logit not converging

[Ajay Saharan]

I am running a logit regression- logit roads_any_project_LS c.Roads_index#i.Constituency i.Constituency, r cluster(BLOCK) noconstant
and after 300 iterations, I am getting the message "convergence not achieved". I need constituency-level coefficient estimates, hence an interaction term. I am not able to understand why am I getting this issue and how to solve it? Any suggestions/comments are welcome. My aim is to get the constituency-level coefficient estimates, any suggestion for other models are also welcome.

My dependent variable is a binary variable. It has the following distribution.

. sum roads_any_project_LS

Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
roads_any~LS | 390,103 .0183208 .1341089 0 1


I have data on 314 constituencies (390,103 villages), and all have at least one village where the dependent variable takes value 1. When I look at the constituency level distribution, there are 20 constituencies with only one village (out of more than 1000 villages) have received projects. Mean number of villages having dependent variable value 1 are 29 and its std deviation is 35.


Logistic regression Number of obs = 390,103
Wald chi2(626) = .
Log pseudolikelihood = -30721.741 Prob > chi2 = .

(Std. Err. adjusted for 4,769 clusters in BLOCK)
--------------------------------------------------------------------------------------------
| Robust
roads_any_project_LS | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
Constituency#c.Roads_index |
1 | -.6608474 .0655114 -10.09 0.000 -.7892472 -.5324475
2 | .4458982 .1757362 2.54 0.011 .1014615 .7903348
3 | .2181932 .2257075 0.97 0.334 -.2241854 .6605719
7 | .120353 .0694249 1.73 0.083 -.0157174 .2564233
8 | .1958896 .2202225 0.89 0.374 -.2357387 .6275178
9 | .1091651 .1157878 0.94 0.346 -.1177748 .3361049
10 | .1542245 .0416943 3.70 0.000 .0725053 .2359438
11 | .3365478 .3147167 1.07 0.285 -.2802856 .9533812
12 | .2002518 .1097046 1.83 0.068 -.0147654 .4152689
13 | -.0542538 .0937007 -0.58 0.563 -.2379039 .1293962
14 | .2941955 .1803958 1.63 0.103 -.0593739 .6477648
15 | .0977584 .0928378 1.05 0.292 -.0842003 .2797171
16 | -.115236 .0936234 -1.23 0.218 -.2987346 .0682625
18 | .1462729 .1154289 1.27 0.205 -.0799635 .3725093
19 | .3670984 .0890255 4.12 0.000 .1926115 .5415852
20 | .3560342 .1593247 2.23 0.025 .0437635 .6683048
21 | .2354457 .1645342 1.43 0.152 -.0870353 .5579267
22 | -.0149209 .118731 -0.13 0.900 -.2476294 .2177875
23 | .1795492 .0820157 2.19 0.029 .0188014 .3402969
25 | .1203832 .0581845 2.07 0.039 .0063436 .2344228
26 | .080957 .1021105 0.79 0.428 -.1191759 .28109
28 | .1525234 .1413528 1.08 0.281 -.124523 .4295699
31 | .3438291 .2499364 1.38 0.169 -.1460373 .8336954
33 | .3965649 .1451224 2.73 0.006 .1121302 .6809995
34 | -.0766231 .139184 -0.55 0.582 -.3494187 .1961724
35 | .488826 .3103768 1.57 0.115 -.1195014 1.097153
36 | .0231568 .1627588 0.14 0.887 -.2958446 .3421582
37 | 1.686467 .9838046 1.71 0.086 -.2417549 3.614688
38 | .0299327 .5698798 0.05 0.958 -1.087011 1.146877
39 | .7815796 .4578359 1.71 0.088 -.1157622 1.678921
