National Survey of Household Income and Expenditures (ENIGH)

Description

MexicanHH_foodConsumption is a 10 percent sample of the data described in Beckman et al. (2024)'s Land Competition and Welfare Effects from Mexico's Proposal to Ban Genetically Modified Corn. An overview of the data construction and assessment is further discussed in the study.

Usage

MexicanHH_foodConsumption

Format

## 'MexicanHH_foodConsumption' A data frame with 8,777 rows and 17 columns:

s1

Share of tortilla consumption by household.

s2

Share of cereal consumption by household.

s3

Share of meat consumption by household.

s4

Share of dairy consumption by household.

s5

Share of fruits and vegetables consumption by household.

s6

Share of other consumption by household.

lnp1

Logged price of tortilla.

lnp2

Logged price of cereal.

lnp3

Logged price of meat.

lnp4

Logged price of dairy.

lnp5

Logged price of fruits and vegetables.

lnp6

Logged price of other.

lnw

Logged food expenditure by household .

age

Logged age of head of household.

size

Inverse (1/x) of size of household.

sex

Sex (female == 1) of head of household.

educ

Educational attaintment of head of household.

' @source <https://en.www.inegi.org.mx/programas/enigh/nc/2022/>

Calculate the demand share equations of a AI or QUAI demand system, including demographic variable.

Description

Calculate the demand share equations of a AI or QUAI demand system, including demographic variable.

Usage

aidsCalculate(
  Prices = matrix(),
  Budget = matrix(),
  ShareNames = NULL,
  Demographics = matrix(),
  DemographicNames = NULL,
  Params = matrix(),
  quaids = FALSE
)

Arguments

Value

A matrix of estimated shares with (nxm) dimensions where n is the number of observations and m the number of shares.

Examples

## Not run: 
testing_data <- censoredAIDS::MexicanHH_foodConsumption

# Organizing the data for comfort
s1 <- testing_data$s1
s2 <- testing_data$s2
s3 <- testing_data$s3
s4 <- testing_data$s4
s5 <- testing_data$s5
s6 <- testing_data$s6

lnp1 <- testing_data$lnp1
lnp2 <- testing_data$lnp2
lnp3 <- testing_data$lnp3
lnp4 <- testing_data$lnp4
lnp5 <- testing_data$lnp5
lnp6 <- testing_data$lnp6

age <- testing_data$age
size <- testing_data$size
sex <- testing_data$sex
educ <- testing_data$educ

# Alpha
b0 <- rep(0, 5)

# Beta
b0 <- c(b0, rep(0.003, 5))

# Gamma
b0 <- c(b0,0.01,0,0.01,0,0, 0.01,0,0,0,0.01,0,0,0,0,0.01)

# Demos
b0 <- c(b0,rep(0.002, 20))

# Sigma
b0 <- c(b0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,1)

li1 <- censoredaidsLoglike(
  Params = b0,
  Shares = matrix(c(s1, s2, s3, s4, s5, s6), ncol = 6),
  Prices = matrix(c(lnp1, lnp2, lnp3, lnp4, lnp5, lnp6), ncol = 6),
  Budget = matrix(testing_data$lnw),
  Demographics = matrix(c(age, size, educ, sex), ncol = 4),
  quaids = FALSE
)


## End(Not run)

Numerical approximation to censored AI or QUAI demand system elasticities, including demographic variable.

Description

Numerical approximation to censored AI or QUAI demand system elasticities, including demographic variable.

Usage

censoredElasticity(
  Prices = matrix(),
  Budget = matrix(),
  ShareNames = NULL,
  Demographics = matrix(),
  DemographicNames = NULL,
  Params = matrix(),
  quaids = FALSE,
  vcov = matrix(),
  func,
  ...
)

Arguments

Value

A list containing a matrix of price and income elasticities, a matrix with their respective standard errors approximated using the delta method, and a matrix of expected shares that can serve to run further post-estimation analyses.

