Why does the Schooling Gap Close while the Wage Gap Persists across Country Income Comparisons?

with Paul Reimers (Deutsche Bundesbank)

November 9th, 2022

Organizations, Markets, and Policy Interventions

EBS Business School

Pantelis Karapanagiotis

@pi_kappa_

Summary

  • Why does the Schooling Gap Close when the Wage Gap Persists?
  • Because the paid hours gap closes as the economy's service sector grows.

Motivation

Motivation

Motivation

Motivation

Motivation

Previous Literature

Ngai & Petrongolo (2017) Restuccia & Vandenbroucke (2014) Bils & Klenow (2000) Bick, Fuchs-Schündeln, & Lagakos (2018) Barro & Lee (2013)

Household's Decision Problem

\[ U(c,\ell, s^{f}, s^{m}) = \int_{t=0}^{T} \mathrm{e}^{-\rho t} \left( \log(c - \bar c) + \varphi \log(\ell) - \beta^{f}\unicode{x1D7D9}_{t\le s^{f}} - \beta^{m}\unicode{x1D7D9}_{t\le s^{m}}\right) \mathrm{d}t. \]

  • Two individuals with distinct genders
  • Traditional production throughout their lifespan
  • Modern production after schooling finishes

Household Working Life Choices

Consumption Aggregation

  • Cross-sector aggregator \[ c = \left( \sum_{i=A,M,S} \omega_{i} c_{i}^{\frac{\varepsilon-1}{\varepsilon}} \right)^{\frac{\varepsilon}{\varepsilon-1}} \]
  • Within-sector aggregator \[ c_{i} = \left( \psi_{i} (c_{ir} )^{\frac{\sigma-1}{\sigma}} + (1-\psi_{i})(c_{ih} )^{\frac{\sigma-1}{\sigma}} \right)^{\frac{\sigma}{\sigma-1}} \]

Consumption Aggregation

  • Cross-sector aggregator \[ c = \left( \sum_{i=A,M,S} \omega_{i} c_{i}^{\frac{\varepsilon-1}{\varepsilon}} \right)^{\frac{\varepsilon}{\varepsilon-1}} \]
  • Within-sector aggregator \[ c_{i} = \left( \psi_{i} (c_{ir} )^{\frac{\sigma-1}{\sigma}} + (1-\psi_{i})(c_{ih} )^{\frac{\sigma-1}{\sigma}} \right)^{\frac{\sigma}{\sigma-1}} \]

Consumption Aggregation

  • Cross-sector aggregator \[ c = \left( \sum_{i=A,M,S} \omega_{i} c_{i}^{\frac{\varepsilon-1}{\varepsilon}} \right)^{\frac{\varepsilon}{\varepsilon-1}} \]
  • Within-sector aggregator \[ c_{i} = \left( \psi_{i} (c_{ir} )^{\frac{\sigma-1}{\sigma}} + (1-\psi_{i})(c_{ih} )^{\frac{\sigma-1}{\sigma}} \right)^{\frac{\sigma}{\sigma-1}} \]

Leisure and Traditional Production

  • Leisure aggregator \[ \ell = \left( \xi^{f}_{l} (\ell^{f})^\frac{\eta_{l}-1}{\eta_{l}} + \xi^{m}_{l} (\ell^{m})^\frac{\eta_{l}-1}{\eta_{l}} \right)^\frac{\eta_{l}}{\eta_{l} - 1} \]
  • Traditional production aggregator \[ c_{ih} = Z_{ih}\left( \xi^{f}_{ih} \left(L^{f}_{ih}\right)^{\frac{\eta-1}{\eta}} + \xi^{m}_{ih} \left(L^{m}_{ih}\right)^{\frac{\eta-1}{\eta}} \right)^{\frac{\eta}{\eta-1}} \]

