# 2.3 Sample selectivity bias  (Page 6/6)

 Page 6 / 6
 Observation As calculated from probit estimate As reported by the Heckman two-step 1 1.2821240 1.2821240 2 0.9313837 0.9313837 3 1.1269680 1.1269680 4 0.9079438 0.9079438 5 0.5900134 0.5900134 6 0.4652062 0.4652061 7 0.2974918 0.2974918 8 0.5300468 0.5300469 9 0.7864666 0.7864666 10 0.6024283 0.6024283

## Exercise

We are interested in understanding the decision of married Portugese women to enter the labor force. We have available data from Portugal. The data set is a sample from Portuguese Employment Survey, from the interview year 1991, and has been provided by the Portuguese National Institute of Statistics (INE). The data are in the Excel file Martins. This file is organized in the following way. There are seven columns, corresponding to seven variables, and 2,339 observations.

a) Estimate the following equation using OLS: $Wages=f\left(age,ag{e}^{2},education\right)$ using the observations for women actually working.

b) What is the potential source of selection bias?

c) Estimate a wage equation for the Portuguese data three ways: (1) using OLS, (2) using the Heckman two-step method, and (3) using the ML method. Report all three estimates in a single table. For consistency, we will assume that the appropriate explanatory variables for wages are (1) age, (2) the square of age, and (3) the years of education. Further, assume that women do not enter the labor force because (1) presence of children under the age of 3, (2) presence of children between 3 and 18, (3) husband's wage level, (4) the level of education of the woman, and (5) the age of the woman.

## Appendix a.

 z 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.0 0 0.004 0.008 0.012 0.016 0.0199 0.0239 0.0279 0.0319 0.0359 0.1 0.0398 0.0438 0.0478 0.0517 0.0557 0.0596 0.0636 0.0675 0.0714 0.0753 0.2 0.0793 0.0832 0.0871 0.091 0.0948 0.0987 0.1026 0.1064 0.1103 0.1141 0.3 0.1179 0.1217 0.1255 0.1293 0.1331 0.1368 0.1406 0.1443 0.148 0.1517 0.4 0.1554 0.1591 0.1628 0.1664 0.17 0.1736 0.1772 0.1808 0.1844 0.1879 0.5 0.1915 0.195 0.1985 0.2019 0.2054 0.2088 0.2123 0.2157 0.219 0.2224 0.6 0.2257 0.2291 0.2324 0.2357 0.2389 0.2422 0.2454 0.2486 0.2517 0.2549 0.7 0.258 0.2611 0.2642 0.2673 0.2704 0.2734 0.2764 0.2794 0.2823 0.2852 0.8 0.2881 0.291 0.2939 0.2967 0.2995 0.3023 0.3051 0.3078 0.3106 0.3133 0.9 0.3159 0.3186 0.3212 0.3238 0.3264 0.3289 0.3315 0.334 0.3365 0.3389 1.0 0.3413 0.3438 0.3461 0.3485 0.3508 0.3531 0.3554 0.3577 0.3599 0.3621 1.1 0.3643 0.3665 0.3686 0.3708 0.3729 0.3749 0.377 0.379 0.381 0.383 1.2 0.3849 0.3869 0.3888 0.3907 0.3925 0.3944 0.3962 0.398 0.3997 0.4015 1.3 0.4032 0.4049 0.4066 0.4082 0.4099 0.4115 0.4131 0.4147 0.4162 0.4177 1.4 0.4192 0.4207 0.4222 0.4236 0.4251 0.4265 0.4279 0.4292 0.4306 0.4319 1.5 0.4332 0.4345 0.4357 0.437 0.4382 0.4394 0.4406 0.4418 0.4429 0.4441 1.6 0.4452 0.4463 0.4474 0.4484 0.4495 0.4505 0.4515 0.4525 0.4535 0.4545 1.7 0.4554 0.4564 0.4573 0.4582 0.4591 0.4599 0.4608 0.4616 0.4625 0.4633 1.8 0.4641 0.4649 0.4656 0.4664 0.4671 0.4678 0.4686 0.4693 0.4699 0.4706 1.9 0.4713 0.4719 0.4726 0.4732 0.4738 0.4744 0.475 0.4756 0.4761 0.4767 2.0 0.4772 0.4778 0.4783 0.4788 0.4793 0.4798 0.4803 0.4808 0.4812 0.4817 2.1 0.4821 0.4826 0.483 0.4834 0.4838 0.4842 0.4846 0.485 0.4854 0.4857 2.2 0.4861 0.4864 0.4868 0.4871 0.4875 0.4878 0.4881 0.4884 0.4887 0.489 2.3 0.4893 0.4896 0.4898 0.4901 0.4904 0.4906 0.4909 0.4911 0.4913 0.4916 2.4 0.4918 0.492 0.4922 0.4925 0.4927 0.4929 0.4931 0.4932 0.4934 0.4936 2.5 0.4938 0.494 0.4941 0.4943 0.4945 0.4946 0.4948 0.4949 0.4951 0.4952 2.6 0.4953 0.4955 0.4956 0.4957 0.4959 0.496 0.4961 0.4962 0.4963 0.4964 2.7 0.4965 0.4966 0.4967 0.4968 0.4969 0.497 0.4971 0.4972 0.4973 0.4974 2.8 0.4974 0.4975 0.4976 0.4977 0.4977 0.4978 0.4979 0.4979 0.498 0.4981 2.9 0.4981 0.4982 0.4982 0.4983 0.4984 0.4984 0.4985 0.4985 0.4986 0.4986 3.0 0.4987 0.4987 0.4987 0.4988 0.4988 0.4989 0.4989 0.4989 0.499 0.499

