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<div class="my-footer"><span>Yannis Galanakis (UoK) • "Are you in the right job?" Human Capital Mismatch in the UK</span></div>

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# "Are you in the right job?" Human Capital Mismatch in the UK
![](https://img.shields.io/badge/project_status-WIP-yellow?style=flat)

.large[Yannis Galanakis <br> 
<span style = 'font-size: 75%;'> University of Kent</span> <br> <br>
November 2021
]

]

.pull-right[
<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <i.galanakis@kent.ac.uk> <br>
<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> [@YannisGalanakis](https://twitter.com/YannisGalanakis) <br>
<svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>  [ygalanak](http://github.com/ygalanak)
<svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M224 256c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128zm95.8 32.6L272 480l-32-136 32-56h-96l32 56-32 136-47.8-191.4C56.9 292 0 350.3 0 422.4V464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48v-41.6c0-72.1-56.9-130.4-128.2-133.8z"/></svg> Job Market Candidate

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---

# <span style = 'color:#D8DEE9; font-size: 60%;'><svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34zm192-34l-136-136c-9.4-9.4-24.6-9.4-33.9 0l-22.6 22.6c-9.4 9.4-9.4 24.6 0 33.9l96.4 96.4-96.4 96.4c-9.4 9.4-9.4 24.6 0 33.9l22.6 22.6c9.4 9.4 24.6 9.4 33.9 0l136-136c9.4-9.2 9.4-24.4 0-33.8z"/></svg></span> Job allocation in the labour market

.pull-left[
* Complementarity between human and physical capital `$\Rightarrow$` market imperfections

* Job allocation explains wage heterogeneity relative to skills
> * inequality among high-skilled workers
> * skills vs. good job
> * search frictions `$\rightarrow$` privilege
>       * e.g. father's background

]

<img src="index_files/figure-html/unnamed-chunk-1-1.png" width="75%" style="display: block; margin: auto;" />
<center>
<small><p class="caption"> Figure: Wage inequality among high-skilled workers </p></small>
</center>
]

---

.pull-left[
- Higher-skilled father: `$\uparrow$` probability of graduate job
> <span style = 'color:#8FBCBB;'>Mechanism:</span> More paternal skills = resources to transmit skills (e.g. stronger network)

- Do individual skills matter?
 - When controlling for skills, probability `$\downarrow$` `$\Rightarrow$` frictions
]

.pull-right[
## <span style = 'font-size: 60%;'> Effect of father's education or social class </span>
<img src="index_files/figure-html/unnamed-chunk-2-1.png" width="75%" style="display: block; margin: auto;" />
]

---

<dl>
    <dt>Question</dt>
    <dd>What is the role of job allocation in mismatch? Does job mismatch come from individual heterogeneity?</dd>

<dt>Model</dt> 
  <ul>  
    <dt>based</dt>
    <dd>Burdett & Mortensen (1998)</dd>
    <dt>finding</dt>
    <dd>frictions generate mismatch</dd>
  </ul>

<dt>Data</dt> <dd>BHPS/UKHLS and BCS70</dd>

<dt>ID</dt> 
<dd>novel measure of mismatch based on the observed distributions of human capital and jobs</dd>

<dt>Findings</dt>
 <dd> <ol>
  <li>Increase of mismatch after the Great Recession</li>
  <li>Employees can find better jobs or their mobility occurs earlier than the aggregate change of skills</li>
  <li>Unobserved productivity does not generate mismatch in the market</li>
 </ol></dd>

</dl>

---

---

# Model

---

## Model motivation

---

1. `$M_w$` workers: employed or Out-of-Work (OoW)
 - 3-types: low-, middle-, high-skilled workers
 - `$b$`: flat benefit of OoW 
2. `$M_f$` firms: 
 - 3 types: low-, middle-, high-productivity firms
 - `$p_l \lt p_m \lt p_h$`: flow of revenue per employee
 - `$\sigma_i$`: fraction of each type of firms
3. Wage setting:
 - Employers set wages once-for-all to maximize steady-state profits
 - Each type of worker within a firm earns the same `$w$`
4. Matching technology:
 - `$\lambda$`: Arrival rate to employment. Ass: `$0 < \lambda < \infty$`
 - `$\delta$`: Job destruction rate. Ass: `$0 < \delta < \infty$`