Leisure and Traditional Production

  • Leisure aggregator \[ \ell = \left( \xi^{f}_{l} (\ell^{f})^\frac{\eta_{l}-1}{\eta_{l}} + \xi^{m}_{l} (\ell^{m})^\frac{\eta_{l}-1}{\eta_{l}} \right)^\frac{\eta_{l}}{\eta_{l} - 1} \]
  • Traditional production aggregator \[ c_{ih} = Z_{ih}\left( \xi^{f}_{ih} \left(L^{f}_{ih}\right)^{\frac{\eta-1}{\eta}} + \xi^{m}_{ih} \left(L^{m}_{ih}\right)^{\frac{\eta-1}{\eta}} \right)^{\frac{\eta}{\eta-1}} \]

Time and Budget Constraints

  • Time constraint

    \begin{align} M^{g} = L^{g} - \ell^{g} - \sum_{i=A,M,S} L^{g}_{ih}. \label{eq:total-labor-allocation} \end{align}
  • Budget constraint

    \begin{align} \int_{t=0}^{T} \mathrm{e}^{-\rho t} \sum_{i=A,M,S} p_{ir}c_{ir} \mathrm{d}t & = \sum_{g = m,f} \int_{t=s^{g}}^{T} \mathrm{e}^{-\rho t} M^{g} w^{g} H(s^{g}) \mathrm{d}t. \label{eq:budget-constraint} \end{align}

Firms' Decision Problems

  • Modern technology production function \[ y_{ir} = Z_{ir} l_{ir}, \label{eq:modern-production} \] \[ l_{ir} = \left( \xi^{f}_{ir} \left( \delta(s^{f}) H(s^{f}) l^{f}_{ir}\right)^{\frac{\eta-1}{\eta}} + \xi^{m}_{ir} \left(\delta(s^{m})H(s^{m}) l^{m}_{ir}\right)^{\frac{\eta-1}{\eta}} \right)^{\frac{\eta}{\eta-1}} \]
  • Human capital \[ H(s^{g}) = \exp\left(\frac{\zeta}{1-\nu}\left(s^{g}\right)^{1-\nu}\right) \]

General Equilibrium

A competitive equilibrium is a collection of market prices \(\{p_{ir}\}_{i = A,M,S}\), wages \(\{w^{f}, w^{m}\}\), consumption and labor supply allocations \(\{c_{ir}, L^{f}_{ih}, L^{m}_{ih}, L^{f}_{ir}, L^{m}_{ir}\}_{i = A,M,S}\), leisure choices \(\{\ell^{f}, \ell^{m}\}\), years of schooling \(\{s^{f}, s^{m}\}\), and labor demand and output choices \(\{l^{f}_{ih}, l^{m}_{ih}, y_{ir}\}_{i = A,M,S}\) such that

  • households maximize their preferences subject to their budget and time constraints, the human capital, and the traditional production technology;
  • firms maximize their profits subject to modern production technology;
  • commodity and services markets clear (\(c_{ir} = y_{ir}\) for \(i = A,M,S\));
  • traditional production is in autarky (\(c_{ih} = Z_{ih} L_{ih}\) for \(i = A,M,S\)); and
  • labor markets clear (\(l^{g}_{ir} = L^{g}_{ir}\) for \(i = A,M,S\) and \(g=f,m\)).

Impact of Productivity Changes

Impact of Gender Production Share Changes

Proposition:

Suppose that \(\zeta < ((1-\nu) / \rho)^{\nu-1}\). For any equilibrium allocation, the elasticity of paid hours ratio with respect to the ratio of schooling years is positive along paths for which wages are constant.

\[ \left. \frac{\partial \log \tilde M}{\partial \log \tilde s} \right|_{\tilde w} > 0 \]

Proposition:

Suppose that \(\zeta < ((1-\nu) / \rho)^{\nu-1}\). For any equilibrium allocation, the elasticity of paid hours ratio with respect to the ratio of schooling years is positive along paths for which wages are constant.

\[ \left. \frac{\partial \log \tilde M}{\partial \log \tilde s} \right|_{\tilde w} > 0 \]

Proposition:

Suppose that \(\zeta < ((1-\nu) / \rho)^{\nu-1}\). For any equilibrium allocation, the elasticity of paid hours ratio with respect to the ratio of schooling years is positive along paths for which wages are constant.

\[ \left. \frac{\partial \log \tilde M}{\partial \log \tilde s} \right|_{\tilde w} > 0 \]

