Counter-sanctions and agricultural dynamics in Russian regions

New approach for aggregated labor productivity comparisons: accounting for structural differences. The Russian regions case

Zaytsev Alexander,

Deputy Head, Sector of International Economic Research, Research fellow, Ph.D.,

Center for Comprehensive European and International Studies (CCEIS), Higher School of Economics (HSE)

Russia, Moscowalex.zaytsev@hse.ru

Klochko Olga,Associate Professor, Ph.D.,

Faculty of World Economy and World Politics HSE,Department of World Economy,

Oklochko@hse.ru

Sirotin Ernest 1st year master student,

Faculty of World Economy and World Politics HSE,esirotin@hse.edu.ru

Introduction International and interregional comparisons on whole economy level – does it make any sense? (1)

• Do really Saudi Arabia or Arab Emirates have the same economic efficiency as US, Sweden or France?

2

Source: Penn World Tables 10.0

0

20

40

60

80

100

120

140

Ths.

do

llars

PP

P

Labor productivity per employed 2019 (among top 40 biggest GDP countries)

Introduction International and interregional comparisons on whole economy level – does it make any sense? (2)

• Regions with resource specialization have higher LP, while regions with agricultural specialization have smaller LP

• Economic structure plays significant defines LP levels.

• Comparisons based on traditional approach to LP estimation are biased due to different specialization of economies. It shows not the whole economy economic efficiency, but efficiency of dominating industries. So using traditional approach we can not obtain correct and precise picture of economic efficiency between countries and regions within single country.

3

Source: calculations based on Rosstat data

0

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1 000 000

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3 000 000

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Top 25 Russian regions on ALP bottom 25 regions

Reasons of biased rankings

• Industrial differences in LP

• Different structural differences

250.0

2500.0

Labor productivity, Russian industries, 2018, ths. rubles

Research questions

• How to deal with the bias in ALP comparisons caused by structural differences?

• How to correct our methodology of ALP estimation to take into account the structural differences between economies?

Literature

Different adhoc solutions to correct for structural differences:

• (Hall & Jones, 1999) proposed to completely exclude the gross added value of the resource sector from the GDP

(Caselli, 2005) criticizes this approach. Following this logic, we should to exclude the agriculture, forestry, and fishing industries. However, such estimates of ALP will no longer be “aggregated”

• exclude resource rent from GDP ((Mamonov & Pestova, 2015), (Zaytsev, 2016)). Better way, but does not solve issue with different structure of non resource part of the economy

• Pen world tables and TED databases – no any correction

Russian regions evidence:

• Russian regions ALP analysis (Mikheeva, 2014, 2015), (Nagaeva, Popod`ko, 2019) – authors stress the importance of structural differences, but no correction done

No any systemic approach exists in the literature to account for structural differences between economies in ALP comparisons

Methodology (1)

Aggregated labor productivity (ALP) in the country or region 𝑗 =

𝑌𝑗

𝐿 𝑗=

σ𝑖=1𝑁 𝑦𝑖

𝑗

𝐿 𝑗=

σ𝑖=1𝑁 𝑦𝑖

𝑗∗𝑙𝑖𝑗

𝑙𝑖𝑗

𝐿 𝑗=

σ𝑖=1𝑁 𝐿𝑝𝑖

𝑗∗𝑙𝑖

𝑗

𝐿= σ𝑖=1

𝑁 𝐿𝑝𝑖𝑗∗ 𝑤𝑖

𝑗

Industrial labor productivity 𝐿𝑝𝑖 =𝑦𝑖𝑗

𝑙𝑖𝑗

• σ𝑖=1𝑁 𝑤𝑖

𝑗= 1

• i - industry index, j - country (region) index.

• Y - GDP (GRP) of the country (region)

• L is the number of people employed in the economy as a whole

• yi- gross value added of industry i

• li- number of people employed in the industry i

• Lpi - labor productivity in industry i

• 𝑤i is the share of industry i in the total number of people employed in the economy (an indicator of the structure of the economy).

• N is the total number of industries (sectors) into which the economy is divided.

ALP depends on industrial LPs, but also on economic

structure!

But labor structure is very different across Russian regions

Agri-c, forestry, fishing and hunting;

7%

Mining; 1.6%

Manufacturing; 14%

Energetics; 2%

Water supply, etc.; 1%

Construction; 9%

Trade; 19%

Transport; 7%

Accomondation and restaurants; 2%

Information and communication; 2%

Finance and insurance; 2%

Real estate activities; 3%

Professional, technical, scientific

activity; 4%

Administrative activity; 3%

Public administration; 5%

Education; 8%

Health care; 6%

Culture and sport; 2%

Other services; 2%

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Agri-c, forestry,fishing and hunting

Mining

Manufacturing

Energetics

Water supply, etc.

