Industry (sector data)

Data - ec

Author

Info

source	dataset	.html	.RData
ec	INDUSTRY	2024-06-19	2023-10-01

Data on industry

source	dataset	.html	.RData
ec	INDUSTRY	2024-06-19	2023-10-01
eurostat	ei_isin_m	2024-06-18	2024-06-08
eurostat	htec_trd_group4	2024-06-18	2024-06-08
eurostat	nama_10_a64	2024-06-19	2024-06-18
eurostat	nama_10_a64_e	2024-06-18	2024-06-18
eurostat	namq_10_a10_e	2024-06-19	2024-06-08
eurostat	road_eqr_carmot	2024-06-19	2024-06-08
eurostat	sts_inpp_m	2024-06-19	2024-06-18
eurostat	sts_inppd_m	2024-06-19	2024-06-08
eurostat	sts_inpr_m	2024-06-19	2024-06-08
eurostat	sts_intvnd_m	2024-06-19	2024-06-08
fred	industry	2024-06-18	2024-06-07
oecd	ALFS_EMP	2024-04-16	2024-05-12
oecd	BERD_MA_SOF	2024-04-16	2023-09-09
oecd	GBARD_NABS2007	2024-04-16	2023-11-22
oecd	MEI_REAL	2024-05-12	2024-05-03
oecd	MSTI_PUB	2024-06-19	2023-10-04
oecd	SNA_TABLE4	2024-06-18	2024-04-30
wdi	NV.IND.EMPL.KD	2024-01-06	2024-04-14
wdi	NV.IND.MANF.CD	2024-06-19	2024-06-09
wdi	NV.IND.MANF.ZS	2024-01-06	2024-04-14
wdi	NV.IND.TOTL.KD	2024-01-06	2024-04-14
wdi	NV.IND.TOTL.ZS	2024-01-06	2024-04-14
wdi	SL.IND.EMPL.ZS	2024-01-06	2024-04-14
wdi	TX.VAL.MRCH.CD.WT	2024-01-06	2024-04-14

Données sur l’industrie

source	dataset	.html	.RData
eurostat	mar_mg_am_cvh	2024-06-18	2024-06-08
eurostat	namq_10_a10	2024-06-19	2024-06-18
insee	CNA-2014-EMPLOI	2024-06-07	2024-06-18
insee	CNT-2014-CB	2024-06-19	2024-06-18
insee	CNT-2014-OPERATIONS	2024-06-19	2024-06-18
insee	ENQ-CONJ-ACT-IND	2024-06-19	2024-06-18
insee	ICA-2015-IND-CONS	2024-06-19	2024-06-18
insee	IPI-2021	2024-06-08	2024-06-06
insee	IPPI-2015	2024-06-19	2024-06-19
insee	t_5407	2024-06-19	2021-08-01
insee	TCRED-EMPLOI-SALARIE-TRIM	2024-06-19	2024-06-19
oecd	ALFS_EMP	2024-04-16	2024-05-12
oecd	SNA_TABLE3	2024-06-18	2024-04-16

LAST_COMPILE

LAST_COMPILE
2024-06-20

Last

Code

INDUSTRY %>%
  group_by(period) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(period)) %>%
  head(2) %>%
  print_table_conditional()

period	Nobs
2023-05-31	528
2023-04-30	528

sector

Code

INDUSTRY %>%
  group_by(sector, Sector) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

sector	Sector	Nobs
TOT	TOTAL Manufacturing	300243

question

Code

INDUSTRY %>%
  group_by(question, Question) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

question	Question	Nobs
1	Production trend observed in recent months	25246
10	Duration of production assured by current order-book levels	7403
11	New orders in recent months	7417
12	Export expectations for the months ahead	7672
13	Current level of capacity utilization	8100
14	Competitive position domestic market	6292
15	Competitive position inside EU	6132
16	Competitive position outside EU	6098
2	Assessment of order-book levels	25234
3	Assessment of export order-book levels	24828
4	Assessment of stocks of finished products	25078
5	Production expectations for the months ahead	25414
6	Selling price expectations for the months ahead	24765
7	Employment expectations for the months ahead	24650
8	Factors limiting the production	43077
9	Assessment of current production capacity	7939
COF	Confidence Indicator (Q2 - Q4 + Q5) / 3	24898

answers

Code

INDUSTRY %>%
  group_by(answers, Answers) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

answers	Answers	Nobs
B	Balance not seasonally adjusted (n.s.a)	120837
BS	Balance seasonally adjusted (s.a)	120826
F1	None (% n.s.a - quarterly question 8)	3714
F1S	None (% s.a - quarterly question 8)	3739
F2	Demand (% n.s.a - quarterly question 8)	3738
F2S	Demand (% s.a - quarterly question 8)	3739
F3	Labour (% n.s.a - quarterly question 8)	3736
F3S	Labour (% s.a - quarterly question 8)	3739
F4	Equipment (% n.s.a - quarterly question 8)	3737
F4S	Equipment (% s.a - quarterly question 8)	3739
F5	Other (% n.s.a - quarterly question 8)	3603
F5S	Other (% s.a - quarterly question 8)	3562
F6	Financial (% n.s.a - quarterly question 8)	2938
F6S	Financial (% s.a - quarterly question 8)	3093
QM	months (n.s.a - quarterly question 10)	3701
QMS	months (s.a - quarterly question 10)	3702
QP	% (n.s.a - quarterly question 13)	4049
QPS	% (s.a - quarterly question 13)	4051

country

Code

INDUSTRY %>%
  group_by(country, Country) %>%
  summarise(Nobs = n()) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

freq

Code

INDUSTRY %>%
  group_by(freq, Frequency) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

freq	Frequency	Nobs
M	Monthly	200113
Q	Quarterly	100130

Germany

Competitive positions

NSA

Code

INDUSTRY %>%
  filter(country == "DE",
         question %in% c("14", "15", "16"),
         answers == "B") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Balance not seasonally adjusted (s.a)") +
  geom_line(aes(x = period, y = value, color = Question)) +
  
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.1),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-60, 60, 2))

SA

Code

INDUSTRY %>%
  filter(country == "DE",
         question %in% c("14", "15", "16"),
         answers == "BS") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Balance not seasonally adjusted (s.a)") +
  geom_line(aes(x = period, y = value, color = Question)) +
  
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.1),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-60, 60, 2))

Export Expectations, New Orders, Producion Trend

Code

INDUSTRY %>%
  filter(country == "DE",
         question %in% c("12", "11", "1"),
         answers == "BS") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Balance not seasonally adjusted (s.a)") +
  geom_line(aes(x = period, y = value, color = Question)) +
  
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.15),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-60, 60, 5))

France

Competitive positions

NSA

Code

INDUSTRY %>%
  filter(country == "FR",
         question %in% c("14", "15", "16"),
         answers == "B") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Balance not seasonally adjusted (s.a)") +
  geom_line(aes(x = period, y = value, color = Question)) +
  
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.1),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-60, 60, 2))

SA

Code

INDUSTRY %>%
  filter(country == "FR",
         question %in% c("14", "15", "16"),
         answers == "BS") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Balance not seasonally adjusted (s.a)") +
  geom_line(aes(x = period, y = value, color = Question)) +
  
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.1),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-60, 60, 2))

Export Expectations, New Orders, Producion Trend

Code

INDUSTRY %>%
  filter(country == "FR",
         question %in% c("12", "11", "1"),
         answers == "BS") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Balance not seasonally adjusted (s.a)") +
  geom_line(aes(x = period, y = value, color = Question)) +
  
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.15),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(-60, 60, 5))