Russian consumer price inflation and cost of living trend 2022 t… — Data analysis

Russian consumer price inflation and cost of living trend 2022 to 2026

Model: DeepSeek V4.1 Flash Checked on: 14/10/2026

Russian consumer price inflation & cost of living, 2022–2026

Data note: Detailed monthly Russian CPI micro-data in the OECD price database stops in March 2022 (collection halted after the invasion of Ukraine)¹². Eurostat does not publish Russian HICP — its ENP-East CPI table lists a RU code but contains no Russian observations (only Armenia, Azerbaijan, Belarus, Georgia, Moldova, Ukraine)³⁴. The continuous 2022–2026 picture therefore comes from the OECD Economic Outlook (No. 119) database, which carries a Russian CPI index⁵.

Headline inflation (annual average, % change)

Year	YoY inflation	Status
2022	+13.8 %	actual
2023	+5.9 %	actual
2024	+8.5 %	actual
2025	+8.7 %	estimate
2026	+6.5 %	projection
2027	+4.6 %	projection

Source: OECD Economic Outlook 119 CPI index, converted to growth rates⁶⁷⁸. The vintage check confirms the framing: EO 117 (May-2025) carried 2025–2026 as projections, while the latest EO 119 has extended the horizon to 2027, making 2025 a near-final estimate and 2026–2027 projections⁹¹⁰.

Cost-of-living index (2021 = 100)

2021	2022	2023	2024	2025	2026*	2027*
100.0	113.8	120.5	130.6	142.0	151.2	158.2

This implies the general price level rose by about +51 % between 2021 and 2026 (and ~+58 % by 2027)⁷.

The trend in words

2022 – the shock year. Inflation had already been climbing through 2021 (monthly YoY went from ~3.6 % in Aug-2020 to 8.7 % in Jan-2022). After the February 2022 invasion and sanctions, the monthly rate jumped to 16.7 % YoY in March 2022, the last month in the detailed series⁶¹¹.
2023 – disinflation. Inflation fell back to ~5.9 %, the low point of the period.
2024–2025 – renewed pressure. Inflation re-accelerated to ~8.5 % in 2024 and ~8.7 % in 2025, keeping the cost of living on a steep upward path.
2026–2027 – gradual easing (projected). The OECD expects inflation to moderate to ~6.5 % in 2026 and ~4.6 % in 2027, but this is a projection and even then prices would remain well above their 2021 level⁹⁸⁷.

A two-panel chart (monthly + annual inflation, and the cost-of-living index, with the projection window shaded) was generated in ⁷.

Caveat: the 2026 figure the user asked about is a forecast, not an observed outcome; actual 2026 data are not yet available in these databases, and Russian data coverage in OECD/Eurostat products has been limited since 2022.

Code Run 1

Dataset: DSD_PRICES@DF_PRICES_ALL

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Filter	Value(s)
REF_AREA	RUS
MEASURE	CPI
EXPENDITURE	_T

