pooled cross section or panel

[Nancy Barakat]

Hi all
I am working on my master thesis tackling the impact of firm-level adoption of mobile money (a binary variable) on its energy intensity (a variable that ranges from 0 to 1). Actually I am a little confused about whether to define my data as panel or pooled cross section. I combined the panel dataset of Kenya , Tanzania and Uganda from the World-Bank enterprise survey, but the year of waves of Kenya differ than those of Tanzania and Uganda, they are only common in year 2013.
So, I have couple of questions regarding my model:
- should I deal with my data as panel or pooled cross section?
- if it will be pooled cross-section, what is the appropriate model for the relationship, Pooled OLS?
- if it is Polled OLS, what is the code? should I define my dataset first as panel using the xttest command before running the model.
- Another issue, since the survey limits me within certain range of variables, my model includes lots of binary variables but two continuous ones, Will this affect my coefficients, if it will how can I account for it?

here is a sample of the data

clear
input str8 country float(year unique_id_01) byte MM float EE byte a0 float(privatedomestic_ownership labor_productivity) byte human_capital
"Kenya" 2018 4521 1 .06129384 2 0 . 100
"Kenya" 2013 5001 1 . 1 40 6875000 90
"Kenya" 2018 4630 1 . 3 100 . 100
"Tanzania" 2006 2028 . .02367347 . 50 3363636 .
"Kenya" 2018 4750 0 . 3 100 . 100
"Kenya" 2013 4750 0 . 3 100 . 80
"Kenya" 2013 5161 0 .026 1 100 9240741 90
"Kenya" 2018 4735 1 . 1 100 725000 5
"Tanzania" 2013 2957 0 .00013333333 1 100 6.00e+08 100
"Kenya" 2007 4275 . .017828345 1 100 711349.7 .
"Kenya" 2013 5181 1 . 1 100 5875001 100
"Kenya" 2013 5139 1 . 1 100 9000 80
"Kenya" 2013 4898 0 . 3 . . 60
"Kenya" 2018 5654 0 .010005834 3 100 . 90
"uganda" 2006 247 . .015748031 . 10 141075792 .
"Kenya" 2007 4361 . . 1 100 422855.9 .
"uganda" 2013 807 0 .021352 1 100 6.50e+07 16
"uganda" 2013 692 1 . 2 100 . .
"uganda" 2006 362 . . . 0 20923076 .
"Kenya" 2018 5632 1 .05307692 3 100 . 4
"Kenya" 2013 4828 0 . 1 100 . 100
"Kenya" 2013 4867 0 . 1 100 8.00e+07 100
"Kenya" 2018 5872 1 . 1 0 . 100
"Kenya" 2013 5177 1 . 1 100 10633218 65
"Kenya" 2007 4296 . . 1 100 3230770 .
"Kenya" 2018 5084 0 . 1 100 3190000 80
"Kenya" 2007 4028 . . 1 100 117647.06 .
"Kenya" 2007 4555 . .023700954 3 100 . .
"Tanzania" 2006 2381 . . . 100 . .
"Kenya" 2013 4555 0 .009 3 100 . 9
"Kenya" 2018 4555 1 .009309065 3 100 . 100
"Kenya" 2018 4829 0 . 1 100 33333.332 .
"Tanzania" 2006 2001 . . . 11 169538448 .
"Kenya" 2018 5455 1 . 1 72 . 100
"Kenya" 2013 5006 1 . 2 100 . 0
"Kenya" 2018 5548 0 .0012018988 1 100 . 100
"Kenya" 2007 4343 . . 1 0 235000 .
