Load data

data <- read_rds('../../temp/data_reg.rds')

Regression data prep

Sorting etc

# Just to be sure
data %<>%
  distinct(AID, year, .keep_all = TRUE) %>%
  relocate(AID, year, type, transition, transited_t, seniority)

Lagged variables

# Decide what to lag
data %<>%
  arrange(AID, year) %>%
  group_by(AID) %>%
  mutate(
    across(c(concepts,
             DL,
             paper_n, 
             citation_n, 
             author_mean,
             oa_mean, 
             novelty_mean, 
             deg_cen, 
             deg_cen_comp, 
             novelty_fw, 
             citation_fw,
             aff_citation_fw,
             aff_novelty_fw
             ), ~ lag(.x, 1))
  ) %>%
  ungroup()

# Some last preprocessing
data %<>%
  filter(year >= 2000,
         year <= 2021) %>%
  mutate(co_author_industry = deg_cen_comp > 0) %>% #,year_min = ifelse(year_min < 2000, 2000, year_min))
    group_by(AID) %>%
  mutate(t = 1:n(),
         t_max = n()) %>%
  ungroup()

# Check missing data
data  %>% naniar::gg_miss_var()

# Replace again missing
data %<>%   
  group_by(AID) %>%
    arrange(AID, year) %>%
    fill(DL, concepts, 
         aff_id, aff_type, aff_novelty_fw, aff_citation_fw, 
         novelty_fw, citation_fw, paper_n, oa_mean, novelty_mean, deg_cen_comp, deg_cen, # IS not really correct, but can be done. Consider
         co_author_industry, citation_n, author_mean, # IS not really correct, but can be done. Consider
         .direction = "downup") %>%
  ungroup()

# Replace NA. All others are by construction
data %<>%
  replace_na(list(DL = 0, concepts = 'unknown'))

data %>% count(concepts, sort = TRUE) %>% 
  mutate(rank_pct = percent_rank(n))

Survival analysis

data_surv <- data %>% 
  # Filter for last observation (=right cernsoring or transition)
  filter(is.na(transited_t) | transited_t <= 1) %>%
  group_by(AID) %>%
  slice_max(order_by = t, n= 1, with_ties = FALSE) %>%
  ungroup() %>%
  # Add some variables
  mutate(auth_star = citation_fw >= 0.9,
         aff_star = aff_citation_fw >= 0.9,
         )


# Previous filtering approach:
# filter(  (type == 'education' & year == year_max) | (type == 'switcher' & transition == 1)) %>%

data_surv %>% count(type)

# Surf object
surv_object <- Surv(time = data_surv$t, event = data_surv$transition)

table(surv_object)

surv_object
    1     2     3     4    4+     5    5+     6    6+     7    7+     8    8+     9    9+    10   10+    11   11+    12   12+    13   13+    14   14+    15 
    2    71    97   117 17504   110 13583   128 10859    95  8588    63  7404    51  6341    33  5430    19  5214    27  4649    16  4068    13  3409     3 
  15+    16   16+    17   17+    18   18+    19   19+    20   20+    21   21+   22+ 
 3012     7  2689     3  2276     8  1917     4  1739     5  1610     9  1547  2576

Kaplan-Meier Method and Log Rank Test

fit_km0 <- survfit(surv_object ~ 1, 
                   data = data_surv)
fit_km1 <- survfit(surv_object ~ DL, 
                   data = data_surv)
fit_km2 <- survfit(surv_object ~ auth_star, 
                   data = data_surv)
fit_km3 <- survfit(surv_object ~ aff_star,
                   data = data_surv)

p1 <- fit_km0 %>% autoplot() + labs(title= 'KM: baseline')
p2 <- fit_km1 %>% autoplot() + labs(title= 'KM: DL researcher')
p3 <- fit_km2 %>% autoplot() + labs(title= 'KM: Star researcher')
p4 <- fit_km3 %>% autoplot() + labs(title= 'KM: Star institution')

(p1 + p2) / (p3 + p4) +  plot_layout(guides = "collect") & theme(legend.position = 'bottom') & labs(x = 'Time', y = 'Survival Probability')

rm(fit_km0, fit_km1, fit_km2, fit_km3,
   p1, p2, p3, p4)

Cox Proportional Hazard Model

### Fit Cox proportional hazard model
set.seed(1337)

#Baseline
fit_cox0 <- coxph(surv_object ~ seniority + concepts + year,
                  data = data_surv)