Program output

(1522, 10)
['REF_AREA', 'FREQ', 'METHODOLOGY', 'MEASURE', 'UNIT_MEASURE', 'EXPENDITURE', 'ADJUSTMENT', 'TRANSFORMATION', 'TIME_PERIOD', 'value']
   REF_AREA FREQ METHODOLOGY MEASURE UNIT_MEASURE EXPENDITURE ADJUSTMENT TRANSFORMATION TIME_PERIOD      value
0       RUS    M           N     CPI           PA          _T          N             GY     1993-01   850.4569
1       RUS    M           N     CPI           PA          _T          N             GY     1993-02   758.8549
2       RUS    M           N     CPI           PA          _T          N             GY     1993-03   694.0606
3       RUS    M           N     CPI           PA          _T          N             GY     1993-04   674.4864
4       RUS    M           N     CPI           PA          _T          N             GY     1993-05   717.3981
5       RUS    M           N     CPI           PA          _T          N             GY     1993-06   722.8885
6       RUS    M           N     CPI           PA          _T          N             GY     1993-07   810.6832
7       RUS    M           N     CPI           PA          _T          N             GY     1993-08   956.5938
8       RUS    M           N     CPI           PA          _T          N             GY     1993-09  1065.5700
9       RUS    M           N     CPI           PA          _T          N             GY     1993-10  1033.3250
10      RUS    M           N     CPI           PA          _T          N             GY     1993-11   946.1458
11      RUS    M           N     CPI           PA          _T          N             GY     1993-12   840.0271
12      RUS    M           N     CPI           PA          _T          N             GY     1994-01   780.9952
13      RUS    M           N     CPI           PA          _T          N             GY     1994-02   682.9341
14      RUS    M           N     CPI           PA          _T          N             GY     1994-03   600.2078
15      RUS    M           N     CPI           PA          _T          N             GY     1994-04   539.9793
16      RUS    M           N     CPI           PA          _T          N             GY     1994-05   479.3412
17      RUS    M           N     CPI           PA          _T          N             GY     1994-06   412.1782
18      RUS    M           N     CPI           PA          _T          N             GY     1994-07   340.7494
19      RUS    M           N     CPI           PA          _T          N             GY     1994-08   265.9619
---- unique values per column ----
REF_AREA 1 <ArrowStringArray>
['RUS']
Length: 1, dtype: str
FREQ 3 <ArrowStringArray>
['M', 'Q', 'A']
Length: 3, dtype: str
METHODOLOGY 1 <ArrowStringArray>
['N']
Length: 1, dtype: str
MEASURE 1 <ArrowStringArray>
['CPI']
Length: 1, dtype: str
UNIT_MEASURE 3 <ArrowStringArray>
['PA', 'PC', 'IX']
Length: 3, dtype: str
EXPENDITURE 1 <ArrowStringArray>
['_T']
Length: 1, dtype: str
ADJUSTMENT 1 <ArrowStringArray>
['N']
Length: 1, dtype: str
TRANSFORMATION 3 <ArrowStringArray>
['GY', 'G1', '_Z']
Length: 3, dtype: str
TIME_PERIOD 514 <ArrowStringArray>
['1993-01', '1993-02', '1993-03', '1993-04', '1993-05', '1993-06', '1993-07',
 '1993-08', '1993-09', '1993-10', '1993-11', '1993-12', '1994-01', '1994-02',
 '1994-03', '1994-04', '1994-05', '1994-06', '1994-07', '1994-08']
Length: 20, dtype: str
value 1346 [ 850.4569  758.8549  694.0606  674.4864  717.3981  722.8885  810.6832
  956.5938 1065.57   1033.325   946.1458  840.0271  780.9952  682.9341
  600.2078  539.9793  479.3412  412.1782  340.7494  265.9619]

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE)
print(df.shape)
print(df.columns.tolist())
print(df.head(20).to_string())
print("---- unique values per column ----")
for c in df.columns:
    u = df[c].unique()
    print(c, len(u), u[:20])

Code Run 2

Dataset: DSD_PRICES@DF_PRICES_ALL

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Filter	Value(s)
REF_AREA	RUS
MEASURE	CPI
EXPENDITURE	_T

Program output

combos of FREQ, UNIT_MEASURE, TRANSFORMATION, ADJUSTMENT:
FREQ  UNIT_MEASURE  TRANSFORMATION  ADJUSTMENT
A     IX            _Z              N              30
      PA            GY              N              29
      PC            G1              N              29
M     IX            _Z              N             363
      PA            GY              N             351
      PC            G1              N             362
Q     IX            _Z              N             121
      PA            GY              N             117
      PC            G1              N             120
dtype: int64

A IX _Z N -> 1992 to 2021 n= 30
A PA GY N -> 1993 to 2021 n= 29
A PC G1 N -> 1993 to 2021 n= 29
M IX _Z N -> 1992-01 to 2022-03 n= 363
M PA GY N -> 1993-01 to 2022-03 n= 351
M PC G1 N -> 1992-02 to 2022-03 n= 362
Q IX _Z N -> 1992-Q1 to 2022-Q1 n= 121
Q PA GY N -> 1993-Q1 to 2022-Q1 n= 117
Q PC G1 N -> 1992-Q2 to 2022-Q1 n= 120

