metadata: authors: 0: author: Lofgren,Hans
1: author: Cicowiez,Martin
count: Africa
docna: 0: docna: A proximity-based approach to labor mobility
            in CGE models with an application to Sub-Saharan Africa
docty: Policy Research Working Paper
subtopic: Labor Markets,International Trade and Trade Rules,Macroeconomic Management
teratopic: International Economics and Trade,Rural Development
abstracts: cdata!: The ease with which workers can move
            between sectors has a strong impact on the effects on labor
            markets  of shocks such as changes in world prices or
            migration flows. This paper introduces an approach to labor
            mobility with frictions under which worker capabilities
            (their efficiencies in different sectors) depend on their
            sector affiliation. If workers in sector a move to sector
            a', their efficiency shortfall due to a capability
            misfit compared to what is needed in a' (and possessed
            by workers already in a') is measured by a proximity
            parameter, 0 ≤ proxa,a' ≤ 1. If proxa,a' < 1,
            the efficient quantity reaching a' is below the
            physical quantity. In this setting, profit-maximizing
            producers are willing to pay the same wage per efficiency
            unit irrespective of worker origin and thus pay less
            efficient workers a lower wage per physical unit. This
            approach to labor mobility is tested in a static CGE model
            that is applied to an illustrative sub-Saharan African
            dataset with sector proximities defined using the approach
            of the product-space literature. Simulations of positive
            export price shocks show that, the higher the proximities,
            the stronger the labor reallocation and the welfare gains.
repnme: repnme: A proximity-based approach to labor
            mobility in CGE models with an application to Sub-Saharan Africa
display_title: A proximity-based approach to labor
            mobility in CGE models with an application to Sub-Saharan Africa
listing_relative_url: /research/2017/07/27814289/proximity-based-approach-labor-mobility-cge-models-application-sub-saharan-africa
topicv3: Current Account m1326278 1135,Trade m1326924 698,Balance of Payments m1326965 441,Static Computable General Equilibrium
            m1504538 3116,Labor Mobility m1582535 4713
publishtoextweb_dt: 2017-07-20T00:00:00Z
historic_topic: International Economics and Trade,Rural Development
no_of_pages: 47
author_keys: 0: author_key: 747317
1: author_key: 942699
model_extractions: WPS8151 Policy Research Working Paper 8151 A Proximity-Based Approach to Labor Mobility in CGE Models with an Application to Sub-Saharan Africa _Hans Lofgren Martín Cicowiez_ Development Economics Development Prospects Group July 2017
Policy Research Working Paper 8151

# **Abstract**

The ease with which workers can move between sectors has a strong impact on the effects on labor markets of shocks such as changes in world prices or migration flows. This paper introduces an approach to labor mobility with frictions under which worker capabilities (their efficiencies in different sectors) depend on their sector affiliation. If workers in sector a move to sector a’, their efficiency shortfall due to a capability misfit compared to what is needed in a’ (and possessed by workers already in a’) is measured by a proximity parameter, 0 ≤ proxa,a’ ≤ 1. If proxa,a’ < 1, the efficient quantity reaching a’ is below the physical quantity. In this setting, profit-maximizing producers are willing to pay the same wage per efficiency unit irrespective of worker origin and thus pay less efficient workers a lower wage per physical unit. This approach to labor mobility is tested in a static CGE model that is applied to an illustrative sub-Saharan African dataset with sector proximities defined using the approach of the product-space literature. Simulations of positive export price shocks show that, the higher the proximities, the stronger the labor reallocation and the welfare gains.

