Mauritius: Selected Issues
Author: International Monetary Fund. African Dept.
Publication Date:
29 Apr 2019
eISBN:
9781498311991
Language:
English
Keywords:
ISCR; CR; Mauritius; GDP; saving; saving rate; export sophistication; export complexity index; arise from a slowdown; exports goods; export complexity; Productivity; Human capital; Service exports; Infrastructure
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Selected Issues

Abstract

Selected Issues

Unlocking Structural Transformation in Mauritius: Challenges and Opportunities1

Structural transformation by moving up the value chain is an integral part of the Mauritian authorities’ efforts to boost competitiveness and growth and achieve the high-income country status by 2030. This note analyzes trends in economic growth, productivity and economic diversification in the country to assess the key factors that could help Mauritius achieve its long-term vision. The findings show that the slowdown in growth relative to the 1990s can be attributed to a decline in the rate of physical and human capital accumulation, and uneven productivity growth across sectors. Efforts to improve diversification are hampered by limited infrastructure and innovation capacity, and by a shortage of an adequately skilled workforce. Addressing these challenges could help Mauritius to expand its export base and improve export sophistication.

A. Introduction

1. Mauritius has experienced a robust economic growth momentum since its independence in 1968. Real GDP grew at an average of 4.7 percent over 1968–2017, enabling the country to achieve the upper middle-income country status in less than fifty years. A key reason for such impressive economic performance has been the ability of Mauritius to transform itself from an agriculture-based economy to a more diversified manufacturing and services-oriented economy and reap large productivity gains.

2. Ambitious plans are being pursued to further diversify Mauritius into high-value added sectors. To climb up the economic development ladder, the authorities are pursuing ambitious plans to boost productivity, improve diversification, and spur private investment. The “Vision 2030” put forward by the authorities includes positioning Mauritius as a major regional investment gateway and a financial services hub, revamping the manufacturing base, and developing the information, technology and communications (ICT) sector.

3. This paper analyzes trends in economic growth, productivity and diversification to determine the key factors that could help Mauritius achieve its long-term vision. The analysis shows that the challenges faced by Mauritius arise from a slowdown in physical and human capital accumulation, and uneven productivity growth across sectors. Addressing these challenges, notably, through upgrading infrastructure, skill development, building innovation capacity, and increasing female labor force participation could help Mauritius to unleash the second wave of structural transformation.

B. Long-Term Growth and Growth Decomposition

4. Potential output growth has slowed down considerably in Mauritius over the last three decades. Applying several approaches including the Hodrick-Prescott (HP) filter, the production function approach and a multivariate filter (MVF) to separate potential output from cyclical components, the analysis shows that the potential output growth rate in Mauritius has dropped from over 6 percent in the late 1980s to below 4 percent in 2017 (Figure 1).2

Figure 1.
Figure 1.

Mauritius: Potential Output Growth, 1987–2017

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Sources: Statistics Mauritius, and IMF staff calculations.
Figure 2.
Figure 2.

Mauritius: Growth Accounting, 1981–2017

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Sources: Statistics Mauritius, and IMF staff calculations.

5. The decline in the growth grate can be largely explained by a slowdown in labor and capital accumulation. A standard growth accounting exercise shows that an increase in female labor force participation spurred growth in the 1980s (labor contribution to real GDP was 3.8 percent).3 Physical and human capital accumulation, as well as total factor productivity (TFP) growth contributed to economic growth in the 1990s (Figure 2). Since 2000, however, labor growth has been about 1.2 percent (vis-à-vis about 3 percent over 1980–2000), while capital accumulation has contributed only 1.4 percent (compared to 2.3 percent in the 1990s).

6. Growth contribution of capital and labor is projected to remain low in the medium term. With a rapidly aging population, labor force participation is expected to decline in the future, which will further reduce the contribution of labor to economic growth. Similarly, with medium-term investment projected to remain lower than in the 1990s, the contribution of capital to growth is also likely to be low.

