Fixed effects and panel data

[Aisha Khan]

I have data for crop production for three time periods for around 2000 households. The data is collected for agricultural plots. these plots are nested within households (as some households cultivate more than one agricultural plot), the hosueholds are located within different agro-ecological which are in turn located within different divisions.

Over the three time periods all households do not cultivate the same plots. for example it is possible that in round 1 a household may cultivate three plots but in round 2 it may cultivate only 2 plots.
I for a panel data by defining a variable using households and plot

egen Panel_var=group(Household Plot)

I run a fixed effects model where the dependent variable is Crop Yield and the main independent variable is a dummy of whether the household acquired Credit. I also control for several plot, and household characteristics.
I include Round fixed effects and agro-ecological fixed effects.

I am having difficulty interpreting the plot fixed effects and the agro-ecological fixed effects.
If i run a fixed effects model (which controls for plot fixed effects) then how can it also allow me to include agro-ecological fixed effects as the former is nested in the latter?

[Clyde Schechter]

You are right, with plot nested in agro-ecological, you should not be able to estimate agro-ecological effects. But you don't show the output, nor the code which generated it, so it is hard to know what to tell you. If you used -xtreg, fe-, then you would have had to specify the agro_ecological indicators as covariates in the model, and they would be explicitly eliminated. If this is what you did, but they were not eliminated, then the logical conclusion is that there is an error in your data. If this is your situation then you should find the offending observations:

by panel_var (agro_ecological), sort: gen byte problem = agro_ecological[1] != agro_ecological[_N]
browse if panel_var

and then figure out how to fix the data errors.

On the other hand, if you used -reghdfe- and absorbed both your panel var and agro_ecological, then you will not get any explicit warning about colinearity. Rather, if you look at the table of absorbed degrees of freedom at the end of the output, you will see that all of the categories for agro_ecological are redundant and 0 coefficients have been assigned to it.

[Aisha Khan]