This paper is a product of the he Development Prospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hlofgren@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# A Proximity-Based Approach to Labor Mobility in CGE Models with an Application to Sub-Saharan Africa

BY HANS LOFGREN AND MARTÍN CICOWIEZ<sup>a</sup> JEL codes: C68, J31, O24 Keywords: Computable General Equilibrium Models; Labor Mobility; Factor Mobility; Wage Differentials; Development Planning and Policy.

a Hans Lofgren: Development Prospects Group, World Bank; Martín Cicowiez: CEDLASUniversidad Nacional de La Plata, La Plata. The paper has also been published in the Journal of Global Economic Analysis, the website of which provides model code and data (www.jgea.org/resources/jgea/ojs/index.php/jgea/article/view/39) and readers are encouraged to cite this version. The authors are grateful for financial support from the Knowledge for Change Program (KCP) of the World Bank and thank three anonymous journal referees for their very helpful comments. The corresponding author is Hans Lofgren (hlofgren@worldbank.org).
# **1. Introduction**

The movement of labor between sectors is a core aspect of structural change in economies throughout the world.<sup>1</sup> The effects of changes in prices, technology, and policies on production, trade and living standards are strongly influenced by the extent to which labor is able to relocate. In a literature survey, Hertel and Reimer (2005, p. 400) find that “the poverty impacts of trade reform often hinge crucially on how well the increased demand for labour in one part of the economy is transmitted to the rest of the economy.” Drawing on a global CGE analysis, van der Mensbrugghe (2007) indicates that the treatment of labor market segmentation and sectoral wage differences has a strong impact on simulated welfare impacts of the Doha round of trade reform, especially for developing countries. The findings reported in Porto and Hoekman (2010) indicate that labor market frictions in developing countries are substantial, giving rise to costly labor market adjustments and limiting labor reallocation. Similarly, even though the analysis has typically been done from a firm (as opposed to sector) perspective, labor adjustments costs are a major focus in the labor economics literature (for example, see Hamermesh 1989, Cooper and Willis 2009, and Cooper et al. 2015).

The Computable General Equilibrium (CGE) literature includes a variety of approaches to mobility, the most common being to assume that mobility between any pair of sectors is either impossible or unconstrained. In this paper, we present and test an innovative approach that, in contrast with this dichotomy, views intersectoral labor mobility as falling along a continuum. It offers a means of making sure that simulations with CGE models more closely approximate empirical patterns for labor movements and/or wage differences between workers of different origins (including incumbent workers) that are employed in a sector. The approach is inspired by the classic transportation model of the linear programming literature (for references, see Gass and Assad 2005, pp. 51-52) and the product-space (PS) literature due to Hausmann and coauthors (Hausmann and Klinger 2006; Hausmann et al. 2011).

The essence of our approach is that workers are viewed as having capabilities that to varying degrees permit them to be efficient in different sectors. As a consequence, reallocation of workers between sectors comes at a cost in the form of lost efficiency: labor that comes from another sector is less efficient than the labor that already works in the receiving sector. These efficiency losses are related to proximity between pairs of sectors, a key concept in the PS literature. It is valued between 0 and 1 and here used to measure the degree to which a worker in the 1 We use the two terms “sector” and “activity” interchangeably with the same definition as “industry” in the System of National Accounts, i.e. a grouping of establishments (or producers) engaged in the same or similar kinds of activities classified in the same International Standard Industrial Classification of All Economic Activities (ISIC) code (United Nations 2003, p. 74).

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sector of origin possesses the capabilities that are required in the sector of destination. In the PS literature, proximity is defined on the basis of revealed comparative advantage (RCA). We also draw on RCA-related data in the illustrative application in this paper even though we do not consider this approach as ideal and, in the conclusions, discuss other options for future research.<sup>2</sup> The efficiency losses due to reallocation are related to a distinction between labor quantities in physical and efficiency units. For labor that stays within its sector of origin (or moves to a sector considered to be in the same location as it requires the same worker capabilities), physical and efficient labor quantities are identical and the proximity is equal to 1. If labor moves to a different location (i.e., its proximity is less than 1), the efficient labor quantity received in the sector of destination falls short of the efficient (and physical) quantity of labor that departs from the sector of origin – the lower the proximity, the larger this shortfall. Profitmaximizing producers consider the gap between efficient and physical labor in their hiring decisions. They equalize payments to labor measured in efficiency units, as a result being willing to pay a higher wage (per physical unit) for a worker who already is in the sector, due to his/her higher degree of efficiency compared to a worker from a different location. This treatment of wages echoes the writings of Alfred Marshall, who coined and used the efficiency wage term to refer to wages that reflect the exertion of ability and efficiency in work. He argued that competition tends to equalize efficiency wages as opposed to the wages per time unit, which differ across workers with different levels of efficiency (Marshall 1920, Book VI, Chapter III); this is the same outcome that prevails at equilibrium in our labor mobility model.<sup>3</sup> Given that proximities between sectors fall along a continuum, labor segmentation also appears along a continuum as opposed to the common dichotomy of treating labor in different sectors as belonging either to the same segment or to different, separate segments. However, the two extremes of this common dichotomy can still be captured as the special cases with all sectors having proximities of 1 or very low proximities for the cases of belonging to the same and fully separate segments, respectively. In the latter case, migration would not take place due to the fact that the (efficiency-based) wage offered in the destination sector would fall short of the wage in the sector of origin. In other respects, our approach is consistent with alternative, common treatments of other 2 Strict adherence to the PS approach to the calculation of proximities would have the drawback of excluding values that differ across labor types (since PS proximities are singledimensional, defined by sector) or are asymmetric (since the PS proximity or distance from activity _a_ to activity _a’_ is the same as the one from _a’_ to _a_ ).

