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Heterogeneous labor and structural change in low- and middle-income, resource-dependent countries

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Abstract

In this paper we analyse structural change and its implications for labor productivity growth in Kazakhstan, Malawi, and Zambia, three resource-dependent countries, during the resource boom that lasted from 2001 to 2013. We pay particular attention to the effects of labor heterogeneity by assessing the demand for pre-determined occupations. The effects of structural change on heterogeneous labor markets are studied by developing a model to explain the observed patterns of labor migration between sectors. We use labor force survey data from Kazakhstan and census microdata for Malawi and Zambia available from the IPUMS International database. In-depth examinations are performed using a decomposition technique and multinomial logit regression to examine labor demand patterns. Results show that private services experienced the largest increase in employment across all occupations and relative skill deepening. Substantial decreases in managers occurred in public services. The results indicate that structural change is fundamental in shaping the allocation of individuals across different occupations within the labor market. Moreover, during a resource boom, the results indicate that the public sector experiences a skill-drain that may affect the quality of governance.

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Notes

  1. The static-shift effect is calculated as the sum of relative changes in the share of labor across sectors between a base year and a terminal year, weighted by the value of labor productivity in the base year.

  2. For calculating the contribution of structural change to labor productivity, we used the Groningen Growth and Development Center (GGDC) database, which provides employment and real-valued added statistics for both Malawi and Zambia disaggregated into ten sectors (Timmer and de Vries 2007, 2009). We collected our own data from Kazakhstan (Labor Force Survey and National Accounts). To maintain consistency with the GGDC data, we followed the data compilation procedures outlined by the GGDC authors.

  3. According to user privileges of the IPUMS International dataset, we acknowledge the statistical agencies that originally produced the data; specifically, the National Statistical Office of Malawi and the Central Statistical Office of Zambia.

  4. The World Bank (2017) defines low-income economies as those with a GNI per capita of $1005 or less in 2016; lower middle-income economies are those with a GNI per capita between $1006 and $3955; upper middle-income economies are those with a GNI per capita between $3956 and $12,235; high-income economies are those with a GNI per capita of $12,236 or more.

  5. However, with high mineral prices the mineral sector’s total exports in 2007 were estimated at USD 725 million (World Bank 2018).

  6. Public services include public administration, defence, education, health care, social services, and utilities. Private services include wholesale and retail trade; hotel and restaurant services; transport, storage and communications; finance, insurance and real estate; and business and technical services.

  7. Chenery et al. (1986) provide an in-depth discussion on the criteria used to distinguish semi-industrialized economies.

  8. At the Kiel Institute of World Economics during the 1970’s, fifteen semi-industrialized economies were studied to examine the process of economic development (Donges 1976). The Kiel Institute of World Economics defined semi-industrialized countries as those economies that have already left the early phase of industrialization (in which capital, skill and infrastructural requirements are rather low); but, have not joined the ranks of industrialized countries (i.e., semi-industrialized countries may be perceived as middle-income countries).

  9. We follow Spatafora et al. (2012) and separate the private services into modern and traditional services. We define traditional private-sector services to include construction; wholesale and retail trade; repair of motor vehicles and motorcycles; accommodation and food service activities; and transportation and storage.

  10. We define modern private-sector services to include financial and insurance activities; real estate activities; professional, scientific and technical activities; and administrative and support service activities.

  11. Public services comprise public utilities and government services.

  12. We could incorporate manufactures into both the utility function as well as the production function in Sect. 3.2; however, this would not change the outcome of our model unless the income elasticity of manufactures is greater than one. Furthermore, manufacturing is a very small component of most low- and middle-income, resource-dependent countries. For example, manufacturing employed only 6.8% of Kazakhstan’s labor force in 2011; 5.8% of Malawi’s labor force in 2008; and 4.4% of Zambia’s labor force in 2010.

  13. Falvey and Gemmell (1996) find government services to be price elastic and income unit elastic.

  14. The assumption of only two skill levels can be relaxed without any major effects if the third factor is the least intensively used factor. We use three skill levels in our empirical analysis.

  15. The percentage of labor employed in mining for Malawi in 1998 and 2008 were 0.06% and 0.13%, respectively; for Zambia in 2000 and 2010 were 2.03% and 2.46%, respectively; for Kazakhstan in 2001 and 2011 were 3.63% and 2.63%, respectively (authors’ calculations). Iinput-output analysis of the South African economy by Stilwell et al. (2000) finds few linkages between mining and the rest of the economy. Finally, studies from resource-dependent high-income jurisdictions show that output growth during the 2001–2013 resource boom was primarily driven by capital growth [see Centre for the Study of Living Standards (2012) for Alberta and Mason (2013) for Australia]. As both the oil and gas and mining industries are extensively global, we assume a similar pattern for Kazakhstan, Malawi and Zambia.

  16. This corresponds well with Rodrik’s (2007) findings that during periods of sharp global competition states are pushed to implement new labor market reforms, reduce government spending, or sharpen economic and policy divergence through innovative institutional measures and new spending initiatives that create new policy spaces. All these initiatives require high-skilled labor in the public sector.

