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Econometrics Report and FE regression on female labour force participation in OECD countries
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ECONOMETRICS REPORT
Participation of Women in the Labour Market Group 6
Ajay Kumar 1411211
Archana Valsan 1411215
M. Krubakar 1411237
EXECUTIVE SUMMARY
INTRODUCTIONIn the current scenario, female labor force participation is an important driver and an outcome
of growth and development of a country. Women’s labor force participation tends to increase
with economic development of the country and this relationship is not uniform at a country
level. Most of the OECD countries have experienced an increase in female labour
participation during the last few decades to the order of 54% in 1980 to 71% in 2010. i The
rate of growth has varied across countries and despite an overall increase in female labour
participation rates, these differences were significant in the early 2000s. This difference in
labor force participation across countries is driven by various economic factors like economic
growth, education and social norms and initiatives taken by the respective governments.
Apart from economic advancement at a country level, another salient factor that drives
female labour participation is the changes in labour demand- for instance, with the emergence
of service sector and new production activities, the demand for female labour force is on the
rise. In 2008, nearly one third of the female working population was involved in the service
sector.ii Another major determinant to female labour participation is the government policies
that enable parents to achieve work-life balance and since 1980, there has a rising focus by
the government of OECD to countries to expand these expenditure. The major policy
instruments are leave from work provided after child birth, extent of childcare services and
tax benefits provided for female workers.
MOTIVATIONThis empirical study aims to determine the participation of women in the labour force in nine
OECD countries having similar GDP per capita values namely, Australia, Canada, Spain,
UK, Italy, The Netherlands, France, Germany and Norway. This study aims to highlight the
key trends and factors that affect the female labor force participation and explain the
underlying reason for such an observed causality. Gender wage gap exists in most of the
OECD countries despite having a legislation to ensure equal pay for equal work, irrespective
of gender. Historically, there is a significant wage gap between women and men with women
being paid lesser wages than men. In all OECD countries, women on an average earned 16%
less than men in 2010iii compared to 2000, when the difference in wages was 4 percentage
points higher. In many OECD countries, the wage gap at the top of the earnings distribution
is much higher than at the median indicating the presence of glass ceiling- a phenomena that
prevents women from moving up the career ladder to top notch salaries. In certain countries,
notably in in Germany, Austria, Spain and Italy wage gap is significant among the male and
female low earners as well. Gender wage gap also increases with age and child bearing as
indicated by OECD studies that in 2010, the gender wage gap for 25-29 years was 9%
compared to 24% for 55-59 year olds.iv There is a growing need to focus the attention of
CEOs and senior managers on improving gender balance backed by the following reasons
namely, a) To attract and retain the best talent, b) To enhance diversity c) To serve consumer
markets having women as the major customers.v Hence, with increasing competitive pressure,
firms are in need of the best talent and in this regard, women account for a growing share of
talent emerging from the education system and firms risk losing out if they fail to leverage
this resourceful pool.vi Despite the potential benefits that firms can obtain from providing a
more prominent role to women, they are under-represented in the business sector. viiHence,
the topic of female labor force participation makes for an interesting study.
DATA AND VARIABLES USED
The data source used was OECD website (http://www.oecd.org/).
The variables used are listed in the table with explanations
Variables Notation Explanation(As per OECD)viii
Dependent variable
Female labour participation flpr The labour force participation rates is calculated as
the labour force divided by the total working-age
population. This indicator is broken down by age
group and it is measured as a percentage of each
age group.
Independent variables
Labour market characteristics
Part time female employment pef Ratio of females employed as part time (who
usually work less than 30 hours per week in their
main job)ix to the total females employed.
Percentage of services in
employment
empinser
Unemployment rate une Unemployment rate is the number of unemployed
people as a percentage of the labour force, where
the latter consists of the unemployed plus those in
paid or self-employment.
Employees, services, female femser Ratio of females employed in services to the total
number of females who are employed expressed as
a percentage.
Strictness of employment
protection
strict The OECD indicators of employment protection
legislation measure the procedures and costs
involved in dismissing individuals or groups of
workers and the procedures involved in hiring
workers on fixed-term or temporary work agency
contracts.x
Gender Wage Gap gendwagg
ap
The gender wage gap is unadjusted and is defined
as the difference between median earnings of men
and women relative to median earnings of men.