     REF_AREA FREQ METHODOLOGY MEASURE UNIT_MEASURE EXPENDITURE ADJUSTMENT TRANSFORMATION TIME_PERIOD     value
1046      RUS    M           N     CPI           IX          _T          N             _Z     2019-10  119.4803
1047      RUS    M           N     CPI           IX          _T          N             _Z     2019-11  119.8148
1048      RUS    M           N     CPI           IX          _T          N             _Z     2019-12  120.2462
1049      RUS    M           N     CPI           IX          _T          N             _Z     2020-01  120.7271
1050      RUS    M           N     CPI           IX          _T          N             _Z     2020-02  121.1255
1051      RUS    M           N     CPI           IX          _T          N             _Z     2020-03  121.7917
1052      RUS    M           N     CPI           IX          _T          N             _Z     2020-04  122.8026
1053      RUS    M           N     CPI           IX          _T          N             _Z     2020-05  123.1342
1054      RUS    M           N     CPI           IX          _T          N             _Z     2020-06  123.4051
1055      RUS    M           N     CPI           IX          _T          N             _Z     2020-07  123.8370
1056      RUS    M           N     CPI           IX          _T          N             _Z     2020-08  123.7875
1057      RUS    M           N     CPI           IX          _T          N             _Z     2020-09  123.7008
1058      RUS    M           N     CPI           IX          _T          N             _Z     2020-10  124.2327
1059      RUS    M           N     CPI           IX          _T          N             _Z     2020-11  125.1148
1060      RUS    M           N     CPI           IX          _T          N             _Z     2020-12  126.1532
1061      RUS    M           N     CPI           IX          _T          N             _Z     2021-01  126.9984
1062      RUS    M           N     CPI           IX          _T          N             _Z     2021-02  127.9890
1063      RUS    M           N     CPI           IX          _T          N             _Z     2021-03  128.8338
1064      RUS    M           N     CPI           IX          _T          N             _Z     2021-04  129.5810
1065      RUS    M           N     CPI           IX          _T          N             _Z     2021-05  130.5399
1066      RUS    M           N     CPI           IX          _T          N             _Z     2021-06  131.4406
1067      RUS    M           N     CPI           IX          _T          N             _Z     2021-07  131.8481
1068      RUS    M           N     CPI           IX          _T          N             _Z     2021-08  132.0722
1069      RUS    M           N     CPI           IX          _T          N             _Z     2021-09  132.8647
1070      RUS    M           N     CPI           IX          _T          N             _Z     2021-10  134.3395
1071      RUS    M           N     CPI           IX          _T          N             _Z     2021-11  135.6291
1072      RUS    M           N     CPI           IX          _T          N             _Z     2021-12  136.7413
1073      RUS    M           N     CPI           IX          _T          N             _Z     2022-01  138.0950
1074      RUS    M           N     CPI           IX          _T          N             _Z     2022-02  139.7107
1075      RUS    M           N     CPI           IX          _T          N             _Z     2022-03  150.3470

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE)
print("combos of FREQ, UNIT_MEASURE, TRANSFORMATION, ADJUSTMENT:")
print(df.groupby(['FREQ','UNIT_MEASURE','TRANSFORMATION','ADJUSTMENT']).size())
print()
# last time period per combo
for (f,u,t,a), g in df.groupby(['FREQ','UNIT_MEASURE','TRANSFORMATION','ADJUSTMENT']):
    print(f, u, t, a, "->", g['TIME_PERIOD'].min(), "to", g['TIME_PERIOD'].max(), "n=",len(g))
print()
print(df[df['FREQ']=='M'].tail(30).to_string())

