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Empirical Economics

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15.10.2018

Determinants of structural unemployment in Colombia: a search approach

verfasst von: Luis E. Arango, Luz A. Flórez

Erschienen in: Empirical Economics | Ausgabe 5/2020

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Abstract

We provide evidence of the structural unemployment rate determinants in Colombia between 1984 and 2015. In the first stage, we estimate different measures of this variable including the ones that emerge from the search-theoretic, the Phillips curve and a structural vector autoregressive model. Next, by using a fully modified ordinary least squares cointegration approach, we test the determinants in the Colombian case following the search model guidelines. Accordingly, the main explanations of the structural unemployment rate are the real minimum wage, the real interest rate, a hiring cost indicator, sectoral shifts, non-wage labour costs and some demographic factors such as the proportion of workers with no college education. Finally, we use these variables to estimate a new measure of the structural unemployment rate called the true structural unemployment rate.

Vorheriger Artikel The impact of productive and non-productive government expenditure on economic growth: an empirical analysis in high-income versus low- to middle-income economies

Nächster Artikel Recasting the trade impact on labor share: a fixed-effect semiparametric estimation study

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Defined by Friedman (1968, p. 8) as: “…the level that would be ground out by the Walrasian system of general equilibrium equations, provided there is imbedded in them the actual structural characteristics of the labor and commodity markets, including market imperfections, stochastic variability in demands and supplies, the cost of gathering information about job vacancies and labor availabilities, the costs of mobility and so on”.

Modigliani and Papademos (1975) defined the Nairu as the unemployment rate consistent with a stable inflation. The controversy about the difference or similarity of the concept of natural rate and Nairu has not been solved. Nevertheless, Ball and Mankiw (2002) and Brauer (2007), among other authors, argue that in the long run there is no difference between them. Aysun et al. (2014) use a more detailed definition of structural unemployment that differs from ours. For them, structural unemployment is the component of unemployment that emerges due to the mismatch between the characteristics of the unemployed workers and vacancies.

In some cases dismissal costs have been identified as a cause of unemployment (OECD 1994; Millán et al. 2013). Less clear results on aggregate employment have been found both on theoretical and on empirical grounds in some others (see, for instance, Bertola et al. 2000).

This reform also introduced a contingency on the wages of apprentices effective since 2014. The contingency consists of an increase in the wage of apprentices up to 100% of the minimum wage if the unemployment rate is lower than 10%. Otherwise, it will be 75% of the minimum wage. This measure might have adverse effects on youth employment.

Mortensen and Pissarides (1999a, b) analyse explicitly the introduction of different policies such as dismissal costs or severance payments and labour taxes that affect the decision of firms to create jobs and then the equilibrium unemployment rate.

Given an homogeneous matching function, \( m\left( {u,v} \right) \), \( q\left( \theta \right) \) corresponds to \( m\left( {u/v,1} \right) \) where \( q'\left( \theta \right) \le 0 \). Thus, \( m\left( {u,v} \right) = \theta q\left( \theta \right)u \). Moreover, any inward shift of the matching function represents an increase in the efficiency of the matching.

You can think of this as all cost items beyond the wage such as the recruitment costs (job posting, review of applicants, pre-screening, interviewing, etc.), training, benefit packages, equipment endowments, etc.

Equations (2) and (3) are the labour demand and labour supply curves in the neoclassical framework. These are usually depicted in the \( w, \theta \) space.

The search model also implies the existence of a long-run Phillips curve in an inflation–unemployment quadrant. A negative slope could emerge if there is an inflation–tax effect on the equilibrium real interest rate (Pissarides 2000, xvii).

The parameter a can be estimated from the regression of \( \Delta \pi_{t} \) on the unemployment rate and a constant.

Balmaseda et al. (2000) use the SVAR methodology and impose the Blanchard and Quah (1989) restrictions to determine the natural rate of unemployment for a set of different OECD countries. Similarly, Groenewold and Hagger (2000, 2003) find an estimate for the Australian SUR. For the case of Colombia this approach was used by Arango et al. (2006b) and Echavarría et al. (2013).

The correction introduced by Arango et al. (2006a) to the unemployment rate is used all over the article. However, its use is not possible for the Perry adjustment given the number of groups required. To remedy this drawback, we try to correct the change in the level of the resulting Perry-adjusted unemployment due to the introduction of a new survey in 2000. To this end, we add a coefficient (0.016856) obtained from a regression of the resulting Perry-adjusted unemployment rate on a constant and a dummy which takes the value of 1 after March 2000. This correction assumes that the methodological change affected all groups in a homogenous fashion.

