language	n	freq
0	2745	0.9852836
1	41	0.0147164

ethnicity	n	freq
1	2502	89.81%
2	219	7.86%
999	65	2.33%

role	first	second	total	freq
0	1059	353	1412	100%
1	1019	355	1374	100%

Some descriptives:

City	Wave	Sender	Responder	Total
Kazan'	Both	108	111	219
Moscow	Both	116	113	229
Arkhangelsk	First	43	51	94
Ekaterinburg	First	108	103	211
Khabarovsk	First	69	63	132
Makhachkala	First	29	19	48
Novosibirsk	First	106	104	210
Perm'	First	109	105	214
Rostov-Na-Donu	First	110	104	214
St. Petersburg	First	110	101	211
Vladivostok	First	56	55	111
Voronezh	First	108	100	208
Chelyabinsk	Second	43	42	85
Irkutsk	Second	36	36	72
Kaliningrad	Second	14	15	29
Krasnodar	Second	45	44	89
Krasnoyarsk	Second	41	42	83
Nnovgorod	Second	43	44	87
Omsk	Second	42	45	87
Sochi	Second	15	17	32
Ufa	Second	43	46	89
Volgograd	Second	46	47	93

Mean sending values per city

Mean return values per city

let’s melt the data on sender_decision_

Return decisions:

let’ build a chart showing for each city involved average sending decisions to away city, home city, and difference between them

let’s analyse sender and return decision by target cities

Reading median salary data

some fixed effects models on sender and return decisions

[1] 17028    14

	DV: odds to send full endowment
	Home city only	Distance without home cities	Distance+diff in income	Full
home_cityhome	2.562***
	(0.134)
target_cityChelyabinsk	−0.356	−0.498	−0.260	−0.166
	(0.289)	(0.317)	(0.313)	(0.289)
target_cityEkaterinburg	0.386**	0.320*	0.492***	0.582***
	(0.131)	(0.138)	(0.128)	(0.121)
target_cityIrkutsk	−0.369	0.303	0.385	0.593+
	(0.297)	(0.338)	(0.334)	(0.324)
target_cityKaliningrad	0.004	0.517+	0.771**	0.878**
	(0.269)	(0.288)	(0.286)	(0.272)
target_cityKazan'	−0.351*	−0.506***	−0.266+	−0.300*
	(0.137)	(0.147)	(0.141)	(0.131)
target_cityKhabarovsk	0.058	0.897***	0.870***	0.940***
	(0.128)	(0.201)	(0.205)	(0.191)
target_cityKrasnodar	−0.438+	−0.259	0.020	0.138
	(0.257)	(0.276)	(0.276)	(0.266)
target_cityKrasnoyarsk	−0.293	0.186	0.249	0.391
	(0.290)	(0.312)	(0.308)	(0.305)
target_cityMakhachkala	−1.528***	−1.484***	−1.009***	−0.829***
	(0.154)	(0.163)	(0.220)	(0.203)
target_cityMoscow	−0.794***	−0.753***	−1.259***	−1.510***
	(0.156)	(0.162)	(0.314)	(0.296)
target_cityNnovgorod	−0.150	−0.254	−0.013	0.117
	(0.270)	(0.294)	(0.290)	(0.270)
target_cityNovosibirsk	0.071	0.490***	0.694***	0.992***
	(0.124)	(0.144)	(0.146)	(0.130)
target_cityOmsk	−0.270	−0.236	0.085	0.253
	(0.284)	(0.302)	(0.306)	(0.291)
target_cityPerm'	−0.348**	−0.524***	−0.292*	−0.189
	(0.123)	(0.128)	(0.129)	(0.119)
target_cityRostov-Na-Donu	−0.082	−0.122	0.188	0.306*
	(0.124)	(0.134)	(0.140)	(0.126)
target_citySochi	−0.900***	−0.701**	−0.422	−0.385
	(0.258)	(0.266)	(0.267)	(0.257)
target_citySt. Petersburg	0.538***	0.622***	0.436*	0.358+
	(0.148)	(0.158)	(0.200)	(0.187)
target_cityUfa	−0.814**	−1.053***	−0.799**	−0.577*
	(0.251)	(0.278)	(0.275)	(0.247)
target_cityVladivostok	0.201	1.075***	1.103***	1.024***
	(0.125)	(0.209)	(0.206)	(0.191)
target_cityVolgograd	−0.259	−0.117	0.215	0.143
	(0.272)	(0.293)	(0.297)	(0.274)
target_cityVoronezh	−0.172	−0.296*
	(0.114)	(0.126)
distance		−0.655***	−0.655***	−1.244***
		(0.099)	(0.099)	(0.075)
I(distance^2)		0.057***	0.057***	0.136***
		(0.014)	(0.014)	(0.011)
diff_salary			0.019*	0.022**
			(0.008)	(0.007)
Num.Obs.	11064	8811	8811	11064
R2	0.300	0.234	0.234	0.297
R2 Adj.	0.173	0.095	0.095	0.170
R2 Within	0.098	0.052	0.052	0.094
R2 Within Adj.	0.094	0.047	0.047	0.090
AIC	12512.7	10978.4	10978.4	12557.6
BIC	19568.2	16964.1	16964.1	19620.4
RMSE	0.40	0.42	0.42	0.40
Std.Errors	by: participant_label	by: participant_label	by: participant_label	by: participant_label
FE: city_eng	X	X	X	X
FE: participant_label	X	X	X	X
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001