Data refer to full-time employeesxi
Demographic characteristics
GDP per capita gdpc A measure of the total output of a country where
the gross domestic product (GDP) is divided by the
number of people in the country.xii
Agriculture/Fishing/Forestry empagri Percentage of labour force engaged in agriculture,
fishing and forestry.
Services Employment empser Percentage of labour force engaged in services
industry.
Manufacturing empman Percentage of labour force engaged in
manufacturing.
Value added services gdpser
Log of population lpoptot Logarithm All nationals present in, or temporarily
absent from a country, and foreigners settled in a
country. This indicator shows the total number of
people living in an areaxiii
Population ages >=65 pop65 The elderly population is defined as people aged 65
and over. It is calculated as ratio of people aged 65
years and above to the total population.xiv
Age dependency ratio, old agedep Ratio of the elderly population and the working age
(15-64 years) population.xv
Employment to population
ratio
empl Ratio of total people who are employed to the total
population expressed as a percentage.
Female enrolled-secondary-
education
rawsec Total number of females enrolled in secondary
educationxvi
Log number of female
enrolled-tertiary education
femter Logarithm of Ratio of number of females enrolled
for tertiary education- (highest level of education,
by age group. This includes theoretical
programmes for advanced research or high skill
professions such as medicine and vocational
programmes leading to the labour market.)xvii to the
total number of people enrolled for tertiary
education expressed as a percentage.
Log number of female
enrolled -secondary education
femsec Ratio of number of females enrolled for secondary
education to the total number of students enrolled
for secondary education expressed as a
percentage.xviii
Fertility rate The total fertility rate in a specific year is defined
as the total number of children that would be born
to each woman if she were to live to the end of her
child-bearing years and give birth to children in
alignment with the prevailing age-specific fertility
rates.xix
Mobile access Measured as the number of mobile subscriptions
per 100 inhabitants.xx
Internet access Internet access is defined as the percentage of
households who reported that they had access to the
Internet. In almost all cases this access is via a
personal computer either using a dial-up, ADSL or
cable broadband access. This indicator is measured
in percentage of all householdsxxi
Policies promoting work life balance
Government expenditure on
education
gexp This includes direct expenditure on educational
institutions and educational-related public subsidies
provided to households and administered by
educational institutions.xxii It is expressed as a
percentage of GDP and calculated as an index with
a base year of 2000.xxiii
METHODOLOGY
We started collecting data from 1980 onwards till 2010. As we kept on adding explanatory
variables the gap in the data available started becoming more prominent. So finally we
truncated the data set from 1998 to 2009 as data gaps was less sparse for this range. In case
data for one of the years was missing in between two years, we extrapolated the value by
recognising the trend in the explanatory variable.
Since we have panel data for 12 years, we have to make a selection between either of the two
Panel Data methods.
Step1: Creating ‘lag’ and ‘log’ variables
Lag variables were created for the following Explanatory Variables-
a) Tertiary Education - We expect the female enrolment in yesteryear’s Tertiary
Education to differently impact the job proportion of female in the two years.
Intuitively, if the enrolment of female is higher this year we expect female
participation in labour to be higher in future when they graduate.
Log variables is created for the following Explanatory Variables-
a) GDP per Capita
b) Total Population
c) Number of female enrolled for Tertiary Education
Step 2: Deciding between Fixed Effect or Random Effect Method of Estimation
We expect the data to have country fixed effects. We are dealing with 9 different countries,
many of which have been involved in World War and inter-country fights in the 19th Century.
Difference of culture, political ideology and financial upbringing gives us sufficient reason to
believe the existence of heterogeneity bias. The ‘unobserved effects’ are negatively co-related
with the explanatory variables and we try out Hausman test to statistically test the preference
of ‘Random Effect’ Estimation over ‘Fixed Effect’ Estimation.
Results are attached in Exhibit 1.
RESULTS AND DISCUSSION
Relationship between Fertility and Female Labour Force Participation
Rationale- A lot has been focussed, discussed and debated on the correlation between
Fertility and Labour Force Participation. However, Benjamin Cheng1 in his vast study has
concentrated on causality of these two factors in the Japanese and US labour force market. He
has shown that Fertility Rate Granger Causes Female Labour Participation. This pristinely
indicates the number of children (past fertility) exerts a prominent negative effect on female
labour participation discouraging them from seeking impactful and full time employment
outside from house. As birth rate climbs up, new mother choose to withdraw from employee
workforce and concentrate on childcare. This result has been further corroborated by works
of Paresh Kumar Narayana and Russel Smyth2 that Fertility negatively Granger causes
Female Employment for Australia.