7. Productivity growth would be the key driver of economic growth in the future. The authorities aim to achieve an average annual growth rate of 5–6 percent over the next decade or so.4 Assuming the same level of contribution from capital and labor as in 2000–17, TFP growth will have to rise to about 3 percent—much higher than the average TFP growth of about 1 percent observed over the last forty years—to meet this goal.

C. Recent Productivity Trends

8. Analyzing recent trends in productivity growth shows a general slowdown relative to the 1990s. Average multifactor productivity (MFP) growth rate for the overall economy has been around 1.1 percent since 2000, which is half of the rate observed during the 1990s (Figure 3).5 While MFP growth for the manufacturing sector has rebounded since 2010 (3.1 percent relative to 1.2 percent in the 2000s), it is still below the average growth rate of the 1990s (3.7 percent). In addition, at a little over 1 percent, productivity growth of the export-oriented enterprises (EOE) has dropped significantly compared to the 1990s (5 percent).6 The slowdown in MFP growth is common to both textile and non-textile EOEs (Figure 4).

Figure 3.
Figure 3.

Mauritius: MFP Growth Rate,1983–2017

(In Percent)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: Statistics Mauritius
Figure 4.
Figure 4.

Mauritius: MFP Growth Rate: Export-Oriented Enterprises 1983–2017

(In Percent)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: Statistics Mauritius

9. Productivity growth varies significantly across sectors. At 6.4 percent, the ICT sector has experienced the fastest MFP growth over 2009–16 (Figure 5). However, annual productivity growth in the finance and insurance sectors has been negligible, while that in the professional, scientific and technical activities category has seen a sharp decline.

Figure 5.
Figure 5.

Mauritius: MFP Index by Sector

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: Statistics Mauritius.Note: ICT= Information, communications, and technology.

10. Notably, wage increases have outstripped labor productivity across the economy. Average wage has risen at a higher rate than labor productivity, resulting in an increase in unit labor costs over the last few years (Figure 6). The divergence between real wages and productivity has been particularly pronounced among the EOEs (Figure 7).7 The loss of competitiveness of the EOEs is evident from the sharp decline in the real export of goods in recent years, which have fallen by about 20 percent over 2014–18.

Figure 6.
Figure 6.

Mauritius: Unit Labor Cost

Index (1982=100)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: Statistics Mauritius.
Figure 7.
Figure 7.

Mauritius: Real Wages and Labor Productivity in EOEs

Index (2009 = 100)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: Statistics Mauritius.

D. Growth Diagnostic Analysis

11. A growth diagnostic framework is applied to analyze the key impediments to boosting productivity in Mauritius. Pioneered by Hausmann, Rodrik, and Velasco (2005), the approach uses a diagnostic decision tree to assess the most binding constraints to economic growth (Figure 8).8 At the most general level, growth is constrained by a lack of finance for entrepreneurs and/or by low economic returns. These factors could in turn be driven by several other variables (including geography, availability of human capital, infrastructure, appropriability of returns, etc.). The growth diagnostic methodology is based on moving down the tree, eliminating factors that are unimportant in explaining the growth performance. Elimination is done by comparing prices/shadow prices for the constraints across time and with peers.9 In addition, the analysis examines if many agents in the economy are trying to overcome certain obstacles.10

Figure 8.
Figure 8.

Mauritius: Growth Diagnostic Tree

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: Hausmann, Rodrik, Velasoo (2005).

12. High cost of finance does not appear to be a binding growth constraint in Mauritius. Countries where weak financial access primarily constrains private investment often display high interest rates and large spreads between lending and deposit rates. Mauritius’ interest rate spreads are slightly lower than in other middle-income countries and close to the average for advanced global financial centers (GFCs; Figure 9).11 Mauritius also has a strong credit rating (Baa1 by Moody’s), while the World Economic Forum’s (WEF) global competitiveness index lists access to finance as the sixth (out of sixteen) most important business problem.12

Figure 9.
Figure 9.

Mauritius: Lending Spread of Mauritius vs. Comparators

(In percentage points)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: World Bank WDI database.

