Skip to main content

Production linkages and dynamic fiscal employment effects of the extractive industries: input-output and nonlinear ARDL analyses of Azerbaijani economy


In this paper, we address the production linkages and employment effects of the petroleum sector on the rest of the Azerbaijani economy. The availability of the input-output tables for the years 2006, 2008, and 2009 enables the assessment of the changes with regard to the multiplier effects of the extractive industries over the first 3 years of the oil boom. We find that despite advanced infrastructure, well-developed petrochemical complex, and local content policies, the degree of integration of the international oil and gas business into the domestic economy is rather weak. In addition, both production and job creation multipliers slightly decreased after 3 years of exponential growth rates of oil production. The assessment of the production multipliers indicates that additional investments in processing, construction, and network industries have the highest production linkages. Concerning employment multipliers agriculture, education, health care, and public sector have the greatest job creation effects. To assess the fiscal employment effects of the oil revenues, which cannot be captured over the static input-output analysis, we employ the cointegrating nonlinear autoregressive distributed lag model. The model reveals a sustainable job creation effect of oil revenues in the case of Azerbaijan.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. The mentioned tables can be downloaded from the following webpage of the SSC

  2. For a comprehensive theoretical analysis of the informal sector employment in the developing countries and possible instruments for the statistical assessment, see OECD (2002) and Loewenstein and Bender (2017).

  3. For the full derivation of the job creation effect, see Hasanli (2011) and Tordo et al. (2011).


  • Adeolu OA, Oyejide TA (2012) Determinants of backward linkages of oil and gas industry in the Nigerian economy. Res Policy 27:452–460

    Google Scholar 

  • Auty R (2006) Improving the beneficial socio-economic impact from hydrocarbon extraction on local/regional development in the Caspian region

    Google Scholar 

  • Bacon R, Kojima M (2011) Issues in estimating the employment generated by energy sector activities. WB WP 82732. Available from: Resources/Measuring_the_employment_impact_of_energy_sector1.pdf. [Last retrieved on 2018 Aug 01]

  • Banerjee A, Dolado J, Mestre R (1998) Error-correction mechanism tests for cointegration in single-equation framework. J Time Ser Anal 19:267–283

    Article  Google Scholar 

  • Bender D (1976) Makroökonomik des Umweltschutzes. Vandenhoeck & Ruprecht, Göttingen

    Google Scholar 

  • Brand MV (2017) Local content policies in oil industry: a comparative analysis. Accessed on 22 May 2019

  • Calvacanti TV de, Mohaddes K, Raissi M (2011) Does oil abundance harm growth? Appl Econ Lett 18(12):1181–1184

  • Calzaroni M (2000) The exhaustiveness of production estimates: new concepts and methodologies. Proceedings of the International Conference on Establishment Surveys, Ottawa

    Google Scholar 

  • Cardenete MA, Guerra A-I, Sancho E (2006) Applied general equilibrium. Springer, Berlin

    Google Scholar 

  • Cordes K, Östensson O, Toledano P (2016) Employment from mining and agricultural investments: how much myth, how much reality? Columbia Center on Sustainable Investment. Columbia University, New Delhi

    Google Scholar 

  • Ericsson M, Löf O (2017) Mining’s contribution to low- and middle-income countries. UNI-WIDER Working Paper 2017/148. Available from [Last retrieved on 2018 Jul 17]

  • Feige EL, Urban I (2008) Measuring underground (unobserved, non-observed, unrecorded) economies in transition countries: can we trust GDP? William Davidson Institute Working Paper No. 913. Available from: nload?doi= [Last retrieved on 2018 Aug 06].

  • Frimpong, J.M., Oteng-Abayie, E.F. (2006), Bounds testing approach: an examination of foreign direct investment, trade, and growth relationships. MPRA WP No 352

  • Grossman GM (1981) The theory of domestic content protection and content preference. Q J Econ 96:583–603

    Article  Google Scholar 

  • Gurbanov S, Nugent J, Mikayilov J (2017) Management of Oil Revenues: Has That of Azerbaijan Been Prudent?. Economies 5 (2):19.