In the nine OECD countries which are the focus of our study, year on year increase in female
labour force participation over 1998-2009 has been positive, with an average CAGR of
0.8107 percent. The population during the same time saw an average CAGR of 0.7241
percent. The proportion of female joining the labour force outpaced the increase in
population. The most striking example was Germany, where population has remained
stagnant from 1998-2009, but the women who came out to be a part of labour force increased
from 62.54% in 1998 to 70.37% in 2009. Surprisingly, during 1998-2009, there has been a
positive correlation between fertility rates and female labour supply in these 9 countries. This
is contrary to the microeconomic predictions and seminal work done by Becker and Lewis
(1973), and empirical work done by Butz and Ward (1979) for U.S. and Mincer in which
Economic models of fertility relate higher women education level with an increase in labour
supply and a reduction in fertility.3
1 The causality between fertility and female labour force participation in Japan, pp1142 Female labour force participation, fertility and infant mortality in Australia: some empirical evidence from Granger causality tests, Applied Economics, February 2006, pp 5703 Labour Market Participation of Women and Fertility: the Effect of Social Policies, Daniela Del Boca, Rolf Aaberge, Ugo Colombino, John Ermisch, Marco Francesconi, Silvia Pasqua and Steinar Strøm, pp3
The rationale behind the microeconomic prediction is logical and intuitive. Women face a
disproportionate trade-off between raising children and having a career. When we regress
‘female labour proportion’ with ‘fertility’ we ought to expect a negative relation. However,
the situation is complicated by endogeneity. For instance women with high career ambition
will be negatively correlated with fertility, and the regression between ‘fertility’ and ‘labour
force participation’ will be spurious. To a larger extent panel data takes care of the
endogeneity by averaging out the “unobservable” and eliminating it when making a “Fixed
Effect Method” estimation. The ‘Fixed Effect Method’ output between Female Labour Force
Participation and Fertility is mentioned below for the 9 OECD countries, and it is positive.
Flpr Coef.
Std.
Err. t P>t P>t [95% Conf. Interval]
Fert
0.24213
9
0.02768
8 8.75 0.00 .1871918 .2970854
_cons
0.26130
7
0.04493
9 5.81 0.00 .1721272 .3504857
Reasons for positive correlation
‘Societal Response Hypothesis’ puts forth the changing attitude towards working mothers,
increased support from the government in form of social expenditure, maternity leaves,
extended child-care services from the society have mellowed down the trade-off faced by
women while being a mother and an employee at the same time. Vinod Mishra, Ingrid
Nielsen and Russell Smyth in their discussion paper4 conclude that if causation runs with a
positive correlation from fertility rates to female labour force participation then this would be
in tandem with the ‘Societal Response Hypothesis’ and would make the return of women to
workforce more easier. However, much criticism from economists has prevented this
hypothesis from being concrete.
Another explanation that has been put forward has been that the positive correlation is due to
increasing part time employment, increasing educational attainment of female, and enrolment
of females in primary and secondary education. We ran a regression between female labour
force participation, and fertility while controlling for education, part time employment and
4 The relationship between female labour force participation and fertility in G7 countries: evidence from panel cointegration and granger causality, pp6
country fixed effects to find a negative relation between fertility and female labour force
participation.
Flpr Coef. Std. Err. t P>t
Pef 0.476024 0.052595 9.05 0
Lpoptot 1.425502 0.158437 9 0
Fert -0.00254 0.030887 -0.08 0.935
primsec 0.401591 0.280239 1.43 0.155
_cons -10.4058 1.136695 -9.15 0
Government Expenditure
The question of causality between Fertility and Female Employment leads to how should
government direct policies towards the population. If Female Labour Force Participation
negatively causes fertility then the government needs to improve “paid maternity leaves” to
encourage working women to have more children in OECD countries. On the contrary if
higher Fertility Rates, negatively causes Female Labour Force participation then the
government policies should be directed towards reducing the cost of mothers from re-entering
the labour force and increasing child care facilities thus reducing the opportunity cost for
women to tend to their children.