13. Mauritius has a stable macroeconomic and political environment, and strong institutions, implying a low risk of appropriability and expropriation. Mauritius has had a vibrant democracy and has enjoyed years of political and macroeconomic stability. It scores highly on the security and property rights indicators (including the WEF). The overall cost of starting businesses is about 1 percent of income per capita, comparable to the best performers worldwide.13 The high public-sector wage premium is a problem (median public-sector wage increased from 218 percent of median private wage in 2011 to 240 percent in 2016), however, which disincentivizes workers to respond to private sector needs.

Figure 10.
Figure 10.

Mauritius: Education, R&D and Internet Speeds Comparison

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Sources: World Bank and Speedtest.

14. Mauritius lags behind peers in innovation capacity, notably, in skilled labor, research and development (R&D), and information technology (IT) infrastructure. R&D expenditure in Mauritius is only 0.2 percent of GDP compared to over 2 percent in advanced GFCs. Among the middle-income countries also, Mauritius lags in the quality of research institutions.14 The IT infrastructure is also weaker than in other major GFCs (Figure 10). In terms of human capital, while primary and secondary enrollment rates are over 95 percent in Mauritius, the tertiary enrollment rate (at 38.7 percent) and average years of schooling (10.1 years) are significantly lower than other upper-middle income economies and advanced GFCs.15

15. Co-ordination externalities can also be considered as a binding growth constraint in Mauritius. Recent research argues that value upgrading is a primary driver of economic development (Hausmann et. al., 2007; Verhoogen, 2008; Khandelwal, 2010). However, economic development through value upgrading is difficult—on the one hand, many products and services cannot be produced without specific inputs and know-how; and on the other hand, investments into producing such inputs and know-how are not initially profitable. This implies some path dependence, as economies move from products they already produce into products that use similar know-how and inputs. It also creates co-ordination externalities, where social benefits to coordination are large but cannot be reaped easily by private actors. When these co-ordination externalities remain unresolved, it is difficult to build the inputs and know-how needed to move into higher value-added sectors—resulting in a less sophisticated and diversified production and export base.16 The low levels of export complexity, in turn, tend to result in low growth rates and level of economic development.17

E. Export Structure

16. Mauritius has much room to improve the sophistication of its export basket. Mauritius’ goods export complexity has been consistently below that of other major GFCs (Figure 11). This gap is driven by the high ubiquity (or low uniqueness) of Mauritius’ exports (see Box 1). Mauritius’ export basket also lacks diversification relative to its global competitors, as indicated by the inverse Herfindahl-Hirschman Index (HHI) in Figure 12.18 The complexity of its services exports has also lagged that of other global competitors. Calculating the average complexity of service export categories (a la Hausmann et al., 2007; Anand et. al. 2012), Mauritius fares much lower than other GFCs.19 Mauritius’ dependence on tourism (with a low complexity score) and a relatively narrow base in high complexity sectors such as finance are the key reasons for this gap. In 2014, for example, finance comprised only 3 percent of Mauritius’ services exports, while the average for other GFCs was 14 percent. By contrast, travel constituted 45 percent of Mauritius’ services exports in 2014, which is over four times the average for its comparators (11 percent).

Figure 11.
Figure 11.

Mauritius: Export Complexity of Goods

(Index)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Sources: Harvard CID and IMF staff calculations.Notes: Higher values of the index indicate greater complexity. SSA=Sub-Saharan Africa; Regional peers= Non-oil exporting SSA countries (Botswana, Namibia, South Africa); Other GFCs=Ireland, Singapore, Switzerland, UAE.
Figure 12.
Figure 12.

Mauritius: Export Diversification of Goods

(Index)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Sources: Harvard CID, and IMF staff calculations.Note: Higher values of the index indicate greater complexity.
Figure 13.
Figure 13.

Mauritius: Export Complexity of Services

(Index)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: IMF staff calculations.Note: Higher values of the index indicate greater complexity.
Figure 14.
Figure 14.