---

- Search among employers
- an offer is assumed to be the realization of a random draw from `$F(·)$`: the distribution of wage offers across employers
- `$F(·)$` is the weighted average salary offer made by the 3 types of firms:
  $$
  F(w)= \sigma_1F_1(w) + \sigma_2F_2(w)+\big(1-(\sigma_1+\sigma_2)\big)F_3(w)
  $$
- Accept-an-offer conditions:
> <span style = 'color:#8FBCBB;'>OoW: </span> if `$w > \phi$`, where `$\phi$` is the reservation wage. Here `$\phi = b$`

> <span style = 'color:#8FBCBB;'>Employed: </span> if wage offer is above their current wage

---

- They choose wage so that

$$
	\max_{w \geq \phi}\pi_i = (p_i-w)\ell(w)
  $$

where `$\ell(w)$` is the size of firms and equals to `$\frac{\overbrace{g(w)}^{\text{# of workers in firms in that range}}}{\underbrace{f(w)}_{\text{# of firms}\equiv \text{average empl per firm}}}$`

???
_- In the steady-state, firms max `$\pi$` subject to the steady-state employment condition (eq. 3, slide 6)_

---

- **The non-employment rate**: Flow of workers exiting work should be equal to the flow of workers OoW
  $$
  u=\frac{\delta}{\delta+\lambda}=\frac{1}{1+\kappa}
  $$
  where `$\kappa=\frac{\lambda}{\delta}$` is a market-friction parameter

- **Distribution of salaries (across workers)**: Flow of workers into jobs providing a wage no larger than `$w$` should be equal to flow of workers out of jobs providing a wage no larger than `$w$`
  $$
  G(w) = \frac{F(w)}{1+\kappa\big(1-F(w)\big)}
  $$
  where `$G(w)$` is the cumulative distribution of salaries
  
---

- **Average size of firms**: Assuming uniform hiring effort,
 $$
 \ell(w) = \frac{1+\kappa}{\Big(1+\kappa \big(1-F(w)\big)\Big)^2}
 $$

- **Profits**
 $$
 \pi_i = \frac{\kappa(p_i-w)}{\Big(1+\kappa \big(1-F(w)\big)\Big)^2}
 $$
---

- **Equal profit condition**: Since `$w_3\geq w_2 \geq w_1$` `$\forall w_i$` on `$\text{supp}(F_i)$`, it holds that
 $$
`\begin{cases}
(p_1-w_1)\frac{1+\kappa}{\Big(1+\kappa \big(1-F(w)\big)\Big)^2}= (p_1-b) \frac{1+\kappa}{(1+\kappa)^2},& \text{if } w<w_1\\
(p_2-w_2)\frac{1+\kappa}{\Big(1+\kappa \big(1-F(w)\big)\Big)^2}= (p_2-w_1) \frac{1+\kappa}{\big(1+\kappa\sigma_1\big)^2},              & \text{if } w_1<w<w_2 \\
(p_3-w_3)\frac{1+\kappa}{\Big(1+\kappa \big(1-F(w)\big)\Big)^2}= (p_3-w_2) \frac{1+\kappa}{\big(1+\kappa(1-(\sigma_1+\sigma_2))\big)^2},              & \text{if } w>w_2
\end{cases}`
 $$
 
 
---

# Simulation I - Discrete skills

---

<table class=" lightable-paper lightable-striped" style='font-family: "Arial Narrow", arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;'>
 <thead>
  <tr>
   <th style="text-align:left;color: white !important;background-color: #3B4252 !important;"> Parameter </th>
   <th style="text-align:left;color: white !important;background-color: #3B4252 !important;"> Value </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$b$` </td>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> 0.8 </td>
  </tr>
  <tr>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$\delta$` </td>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> 0.287 </td>
  </tr>
  <tr>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$\lambda$` </td>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> 0.142 </td>
  </tr>
  <tr>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$p_1$` </td>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$p_2$` </td>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> 2.5 </td>
  </tr>
  <tr>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$p_3$` </td>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> 3 </td>
  </tr>
  <tr>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$\sigma_1$` </td>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$\frac{1}{3}$` </td>
  </tr>
  <tr>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$\sigma_2$` </td>
   <td style="text-align:left;color: white !important;background-color: #3B4252 !important;"> `$\frac{1}{3}$` </td>
  </tr>
</tbody>
</table>