Examples

## Not run: 

testing_data <- censoredAIDS::MexicanHH_foodConsumption

# Organizing the data for comfort
s1 <- testing_data$s1
s2 <- testing_data$s2
s3 <- testing_data$s3
s4 <- testing_data$s4
s5 <- testing_data$s5
s6 <- testing_data$s6

lnp1 <- testing_data$lnp1
lnp2 <- testing_data$lnp2
lnp3 <- testing_data$lnp3
lnp4 <- testing_data$lnp4
lnp5 <- testing_data$lnp5
lnp6 <- testing_data$lnp6

age <- testing_data$age
size <- testing_data$size
sex <- testing_data$sex
educ <- testing_data$educ

# Alpha
b0 <- rep(0, 5)

# Beta
b0 <- c(b0, rep(0.003, 5))

# Gamma
b0 <- c(b0,0.01,0,0.01,0,0, 0.01,0,0,0,0.01,0,0,0,0,0.01)

# Demos
b0 <- c(b0,rep(0.002, 20))

# Sigma
b0 <- c(b0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,1)

vcov <- matrix(0, nrow = length(b0), ncol = length(b0))
vcov[upper.tri(vcov, diag = TRUE)] <- runif(.5*length(b0)*(1+length(b0)))
vcov <- t(vcov) %*% vcov

list_etas <- censoredElasticity(
Params = b0,
Shares = matrix(c(s1, s2, s3, s4, s5, s6), ncol = 6),
Prices = matrix(c(lnp1, lnp2, lnp3, lnp4, lnp5, lnp6), ncol = 6),
Budget = matrix(testing_data$lnw),
Demographics = matrix(c(age, size, educ, sex), ncol = 4),
quaids = FALSE,
func = mean,
na.rm = TRUE,
vcov = vcov
)

## End(Not run)

Calculates the truncated (censored) log-likehood function of a share system of equations of a AI or QUAI demand system.

Description

Calculates the truncated (censored) log-likehood function of a share system of equations of a AI or QUAI demand system.

Usage

censoredaidsLoglike(
  Shares = matrix(),
  Prices = matrix(),
  Budget = matrix(),
  ShareNames = NULL,
  Demographics = matrix(),
  DemographicNames = NULL,
  Params = matrix(),
  quaids = FALSE
)

Arguments

Value

A numeric vector for individual loglikehood contributions with dimensions (nx1) where n is the number of observations.

Examples

## Not run: 
testing_data <- censoredAIDS::MexicanHH_foodConsumption

# Organizing the data for comfort
s1 <- testing_data$s1
s2 <- testing_data$s2
s3 <- testing_data$s3
s4 <- testing_data$s4
s5 <- testing_data$s5
s6 <- testing_data$s6

lnp1 <- testing_data$lnp1
lnp2 <- testing_data$lnp2
lnp3 <- testing_data$lnp3
lnp4 <- testing_data$lnp4
lnp5 <- testing_data$lnp5
lnp6 <- testing_data$lnp6

age <- testing_data$age
size <- testing_data$size
sex <- testing_data$sex
educ <- testing_data$educ

# Alpha
b0 <- rep(0, 5)

# Beta
b0 <- c(b0, rep(0.003, 5))

# Gamma
b0 <- c(b0,0.01,0,0.01,0,0, 0.01,0,0,0,0.01,0,0,0,0,0.01)

# Demos
b0 <- c(b0,rep(0.002, 20))

# Sigma
b0 <- c(b0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,1)

  f <- censoredaidsLoglike(
Params = b0,
Shares = matrix(c(s1, s2, s3, s4, s5, s6), ncol = 6),
Prices = matrix(c(lnp1, lnp2, lnp3, lnp4, lnp5, lnp6), ncol = 6),
Budget = matrix(testing_data$lnw),
Demographics = matrix(c(age, size, educ, sex), ncol = 4),
quaids = FALSE
)

## End(Not run)

Calculate the demand share equations of a AI or QUAI demand system, including demographic variable, using point estimates (e.g., mean, median, percentiles).

Description

Calculate the demand share equations of a AI or QUAI demand system, including demographic variable, using point estimates (e.g., mean, median, percentiles).

Usage

muaidsCalculate(
  muPrices = numeric(),
  muBudget = numeric(),
  muDemographics = numeric(),
  Params = matrix(),
  quaids = FALSE,
  m = numeric(),
  t = numeric(),
  dems = FALSE
)

Arguments

Value

A matrix of estimated shares with (1xm) dimensions where n is the number of observations and m the number of shares.