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1. If people have no earnings, then you could code their earnings as 0
and include them in the sample.
2. If people have no earnings, then they don't belong in your main sample.
The choice is yours, depending on your research question.
Either way, you have now got good specific advice from Maarten and
others which gives you scope for experiment. Either way, this is not
coming across to me as a logit or probit problem at all.
On Wed, Dec 14, 2011 at 10:16 AM, Muhammad Anees <email@example.com> wrote:
> There are actually two variables describing earnings, one as a
> dichomous (recorded in Yes or No) and and other as groups or interval.
> The objective is to assess if such earnings from secondardy jobs also
> depend on foreign qualification and if yes, then what is difference
> between the regressions across the two groups based on foreign
> qualification or no qualification.
> On Wed, Dec 14, 2011 at 3:03 PM, Nick Cox <firstname.lastname@example.org> wrote:
>> Thanks for the example.
>> I see earnings as a coarsely categorised variable, fit for -intreg- or
>> -ologit-. In what sense is earnings dichotomous (means, has two
>> categories)? Why did you say you were interested in logit models?
>> On Wed, Dec 14, 2011 at 9:48 AM, Muhammad Anees <email@example.com> wrote:
>>> Thanks Nick for your suggestion.
>>> Sure, the data looks like which contains two different sample but
>>> related. Foreign qualified and no foreign qualification are two
>>> different dataset earch with the following sample data, only a sample,
>>> actually the data consist two different samples in two different
>>> files, which I can deal how to combine in stata for my purpose.
>>> ear exper gender subject area language
>>> 0-5000 20 m IT rural Urdu
>>> 5000-10000 22 m ENGINEER urban English
>>> 10001-15000 15 f ECONOMICS rural Urdu
>>> 5000-10000 10 m HR urban Urdu
>>> 5000-10000 5 f STRT MGT urban English
>>> 10001-15000 8 f MARK urbna English
>>> 0-5000 9 m SOCIOLOGY rural Urdu
>>> 0-5000 17 m IT urban Urdu
>>> On Wed, Dec 14, 2011 at 2:39 PM, Nick Cox <firstname.lastname@example.org> wrote:
>>>> It's categorical/dichotomous, yet the example is [pro]portion of
>>>> earnings from outside main job. Sounds like a fractional response from
>>>> the latter. Muhammad: Give us an example of what observations look
>>>> like before this gets any more obscure, please!
>>>> On Wed, Dec 14, 2011 at 9:18 AM, Maarten Buis <email@example.com> wrote:
>>>>> On Wed, Dec 14, 2011 at 6:25 AM, Muhammad Anees wrote:
>>>>>> Sorry for not clarifying the story about the types of variables, like
>>>>>> earnings which I have at hand as a categorical/dichotomous variable.
>>>>>> For example if an individual has a portion of earnings from doing
>>>>>> consultancies or involved in any R&D organizations beside their normal
>>>>>> routine jobs. In this case, I was interested in comparing the
>>>>>> regression models (across foreign qualified and not foreign qualified)
>>>>>> of earnings on other predictors say experience, research training, job
>>>>>> nature, industry, region (rural and urban) using logit/probit in case
>>>>>> of categorical variables and similarly using OLS for continuous
>>>>>> dependent variable which at least I do not have at this stage.
>>>>> This is still not clear. The independent/explanatory/right-hand-side/x
>>>>> variables aren't relevant here, they can be of any type, it is the
>>>>> type of the dependent/explained/left-hand-side/y variable that
>>>>> matters. Earnings is typically collected as either a continuous
>>>>> variable (how much do you earn?) or as a choice from a set of
>>>>> intervals (did you earn less than x$, between x$ and y$, etc.?). None
>>>>> of these are correctly modeled as a logit/probit. In the former case I
>>>>> would use a -glm- with the -link(log)- option, in the latter case I
>>>>> would start with assigning each category with a reasonable
>>>>> representative number and than use -glm- with the -link(log)- option.
>>>>> There are other solutions for the latter problem, e.g. -intreg-, but
>>>>> if the underlying distribution is non-normal, which is likely to be
>>>>> the case with earnings, then it is unclear whether these alternatives
>>>>> are any better. The comparison is than just a matter of adding the
>>>>> appropriate dummies and/or interactions.
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