"Kenya" 2013 5178 0 . 1 100 2130000 90
"Kenya" 2018 4345 0 . 1 0 5960000 100
"Kenya" 2013 5084 0 . 1 100 1818182 15
"Kenya" 2007 4234 . . 1 100 400000 .
"Kenya" 2007 4047 . .05703125 1 30 4095192 .
"Kenya" 2013 4047 0 .1205986 1 35 . 100
"Kenya" 2018 5862 0 . 1 88 404500 .
"Kenya" 2007 4202 . . 1 100 800000 .
"Kenya" 2018 4002 1 .0675 1 100 806451.6 60
"Kenya" 2007 4002 . .0345 1 0 352941.2 .
"Kenya" 2013 5185 0 . 1 0 2666667 100
"Kenya" 2013 4347 0 . 1 100 . 80
"Kenya" 2013 4002 0 . 1 30 1333333.4 .
"Kenya" 2007 4344 . . 1 0 4000000 .
"Kenya" 2007 4578 . . 3 100 . .
"Kenya" 2013 4735 1 . 1 100 290909.1 95
"uganda" 2006 246 . .05316667 . 100 15384615 .
"uganda" 2006 310 . . . 0 6666667 .
"Kenya" 2018 5764 1 .01875 3 100 . 100
"Kenya" 2007 4294 . . 1 0 7236842 .
"Kenya" 2013 5132 0 . 1 100 . 85
"Kenya" 2018 5600 1 .005272727 1 38 929166.7 50
"Tanzania" 2006 2104 . .03075 . 100 41666668 .
"Kenya" 2018 5179 1 .086 2 100 . 100
"Kenya" 2013 5095 0 . 1 100 1666666.6 100
"Kenya" 2007 4315 . . 1 100 1053333.4 .
"Kenya" 2013 4802 0 . 2 100 . 100
"Kenya" 2007 4114 . . 1 0 2142857.3 .
"Kenya" 2018 5776 1 .032 3 100 . 100
"uganda" 2013 252 1 . 1 100 . 80
"Kenya" 2013 5180 0 .013333334 1 100 2350000 90
"Kenya" 2018 5988 1 . 1 100 337500 100
"Kenya" 2018 5816 1 . 3 20 . 100
"Kenya" 2018 5556 0 .066 1 100 4542857 100
"uganda" 2006 2 . .002708333 . 75 26666666 .
"Kenya" 2013 4934 1 . 1 51 63196204 100
"Kenya" 2018 5829 1 . 3 0 . .
"Kenya" 2013 4992 1 . 1 100 . 12
"Kenya" 2018 5685 1 .1 3 100 . 100
"Kenya" 2007 4009 . . 1 100 796460.2 .
"Kenya" 2013 4702 1 . 1 0 3332969 25
"Kenya" 2018 5994 1 . 3 100 . 100
"Kenya" 2013 4978 0 . 3 35 . 100
"Kenya" 2013 5278 0 . 1 0 . 80
"Kenya" 2007 4014 . .08333334 1 100 1028846.1 .
"Kenya" 2007 4210 . .21454546 1 100 1354545.5 .
"Kenya" 2013 4985 1 . 3 100 . 100
"Kenya" 2018 4210 1 .13047619 2 100 . 100
"Kenya" 2013 5131 0 . 1 51 . 60
"Kenya" 2007 4213 . . 1 0 2357142.8 .
"Kenya" 2013 4294 0 .1 1 100 . 100
"Kenya" 2007 4521 . .01875 2 100 625000 .
"uganda" 2013 867 0 .032028 1 100 59750000 16
"Kenya" 2013 4300 1 .17 1 3 27490000 100
"uganda" 2013 591 0 . 3 100 . 40
"Kenya" 2007 4048 . . 1 75 816666.7 .
"Kenya" 2013 4704 0 . 1 100 4400000 90
"Kenya" 2013 4752 1 . 1 0 623333.3 4
"uganda" 2013 640 0 . 1 100 . 17
"Kenya" 2007 4091 . . 1 100 3866667 .
"uganda" 2013 281 0 . 1 . . 100
"Kenya" 2013 4075 0 . 1 100 518918.9 80
"Kenya" 2007 4128 . . 1 40 1891892 .
end

[Carlo Lazzaro]

Nancy:
welcome to this forum.
1) if your data come from a survey, you should probably go pooled OLS with the -svy:- prefix;
2) a pooled OLS requires -regress- with clustered standard errors;
3) if you go pooled OLS you do not need to identify your dataset as a panle one.

