 1995
is the only year in our data when employment fell. One natural question is which firms were hurt
worst by the crisis. Since problems in the credit markets were an important component of the
economic crisis, one might expect that smaller firms would be particularly hard hit by the crisis.
Table 6 shows net-growth rates from 1994-2000 separated by the same firm-size
categories used in table 5 and indeed shows that small firms suffered the most during the
economic crisis. Over the whole period, firms with average employment less than 50 grew
slightly faster than did firms with average employment of at least 250 employees. Growth rates
were highest in the middle two firm-size categories.
In 1995, however, the pattern was quite different. In particular, growth rates rose
monotonically with firm size. For example, the growth rate in firms with average employment of
at least 250 employees was 0.5%. The same figure for firms with less than 50 employees is –
8.0%. Beginning in 1996, as the economy began to recover its losses, smaller firms outperformed
their larger counterparts.
One attractive feature of our data is that we observe the gender of 95% of the workers in
our data. This allows us to calculate job- and worker-flows statistics separately for men and
women. Tables 7 and 8 show these statistics separately for men and women from 1994-2000.8
We do not observe many important differences between tables 7 and 8. Perhaps the most
8
For these tables, a birth continues to mean that the firm had no employees in the previous year. For example, if a
firm had 5 male employees and later hired its first female employee, we would call the a job created by an expansion
(not a birth) for women. Similarly, a death implies that firm employment for both sexes (and the missing category)
dropped to zero.
11
important difference is that the net-growth rate for women is one percentage point higher on
average. That is, female participation in the formal sector of the economy increased faster than
male participation. Despite this trend, female employment was only 34% of total employment in
2000.9
When we interact gender with firm age, however, we see an interesting result on the life
cycle of firms. We divide firms into the same firm-age categories as in table 4. In table 9 we
present net-growth rates separately for men and women along with the percentage of male and
female employment in each firm-age category.10 We find the percent of men working in the
youngest firms (as a percentage of the male labor force) is larger than the analogous statistic for
women. That is, men are more likely to work in firms that are less than one year old (new firms).
Firms one to two years old, however, show a net-growth rate for men of –12.7 percent (on
average from 1998-2000) while the analogous statistic for women is 12.4%. In other words,
firms are born mainly with men, but then quickly move to hire women in their early years. These
differences disappear for the older firms. This result raises the possibility that differences in risk
aversion may explain participation decisions in young firms.11
Another attractive feature of our data is that we observe the age of the workers. Table 10,
for example, shows averages of job and worker-flows statistics separately for several age-ofworker categories over the period 1994-2000. The first category is at least 15 years old but less
than 20 years old. We use categories of five-year increments until the oldest category of at least
9
This is the percent of female employment in 2000 as a total of all employment for which we have a gender code.
The employment figure from which each percentage of the male or female labor force is calculated is the average
of current- and prior-year employment.
11
Gender differences in risk aversion have been the subject of growing academic debate. See Schubert et al (1999)
for a review of an aspect of this debate.
10
12
60 years old but less than 65 years old.12 Net-growth rates decline as the age of the workers
increases.
At the extreme ends of the table we see some extreme results. For example, net
employment growth for workers 15-20 years old is 54.9% while net growth is 12.6% for workers
20-25 years old. To what is this difference due? The accession rate for the younger group is 45.1
percentage points higher while the separation rate is only 2.8 percentage points higher. That is,
almost all of the difference is due to differences in accession rates. Turning to job creation and
destruction, we see that the job creation rate for the younger group is 40.5 percentage points
higher. Interestingly, the younger group also has a slightly higher job destruction rate (a
difference of 1.7 percentage points).
As workers approach retirement, we again see dramatic results. Net growth for workers
50-55 years old is –1.0% while net growth is –16.2% for workers 55-60 years old. The difference
in accession rates is minimal; the younger group has an accession rate that is 0.4 percentage
points higher. The difference in separation rates, however, is dramatic. The separation rate for
workers 55-60 years old is 22.3% while the separation rate for workers 60-65 years old is 37.1%.
That is, almost all of the difference between these two groups comes from the separation rate.
Turning to the age groups between 20 and 55 years old, the pattern is clear. As we move
from a younger age category to an older one, both the accession rate and the separation rate fall.
