Explaining UK wage inequality in the past globalisation period, 1880–1913


The current era of globalisation has witnessed a rising premium paid to skilled workers resulting in increasing wage inequality in most OECD countries. This pattern differs from that observed during the past globalisation period (1880–1913), in which wage inequality decreased in most of the Old World countries. The present debate over wage inequality focuses on the implications of globalisation, technological change, the role of labour market institutions and education. Similar factors were at work in the past globalisation process. In order to disentangle the main factors that contribute to wage inequality, we calibrate a general equilibrium model for the UK economy in the past globalisation period. The results show that a trade shock and a skilled-biased technology shock increased wage inequality. However, education and emigration had a more significant impact and led to a decrease in wage inequality.

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  1. 1.

    In addition to already mentioned references see also, for example Taylor and Williamson 1997, Harley 2002 and Voigtländer and Voth 2006.

  2. 2.

    Unfortunately, calibration does not allow us to offer confidence intervals for the results.

  3. 3.

    As Kehoe (1996) points out, one can think of these variables as price indices, which are naturally set to one in the base case. Only one of these prices (or a combination of some prices) can be considered the numeraire. That means that all prices change after a shock except the numeraire. Thus, the problem is defined in terms of relative prices.

  4. 4.

    Although the exports of mining and quarrying were very important (due mainly to exports of coal) the UK imported other mining and quarrying products such as iron ore, copper, lead, or petroleum. The trade statistics did not show exactly whether the sector was net exporter or importer. We have estimated it and obtained that the mining and quarrying sector in 1880 and 1900 was a net importer.

  5. 5.

    Later on, we will check the robustness of the results when a Hicksian technology shock is considered.

  6. 6.

    Note that our measure of capital also does include land.

  7. 7.

    The wage inequality for “Transport and Railways” sub-sector in 1906 was 1.57 and the average for the industrial sector was 1.58.

  8. 8.

    Following the same procedure that for the industrial sector, we have obtained that in the service sector the 71.5% of the labour force was unskilled. Although in this sector there were some high skilled sub-sectors such as “Insurance, banking and finance” (with the 69.3% of skilled workers) or the “Professional services” (with the 74.4% of skilled workers), they only represented a very small portion of the total labour force of the service sector (the two sectors represented only the 11.78% of total labour force in the service sector in 1911). However, the majority sectors such as “Miscellaneous” (pubs, hotels, domestic service, etc.) and “Distributive trades” (both sectors represented the 61.3% of the total labour force in the service sector) were unskilled labour sectors (with the 83.2 and the 81.6% of unskilled workers on their total labour force respectively).


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Concha Betrán and María A. Pons acknowledge financial support from CICYT Grant SEJ2004-07402. Financial support from CICYT Grant SEJ2005-01365, GV/2007/268 Fundación Rafael del Pino and EFRD is gratefully acknowledged by Javier Ferri. Preliminary versions of this paper were presented at the Sixth Conference of the European Historical Economics Society, the European Social History Society Congress in Amsterdam, IX Encuentro de Economía Aplicada, 18th Annual Conference of the European Association of Labour Economists, XXXI Simposio de Analisis Economico, and the 8th Global Conference on Business and Economics. Thanks to all the participants for their comments and suggestions. A previous version of the paper has been published as a working paper in FUNCAS, num. 352/2007. Finally, we are very grateful for the relevant and constructive comments made by a referee.

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Correspondence to Maria A. Pons.


Appendix 1: Parameters, variables and equations

See Tables 9, 10, 11

Table 9 Parameters of the model
Table 10 Exogenous variables
Table 11 Endogenous variables

A1.1 Production functions

Each sector produces using capital and a composite of skilled and unskilled labour:

$$ X_{j} = \alpha_{j}^{x} \left[ {\delta_{j}^{x} K_{j}^{{\upsilon_{j}^{x} }} + \left( {1 - \delta_{j}^{x} } \right)L_{j}^{{\upsilon_{j}^{x} }} } \right]^{{\frac{1}{{\upsilon_{j}^{x} }}}} $$

where = 1, 2 stands for the skilled (1) and unskilled (2) sector. The composite of labour for each sector takes the form:

$$ L_{j} = \alpha_{j}^{l} \left[ {\delta_{j}^{l} \left( {\beta^{\text{s}} S_{j} } \right)^{{\upsilon_{j}^{l} }} + \left( {1 - \delta_{j}^{l} } \right)\left( {\beta^{\text{u}} U_{j} } \right)^{{\upsilon_{j}^{l} }} } \right]^{{\frac{1}{{\upsilon_{j}^{l} }}}} $$