Mauritius: Export Diversification of Services

(Index)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: IMF staff calculations.Note: Higher values of the index indicate greater diversification.

17. Why does Mauritius lag in export complexity? To address this question, an export complexity function—linking the export complexity index with various structural and macroeconomic factors—is estimated using panel data for 121 advanced and emerging market countries over 1995–2016:

ECIit=β0+β1Xit+β2Di+β3ECIit−j+ψt+μit(1)

where ECIit is the export complexity index of country i in time t; Xit includes economic and structural variables that are likely to affect the comparative advantage of countries, and thereby their export sophistication (such as human capital proxied by the average years of schooling; financial development proxied by the private credit to GDP ratio; institutional quality proxied by the World Governance Indicator’s governance effectiveness rating; political stability; and economic size proxied by the total population); and ψt are time effects to capture common shocks across countries. Given that several of these variables are slow-moving, country-fixed effects are not included in the benchmark regression, but several dummy variables, Dt, are included to capture the time-invariant country characteristics (such as the resource exporter status; income status, regional grouping, distance to major markets etc.). In addition, to capture the persistence in the export structure, the lagged term of the dependent variable (ECIit-j; j=1) is also included.

18. Findings suggest that innovation capacity, political stability, and governance are key drivers of export complexity. Table 1 shows the regression results for equation (1)—both with and without the lag of the ECI variable. The results show that the average years of schooling, R&D expenditure, governance and political stability, communications infrastructure and trade openness are significant determinants of economic complexity. The lag of the ECI is also highly significant, suggesting strong path dependence in export complexity and the difficulties associated with moving up the value chain. Distance to major markets is strongly negatively associated with higher levels of export complexity, suggesting that geographically remote countries are at a locational disadvantage for export sophistication. This could be because of the high information and transportation costs associated with remoteness, which lowers the economic returns for enterprises, deterring private investment.20

19. Mauritius has significant room to improve its export complexity. Based on the estimation results, Mauritius appears to have a lower ECI than that predicted by its macro-structural characteristics. Thus, while it scored as the 44th percentile in 2016, its predicted level turns out to be 53 (Figure 15). This suggests a misallocation of resources (e.g., from skill mismatches), and/or deficiencies in the physical infrastructure and investment climate not well captured by the indices used. Notwithstanding the difference between actual and potential ECI, Mauritius also has considerable room to boost its potential by improving the factors positively associated with ECI—notably, human capital, the ICT infrastructure and R&D.

Figure 15.
Figure 15.

Mauritius: Predicted vs Actual Economic Complexity, 2016

(Percentile)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: Harvard CID, IMF staff calculations.Note: Predicted percentile is calculated using the average of the regression models.

Export Clusters for Mauritius: A Comparative Analysis

In recent work, Jankowska et al. (2012) explore the differences between Asian economies which escaped the middle-income trap (South Korea, Hong Kong, Singapore) and Latin American economies which did not (Brazil, Colombia and Mexico). They calculate a proximity metric across products (product A is proximate to product B if it is easier to produce A once a country already produces B). Their analysis finds that the successful Asian economies expanded into proximate products and accrued more sophisticated capabilities in this process. In this framework, some products are unique, as they are: a) proximate to many other products, creating a cluster, and b) and are high value. Some examples of such high connectivity, high value clusters are electronics, vehicles and chemicals. Jankowska et al. (2012) show that highly successful economies built up comparative advantage in at least some of these clusters.