## References

Bourguignon, François, Martin Fournier, and Marc Gurgand (2007). Selection Bias Corrections Based on the Multinomial Logit Model: Monte Carlo Comparisons. Journal of Economic Surveys 21 (1): 174-205.

Chiburis, Richard and Michael Lokshin (2007). Maximum Likelihood and Two-Step Estimation of an Ordered-Probit Selection Model. The Stata Journal 7 (2): 167-182.

Dahl, G. B. (2002). Mobility and the Returns to Education: Testing a Roy Model with Multiple Markets. Econometrica 70 (6): 2367-2420.

Dubin, Jeffrey A. and Douglas Rivers (1989). Selection Bias in Linear Regression, Logit and Probit Models. Sociological Methods and Research 18 (2&3): 360-390.

Heckman, James (1974). Shadow Prices, Market Wages and Labor Supply. Econometrica 42 (4):679-694.

Heckman, James (1976) “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models,” The Annals of Economic and Social Measurement 5 : 475-492.

Heckman, James (1979). Sample Selection Bias as a Specification Error. Econometrica 47 (1): 153-161.

Jimenez, Emanuel and Bernardo Kugler (1987). The Earnings Impact of Training Duration in a Developing Country: An Ordered Probit Model of Colombia's Servicio Nacional de Aprendizaje (SENA). Journal of Human Resources 22 (2): 230-233.

Lee, Lung-Fei (1983). Generalized Econometric Models with Selectivity. Econometrica 51 (2): 507-512.

McFadden, Daniel L. (1973). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka Frontiers in Econometrics (New York: Academic Press).

Newey, W. K. and Daniel L. McFadden (1994). Large Sample Estimation and Hypothesis Testing. In R. F. Engle and D. L. McFadden (eds.) Handbook of Econometrics (Amsterdam: North Holland).

Schmertmann, Carl P. (1994). Selectivity Bias Correction Methods in Polychotomous Sample Selection Models. Journal of Econometrics 60 (1): 101-132.

Vella, Francis (1998). Estimating Models with Sample Selection Bias: A Survey. The Journal of Human Resources 33 (1):127-169.

so some one know about replacing silicon atom with phosphorous in semiconductors device?
how to fabricate graphene ink ?
for screen printed electrodes ?
SUYASH
What is lattice structure?
of graphene you mean?
Ebrahim
or in general
Ebrahim
in general
s.
Graphene has a hexagonal structure
tahir
On having this app for quite a bit time, Haven't realised there's a chat room in it.
Cied
what is biological synthesis of nanoparticles
what's the easiest and fastest way to the synthesize AgNP?
China
Cied
types of nano material
I start with an easy one. carbon nanotubes woven into a long filament like a string
Porter
many many of nanotubes
Porter
what is the k.e before it land
Yasmin
what is the function of carbon nanotubes?
Cesar
I'm interested in nanotube
Uday
what is nanomaterials​ and their applications of sensors.
what is nano technology
what is system testing?
preparation of nanomaterial
Yes, Nanotechnology has a very fast field of applications and their is always something new to do with it...
what is system testing
what is the application of nanotechnology?
Stotaw
In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
Azam
anybody can imagine what will be happen after 100 years from now in nano tech world
Prasenjit
after 100 year this will be not nanotechnology maybe this technology name will be change . maybe aftet 100 year . we work on electron lable practically about its properties and behaviour by the different instruments
Azam
name doesn't matter , whatever it will be change... I'm taking about effect on circumstances of the microscopic world
Prasenjit
how hard could it be to apply nanotechnology against viral infections such HIV or Ebola?
Damian
silver nanoparticles could handle the job?
Damian
not now but maybe in future only AgNP maybe any other nanomaterials
Azam
Hello
Uday
I'm interested in Nanotube
Uday
this technology will not going on for the long time , so I'm thinking about femtotechnology 10^-15
Prasenjit
can nanotechnology change the direction of the face of the world
At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
the Beer law works very well for dilute solutions but fails for very high concentrations. why?
how did you get the value of 2000N.What calculations are needed to arrive at it
Privacy Information Security Software Version 1.1a
Good
Got questions? Join the online conversation and get instant answers!