3 This usage deviates from that of modern efficiency-wage theories with links between wages and worker productivity, which may give raise to wage setting above market equilibrium. For a survey, see Weiss (1991).

3
(non-mobility) aspects of labor market functioning, including the determination of total employment for each labor type (with or without unemployment or labor/leisure decisions), and the treatment of substitutability between labor categories within sectors.

A sector-based labor classification is natural given our focus on developing an approach that is applicable to multi-sector CGE models. However, data permitting, it would be both easy and often desirable to extend the labor classification in the model to additional dimensions, such as educational attainment or achievement or what may be labeled as skill or occupation. If so, proximities would be defined in terms of the different dimensions taken together. To exemplify, if the two dimensions are sector and skill, the proximity between two service sectors may be higher for IT staff (if they use and possess very similar skills in different sectors) than for engineers (if their skills differ more depending on the specific sector of employment).

In this paper, we test our capability-based approach in simulations with a simple CGE model applied to an illustrative database for a Sub-Saharan African (SSA) economy. We selected a database for SSA since, compared to other regions, CGE models for this region tend to treat labor markets as competitive; i.e. they do not tend to consider non-competitive aspects like unionization and minimumwage legislation.<sup>4</sup> Apart from the introduction of the proximity treatment, this is also how we treat the labor market. However, we consider the core innovation of our model as equally valid for other regions.

We proceed as follows: after a brief discussion of alternative approaches to labor mobility, Section 2 presents our approach. The approach is tested in Section 3 in a set of comparative-static CGE simulations of the effects of export price shocks for processed food products with alternative parameterizations of the labor market. Conclusions and a discussion of future research are presented in Section 4. A full mathematical statement of the CGE model is provided in an appendix and the model code and data are available as supplementary materials.

# **2. Method and data**

After a brief review of alternative treatments of labor mobility in CGE models and the broader labor economics literature in Section 2.1, Section 2.2 presents and analyzes the producer profit maximization problem on which we base the proximity-based formulation for labor mobility. It also includes a brief description of the simple CGE model into which it is embedded. (A full mathematical

> 4 To exemplify, Boccanfuso and Savard (2007), Go et al. (2010), and Diao and Thurlow (2012) all assume competitive (but in some cases segmented) labor markets. CGE applications to Sub-Saharan Africa with non-competitive labor markets are very rare. Two examples, both related to minimum wages in South Africa, are Pauw and Leibbrandt (2012) and Erero (2016).

4
statement of the CGE model is in the appendix.) The illustrative database for the CGE application is presented in Section 2.3.