47 | .5706566 .3132112 1.82 0.068 -.0432262 1.184539
48 | .3613716 .0437837 8.25 0.000 .2755572 .447186
50 | .2657646 .0987458 2.69 0.007 .0722264 .4593027
51 | .2042784 .0724962 2.82 0.005 .0621885 .3463683
52 | -.0053896 .1571352 -0.03 0.973 -.3133689 .3025897
55 | -.0309479 .0563627 -0.55 0.583 -.1414168 .0795209
56 | -.0245893 .1764528 -0.14 0.889 -.3704303 .3212518
58 | .5417619 .5237293 1.03 0.301 -.4847286 1.568252
59 | .0808226 .0785701 1.03 0.304 -.073172 .2348172
61 | .234522 .0791768 2.96 0.003 .0793384 .3897056
62 | .1099259 .124271 0.88 0.376 -.1336408 .3534925
63 | .2400244 .1537519 1.56 0.118 -.0613237 .5413725
65 | .1866645 .1336812 1.40 0.163 -.0753457 .4486748
66 | -.0282779 .1565299 -0.18 0.857 -.3350708 .278515
67 | .8259951 .624605 1.32 0.186 -.3982081 2.050198
68 | -.0599338 .1244139 -0.48 0.630 -.3037805 .183913
70 | -.4226502 .0359109 -11.77 0.000 -.4930343 -.3522661
71 | .3371348 .093079 3.62 0.000 .1547034 .5195662
72 | .0569631 .1446507 0.39 0.694 -.226547 .3404732
73 | .1970157 .0787471 2.50 0.012 .0426742 .3513572
74 | .2106958 .1479022 1.42 0.154 -.0791871 .5005788
75 | .0195349 .0945959 0.21 0.836 -.1658697 .2049395
76 | .0737547 .1076251 0.69 0.493 -.1371865 .284696
79 | -.1264817 .0873495 -1.45 0.148 -.2976837 .0447203
80 | -.0247965 .2585267 -0.10 0.924 -.5314994 .4819065
81 | .1180961 .1635918 0.72 0.470 -.202538 .4387301
82 | -.0684729 .0467244 -1.47 0.143 -.1600511 .0231053
83 | .1907104 .1266487 1.51 0.132 -.0575164 .4389372
85 | -.1060408 .0584821 -1.81 0.070 -.2206636 .0085819
88 | .1176512 .0885092 1.33 0.184 -.0558237 .2911261
90 | .081203 .0650858 1.25 0.212 -.0463628 .2087689
92 | .1148136 .1121654 1.02 0.306 -.1050266 .3346538
93 | .4887394 .2223114 2.20 0.028 .0530171 .9244617
94 | -.0788945 .082853 -0.95 0.341 -.2412834 .0834943
96 | .1815226 .0724197 2.51 0.012 .0395826 .3234627
97 | .2350458 .2695767 0.87 0.383 -.2933149 .7634064
99 | -.0101564 .1173261 -0.09 0.931 -.2401113 .2197985
101 | .0531907 .0916401 0.58 0.562 -.1264207 .232802
102 | -.1600988 .142758 -1.12 0.262 -.4398993 .1197016
103 | .078361 .1967812 0.40 0.690 -.3073231 .4640452
104 | -.0304649 .0654481 -0.47 0.642 -.1587409 .0978111
105 | .1232343 .0498378 2.47 0.013 .025554 .2209146
107 | -.0260969 .0506596 -0.52 0.606 -.1253879 .0731941
108 | -.0266757 .0937611 -0.28 0.776 -.2104441 .1570927
109 | .4251732 .1620749 2.62 0.009 .1075121 .7428342
111 | .5153267 .4040994 1.28 0.202 -.2766936 1.307347
112 | .0748976 .0722194 1.04 0.300 -.0666499 .2164452
115 | .1237639 .0739255 1.67 0.094 -.0211273 .2686552
116 | .082045 .1538435 0.53 0.594 -.2194827 .3835726
117 | .1235557 .147288 0.84 0.402 -.1651236 .4122349
118 | 1.03302 .1585255 6.52 0.000 .7223156 1.343724
120 | .4643969 .0877099 5.29 0.000 .2924886 .6363052
121 | .1773 .143952 1.23 0.218 -.1048408 .4594408
122 | .3590351 .2431092 1.48 0.140 -.1174501 .8355203
123 | -.015444 .0801791 -0.19 0.847 -.1725922 .1417041
125 | 11.58369 1.013573 11.43 0.000 9.59712 13.57025
127 | .1546807 .1457843 1.06 0.289 -.1310513 .4404128