There is a utility function defined over domestic and imported goods:

$$ W = \alpha^{w} \left[ {\delta^{w} X_{1}^{{\upsilon^{w} }} + (1 - \delta^{w} )Y^{{\upsilon^{w} }} } \right]^{{\frac{1}{{\upsilon^{w} }}}} $$

where Y is a composite of the domestic produced unskilled good and an equivalent imported good (Armington assumption):

$$ Y = \alpha^{y} \left[ {\delta^{y} X_{2}^{{\upsilon^{y} }} + \left( {1 - \delta^{y} } \right)M_{2}^{{\upsilon^{y} }} } \right]^{{\frac{1}{{\upsilon^{y} }}}} $$

The model is composed of the following equations determined by zero profit conditions, market clearing conditions, income balance and the macroeconomic closure rule.

A1.2 Zero profit conditions

Perfect competition and free entry imply that firms do not have extraordinary profits.

$$ \begin{aligned} P_{{_{j} }} & = \left( {\alpha_{j}^{x} } \right)^{ - 1} \left[ {\left( {\delta_{j}^{x} } \right)^{{\eta_{j}^{x} }} P_{K}^{{\left( {1 - \eta_{j}^{x} } \right)}} + \left( {1 - \delta_{j}^{x} } \right)^{{\eta_{j}^{x} }} P_{{L_{j} }}^{{\left( {1 - \eta_{j}^{x} } \right)}} } \right]^{{\frac{1}{{\left( {1 - \eta_{j}^{x} } \right)}}}} \\ P_{{L_{j} }} & = \left( {\alpha_{j}^{l} } \right)^{ - 1} \left[ {\left( {\delta_{j}^{l} } \right)^{{\eta_{j}^{l} }} \left( {\frac{{W_{\text{s}} }}{{\beta^{\text{s}} }}} \right)^{{\left( {1 - \eta_{j}^{l} } \right)}} + \left( {1 - \delta_{j}^{l} } \right)^{{\eta_{j}^{l} }} \left( {\frac{{W_{\text{u}} }}{{\beta^{\text{u}} }}} \right)^{{\left( {1 - \eta_{j}^{l} } \right)}} } \right]^{{\frac{1}{{\left( {1 - \eta_{j}^{l} } \right)}}}} \\ P_{W} & = \left( {\alpha^{w} } \right)^{ - 1} \left[ {\left( {\delta^{w} } \right)^{{\eta^{w} }} P_{1}^{{\left( {1 - \eta^{w} } \right)}} + \left( {1 - \delta^{w} } \right)^{{\eta^{w} }} P_{Y}^{{\left( {1 - \eta^{w} } \right)}} } \right]^{{\frac{1}{{\left( {1 - \eta^{w} } \right)}}}} \\ P_{Y} & = (\alpha^{y} )^{ - 1} \left[ {\left( {\delta^{y} } \right)^{{\eta^{y} }} P_{2}^{{\left( {1 - \eta^{y} } \right)}} + \left( {1 - \delta^{y} } \right)^{{\eta^{y} }} P_{{F_{2} }}^{{\left( {1 - \eta^{y} } \right)}} } \right]^{{\frac{1}{{\left( {1 - \eta^{y} } \right)}}}} \\ P_{1} & = \overline{{P_{{E_{1} }} }} P_{\text{FX}} \\ P_{F2} & = \overline{{P_{{M_{2} }} }} P_{\text{FX}} \\ \end{aligned} $$

where unitary revenue is on the left hand side of the equations and unitary cost on the right.

