Industry (sector data)

Data - ec

Info

source dataset .html .RData

ec

INDUSTRY

2024-09-08 2023-10-01

Data on industry

source dataset .html .RData

ec

INDUSTRY

2024-09-08 2023-10-01

eurostat

ei_isin_m

2024-09-14 2024-09-14

eurostat

htec_trd_group4

2024-09-14 2024-09-14

eurostat

nama_10_a64

2024-09-14 2024-09-14

eurostat

nama_10_a64_e

2024-09-14 2024-09-14

eurostat

namq_10_a10_e

2024-09-14 2024-09-14

eurostat

road_eqr_carmot

2024-09-14 2024-09-14

eurostat

sts_inpp_m

2024-06-24 2024-09-14

eurostat

sts_inppd_m

2024-09-15 2024-09-14

eurostat

sts_inpr_m

2024-09-15 2024-09-14

eurostat

sts_intvnd_m

2024-06-24 2024-09-14

fred

industry

2024-09-14 2024-09-14

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

2024-09-11 2023-10-04

oecd

SNA_TABLE4

2024-09-11 2024-04-30

wdi

NV.IND.EMPL.KD

2024-01-06 2024-09-14

wdi

NV.IND.MANF.CD

2024-09-14 2024-09-14

wdi

NV.IND.MANF.ZS

2024-01-06 2024-09-14

wdi

NV.IND.TOTL.KD

2024-01-06 2024-09-14

wdi

NV.IND.TOTL.ZS

2024-01-06 2024-09-14

wdi

SL.IND.EMPL.ZS

2024-01-06 2024-09-14

wdi

TX.VAL.MRCH.CD.WT

2024-01-06 2024-09-14

Données sur l’industrie

source dataset .html .RData

eurostat

mar_mg_am_cvh

2024-09-14 2024-09-14

eurostat

namq_10_a10

2024-09-14 2024-09-14

insee

CNA-2014-EMPLOI

2024-06-07 2024-09-14

insee

CNT-2014-CB

2024-09-14 2024-09-14

insee

CNT-2014-OPERATIONS

2024-09-14 2024-09-14

insee

ENQ-CONJ-ACT-IND

2024-09-14 2024-09-14

insee

ICA-2015-IND-CONS

2024-09-14 2024-09-14

insee

IPI-2021

2024-09-14 2024-09-08

insee

IPPI-2015

2024-09-14 2024-09-14

insee

t_5407

2024-09-14 2021-08-01

insee

TCRED-EMPLOI-SALARIE-TRIM

2024-09-14 2024-09-14

oecd

ALFS_EMP

2024-04-16 2024-05-12

oecd

SNA_TABLE3

2024-09-11 2024-04-16

LAST_COMPILE

LAST_COMPILE
2024-09-15

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))