Warning: Loglik converged before variable  13,31,38,40,43,48,51,53,54,56,58,60,61,62,64,67,70,72,76,78,79,85,87,89,90,94,95,96,98,99,101 ; coefficient may be infinite.

fit_cox1 <- coxph(surv_object ~ seniority + concepts + year 
                  + DL,
                  data = data_surv)

Warning: Loglik converged before variable  13,31,38,40,43,48,51,53,54,56,58,60,61,62,64,67,70,72,76,78,79,85,87,89,90,94,95,96,98,99,101 ; coefficient may be infinite.

fit_cox2 <- coxph(surv_object ~ seniority + concepts + year 
                  + deg_cen + deg_cen_comp,
                  data = data_surv)

Warning: Loglik converged before variable  55 ; coefficient may be infinite.

fit_cox3 <- coxph(surv_object ~ seniority + concepts + year 
                  + citation_fw + novelty_fw,
                  data = data_surv)

Warning: Loglik converged before variable  13,31,38,40,48,51,53,54,56,58,60,61,62,67,70,72,76,78,79,85,89,90,94,95,96,98,99,101 ; coefficient may be infinite.

fit_cox4 <- coxph(surv_object ~ seniority + concepts + year 
                  + aff_citation_fw,
                  data = data_surv)

Warning: Loglik converged before variable  13,31,38,40,43,48,51,53,54,56,58,60,61,62,64,67,70,72,76,78,79,85,87,89,90,94,95,96,98,99,101 ; coefficient may be infinite.

fit_cox5 <- coxph(surv_object ~ seniority + concepts + year 
                  + DL + deg_cen + deg_cen_comp + citation_fw + novelty_fw + aff_citation_fw,
                  data = data_surv)

#stargazer(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5, type = 'latex', 
# omit = c( 'concepts', 'year', 'Constant'), out ='../../output/surv_res1.tex')

#stargazer(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5, type = 'text',  omit = c( 'concepts', 'year', 'Constant'))

stargazer(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5, type = 'html', omit = c( 'concepts', 'year', 'Constant'))

Warning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: number of rows of result is not a multiple of vector length (arg 2)Warning: number of rows of result is not a multiple of vector length (arg 2)Warning: number of rows of result is not a multiple of vector length (arg 2)Warning: number of rows of result is not a multiple of vector length (arg 2)Warning: number of rows of result is not a multiple of vector length (arg 2)


	Dependent variable:

	surv_object
	(1)	(2)	(3)	(4)	(5)	(6)

seniority	-0.232^***	-0.226^***	-0.239^***	-0.213^***	-0.220^***	-0.209^***
	(0.011)	(0.011)	(0.011)	(0.011)	(0.011)	(0.011)

DL		1.349^***				0.692^***
		(0.076)				(0.081)

deg_cen			0.190^***			-0.308^***
			(0.028)			(0.047)

deg_cen_comp			0.129^***			0.127^***
			(0.007)			(0.007)

citation_fw				4.261^***		4.181^***
				(0.130)		(0.156)

novelty_fw				-0.876^***		-0.880^***
				(0.157)		(0.159)

aff_citation_fw					5.379^***	0.292
					(0.275)	(0.326)


Observations	105,296	105,296	105,296	105,296	105,296	105,296
R²	0.017	0.020	0.020	0.030	0.020	0.033
Max. Possible R²	0.169	0.169	0.169	0.169	0.169	0.169
Log Likelihood	-8,867.794	-8,709.206	-8,684.612	-8,173.723	-8,697.137	-8,017.109
Wald Test	262.910^*** (df = 99)	597.870^*** (df = 100)	2,188.910^*** (df = 70)	1,383.660^*** (df = 98)	642.810^*** (df = 100)	3,284.940^*** (df = 74)
LR Test	1,781.754^*** (df = 99)	2,098.929^*** (df = 100)	2,148.117^*** (df = 70)	3,169.896^*** (df = 98)	2,123.067^*** (df = 100)	3,483.124^*** (df = 74)
Score (Logrank) Test	1,560.421^*** (df = 99)	1,926.216^*** (df = 100)	7,626.015^*** (df = 70)	3,211.857^*** (df = 98)	1,954.019^*** (df = 100)	8,859.206^*** (df = 74)

Note:	p<0.1; p<0.05; p<0.01

#fit_cox5 %>% ggforest(data = data_surv)