Code Run 3

Dataset: DSD_EO@DF_EO

Dataset filters for `DSD_EO@DF_EO`

Filter	Value(s)
REF_AREA	RUS
MEASURE	CPI

Program output

(33, 5)
   REF_AREA MEASURE FREQ  TIME_PERIOD     value
0       RUS     CPI    A         1995  0.031732
1       RUS     CPI    A         1996  0.046908
2       RUS     CPI    A         1997  0.053932
3       RUS     CPI    A         1998  0.068857
4       RUS     CPI    A         1999  0.127681
5       RUS     CPI    A         2000  0.154396
6       RUS     CPI    A         2001  0.187508
7       RUS     CPI    A         2002  0.216982
8       RUS     CPI    A         2003  0.246833
9       RUS     CPI    A         2004  0.273674
10      RUS     CPI    A         2005  0.308290
11      RUS     CPI    A         2006  0.338141
12      RUS     CPI    A         2007  0.368619
13      RUS     CPI    A         2008  0.420544
14      RUS     CPI    A         2009  0.469585
15      RUS     CPI    A         2010  0.501819
16      RUS     CPI    A         2011  0.544212
17      RUS     CPI    A         2012  0.571679
18      RUS     CPI    A         2013  0.610310
19      RUS     CPI    A         2014  0.658096
20      RUS     CPI    A         2015  0.760191
21      RUS     CPI    A         2016  0.813997
22      RUS     CPI    A         2017  0.843723
23      RUS     CPI    A         2018  0.867929
24      RUS     CPI    A         2019  0.906685
25      RUS     CPI    A         2020  0.937288
26      RUS     CPI    A         2021  1.000000
27      RUS     CPI    A         2022  1.137589
28      RUS     CPI    A         2023  1.204565
29      RUS     CPI    A         2024  1.306409
30      RUS     CPI    A         2025  1.419917
31      RUS     CPI    A         2026  1.511783
32      RUS     CPI    A         2027  1.581504

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE)
print(df.shape)
df = df.sort_values('TIME_PERIOD')
print(df.to_string())
print()
# Also check what PRI / deflator looks like

Code Run 4

Dataset: DSD_PRICES@DF_PRICES_ALL Dataset: DSD_EO@DF_EO

Images

russia_inflation.svg

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Filter	Value(s)
REF_AREA	RUS
MEASURE	CPI
TRANSFORMATION	GY
UNIT_MEASURE	PA
FREQ	M
EXPENDITURE	_T, CP01, CP04, CP07
sinceTimePeriod	2019

Dataset filters for `DSD_EO@DF_EO`

Filter	Value(s)
REF_AREA	RUS
MEASURE	CPI

Program output

monthly rows: (153, 10) freqs: <ArrowStringArray>
['M']
Length: 1, dtype: str exp: <ArrowStringArray>
['CP07', 'CP04', 'CP01', '_T']
Length: 4, dtype: str

Monthly YoY CPI inflation (%), Russia (last 20 months available):
              CP01  CP04   CP07     _T
TIME_PERIOD                           
2020-08-01    4.80  3.15   2.77   3.57
2020-09-01    4.85  3.29   3.10   3.67
2020-10-01    5.39  3.43   3.53   3.98
2020-11-01    6.55  3.59   4.05   4.42
2020-12-01    7.68  3.74   4.52   4.91
2021-01-01    8.03  3.63   5.07   5.19
2021-02-01    8.86  3.81   5.77   5.67
2021-03-01    8.63  3.98   6.86   5.78
2021-04-01    7.38  4.25   7.40   5.52
2021-05-01    8.35  4.84   7.51   6.01
2021-06-01    8.96  5.64   8.06   6.51
2021-07-01    8.37  6.55   8.59   6.47
2021-08-01    8.72  6.52   9.26   6.69
2021-09-01   10.60  6.36   9.60   7.41
2021-10-01   12.61  6.37  10.17   8.14
2021-11-01   12.43  6.42  10.75   8.40
2021-12-01   12.15  6.32  11.35   8.39
2022-01-01   12.68  6.34  11.69   8.74
2022-02-01   13.02  6.97  11.33   9.16
2022-03-01     NaN   NaN    NaN  16.70

Annual CPI index (2021=1) and YoY inflation, Russia:
 TIME_PERIOD  value  inflation
        2015  0.760     15.514
        2016  0.814      7.078
        2017  0.844      3.652
        2018  0.868      2.869
        2019  0.907      4.465
        2020  0.937      3.375
        2021  1.000      6.691
        2022  1.138     13.759
        2023  1.205      5.888
        2024  1.306      8.455
        2025  1.420      8.689
        2026  1.512      6.470
        2027  1.582      4.612

saved chart

Code


import pandas as pd, numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

# ---- Monthly YoY inflation (dataset 10, COICOP1999) ----
m = pd.read_csv(DATASET_FILE0)
print("monthly rows:", m.shape, "freqs:", m['FREQ'].unique(), "exp:", m['EXPENDITURE'].unique())
m['TIME_PERIOD'] = pd.to_datetime(m['TIME_PERIOD'], format='%Y-%m')
piv = m.pivot_table(index='TIME_PERIOD', columns='EXPENDITURE', values='value')
piv.columns = [{'\_T':'_T'}.get(c,c) for c in piv.columns]
print("\nMonthly YoY CPI inflation (%), Russia (last 20 months available):")
print(piv.tail(20).round(2).to_string())

