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Life after coronavirus: Strengthening labor markets through active policy

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Prior to the COVID-19 crisis, the growing consensus was that the central challenge to achieving inclusive economic prosperity was the creation of good jobs that bring more workers closer to a true “middle-class” lifestyle (Rodrik, 2019). This simple goal will be hard to meet. The lingering effects of the coronavirus crisis will add to the structural changes that were already shifting labor demand and skill content of traditional occupations—exposing workers to displacement, income cuts, or inactivity. This crisis will have persistent effects on economic activity, as the affected, mostly labor-intensive sectors, will need months to come back to speed—if those sectors recover at all. To meet this uphill challenge, it is essential to understand what works in terms of off-the-shelf labor market policies and to learn how to calibrate them to the particular time and space context faced by individual countries and regions—and, last but not least, to put fiscal resources to work to that end.


Technological change and the automation of employment put a wide range of routine occupations at risk and increase wage inequality among employees (Goos et al., 2014; Autor, 2015) and between capital and labor (Das et al., 2017). Globalization and the emergence of global value chains stimulate an increasingly aggressive global competition where a programmer in New Delhi, an industrial operator in Beijing, or customer service provider in Mexico City competes directly with their counterparts in Paris, New York, or Berlin (Baldwin, 2013). And demographic change and aging populations represent formidable challenges for the financing of social security systems based on the output of a declining mass of formal wage workers (Holzmann, 2013; Bloom et al., 2015).

M

Martín Montané

Researcher, Center for Evidence-Based Policy of the School of Government - Universidad Torcuato Di Tella

L

Luca Sartorio

Researcher, Center for Evidence-Based Policy of the School of Government - Universidad Torcuato Di Tella.

How can governments respond to these shocks to labor demand? Leaving aside the (often ill-advised and self-defeating) protectionist and regulatory barriers, and distribution policies that require a fiscal space that only a few countries can afford, the natural answer is active labor market policies (ALMP). ALMP is a general denomination for specific policies that could be broadly grouped into four big policy clusters—vocational training, assistance in the job search process, wage subsidies or public works programs, and support to microentrepreneurs or independent workers. These policies are a big fiscal item in most countries with well-funded welfare states (as a reference, on average, ALMPs account for more than 0.5 percent of GDP in OECD countries).1

ALMP vary widely in design, target, and implementation. Do they work? Where to start? To provide an informed starting point for policymakers, in a recent paper (Levy Yeyati et al. 2019), we conducted a systematic review of experimental evaluations of the effectiveness of ALMPs worldwide. Specifically, we focus exclusively on programs evaluated through randomized control trials (RCTs)—more precisely: 652 impact estimates on employment and income from 102 interventions around the globe, evaluated through 73 rigorous impact evaluations—exploiting the fact that the past five years have witnessed a flurry of RCTs that shed new light on the impact and cost-effectiveness of ALMPs (Figure 1).2 This exclusive focus on RCTs reduces the number of relevant evaluations, but allows us to focus on estimates with high internal validity and to refine the metrics used to compare results, making the findings from individual evaluations more naturally comparable.3

Figure 1. Distribution of studies included in our sample according to the year of publication

Figure 1. Distribution of studies included in our sample according to the year of publication

Note: 2018 data point includes only studies published up to June of 2018.

Do these programs work—in the sense of either increasing labor earnings or employability (the chance to get a job)? To answer this while accounting for the dimensionality of the problem (programs differ in design, quality of implementation, context, target population), we constructed a “design space” (that is, a parsimonious version of the space of all possible instances of a class of policy) characterized by: (i) the specific components into which the programs can be decomposed; (ii) the implementation features and the type of public-private participation; and (iii) the economic context and the target population of the programs (Figure 2). This allows us to refine the analysis and identify why policies that are similar on paper can differ in their impact and cost-effectiveness.

Figure 2. Proposed design space to characterize active labor market policies

Figure 2. Proposed design space to characterize active labor market policies

Comparing the impact on earnings of the four policy clusters, we find that: Wage subsidies and support to microentrepreneurs or independent workers (training often coupled with loans or lease of productive assets) show the greatest median impact on earnings relative to the control group (improvements of 16.7 percent and 16.5 percent); vocational training programs have a median impact of 7.7 percent; and employment services show an almost negligible impact.

The median impact on employment exhibit a similar pattern: The dividends from wage subsidies are comparable with the ones for earnings, whereas independent worker assistance and vocational training show median impacts on employment of 11 percent and 6.7 percent. Interestingly, employment services interventions have a median impact of 2.6 percent on this target variable, consistent with short-lived and inexpensive interventions that do not attempt to help build human capital, but rather to improve the propensity to find employment. Importantly, there is substantial variability in reported impacts (Figure 3).

Figure 3. Boxplot of the 652 coefficients according to the estimated effect relative to the control group

Figure 3. Boxplot of the 652 coefficients according to the estimated effect relative to the control group

Notes: Estimates are grouped by type of program and outcome category. Boxes represent the 50 percent central coefficients reported. The horizontal lines show the median value. The vertical lines show the last coefficient that falls into the +/- 1.5 times the interquartile range. Points are observations that lay above or below the +/- 1.5 times the interquartile range.

Impact evaluation may be misleading if costs are not taken into account. For example, wage subsidies may be more effective than employment assistance, but they are also significantly costlier. Only 51 interventions report costs, of which only 22 carried out a rigorous cost-benefit analysis (looking at net present values, internal rates of return, or payback times). When information is available, we added a continuous variable to identify the average cost per person in 2010 PPP dollars. With the caveat of a smaller sample, we did identify some indicative patterns: Wage subsidies, worker assistance, and microentrepreneur and vocational trainings have comparable median cost, while employment services are notably less expensive (Figure 4).