#save models
list(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5) %>% write_rds('../../temp/reg_surv1.rds')

#rm(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5)

Propensity Score matching

library(MatchIt)

data_match <- data %>%
  filter(type == 'education' | type == 'switcher' & transition == 1) %>%
  left_join(read_rds('../../temp/author_concepts_main.rds') %>% select(AID, concepts) %>% rename(concept_main = concepts), by = 'AID') %>%
  select(-transition, -transited_t, -year_transit) %>%
  mutate(type = (type %>% factor() %>% as.numeric()) -1,
         aff_id = aff_id %>% factor(),
         concepts = concepts %>% factor(),
         concept_main = concept_main %>% factor()) %>%
  drop_na()

# Do the PSM
set.seed(1337)
match_out <-  data_match %>% 
  # Matching
  matchit(type ~ paper_n + author_mean + oa_mean + DL + t + t_max + co_author_industry + aff_citation_fw + aff_citation_fw, exact = c('year', 'seniority', 'concept_main'),
          data = ., verbose = FALSE, method = "nearest", ratio = 1) # OR method  "genetic" ... takes forever.....

# GEt the matched pairs
matched_pairs  <- bind_cols(data_match[row.names(match_out$match.matrix),"AID"] , data_match[match_out$match.matrix,"AID"] ) %>%
  rename(orig_AID = `AID...1`,
         match_AID = `AID...2`)

New names:

# construct the matched dataset
data_did <- matched_pairs %>% 
  mutate(pair_id = 1:n())

data_did <- data_did %>%
  select(orig_AID, pair_id) %>%
  rename(AID = orig_AID) %>%
  bind_rows(data_did %>%
  select(match_AID, pair_id) %>%
  rename(AID = match_AID) ) %>%
  arrange(pair_id) %>%
  left_join(data, by = 'AID')

# compute transition point for matched partner
data_did %<>%
  group_by(pair_id) %>%
  mutate(switcher = type == 'switcher',
         year_transit = max(year_transit, na.rm = TRUE),
         transition = year == year_transit,
         transited = year >= year_transit,
         transited_t = year - year_transit + 1) %>%
  mutate(across(c(year, concepts, transited), ~ factor(.x))) %>%
  mutate(transited_t = ifelse(transited_t > 0, transited_t, 0)) %>%
  drop_na()

data_did %>% saveRDS('../temp_reg/reg_data_did.rds')

Diff-in-Diff

# Delete all objects
rm(list = ls(all.names = TRUE)) #will clear all objects includes hidden objects.
gc() #free up memrory and report the memory usage.

           used  (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells  3874576 207.0   12010248   641.5         NA   12010248   641.5
Vcells 23545267 179.7 5445164727 41543.4      65536 8507759093 64909.1

data_did <- read_rds('../temp_reg/reg_data_did.rds')

# For the interaction plot code logical to numeric
# data_did %<>%  mutate(switcher = switcher %>% as.numeric(), transited = transited %>% as.numeric())

data_did %>% 
  count(concepts, sort = TRUE) %>% 
  mutate(rank_pct = percent_rank(n))

Models

# fit_did_0 <- lm(citation_rank ~ switcher + field_of_study_name, data = data_did)

fit_did_1 <- lm(citation_fw ~ (type + transited)^2 + year + seniority + year, # + concepts, 
                data = data_did)

fit_did_2 <- lm(citation_fw ~ (type + transited)^2 + (type + transited_t)^2 + year  + seniority, # + concepts, 
                data = data_did)

fit_did_3 <- lm(novelty_fw ~ (type + transited)^2 + year + seniority + year, # + concepts, 
                data = data_did)

fit_did_4 <- lm(novelty_fw ~ (type + transited)^2 + (type + transited_t)^2 + year + seniority, # + concepts , 
                data = data_did)

# + paper_n ?

#stargazer(fit_did_1, fit_did_2, fit_did_3, fit_did_4, type = 'latex', omit = c( 'concepts', 'year', 'Constant'), out ='../../output/did_res1.tex')

stargazer(fit_did_1, fit_did_2, fit_did_3, fit_did_4, type = 'text', omit = c('year', 'Constant'))

Warning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: number of rows of result is not a multiple of vector length (arg 2)Warning: number of rows of result is not a multiple of vector length (arg 2)