[Nancy Barakat]

Thanks Carlo for your reply
so my dataset is pooled cross section even though Uganda and Tanzanian firms' are both surveyed in years 2006 and 2013, while Kenya's are in 2007, 2013 and2018? To be honest, I am concerned that POLS wont be that efficient as it doesnt account for time invariant observants or differences across firms. Plus, it brought about insignificant estimates, though, I correctly specify my model upon literature and checked for heteroskedasticity, auto-correlation and existence of outliers.
Thanks again

[Carlo Lazzaro]

Nancy:
you mentioned a survey, that is something different from a panel dataset (unfortunately, I do not know the data you're dealing with).
If you want to go panel (considering countries as your -panelid-), POLS can be the way to go. That said, I do not inderstand how POLS cannot take time invariant variabkes into account (this happens with the -fe- estimator).
As you can see in the following toy example, POLS returns a coefficient for the time-invariant predictor -race-:

. use "https://www.stata-press.com/data/r17/nlswork.dta"
(National Longitudinal Survey of Young Women, 14-24 years old in 1968)

. regress ln_wage i.race, vce(cluster idcode)

Linear regression                               Number of obs     =     28,534
                                                F(2, 4710)        =      58.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0187
                                                Root MSE          =     .47363

                             (Std. err. adjusted for 4,711 clusters in idcode)
------------------------------------------------------------------------------
             |               Robust
     ln_wage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        race |
      Black  |  -.1427862   .0133808   -10.67   0.000    -.1690188   -.1165536
      Other  |    .080671   .0647742     1.25   0.213    -.0463166    .2076587
             |
       _cons |   1.714338   .0071195   240.80   0.000     1.700381    1.728296
------------------------------------------------------------------------------

.

whereas -fe- does not:

. xtreg ln_wage i.race, fe vce(cluster idcode)
note: 2.race omitted because of collinearity.
note: 3.race omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =     28,534
Group variable: idcode                          Number of groups  =      4,711

R-squared:                                      Obs per group:
     Within  = 0.0000                                         min =          1
     Between = 0.0050                                         avg =        6.1
     Overall =      .                                         max =         15

                                                F(0,4710)         =          .
corr(u_i, Xb) =      .                          Prob > F          =          .

                             (Std. err. adjusted for 4,711 clusters in idcode)
------------------------------------------------------------------------------
             |               Robust
     ln_wage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        race |
      Black  |          0  (omitted)
      Other  |          0  (omitted)
             |
       _cons |   1.674907          .        .       .            .           .
-------------+----------------------------------------------------------------
     sigma_u |  .42456905
     sigma_e |  .32028665
         rho |  .63731204   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.

I agree with the proposed post estimateion routine.

[Nancy Barakat]