128 | -.11968 .0859668 -1.39 0.164 -.2881719 .0488119
129 | .6831128 .188329 3.63 0.000 .3139947 1.052231
132 | -.1149689 .0600724 -1.91 0.056 -.2327088 .0027709
133 | .1881041 .2009844 0.94 0.349 -.2058181 .5820264
134 | -.0673733 .1162911 -0.58 0.562 -.2952998 .1605531
135 | .3194773 .1812864 1.76 0.078 -.0358374 .6747921
136 | .2270449 .1127692 2.01 0.044 .0060213 .4480685
137 | .1108311 .0911032 1.22 0.224 -.0677279 .2893901
138 | -.0302023 .0906149 -0.33 0.739 -.2078043 .1473997
141 | -.0269314 .1594216 -0.17 0.866 -.3393919 .2855292
142 | .1876524 .1032194 1.82 0.069 -.0146538 .3899586
143 | .0705556 .081043 0.87 0.384 -.0882857 .2293969
145 | .9079657 .1742489 5.21 0.000 .5664442 1.249487
147 | -.0356222 .175612 -0.20 0.839 -.3798154 .308571
148 | -.1684515 .1371322 -1.23 0.219 -.4372258 .1003227
149 | .0650085 .0749801 0.87 0.386 -.0819498 .2119669
150 | .1022375 .116453 0.88 0.380 -.1260062 .3304811
151 | .2524813 .1113974 2.27 0.023 .0341465 .4708161
154 | .1055224 .0442386 2.39 0.017 .0188163 .1922285
155 | .1612263 .1032679 1.56 0.118 -.041175 .3636276
156 | .2159575 .1807413 1.19 0.232 -.1382889 .5702039
157 | .1493157 .0600223 2.49 0.013 .0316742 .2669572
158 | .4026111 .1749493 2.30 0.021 .0597167 .7455055
159 | .3407883 .1874535 1.82 0.069 -.0266139 .7081905
160 | .0012592 .1455405 0.01 0.993 -.2839949 .2865132
161 | .1358313 .0715826 1.90 0.058 -.0044679 .2761305
163 | -.1161922 .109372 -1.06 0.288 -.3305574 .098173
164 | .2929143 .0854986 3.43 0.001 .1253401 .4604886
165 | -.3221409 .1290194 -2.50 0.013 -.5750142 -.0692675
166 | .3102276 .0921346 3.37 0.001 .129647 .4908081
168 | 1.130028 .322774 3.50 0.000 .4974025 1.762653
170 | .1649246 .0623946 2.64 0.008 .0426335 .2872157
171 | .150621 .0649716 2.32 0.020 .0232791 .2779629
172 | .0347216 .332317 0.10 0.917 -.6166079 .686051
173 | .1534114 .0629638 2.44 0.015 .0300046 .2768182
174 | -.0311672 .1306873 -0.24 0.812 -.2873096 .2249751
175 | .0274888 .0743977 0.37 0.712 -.118328 .1733056
176 | .1724106 .0497797 3.46 0.001 .0748442 .269977
177 | .172802 .0850523 2.03 0.042 .0061026 .3395015
179 | .2716042 .1702945 1.59 0.111 -.0621669 .6053753
180 | .3713476 .1062206 3.50 0.000 .163159 .5795362
182 | .0193702 .1530052 0.13 0.899 -.2805146 .3192549
184 | .3210185 .0990776 3.24 0.001 .1268301 .515207
185 | .0462149 .1563764 0.30 0.768 -.2602771 .3527069
187 | .0698952 .0695679 1.00 0.315 -.0664553 .2062457
188 | -.0334251 .1064789 -0.31 0.754 -.2421199 .1752698
190 | .3565551 .0592554 6.02 0.000 .2404167 .4726935
191 | .0690217 .0645224 1.07 0.285 -.0574399 .1954832
192 | .8476464 .1256336 6.75 0.000 .601409 1.093884
193 | .2515981 .0967901 2.60 0.009 .061893 .4413032
195 | .1601033 .137404 1.17 0.244 -.1092036 .4294102
197 | .3574607 .1684967 2.12 0.034 .0272133 .6877082
198 | .3709626 .3092596 1.20 0.230 -.2351751 .9771003
204 | .0174285 .0541959 0.32 0.748 -.0887935 .1236504
206 | -.3798898 .4004283 -0.95 0.343 -1.164715 .4049353
209 | .23999 .0926316 2.59 0.010 .0584355 .4215445
210 | .0284985 .0365235 0.78 0.435 -.0430861 .1000832
211 | -.2325995 .1008773 -2.31 0.021 -.4303154 -.0348835