A1.3 Market clearing conditions

These conditions imply that demand equals supply for each good and factor.

$$ \begin{aligned} X_{1} - E_{1} & = \left( {\alpha^{w} } \right)^{ - 1} \left[ {\left( {\delta^{w} } \right) + \left( {1 - \delta^{w} } \right)\left( {\frac{{\delta^{w} P_{Y} }}{{\left( {1 - \delta^{w} } \right)P_{1} }}} \right)^{{\left( {1 - \eta^{w} } \right)}} } \right]^{{\frac{{\eta^{w} }}{{\left( {1 - \eta^{w} } \right)}}}} W \\ Y & = \left( {\alpha^{w} } \right)^{ - 1} \left[ {\left( {\delta^{w} } \right)\left( {\frac{{\left( {1 - \delta^{w} } \right)P_{1} }}{{\delta^{w} P_{Y} }}} \right)^{{\left( {1 - \eta^{w} } \right)}} + \left( {1 - \delta^{w} } \right)} \right]^{{\frac{{\eta^{w} }}{{\left( {1 - \eta^{w} } \right)}}}} W \\ X_{2} & = \left( {\alpha^{y} } \right)^{ - 1} \left[ {\left( {\delta^{y} } \right) + \left( {1 - \delta^{y} } \right)\left( {\frac{{\delta^{y} P_{{F_{2} }} }}{{\left( {1 - \delta^{y} } \right)P_{2} }}} \right)^{{\left( {1 - \eta^{y} } \right)}} } \right]^{{\frac{{\eta^{y} }}{{\left( {1 - \eta^{y} } \right)}}}} Y \\ M_{2} & = \left( {\alpha^{y} } \right)^{ - 1} \left[ {\left( {\delta^{y} } \right)\left( {\frac{{\left( {1 - \delta^{y} } \right)P_{2} }}{{\delta^{y} P_{{F_{2} }} }}} \right)^{{\left( {1 - \eta^{y} } \right)}} + \left( {1 - \delta^{y} } \right)} \right]^{{\frac{{\eta^{y} }}{{\left( {1 - \eta^{y} } \right)}}}} Y \\ W & = \frac{I}{{P_{W} }} \\ \end{aligned} $$
$$ \begin{aligned} \overline{K} & = \mathop \sum \limits_{j = 1}^{2} \left[ {\left( {\alpha_{j}^{x} } \right)^{ - 1} \left[ {\left( {\delta_{j}^{x} } \right) + \left( {1 - \delta_{j}^{x} } \right)\left( {\frac{{\delta_{j}^{x} P_{{L_{j} }} }}{{\left( {1 - \delta_{j}^{x} } \right)P_{\text{K}} }}} \right)^{{\left( {1 - \eta_{j}^{x} } \right)}} } \right]^{{\frac{{\eta_{j}^{x} }}{{\left( {1 - \eta_{j}^{x} } \right)}}}} X_{j} } \right] \\ L_{1} + L_{2} & = \mathop \sum \limits_{j = 1}^{2} \left[ {\left( {\alpha_{j}^{x} } \right)^{ - 1} \left[ {\left( {\delta_{j}^{x} } \right)\left( {\frac{{\left( {1 - \delta_{j}^{x} } \right)P_{\text{K}} }}{{\delta_{j}^{x} P_{{L_{j} }} }}} \right)^{{\left( {1 - \eta_{j}^{x} } \right)}} + \left( {1 - \delta_{j}^{x} } \right)} \right]^{{\frac{{\eta_{j}^{x} }}{{\left( {1 - \eta_{j}^{x} } \right)}}}} X_{j} } \right] \\ \overline{U} & = \mathop \sum \limits_{j = 1}^{2} \left[ {\left( {\alpha_{j}^{l} } \right)^{ - 1} \left( {\frac{1}{{\beta^{\text{u}} }}} \right)\left[ {\left( {\delta_{j}^{l} } \right)\left( {\frac{{\left( {1 - \delta_{j}^{l} } \right)\beta^{\text{u}} W_{\text{s}} }}{{\delta_{j}^{l} \beta^{\text{s}} W_{\text{u}} }}} \right)^{{\left( {1 - \eta_{j}^{l} } \right)}} + \left( {1 - \delta_{j}^{l} } \right)} \right]^{{\frac{{\eta_{j}^{l} }}{{\left( {1 - \eta_{j}^{l} } \right)}}}} L_{j} } \right] \\ \overline{S} & = \mathop \sum \limits_{j = 1}^{2} \left[ {\left( {\alpha_{j}^{l} } \right)^{ - 1} \left( {\frac{1}{{\beta^{\text{s}} }}} \right)\left[ {\left( {\delta_{j}^{l} } \right) + \left( {1 - \delta_{j}^{l} } \right)\left( {\frac{{\delta_{j}^{l} \beta^{\text{s}} W_{\text{u}} }}{{\left( {1 - \delta_{j}^{l} } \right)\beta^{\text{u}} W_{\text{s}} }}} \right)^{{\left( {1 - \eta_{j}^{l} } \right)}} } \right]^{{\frac{{\eta_{j}^{l} }}{{\left( {1 - \eta_{j}^{l} } \right)}}}} L_{j} } \right] \\ \end{aligned} $$