#Plotting km vs probability to donate all

return models

	DV: returned amount (as % of endowment)
	Home city only	Distance without home cities	Distance+diff in income	Full
Home Cityhome	0.096***
	(0.005)
Target cityChelyabinsk	0.009	0.003	0.004	0.015
	(0.011)	(0.011)	(0.011)	(0.010)
Target cityEkaterinburg	0.013*	0.016**	0.014**	0.018***
	(0.005)	(0.005)	(0.004)	(0.004)
Target cityIrkutsk	−0.002	0.022*	0.020+	0.029**
	(0.011)	(0.011)	(0.011)	(0.011)
Target cityKaliningrad	0.012	0.023*	0.020+	0.040***
	(0.011)	(0.010)	(0.010)	(0.010)
Target cityKazan'	−0.012*	−0.014**	−0.013**	−0.012*
	(0.005)	(0.005)	(0.004)	(0.005)
Target cityKhabarovsk	0.020**	0.044***	0.049***	0.050***
	(0.006)	(0.007)	(0.007)	(0.008)
Target cityKrasnodar	−0.015	−0.004	−0.005	0.004
	(0.010)	(0.010)	(0.010)	(0.009)
Target cityKrasnoyarsk	0.009	0.020+	0.018	0.030**
	(0.011)	(0.011)	(0.011)	(0.011)
Target cityMakhachkala	−0.032***	−0.026***	−0.030***	−0.011**
	(0.005)	(0.005)	(0.004)	(0.004)
Target cityMoscow	−0.042***	−0.041***	−0.035***	−0.064***
	(0.006)	(0.007)	(0.009)	(0.011)
Target cityNnovgorod	0.008	0.004	0.004	0.017+
	(0.010)	(0.009)	(0.009)	(0.009)
Target cityNovosibirsk	0.004	0.018**	0.010*	0.035***
	(0.005)	(0.005)	(0.004)	(0.005)
Target cityOmsk	0.005	0.012	0.010	0.022*
	(0.011)	(0.011)	(0.011)	(0.010)
Target cityPerm'	−0.001	−0.006	−0.006	0.003
	(0.005)	(0.005)	(0.004)	(0.004)
Target cityRostov-Na-Donu	0.003	0.003	0.002	0.016***
	(0.006)	(0.005)	(0.004)	(0.005)
Target citySochi	−0.023*	−0.018+	−0.018+	−0.009
	(0.010)	(0.010)	(0.010)	(0.009)
Target citySt. Petersburg	0.006	0.013*	0.016*	0.001
	(0.006)	(0.006)	(0.007)	(0.007)
Target cityUfa	−0.008	−0.015	−0.014	−0.003
	(0.010)	(0.011)	(0.011)	(0.010)
Target cityVladivostok	0.012**	0.041***	0.048***	0.041***
	(0.005)	(0.008)	(0.007)	(0.008)
Target cityVolgograd	0.009	0.010	0.010	0.022*
	(0.010)	(0.010)	(0.010)	(0.009)
Target cityVoronezh	−0.005	−0.005	−0.004	0.000
	(0.005)	(0.005)	(0.003)	(0.004)
Distance		−21.508***	−9.220***	−46.630***
		(3.514)	(1.363)	(3.178)
(Distance^2)		1893.187***		5137.126***
		(511.802)		(494.425)
Diff Salary			0.000	0.001**
			(0.000)	(0.000)
Num.Obs.	16548	15169	15169	16548
R2	0.745	0.772	0.772	0.744
R2 Adj.	0.721	0.749	0.749	0.720
R2 Within	0.060	0.026	0.025	0.057
R2 Within Adj.	0.059	0.024	0.023	0.056
AIC	−18701.4	−18895.4	−18877.9	−18651.2
BIC	−7732.1	−8042.2	−8024.6	−7666.5
RMSE	0.13	0.12	0.12	0.13
Std.Errors	by: participant_label	by: participant_label	by: participant_label	by: participant_label
FE: city_eng	X	X	X	X
FE: participant_label	X	X	X	X
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001