Thus either ways government expenditure on social causes have a role in improving the
Female Labour Force Participation. The only point worth debating can be the causality of
Government Expenditure enhancing Fertility Rates and Fertility Rates hence increasing
Female Labour Force Participation or government Expenditure directly impacting Female
Participation Rate which in turn causally impacts fertility. Which in anyways is beyond the
scope of this work.
From our regression analysis we found Government Expenditure positively impacts Female
Labour Force Employment. Since social expenditure, rather than infrastructure building was
regressed we expect the lag of government expenditure not to make much of a difference, and
was proved with our result. The lag of government expenditure wasn’t significant while
controlling for other factors.
Florence Jaumotte5 in her work has shown that inadequate childcare childcare is a constraint
for full time employees than for part time employees, and even his result are in line with our
results about the positive significance of Social Expenditure on Female Labour Force
Participation.
Co-eff Std. Error t p>t
gexp 0.3623326 0.1532204 2.36 0.02
laggex
p 0.1138038 0.1329684 0.86 0.395
_cons -11.11923 2.065181 -5.38 0
Part Time Female Employment
Various published works of econometricians on Female Labour Force Participation such as
Jaumotte (2003)5 and Olivier Thévenon6 have categorised the determinants of female labour
participation between “labour market” characteristics and “family friendly policies”. Labour
Market Characteristics have ‘Flexibility of Working-time” as an explanatory variable. The
reasons put forth by them revolve around two buckets- “flexi work timings to accommodate
work and family” and “employers trying to avoid restrictive employment contracts”.
According to 2001 European Labour Force Survey7, the proportion of female part time
workers on account of household activities was more than 40 percent. From our data we
found the correlation between Female Labour Force Proportion to be positively correlated to
Part Time Employment, and countries, save Netherlands, with higher Female Labour Force
Proportion had higher proportion for Part Time Female Employees. Thus policies that tend to
remove distortions against part time working favour women contribution to work force.
5 FEMALE LABOUR FORCE PARTICIPATION: PAST TRENDS AND MAIN DETERMINANTS IN OECD COUNTRIES ECONOMICS DEPARTMENT WORKING PAPERS NO.376, pp196 Thévenon, O. (2013), “Drivers of Female Labour Force Participation in the OECD”, OECD Social, Employment and Migration Working Papers, No. 145, OECD Publishing; pp 217 http://ec.europa.eu/eurostat/statistics-explained/index.php/Statistics_in_focus
A Fixed Estimate Regression is given between Female Labour Force Participation and Part
Time Female Employment while controlling for other variables. The relationship is
significant and the coefficient is positive.
Flpr Coef. Std. Err. T P>t [95% Conf. Interval
Pef
0.282544
2
0.051724
4 5.46 0
.1796846 .38540
38
Proportion of Dependent Population
The proportion of older people in labour force has been on a constant decline on a worldwide
basis. In 1950, 33.33 percent of people above 65 participated in labour force, today it is just
20 percent8. In European countries the same set of figures were 22 percent and 5 percent
respectively8. This change increases the pressure on government expenditure for pensions and
healthcare for the elderly. As the proportion increases further (none of the 9 countries in the
1998-2009 range have a fertility rate above 2- the self-sustaining level) three changes are
simultaneously happening9 which is improving the female labour proportion in the labour
force. First is a social change, as the generation prior to 1970 moves out of the workforce the
social change is integrating women faster into the formal labour force because of break of
social traditions. Second is, the economic dimension that female contribution leads to higher
economic growth. Third is, more female participation leads to financial sustainability of the
welfare situation of these countries. With respect to the third point governments will be
further propelled to promote female labour participation to reduce the fiscal gap due to rising
welfare expenditures.10
Fixed Effect regression while controlling for the rest of the variables gives the following
output-
flpr Coef. Std. Err. t
P>t
[95% Conf. Interval]
8 World Population Ageing 1950-2050, Population Division, DESA, United Nations; pp 19 The Trend in Female Labour Force Participation: What Can Be Expected for the Future? Marike Knoef, Rob Euwals, Daniel van Vuuren; pp 2610 The Trend in Female Labour Force Participation: What Can Be Expected for the Future? Marike Knoef, Rob Euwals, Daniel van Vuuren; pp 2
pop65
0.468244
9
0.250205
7 1.87 0.065
-0.029876
0.966366
Proportion of female enrolled for secondary education:
Educational attainment helps us to explain the differences in labour force participation among
men and womenxxiv Studies have shown that a key factor driving women’s aspiration to join
the labour force is the sharp increase in girl’s educational attainment in the recent decades.xxv
Logarithm of females employed for tertiary education