  17. We could introduce inequality in resource rent earnings, similar to Howie and Atakhanova (2014); but, given the Linear Expenditure System utility function the aggregate expenditure share on each good would still be \(\alpha_{n}\).

  18. The increase of factors of production do not affect the labor demand dynamics as the additional skill level is the one least intensively used in the production process.

  19. Katz and Murphy (1992) state that under the assumption of fixed relative wages between-sector results will be downward biased in groups with relative wage increases.

  20. Due to data availability, the base year for Kazakhstan is 2001; for Malawi is 1998; and for Zambia 2000.

  21. Between 1990 and 1995, Kazakhstan’s gross agriculture output decreased 47% and agricultural labor productivity decreased 63% (Macours and Swinnen 2002).

  22. The Kazakhstani Ministry of Agriculture's budget increased from 26 billion tenge in 2001–81 billion tenge in 2005, and its share of the total central budget went from 2.5% to 6.5% (Petrick and Pomfret 2016).

  23. We acknowledge that the time periods for each country are not consistent. However, as we are mainly interested in examining the changes in probabilities of selecting employment between the private and public sectors for professionals, managers, clerks, service workers, and laborers the differing time periods have minimal effects on our results. This is supported by the fact that we do not compare results across countries.

  24. In Kazakhstan, unskilled farmers were classified as those workers that worked in the agricultural sector and have completed up to 9 years of education. In Kazakhstan, the data indicate that less than 0.1% of unskilled farmers worked in public sector in KZ in both 2001 and 2011. In both Malawi and Zambia, unskilled farmers were classified as those workers that worked in the agricultural sector and have completed up to 6 years of education. In Malawi, the data indicate that 3.0% and 0.5% of unskilled farmers worked in public sector in 1998 and 2008, respectively. In Zambia, the data indicate that 1.4% and 0.2% of unskilled farmers worked in public sector in 1998 and 2008, respectively.

  25. Sales workers in Kazakhstan were classified as those workers that worked in the ILO’s ISCO-88 “Models, Salespersons and Demonstrators” occupations. In Kazakhstan, the data indicate that 0.5% and 0.2% of sales workers worked in public sector in 2001 and 2011, respectively. As the IPUMS data “Occupation, ISCO general” aggregates “Personal and Protective Service Workers” with “Models, Salespersons and Demonstrators”, we use country-specific data for Malawi and Zambia to extract and exclude “Models, Salespersons and Demonstrators”. In Malawi, the data indicate that 1.5% and 0.3% of sales workers worked in public sector in 1998 and 2008, respectively. In Zambia, the data indicate that 0.6% and 2.6% of sales workers worked in public sector in 2000 and 2010, respectively.

  26. Craft workers in Kazakhstan, Malawi and Zambia were classified as those workers that worked in the ILO’s ISCO-88 “Craft and Related Trades Workers”. In Kazakhstan, the data indicate that 3.2% and 1.0% of craft workers worked in public sector in 2001 and 2011, respectively. In Malawi, the data indicate that 3.8% and 1.7% of sales workers worked in public sector in 1998 and 2008, respectively. In Zambia, the data indicate that 2.0% and 0.9% of sales workers worked in public sector in 2000 and 2010, respectively.

  27. It should be noted that the sector choice probabilities are calculated similar to Blank (1985), van der Gaag and Vijverberg (1988) and Stelcner et al. (1989) by using the assumption that occupation-specific experience is equally valuable to all sectors of the economy.

  28. The mining and oil and gas extraction sector in Alberta experienced − 6.1% annual growth of multifactor productivity during 1997–2010 (Centre for the Study of Living Standards 2012).

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Acknowledgements

The authors would like to thank the editor and the two anonymous referees for their helpful and constructive comments. The usual disclaimer applies.

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  1. Graduate School of Public Policy, Nazarbayev University, 53 Kabanbay Batyr Avenue, Block C3, Astana, Kazakhstan, 010000

    Peter Howie

  2. Academy of Public Administration of Kazakhstan, 33a Abay Street, Astana, Kazakhstan, 010000

    Zauresh Atakhanova

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Appendices

Appendix 1

See Tables 6, 7 and 8.

Table 6 Kazakhstan’s percentage share and change in occupational mix by sector: 2001–2011
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Table 7 Malawi’s percentage share and change in occupational mix by sector: 1998–2008
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Table 8 Zambia’s percentage share and change in occupational mix by sector: 2000–2010
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Appendix 2

See Tables 9, 10 and 11.

Table 9 Results of sector-choice equations for Kazakhstan
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Table 10 Results of sector-choice equations for Malawi
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Table 11 Results of sector-choice equations for Zambia
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Howie, P., Atakhanova, Z. Heterogeneous labor and structural change in low- and middle-income, resource-dependent countries. Econ Change Restruct 53, 297–332 (2020). https://doi.org/10.1007/s10644-018-9242-9

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