# _2.1 Alternative treatments of labor mobility in CGE models_

The CGE literature includes a large variety of treatments of the labor market, including labor mobility, which is the focus of this paper.<sup>5</sup> As a reference point, it may be helpful to remind the reader of the relatively standard and simple CGE treatment of labor that is found in Lofgren et al. (2002). In this model, for each labor type (one or more, often categorized on the basis of education or “skill”), (a) the demand side is represented by first-order conditions for producer profitmaximization in the form of a value-added function and an equation that, for each production sector, imposes equality between the wage and the marginal value product of labor; (b) the quantity of labor supplied for employment is exogenous; and (c) a labor market equilibrium condition imposes equality between quantities demanded and supplied, an outcome that is realized via adjustments in an economy-wide wage variable. Labor is measured in physical units (e.g., number of full-time workers employed during one year). Base-year wages, which vary across activities, are computed on the basis of data on labor incomes (from the SAM) and employment quantities. For any labor type, this market-clearing mechanism does not change base-year relative wages by sector; it merely imposes a uniform scaling of the wages across all activities. Each labor type is perfectly mobile across the activities in which it is employed; a worker that moves from one sector to another earns the same wage and is as productive as those who already work in the receiving sector.

The CGE literature includes alternative approaches that deviate from this simple formulation in various ways, including the treatment of labor supply (which alternatively may be endogenous, as a point on a wage curve or driven by utility-maximizing household decisions), and sectoral wage differences (which vanish if in every employing sector, one unit of each labor type is defined as the quantity that earns a wage of one). An additional dimension of variation lies in the treatment of value-added, where different functional forms and nestings of labor and other factors have been used.

5 Boeters and Savard (2013) review the literature on labor markets in CGE models; their discussion of labor market coordination and heterogeneity (pp. 1679-1706) covers some of the issues addressed here. Annabi (2003) reviews the endogenization of labor supply, and the modeling of unions and efficiency wages. CGE analyses that present and apply approaches that deviate from the standard competitive market model are found in Thierfelder and Shiells (1997), and Maechler and Roland-Holst (1997).

5
With regard to labor mobility, the focus of this paper, instead of perfect mobility, different types of segmentation are common with each segment including one or more activities. Segments may either be watertight compartments or allow for imperfect mobility (migration) between segments. According to one type of segmentation, labor-demanding activities are classified as rural or urban. The treatment of migration between the two areas may be based on the HarrisTodaro model with urban unemployment and migration ensuring equality between the rural wage and the expected urban wage; in the presence of urban unemployment, the wage in urban areas is fixed and therefore not free to clear the urban labor market (Dervis et al., 1982, pp. 178-180).<sup>6</sup> Apart from the assumption of urban unemployment, the rural-urban migration model is in the same spirit as the formulation in Lofgren et al. (2002; described above) in the sense that, in both cases, in response to shocks, labor would be reallocated to maintain exogenous relative wage differences. Under a related approach, proposed by Flaig (2014, pp. 119-121), labor migrates to pools linked to multiple segments (with perfect mobility within each pool) or between pairs of segments, with each segment representing labor employed in one or more sectors. In both cases, migration depends on relative wage changes in different segments and response elasticities. In the GTAP model, the allocation of labor and other factors with imperfect mobility (which are “sluggish”) is determined by a constant-elasticity-oftransformation (CET) function, with the allocations responding to changes in relative wages across activities (Hertel and Tsigas, 1997, pp. 51-52). Like formulations with labor units derived from normalization of wages to unity, the units of labor employed in different sectors do not correspond to physical units.

A review of the econometric labor economics literature indicates that, as opposed to the CGE literature, it has paid little attention to sectors. Its canonical model for wage (and inequality) analysis disaggregates labor on the basis ‘skills’ (in practice a small number, sometimes proxied by educational attainment) without any explicit reference to the sector dimension (Acemoglu and Autor 2011, pp. 1096-1118). As with CGE models, their model is in the neoclassical tradition. Wage changes for different skills depend on changes in labor market supplies and demands, the latter influenced by technological change. Implicitly (in the absence of any reference to sectors), the canonical model views labor in each skill group as 6 Empirically, rural-urban segmentation is problematic: available data do not permit sectors to be split into rural and urban as, to varying degrees, production in all sectors takes place both in rural and urban areas. If this is linked to household disaggregation (inside the model or in a microsimulation module), then a further complication is that workers residing in rural (urban) areas may work in urban (rural) areas. The assumption of the Harris-Todaro model that rural labor is fully employed is also problematic and in sharp contrast with what is assumed in the Lewis model with unlimited supplies of labor (Lewis 1954).