  • Hailu D, Gankhuyag U, Kipgen C (2014) How does the extractive industry promote growth, industrialization and employment generation? Paper Presented to UNDP of Brazil. Available from: HowDoestheExtractive1.pdf [Last retrieved on 2018 Jul 25]

  • Hansen MW (2014) From enclave to linkage economies? A review of the literature on linkages between extractive multinational corporations and local industry in Africa. DIIS Working Paper 2014:02. Available from: publications/import/extra/wp2014michael_hansen_for_web_1.pdf. [Last retrieved on 2017 Jan 14]

  • Hartwick J (1977) Population growth, technical progress, intergenerational equity and the investment of resource rents. Am Econ Rev 67(5):972–974

    Google Scholar 

  • Hasanli Y (2011) Modelling of cross-sectoral relationships in Azerbaijani economy. Baku, Elm

    Google Scholar 

  • Hasanli Y, Bayzakov S, Valiyev V (2012) Modeling of the multiplicative effects of opening of the work places on the basis of Intersectoral labor balance (on example of Azerbaijan and Kazakhstan). EcoMod 2011: International Conference on Economic Modeling, Portugal. Available from: Full%20Pape%20 eng. [Last retrieved on 2017 Mar 14]

  • Hasanov F (2010) The impact of real effective exchange rate on the non-oil exports: The case of Azerbaijan. MPRA Working Paper 29556.

  • Hernandez M (2014) Big oil, small jobs: a look at the oil industry’s dubious job claims. Energy and Environment. Center for American Progress. Available from: issues/green/news/2014/01/22/82571/big-oil-small-jobs-a-look-at-the- oil-industrys-dubious-job-claims/. [Last retrieved on 2018 Jan 25]

  • ICMM (2007) Ghana: the challenge of mineral wealth: using resource endowments to foster sustainable development. ICMM, Ghana, Tanzania

    Google Scholar 

  • ICMM (2011) Utilizing mining and mineral resources to foster the sustainable development of the Lao PDR. Partnerships for Development, Mining

    Google Scholar 

  • ICMM (2016) The role of mining in national economies. Available from: [Last retrieved on 2015 Jul 25]

  • ILO (2015) Hernessing the potential of ectractive industries – decent work in the rural economy. Available from: wcmsp5/groups/public/---ed_emp/---emp_policy/documents/ publication/wcms_437199.pdf. [Last retrieved on 2018 Jul 25]

  • IPIECA, API (2016) Estimating petroleum industry value chain (Scope 3) greenhouse gas emissions. Overview and methodologies. Accessed 11 Nov 2018

  • Katrakilidis C, Trachanas E (2012) What drives housing price dynamics in Greece: new evidence from asymmetric ARDL cointegration. Econ Model 29(4):1064–1069

    Article  Google Scholar 

  • Kazzazi A, Nouri B (2012) A conceptual model for local content development in petroleum industry. Manag Sci Lett 2(2012):2165–2174

    Article  Google Scholar 

  • Koitsiwe K, Adachi T (2017) Linkages between mining and non- mining sectors in Botswana. Miner Econ 30:95–105

    Article  Google Scholar 

  • Kuhn G, Jansen R (1997) Input-output analysis – sectoral multipliers for South Africa in 1993. The Industrial Development Corporation of South Africa Ltd., Johannesburg

    Google Scholar 

  • LeVine S (2007) The oil and the glory: the pursuit of empire and fortune of the Caspian Sea. Random House, Inc., NY

    Google Scholar 

  • Loewenstein W, Bender D (2017) Labour market failure, capital accumulation, growth and poverty dynamics in partially formalized economies: why developing countries’ growth patterns are different. Available from: . Accessed 14 Jan 2018

  • McKinsey Global Institute (2013) Reverse the curve: maximizing the potential of resource-driven economies. Available from: and%20Mining/Our%20Insights/Reverse%20the%20curse%20 Maximizing%20the%20potential%20of%20resource%20driven%20 economies/MGI_Reverse_the_curse_Full_report.ashx. [Last retrieved on 2018 Jul 25]

  • Mikesell RF (1997) Explaining the resource curse, with special reference to mineral-exporting countries. Res Policy 23(4):191–199

