Persistent differences in per capita incomes are usually attributed to productivity differentials. But countries also converge to widely different employment-to-population ratios. This column shows that in fact higher productivity growth in emerging market and developing economies tends to be associated with declines in the employment-to-population ratio (‘jobless development’). Convergence towards low steady-state employment ratios appears to reflect a broken link in structural transformation caused by weak job creation in the non-agricultural sector.
Over the next two decades, between 2024 and 2044, the world’s working-age population (aged between 15 and 64) will grow by 0.7 billion, almost all of these in emerging market and developing economies (EMDEs). Will growth be sufficient to deliver enough jobs for these job market entrants? Judging by past experience, in many cases it will not.
Over the period 2000–2019, one in three EMDEs enjoyed rapid, productivity-driven per capita income growth while also suffering from declining employment-to-working-age population ratios. We coin the term ‘jobless development’ to describe this phenomenon.
Consider, for example, South Korea and India (Figure 1). Both countries experienced sustained periods of rapid growth – South Korea during 1965–1987, and India during 1990–2018. Whereas in South Korea employment also grew rapidly as a ratio of working-age population, it declined during India’s period of rapid growth.
Figure 1 Evolution of employment to working-age population ratio in South Korea and India
Sources: Penn World Tables; World Development Indicators (database).
Although jobless development is common, the dynamics of the employment-to-population ratio have received little attention in the macroeconomic development literature, in part because the ratio is typically uncorrelated with the level of development in a broad cross-section of countries. But the finding of no systematic correlation between the employment-to-population ratio and development does not preclude large differences across countries. Moreover, the dynamics of employment-to-population ratios across countries deserve study in their own right, since employment ratios have been stagnant over the past two decades in EMDEs and low employment ratios have often been associated with social discontent (Figure 2). The challenge of creating a sufficient number of jobs compounds the challenge of creating jobs of sufficient quality in EMDEs (Kenna et al. 2018) and is itself compounded by the population aging that allows workers to remain in the labour force beyond the age of 64 years (Kotschy and Bloom 2023).
Figure 2 Protests by employment-to-working-age-population ratio, 2010-19 averages
Sources: Clark and Regan (2016); Penn World Tables; World Development Indicators (database).
Note: Chart shows annual average number of protest days or number of protest events, during 2010–2019, in EMDEs that rank in the bottom or top quintiles by average employment-to-working-age population ratios during 2010–2019. Sample includes 118 EMDEs.
Cross-country dispersion in steady-state employment ratios
The growth literature has a long tradition of studying the conditional convergence of GDP per capita (Barro and Sala-i-Martin 1992, Kremer et al. 2022, Patel et al. 2021). We borrow from this literature to examine the dynamics of employment-to-working-age-population ratios (EWAP) (Ohnsorge et al. 2024). In particular, using data for 2000–2019, we estimate the long-run steady-state level of EWAP, allowing each country to have its own steady-state level and controlling for labour productivity growth and working-age population growth. The results suggest that many countries converge towards similar steady-state levels of EWAPs.
But there is a large group of exceptions with sizable differences from the EMDE average steady-state EWAP (Figure 3). Out of a sample of 103 EMDEs, 28 countries converge to steady-state aggregate EWAPs at least one standard deviation below the sample average, while 18 countries converge to steady-state aggregate EWAPs at least one standard deviation above the sample average. Countries with below-average steady-state aggregate EWAP include two-thirds of the countries in the Middle East and North Africa region, almost half of the countries in Europe and Central Asia, and one-quarter of sub-Saharan African countries.
Figure 3 Distribution of deviations of steady-state employment ratios from sample average
Source: Authors’ estimates.
Note: Deviation of steady-state employment ratios from sample average. Charts show only deviations that exceed 1 standard deviation. Derived from estimated fixed effects from Table 1 of Ohnsorge et al. (2024), divided by estimated coefficient on the lagged employment ratio.