========================================================================================================================================
                                                                       Dependent variable:                                              
                         ---------------------------------------------------------------------------------------------------------------
                                               citation_fw                                             novelty_fw                       
                                     (1)                         (2)                         (3)                         (4)            
----------------------------------------------------------------------------------------------------------------------------------------
typeswitcher                      0.050***                    0.051***                      0.007                       0.007           
                                   (0.007)                     (0.007)                     (0.005)                     (0.005)          
                                                                                                                                        
transited                         0.123***                    0.203***                    0.101***                    0.112***          
                                   (0.009)                     (0.011)                     (0.005)                     (0.007)          
                                                                                                                                        
transited_t                                                   -0.021***                                               -0.003***         
                                                               (0.002)                                                 (0.001)          
                                                                                                                                        
seniority                         0.004***                    0.007***                    0.006***                    0.006***          
                                  (0.0004)                    (0.0004)                    (0.0002)                    (0.0002)          
                                                                                                                                        
typeswitcher:transited            0.114***                    0.172***                    -0.020***                    -0.010           
                                   (0.011)                     (0.014)                     (0.007)                     (0.009)          
                                                                                                                                        
typeswitcher:transited_t                                      -0.015***                                                -0.003*          
                                                               (0.002)                                                 (0.001)          
                                                                                                                                        
----------------------------------------------------------------------------------------------------------------------------------------
Observations                       17,151                      17,151                      17,151                      17,151           
R2                                  0.165                       0.195                       0.142                       0.143           
Adjusted R2                         0.164                       0.194                       0.140                       0.142           
Residual Std. Error          0.349 (df = 17125)          0.343 (df = 17123)          0.222 (df = 17125)          0.222 (df = 17123)     
F Statistic              135.153*** (df = 25; 17125) 153.683*** (df = 27; 17123) 113.069*** (df = 25; 17125) 106.066*** (df = 27; 17123)
========================================================================================================================================
Note:                                                                                                        *p<0.1; **p<0.05; ***p<0.01

stargazer(fit_did_1, fit_did_2, fit_did_3, fit_did_4, type = 'html', omit = c('year', 'Constant'))


	Dependent variable:

	citation_fw		novelty_fw
	(1)	(2)	(3)	(4)

typeswitcher	0.050^***	0.051^***	0.007	0.007
	(0.007)	(0.007)	(0.005)	(0.005)

transited	0.123^***	0.203^***	0.101^***	0.112^***
	(0.009)	(0.011)	(0.005)	(0.007)

transited_t		-0.021^***		-0.003^***
		(0.002)		(0.001)

seniority	0.004^***	0.007^***	0.006^***	0.006^***
	(0.0004)	(0.0004)	(0.0002)	(0.0002)

typeswitcher:transited	0.114^***	0.172^***	-0.020^***	-0.010
	(0.011)	(0.014)	(0.007)	(0.009)

typeswitcher:transited_t		-0.015^***		-0.003^*
		(0.002)		(0.001)


Observations	17,151	17,151	17,151	17,151
R²	0.165	0.195	0.142	0.143
Adjusted R²	0.164	0.194	0.140	0.142
Residual Std. Error	0.349 (df = 17125)	0.343 (df = 17123)	0.222 (df = 17125)	0.222 (df = 17123)
F Statistic	135.153^*** (df = 25; 17125)	153.683^*** (df = 27; 17123)	113.069^*** (df = 25; 17125)	106.066^*** (df = 27; 17123)

Note:	p<0.1; p<0.05; p<0.01

#library(interplot)
#fit_did_2 %>% interplot(var1 ="switcher", var2 = "transited_t", hist = TRUE)

library(interactions)
fit_did_2 %>% interact_plot(pred = transited_t, modx = switcher,
                            interval = TRUE) +
  theme(legend.position = 'bottom')

ggsave('../../output/fig_interaction_1.png', width = 10, height = 7.5, units = 'cm')

library(interactions)
fit_did_2 %>% interact_plot(pred = transited_t, modx = switcher,
                            interval = TRUE, linearity.check = TRUE)

—>

Descriptives

data %>% select(seniority, deg_cen, deg_cen_comp, co_author_industry, DL, paper_n, author_mean, citation_fw, novelty_fw, aff_citation_fw, aff_novelty_fw) %>% as.data.frame() %>%
  stargazer(summary = TRUE, type = 'html')