---

.pull-left[
<span style = 'color:#8FBCBB;'> Implication 1: </span> Kinked distributions
  - 2 kinks for 3 types of firms
  - `$G(w)<F(w)$`: `$F(w)$` first-order stochastically dominates `$G(w)$`
  
<span style = 'color:#8FBCBB;'> Implication 2: </span> More productive firms offer higher wages
]

<img src="index_files/figure-html/unnamed-chunk-4-1.png" width="75%" style="display: block; margin: auto;" />
]

---

.pull-left[
- Jumps separate firms
  > <span style = 'color:#8FBCBB;'> `$\overline{w_1}=\underline{w_2}$` </span>: low- to middle-productivity firms
  
  > <span style = 'color:#8FBCBB;'> `$\overline{w_2}=\underline{w_3}$` </span>: middle- to high-productivity firms

<span style = 'color:#8FBCBB;'> Implication 3 </span>: Overlap: Number of workers in middle-prod firms exceeds the average employment per firm.
  - High-skilled workers wait until they find a better job
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-5-1.png" width="75%" style="display: block; margin: auto;" />
]

---

.pull-left[
<span style = 'color:#8FBCBB;'> Implication 4: </span> More productive firms:
 - are more profitable
 - attract more workers
 - lose less workers to other employers
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-6-1.png" width="75%" style="display: block; margin: auto;" />

]

---

</center>
</figure>
]

.pull-right[
<figure>
<center>
<image src="fromTEX/proportionOfSkillTypeinEachJob.png" style="width:85%">
</center>
</figure>
]

---

.pull-left[
- Women face more frictions than men
- BM limitation: job transitions based on better wage offers
> <span style = 'color:#8FBCBB;'>higher `$\delta$`:</span> e.g. move because of partner's new job, get pregnant, nursery's availability changes
> <span style = 'color:#8FBCBB;'>lower `$\lambda$`:</span> e.g. searching for a firm in the area where partner's job is or near school

]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-7-1.png" width="75%" style="display: block; margin: auto;" />
]

---

> <span style = 'color:#8FBCBB;'>Mechanism 1:</span> Fewer firms in each category `$\Rightarrow$` more high-skilled workers

> <span style = 'color:#8FBCBB;'>Mechanism 2:</span> `$\sigma$` determines the penalty of mismatch
  - `$\kappa$` large `$\Rightarrow$` variation in `$\sigma$` has *no* effect
  - `$\kappa$` small `$\Rightarrow$` variation in `$\sigma$` has a *big* effect

- Smaller `$\sigma$` + greater `$\kappa$` `$\Rightarrow$` `$\downarrow$` mismatch
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-8-1.png" width="75%" style="display: block; margin: auto;" />

---

# Simulation II - Continuous skills

---

.pull-left[
- 3-types of firms and workers
 - under-estimate the extent of mismatch
    
> - <span style = 'color:#8FBCBB;'> `$\kappa$` `$\rightarrow$` `$\infty$`:</span> No frictions in LM. Relative productivity is determined by relative wages
    
> - <span style = 'color:#8FBCBB;'> `$\kappa$` `$\rightarrow$` 0: </span> More frictions weaken the relationship of `$w$` and `$p$`
  
- `$>3$` categories to proxy the data
]

.pull-right[
<figure>
<center>
<image src="fromTEX/frictionsarrow.png" style="width:100%">
</center>
</figure>
]

---

.pull-left[
- `$\kappa \rightarrow \infty$` collapse to PC, where matching is perfect
- Expected wage:
$$
E(w) = \frac{\delta}{\delta +\lambda}b+\frac{\lambda}{\delta + \lambda}p
$$
  <span style = 'color:#8FBCBB;'>Mechanism 1:</span> Frictions in LM
  