Construction

Trade

Transport

Accomondation andrestaurantsInformation and

communicationFinance and

insurance

Real estate activities

Professional,technical, scientific…

Administrativeactivity

Publicadministration

Education

Health care

Culture and sport

Other services

Coefficient of localization in different regions

Tyumen Obl. Krasnodar Krai Saint Petersburg

Russian labor structure by industry, 2018

Methodology (2)

Aggregated labor productivity𝑗 =

𝑖=1

𝑁

𝐿𝑝𝑖𝑗∗ 𝑤𝑖

𝑤𝑖- fixed labor structure for each region j. We use average Russian labor structure as benchmark

Our idea is close to approaches used in the index theory and the international comparisons program (World bank, 2014) in calculating purchasing power parities

Our idea - fix and use the same (‘’benchmark’’) labor structure  

Data

• Data on industrial GVA and employment on 85 Russian regions

• 2018 year

• 19 industries

• Source of data: official Rosstat data

11Source: calculations based on Rosstat database

Weighed approach shows that the real gap in productivity (ALP) between the Russian regions is 2 times smaller

Traditional approach Weighted approach

The traditional ALP of the most productive region in Russia is 15 times higher than the level of the least productive region (4813 vs. 325 thnd RUR per employee). A weighted approach reduces this gap down to 7 times (2232 vs. 312 thnd RUR per employee).

rubles per employee rubles per employee

Due to changes in ALP, 77 out of 85 regions changed their positions in the Russian ranking of productivity

1

2

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4

5

6

7

8

9

10

76

77

78

79

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81

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85

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3

7

5

6

9

51

73

74

77

80

79

82

81

83

85

84

86

100 80 60 40 20 0 20 40 60 80 100

32 regions lost positions

45 regions gained positions in the ranking

Ingushetiya6

Chechnya

Kabardino-Balkariya

Sevastopol

Inanovo

Karachaevo-Cherkessiya

Krym

Severnaya Osetiya

Pskov

Buryatiya

Altayskiy krai

Yakutiya

Belgorod

Krasnoyarsk

Leningradskaya r.

Tatarstan

Tyumen

Khanty-Mansiysk

Sakhalin

Yamalo-Nenetsk

Nenetsk

Regions with the strongest change in the ranking are regions with specialization in mining (loss) or agriculture (gain)

-50

-40

-30

-20

-10

0

10

20

30

Strongest ALP decline and position loss in regions with specialization of the regional economy in mining, esp. in oil and gas extraction

Strongest ALP increase and position gain in2 types of regions:• agricultural regions • regions with the structure of employment very close

to the structure of those employed in the Russian economy as a whole

18

15 14 14

11 11 119 9 9 8 8 8 8 8

41

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13 1311

9 9 9 8 76 5

LOSS GAIN

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There are no significant changes in the group of Russian most productive regions

Top-10

regions (weighted

approach)

Traditional ALP,

RUR/empl

Weighted ALP,

RUR/empl

ALP

Difference %

Position in

traditional

ranking

Position in

weighted

ranking

Difference in

positions

Yamalo-Nenetsk r. 4 774 173 2 232 117 -53% 2 1 1Sakhalin r. 3 161 454 1 885 458 -40% 3 2 1Tumen r. 1 667 803 1 837 856 10% 5 3 2Moscow 1 288 797 1 383 264 7% 12 4 8Leningrad r. 1 463 280 1 381 567 -6% 7 5 2Krasnoyarsk 1 432 336 1 341 271 -6% 8 6 2Tatarstan R. 1 496 644 1 333 947 -11% 6 7 -1St. Petersburg 1 245 818 1 252 275 1% 16 8 8Belgorod r. 1 419 653 1 232 314 -13% 9 9 0Nenetskiy AR 4 812 895 1 155 786 -76% 1 10 -9

• 8 regions out of traditional TOP-10 stayed in our approach in the leading group• 2 new regions entered the leading group – Moscow and St-Petersburg.

• Moscow thanks to its high productivity in a manufacturing sector. • Petersburg primarily thanks to an extraction industry and public administration, where the city has

less employees and high productivity than the Russian average.

14Source: calculations based on Rosstat database

The change in the ALP depends on the distance between the region’sindustrial structure of employed and the Russian structure

Hackman Index

ALP change, |%|

The case of Kuzbass

16Source: calculations based on Rosstat database

The sectoral contribution to the the

adjustment of the region’s productivity can

be either positive or negative

The size of the sectors’ contribution

depends on two factors:

• Difference between the share of

employed in the industry in the region

and share of employed in the same

industry in Russia

• Level of the region’s productivity in a

given industry: the higher it is, the

stronger the industry's contribution to

the adjustment of the traditional PT

Summary of results

• The real gap in productivity between the Russian regions is 2 times smaller

• 77 out of 85 regions changed their positions in the Russian productivity ranking – the role of some regions in generating country’s ALP has changed

• Regions with the strongest change in the ALP and Ranking are regions that specialize in a particular industry

• The change in the productivity depends on the distance between the region’s industrialstructure of employed and the Russian structure

• ALP adjustment depends on the industrial inputs, that can be positive or negative. The contribution size of a particular industry depends on the region’s share of employed and level of the region’s productivity in it

Thank you!!!

Zaytsev Alexander,

Ph.D., Research fellow, Center for Comprehensive European and International Studies

(CCEIS), Higher School of Economics (HSE)

Russia, Moscowalex.zaytsev@hse.ru

Klochko Olga,Associate Professor, Ph.D.,

Faculty of World Economy and World Politics HSE,Department of World Economy,

Oklochko@hse.ru

Sirotin Ernest 1st year master student,

Faculty of World Economy and World Politics HSE,esirotin@hse.edu.ru

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TFP levels, 2017

Penn World Tables 9.020

101% 100% 99% 97% 97% 95%91% 90% 89% 86% 86% 85% 84% 82% 81% 80% 80% 78% 77% 75%

71%63% 62% 60%

54% 53% 51%47% 45% 43%

38%

0%

20%

40%

60%

80%

100%

120%USA = 100%