Abstract
We analyze the effect of an exogenous economic shock on youth, specifically their incentive for preferring to participate in the labor market over continuing their education. We examine the Chilean case and the sustained increase in the market price of the minerals extracted there during the period of Metal Mining Price Boom between the years 2003 and 2011. Chile is a large-scale producer of metals, being the world's largest producer of copper and the third largest producer of molybdenum. The prices of such metals increased steadily during the shock, what boosted the economic growth and the demand for labor. This shock altered the skill composition in the labor market, increasing the jobs for low- and medium–low-skilled workers in the counties more exposed to the shock, thus being the metal mining counties the most affected by its impact. According to this, our results show a negative impact of the shock on returns to schooling that simultaneously reduced school enrollment rates while increasing youth labor participation in those counties.
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Source Self-elaborated using CASEN database for the period 2000, 2003, 2006, 2009, 2011, 2013 and 2015. Averages only consider counties with a population greater than 25,000 individuals, since the data provided by the CASEN for small-sized counties might not be representative (Modrego & Berdegué, 2015). As a result, an average of 19 counties have a population of at least 25,000 individuals and are identified as metal mining producers between 2000 and 2015
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Notes
As an example, the price of copper, which is the principal metal exported from Chile, went from nearly USD 0.80 per pound in 2003 to over USD 4.00 per pound in the second quarter of 2008. The price then stabilized between USD 3.00 and USD 4.00 through 2011.
Data from the Chilean National Statistics Institute (INE) Labor Force Survey comparing the levels of employment between the years 2003 and 2011.
In this paper, we will use the US term “county” as they have similar administrative features and size, comparable in some ways to comunas. Counties can contain typically several municipalities in much the way that some comunas do.
The difference of the wages paid to high-skilled workers relative to wages paid to low-skilled workers.
According to Ferreira and Schady (2009), ‘declines in overall income levels raise the marginal utility of whatever the child can contribute to the family’s budget today ([thus, having] an “antischooling” income effect) (p. 2).’
Other studies analyze the impact of mining on consumption, poverty and inequality in local communities in Peru (Loayza and Rigolini 2016), poverty rates in Chile (Álvarez et al. 2018), or the negative externalities of mining in relation to the economic activity of other economic sectors through competition for key inputs (such as land and labor) and environmental pollution in Ghana (Aragón and Rud 2012).
For the sake of simplicity, we will assume that workers from one sector cannot move to a company belonging to another sector. We will also assume that the market price has a unit value.
We will only consider two sectors in our analysis: (i) a sector which only operates with high-skilled workers \(~\left( {j = HS} \right)\) and (ii) the remaining sector operating with only low-skilled workers \(\left( {j = n - HS} \right)\).
In order to simplify the present analysis, we will assume that the individual cannot choose to work and study at the same time, and that once they decide to join the labor market then they cannot return to school.
When \(\omega = 0\), the individual doesn’t participate in the labor market.
The process of individual data aggregation requires the assumption that the individual behavior theoretically identified in Sect. 2 is the one that defines the behavior of the population average.
According to Álvarez et al. (2018) ‘these metals represent over 99.5% of the production value between 1998 and 2013. Copper is by far the most important: it accounts for over 85% of the total production value for each year (p. 4).’.
The first year of the sample is taken to avoid the potential existence of simultaneous causality in our estimates.
See for example: http://www.sernageomin.cl/mineria/anuario-2016-sernageomin/.
Unfortunately, due to a lack of information, there is no AMC information for 2000 or 2003.
Long-distance commuting is a phenomenon related to individuals that work in a county/region different from that in which they live. In Chile, this phenomenon is important, particularly in counties in the north of the country where it represents approximately 20–25% of total employment (Aroca and Atienza 2008).
It is important to consider that the information provided by CASEN for the year 2000 does not allow us to identify LDC workers. Therefore, there is no variable capable of improving the accuracy of the employment rate in the metal mining sector.