By university vs. switcher career

data %>% 
  select(type, seniority, deg_cen, deg_cen_comp, co_author_industry, DL, paper_n, author_mean, citation_fw, novelty_fw, aff_citation_fw, aff_novelty_fw) %>%
  as.data.frame() %>%
  split(.$type) %>% 
  walk(~ stargazer(., summary = TRUE, type = 'html'))

data %>% 
  filter(transited_t <= 0) %>%
  mutate(auth_star = citation_fw >= 0.9,
         aff_star = aff_citation_fw >= 0.9)

–>

---
title: 'Privatization of AI research: Executing regressions'
author: "Daniel S. Hain (dsh@business.aau.dk)"
date: "Updated `r format(Sys.time(), '%B %d, %Y')`"
output:
  html_notebook:
    code_folding: hide
    df_print: paged
    toc: false
    toc_depth: 2
    toc_float:
      collapsed: false
    theme: flatly
---

```{r setup, include=FALSE}
# Knitr options
### Generic preamble
rm(list = ls(all.names = TRUE)) #will clear all objects includes hidden objects.
gc() #free up memrory and report the memory usage.
graphics.off()

Sys.setenv(LANG = "en") # For english language
options(scipen = 5) # To deactivate annoying scientific number notation

### Load packages
library(tidyverse) # Collection of all the good stuff like dplyr, ggplot2 ect.
library(magrittr) # For extra-piping operators (eg. %<>%)

# Descriptives
#library(skimr)
library(stargazer)

# Viz
library(patchwork)

# Survival analysis
library(survival)
library(survminer)
library(ggfortify)
```

# Load data

```{r}
data <- read_rds('../../temp/data_reg.rds')
```


# Regression data prep

## Sorting etc

```{r}
# Just to be sure
data %<>%
  distinct(AID, year, .keep_all = TRUE) %>%
  relocate(AID, year, type, transition, transited_t, seniority) 
```

## Lagged variables

```{r}
# Decide what to lag
data %<>%
  arrange(AID, year) %>%
  group_by(AID) %>%
  mutate(
    across(c(concepts,
             DL,
             paper_n, 
             citation_n, 
             author_mean,
             oa_mean, 
             novelty_mean, 
             deg_cen, 
             deg_cen_comp, 
             novelty_fw, 
             citation_fw,
             aff_citation_fw,
             aff_novelty_fw
             ), ~ lag(.x, 1))
  ) %>%
  ungroup()
```

```{r}
# Some last preprocessing
data %<>%
  filter(year >= 2000,
         year <= 2021) %>%
  mutate(co_author_industry = deg_cen_comp > 0) %>% #,year_min = ifelse(year_min < 2000, 2000, year_min))
    group_by(AID) %>%
  mutate(t = 1:n(),
         t_max = n()) %>%
  ungroup()
```

```{r}
# Check missing data
data  %>% naniar::gg_miss_var()
```

```{r}
# Replace again missing
data %<>%   
  group_by(AID) %>%
    arrange(AID, year) %>%
    fill(DL, concepts, 
         aff_id, aff_type, aff_novelty_fw, aff_citation_fw, 
         novelty_fw, citation_fw, paper_n, oa_mean, novelty_mean, deg_cen_comp, deg_cen, # IS not really correct, but can be done. Consider
         co_author_industry, citation_n, author_mean, # IS not really correct, but can be done. Consider
         .direction = "downup") %>%
  ungroup() 
```

```{r}
# Replace NA. All others are by construction
data %<>%
  replace_na(list(DL = 0, concepts = 'unknown'))
```

```{r}
data %>% count(concepts, sort = TRUE) %>% 
  mutate(rank_pct = percent_rank(n))
```

# Survival analysis

```{r}
data_surv <- data %>% 
  # Filter for last observation (=right cernsoring or transition)
  filter(is.na(transited_t) | transited_t <= 1) %>%
  group_by(AID) %>%
  slice_max(order_by = t, n= 1, with_ties = FALSE) %>%
  ungroup() %>%
  # Add some variables
  mutate(auth_star = citation_fw >= 0.9,
         aff_star = aff_citation_fw >= 0.9,
         )


# Previous filtering approach:
# filter(  (type == 'education' & year == year_max) | (type == 'switcher' & transition == 1)) %>%
```

```{r}
data_surv %>% count(type)
```

```{r}
# Surf object
surv_object <- Surv(time = data_surv$t, event = data_surv$transition)
```

```{r}
table(surv_object)
```