Hi Clyde Schechter. Thank you for your answer.
This is my data

clear
input double(Round Household Plot) long Agro_Eco_zones float(Yields Credit_Accessed)
1   1                  3  7 4118.3335 0
2   1                3.1  7    6177.5 1
3   1                  5  7  8086.909 0
3   2                  2  7  9565.162 0
1   3                  2  7 66.526924 1
2   3                2.1  7  6816.552 1
1   3                  3  7  4612.534 1
1   4                  5  7  2671.351 0
3   4                  7  7  6678.378 0
1   5                  2  7 1317.8667 0
3   5                  2  7      7413 0
2   5                2.1  7  1372.778 1
2   5                3.1  7  4612.533 1
1   6                  2  7  4612.534 0
1   9                  2  7  4561.846 0
1  10                  3  7 1560.6316 0
2  12                  4  7   3162.88 0
3  12                  5  7  5814.118 0
1  13                  2  7  4612.534 0
1  14                  7  7      2471 1
3  14                  7  7    5683.3 1
2  14                7.1  7 4706.6665 1
1  14                  8  7 1900.7693 1
3  14                  8  7    5683.3 1
2  14                8.1  7      3530 1
1  14                  9  7  4612.534 1
2  14                9.1  7 4667.4443 1
1  14                 10  7    1976.8 1
2  14               10.1  7      4942 1
1  14                 11  7 1900.7693 1
2  14               11.1  7      3530 1
2  14               12.1  7      4942 1
1  15                  3  7 2635.7334 0
3  15                  3  7  8086.909 0
2  15                3.1  7    5930.4 0
2  15                  4  7 2196.4446 0
2  15                  5  7  2534.359 0
1  16                  3  7  4612.534 1
3  16                 10  7  5702.308 1
2  16               10.1  7 4612.5337 1
3  17                  2  7  5068.718 0
3  18                  4  7  9503.847 0
2  18                4.1  7  3421.385 1
2  18                6.1  7      4942 1
1  20                  2  7  285.1154 0
2  20                2.1  7    4800.8 0
1  66                  3 16    3953.6 0
1  66                  8 16 2396.1213 0
1  66                  9 16 4118.3335 0
1  69                  2 16    3953.6 0
2  69                  2 16  3294.667 0
3  69                  2 16    3459.4 1
1  69                  3 16  2882.833 0
2  69                  3 16  3294.667 0
3  69                  3 16    3459.4 1
1  69                  4 16 1647.3333 0
1  69                  5 16 1647.3333 0
1  69                  6 16 3294.6665 0
1  69                  7 16 1647.3333 0
1  69                  8 16 1921.8888 0
1  69                  9 16 1647.3333 0
3  69                 11 16 3760.2175 1
3  69                 12 16 3928.2566 1
3  69                 13 16 3552.0625 1
3  69                 14 16  3474.844 1
2  72                  2 16 3743.9395 1
1  74                  2 16    3706.5 1
2  74                  2 16    6177.5 0
3  74                  2 16   4677.25 1
1  74                  4 16 2995.1516 1
1  74                  5 16  3624.133 1
2  74                  5 16  3624.133 0
3  74                  5 16      4942 1
2  74                  6 16   6375.18 0
3  74                  7 16  4867.121 1
3  74                  8 16 4792.2427 1
3  74                  9 16 4492.7275 1
3  74                 10 16  5042.857 1
2  78                  2 16  4492.727 0
2  78                  3 16 4492.7275 0
2  78                  4 16 4492.7275 0
2  78                  5 16 4118.3335 0
1  80                  2 16      2824 1
2  80                  2 16    4518.4 1
3  80                  2 16    2965.2 1
1  80                  3 16  3294.667 1
2  80                  3 16 4521.9297 1
3  80                  3 16    2965.2 1
1  80                  5 16  3144.909 1
1  80                  6 16 3743.9395 1
3  80                  7 16 2995.1516 1
3  80                  8 16 2995.1516 1
3  80                  9 16 2995.1516 1
3  80                 10 16  2779.875 1
3  80                 11 16    2718.1 1
3  80                 12 16 2995.1516 1
3  80                 13 16 2995.1516 1
3  80                 14 16  4633.125 1
3  80                 15 16 3743.9395 1
3  80                 16 16 3743.9395 1
1  94                  2  1    3706.5 0
2  94                  2  1   8030.75 0
3  94                  2  1    3706.5 0
1  96                  2  1    1976.8 0
1  97                  3  1      2471 0
1 102                  2 16 1647.3334 0
2 102                  2 16 4118.3335 0
1 105                  4 16  4005.079 0
2 105                  4 16  3277.507 1
3 105                  4 16 3294.6665 0
1 105                  5 16 4000.6665 0
2 105                  5 16 3294.6665 1
3 105                  5 16 3294.6665 0
1 105                  6 16 4145.7886 0
2 105                  6 16 3226.0276 1
3 105                  6 16 3294.6665 0
1 105                  8 16 4390.1436 0
2 105                  8 16  3294.667 1
3 105                  8 16  3294.667 0
1 105                  9 16   4003.02 0
2 105                  9 16  3294.667 1
3 105                  9 16  3294.667 0