Figure 4. Boxplot of unit costs, cost per treated participant by four-way program classification, 2010 PPP U.S. dollars

Figure 4. Boxplot of unit costs, cost per treated participant by four-way program classification, 2010 PPP U.S. dollars

Notes: Estimates are grouped by type of program and outcome category. Boxes represent the 50 percent central coefficients reported. The horizontal lines show the median value. The vertical lines show the last coefficient that falls into the +/- 1.5*interquartile range limit. Points are observations that lay above or below the +/- 1.5* interquartile range limit.

The reported impacts of ALMPs on employment and earnings outputs, although moderately positive on average, are subject to great variability due to the multidimensional design space of these policies. As we pointed out, ALMPs are generally complex policies with high-dimensional design spaces, highly dependent on contextual factors and the quality of their implementation. Any systematic review that does not describe the design space of the policies evaluated and considers the existing variability within the same intervention class or their interactions with the context and the target population, may have limited use from a practical policy perspective (Pritchett et al., 2013).

Which features of the design and implementation of policies are associated with a greater impact? In which contexts is the application of ALMPs more effective? On what populations do they tend to be more successful? As a first take on these questions, we ran meta-analytic regressions that exploited the descriptive granularity of our design space, identifying policy components, contextual factors, and demographic groups associated with a greater probability of success.

Figure 5 summarizes the main findings with statistical significance at a conventional level of eight different models. They are the combination of two cutoffs for the positive and statistically significant binary variable (5 percent and 10 percent) and four subsamples. Several insights arise from this exercise:

  • Individualized coaching or follow-up of the participants, specialized training exclusively focused on a specific industry, and the provision of monetary incentives to trainees all correlated with better outcomes in vocational training programs (the most frequent ALMPs in our dataset).
  • ALMPs are procyclical: The effectiveness of a program correlated positively with economic growth and negatively with national unemployment.
  • Training programs tend to be more effective for young people and we found no significant differences across genders or educational levels.

Figure 5. Main findings with statistical significance at conventional levels in meta-analytic regressions

Figure 5. Main findings with statistical significance at conventional levels in meta-analytic regressions

The shock of the new technology, the coronavirus crisis, and an uncertain future require an increasingly proactive policy oriented to ensure that workers work—in other words, mitigating the deleterious effect of technological change and systemic crises, such as the current COVID-19 pandemic, on human capital and, indirectly, on equity, and, ultimately, macroeconomic sustainability. The knowledge about what works and when is out there to be used. The challenge is to walk the last mile and bring home those lessons. There is not—and, for a while, there will not be—a better allocation of research and fiscal efforts.

References

Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of economic perspectives29(3), 3-30.

Baldwin, R. (2013). Trade and industrialization after globalization’s second unbundling: How building and joining a supply chain are different and why it matters. In Globalization in an age of crisis: Multilateral economic cooperation in the twenty-first century (pp. 165-212). University of Chicago Press.

Bloom, D. E., Chatterji, S., Kowal, P., Lloyd-Sherlock, P., McKee, M., Rechel, B., … & Smith, J. P. (2015). Macroeconomic implications of population ageing and selected policy responses. The Lancet385(9968), 649-657.

Card, D., Kluve, J., & Weber, A. (2010). Active labour market policy evaluations: A meta‐analysis. The economic journal120(548), F452-F477.

Card, D., Kluve, J., & Weber, A. (2018). What works? A meta analysis of recent active labor market program evaluations. Journal of the European Economic Association16(3), 894-931.

Dao, M. C., Das, M. M., Koczan, Z., & Lian, W. (2017). Why is labor receiving a smaller share of global income? Theory and empirical evidence. International Monetary Fund.

Goos, M., Manning, A., & Salomons, A. (2014). Explaining job polarization: Routine-biased technological change and offshoring. American economic review104(8), 2509-26.

Holzmann, R. (2017). An optimistic perspective on population ageing and old-age financial protection. Malaysian Journal of Economic Studies50(2), 107-137.

Kluve, J., Puerto, S., Robalino, D., Romero, J. M., Rother, F., Stöterau, J., … & Witte, M. (2019). Do youth employment programs improve labor market outcomes? A quantitative review. World Development114, 237-253.

Levy Yeyati, E., Montané, M., & Sartorio, L. (2019). What works for active labor market policies? (No. 201903). Universidad Torcuato Di Tella.

Pritchett, L., Samji, S., & Hammer, J. S. (2013). It’s all about MeE: Using Structured Experiential Learning (‘e’) to crawl the design space. Center for Global Development Working Paper, (322).

Rodrik, D. (2019, February 7). The Good Jobs Challenge. Project Syndicate.

Footnotes

  1. OECD data for 2017 available at https://www.oecd.org/employment/activation.htm.
  2. Because of the relatively small number of RCTs, prior to 2014, they represent a minor share of the sample covered by previous surveys, see Card et al. (2010), and Card et al. (2017). Escudero et al.’s (2018) meta-analysis also benefits from this recent batch of RCTs ALMPs, but they restrict attention to youth-targeted programs and complement their sample with other evaluation approaches.
  3. The dataset is available at bit.ly/quefuncionacepe and will be periodically updated with new evaluations and descriptive variables.

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