Thanks Carlo again for your instant reply
let me make my data clearer, I extracted firm-level data from world bank enterprise survey. World bank recently provides panel file for each country entailing the results of tracking the same firms in different waves. For Kenya, the rounds were in 2007,2013 and 2018 while both Uganda and Tanzania in 2006 and 2013. Since I focus my study on the eastern African countries with high performance in mobile money, I chose these three countries and combined their data sets together in a separate file. So now I have around 5000 firms from three different countries for years 2006, 2007, 2013 and 2018. In that case I am working on a micro-level analysis, so if I run panel modelling, Shouldn't the panelid in that case be the firm_id?
Regarding my concerns about using POLS, I meant earlier that POLS assume that Xs arent correlated with ui in errorterm, it doesnt take into account that Xs can be affected and related with different firms' characteristics. It will deal with all firms as if they are independent when actually same firms are tracked in different years and therefore dependent. Please correct me if I am missing something of the underlying assumption of POLS and fixed effect because my dataset is really confusing me and I cant place under which umbrella.
Thanks again for your help and regards
clear
input float EE byte(MM bankaccount R_D human_capital) float exporter str8 country float(year unique_id_01)
.02419355 . 0 . . 0 "uganda" 2006 135
. . 1 . . 0 "uganda" 2006 374
.02488336 . 1 . . 0 "Tanzania" 2006 2105
. . 1 . . 0 "uganda" 2006 434
.10666667 . 1 . . 0 "Tanzania" 2006 2076
.005066667 . 1 . . 0 "Tanzania" 2006 2235
.035555556 . 1 . . 0 "uganda" 2006 221
.07333333 . 1 . . 1 "Tanzania" 2006 2019
.033333335 . 1 . . 0 "uganda" 2006 224
.12 . 1 . . 0 "uganda" 2006 27
.023893805 . 1 . . 0 "Tanzania" 2006 2232
.00024666666 . 1 . . 0 "Tanzania" 2006 2231
.02777778 . 1 . . 0 "uganda" 2006 181
. . 1 . . 0 "Tanzania" 2006 2281
. . 1 . . 0 "uganda" 2006 562
. . 1 . . 0 "Tanzania" 2006 2325
.033333335 . 1 . . 0 "uganda" 2006 214
.08097687 . 1 . . 0 "Tanzania" 2006 2213
. . 0 . . 0 "uganda" 2006 538
.0044230768 . 1 . . 0 "Tanzania" 2006 2220
. . 1 . . 0 "Tanzania" 2006 2341
.0022413793 . 1 . . 0 "Tanzania" 2006 2218
. . 1 . . 0 "Tanzania" 2006 2297
. . 1 . . 0 "uganda" 2006 473
.02510638 . 1 . . 0 "Tanzania" 2006 2129
. . 1 . . 0 "uganda" 2006 447
.012777777 . 1 . . 0 "uganda" 2006 16
. . 1 . . 0 "Tanzania" 2006 2417
.02758333 . . . . 0 "uganda" 2006 8
. . 1 . . 0 "uganda" 2006 469
. . 1 . . 0 "uganda" 2006 222
.036 . 1 . . 0 "uganda" 2006 43
. . 1 . . 0 "Tanzania" 2006 2415
. . 1 . . 0 "uganda" 2006 552
.0016875 . 1 . . 0 "uganda" 2006 140
.14117648 . 1 . . 0 "uganda" 2006 307
. . 0 . . 0 "uganda" 2006 393
.015384615 . 1 . . 0 "Tanzania" 2006 2201
. . 1 . . 0 "Tanzania" 2006 2413
.056 . 1 . . 0 "Tanzania" 2006 2117
. . 1 . . 0 "Tanzania" 2006 2307
.00793 . 1 . . 0 "uganda" 2006 220
.0378125 . 1 . . 0 "uganda" 2006 83