Supply is on the left hand side while the right captures demand. The equation W = I/P W represents the budget constraint, P W being the minimum cost at a given commodity prices of buying one unit of utility (the expenditure function) and I the total income of the representative household (see definition in the below equation).

A1.4 Income balance

The following equation defines total income as revenues from total capital endowment, skilled and unskilled labour endowments and current trade deficit.

$$ I = P_{\text{K}} \overline{K} + W_{\text{s}} \overline{S} + W_{\text{u}} \overline{U} + P_{\text{FX}} \overline{\text{CTD}} $$

A1.5 Macro closure rule

This rule reflects the fact that the current trade deficit is constant.

$$ P_{1} E_{1} - P_{{F_{2} }} M_{2} = P_{\text{FX}} \overline{\text{CTD}} $$

Equations from (A1.2) to (A1.5) determine a model with 19 equations that is solved for 19 endogenous variables (see Table 11 above).

A1.6 Calibration with added restrictions

Up to what degree match the model solution other data than wage premium? To answer this question two changes in calibration are included with respect to exact calibration in which only information for the wage premium is considered. First, we allow for Hicks neutral technology change in sector X 2 as captured by ∆α x2 in addition to the unskilled-biased technological change (∆βu) used in the exact calibration procedure. Second, we choose βu and α x2 for 1880 in order to minimize a criterion function. In particular we choose the sum of squared deviation of model-predicted wage and production ratios (with a hat in the expression below) with respect to the actual values in 1880 (the variables without hat):

$$ \begin{gathered} \min \left( {\Upomega \left( {\frac{{W_{\text{s}}^{1880} }}{{W_{\text{u}}^{1880} }} - \frac{{\hat{W}_{\text{s}}^{1880} }}{{\hat{W}_{\text{u}}^{1880} }}} \right)^{2} + (1 - \Upomega )\left( {\frac{{X_{1}^{1880} }}{{X_{2}^{1880} }} - \frac{{\hat{X}_{1}^{1880} }}{{\hat{X}_{2}^{1880} }}} \right)^{2} } \right) \hfill \\ {\text{w}} . {\text{r}} . {\text{t}}\quad \beta_{\text{u}}^{1880} ,\alpha_{2}^{x,1880} \hfill \\ \end{gathered} $$

where Ω = 0.5. This particular form implies the same weight for the wage premium and production deviations. Thus, the exact calibration procedure can be considered a special case where both Ω = 1 (in this case we reply exactly the wage premium variation) or Ω = 0 in which case we would replicate exactly variation in production ratios. We solve the above optimization problem numerically creating a grid of values for βu and α x2 , and solve the model for all the possible combinations of values of βu and α x2 , taking the combination that minimize the objective function.

According to our results, the minimum value for this function is 0.004 that is reached for a value of β 1880u  = 0.600 and α x,18802  = 1.230.

Appendix 2: Data

A2.1 Data for wage inequality in the past

For France, the UK, Italy and Spain see Betrán and Pons (2004). For Sweden, we have calculated from Bagge et al. (1933).

A2.2 Data for the UK


Skilled workers:

Skilled manual workers.

Unskilled workers:

Semi-skilled and unskilled manual workers. Males and females for industry and males for agriculture. (in thousands).


Industry (manufacturing, building, gas, electricity and water, mining and quarrying) and agriculture. We have not considered the service sector.


1911 (census year).