The drop in the accession rate, however, is always more pronounced. The patterns for job
creation and destruction are less clear.
12
An employee is placed in an age category based on his or her age on December 31 of the year listed in the table.
For example, if an employee who is 59 on December 31, 1999 is no longer with the firm on December 31, 2000, we
treat this as a separation for the age category 60-65 in the year 2000.
13
5. Conclusions
We present the first ever results on job and worker flows from Mexico. Some results
found in other countries are also true in Mexico. For example, older firms and larger firms
exhibit lower rates of job and worker turnover. One difference between our results and earlier
results in the literature is that older firms have higher net-growth rates than younger firms.
In addition to examining whether our results fit previously established patterns from other
countries, we generate some new results as well. We find, for example, that large firms
dramatically outperformed smaller firms during the economic crisis. We further found that
younger firms (in the two or three years after appearing on the data) exhibit much higher netgrowth rates for women than for men. Men however, disproportionately tend to work in firms
that are just entering the market. Looking at age of workers, we found that net-growth rates
decline with worker age. The general pattern is that accession rates and separation rates both
decline with worker age, but accession rates decline faster.
Finally, we conclude by noting that this is only a preliminary step in our efforts to study
labor dynamics in Mexico. We did not detect any changes over the relatively short period in the
analysis presented here, but we suspect an analysis of more years of data might yield different
results. For this reason we are in the process of acquiring and processing data from the period
1985-1992 as well as data from 2001. The earlier years might be particularly important because
the would allow us to study the effects of the GATT trade agreement.
14
References
Abowd, John M., Patrick Corbel, and Francis Kramarz, 1999, “The Entry and Exit of Workers
and the Growth of Employment: An Analysis of French Establishments.” Review of
Economics and Statistics, v81, n2, 170-187.
Anderson, Patricia M., Bruce D, Meyer, John Pencavel, and Mark J. Roberts, 1994, “The Extent
and Consequences of Job Turnover,” Brookings Papers on Economic Activity:
Microeconomics, v6, 177-248.
Davis, Steven J. and John C. Haltiwanger, 1990, “Gross Job Creation and Destruction:
Microeconomic Evidence and Macroeconomic Implications.” NBER Macroeconomics
Annual, 123-168.
Davis, Steven J. and John C. Haltiwanger, 1992. “Gross Job Creation, Gross Job Destruction,
and Employment Reallocation.” Quarterly Journal of Economics, v107, n3, 819-863.
Davis, Steven J. and John C. Haltiwanger, 1999, “Gross Job Flows,” in Handbook of Labor
Economics, v3b, edited by Orley Ashenfelter and David Card, (Elsevier, Amsterdam).
Hamermesh, Daniel S., Wolter H. J. Hassink, and Jan C. von Ours. (1996) Job Turnover and
Labor Turnover: A Taxonomy of Employment Dynamics Annales d’Économie et de
Statistique, v 41/42, 21-40.
Roberts, M. (1996) Employment Flows and producer turnover. In: Roberts, M. and Tybout, J.
Industrial Evolution in Developing Countries: micro patterns of turnover, productivity
and market structure, edited by M. Roberts and J. Tybout, (Oxford University Press, new
York).
Schubert, Renate; Martin Brown, Matthias Gysler, Hans Wolfgang Brachinger "Financial
Decision-Making: Are Women Really More Risk-Averse?" American Economic Review,
vol. 89, no. 2, May 1999, pp. 381-85
15
Spletzer, James R., 2000, “ The Contribution of Establishment
Births and Deaths To Employment Growth,” Journal of
Business and Economic Statistics, v18, n1, 113-126.