.02830022 . 1 . . 0 "uganda" 2006 79
. . 1 . . 0 "uganda" 2006 543
.0003788295 . 1 . . 0 "uganda" 2006 147
. . 1 . . 0 "Tanzania" 2006 2412
.036363635 . . . . 0 "uganda" 2006 4
.015 . 1 . . 0 "uganda" 2006 293
.06315789 . 1 . . 0 "Tanzania" 2006 2183
. . 1 . . 0 "uganda" 2006 316
.0085 . 1 . . 0 "uganda" 2006 211
. . 1 . . 0 "uganda" 2006 394
. . 1 . . 0 "uganda" 2006 313
. . 0 . . 0 "Tanzania" 2006 2308
. . 1 . . 0 "Tanzania" 2006 2001
. . 1 . . 0 "Tanzania" 2006 2373
.005278226 . 1 . . 0 "uganda" 2006 92
.01084944 . 1 . . 0 "uganda" 2006 40
.0025 . 1 . . 0 "uganda" 2006 26
. . 1 . . 0 "uganda" 2006 58
.002142857 . 1 . . 0 "Tanzania" 2006 2027
. . 0 . . 0 "uganda" 2006 460
.05147059 . 0 . . 0 "uganda" 2006 93
.03783102 . 1 . . 0 "Tanzania" 2006 2066
.005434783 . 1 . . 0 "Tanzania" 2006 2219
.05714286 . 1 . . 0 "Tanzania" 2006 2149
.004 . 1 . . 0 "Tanzania" 2006 2035
. . 1 . . 0 "uganda" 2006 383
.04384615 . 1 . . 0 "Tanzania" 2006 2207
. . 1 . . 0 "uganda" 2006 366
.01967213 . 1 . . 0 "Tanzania" 2006 2202
. . 1 . . 0 "Tanzania" 2006 2326
. . 1 . . 0 "uganda" 2006 325
. . 0 . . 0 "uganda" 2006 314
. . 1 . . 0 "uganda" 2006 426
. . 1 . . 0 "Tanzania" 2006 2331
. . 0 . . 0 "Tanzania" 2006 2332
.03 . 1 . . 0 "Tanzania" 2006 2186
. . 1 . . 0 "Tanzania" 2006 2345
.008888889 . 1 . . 0 "uganda" 2006 245
. . 1 . . 0 "Tanzania" 2006 2390
.015652174 . 1 . . 0 "uganda" 2006 215
.02777778 . 1 . . 0 "uganda" 2006 115
. . 0 . . 0 "Tanzania" 2006 2340
.032470588 . 1 . . 0 "Tanzania" 2006 2095
. . 1 . . 0 "Tanzania" 2006 2348
.07081138 . 1 . . 0 "Tanzania" 2006 2185
. . 0 . . 0 "Tanzania" 2006 2405
.024 . 0 . . 0 "Tanzania" 2006 2088
. . 1 . . 0 "Tanzania" 2006 2355
. . 1 . . 0 "Tanzania" 2006 2356
. . 1 . . 0 "Tanzania" 2006 2357
. . 1 . . 0 "Tanzania" 2006 2303
. . 1 . . 0 "uganda" 2006 357
.0026 . 0 . . 1 "Tanzania" 2006 2257
.073846154 . 1 . . 0 "Tanzania" 2006 2057
.028328214 . 1 . . 0 "uganda" 2006 28
. . 1 . . 0 "uganda" 2006 362
.007512755 . 1 . . 0 "Tanzania" 2006 2253
.26785713 . 1 . . 0 "uganda" 2006 302
. . 1 . . 0 "uganda" 2006 486
.005714286 . 1 . . 0 "Tanzania" 2006 2032
.003140979 . 1 . . 1 "uganda" 2006 248
.05545454 . 1 . . 0 "Tanzania" 2006 2137
.025 . 1 . . 0 "Tanzania" 2006 2133
. . 1 . . 0 "uganda" 2006 412
. . 1 . . 0 "uganda" 2006 368
.007857143 . 0 . . 0 "uganda" 2006 84
. . 1 . . 0 "uganda" 2006 390
.031927735 . 1 . . 0 "uganda" 2006 42
.01 . 1 . . 0 "uganda" 2006 5
. . 1 . . 0 "uganda" 2006 371
. . 1 . . 0 "Tanzania" 2006 2299
. . 1 . . 0 "uganda" 2006 415
.03032491 . 1 . . 1 "Tanzania" 2006 2224
.11333334 . 1 . . 0 "Tanzania" 2006 2268
.04216216 . 0 . . 0 "uganda" 2006 114
. . 1 . . 0 "Tanzania" 2006 2350
. . 1 . . 0 "uganda" 2006 375
. . 1 . . 0 "uganda" 2006 475
. . 1 . . 0 "uganda" 2006 318
.14604463 . 1 . . 0 "Tanzania" 2006 2041
. . 1 . . 0 "Tanzania" 2006 2418
. . 1 . . 0 "uganda" 2006 381
.034444444 . 1 . . 0 "uganda" 2006 112
.005882353 . 1 . . 0 "uganda" 2006 127
.1 . . . . 0 "uganda" 2006 260