    Article  Google Scholar 

  • Muradov A, Hajiyev N, Hasanli Y (2019) World market Price of oil. Impacting factors and forecasting. Springer, Berlin

    Book  Google Scholar 

  • OECD (2002) Measuring the non-observed economy. A handbook. Available from: [Last retrieved on 2018 Aug 02]

  • Onder H (2013) Azerbaijan: inclusive growth in a resource-rich economy. Washington D.C, The World Bank

    Google Scholar 

  • Östensson O (2014) The employment effect of mine employees’ local expenditure. Miner Econ 27(2–3):135–142

    Article  Google Scholar 

  • Pesaran MH, Shin Y, Smith RP (1999) Pooled mean group estimation of dynamic heterogeneous panels. J Am Stat Assoc 94(446):621–634

    Article  Google Scholar 

  • Pirani S (2018) Let’s not exaggerate: southern gas corridor prospects to 2030. OIES Paper: NG 135. Available from: [Last retrieved on 2018 Aug 08]

  • Redqueen S, IFC (2017) Effects of oil, gas and mining investments on jobs. Literature review and estimation tool for Ghana and Peru. Policy Research Paper. Available from: https://www.commdev. org/wp-content/uploads/2015/05/P_Effects_oil_gas_mining_ investments_jobs-Literature_review_Estimation_tool_for_Ghana_ and_Peru_31012017.pdf. [Last retrieved on 2018 Jul 25]

  • Rzayeva G (2015) The outlook of Azerbaijani gas supplies to Europe: challenges and perspectives. OIES Paper: NG 97

  • Sabiroglu IM, Bashirli S (2012) Input-output analysis of an oil-rich economy: the case of Azerbaijan. Res Policy 37:73–80

    Article  Google Scholar 

  • Sadik-Zada ER (2016) Oil abundance and economic growth. Logos Verlag, Berlin

    Google Scholar 

  • Safaraliyeva U (2018) Azerbaijan oil and gas sector briefing. Department for International Trade. Accessed on 22 May 2019

  • Shankar S (1989) Role of petroleum industry in Singapore’s economy. Research Notes and Discussion Paper No. 67. Singapore: Institute of Southeast Asian Studies Press

  • Schroedet Y (2003) Asymmetric cointegration. University of Geneva, Mimeo

    Google Scholar 

  • Shin Y, Yu B (2004) An ARDL approach to an analysis of asymmetric long-run cointegrating relationships. Leeds University Business School, Mimeo

    Google Scholar 

  • Shin Y, Yu B, Greenwood-Nimmo M (2014) Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In: Sickles, R.C., Horrace, W.C. (eds.) Festschrift in Honor of Peter Schmidt. New York: Springer

  • Sicotte J (2018) Baku and its oil industry through war and revolution: 1914-1920. Extractive Industries and Society 5(3):384–392

    Article  Google Scholar 

  • Solow R (1974) Intergenerational equity and exhaustible resources. Rev Econ Stud 41:29–45

    Article  Google Scholar 

  • Stilwell LC, Minnitt RCA, Monson TD, Kuhn G (2000) An input-output analysis of the impact of mining on the south African economy. Res Policy 26:17–30

    Article  Google Scholar 

  • Teka Z (2011) Backward linkages in the manufacturing sector in the oil and gas value chain in Angola. MMCP Discussion Paper. Cape Town: University of Cape Town

  • Tordo S, Tracy B, Arfaa N (2011) National oil companies and value creation. World Bank Working Paper No. 218. Washington D.C.: The World Bank

  • Tordo S, Warner M, Manzano OE, Anouti Y (2013) Local content policies in the oil and gas sector. World Bank, Washington DC

    Book  Google Scholar 

  • UNO, AZSTAT (2008) Учет и оценка ненаблюдаемой экономики в статистической практике Азербайджана. Available from: eadmin/DAM/stats/documents/ece/ces/ge.20/2008/sp.8.r.pdf. [Last retrieved on 2018 Aug 06]