16
Table 1: Comparisons with Official Statistics
Official Statistics on Cotizantes
Employment in our Data
%
empl change
%
empl change
year
1993
1994
1995
1996
1997
1998
1999
2000
8,514,279
8,795,812
8,283,045
8,993,670
10,154,944
11,050,796
11,807,827
12,406,565
Mean empl change
SD of empl changes
-3.25
-6.00
8.23
12.13
8.45
6.62
4.95
5.37
5.76
year
1993
1994
1995
1996
1997
1998
1999
2000
8,861,568
9,114,508
8,716,941
9,517,710
10,948,265
11,610,921
12,294,693
12,859,215
Mean empl change
SD of empl changes
-2.81
-4.46
8.78
13.98
5.87
5.72
4.49
5.31
5.63
Jobs in our Data
year
1993
1994
1995
1996
1997
1998
1999
2000
%
empl change
9,024,937
9,319,949
8,971,324
9,931,151
11,604,092
12,361,461
13,128,253
13,743,544
Mean empl change
SD of empl changes
-3.22
-3.81
10.16
15.54
6.32
6.02
4.58
6.00
5.98
Notes: Official Statistics come from the web site of the Instituto Mexicano del Seguro Social (IMSS) at
http://www.imss.gob.mx/nr/IMSS/ventunica/memoria_2001/2/024000.htm. The other statistics from our calculations from Social
Security Records from the Instituto Mexicano del Seguro Social (IMSS). Employment is defined as the number of people
working. Each job is a worker-firm pair. The denominator for all percent changes is the average of employment in the current
and previous year. Employment measurements are taken on December 31 of every year. See text for details.
Table 2: Annual Worker Flows and Job Flows from Mexico from 1994 - 2000
year
sum of
accession
and
net
growth accession separation separation
rates
rate
rate
rate
sum of
creation
and
job
job
creation destruction destruction
rates
rate
rate
excess
worker
flows
1994
1995
1996
1997
1998
1999
2000
3.2
-3.8
10.2
15.5
6.3
6.0
4.6
37.0
30.1
35.4
40.6
37.6
36.7
36.8
33.8
33.9
25.3
25.0
31.2
30.7
32.2
70.8
64.1
60.7
65.6
68.8
67.5
69.0
19.6
14.8
20.6
25.4
20.2
18.4
17.8
16.3
18.6
10.4
9.9
13.8
12.4
13.2
35.9
33.3
31.0
35.3
34.0
30.9
30.9
34.9
30.7
29.7
30.3
34.8
36.6
38.1
means
6.0
36.3
30.3
66.6
19.5
13.5
33.0
33.6
Notes: All Data come from Social Security Records from the Instituto Mexicano del Seguro Social (IMSS). The denominator for
all percent changes is the average of employment in the current and previous year. Employment measurements are taken on
December 31 of every year. See text for details.
Table 3: Decomposition of Job Flows
total
job
creat
deaths
contrs
total
job
destr
sum of
creat
and
destr
year
net
growth
rate
1994
1995
1996
1997
1998
1999
2000
3.2
-3.8
10.2
15.5
6.3
6.0
4.6
6.7
5.8
6.7
9.1
7.9
7.4
7.3
12.8
8.9
13.8
16.4
12.3
11.0
10.4
19.6
14.8
20.6
25.4
20.2
18.4
17.8
4.4
5.0
3.4
3.2
4.5
4.4
4.4
12.0
13.6
7.0
6.7
9.4
8.1
8.8
16.3
18.6
10.4
9.9
13.8
12.4
13.2
35.9
33.3
31.0
35.3
34.0
30.9
30.9
3.2
3.8
10.2
15.5
6.3
6.0
4.6
0.0
0.8
0.0
0.0
0.1
0.3
0.6
32.6
28.7
20.8
19.8
27.6
24.5
25.8
means
6.0
7.3
12.2
19.5
4.2
9.4
13.5
33.0
7.1
0.3
25.7
births expans
aggreg industry
comp
comp
idiosyn
comp
Notes: All Data come from Social Security Records from the Instituto Mexicano del Seguro Social (IMSS). The denominator for all
percent changes is the average of employment in the current and previous year. Employment measurements are taken on December
31 of every year. See text for details.