.0024 . 1 . . 1 "Tanzania" 2006 2180
.04878049 . 0 . . 0 "uganda" 2006 203
. . 1 . . 0 "uganda" 2006 382
. . 1 . . 0 "uganda" 2006 331
. . 1 . . 0 "Tanzania" 2006 2377
.18333334 . 1 . . 1 "Tanzania" 2006 2266
.008571428 . 1 . . 0 "Tanzania" 2006 2200
.003157895 . 0 . . 0 "Tanzania" 2006 2163
.01375 . 1 . . 0 "uganda" 2006 67
.036169186 . 1 . . 0 "Tanzania" 2006 2230
.027508514 . 1 . . 0 "uganda" 2006 71
.03515625 . 1 . . 0 "uganda" 2006 280
.08 . 0 . . 0 "uganda" 2006 200
. . 1 . . 0 "uganda" 2006 373
. . 0 . . 0 "Tanzania" 2006 2382
.0252 . 1 . . 0 "Tanzania" 2006 2073
.1734104 . 1 . . 0 "uganda" 2006 236
. . 1 . . 0 "Tanzania" 2006 2075
. . 1 . . 0 "uganda" 2006 333
. . 1 . . 0 "uganda" 2006 345
.03758865 . 1 . . 0 "Tanzania" 2006 2246
. . 0 . . 0 "uganda" 2006 387
. . 1 . . 0 "Tanzania" 2006 2397
. . 1 . . 0 "uganda" 2006 444
. . 1 . . 0 "uganda" 2006 330
.10428572 . 1 . . 1 "uganda" 2006 13
. . 1 . . 0 "uganda" 2006 404
. . 0 . . 0 "Tanzania" 2006 2349
.018666666 . 1 . . 0 "uganda" 2006 150
. . 0 . . 0 "Tanzania" 2006 2359
.0176 . 0 . . 0 "Tanzania" 2006 2176
. . 1 . . 0 "Tanzania" 2006 2402
.012 . 0 . . 0 "uganda" 2006 261
.00021980295 . 1 . . 0 "uganda" 2006 146
.08494502 . 1 . . 0 "Tanzania" 2006 2214
. . 1 . . 0 "Tanzania" 2006 2313
. . 1 . . 0 "Tanzania" 2006 2287
.024896266 . 1 . . 0 "uganda" 2006 286
.016666668 . 1 . . 0 "uganda" 2006 104
.0305 . 1 . . 0 "Tanzania" 2006 2061
.02722222 . 1 . . 0 "uganda" 2006 288
.0075 . 1 . . 0 "Tanzania" 2006 2191
. . 1 . . 0 "uganda" 2006 471
.04779686 . 1 . . 0 "Tanzania" 2006 2112
.014816754 . 0 . . 0 "uganda" 2006 108
. . 0 . . 0 "Tanzania" 2006 2351
. . 1 . . 0 "uganda" 2006 508
.04931298 . 1 . . 0 "Tanzania" 2006 2247
.03125 . 1 . . 0 "uganda" 2006 45
. . 1 . . 0 "uganda" 2006 378
. . 1 . . 0 "Tanzania" 2006 2408
.0125 . 1 . . 0 "uganda" 2006 68
.03075 . 1 . . 0 "Tanzania" 2006 2104
.015748031 . 1 . . 1 "uganda" 2006 247
.23214285 . 1 . . 0 "uganda" 2006 3
. . 1 . . 0 "uganda" 2006 502
. . 1 . . 0 "uganda" 2006 320
. . 1 . . 0 "uganda" 2006 501
.03157895 . 1 . . 0 "Tanzania" 2006 2100
.04020275 . 1 . . 0 "Tanzania" 2006 2099
.0195 . 1 . . 0 "uganda" 2006 95
.0875 . 0 . . 0 "Tanzania" 2006 2217
. . 1 . . 0 "Tanzania" 2006 2337
.023809524 . 1 . . 0 "Tanzania" 2006 2242
.084 . 1 . . 0 "Tanzania" 2006 2103
.01942446 . 0 . . 0 "uganda" 2006 306
.02109705 . 1 . . 0 "Tanzania" 2006 2011
.12 . 1 . . 0 "uganda" 2006 296
.016033359 . 1 . . 0 "uganda" 2006 55
.023592396 . . . . 0 "uganda" 2006 162
.0040571243 . 1 . . 0 "Tanzania" 2006 2178
.009087883 . 0 . . 0 "Tanzania" 2006 2262
.02522449 . 1 . . 0 "Tanzania" 2006 2172
. . 1 . . 0 "uganda" 2006 317
.0336 . 1 . . 0 "Tanzania" 2006 2060
.13 . 1 . . 0 "uganda" 2006 15
. . 1 . . 0 "uganda" 2006 340
. . 1 . . 0 "Tanzania" 2006 2396
.02 . 1 . . 0 "uganda" 2006 118
.06114769 . 1 . . 0 "uganda" 2006 189
. . 1 . . 0 "uganda" 2006 409
. . 0 . . 0 "uganda" 2006 500
. . 0 . . 0 "Tanzania" 2006 2366
.010344828 . 1 . . 0 "uganda" 2006 37
. . 1 . . 0 "uganda" 2006 545