  <span style = 'color:#8FBCBB;'>Mechanism 2:</span> Mismatch: higher-skilled workers are in mismatch

- The gap increases with `$p$`

]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-9-1.png" width="75%" style="display: block; margin: auto;" />
]

---

.pull-left[
`$\kappa=0.492$`
<img src="index_files/figure-html/unnamed-chunk-10-1.png" width="75%" style="display: block; margin: auto;" />
]

.pull-right[
`$\kappa=2$` 
<img src="index_files/figure-html/unnamed-chunk-11-1.png" width="75%" style="display: block; margin: auto;" />
]

---

# Data - Empirical methodology

---

- Unbalanced Panel:
  1. British Household Panel Survey (BHPS)
  > - waves 1-18;
  > - 1991-2009
  
  2. UK Household Longitudinal Study (Understanding Society)
  >  - waves 2-7;
  >  - 2010-2015

- Balanced Panel: British Cohort Study (BCS70)
  - waves 1-7 (age 5-38);
  - rich cognitive and non-cognitive test scores in childhood

???
_- Contribution: No study employs these datasets to explore_

---

- How many workers in low-productivity occupations hold similar human capital to those in middle-productivity ones?

- Wage equation:

`$\ln [\text{wage}]_{i,t}= \alpha + \beta_1 x_i + \sum_{k=2}^7 \beta_k S_{k,i,t} + \vartheta_t +u_{i,t}$`

- Selection equation:

`$\text{Labour force}_{i,t} = \alpha + \delta_1 z_{i,t} + \delta_2 FS_{i,t} + \delta_3 \text{HHmembers}_{i,t}+ \vartheta_t + v_{i,t}$`

---

.pull-left[
- Who is in mismatch?
  
  `$\text{mismatched}_{i,t} = \widehat{HC}_{i,t} | occ_{j,t}> \bigg(\widetilde{\widehat{HC}}_t | occ_{j-1,t} \bigg)$`
  
where `$\widetilde{\widehat{HC}}$`: median of the estimated HC
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-12-1.png" width="75%" style="display: block; margin: auto;" />

]

---

# Results

---

.pull-left[
- steep rise after the Great Recession
- HCM follows the augmenting unemployment trend
  - reduced female LF participation
  - Should we blame the dataset? <br>
    Maybe not <br>
    <small> (Postel-Vinay & Sepahsalari; 2019) </small>

- Robustness checks
  1. Measurement .content-box-green[<span style = 'font-size: 60%;'> <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M302.189 329.126H196.105l55.831 135.993c3.889 9.428-.555 19.999-9.444 23.999l-49.165 21.427c-9.165 4-19.443-.571-23.332-9.714l-53.053-129.136-86.664 89.138C18.729 472.71 0 463.554 0 447.977V18.299C0 1.899 19.921-6.096 30.277 5.443l284.412 292.542c11.472 11.179 3.007 31.141-12.5 31.141z"/></svg> [graph](#MeasurementRobustness)</span>  ]
  2. Mismatch vs. job satisfaction 
  
  .content-box-green[<span style = 'font-size: 60%;'> <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M302.189 329.126H196.105l55.831 135.993c3.889 9.428-.555 19.999-9.444 23.999l-49.165 21.427c-9.165 4-19.443-.571-23.332-9.714l-53.053-129.136-86.664 89.138C18.729 472.71 0 463.554 0 447.977V18.299C0 1.899 19.921-6.096 30.277 5.443l284.412 292.542c11.472 11.179 3.007 31.141-12.5 31.141z"/></svg> [Job satisfaction](#JobSatisfactionMismatch)</span>  ]
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-13-1.png" width="75%" style="display: block; margin: auto;" />