6
Pa’ Qa a! Wf ase q! ‘ QFAAg aa ~~71~~ p Qa = Aq », Ofa : QF f QF rg = » PFia'ja’ QFAA faa a’ QFAAg aa Pa Qa wf asp q! QFAAg aa Qa, ofa p
QF ra Pra'a 0< Pf,a'a ~~=i~~ p L = Ppa‘ Qa - »f >.a’ WfaSp qt QFAAFa! aq + Va} Ga >.f Spa QFa} Qa + », WFAD; a » Prala’ QFAAS ata — oF V, f a’ WFAD¥a <u>OL</u> OL <u>AQq</u><sup>—Pa</sup> Va -1 ~~—-~~ 1 -p , QF e | —WFADy

p, and Wf as ¢
aL QFAAFala QFAA faa(—Wfas¢qi + WFADs a" Ppa'a) = 0 ~~=1~~ <u>aL</u> pl ~~av,~~ % >. 0° OF — Qa a f ~~OWFADya~~ aL , ~ »a’ Pala’ QFAAS aa — QFra -1 ~~po~~ - -p-1 Da Ug >. 56a QF? | [Spq* QF2 "| = WFADpa f' Wf ase q! > WFAD¥ a “Drala QFAArata QFAApala WFAD¢ q ‘Prala — Wh AS¢ a!) = 0 ~~=1~~ p Qa = Ga », Opa’ QF, e f QF rg = » PFia'ja’ QFAA faa a’

p)(-p) =1 WFADsa wfaspaq! WFADsa pala
any _a’_ to _a_ have to be non-negative (14). In equation 15, for any allocation that takes place (is positive), the parenthesized expression with wage terms must be zero, i.e., equation 13 must hold as an equality. The remaining two equations define the value-added function (16) and the link between labor hiring in efficiency units (on the left) to labor hiring in physical units (on the right) (17) In order to see the economic logic behind and implications of equations 13 and 15, it may help to restate equation 13 as: If labor in _a’_ is allocated to _a_ (�����,��,� > 0), then the equality between the leftand right-hand sides of (13’) imposed by equation 15 means that the physical wage in the location of origin _a_ ’, adjusted for the proximity parameter (the left side), equals the efficiency wage of labor in _a_ (the right side). This shows that activity _a_ allocates its hiring of labor from different sources so that the wage per efficiency unit of labor is equal across all sources. It means that activities pay less efficient workers a lower wage per physical unit. Drawing on equation 13’, if _a_ employs workers from two sources, _a_ ’ and _a_ ’’, and the proximity of _a_ ’ is higher (��,��,� > ��,���,�), then, given �����,� = �����,��⁄��,��,� = �����,���⁄��,���,�, �����,��⁄�����,��� = ��,��,�⁄��,���,� > 1.

On the other hand, for the case where workers from _a’_ are not hired in _a_ (�����,��,� = 0), equation 15 permits the right-hand side of equation 13’ to be strictly larger than the left-hand side. This means that, after corrected for low efficiency (a low proximity), a worker from _a’_ requires a wage that is too high to be competitive in activity _a_ . The implication is that efficiency-based wage setting stacks the cards against reallocation of labor from _a’_ to _a_ if the proximity is low (strong misfit between the capabilities of the worker associated with _a’_ and those required in _a_ ).<sup>12,13</sup> The simulations in Section 3 use a simple static CGE model into which the proximity-based labor treatment is embedded. Apart from the labor treatment

> 12 With regard to optimal outcomes, they do not include the case where �����,��⁄��,��,� < �����,� since, if so, profits could be increased by hiring more labor from _a’_ in _a_ ; i.e. after correction for efficiency losses, the wage paid for labor from _a’_ is below the labor efficiency wage for _a_ .

13 Similarly, willingness to pay a higher wage to workers already in their current employment is also generated by labor market insider-outsider theories. However, as opposed to our model, the focus is here on firms (not sectors) and the reasons are different: labor turnover costs among which lower productivity due to absent skills is only one part (cf. Lindbeck and Snower, 1988).