1 105                 10 16   4003.02 0
2 105                 10 16  3294.667 1
3 105                 10 16  3294.667 0
1 105                 11 16   3582.95 0
2 105                 11 16  3157.389 1
3 105                 11 16 3294.6665 0
2 105                 13 16    3212.3 1
3 105                 13 16  3294.667 0
3 105                 14 16  3294.667 0
3 105                 15 16  3294.667 0
3 105                 16 16   3088.75 0
1 106                  3 16  2977.555 0
2 106                  3 16  3274.075 0
3 106                  3 16    2965.2 0
1 106                  4 16  2952.845 0
1 106                  5 16  2977.555 0
2 106                  6 16  3171.117 0
3 106                  7 16 2196.4443 0
3 106                  8 16  3059.333 0
1 108                  2 16  3294.667 0
1 108                  3 16  3294.667 0
1 108                  4 16  3294.667 0
1 108                  5 16  3294.667 0
1 108                  6 16  5216.555 0
2 108                  8 16   3409.98 0
1 109                  2 16   3088.75 0
1 109                  3 16    2965.2 0
1 109                  4 16  3020.111 0
1 109                  5 16  3294.667 0
1 109                  6 16  3294.667 0
1 111                  2 16 3294.6665 1
2 111                  2 16 3061.2944 1
3 111                  2 16      2471 1
1 111 2.0999999046325684 16 1647.3333 1
1 111  2.200000047683716 16 1647.3333 1
1 111                  3 16  3294.667 1
2 111                  3 16  2886.952 1
1 111 3.0999999046325684 16    1976.8 1
1 111  3.200000047683716 16 1647.3334 1
2 111                  4 16  3151.691 1
2 111                4.1 16  2988.511 1
2 111                4.2 16 2620.1924 1
2 111                  5 16  2882.833 1
3 111                  5 16 2141.5334 1
3 111                  6 16  3397.625 1
2 113                  3 16 4200.6997 0
2 113                  4 16  4204.444 0
3 113                  4 16  3594.182 0
2 113                  5 16  4204.444 0
2 113                  6 16    4200.7 0
2 113                  7 16    4200.7 0
2 113                  8 16    4200.7 0
2 113                  9 16    4200.7 0
2 113                 10 16    4200.7 0
3 113                 10 16   4324.25 0
2 113                 11 16    4200.7 0
2 113                 12 16    4200.7 0
2 113                 13 16    4200.7 0
2 113                 14 16    4200.7 0
2 113                 15 16 4241.8833 0
2 113                 16 16    4200.7 0
2 113                 17 16 1776.0313 0
3 114                  3 16 4667.4443 0
3 116                  3 16  3129.933 0
3 116                  4 16 3634.7615 0
3 116                  5 16  3129.933 0
3 116                  6 16  3129.933 0
3 116                  7 16   3755.92 0
1 117                  4 16 2635.7334 0
1 117                  5 16  2631.615 0
1 117                  6 16  2631.615 0
3 117                  6 16    6177.5 0
1 117                  7 16 2635.7334 0
3 117                  7 16    6177.5 0
1 117                  8 16   3830.05 0
3 117                  8 16    6177.5 0
3 117                  9 16    6177.5 0
3 118                  3 16 2316.5625 0
3 118                  4 16 2196.4443 0
3 118                  5 16 1830.3704 0
1 118                  6 16 2414.8408 0
2 118                  6 16  3223.532 0
1 142                  2 16 1317.8667 0
1 142                  3 16 1043.9976 0
1 142                  4 16 1550.5525 0
1 142                  5 16 1550.5525 0
2 142                  6 16      2471 0
2 142                  7 16      2471 0
2 142                  8 16  2882.833 0
2 142                  9 16      2471 0
2 142                 10 16    1976.8 0
3 142                 10 16      2471 0
2 142                 11 16  2882.833 0
3 142                 12 16      2471 0
3 142                 13 16    1482.6 0
1 143                  4 16    1976.8 0
2 143                  4 16    2541.6 0
3 143                  4 16      2471 0
1 143                  5 16    1729.7 0
2 143                  5 16      2471 0
3 143                  5 16   3088.75 0
2 146                  3 16   1853.25 0
2 146                  4 16   1853.25 0
3 146                  4 16   1359.05 0
3 146                  5 16 1647.3334 0
3 146                  6 16 1647.3334 0
3 146                  7 16 1647.3334 0
1 147                  3 16  2409.225 0
1 147                  4 16 1647.3334 0
2 147                  5 16      2471 0
3 147                  5 16    1482.6 0
2 147                  6 16      2471 0
3 147                  6 16   1111.95 0
3 147                  7 16  658.9333 0
3 147                  8 16  921.0091 0
1 148                  3 16      2471 0
1 148                  4 16      2471 0
1 148                  5 16      2471 0
1 148                  6 16      2471 0
1 149                  3 16      2471 0
3 149                  5 16 1647.3333 0
3 149                  6 16 1647.3334 0
3 149                  7 16    2718.1 0
2 151                  2 16  4283.067 1
3 151                  2 16    3212.3 0
2 151                  4 16  1544.375 1