---

.pull-left[
- Productivity: cognitive + non-cognitive skills
- Findings:
  - Incidence does *not* fluctuate much over time
      - mobility: match vs. mismatch 
.content-box-green[<span style = 'font-size: 60%;'> <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M302.189 329.126H196.105l55.831 135.993c3.889 9.428-.555 19.999-9.444 23.999l-49.165 21.427c-9.165 4-19.443-.571-23.332-9.714l-53.053-129.136-86.664 89.138C18.729 472.71 0 463.554 0 447.977V18.299C0 1.899 19.921-6.096 30.277 5.443l284.412 292.542c11.472 11.179 3.007 31.141-12.5 31.141z"/></svg> [Transitions](#transitionsBCS70)</span>  ]
  
  - Instance of mismatch *increases* when controlling for skills
  - Sample sizes between BHPS and BCS70 differ: weighting

.content-box-green[<span style = 'font-size: 60%;'> <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M302.189 329.126H196.105l55.831 135.993c3.889 9.428-.555 19.999-9.444 23.999l-49.165 21.427c-9.165 4-19.443-.571-23.332-9.714l-53.053-129.136-86.664 89.138C18.729 472.71 0 463.554 0 447.977V18.299C0 1.899 19.921-6.096 30.277 5.443l284.412 292.542c11.472 11.179 3.007 31.141-12.5 31.141z"/></svg> [Robustness I: Differences in skills test scores](#RobustnessI)</span>  ]
.content-box-green[<span style = 'font-size: 60%;'> <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M302.189 329.126H196.105l55.831 135.993c3.889 9.428-.555 19.999-9.444 23.999l-49.165 21.427c-9.165 4-19.443-.571-23.332-9.714l-53.053-129.136-86.664 89.138C18.729 472.71 0 463.554 0 447.977V18.299C0 1.899 19.921-6.096 30.277 5.443l284.412 292.542c11.472 11.179 3.007 31.141-12.5 31.141z"/></svg> [Robustness II: PCA](#RobustnessII)</span>  ]
  
]

]

---

- Model:
  1. Job search frictions generate mismatch between workers and firms.
  2. In a continuous skills setting, higher-skilled workers have a lower expected wage.

- BHPS/UKHLS: Mismatch increases after the Great Recession

- BCS70: Unobserved productivity does not generate mismatch in the market

<font size="+3">
<strong>Takeway:</strong> 
Mismatch = `$f(\underbrace{\text{frictions}}_{😟}, \underbrace{\text{unobserved productivity}}_{😶})$`
</font>

---

class: inverse
background-image: url(img/Kent_Economics_White.svg)
background-size: 260px
background-position: 5% 95%

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---

.footnote[
.content-box-green[<span style = 'font-size: 60%;'> <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M302.189 329.126H196.105l55.831 135.993c3.889 9.428-.555 19.999-9.444 23.999l-49.165 21.427c-9.165 4-19.443-.571-23.332-9.714l-53.053-129.136-86.664 89.138C18.729 472.71 0 463.554 0 447.977V18.299C0 1.899 19.921-6.096 30.277 5.443l284.412 292.542c11.472 11.179 3.007 31.141-12.5 31.141z"/></svg> [Back](#FrictionsMatter)</span>  ]
]

---

---

---

---

---

- Differences in skills test scores by mismatch and gender
  - Report: cognitive and non-cognitive skill test scores of those who hold a degree or not by mismatch status (and by gender)
  - Findings:
  
>    <span style = 'color:#8FBCBB;'>Non-cognitive:</span>
      1. no difference by mismatch for degree and non-degree holders
      2. Women have greater non-cognitive test scores: earlier maturity
>    <span style = 'color:#8FBCBB;'>Cognitive:</span>
      1. middle-skilled in match: lower cognitive skills
      2. high-skilled: no statistically significant difference
      3. women outperform men

`$\Rightarrow$` *no* evidence that unobserved skill would explain mismatch for degree holders

---

- Principal Component Analysis on skills
  - reduce the dimensionality of the data: important when correlation is high
  - k-fold cross-validation: out-of-sample variation prediction
  
    <span style = 'color:#8FBCBB;'>Findings:</span> how skills interact with mismatch
      1. Number of components: cognitive and non-cognitive skills substitute each other
      2. Drivers of personality → wages ⇒ returns to skills