For Ball and Mankiw’s (2002) and King and Morley’s (2007) methodologies we use the headline inflation. For the VAR of King and Morley (2007) we choose four lags; this is the minimum number for which the cyclical component is stationary. Following Tasci and Zaman (2010) we use the Hodrick–Prescott filter to compute the trend of the job finding and job separation rates for the Shimer’s (2012) approach. For Ball and Mankiw (2002), we estimate the coefficient \( \alpha \) with annual data.

In “Appendix B” we show the behaviour of the determinants jointly with the unemployment rate.

See Álvarez and Hofstetter (2014) and Arango (2013b) for the construction of the vacancy rate.

The Help-Wanted Index (HWI) is a variable used instead of the vacancy rate (see Table 4).

Another limitation of our empirical work is the use of the vacancy rate constructed by Álvarez and Hofstetter (2014) and Arango (2013b) based on help-wanted announcements of the newspapers (vacancies). Even though we use correction of Barnichon (2010) for the online vacancies it might still not be the most precise estimation of the vacancy rate in Colombia.

Recently, the 1607 Act of 2012 reduced the total non-wage labour costs by 13.5 percentage points for firms that employ workers that earn less than two minimum wages.

This is a limitation of our empirical work. It would be desirable to have better measures of the reservation wages, but unfortunately there is no information available about that variable.

The inverse of \( \theta q\left( \theta \right) \) is the mean duration of unemployment.

In Colombia, the female unemployment rate is about four percentage points higher than the male unemployment rate. The volume edited by Arango et al. (2016) provides some explanations for this fact.

Given the discrepancy between the ADF and Phillips–Perron tests, the stationarity of real interest rate, vacancy rate and the proportion of less educated labour force was corroborated with the Ng–Perron test.

Nevertheless, we retain Ball & Mankiw’s SUR measure along the paper to observe its performance.

Phillips (1995, section 4) shows that FMOLS can also be used with a mix of I(1) and stationary regressors. According to Phillips, “This approach provides a general framework which makes it possible to study the asymptotic behaviour of FMOLS in models with full rank I(1) regressors, models with I(1) and I(0) regressors, models with unit roots, and models with only stationary regressors” (see page 1023).

Under dynamic OLS, DOLS, methodology, with one lag, the results are almost the same.

It is important to mention that in Table 2 we retain the significant variables. Thus, a variable such as labour force growth was not included in the regressions since it was not statistically significant.

This assertion is based on the information processed by the Labour Market Analysis Group of the Banco de la República, the Colombian central bank (see Banco de la República 2015).

Column (1) shows the model of Shimer already presented in Table 2.

Table 8 in “Appendix A” presents the evidence of cointegration for the subsample period.

For a panel of 19 Latin American countries, Ball et al. (2013) also found that higher social security contributions imply higher structural unemployment. They explore different measures of economic development as rural population as a percentage of total population and find that this variable has a negative sign and is significant to explain the structural unemployment across the different Latin American countries. Recall that our study focusses on the urban SUR. On the other hand, Ball et al. (2013) also argue that for countries like Chile, Colombia and Venezuela, the effect of a tightness monetary policy may affect the long-term unemployment. These results would be similar to ours if we accept that monetary policy can affect the real interest rate.

The estimation of the T-SUR comes from getting the forecast of the unemployment rate using the FMOLS estimation presented in Table 4. As for the previous estimates of SUR, the cyclical unemployment rates are stationary processes.

One remaining issue is related to the macroeconomic relevance of this battery of SUR estimates (see the tables in “Appendix C”). In the first place, apart from the Perry-adjusted T-SUR, none of the measures is cointegrated with inflation (see Tables 11, 12). In other words, with that exception, none of the structural components of unemployment rate exhibit a long-run relationship with inflation. Thus, any of these measures could be regarded as a Nairu. We exclude the Ball & Mankiw SUR, since this variable does not cointegrate with the set of variables used to obtain the determinants (see Table 1).

It is worth mentioning that real interest rate, hiring cost indicator and sectoral shifts are not city specific; that is, they are aggregate variables. The vacancy rate, the other endogenous variable of the model, is also a determinant of the SUR under the assumption that the FMOLS takes such endogeneity into account.

Recall that Act 1607 of 2012 reduced the non-wage labour costs by 13.5 percentage points.

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Titel: Determinants of structural unemployment in Colombia: a search approach
verfasst von: Luis E. Arango
Luz A. Flórez
Publikationsdatum: 15.10.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 5/2020
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-018-1572-y

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