Table 4: Average Job and Worker Flows Statistics by Age of Firm
firm age
1 <= age < 2
2 <= age < 3
3 <= age < 4
4 <= age < 5
age >= 5
sum of
accession
and
net
growth accession separation separation
rates
rate
rate
rate
-6.5
-4.3
-2.8
-2.6
-0.5
56.2
47.6
43.8
39.3
26.0
62.7
51.9
46.6
41.9
26.5
118.9
99.5
90.5
81.2
52.6
sum of
job
creation
and
job
job
creation destruction destruction
rates
rate
rate
31.8
21.6
18.7
15.5
8.9
38.4
25.9
21.5
18.2
9.5
70.2
47.4
40.2
33.7
18.4
excess
worker
flows
48.7
52.0
50.2
47.5
34.1
Notes: All Data come from Social Security Records from the Instituto Mexicano del Seguro Social (IMSS). The denominator for all
percent changes is the average of employment in the current and previous year. Employment measurements are taken on
December 31 of every year. Averages are from the 1998-2000 results. See text for details.
Table 5: Average Job and Worker Flows Statistics by Average Size of Firm
average size
0 <= size < 50
50 <= size < 100
100 <= size < 250
size >= 250
sum of
accession
and
net
growth accession separation separation
rates
rate
rate
rate
5.4
6.7
7.9
5.9
44.5
41.6
40.2
27.9
39.1
34.9
32.2
22.0
83.6
76.5
72.4
49.9
sum of
job
creation
and
job
job
creation destruction destruction
rates
rate
rate
28.5
20.8
19.3
12.4
23.2
14.1
11.4
6.5
51.7
34.9
30.7
18.9
excess
worker
flows
31.9
41.7
41.7
31.1
Notes: All Data come from Social Security Records from the Instituto Mexicano del Seguro Social (IMSS). The denominator for all
percent changes is the average of employment in the current and previous year. Employment measurements are taken on December
31 of every year. Averages are from the 1994-2000 results. See text for details.
Table 6: Net Growth Rates by Firm-Size Category
net growth
net growth
(50 <= ave empl < 100) (100 <= ave empl < 250)
net growth
(ave empl >= 250)
year
net growth
(ave empl < 50)
1994
1995
1996
1997
1998
1999
2000
1.9
-8.0
9.0
15.7
6.7
6.5
5.7
4.8
-5.9
10.7
18.6
8.1
6.2
4.8
5.5
-4.5
12.9
19.7
9.3
7.6
5.3
3.4
0.5
10.2
13.7
4.9
5.3
3.6
means
5.4
6.7
7.9
5.9
Notes: All Data come from Social Security Records from the Instituto Mexicano del Seguro Social (IMSS). The denominator for all
percent changes is the average of employment in the current and previous year. Employment measurements are taken on
December 31 of every year. See text for details.
Table 7: Annual Worker Flows and Job Flows from Mexico from 1994 - 2000
(Female Workers Only)
year
sum of
accession
and
net
growth accession separation separation
rates
rate
rate
rate
sum of
creation
and
job
job
creation destruction destruction
rates
rate
rate
excess
worker
flows
1994
1995
1996
1997
1998
1999
2000
5.2
-1.8
11.3
13.5
6.6
8.0
6.8
38.6
31.2
36.4
39.1
37.0
37.4
38.0
33.4
33.0
25.0
25.5
30.4
29.4
31.3
72.0
64.1
61.4
64.6
67.4
66.8
69.3
22.6
16.2
21.9
23.9
19.6
19.0
18.8
17.4
18.0
10.6
10.4
13.0
11.0
12.0
40.0
34.2
32.5
34.3
32.5
30.0
30.7
32.0
29.9
28.9
30.3
34.9
36.8
38.6
means
7.1
36.8
29.7
66.5
20.3
13.2
33.5
33.0
Notes: All Data come from Social Security Records from the Instituto Mexicano del Seguro Social (IMSS). The denominator for
all percent changes is the average of employment in the current and previous year. Employment measurements are taken on
December 31 of every year. See text for details.