212 | .3302791 .0317353 10.41 0.000 .268079 .3924791
213 | .665213 .0836916 7.95 0.000 .5011805 .8292455
214 | .4015228 .2890704 1.39 0.165 -.1650447 .9680903
215 | .0798686 .1942914 0.41 0.681 -.3009355 .4606727
217 | -.5452624 .0660233 -8.26 0.000 -.6746657 -.415859
221 | .2717994 .2665553 1.02 0.308 -.2506393 .7942382
224 | .0067209 .3926434 0.02 0.986 -.7628461 .7762879
225 | -.3512399 .0601539 -5.84 0.000 -.4691393 -.2333405
226 | -.1508163 .3207543 -0.47 0.638 -.7794832 .4778506
227 | .2782354 .1112085 2.50 0.012 .0602706 .4962001
229 | 1.166153 .6404576 1.82 0.069 -.0891207 2.421427
231 | .371917 .369513 1.01 0.314 -.3523152 1.096149
234 | .1069477 .0660999 1.62 0.106 -.0226056 .236501
235 | 17.29088 1.61e-07 1.1e+08 0.000 17.29088 17.29088
238 | .6178964 .1953691 3.16 0.002 .2349801 1.000813
239 | -.6548 .0710836 -9.21 0.000 -.7941213 -.5154788
241 | .3987505 .1981316 2.01 0.044 .0104198 .7870812
242 | .390587 .1504276 2.60 0.009 .0957544 .6854197
243 | -.0959164 .2836754 -0.34 0.735 -.65191 .4600771
244 | -1.331163 .1051585 -12.66 0.000 -1.53727 -1.125057
247 | .1055609 .1954247 0.54 0.589 -.2774644 .4885862
249 | .1528566 .0722969 2.11 0.034 .0111574 .2945559
250 | .1496109 .1239498 1.21 0.227 -.0933262 .392548
252 | -.1634168 .1322389 -1.24 0.217 -.4226003 .0957666
255 | .0566075 .0683949 0.83 0.408 -.0774441 .1906591
256 | .5234833 .1458697 3.59 0.000 .237584 .8093827
257 | .0238993 .1151488 0.21 0.836 -.2017883 .2495868
258 | .0926211 .1125279 0.82 0.410 -.1279296 .3131718
260 | -.0563441 .1326084 -0.42 0.671 -.3162518 .2035635
261 | .1160909 .2112703 0.55 0.583 -.2979913 .530173
262 | .1784299 .0489056 3.65 0.000 .0825768 .274283
264 | .129385 .057469 2.25 0.024 .0167479 .2420221
266 | -.143771 .0907939 -1.58 0.113 -.3217239 .0341818
267 | .0672449 .066975 1.00 0.315 -.0640237 .1985134
268 | .0312796 .1340351 0.23 0.815 -.2314244 .2939835
269 | .161825 .0452222 3.58 0.000 .0731911 .2504589
271 | .1161613 .2418874 0.48 0.631 -.3579293 .590252
273 | -.1508855 .0619568 -2.44 0.015 -.2723186 -.0294523
275 | -.4396436 .0401815 -10.94 0.000 -.5183979 -.3608893
276 | .2840709 .0579226 4.90 0.000 .1705446 .3975971
277 | -.0038655 .1291965 -0.03 0.976 -.2570859 .2493549
278 | .4189674 .1350899 3.10 0.002 .154196 .6837388
279 | -.0216295 .0333402 -0.65 0.517 -.0869751 .0437162
281 | .1568697 .1356994 1.16 0.248 -.1090962 .4228356
283 | -.0274898 .0772257 -0.36 0.722 -.1788495 .1238698
284 | .3732391 .1486632 2.51 0.012 .0818645 .6646136
287 | .1700929 .0729812 2.33 0.020 .0270523 .3131335
288 | .1882206 .0547304 3.44 0.001 .0809509 .2954903
289 | .2286227 .0999133 2.29 0.022 .0327963 .4244492
293 | .2556535 .0975158 2.62 0.009 .0645261 .4467809
294 | .2348916 .1140465 2.06 0.039 .0113647 .4584186
295 | .1965625 .0719356 2.73 0.006 .0555714 .3375536
296 | .2558414 .1342931 1.91 0.057 -.0073683 .519051
297 | .3467153 .2311446 1.50 0.134 -.1063198 .7997503
298 | .2470408 .0645706 3.83 0.000 .1204847 .3735968
299 | -.291278 .0440853 -6.61 0.000 -.3776836 -.2048724
300 | -.0326524 .2008097 -0.16 0.871 -.4262322 .3609274