3 151                  4 16  3026.975 0
2 151                  5 16   4324.25 1
2 152                  2 16   3088.75 0
2 152                  3 16   3088.75 0
3 152                  3 16    3706.5 1
2 152                  5 16   3088.75 0
2 152                  6 16   3088.75 0
2 152                  7 16   3088.75 0
2 152                  9 16   4324.25 0
2 152                 10 16  2779.875 0
3 152                 12 16    2965.2 1
3 152                 13 16      3177 1
3 152                 14 16  3294.667 1
1 153                  3 16   1877.96 0
2 153                  3 16   2569.84 1
3 153                  3 16   2668.68 1
1 153                  4 16   1877.96 0
2 153                  4 16   4744.32 1
2 153                  5 16   2372.16 1
2 153                  6 16 2372.1602 1
3 153                  6 16  2537.784 1
3 153                  7 16    5436.2 1
1 154                  3 16 2419.5208 0
2 154                  3 16 2059.1667 0
3 154                  3 16 4736.0835 0
1 154                  4 16  2162.125 0
1 154                  5 16 2273.3198 0
2 154                  5 16   3162.88 0
3 154                  5 16    3212.3 0
1 154                  6 16 2273.3198 0
2 154                  6 16 2635.7334 0
3 154                  6 16    3212.3 0
1 154                  7 16 2509.6094 0
1 154                  8 16  2426.875 0
1 154                  9 16 2333.7222 0
2 154                  9 16 4392.8887 0
3 154                 10 16  3201.068 0
3 154                 11 16 4736.0835 0
3 154                 12 16    4694.9 0
1 155                  3 16    1976.8 0
2 155                  5 16    1729.7 0
3 155                  7 16  3067.448 0
2 156                  2 16 3057.8625 0
1 157                  2 16   1853.25 0
1 157                  3 16     988.4 0
2 157                  5 16    3953.6 0
2 157                  6 16    3706.5 0
2 157                  7 16  4015.375 0
2 157                  8 16      4942 0
2 157                  9 16      4942 0
2 157                 10 16      2471 0
2 157                 11 16      4942 0
1 158                  2 16  3369.546 0
2 202                  5 16    3459.4 0
1 203                  2 16    4200.7 0
1 204                  4 16    1482.6 0
2 204                  4 16      2471 1
3 204                  4 16    3459.4 1
1 204                  5 16     988.4 0
1 204                  6 16    1235.5 0
2 204                  6 16      2471 1
1 204                  7 16 1098.2222 0
1 204                  8 16 1187.9808 0
1 204                  9 16 1187.9808 0
3 204                  9 16    3459.4 1
1 204                 10 16 1184.0209 0
2 204                 10 16      2471 1
1 204                 11 16    1482.6 0
1 204                 12 16    1482.6 0
1 204                 13 16    1482.6 0
1 204                 14 16    1482.6 0
3 204                 15 16    3706.5 1
3 204                 16 16    3706.5 1
3 204                 17 16    3706.5 1
1 206                  2 16  2779.875 0
1 207                  2 16  3294.667 0
2 209                  2 16      2471 0
2 209                  3 16      2471 0
3 209                  3 16    3706.5 0
2 209                  4 16      2471 0
3 209                  5 16      2824 0
1 210                  3 16 3169.0576 0
1 210                  4 16 2886.5774 0
1 213                  3 16   1853.25 0
1 217                  2 16    794.25 0
1 218                  2 16  5106.733 0
2 218                  2 16      2471 0
2 218                  5 16    2294.5 0
3 218                  5 16      2824 0
2 218                  6 16   2841.65 0
3 218                  6 16      2471 0
3 218                  7 16      2471 0
1 219                  3 16      2471 0
2 219                  3 16 2745.5554 1
1 219                  4 16      2471 0
1 219                  5 16 2745.5554 0
1 219                  6 16 2745.5554 0
2 219                  6 16 2745.5554 1
3 219                  6 16   3088.75 0
1 219                  7 16 2635.7334 0
1 219                  8 16 2635.7334 0
3 219                  8 16  3129.933 0
2 219                  9 16  3369.546 1
3 219                  9 16    3706.5 0
3 220                  2 16      2471 0
1 341                  3 16 1647.3334 0
3 341                  3 16  3294.667 0
3 341                  4 16    4059.5 0
2 343                  3 16  4815.282 0
3 343                  3 16  4561.846 1
1 346                  2 16      2471 0
3 346                  3 16    4447.8 0
3 346                  4 16      4942 0
2 346                  5 16    6671.7 0
3 346                  5 16   3830.05 0
2 346                  6 16    2965.2 0
3 346                  6 16    3953.6 0
2 346                  7 16      2471 0
3 346                  7 16 2265.0833 0
2 347                  4 16      4942 0
3 347                  4 16    3706.5 0
2 347                  5 16  6589.333 0
3 347                  5 16    3706.5 0
2 347                  6 16      7413 0
3 347                  6 16    3459.4 0
3 347                 11 16  3912.417 0