Data and Calibration

Labor and Leisure Allocations Data

Wage Ratio Data

Wage Ratio Data

\[ \log(w_{ig}) = c_g + \beta^g_1 age_i + \beta^g_2 age_i^2 + \rho^g_M Ind^M_i + \rho^g_S Ind^S_i + \theta^g_m Educ^m_i + \theta^g_h Educ^h_i + \epsilon_{ig} \]

Production Shares Data

\[ \frac{L^{f}_{ih}}{L^{m}_{ih}} = \left(\frac{\xi^{f}_{ir}}{\xi^{m}_{ir}}\right)^{\eta} \left(\frac{\xi^{f}_{ir}d(s^{f})H(s^{f})}{\xi^{m}_{ir}d(s^{m})H(s^{m})}\right)^{-\eta} = \tilde\xi_{ih}^{\eta} \left(\tilde{w} \tilde{d} \tilde{H}\right)^{-\eta}. \]

Production Shares Data

\[ \frac{L^{f}_{ih}}{L^{m}_{ih}} = \left(\frac{\xi^{f}_{ir}}{\xi^{m}_{ir}}\right)^{\eta} \left(\frac{\xi^{f}_{ir}d(s^{f})H(s^{f})}{\xi^{m}_{ir}d(s^{m})H(s^{m})}\right)^{-\eta} = \tilde\xi_{ih}^{\eta} \left(\tilde{w} \tilde{d} \tilde{H}\right)^{-\eta}. \]

Comparative Advantages

\[ \tilde \xi_{is} = \frac{\xi^{f}_{is}}{\xi^{m}_{is}} > \frac{\xi^{f}_{jq}}{\xi^{m}_{jq}} = \tilde \xi_{jq} \]

Comparative Advantages

\[ \tilde \xi_{is} = \frac{\xi^{f}_{is}}{\xi^{m}_{is}} > \frac{\xi^{f}_{jq}}{\xi^{m}_{jq}} = \tilde \xi_{jq} \]

Comparative Advantages

Baseline Calibration Parameters

Benchmark and Counterfactuals

Quantitative Results

Schooling Predictions Benchmark

Individual Margin: Predictions for Years of Schooling.

Household Margin: Wage, Paid Hours, and Schooling Gaps

Counterfactual Analyses

Why does the Schooling Gap Close when the Wage Gap Persists?

  • Data:
    • household survey data from (Bick et al., 2018),
    • schooling data from (Barro & Lee, 2013) gathered from censuses,
    • wage gap persists in cross-country income comparisons,
    • but paid hours and schooling gaps are positively correlated.
  • Model:
    • General equilibrium, multi-sector, -gender, and -production technology model,
    • qualitatively explains the positive elasticity between paid hours and schooling for fixed wages, and
    • makes accurate quantitative predictions for schooling choices across country income groups.
  • Communique:
    • As the service sector of the economy grows, the paid hours gap closes,
    • leads to a schooling gap decline,
    • even when the wage gap stays constant.

Appendix

Low-Income Data Sources

Middle-Income Data Sources

High-Income Data Sources

References

Barro, R. J., & Lee, J. W. (2013). A new data set of educational attainment in the world, 1950 2010. Journal of Development Economics, 104, 184–198. https://doi.org/10.1016/j.jdeveco.2012.10.001
Bick, A., Fuchs-Schündeln, N., & Lagakos, D. (2018). How do hours worked vary with income? Cross-country evidence and implications. American Economic Review, 108(1), 170–199. https://doi.org/10.1257/aer.20151720
Bils, M., & Klenow, P. J. (2000). Does schooling cause growth? American Economic Review, 90(5), 1160–1183. https://doi.org/10.1257/aer.90.5.1160
Ngai, L. R., & Petrongolo, B. (2017). Gender gaps and the rise of the service economy. American Economic Journal: Macroeconomics, 9(4), 1–44. https://doi.org/10.1257/mac.20150253
Restuccia, D., & Vandenbroucke, G. (2014). Explaining educational attainment across countries and over time. Review of Economic Dynamics, 17(4), 824–841. https://doi.org/10.1016/j.red.2014.03.002