# ---- Annual CPI index (dataset 31, Economic Outlook) ----
a = pd.read_csv(DATASET_FILE1).sort_values('TIME_PERIOD')
a['TIME_PERIOD'] = a['TIME_PERIOD'].astype(int)
a['inflation'] = a['value'].pct_change()*100
print("\nAnnual CPI index (2021=1) and YoY inflation, Russia:")
print(a[a['TIME_PERIOD']>=2015][['TIME_PERIOD','value','inflation']].round(3).to_string(index=False))

# ---- chart ----
fig, ax1 = plt.subplots(figsize=(9,5))
ax1.plot(a['TIME_PERIOD'], a['value'], color='tab:red', marker='o', lw=2, label='CPI index (2021=100)')
ax1.set_xlabel('Year'); ax1.set_ylabel('CPI index (2021=100)', color='tab:red')
ax1.tick_params(axis='y', labelcolor='tab:red')
ax1.set_xlim(2019, 2027)
ax2 = ax1.twinx()
ax2.bar(a['TIME_PERIOD'], a['inflation'], color='tab:blue', alpha=0.4, width=0.5, label='YoY inflation (%)')
ax2.set_ylabel('YoY inflation (%)', color='tab:blue')
ax2.tick_params(axis='y', labelcolor='tab:blue')
ax2.axvline(2025.5, color='grey', ls='--', lw=1)
ax2.text(2025.6, ax2.get_ylim()[1]*0.9, 'projection ->', color='grey', fontsize=9)
plt.title('Russia: consumer price inflation & cost-of-living index (2019-2027)')
fig.tight_layout()
fig.savefig('russia_inflation.svg')
print("\nsaved chart")

Code Run 5

Dataset: enpe_cpi

Dataset filters for `enpe_cpi`

Filter	Value(s)
geo	RU

Program output

(0, 5)
['freq', 'unit', 'geo', 'time', 'value']
Empty DataFrame
Columns: [freq, unit, geo, time, value]
Index: []

freq []
unit []
geo []
time []

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE)
print(df.shape)
print(df.columns.tolist())
print(df.head(30).to_string())
print()
for c in df.columns:
    if c not in ('TIME_PERIOD','value'):
        print(c, df[c].unique()[:30])

Code Run 6

Dataset: enpe_cpi

Program output

(221, 5)
['freq', 'unit', 'geo', 'time', 'value']
geo
GE    40
MD    40
AM    40
AZ    40
BY    32
UA    29
unit
RCH_A_AVG    116
INX_A_AVG    105
   freq       unit geo  time  value
0     A  INX_A_AVG  GE  2009   81.4
1     A  INX_A_AVG  GE  2019  116.5
2     A  INX_A_AVG  MD  2013   86.8
3     A  INX_A_AVG  AM  2010   80.1
4     A  INX_A_AVG  AZ  2014   96.1
5     A  INX_A_AVG  UA  2021  175.3
6     A  RCH_A_AVG  AM  2016   -1.4
7     A  RCH_A_AVG  AZ  2006    8.3
8     A  RCH_A_AVG  AZ  2018    2.3
9     A  RCH_A_AVG  BY  2007    8.4
10    A  RCH_A_AVG  GE  2020    5.2
11    A  INX_A_AVG  BY  2005   15.9
12    A  INX_A_AVG  GE  2024  155.7
13    A  RCH_A_AVG  AZ  2013    2.4
14    A  INX_A_AVG  UA  2015  100.0
15    A  INX_A_AVG  AZ  2011   91.6
16    A  INX_A_AVG  AM  2019  103.5
17    A  INX_A_AVG  BY  2016  111.8
18    A  INX_A_AVG  MD  2012   83.0
19    A  INX_A_AVG  MD  2024  204.1
20    A  INX_A_AVG  AM  2011   86.3
21    A  INX_A_AVG  AM  2015  100.0
22    A  RCH_A_AVG  AM  2008    9.0
23    A  RCH_A_AVG  AM  2020    1.2
24    A  RCH_A_AVG  AM  2009    3.4
25    A  RCH_A_AVG  AM  2023    2.0
26    A  RCH_A_AVG  GE  2015    4.0
27    A  RCH_A_AVG  BY  2017    6.0
28    A  RCH_A_AVG  MD  2011    7.6
29    A  INX_A_AVG  AZ  2015  100.0
30    A  RCH_A_AVG  MD  2022   28.7
31    A  RCH_A_AVG  MD  2019    4.8
32    A  RCH_A_AVG  BY  2011   53.2
33    A  RCH_A_AVG  MD  2023   13.4
34    A  RCH_A_AVG  UA  2006    9.1
35    A  INX_A_AVG  AZ  2012   92.6
36    A  RCH_A_AVG  MD  2005   11.9
37    A  RCH_A_AVG  UA  2017   14.4
38    A  RCH_A_AVG  UA  2009   15.9
39    A  RCH_A_AVG  BY  2008   14.8