## Kaplan-Meier Method and Log Rank Test

```{r}
fit_km0 <- survfit(surv_object ~ 1, 
                   data = data_surv)
fit_km1 <- survfit(surv_object ~ DL, 
                   data = data_surv)
fit_km2 <- survfit(surv_object ~ auth_star, 
                   data = data_surv)
fit_km3 <- survfit(surv_object ~ aff_star,
                   data = data_surv)
```

```{r}
p1 <- fit_km0 %>% autoplot() + labs(title= 'KM: baseline')
p2 <- fit_km1 %>% autoplot() + labs(title= 'KM: DL researcher')
p3 <- fit_km2 %>% autoplot() + labs(title= 'KM: Star researcher')
p4 <- fit_km3 %>% autoplot() + labs(title= 'KM: Star institution')

(p1 + p2) / (p3 + p4) +  plot_layout(guides = "collect") & theme(legend.position = 'bottom') & labs(x = 'Time', y = 'Survival Probability')
```

```{r}
rm(fit_km0, fit_km1, fit_km2, fit_km3,
   p1, p2, p3, p4)
```


## Cox Proportional Hazard Model

```{r}
### Fit Cox proportional hazard model
set.seed(1337)

#Baseline
fit_cox0 <- coxph(surv_object ~ seniority + concepts + year,
                  data = data_surv)

fit_cox1 <- coxph(surv_object ~ seniority + concepts + year 
                  + DL,
                  data = data_surv)

fit_cox2 <- coxph(surv_object ~ seniority + concepts + year 
                  + deg_cen + deg_cen_comp,
                  data = data_surv)

fit_cox3 <- coxph(surv_object ~ seniority + concepts + year 
                  + citation_fw + novelty_fw,
                  data = data_surv)

fit_cox4 <- coxph(surv_object ~ seniority + concepts + year 
                  + aff_citation_fw,
                  data = data_surv)

fit_cox5 <- coxph(surv_object ~ seniority + concepts + year 
                  + DL + deg_cen + deg_cen_comp + citation_fw + novelty_fw + aff_citation_fw,
                  data = data_surv)
```


```{r}
#stargazer(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5, type = 'latex', 
# omit = c( 'concepts', 'year', 'Constant'), out ='../../output/surv_res1.tex')
```

```{r}
#stargazer(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5, type = 'text',  omit = c( 'concepts', 'year', 'Constant'))
```

```{r, results='asis'}
stargazer(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5, type = 'html', omit = c( 'concepts', 'year', 'Constant'))
```

```{r}
#fit_cox5 %>% ggforest(data = data_surv)
```

```{r}
#save models
list(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5) %>% write_rds('../../temp/reg_surv1.rds')
```

```{r}
#rm(fit_cox0, fit_cox1, fit_cox2, fit_cox3, fit_cox4, fit_cox5)
```

# Propensity Score matching 

```{r}
library(MatchIt)
```

```{r}
data_match <- data %>%
  filter(type == 'education' | type == 'switcher' & transition == 1) %>%
  left_join(read_rds('../../temp/author_concepts_main.rds') %>% select(AID, concepts) %>% rename(concept_main = concepts), by = 'AID') %>%
  select(-transition, -transited_t, -year_transit) %>%
  mutate(type = (type %>% factor() %>% as.numeric()) -1,
         aff_id = aff_id %>% factor(),
         concepts = concepts %>% factor(),
         concept_main = concept_main %>% factor()) %>%
  drop_na()
```

```{r}
# Do the PSM
set.seed(1337)
match_out <-  data_match %>% 
  # Matching
  matchit(type ~ paper_n + author_mean + oa_mean + DL + t + t_max + co_author_industry + aff_citation_fw + aff_citation_fw, exact = c('year', 'seniority', 'concept_main'),
          data = ., verbose = FALSE, method = "nearest", ratio = 1) # OR method  "genetic" ... takes forever.....
```

```{r}
# GEt the matched pairs
matched_pairs  <- bind_cols(data_match[row.names(match_out$match.matrix),"AID"] , data_match[match_out$match.matrix,"AID"] ) %>%
  rename(orig_AID = `AID...1`,
         match_AID = `AID...2`) 
```

```{r}
# construct the matched dataset
data_did <- matched_pairs %>% 
  mutate(pair_id = 1:n())

data_did <- data_did %>%
  select(orig_AID, pair_id) %>%
  rename(AID = orig_AID) %>%
  bind_rows(data_did %>%
  select(match_AID, pair_id) %>%
  rename(AID = match_AID) ) %>%
  arrange(pair_id) %>%
  left_join(data, by = 'AID')
```

```{r}
# compute transition point for matched partner
data_did %<>%
  group_by(pair_id) %>%
  mutate(switcher = type == 'switcher',
         year_transit = max(year_transit, na.rm = TRUE),
         transition = year == year_transit,
         transited = year >= year_transit,
         transited_t = year - year_transit + 1) %>%
  mutate(across(c(year, concepts, transited), ~ factor(.x))) %>%
  mutate(transited_t = ifelse(transited_t > 0, transited_t, 0)) %>%
  drop_na() %>%
  ungroup()
```

```{r}
data_did %>% saveRDS('../temp_reg/reg_data_did.rds')
```