  • Wood L (2013) Chemical industry of Azerbaijan: business report 2013. Zinn, G.W. (2008), Interpreting Input-Output Multipliers. Department of Economics, East Carolina University. Available from: Accessed 31 Jul 2018

Download references

Author information

Authors and Affiliations


Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material


(XLSX 360 kb)


(XLSX 365 kb)


(XLSX 199 kb)



Table 4. Simulation of the 1 Mio. USD increase of the oil sector output (final demand) based on IO tables of 2009
Table 5. Simulation of the 1 Mio. USD increase of the non-oil sector output (final demand) based on IO tables of 2009
Table 6. Direct linkage effects of extractive industries on the other sectors 2006
Table 7. Direct linkage effects of processing industries on the other sectors of economy 2006
Table 8. Job creation effect of USD 1 Mio. Output growth on the economy-wide employment
Table 9. Leontief multiplier by sector for 2006 and 2009

Appendix 7. IO analysis–based assessment of the employment effects

The aggregate output of all sectors in the economy is the sum of the intermediate, AX, and the final output, Y:

$$ X= AX+Y $$

whereby, A is the direct expenses coefficient matrix which is also called technological matrix for representing production technology. X is the total output of goods and services, and Y is the last product vector. Equation (A.1) could be rewritten as follows in Eq. (A.2), whereby E is the respective identity matrix

$$ X={\left(E-A\right)}^{-1}Y $$

We start the presentation of the labor balance model by introducing the direct labor coefficient ti, which is calculated as the ratio of the total employment and total output in the individual sectors. This corresponds to Eq. (A.3):

$$ {t}_j=\frac{L_j}{X_j},\kern0.5em i=\overline{1,n} $$

whereby Xi is the total product of the j-th sector and Lj - is the labor force which is employed in the j-th sector for the production of the j-th product. As already mentioned, ti captures only the direct job creation effect. If we denote the full job creation coefficient as Tj, then

$$ {T}_j=\sum \limits_{i=1}^n{a}_{ij}{T}_i+{t}_{j,}=\overline{1,n} $$

whereby aij is the direct material expense coefficient and Tj is full the labor coefficient. If we include direct labor coefficient t = (t1, t2, …, tn) and the full labor coefficient T = (T1, …. Tn) as a line vector, then Eq. (A.4) can be written in the matrix form (A.5):

$$ T=T\ A+t $$

The multiplication of the LHS of Eq. (A.5) by the identity matrix, E, yields:

$$ T=t{\left(E-A\right)}^{-1} $$

(E − A)−1 matrix is the well-known full material expense coefficients matrix. If we denote (E − A)−1 as B, then Eq. (A.6) can be rewritten as:

$$ T= tB $$

Equation (A.3) yields:

$$ {L}_j={t}_j{X}_j $$

If we assume that there are n sectors in the economy, then the whole labor force which is employed in the economy, L, can be calculated in accordance with Eq. (A.8):

$$ L=\sum \limits_{j=1}^n{t}_j{X}_j= tX $$

The multiplication of the both sides of (A.7) by the final product vector, Y yields

$$ TY= tBY $$

From (A.2), we know that BY = X. Hence, Eq. (A.9) can also be expressed as follows:

$$ T\ Y=t\ X $$

Because, the RHS of the last equation equals the RHS of Eq. (A.8), which shows the total number of the employed people, we can rewrite Eq. (A.10) as follows:

$$ L= TY $$

This equation shows that the full labor coefficient T and determines the number of the additional employees to produce the final product. Based on Eq. (A.11), the job creation, ∆L, can be calculated as ΔL = tΔX = tB∆Y = T∆Y.Footnote 3 ΔL stands for the direct and indirect job creation effects caused by the change in the amount of the final good, ΔY.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sadik-Zada, E.R., Loewenstein, W. & Hasanli, Y. Production linkages and dynamic fiscal employment effects of the extractive industries: input-output and nonlinear ARDL analyses of Azerbaijani economy. Miner Econ 34, 3–18 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Input-output analysis
  • Employment
  • Nonlinear ARDL (NARDL)
  • Asymmetric error correction model (AECM)

JEL classification

  • C67
  • D58
  • D24
  • P22
  • Q32
  • Q33
  • Q38