301 | -.023373 .0892984 -0.26 0.794 -.1983946 .1516487
302 | .2280407 .1379926 1.65 0.098 -.0424199 .4985013
303 | .3161888 .099734 3.17 0.002 .1207138 .5116639
304 | .109996 .0779635 1.41 0.158 -.0428097 .2628017
306 | .0506314 .403735 0.13 0.900 -.7406747 .8419375
307 | -.4684169 .2255777 -2.08 0.038 -.910541 -.0262928
309 | .2133622 .0602292 3.54 0.000 .095315 .3314093
310 | .1617903 .0351098 4.61 0.000 .0929763 .2306042
311 | -.0192113 .3096359 -0.06 0.951 -.6260866 .587664
314 | .4440804 .1482472 3.00 0.003 .1535213 .7346395
315 | .1580377 .0685329 2.31 0.021 .0237156 .2923597
316 | -.2216158 .120877 -1.83 0.067 -.4585303 .0152988
317 | .1240235 .1905109 0.65 0.515 -.2493711 .497418
319 | .4758018 .0651125 7.31 0.000 .3481837 .6034199
320 | -.0906851 .1661306 -0.55 0.585 -.416295 .2349249
321 | .2125454 .0901161 2.36 0.018 .0359212 .3891697
322 | .0564791 .1340737 0.42 0.674 -.2063005 .3192587
323 | .0290089 .0560552 0.52 0.605 -.0808572 .138875
325 | .0580142 .0925834 0.63 0.531 -.123446 .2394745
326 | .1600362 .0801491 2.00 0.046 .002947 .3171255
328 | .4026419 .0757887 5.31 0.000 .2540988 .5511851
329 | -.6639001 .0356307 -18.63 0.000 -.7337349 -.5940653
331 | -.2288296 .0349707 -6.54 0.000 -.2973708 -.1602884
332 | .0396693 .0681965 0.58 0.561 -.0939934 .173332
333 | .0311444 .0693589 0.45 0.653 -.1047966 .1670854
334 | -.1903436 .1010952 -1.88 0.060 -.3884865 .0077993
336 | -.0992794 .0867983 -1.14 0.253 -.2694009 .0708421
337 | -.0590828 .1306143 -0.45 0.651 -.3150822 .1969166
339 | .4101889 .1473669 2.78 0.005 .121355 .6990228
340 | -.0769234 .1220355 -0.63 0.528 -.3161087 .1622618
341 | .1759138 .1123157 1.57 0.117 -.044221 .3960486
342 | .3070744 .0800013 3.84 0.000 .1502747 .463874
344 | -12.34529 1.029686 -11.99 0.000 -14.36344 -10.32714
348 | -.0293254 .0955934 -0.31 0.759 -.216685 .1580343
349 | -.0016645 .0930732 -0.02 0.986 -.1840846 .1807556
350 | .2168165 .1891021 1.15 0.252 -.1538167 .5874497
351 | .07429 .1819272 0.41 0.683 -.2822809 .4308608
353 | .0338645 .0840969 0.40 0.687 -.1309624 .1986914
354 | .1079581 .1327402 0.81 0.416 -.1522079 .3681242
355 | .2891865 .1776667 1.63 0.104 -.0590338 .6374069
357 | -.0503639 .0649135 -0.78 0.438 -.1775919 .0768642
358 | -.1271652 .0482768 -2.63 0.008 -.221786 -.0325444
359 | .1621126 .2463093 0.66 0.510 -.3206448 .6448701
363 | .1652744 .0572048 2.89 0.004 .0531552 .2773937
381 | .2680742 .0833737 3.22 0.001 .1046648 .4314837
396 | .0634269 .1357077 0.47 0.640 -.2025553 .329409
408 | .3546149 .072967 4.86 0.000 .2116022 .4976276
409 | -.0149893 .0753563 -0.20 0.842 -.162685 .1327063
410 | .2408865 .081701 2.95 0.003 .0807555 .4010175
411 | -.1229853 .0901284 -1.36 0.172 -.2996337 .0536632
412 | .2426856 .1740914 1.39 0.163 -.0985274 .5838985
413 | .2072984 .1416633 1.46 0.143 -.0703566 .4849534
414 | .2251121 .1250205 1.80 0.072 -.0199235 .4701477
415 | .3361365 .255581 1.32 0.188 -.1647931 .8370661
416 | .0455123 .1641154 0.28 0.782 -.276148 .3671727
418 | -.2054615 .1559766 -1.32 0.188 -.5111699 .1002469
419 | .0757543 .0581956 1.30 0.193 -.038307 .1898157