# Diff-in-Diff

```{r}
# Delete all objects
rm(list = ls(all.names = TRUE)) #will clear all objects includes hidden objects.
gc() #free up memrory and report the memory usage.

data_did <- read_rds('../temp_reg/reg_data_did.rds')
```

```{r}
# For the interaction plot code logical to numeric
# data_did %<>%  mutate(switcher = switcher %>% as.numeric(), transited = transited %>% as.numeric())
```

```{r}
data_did %>% 
  count(concepts, sort = TRUE) %>% 
  mutate(rank_pct = percent_rank(n))
```



## Models

```{r}
# fit_did_0 <- lm(citation_rank ~ switcher + field_of_study_name, data = data_did)

fit_did_1 <- lm(citation_fw ~ (type + transited)^2 + year + seniority + year, # + concepts, 
                data = data_did)

fit_did_2 <- lm(citation_fw ~ (type + transited)^2 + (type + transited_t)^2 + year  + seniority, # + concepts, 
                data = data_did)

fit_did_3 <- lm(novelty_fw ~ (type + transited)^2 + year + seniority + year, # + concepts, 
                data = data_did)

fit_did_4 <- lm(novelty_fw ~ (type + transited)^2 + (type + transited_t)^2 + year + seniority, # + concepts , 
                data = data_did)

# + paper_n ?
```

```{r}
#stargazer(fit_did_1, fit_did_2, fit_did_3, fit_did_4, type = 'latex', omit = c( 'concepts', 'year', 'Constant'), out ='../../output/did_res1.tex')
```

```{r}
stargazer(fit_did_1, fit_did_2, fit_did_3, fit_did_4, type = 'text', omit = c('year', 'Constant'))
```

```{r, results='asis'}
stargazer(fit_did_1, fit_did_2, fit_did_3, fit_did_4, type = 'html', omit = c('year', 'Constant'))
```



<!---
### Template new plotting model

```{r}
#library(interplot)
#fit_did_2 %>% interplot(var1 ="switcher", var2 = "transited_t", hist = TRUE) 
```

```{r}
library(interactions)
fit_did_2 %>% interact_plot(pred = transited_t, modx = switcher,
                            interval = TRUE) +
  theme(legend.position = 'bottom')
```

```{r}
ggsave('../../output/fig_interaction_1.png', width = 10, height = 7.5, units = 'cm')
```


```{r}
library(interactions)
fit_did_2 %>% interact_plot(pred = transited_t, modx = switcher,
                            interval = TRUE, linearity.check = TRUE)
```


--->

# Descriptives

<!---

## All data

```{r, results='asis'}
data %>% select(seniority, deg_cen, deg_cen_comp, co_author_industry, DL, paper_n, author_mean, citation_fw, novelty_fw, aff_citation_fw, aff_novelty_fw) %>% as.data.frame() %>%
  stargazer(summary = TRUE, type = 'html')
```

## By university vs. switcher career

```{r, results='asis'}
data %>% 
  select(type, seniority, deg_cen, deg_cen_comp, co_author_industry, DL, paper_n, author_mean, citation_fw, novelty_fw, aff_citation_fw, aff_novelty_fw) %>%
  as.data.frame() %>%
  split(.$type) %>% 
  walk(~ stargazer(., summary = TRUE, type = 'html'))
```


```{r}
data %>% 
  filter(transited_t <= 0) %>%
  mutate(auth_star = citation_fw >= 0.9,
         aff_star = aff_citation_fw >= 0.9)
```


-->





Privatization of AI research: Executing regressions

Daniel S. Hain (dsh@business.aau.dk)

Updated March 28, 2023