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE)
print(df.shape)
print(df.columns.tolist())
print(df['geo'].value_counts().to_string())
print(df['unit'].value_counts().to_string())
print(df.head(40).to_string())

Code Run 7

Dataset: DSD_EO_114@DF_EO_114

Dataset filters for `DSD_EO_114@DF_EO_114`

Filter	Value(s)
REF_AREA	RUS
MEASURE	CPI
FREQ	A

Program output

EO114 (dataset 32) Russia CPI index & YoY inflation:
 TIME_PERIOD  value   infl
        2019  0.907  4.470
        2020  0.937  3.382
        2021  1.000  6.694
        2022  1.137 13.736
        2023  1.203  5.804
        2024  1.290  7.161
        2025  1.358  5.272
last year: 2025

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE).sort_values('TIME_PERIOD')
df['TIME_PERIOD']=df['TIME_PERIOD'].astype(int)
df['infl']=df['value'].pct_change()*100
print("EO114 (dataset 32) Russia CPI index & YoY inflation:")
print(df[df['TIME_PERIOD']>=2019][['TIME_PERIOD','value','infl']].round(3).to_string(index=False))
print("last year:", df['TIME_PERIOD'].max())

OECD dataset: Economic Outlook 119

Code Run 8

Dataset: DSD_EO_117@DF_EO_117

Dataset filters for `DSD_EO_117@DF_EO_117`

Filter	Value(s)
REF_AREA	RUS
MEASURE	CPI
FREQ	A

Program output

EO117 (dataset 35) Russia CPI index & YoY inflation:
 TIME_PERIOD  value   infl
        2019  0.907  4.465
        2020  0.937  3.375
        2021  1.000  6.691
        2022  1.138 13.759
        2023  1.205  5.888
        2024  1.306  8.455
        2025  1.433  9.727
        2026  1.521  6.090
last year: 2026

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE).sort_values('TIME_PERIOD')
df['TIME_PERIOD']=df['TIME_PERIOD'].astype(int)
df['infl']=df['value'].pct_change()*100
print("EO117 (dataset 35) Russia CPI index & YoY inflation:")
print(df[df['TIME_PERIOD']>=2019][['TIME_PERIOD','value','infl']].round(3).to_string(index=False))
print("last year:", df['TIME_PERIOD'].max())

Code Run 9

Dataset: DSD_PRICES@DF_PRICES_ALL Dataset: DSD_EO@DF_EO

Images

russia_inflation.svg

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Filter	Value(s)
REF_AREA	RUS
MEASURE	CPI
TRANSFORMATION	GY
UNIT_MEASURE	PA
FREQ	M
EXPENDITURE	_T
sinceTimePeriod	2018

Dataset filters for `DSD_EO@DF_EO`

Filter	Value(s)
REF_AREA	RUS
MEASURE	CPI
FREQ	A

Program output

saved
 TIME_PERIOD  value   infl
        2018  0.868  2.869
        2019  0.907  4.465
        2020  0.937  3.375
        2021  1.000  6.691
        2022  1.138 13.759
        2023  1.205  5.888
        2024  1.306  8.455
        2025  1.420  8.689
        2026  1.512  6.470
        2027  1.582  4.612
Cumulative price level 2026 vs 2021: 51.2 %
Cumulative price level 2025 vs 2021: 42.0 %