420 | -.1344135 .2467802 -0.54 0.586 -.6180937 .3492667
423 | .1406605 .1641673 0.86 0.392 -.1811014 .4624224
424 | .2739782 .1085204 2.52 0.012 .0612822 .4866743
427 | .133644 .1104307 1.21 0.226 -.0827962 .3500843
428 | -.2010239 .1610079 -1.25 0.212 -.5165934 .1145457
429 | .2540993 .1280242 1.98 0.047 .0031764 .5050222
430 | .1242195 .1638104 0.76 0.448 -.1968429 .4452819
433 | -.1984485 .1292221 -1.54 0.125 -.4517191 .0548221
434 | .1348158 .0895985 1.50 0.132 -.040794 .3104256
Constituency |
2 | -6.767896 1.250029 -5.41 0.000 -9.217907 -4.317884
3 | -6.147615 1.254376 -4.90 0.000 -8.606148 -3.689083
7 | -4.092442 .4738542 -8.64 0.000 -5.021179 -3.163705
8 | -8.762419 1.478712 -5.93 0.000 -11.66064 -5.864197
9 | -5.810071 .6202677 -9.37 0.000 -7.025773 -4.594369
10 | -4.832858 .2811037 -17.19 0.000 -5.383811 -4.281905
11 | -6.557308 2.079503 -3.15 0.002 -10.63306 -2.481556
12 | -5.368246 .7684674 -6.99 0.000 -6.874414 -3.862077
13 | -2.77771 .6068687 -4.58 0.000 -3.967151 -1.58827
14 | -5.770983 1.510337 -3.82 0.000 -8.731189 -2.810778
15 | -3.19787 .5252015 -6.09 0.000 -4.227246 -2.168494
16 | -4.836366 .7696553 -6.28 0.000 -6.344863 -3.327869
18 | -3.770309 .8368468 -4.51 0.000 -5.410499 -2.130119
19 | -5.640576 .6578104 -8.57 0.000 -6.929861 -4.351292
20 | -5.908554 1.237148 -4.78 0.000 -8.33332 -3.483788
21 | -6.588575 1.279 -5.15 0.000 -9.09537 -4.081781
22 | -5.06064 .8768551 -5.77 0.000 -6.779245 -3.342036
23 | -5.184151 .7121997 -7.28 0.000 -6.580036 -3.788265
25 | -4.596716 .2837211 -16.20 0.000 -5.152799 -4.040633
26 | -5.389818 .4829199 -11.16 0.000 -6.336323 -4.443312
28 | -5.423824 .8235405 -6.59 0.000 -7.037934 -3.809714
31 | -8.437725 1.945799 -4.34 0.000 -12.25142 -4.624028
33 | -5.68043 1.088294 -5.22 0.000 -7.813448 -3.547413
34 | -3.770749 1.302201 -2.90 0.004 -6.323016 -1.218483
35 | -7.641496 2.475247 -3.09 0.002 -12.49289 -2.790102
36 | -5.317102 1.150518 -4.62 0.000 -7.572075 -3.062129
37 | -19.45614 9.001638 -2.16 0.031 -37.09902 -1.81325
38 | -5.627724 3.848284 -1.46 0.144 -13.17022 1.914774
39 | -11.39914 3.890688 -2.93 0.003 -19.02475 -3.773531
47 | -9.998111 2.353572 -4.25 0.000 -14.61103 -5.385195
48 | -5.619795 .4949347 -11.35 0.000 -6.589849 -4.649741
50 | -8.177966 .742685 -11.01 0.000 -9.633602 -6.72233
51 | -4.759702 .5145685 -9.25 0.000 -5.768237 -3.751166
52 | -6.029028 .9225236 -6.54 0.000 -7.837141 -4.220915
55 | -4.668295 .7494135 -6.23 0.000 -6.137119 -3.199472
56 | -5.276844 .9829937 -5.37 0.000 -7.203477 -3.350212
58 | -8.038333 3.674721 -2.19 0.029 -15.24065 -.8360112
59 | -4.780714 .8041488 -5.95 0.000 -6.356816 -3.204611
61 | -5.145956 .5563156 -9.25 0.000 -6.236315 -4.055598
62 | -4.180979 1.073345 -3.90 0.000 -6.284696 -2.077262
63 | -6.275662 .8351154 -7.51 0.000 -7.912458 -4.638866
65 | -6.135146 .8899945 -6.89 0.000 -7.879503 -4.390788
(deleted other constituency FE due to character limit)
--------------------------------------------------------------------------------------------
Note: 1666 failures and 0 successes completely determined.
convergence not achieved
r(430);