In addition, we performed an ANOVA to test whether the differences observed in Fig. 1 between the metal mining and non-metal mining producer counties are significant in both the school enrollment rate and labor force participation rate of young individuals. Our results indicate that these differences are significant, in that metal mining counties have a higher rate of school enrolment and a lower rate of labor force participation in comparison with the other group. We have also performed an additional ANOVA to test if the two groups of counties considered present significant differences in the level of poverty. This new analysis is performed under the logic that poorer households might face budget constraints in keeping their children enrolled at school (Carneiro and Heckman 2002). The outcomes of this new analysis show that non-metal mining producer counties have a higher (and significant) level of both poverty and extreme poverty than the metal mining producers. These new results combined with the previous ANOVA indicate that the socioeconomic conditions in non-metal mining counties, particularly the higher level of poverty, is likely to affect the school attendance and labor participation of young individuals in these counties and might explain the differences observed between groups of counties in Fig. 1.
To calculate this variable the following Mincerian wage equation will be estimated for each county, using the information available in CASEN for all the individuals that earn a salary in the county, correcting by sample selection bias:
\(\ln w_{{i,v}} = \alpha _{0} + \alpha _{1} \cdot{\text{Year\_School}}_{{i,v}} + \alpha _{2} \cdot{\text{Age}}_{{i,v}} + \alpha _{3} \cdot{\text{Age}}_{{i,v}}^{2} + \alpha _{4} \cdot{\text{Gender}}_{{i,v}} + \theta _{{h,v}} + \lambda _{{i,v}} + u_{{i,v}}\)
\(i\) being each worker between 15 years old or over resident in a county \(v\). The estimate includes years of schooling (\(Year\_School_{{i,v}}\)), age (\(Age_{{i,v}}\)) -as a proxy of the potential working experience in the labor market (Mincer 1974) and its square, individual’s gender (\(Gender_{{i,v}}\)), the \(h^{{th}}\) economic sector in which he/she participates (\(\theta _{{h,v}}\)) and the Mills inverse ratio associated to the probability of being employed in the county (\(\lambda _{{i,v}}\)). To calculate this last variable a probit model is applied to identify the probability of being employed, using: years of schooling, age and its quadratic mean, gender and the number of people per household as regressors. This regression is applied to each of the counties in the different CASEN surveys used in the model; the predicted value being \(\hat{\alpha }_{1}\) which will be identified as the variable \(schooling\_returns_{{vt}}\) in (9.1, 9.2 and 9.3) (see Appendix 1, Table 9 which shows the yearly regional average of schooling returns estimated).
The measurement error is due to the sample itself, therefore any variable (\(n_{{vt}}\)) originating from the analyzed database and used as an instrument would also carry with it a potential problem of error measurement, which would violate the orthogonality of the residues: \(E\left( {n_{{vt}} \cdot\varepsilon _{{vt}} } \right) \ne 0\), which would limit the use of this technique.
The elimination of counties with small populations from the estimates carried out will produce results that can only be valid for the sample under consideration. However, other authors have applied similar distinctions in their applied studies. Card (2001) analyzes the impact of migration on the local labor markets in the U.S. and includes only 175 of the biggest cities (out of a total of 324), due to a lack of migration data.
See Appendix 1, Table 8 for descriptive statistics.
County where the well-known Chuquicamata mine is located.
Due to the importance of the copper mining industry in the Chilean economy we replicate our empirical analysis using the world price of copper as a measure for the intensity of the shock, thus creating a new measure for \(Ln\left[ {P_{{t,j}} } \right]\) in the simultaneous equations model estimated. The main results remain robust, but in some cases, we find a loss of significance, which can be explained by the lower variation of the copper price. All these additional estimates are available upon request.
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Appendix 1
Appendix 1
See Figs. 2, 3 and Tables 7, 8, 9.
Source Self-elaborated using data from the National Labor Survey (ENE) and COCHILCO
Evolution for national average metal mining employment share between 1986 and 2015, and for national metal mining price index between 1999 and 2015.
Source: Self-elaborated using CASEN database. Note Figure 1 shows the average county employment share for the year 2000. “No data” registers the counties not originally considered in the sample provided by the CASEN
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Trujillo, M.P., Puello, G.O.R. Economic shocks and their effect on the schooling and labor participation of youth: evidence from the metal mining price boom in Chilean counties. Ann Reg Sci 68, 65–93 (2022). https://doi.org/10.1007/s00168-021-01069-8
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DOI: https://doi.org/10.1007/s00168-021-01069-8