We have used employment of manual workers by industry elaborated by Routh (1980). These data are elaborated from the Census of Population to obtain a homogeneous classification. As we need data by industry for skilled, semi-skilled and unskilled manual workers and the data elaborated by Routh (1980) only refer to 1951, we have calculated the proportions of skilled manual workers, semi-skilled and unskilled manual workers and non-manual workers in the labour force for each industry in 1951 and we have considered that these proportions are the same as in 1911.

We have also used the proportion of skilled on semi-skilled and unskilled manual workers to classify industries in skilled and unskilled sectors. The skilled industries are those that display an above average proportion and the unskilled industries a below average proportion.

Classification of sectors in decreasing order:

Skilled sectors:

(1) leather, (2) wood, (3) building, (4) vehicles, (5) paper printing, (6) textiles, (7) engineering, shipbuilding and electric, (8) other manufacturing, (9) metal goods and instruments, (10) metal manufacture and (11) cement, ceramic and glass.

Unskilled sectors:

(12) mining and quarrying, (13) clothing, (14) gas, electricity and water, (15) food, drink, tobacco, (16) chemicals and (17) agriculture.


Routh (1980).


We have obtained the data of production for the different industries for 1924. We have taken the production data from Gross Domestic product at factor cost (million pounds) for 1924 elaborated by Feinstein (1972), Table 9, p. T26 and the share of value added in manufacturing for 1924 in Mathews et al. (1982), chap. 4, p. 239. To obtain the data for the year 1913 we have used the index of production of each industry and agriculture, forestry and fishing elaborated by Feinstein (1972).

We have used the above classification of skilled and unskilled sectors to obtain the skilled and unskilled production for the skilled and unskilled sectors.


The rents of capital are estimated for each sector as a residual obtained from the difference between the value of the production and the income from labour.


Exports (£m):

Mitchell (1990, p.481).


Mitchell (1990).

Terms of trade:

Prices of exports on prices of imports in percentages.

Imports (£m):

Mitchell (1990, p.475–476)


Feinstein (1976).

Average wage

We have calculated an annual average wage for the year 1913 (in pounds), weighted by the participation of each group of workers in the total number of manual workers. We have used the data from Routh (1980, p.99) for 1911 and for obtaining the data for 1913 we use the Index of Money Wages from Bowley (1937).



The percentage of the population over 10–12 years old able to read and write. Source: Flora (1973), in Eisenstadt and Rokkanpp, p. 213–258, 245.

School-enrolment ratio:

Primary school enrolment as a percentage of the population aged 5–14 years old. Calculated from Flora (1987), pp. 78, 559, 624.

Other variables


To calculate the impact of emigration in the labour market, we consider that emigration reduced the unskilled labour force by 16% in 1911 following O’Rourke, Williamson and Hatton (1994, p. 208).


Total population (in thousands) from Mitchell (1998).

Labour force:

We have used the data for the labour force in 1913 elaborated by Routh (1980) which is homogenous with the data of 1951 Census. To calculate the labour force in 1880 we have used the increase in the labour force in the considered sectors from 1880 to 1913 from Mitchell (1998, p. 104)

The growth rate of capital stock:

We have considered the growth of the total gross stock of capital at 1900 prices between 1880 and 1913 from Mitchell (1990, p.864). As in our model, capital involves land and capital, in order to identify productive capital and land accumulation separately we use the percentage which represents the rents of land on GDP in 1841 (from Harley and Crafts 2000) and extrapolate to the year 1913 using the rate of growth of the rents of land and buildings calculated by Feinstein (1972). According to our estimation, the rents of land represented 15% of the production of agriculture (A) and industry (I) in 1913. As the participation of the total rents of capital on GDP (A + I) according to the social accounting matrix was 58.2%, we use the ratio (58.2 − 15)/58.2 to correct the shock in capital of Table 3.

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Betrán, C., Ferri, J. & Pons, M.A. Explaining UK wage inequality in the past globalisation period, 1880–1913. Cliometrica 4, 19–50 (2010). https://doi.org/10.1007/s11698-009-0038-z

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  • Wage inequality
  • Globalisation
  • Technological change
  • General equilibrium

JEL Classification

  • N30
  • C68
  • J31