The relation between female labour force participation and logarithm of tertiary education is
negative which is counter intuitive. One of the possible explanation for this anomaly is the
concept of discouraged worker effect theory according to which well educated women do not
prefer re-joining the work force once they decide to drop out of work force (Sabarwal et al.,
2010). Another possible explanation could be the “income effect” according to which, higher
levels of education leads to jobs with higher hourly wage rates that enables families to afford
having one parent who works part time.xxvi
flpr Coefficient Standard
Error
t p>t 95% Conf. Interval]
Lower Upper
ltertorg -0.0824 0.0308 -2.68 0.009 -0.1435 -0.0211
Unemployment rate:
As expected, unemployment rate and female labour participation are negatively correlated
with each other. Family related and labour market related constraints can curb women’s
ability to help stabilise family incomes. The perceived need for them to seek employment and
join the labour force may not be so severe if earnings losses of men in their households are
temporary or if out-of-work benefits provided by the government is high. In addition, means-
tested unemployment benefits that reduces once one partner in the household starts earning
also acts like a significant barrier to boosting female unemployment. With the onset of
recession in 2009, gender employment gaps reduced across all OECD countries with the
exception of Israel, Sweden, Poland and Korea. Female employment suffered only marginally
compared to male’s since the impact of recession was predominant in the manufacturing,
trade and construction sectors compared to the services sector where most of the females are
employed (In 2008, one-third of the female working population was employed in service
sector in OECD countries)xxvii and the services sector showed a modest decline during the
same period. Evidence from previous studies on recessions have shown that while men are
more likely to lose jobs at the onset of recession, they are more likely to find jobs once the
economy recovers (Maier 2011). However, the same cannot be said for women since the
phenomena of discouraged worker effect is more predominant and they may not try re-
joining the labour force once the economy recovers (Sabarwal et al., 2010).
flpr Coefficient Standard
Error
t p>t 95% Conf. Interval]
Lower Upper
pop65 -0.2545 0.0946 -2.69 0.009 -0.4429 -0.0661
Gender Wage Gap
A whole gamut of historical, cultural, and social reasons have contributed to gender wage
gap. The key factors put forth by Australian Justice Department arexxviii-
a) Men and Women still work in different areas of workforce, workspace and industries.
Jobs of hospitality, catering, nursing are female dominated and have been traditionally
undervalued.
b) Casual and non-career part time jobs, which provide negligible opportunities for
training, development and career progression find healthy proportion of women
employees.
c) Lack of permanent flexible work timings impact women with dependent children
more than man who shrug off child’s responsibility to the mother.
Gender wage gap a) negatively impacts the morale of female b) decreases the opportunity
cost of rearing children c) reduces the income effect. Thus gender wage gap negatively
impact female labour force participation.
flpr Coef. Std. Err. t P>t [95% Conf. Interval]gendwagga
p-
0.14435 0.067606 -2.14 0.036 -0.27882-
0.00988Impact of various sectors on female labour force participation:
What drives women’s labour force participation?
The growth/stagnancy in female labour force participation can be examined by analyzing the labour supply as well as labour demand effects.
Labour supply effects:
Rising male education and income act as catalysts in lowering the female labour force participation due to the popular income effect; house hold income rises and there is no/less necessity for the women to work to sustain family life
Social stigmas and restrictions can lead a woman to not work in certain blue-collar and menial jobs
However the factors mentioned above applies only to the mean and higher level of education distribution whereas these concerns are overcome by economic distress and the woman has to work
Fertility decline in women leads to higher female participation The growing education attainment level among individuals might not be inclined
towards labour market expansion. There is a possibility that the rising female education might be associated with declining labour market orientation (unobserved)
As an epilogue to the previous point, the focus of female education might be to improve the marriage rather than her labour market prospects
Labour demand effects:
Even if the above mentioned labour supply factors are improved upon labour demand is what sets the lower benchmark for female participation
In particular, say the number of educated women needed in a particular white-collar job (service industries, healthcare and education) declines which will naturally propagate to labour supply as well
Therefore the relative difference in labour supply and demand of these type of jobs at the local level will impact the participation rates
Apart from the supply and demand related effects, the following 3 sectors have an important part in determining the female labour force participation.