Breaking down the shares (indicated by the rectangle sizes) of the largest exports for Mauritius and its comparators (at HS 4-digit level) in the figures below, the analysis shows that Mauritius’ export basket in 2016 was dominated by travel and tourism, fish, sugar and ICT, with little or no comparative advantage in high connectivity clusters. By contrast, Singapore, a small open economy which is also a competitive financial center, exports goods and services in a variety of high margin clusters including the electronics and chemicals clusters. The other countries considered here, Switzerland and Ireland, both show a comparative advantage in high connectivity clusters and niche high-margin goods. Switzerland specializes in precious metals, stones, pharmaceutical products, clocks, industrial and electrical machinery. In comparison, Ireland’s export basket focuses more on services, but still includes chemicals, machinery and electronics.

uA02fig01

Exports Clusters for Mauritius (2016)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

uA02fig02

Exports Clusters for Singapore (2016)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

uA02fig03

Exports Clusters for Switzerland (2016)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

Source: Atlas of Economic Complexity. Harvard CID.
uA02fig04

Exports Clusters for Ireland (2016)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A002

F. Conclusions

20. Mauritius faces several challenges in its structural transformation into high value-added sectors. The authorities’ Vision 2030 foresees Mauritius join the ranks of higher-income countries over the next decade by a fundamental transformation of the economy to a more economically diversified and sophisticated economy. However, dwindling productivity, rising unit labor costs, limited innovation capacity, and unfavorable demographic trends in the form of an aging population and a potentially declining labor force, pose significant challenges in achieving this goal.

21. These challenges could be tackled with proactive and pragmatic policies to build innovation capacity, human capital and infrastructure. A comparative analysis with other GFCs suggests there is considerable room for Mauritius to diversify into high-value added sectors, notably services, by improving human and physical capital, and innovation capability. Structural reforms to address labor market inefficiencies, reduce skill mismatches, increase female labor force participation, and promote research and development will help to unlock Mauritius’ economic potential and modernize the economy.

22. Initially focusing on value upgrading in the traditional sectors could help to unlock structural transformation. Mauritius’ traditional exports have comprised sugar, textiles, tourism, and financial services, where there is considerable scope for value upgrading. Given capacity constraints, efforts could focus on increasing the value added in these sectors to build on the initial infrastructure and technical expertise. The strategies to diversify must also be realistic about the existing limitations in terms of human, financial and institutional capacity constraints, and address the fundamental bottlenecks first to prevent a wastage of resources. For example, artificial intelligence/machine learning-based strategies require a strong ICT infrastructure. Similarly, initiatives such as the new life sciences park require a solid R&D set up and collaboration between the research institutions and the industry.

23. Systematic monitoring and evaluation of the initiatives to address the structural challenges is essential. In recent years, Mauritius has introduced a range of initiatives and reforms to boost skill development, support private enterprises, and increase women’s participation in the workforce. These programs should be aligned with the long-term vision of improving export sophistication and productivity growth, and target sectors with the highest potential. Regular monitoring and evaluation of these programs through data collection is essential to track progress, prevent wastage of public resources, and enhance effectiveness.

Table 1.

Mauritius: Drivers of Economic Complexity, 1995–2016

article image
Note: Robust standard errors clustered at country level in parentheses. Time fixed effects included in all regressions. Columns [3–4] and [7–8] use fixed effects at country level as well. ***, ** and * indicate statistical significance at 1,5, and 10 percent levels respectively.

Taken from the Fraser Institute Economic Freedom Database.

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1

Prepared by Sandesh Dhungana.

2

The HP filter separates the trend and cyclical components of output through a purely statistical filtering technique, while the production function approach first uses a standard Cobb-Douglas production function to calculate productivity, and then uses trend values of unemployment and productivity to calculate potential output. The MVF approach allows for the inclusion of two structural relationships, namely Phillips curve and Okun’s law, thereby adding pertinent information on output, inflation, and unemployment to the potential output estimation process (Blagrave et al., 2015).

3

The growth accounting exercise assumes output is generated by a Cobb-Douglas production function. Human capital augmented labor is used. Perpetual inventory method with geometric depreciation rate of 7 percent is used to calculate capital stock. Contributions from TFP are calculated as a residual.

5

Multifactor Productivity (MFP) is calculated by Statistics Mauritius (SM) as the ratio of real output to weighted combination of labor and capital inputs.

6

EOEs comprise manufacturing enterprises, formerly operating with an export certificate (for Export Processing Zones) and export manufacturing enterprises holding a registration certificate issued by the Board of Investment. In 2016, this sector accounted for 40 percent of the manufacturing output and 5.2 percent of total output.

