Can I calculate the variance in this way? If not, what should I do?
Confirming whether your method correctly calculates the standard error is beyond the scope of the user support team. I can however direct you to the variables that you will need to implement the sampling error formula provided in section 3.9 of the documentation that you shared.
In the equation, e indexes the 32 states of Mexico (GEO1_MX), h indexes the design stratum (MX2020H_EST_D), and i indexes the Primary Sampling Unit (i.e., UPM). These sample design variables are provided by IPUMS International as source variables and can be found by selecting Source Variables from the top of the variable selector page. Source variables have not been harmonized across time; they are sample-specific and the corresponding variable for each quarter of the Mexico ENOE that you plan to analyze needs to be added to your data cart. The correct weight to use for person-level estimates is PERWT and the weight for household-level estimates is HHWT. Weights are referred to as expansion factors (factor de expansiĂłn) in the documentation.
Thank you Ivan, you are very warm-hearted.
Here is an another question : If a person whose age > 14 and EMPSTAT == 0, can I categorize him as NILF (not being in the labor force, such as he is currently serving in the military) ?
In other words, is it correct for me to define people with age < 15 or EMPSTAT == 0 as NILF?
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EMPSTAT = 0 identifies persons who are not-in-universe (i.e., NIU) for the questions regarding employment status, meaning that the survey does not inquire into their employment status. The universe statement for EMPSTAT in the Mexico ENOE labor survey is present persons age 12+, indicating that the survey does not ask for the employment status of children under the age of 12 or for persons who do not reside in the household. Instead, these persons are assigned EMPSTAT = 0. When filtering records to those with AGE => 12 and RESIDENT = 1 (present resident), almost all records (99.9%+) will have a value for EMPSTAT > 0.
The ENOE is a rotating panel survey where each household is sampled five times over the course of five quarters. In the first interview, a household roster is created that lists every person who resided in the household at the time of the initial interview. Records for residents who are then noted as absent from the sampled household during any of the subsequent quarters are preserved in the data (these are the persons with RESIDENT = 2). However, their employment status is not recorded and instead set as NIU. There are a number of reasons someone might leave the household including moving in with a new partner, moving away from their previous partner, or passing away. You can tabulate the reasons any particular person left with the source variable MX2020H_CS_AD_MOT (reason for absence of habitual resident). You can also find the interview number (1-5) with the source variable MX2020H_N_ENT (interview number).
Leaving the household does not mean that the respondent is NILF, but only that they are no longer being sampled as part of the ENOE. You might treat those who left the household for work as being in the labor force and those who moved to study as leaving the labor force (although students can still be working). Aside from these cases and persons who have passed away, it is not possible to discern whether a non-resident is in the labor force or NILF. Additionally, none of the reasons for leaving the household specifically cite military service. I am also unable to find any variables that would identify respondents who are sampled while in military service.
Thank you Ivan, your response was very helpful. 
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Hi Ivan, may I ask you a question? Is there any variable to show whether the person is employed formally or informally in Mexico Census 2020? In Mexico Survey 2020 Q1, it is MX2020H_EMP_PPAL, but I can’t find it in Mexico Census 2020.
There is no variable in the Mexico Labor Force Surveys that distinguishes between formal and informal workers. You might however be able to leverage details about a respondent’s class of worker status (see CLASSWK and its source variable MX2020A_CLASSWK) as well as other variables related to employment benefits such as MX2020A_LEAVEPAY (receives paid leave) and MX2020A_RETIRE (receives SAR retirement savings) to determine yourself whether a respondent is a formal or informal worker.
This International Monetary Fund Working Paper, written in collaboration with the Mexican National Institute of Statistics and Geography (INEGI), provides the following definition of informal workers:
An important feature of Mexican informality is that firms classified as formal employ a substantial share of workers in non-salaried informal contractual relationships. The definition of informal firms in this context includes subsistence agriculture, domestic work, and firms classified as informal by the Mexican National Institute of Statistics and Geography (INEGI) based on reported name, family ownership, and accounting practices. All other firms are classified as formal.
From the worker’s side, following criteria from INEGI, the definition of informal workers includes those at non-agricultural informal firms, self-employed agricultural workers, unpaid workers, non-salaried workers (at both formal and informal firms), and workers without access to social security health services in both formal and informal firms. Workers in non-salaried contractual relationships, including those at formal firms, are therefore included in the informal worker category. None of the workers under this definition have access to Mexican Social Security Institute (IMSS). All other workers are defined as formal.
Dear Ivan, I think this variable is the indicator in the Survey Data: https://international.ipums.org/international-action/variables/MX2020H_0487
However, I think there is not such variable in the Census data so I can’t conduct the simulation by design, which is more convincing than the simulation by model.
Sorry, I meant to say that there is no variable in the Mexico 2020 Census that distinguishes between formal and informal workers! Hope this information is helpful in figuring out how to best pursue your project.
I got it, thank you very much.