2 348                  3 16    4200.7 0
3 348                  3 16      4942 0
3 348                  5 16    6177.5 0
1 350                  2 16   3088.75 0
2 350                  4 16      9884 0
3 350                  4 16      4942 0
3 350                  5 16      4942 0
1 351                  3 16      2471 0
2 351                  4 16    3706.5 0
3 351                4.1 16      4942 0
3 351                4.2 16      4942 0
3 351                4.3 16    1235.5 0
3 351                4.4 16    1235.5 0
1 352                  2 16    3706.5 0
3 352                  5 16   4324.25 1
2 352                5.1 16    3706.5 0
2 352                5.2 16    2718.1 0
3 352                  6 16      4942 1
2 352                6.1 16    1482.6 0
2 352                6.2 16    1482.6 0
2 352                  7 16 1647.3334 0
2 352                  8 16   1111.95 0
3 352                  8 16    6177.5 1
3 352                 10 16  4612.534 1
2 353                  3 16   1853.25 0
1 354                  3 16 4406.6167 0
2 354                  3 16  9060.333 0
1 354                  4 16 4419.2886 0
3 354                  4 16 4371.7695 0
2 354                  5 16    1235.5 0
3 354                  5 16    3706.5 0
3 354                  6 16      4236 0
3 354                7.1 16      4942 0
3 354                7.2 16      2471 0
3 354                7.3 16      2471 0
1 357                  2 16      2471 1
2 357                  2 16      2471 0
1 358                  3 16    3706.5 0
2 358                  3 16    3706.5 0
3 358                  3 16  2162.125 1
2 358                  4 16   1111.95 0
3 358                  4 16   3088.75 1
3 358                  5 16   3088.75 1
1 360                  3 16  1797.091 0
2 360                  3 16   1606.15 0
3 360                  3 16   3088.75 0
3 362                  2  2 4566.4077 0
1 363                  2  2      4942 0
3 363                  2  2   7215.32 0
1 363                  3  2   6795.25 0
1 367                  2  2  6376.774 0
3 367                  2  2  5260.839 0
1 367                  3  2  6390.518 0
3 367                  3  2  5282.828 0
1 367                  4  2  6305.311 0
3 367                  4  2      4942 0
1 367                  5  2  5751.466 0
3 367                  5  2  5282.828 0
1 368                  2  2  5212.266 0
2 368                  2  2  5391.273 0
3 368                  2  2  5623.655 0
1 368                  3  2      4942 0
2 368                  3  2  5353.833 0
1 368                  4  2      4942 0
2 368                  4  2  5271.467 0
1 368                  5  2  5147.917 0
2 368                  5  2    5436.2 0
1 368                  6  2  5147.917 0
2 368                  6  2  5353.833 0
3 368                  6  2    5436.2 0
1 368                  7  2      4942 0
2 368                  7  2  5353.833 0
3 368                  7  2    5436.2 0
1 368                  8  2      4942 0
2 368                  8  2  5106.733 0
3 368                  8  2    5436.2 0
1 368                  9  2      4942 0
2 368                  9  2  5600.934 0
3 368                  9  2    5436.2 0
1 368                 10  2      4942 0
2 368                 10  2  5271.467 0
3 368                 10  2    5436.2 0
1 368                 11  2  5271.467 0
2 368                 11  2  5600.934 0
1 368                 12  2  5106.733 0
2 368                 12  2  5353.833 0
1 368                 13  2  5106.733 0
2 368                 13  2  5600.934 0
1 368                 14  2    5683.3 0
2 368                 14  2    5436.2 0
3 368                 14  2    5436.2 0
1 368                 15  2    5683.3 0
2 368                 15  2    5683.3 0
3 368                 15  2    5436.2 0
1 368                 16  2    5683.3 0
2 368                 16  2    5436.2 0
1 368                 17  2      7413 0
2 368                 17  2   5559.75 0
1 368                 18  2      7413 0
2 368                 18  2    5930.4 0
3 368                 18  2    5436.2 0
1 368                 19  2    5436.2 0
2 368                 19  2    5436.2 0
3 368                 19  2    5436.2 0
1 368                 20  2    5436.2 0
2 368                 20  2    5436.2 0
1 368                 21  2    5436.2 0
2 368                 21  2   5806.85 0
1 368                 22  2    3953.6 0
2 368                 22  2   5559.75 0
1 368                 23  2      7413 0
2 368                 23  2    5436.2 0
3 368                 23  2    5436.2 0
1 368                 24  2    3706.5 0
2 368                 24  2    6177.5 0
3 368                 24  2    5436.2 0
1 368                 25  2    7165.9 0
2 368                 25  2    5436.2 0
1 368                 26  2 4736.0835 0
2 368                 26  2  2779.875 0
3 369                  2  2  5491.111 0
3 369                  3  2  5491.111 0
1 371                  2  2 2928.5925 0
2 371                  2  2  7687.556 0
3 371                  2  2  4575.926 0
1 371                  3  2  3474.844 0
end