Service sector Manufacturing sector Agricultural sector
Hence we would look into these factors in detail
Employment in Services
Service Sector in a particular country and female employment are correlated. The correlation
in our data for 9 OECD countries for 12 years is .7871. Historically, it has been shown by
Rogersonxxix that the correlation for OECD countries is 0.82. Rogerson, in his 2007 paper,
finds that the female employment differential between America and Europe can be attributed
to the prevalence of service industry. A large service sector provides larger opportunities for
women for better employment opportunities both in terms of wages and new job opening.
This effect is a demand driven effect, which attracts more women to be a part of labour force.
The supply driven effect rises from the fact that women having better jobs and higher pay
leads to a bigger demand for services and higher consumption for services.
Hence focusing on expansion in service sector demand and supply would be one way to
address the stagnant FLFP economies. One way to go about it would be to decrease the entry
regulations and increase the financial and social incentives for women.
In our analysis we found Proportion of Employment provided by Services to have a positive
effect on Female Labour Force Participation while controlling for other factors.
flpr Coef. Std. Err. t P>t[95% Conf. Interval]
empser 1.359142 0.265847 5.11 0 0.830382 1.887902
Employment in Agriculture
Agriculture being the biggest employer is a characteristic of African and Asian economies.
They are low income countries, mostly backward and with inadequate access to good quality
education. They do employ large number of women in agriculture sector but it is more on
account of ‘push’ factors rather than the ‘pull’ factors. As the employee proportion of
Agriculture increases it can be hypothesised that women engagement in labour force will
increase. However, what becomes more troublesome is the skewness of amount of work put
in by women via men, and the elongating gender wage gap. In agrarian economies women
tend to children, the elderly, the cattle and other household activities. In addition to this
women are also involved in working at agricultural farms for minimal to no wages. Women
are more likely to be involved in part time and seasonal employment and earn lower wages
than menxxx, thus increasing the gender wage gap. The decreasing dependence on agriculture
to fuel the GDP growth is one of the causes why female participation across GDP per capita
is the famous ‘U Curve’. As share of agriculture decreases and manufacturing picks up GDP
per capita increases which dither women from participating in Agriculture, thus leading to a
reduction in Female Labour Participation. However, once Service sector starts picking up
which has better pay and lesser physical demands women once again re-enter the labour
force.
The Fixed Effect Estimators are given below for employment proportion of agriculture and
how it impacts Female Labour Force Participation.
flpr Coef.Std. Err. t P>t [95% Conf.
Interval]
empagri0.52080
50.6670
9 0.78 0.437 -0.806011.84761
9
Exhibit1
hausman remod femod
(b) (B) (b-B)sqrt(diag(V_b-
V_B))
Remod femodDifferenc
e S.E.
pef -0.04409560.227225
8
-0.271321
4 0.026384
lgdpc -0.0301120.009260
8
-0.039372
8 0.0422746
strict -0.0208073 -0.006465
-0.014342
3 .
pop65 0.24811950.547054
4
-0.298934
9 0.3824955lpoptot 0.00952 1.379112 -1.369592 .
une 0.6986535
-0.339093
6 1.037747 0.1725852
primsec -0.62047670.253005
4 -0.873482 0.3260118
empagri -5.8885840.520804
5 -6.409388 0.5608185empser -1.052758 1.359142 -2.4119 0.1157595
empman -1.967496 1.370422 -3.337918 .
gendwaggap 0.3052527
-0.144349
70.449602
4 0.108446
ltertorg -0.208462
-0.067491
5
-0.140970
6 0.0803217
lagtert1 1.28E-08 8.41E-09 4.43E-09 2.00E-08
fert -0.0167517
-0.015662
8
-0.001088
9 0.0209693
govexp 0.4443363 0.1422340.302102
3 0.1792156
chi2(14) = (b-B)'[(V_b-V_B)^(-1)](b-B) 436.15 Prob>chi2 = 0.0000
REFERENCES
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