PS- I have tried firth logit also
It gives me
"
. firthlogit roads_any_project_LS c.Roads_index#i.Constituency i.Constituency

initial: penalized log likelihood = -<inf> (could not be evaluated)
"

[Carlo Lazzaro]

Ajay:
the footnote just below the outcome table explains the reason of the issue you're facing.
In addition, your specification does not allow any dissemination of your results: it is hard to believe that just two predictors along with their interaction can give a fair and true view of the data generating process that you're studying.
Eventually, I'd recommend you to start with revising your code as follows (please double-check if -c.Roads_index- is a continuous variable):

logit roads_any_project_LS c.Roads_index##i.Constituency, r cluster(BLOCK) noconstant

[Ajay Saharan]

Sir,
I have also used the command "logit roads_any_project_LS c.Roads_index##i.Constituency $xlist, r cluster(BLOCK) noconstant" earlier. From 12th iteration, it shows
"Iteration 12: log pseudolikelihood = -30070.523 (not concave)
Iteration 13: log pseudolikelihood = -30070.523 (not concave)
Iteration 14: log pseudolikelihood = -30070.523 (not concave)
Iteration 15: log pseudolikelihood = -30070.523 (not concave)
Iteration 16: log pseudolikelihood = -30070.523 (not concave)"
I have controls like hhd_total bpl_hhd_total i.Post_office_in_village i.Public_transport in the xlist. The regression "logit roads_any_project_LS Roads_index i.Constituency $xlist, r cluster(BLOCK)" works just fine. But I want to know the constituency-specific coefficients and that's why I include the interaction term.

[Joseph Coveney]

. . . I want to know the constituency-specific coefficients and that's why I include the interaction term.

With such a rare outcome (fewer than 2% of villages among your 314 constituencies) you're liable to be out of luck.

It's a bit of a long shot, but you might be able to get something via a so-called borrowing-strength approach; something like the following.

rename (roads_any_project_LS Roads_index Constituency) (rap rin cty)
melogit rap || cty: rin, covariance(unstructured)
predict double ebm*, reffects
bysort cty: generate byte first = _n == 1

list cty ebm? if first, noobs // These are the constituency-specific slopes (ebm1) and intercepts (ebm2) for "Roads_index"

They'll be subject to so-called shrinkage, but that'll be unavoidable, and maybe even a blessing in disguise, given the dataset that you've got to work with.

[Joseph Coveney]

melogit rap || cty: rin, covariance(unstructured)[/code]

It'll be better to include Roads_index in the fixed effects equation, too, in order to capture the population mean slope.

melogit rap c.rin || cty: rin, covariance(unstructured)

[Ajay Saharan]

It'll be better to include Roads_index in the fixed effects equation, too, in order to capture the population mean slope.

melogit rap c.rin || cty: rin, covariance(unstructured)

This is really helpful. Thank you for this.