I am trying to fit a fixed effects model.

egen panel_var=group(Household Plot)
xtset panel_var Round
xtreg Yields Credit_Accessed i.Agro_Eco_zone, fe

would it matter if the number of plots for the households differed across the three rounds?

[Clyde Schechter]

When I run your code on your example data, I get:

note: 2.Agro_Eco_zones omitted because of collinearity.
note: 7.Agro_Eco_zones omitted because of collinearity.
note: 16.Agro_Eco_zones omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =        500
Group variable: panel_var                       Number of groups  =        354

R-squared:                                      Obs per group:
     Within  = 0.0070                                         min =          1
     Between = 0.0001                                         avg =        1.4
     Overall = 0.0029                                         max =          3

                                                F(1, 145)         =       1.02
corr(u_i, Xb) = -0.1449                         Prob > F          =     0.3136

---------------------------------------------------------------------------------
         Yields | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
Credit_Accessed |   294.6203   291.3433     1.01   0.314     -281.208    870.4486
                |
 Agro_Eco_zones |
             2  |          0  (omitted)
             7  |          0  (omitted)
            16  |          0  (omitted)
                |
          _cons |   3595.866   85.88453    41.87   0.000     3426.118    3765.613
----------------+----------------------------------------------------------------
        sigma_u |  1458.4782
        sigma_e |  1201.2393
            rho |   .5958203   (fraction of variance due to u_i)
---------------------------------------------------------------------------------
F test that all u_i=0: F(353, 145) = 2.00                    Prob > F = 0.0000

The parts I have shown in red are where Stata has recognized that Agro_Eco_zones is constant within panel_var, and therefore the Agro_Eco_zones indicators are omitted from the analysis. This is the expected behavior of Stata and it confirms that you have this nesting. Under these circumstances, it is mathematically impossible to estimate Agro_Eco_zones effects using a fixed-effects model.

So, why would you want to include Agro_Eco_zones here? There are two possibilities:

• If your research goals require you to estimate those effects, then you can't do this with a fixed effects model and you will need to use some different kind of analysis. (Consider using -xthybrid-, available from SSC, which implements a Mundlak model.)
• But if you are interested in including them solely to avoid omitted variable bias, then there is no problem and nothing you need to do because the Agro_Eco_zones effects are automatically adjusted for by the panel_var fixed effects--that is the nice thing about fixed-effects models.

[Aisha Khan]

Clyde Schechter Thank you for your answer and the suggestion. I think I understand the point now.
If the goal is to estimate these effects then the Mundlak Chamberlain approach (correlated random effects) seems to be implemented in few papers. Also discussed here (https://www.statalist.org/forums/for...m-effects-code)
My question is that if I apply the correlated random effects as here (https://www.statalist.org/forums/for...m-effects-code) then do I also need to include the mean values for Credit_Accessed (my main variable of interest and a dummy) in the model. I see some papers that do not include the mean values of the independent variable of interest (for example https://onlinelibrary.wiley.com/doi/...477-9552.12537) while some do.

[Clyde Schechter]

I would include Credit_Accessed. Estimates of any of these variables' effects in the absence of the others is potentially problematic due to omitted variable bias, unless the variables themselves are known to be independent of each other.

[Aisha Khan]

Clyde Schechter thank you for your reply.
So, Credit_Accessed and mean values for Credit_Accessed (as in the CRE) should be included ?

[Clyde Schechter]

Yes.

[Aisha Khan]

Thank you Clyde Schechter