Employment weakness appears to be concentrated in the non-agricultural sector and among women. In 36 countries, the non-agricultural EWAP is at least one standard deviation below the sample average. And the EWAP for women is at least one standard deviation below-average in more than three-quarters of countries in the Middle East and North Africa and South Asia.
The broken link in structural transformation: The non-agricultural sector
Development typically proceeds along a path of structural transformation – a shift in employment out of agriculture into non-agriculture. Our estimates suggest that low aggregate EWAPs reflect lagging structural transformation and, in particular, a struggling non-agricultural sector. Outside sub-Saharan Africa, every 1 percentage point higher non-agricultural steady-state EWAP is associated with an 0.72 percentage point higher aggregate steady-state EWAP (Figure 4).
Figure 4 Aggregate and non-agricultural steady-state employment ratios
Source: Authors’ estimates.
Note: Deviation of steady-state employment ratios from sample average for aggregate employment and non-agricultural employment. Excludes Sub-Saharan African countries. Derived from estimated fixed effects from Table 1 of Ohnsorge et al. (2024), divided by estimated coefficient on the lagged employment ratio. The correlations are shown as OLS regression estimates in Table 3 of Ohnsorge et al. (2024).
In the 36 countries with below-average non-agricultural steady-state EWAPs, a link in structural transformation appears to be broken: while the agricultural sector typically sheds unproductive workers broadly in line with other countries, the non-agricultural sector struggles more than elsewhere to absorb them.
Factors associated with weak non-agricultural employment
Why do some EMDEs converge towards below-average non-agricultural EWAPs? In a series of cross-country regressions, we test the correlations of steady-state EWAPs in the non-agricultural sector with structural factors that the literature has shown to be associated with non-agricultural employment (Figure 5).
Firm size
Greater policy distortions have been associated with smaller firm size which, in turn, has been associated with lower steady state levels of EWAP. (Bento and Restuccia 2021, Hsieh and Klenow 2014). In our sample, too, countries with larger establishment sizes in non-agricultural sectors – manufacturing or services – are associated with higher steady-state non-agricultural EWAPs.
Openness to trade
The empirical evidence on the link between openness to trade economy-wide employment is generally mixed. But in our sample, countries with higher exports-to GDP ratios also have significantly higher steady-state non-agricultural EWAPs.
Access to inputs
Access to inputs, finance or land, can promote firms’ growth, including their job creation. In our sample, too, countries with smaller proportions of firms reporting constrained access to finance or land have higher steady-state non-agricultural EWAPs.
Labour and product market flexibility
Less burdensome labour and product market regulations can boost firm’s growth. Indeed, in our sample, countries with more flexible product and labour markets have statistically significantly higher non-agricultural EWAPs.
Skills
Better human capital of the workforce can accelerate the shift from agriculture to non-agricultural activities (Lee and Malin 2013, Porzio et al. 2022). We find evidence of this in our sample, too, where countries with higher literary rates have higher steady-state non-agricultural EWAPs.
Figure 5 Steady-state non-agricultural employment ratios, by country characteristics
Sources: Authors’ estimates.
Note: Bars show the predicted deviations from sample average steady state non-agricultural employment ratios, for the bottom and top quartiles of manufacturing and services establishment size (A) or total exports-to-GDP ratios and proportion of firms that reported constrained access to finance (B). Derived from estimated fixed effects from Table 6 of Ohnsorge et al. (2024). Establishment size data come from Bento and Restuccia (2021). Total exports data come from World Development Indicators.
Policies for job creation
Jobless development is common among EMDEs and appears to be a feature of lagging structural transformation. In about three dozen countries, rapid productivity growth since 2000 has been accompanied by falling employment-to-population ratios in the non-agricultural sector. To kindle non-agricultural employment growth, policies need to remove obstacles to firms’ growth – i.e. obstacles to access to finance, land, and foreign markets, inputs and technologies; obstacles to competition in product and labour markets; and obstacles to human capital accumulation.
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