# Generating a DREAM sample dataset

It is documented how the DREAM sample dataset is generated. Note that it only vagly is similar to the original.

The idea is to have a dataset of a number of persons of different age and gender followed weekly over a long period.

At age 65 these people retire. Before age 65 they can be in four states:

- Working, a non-sticky state
- On early retirement, a sticky state until age 65
- On benefits, a non-sticky state
- Died, the final sticky state

After age 65 the states are Retired or Died.

Sticky states are such that once reached the state is not left before death (also a sticky state).

# Initialisation

We follow 10000 persons (*R*) from the first week (week 32 in 1991) in the DREAM
database to the last week in 2017 (*lstwk*).

Note that for simplicity we use the builtin (American) week notation.

The european notation operates with week 53 in some years.

The generation is mainly done in Mata.

```
mata {
frstwk = 31*53 // 1991w32
lstwk = 58*52-1 // 2017w52
wknames = "y" :+ strofreal(frstwk..lstwk, "%tw")
//select(wknames', regexm(wknames', "53$"))
C = cols(wknames)
rseed(123)
R = 10000
}
```

# Generating age and gender

By design the males are 5 years older on average than the females, on average age 40 versus age 35.

```
mata {
id = 1::R
male = rbinomial(R, 1, 1, 0.48)
age = rpoisson(R, 1, 15) :* !male + rpoisson(R, 1, 20) :* male :+ 20
}
```

# Generating and age and gender dependent score and setting pension at 65

It would be expected that with higher age the lower is the probability of being working.

Further, males are modelled to have a higher score than females.

The score is saved in the variable *scr*. Retirement week is saved in the
variable *retired_at*.

```
mata {
scr = J(R, C, .)
retired_at = J(R,1,.)
for(r=1;r<=R;r++) for(c=1;c<=C;c++) {
curr_age = age[r] + trunc(c / 52) // age at start plus time in years
scr[r,c] = male[r] + curr_age / 10
if ( curr_age >= 65 & retired_at[r] == .) retired_at[r] = c
}
}
```

# Setting working life states

The score is added a normal random component with mean zero and a standard deviation of 5.

The state is saved in the variable *state* with values:

- Working(0)
- On benefits(1) if the score is above 0.65
- Early retirement(2) with probability equal to score divided by 1500.

To ease calculations the function *std_matrix01* is created.

```
real matrix std_matrix01(real matrix M)
{
real scalar level, variation
level = min(M)
variation = max(M) - min(M)
return((M :- level) :/ variation)
}
```

It turns a matrix of scores into a matrix of scores between zero and one.

```
mata {
scr = std_matrix01(scr + rnormal(R,C,0,5) )
state = (scr :> 0.65) + rbinomial(1,1,1, (scr :/ 1500))
}
```

# Early retirement and pension

Early retirement must happen before pension.

First find the first week of early retirement.
A matrix of R rows of values one to C *(J(R,1,1..C)* is divided with a
zero/one variable *(state :== 2)*.
The result is either a number between one and C or missing (.).

```
mata: early_retirement_at = rowmin( (J(R,1,1..C) :/ (state :== 2), J(R, 1, .)) )
```

For each person the weeks after first week with early retirement is set to early retirement.

```
mata {
for(r=1;r<=R;r++) {
if ( early_retirement_at[r] < . ) {
state[r, early_retirement_at[r]..C] = J(1, C - early_retirement_at[r] + 1, 2)
}
}
}
```

Likewise for pension. And since pension is set after early retirement there is no one on early retirement after they have reached the pension age of 65.

```
mata {
for(r=1;r<=R;r++) {
if ( retired_at[r] < . ) {
state[r, retired_at[r]..C] = J(1, C - retired_at[r] + 1, 4)
}
}
}
```

A score of death (*diedscr*) is created from the age and gender score (*scr*)
added some random noise with standard deviation of 15.

```
mata: diedscr = std_matrix01(scr + rnormal(R,C,0,15) )
```

The week of ("first") death is found.

```
mata: died_at = rowmin( (J(R,1,1..C) :/ (diedscr :>= 0.85), J(R, 1, .)) )
```

Finally, every week after a death is set to the state Died.

```
mata {
for(r=1;r<=R;r++) {
if ( died_at[r] < . ) {
state[r, died_at[r]..C] = J(1, C - died_at[r] + 1, 3)
}
}
}
```

These variables are inserted into a dataset.

```
mata {
nhb_sae_addvars(("id", "male", "age"), (id, male, age))
nhb_sae_addvars(wknames, state)
}
```

Labels etc are added.

```
label define state 0 "Working" 1 "On benefits" 2 "Early retirement" 3 "Died" 4 "Retired"
label values y* state
label data "DREAM like dataset"
label variable male "Gender"
label define male 0 "Female" 1 "Male"
label values male male
label variable age "Age at week 32, 1991 (Years)"
label variable id "Id"
notes: Registrations (Working/On benefits/Early retirement/Died/Retired) each week for 10000 participants from start (week 32, 1991) until week 52, 2017
save dream, replace
```

# Validation at start and at end

The distribution of states for the first and last week are shown below

```
crossmat y1991w32 y2017w52
```

y2017w52 | |||||||
---|---|---|---|---|---|---|---|

Working | On benefits | Early retirement | Died | Retired | Total | ||

y1991w32 | Working | 4498 | 452 | 195 | 1180 | 3202 | 9527 |

On benefits | 181 | 19 | 9 | 56 | 206 | 471 | |

Died | 0 | 0 | 0 | 2 | 0 | 2 | |

Total | 4679 | 471 | 204 | 1238 | 3408 | 10000 |

Last update: 2018-12-11, Stata version 15.1