#Plotting km vs probability to return all

Exploring knowledge effect

Correspondence Between Values and Their Meanings
Value	Meaning
0	I live/lived/visited this region
1	From family and friends
2	From social media (vk, Instagram, etc.)
3	From mass media (newspapers, television, internet media, etc.)
4	At school or university
5	Other sources

knowledge_city	0	1	6	2	4	3	5
Arkhangelsk	1%	3%	96%	NA	NA	NA	NA
Chelyabinsk	1%	1%	94%	4%	NA	NA	NA
Ekaterinburg	1%	3%	96%	NA	NA	NA	NA
Irkutsk	7%	NA	93%	NA	NA	NA	NA
Kaliningrad	NA	NA	96%	4%	NA	NA	NA
Kazan'	0%	2%	95%	2%	1%	NA	NA
Khabarovsk	NA	1%	98%	2%	NA	NA	NA
Krasnodar	1%	4%	93%	2%	NA	NA	NA
Krasnoyarsk	3%	3%	92%	1%	NA	1%	NA
Makhachkala	NA	4%	88%	2%	4%	2%	NA
Moscow	1%	4%	89%	3%	1%	2%	1%
Nnovgorod	NA	6%	93%	NA	NA	1%	NA
Novosibirsk	0%	3%	95%	1%	NA	1%	NA
Omsk	2%	NA	96%	1%	NA	NA	NA
Perm'	NA	3%	95%	1%	NA	0%	NA
Rostov-Na-Donu	0%	0%	98%	0%	NA	0%	0%
Sochi	NA	4%	96%	NA	NA	NA	NA
St. Petersburg	0%	6%	90%	1%	2%	1%	NA
Ufa	NA	2%	98%	NA	NA	NA	NA
Vladivostok	1%	4%	94%	1%	NA	1%	NA
Volgograd	1%	NA	99%	NA	NA	NA	NA
Voronezh	0%	3%	96%	NA	NA	0%	NA

Check if knowledge intensity affects decisions

 [1] "participant_label"    "city_eng"             "wave"                
 [4] "target_city_code"     "value"                "target_city"         
 [7] "home_city"            "distance"             "value_standardized"  
[10] "value_binary"         "source_median_salary" "target_median_salary"
[13] "diff_salary"          "abs_diff_salary"      "knowledge_intensity"