Table 8: Annual Worker Flows and Job Flows from Mexico from 1994 - 2000
(Male Workers Only)
year
sum of
accession
and
net
growth accession separation separation
rates
rate
rate
rate
sum of
creation
and
job
job
creation destruction destruction
rates
rate
rate
excess
worker
flows
1994
1995
1996
1997
1998
1999
2000
3.5
-3.0
10.1
16.1
6.4
5.7
3.8
34.7
30.2
35.2
40.9
37.8
37.0
36.7
31.2
33.2
25.1
24.8
31.4
31.2
32.8
65.9
63.4
60.3
65.7
69.3
68.2
69.5
19.4
16.0
21.3
26.8
21.5
19.7
18.7
15.9
19.0
11.2
10.8
15.1
13.9
14.9
35.3
35.0
32.4
37.6
36.7
33.6
33.6
30.6
28.4
27.9
28.1
32.6
34.6
35.9
means
6.1
36.1
30.0
66.0
20.5
14.4
34.9
31.1
Notes: All Data come from Social Security Records from the Instituto Mexicano del Seguro Social (IMSS). The denominator for
all percent changes is the average of employment in the current and previous year. Employment measurements are taken on
December 31 of every year. See text for details.
Table 9: Net Growth Rates for Men and Women by Firm Age
year
1998
1999
2000
1998
1999
2000
1998
1999
2000
1998
1999
2000
1998
1999
2000
1998
1999
2000
(net rate
for
net rate women) - average
for (net rate diff in net
net rate
rates
firm age for men women for men)
less than 1
less than 1
less than 1
1 to 2
1 to 2
1 to 2
2 to 3
2 to 3
2 to 3
3 to 4
3 to 4
3 to 4
4 to 5
4 to 5
4 to 5
5 or more
5 or more
5 or more
200.0
200.0
200.0
-10.5
-12.2
-15.3
-3.2
-7.1
-8.6
0.1
-5.2
-5.8
-1.1
-3.0
-5.4
-0.8
0.0
-1.9
200.0
200.0
200.0
13.3
13.4
10.6
1.5
0.5
3.8
2.8
-1.0
-1.5
0.4
1.2
-2.7
-0.3
2.1
0.4
0.0
0.0
0.0
23.8
25.6
26.0
4.7
7.6
12.4
2.8
4.1
4.2
1.5
4.3
2.7
0.5
2.0
2.3
0.0
25.1
8.2
3.7
2.8
1.6
% of
% of
male female
empl empl
4.0
3.7
3.7
7.8
7.1
6.6
5.7
6.7
6.1
4.9
5.1
6.0
4.8
4.5
4.6
72.8
72.8
72.9
2.9
2.8
2.9
6.3
5.7
5.6
5.5
6.2
5.8
4.8
5.2
5.8
4.7
4.5
4.7
75.9
75.5
75.2
Notes: All Data come from Social Security Records from the Instituto Mexicano del Seguro
Social (IMSS). The denominator for all percent changes is the average of employment in the
current and previous year. Employment measurements are taken on December 31 of every
year. See text for details.
Table 10: Average Job and Worker Flows Statistics by Age of Worker
worker age
15 <= age < 20
20 <= age < 25
25 <= age < 30
30 <= age < 35
35 <= age < 40
40 <= age < 45
45 <= age < 50
50 <= age < 55
55 <= age < 60
60 <= age < 65
change
change
in acc
in sep
rate from
rate from
previous
previous
net acc rate category sep rate category
54.9
12.6
5.2
2.4
1.3
0.7
0.1
-0.4
-1.0
-16.2
97.0
52.0
36.7
30.0
26.9
24.8
23.1
22.0
21.3
20.9
--45.1
-15.2
-6.7
-3.1
-2.1
-1.7
-1.1
-0.7
-0.4
42.1
39.3
31.5
27.6
25.6
24.1
23.0
22.4
22.3
37.1
--2.8
-7.8
-3.9
-2.0
-1.5
-1.1
-0.6
-0.1
14.8
change
in creat
rate from
creat previous
rate category
change
in destr
rate from
destr previous
rate category
72.3
31.8
22.9
19.8
18.8
18.3
17.8
17.5
17.2
16.4
17.5
19.2
17.6
17.3
17.4
17.6
17.7
18.0
18.2
32.6
--40.5
-9.0
-3.1
-1.0
-0.5
-0.5
-0.3
-0.3
-0.8
-1.7
-1.6
-0.3
0.1
0.2
0.1
0.3
0.3
14.4
Notes: All Data come from Social Security Records from the Instituto Mexicano del Seguro Social (IMSS). The denominator
for all percent changes is the average of employment in the current and previous year. Employment measurements are
taken on December 31 of every year. Averages are from the 1994-2000 results. See text for details.