. . 0 . . 0 "uganda" 2006 379
.10857143 . 1 . . 1 "uganda" 2006 47
.037142858 . 1 . . 1 "uganda" 2006 268
. . 1 . . 0 "uganda" 2006 474
. . 1 . . 0 "Tanzania" 2006 2285
.028 . 1 . . 0 "Tanzania" 2006 2043
. . 1 . . 0 "Tanzania" 2006 2334
.021 . 1 . . 0 "Tanzania" 2006 2205
. . 1 . . 0 "uganda" 2006 406
.0576 . 1 . . 0 "uganda" 2006 99
. . 1 . . 0 "Tanzania" 2006 2318
.04285714 . 1 . . 0 "uganda" 2006 44
. . 1 . . 0 "Tanzania" 2006 2416
.0447 . 1 . . 0 "Tanzania" 2006 2151
.05090909 . 0 . . 0 "Tanzania" 2006 2156
.17258064 . 1 . . 0 "uganda" 2006 63
. . 1 . . 0 "uganda" 2006 425
.1764706 . 1 . . 0 "uganda" 2006 291
.0044444446 . 1 . . 0 "Tanzania" 2006 2036
.029310346 . 1 . . 0 "Tanzania" 2006 2101
.044 . 1 . . 0 "Tanzania" 2006 2210
.0045454544 . 1 . . 0 "uganda" 2006 97
. . 1 . . 0 "uganda" 2006 529
. . 1 . . 0 "uganda" 2006 461
.015454546 . 1 . . 0 "Tanzania" 2006 2157
. . 0 . . 0 "uganda" 2006 360
. . 1 . . 0 "uganda" 2006 429
. . 0 . . 0 "uganda" 2006 117
.07777778 . 1 . . 0 "uganda" 2006 72
.15 . 1 . . 0 "uganda" 2006 34
. . 1 . . 0 "uganda" 2006 472
. . 1 . . 0 "uganda" 2006 556
.05227273 . . . . 0 "uganda" 2006 201
.005555556 . 0 . . 0 "Tanzania" 2006 2030
. . 1 . . 0 "uganda" 2006 470
.024 . 1 . . 0 "Tanzania" 2006 2198
. . 0 . . 0 "uganda" 2006 524
.09444445 . 1 . . 0 "uganda" 2006 225
. . 1 . . 0 "Tanzania" 2006 2279
.002962963 . 1 . . 1 "Tanzania" 2006 2018
.0306572 . 1 . . 0 "Tanzania" 2006 2009
. . 1 . . 0 "Tanzania" 2006 2298
. . 1 . . 0 "uganda" 2006 531
.007017544 . 0 . . 0 "Tanzania" 2006 2033
.065625 . 1 . . 0 "uganda" 2006 275
. . 1 . . 0 "Tanzania" 2006 2336
. . 1 . . 0 "uganda" 2006 439
.029649124 . 1 . . 0 "uganda" 2006 87
.0009473684 . 1 . . 0 "uganda" 2006 171
.05398111 . 1 . . 0 "Tanzania" 2006 2016
. . 1 . . 0 "Tanzania" 2006 2131
.028 . 1 . . 0 "Tanzania" 2006 2063
.01111111 . 1 . . 0 "uganda" 2006 39
.06818182 . 1 . . 0 "Tanzania" 2006 2008
.010555555 . 1 . . 0 "uganda" 2006 173
.10738916 . 1 . . 0 "uganda" 2006 305
. . 1 . . 0 "Tanzania" 2006 2312
. . 1 . . 0 "Tanzania" 2006 2339
. . 1 . . 0 "uganda" 2006 554
. . 1 . . 0 "uganda" 2006 349
.0025 . 0 . . 0 "Tanzania" 2006 2010
. . 1 . . 0 "uganda" 2006 555
.04 . 0 . . 0 "uganda" 2006 185
.005615469 . 1 . . 0 "uganda" 2006 234
. . 1 . . 0 "Tanzania" 2006 2411
.0016666667 . 0 . . 0 "Tanzania" 2006 2196
. . 1 . . 0 "uganda" 2006 443
. . 0 . . 0 "uganda" 2006 372
. . 0 . . 0 "Tanzania" 2006 2379
.016375 . 1 . . 1 "Tanzania" 2006 2034
. . 1 . . 0 "Tanzania" 2006 2387
.04329674 . 1 . . 0 "Tanzania" 2006 2140
. . 1 . . 0 "Tanzania" 2006 2394
.19444445 . 1 . . 0 "uganda" 2006 138
. . 1 . . 0 "uganda" 2006 367
.004625 . 1 . . 0 "uganda" 2006 57
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end

[Carlo Lazzaro]

Nancy:
thanks for your clarifications.
1) you can go -xtreg,fe- first, -xtset-ting your data with -firmid- as your -panelid-.
2) yes, POLS does not take into account the ui error term.

[Nancy Barakat]

Thanks a lot for your help. Highly appreciated