	DV: odds to send full endowment
	Home city only	Distance without home cities	Distance+diff in income	Full
home_cityhome	12.962***
	(1.739)
target_cityChelyabinsk	0.700	0.581+	0.769	0.900
	(0.202)	(0.182)	(0.238)	(0.250)
target_cityEkaterinburg	1.471**	1.233	1.536***	1.578***
	(0.192)	(0.169)	(0.196)	(0.189)
target_cityIrkutsk	0.692	1.036	1.320	1.731+
	(0.205)	(0.346)	(0.429)	(0.524)
target_cityKaliningrad	1.004	1.430	1.879*	1.921*
	(0.270)	(0.412)	(0.533)	(0.499)
target_cityKazan'	0.704*	0.579***	0.773+	0.747*
	(0.096)	(0.086)	(0.108)	(0.097)
target_cityKhabarovsk	1.060	2.324***	2.857***	5.662***
	(0.135)	(0.487)	(0.595)	(1.249)
target_cityKrasnodar	0.645+	0.612+	0.956	1.091
	(0.166)	(0.171)	(0.260)	(0.279)
target_cityKrasnoyarsk	0.746	0.974	1.162	1.387
	(0.216)	(0.303)	(0.351)	(0.394)
target_cityMakhachkala	0.217***	0.226***	0.308***	0.305***
	(0.033)	(0.037)	(0.067)	(0.061)
target_cityMoscow	0.452***	0.326***	0.367**	0.404**
	(0.071)	(0.056)	(0.113)	(0.117)
target_cityNnovgorod	0.860	0.747	0.976	1.139
	(0.232)	(0.218)	(0.281)	(0.299)
target_cityNovosibirsk	1.073	1.225	1.542**	1.726***
	(0.133)	(0.168)	(0.203)	(0.215)
target_cityOmsk	0.764	0.750	0.989	1.132
	(0.217)	(0.227)	(0.300)	(0.317)
target_cityPerm'	0.706**	0.604***	0.716**	0.757*
	(0.087)	(0.077)	(0.092)	(0.090)
target_cityRostov-Na-Donu	0.921	0.800	1.148	1.257+
	(0.114)	(0.109)	(0.160)	(0.158)
target_citySochi	0.406***	0.364***	0.615+	0.644+
	(0.105)	(0.100)	(0.163)	(0.159)
target_citySt. Petersburg	1.712***	1.377+	1.693**	1.821**
	(0.254)	(0.226)	(0.337)	(0.335)
target_cityUfa	0.443**	0.364***	0.459**	0.623*
	(0.111)	(0.102)	(0.125)	(0.150)
target_cityVladivostok	1.222	2.820***	3.791***	6.995***
	(0.152)	(0.611)	(0.791)	(1.576)
target_cityVolgograd	0.771	0.781	1.183	1.102
	(0.210)	(0.226)	(0.348)	(0.292)
target_cityVoronezh	0.842	0.781*
	(0.096)	(0.098)
distance		0.792***	0.752***	0.627***
		(0.032)	(0.029)	(0.026)
knowledge_intensity		1.166***
		(0.024)
diff_salary			1.013	1.007
			(0.008)	(0.007)
Num.Obs.	11064	8767	8811	11064
R2	0.300	0.239	0.232	0.283
R2 Adj.	0.173	0.100	0.093	0.156
R2 Within	0.098	0.058	0.049	0.076
R2 Within Adj.	0.094	0.053	0.045	0.072
AIC	12512.7	10861.0	10998.9	12769.8
BIC	19568.2	16814.3	16977.6	19825.3
RMSE	0.40	0.42	0.42	0.40
Std.Errors	by: participant_label	by: participant_label	by: participant_label	by: participant_label
FE: city_eng	X	X	X	X
FE: participant_label	X	X	X	X
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001