Code


import pandas as pd, numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

m = pd.read_csv(DATASET_FILE0)
m['date'] = pd.to_datetime(m['TIME_PERIOD'], format='%Y-%m')
m = m.sort_values('date')
a = pd.read_csv(DATASET_FILE1).sort_values('TIME_PERIOD')
a['TIME_PERIOD'] = a['TIME_PERIOD'].astype(int)
a['infl'] = a['value'].pct_change()*100
a = a[a['TIME_PERIOD']>=2018]

fig, (ax1, ax2) = plt.subplots(2,1, figsize=(9.5,8), sharex=False)

# Panel 1: YoY inflation
ax1.plot(m['date'], m['value'], color='tab:blue', lw=1.6, label='Monthly YoY CPI inflation (OECD/COICOP, to Mar-2022)')
ax1.plot(pd.to_datetime(a['TIME_PERIOD'].astype(str)+'-07-01'), a['infl'], color='tab:red', marker='o', lw=2, label='Annual avg. YoY inflation (OECD Econ. Outlook 119)')
ax1.axvspan(pd.Timestamp('2025-07-01'), pd.Timestamp('2027-12-31'), color='grey', alpha=0.12)
ax1.text(pd.Timestamp('2025-08-01'), 15, 'projections', color='grey', fontsize=9)
ax1.axvline(pd.Timestamp('2022-02-15'), color='k', ls=':', lw=1)
ax1.text(pd.Timestamp('2022-03-01'), 15.5, 'Feb 2022', fontsize=8)
ax1.set_ylabel('YoY inflation (%)')
ax1.set_title('Russia: consumer price inflation (2018–2027)')
ax1.legend(fontsize=8, loc='upper left')
ax1.grid(alpha=0.3)

# Panel 2: cost-of-living index
ax2.plot(a['TIME_PERIOD'], a['value']*100, color='tab:red', marker='o', lw=2)
ax2.axvspan(2025.5, 2027, color='grey', alpha=0.12)
ax2.set_ylabel('CPI index (2021 = 100)')
ax2.set_xlabel('Year')
ax2.set_title('Russia: cost-of-living index (annual average)')
ax2.grid(alpha=0.3)
for _,r in a[a['TIME_PERIOD']>=2022].iterrows():
    ax2.annotate(f"{r['value']*100:.1f}", (r['TIME_PERIOD'], r['value']*100), textcoords="offset points", xytext=(0,7), ha='center', fontsize=8)

fig.tight_layout()
fig.savefig('russia_inflation.svg')
print("saved")

# summary numbers
print(a[['TIME_PERIOD','value','infl']].round(3).to_string(index=False))
print("Cumulative price level 2026 vs 2021:", round(a[a.TIME_PERIOD==2026]['value'].iloc[0]*100-100,1), "%")
print("Cumulative price level 2025 vs 2021:", round(a[a.TIME_PERIOD==2025]['value'].iloc[0]*100-100,1), "%")

OECD dataset: Consumer price indices (CPIs, HICPs), COICOP 1999

Russian consumer price inflation and cost of living trend 2022 to 2026

Russian consumer price inflation & cost of living, 2022–2026

Headline inflation (annual average, % change)

Cost-of-living index (2021 = 100)

The trend in words

Dataset filters for DSD_PRICES@DF_PRICES_ALL

Program output

Code

Dataset filters for DSD_PRICES@DF_PRICES_ALL

Program output

Code

Dataset filters for DSD_EO@DF_EO

Program output

Code

Images

Dataset filters for DSD_PRICES@DF_PRICES_ALL

Dataset filters for DSD_EO@DF_EO

Program output

Code

Dataset filters for enpe_cpi

Program output

Code

Program output

Code

Dataset filters for DSD_EO_114@DF_EO_114

Program output

Code

Dataset filters for DSD_EO_117@DF_EO_117

Program output

Code

Images

Dataset filters for DSD_PRICES@DF_PRICES_ALL

Dataset filters for DSD_EO@DF_EO

Program output

Code

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Dataset filters for `DSD_EO@DF_EO`

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Dataset filters for `DSD_EO@DF_EO`

Dataset filters for `enpe_cpi`

Dataset filters for `DSD_EO_114@DF_EO_114`

Dataset filters for `DSD_EO_117@DF_EO_117`

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Dataset filters for `DSD_EO@DF_EO`