7

As discussed in Country Report No. 17/363, the wage-setting mechanism is a major driver of these trends.

8

The most binding constraint is the one that, if relaxed, would lead to the largest increase in long run growth. See Hausmann, Rodrik and Velasco (HRV, 2005) for the theoretical exposition of the approach. Also see Hausmann, Klinger and Wagner (2008) for a handbook on doing growth diagnostics in practice.

9

If a constraint is binding, the price/shadow price for the constraint should be high. Also, episodes of the constraint loosening (if available), should lead to growth spurts.

10

For example, if political risk is high, large firms invest in security infrastructure. If financing is an issue, firms invest using retained earnings.

11

Singapore, the United Arab Emirates, Ireland and Switzerland are used as comparator GFCs.

12

Among some of the other GFCs, access to finance is rated the most important problem in the United Arab Emirates, the third most important for Ireland and the sixth most important for Singapore and Switzerland.

13

The cost of starting a business is 0.1 percent of per capita income in Singapore, and 1.1 percent for the U.S.

14

According to the WEF index, Mauritius also lags most middle-income countries in terms of R&D industry-university collaboration.

15

Tertiary enrollment rates are higher in Singapore (83 percent), Switzerland (77 percent) and Ireland (58 percent). The average for upper-middle income economies is 52 percent.

16

Production sophistication data is mostly unavailable at a sectoral level; hence export sophistication is generally used in the literature. Overall, the two tend to be highly correlated.

17

See, e.g., the Atlas of Economic Complexity developed by the Harvard Center for International Development (http://atlas.cid.harvard.edu/). The export complexity measure combines diversification and ubiquity. Ubiquity measures the number of countries exporting the same product (with low values indicating a more complex product, as it is only produced by a few countries). An export basket with high diversification and low ubiquity is more complex.

18

The index is calculated as Index=1−ΣNsi2 where st is the share of category i in the overall export basket. Categorization is done at the HS 4-digit level.

19

Due to lack of granular data, this analysis only uses service categories at the 1-digit level. The United Arab Emirates (UAE) is not included as the database does not have any data on the UAE.

20

Mauritius is ranked 18th in the world in remoteness by the Global Connectivity Index (2018). Mauritius is ten times more distant to major world ports compared to Ireland and Switzerland, and 1.5 times farther than Singapore (Gallup et al., 1999).

Same Series

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Other Publishers

Cover IMF Staff Country Reports
Mauritius: Selected Issues
Author: International Monetary Fund. African Dept.
Volume/Issue:
Volume 2019: Issue 109
Publisher:
International Monetary Fund
ISBN:
9781498311991
ISSN:
1934-7685
Pages:
41
DOI:
https://doi.org/10.5089/9781498311991.002

Mauritius: Potential Output Growth, 1987–2017

Mauritius: Growth Accounting, 1981–2017

Mauritius: MFP Growth Rate,1983–2017

(In Percent)

Mauritius: MFP Growth Rate: Export-Oriented Enterprises 1983–2017

(In Percent)

Mauritius: MFP Index by Sector

Mauritius: Unit Labor Cost

Index (1982=100)

Mauritius: Real Wages and Labor Productivity in EOEs

Index (2009 = 100)

Mauritius: Growth Diagnostic Tree

Mauritius: Lending Spread of Mauritius vs. Comparators

(In percentage points)

Mauritius: Education, R&D and Internet Speeds Comparison

Mauritius: Export Complexity of Goods

(Index)

Mauritius: Export Diversification of Goods

(Index)

Mauritius: Export Complexity of Services

(Index)

Mauritius: Export Diversification of Services

(Index)

Mauritius: Predicted vs Actual Economic Complexity, 2016

(Percentile)

Exports Clusters for Mauritius (2016)

Exports Clusters for Singapore (2016)

Exports Clusters for Switzerland (2016)

Exports Clusters for Ireland (2016)