10
(which is based on equations 12-17), the CGE model is typical of models of small open economies with optimizing behavior for households and producers, domestic markets for commodities and factors cleared by flexible prices and wages, respectively, and a government that consumes, saves, taxes, and both receives and pays transfers. With regard to its treatment of factors, in distinction from the producer maximization problem, the CGE model also requires equilibrium conditions for factor markets, which are defined over _f_ and _a’_ (one condition for each factor stock and activity of origin). These conditions may be stated as follows: �������� �,��,� = �����,�� ����� �������� � where �����,�� is the available physical quantity of factor _f_ that has the capabilities required in source activity _a_ ’. The condition imposes equality between this quantity and the sum of the physical quantities of labor _f_ from _a_ ’ that are allocated to any activity _a_ . It is cleared by the wage variable, �����,��, which is exogenous in the producer model but endogenous (and in upper case) in the CGE model.

# _2.3 Data_

The bulk of the dataset for our model consists of a social accounting matrix (SAM) and sectoral employment quantities (both for the simulation base-year), complemented by a set of elasticities (for production, consumption, and trade). Due to our proximity-based treatment, additional data is needed for the proximity parameter. We will here present the model database with special emphasis on the construction of the data for the proximity parameter.

In this application, the SAM is designed to capture the sectoral structure of an SSA country that is not strongly reliant in natural resource exports.<sup>14</sup> The SAM is used to define base-year values for the bulk of the model parameters, including production technologies, sources of commodity supplies (domestic output or imports), demand patterns (for household and government consumption, investment and exports), transfers between different institutions, and tax rates. The disaggregation of our country SAM coincides with that of the rest of the model database. As shown in Table 1, it is disaggregated into 23 sectors (activities and commodities) – 3 in agriculture, 2 in mining, 13 in manufacturing, and 7 in services – with each activity producing a single commodity for which it is the only domestic producer. The factors are split into labor, private capital, and natural resources (5 types: agricultural land, forestry land, fishing resources, and two natural resources used in extractive industries). The institutions are split into households, government, and the rest of world. A set of auxiliary accounts cover 14 Specifically, our SSA SAM is based on the 2010 supply and use tables for Uganda.

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the different tax instruments as well as trade and transport margins on domestic sales, imports and exports.

**Table 1.** Disaggregation of SSA country CGE and SAM.

|Category‐ #||Item|Category‐ #|Item|
|---|---|---|---|---|
|**Sectors**|**Agriculture (3)**|Agriculture|**Factors (7)**|Labor|
|**(activities and**||Forestry||Private capital|
|**commodities)**||Fishing||Land|
|**(25)**|**Mining (2)**|Petroleum and gas<br>Mining||Timber<br>Fish|
||**Manufacturing**|Food||Extractive res in Pet and gas|
||**(13)**|Beverages||Extractive res in Mining|
|||Tobacco products|**Institutions**|Households|
|||Textiles and leather|**(3)**|Government|
|||Wood||Rest of the world|
|||Paper|**Auxiliary**|Taxes on production|
|||Refined petroleum products|**accounts (7)**|Taxes on sales|
|||Chemical products||Taxes on imports|
|||Rubber and plastic||Taxes on income|
|||Non‐metalic mineral products||Trade and transp marg, dom|
|||Metal products||Trade and transp marg, imp|
|||Machinery and vehicles||Trade and transpmarg,exp|
|||Other manufactures|**Savings and**|Savings<br>|
||**Services (7)**|Electricity and gas|**Investment**|Private (non‐government)|
|||Construction|**(4)**|Government|
|||Trade, hotels and resturants||Stock change|
|||Transport|||
|||Communications|||
|||Government|||
|||Other services|||

_Source_ : Authors’ elaboration.

On the basis of the SAM data, Table 2 summarizes the sectoral structure of the illustrative SSA economy: sectoral shares in value-added, production, employment, exports and imports, as well as the split of domestic sectoral supplies between exports and domestic sales, and domestic sectoral demands between imports and domestic output. For instance, while (primary) agriculture represents a significant share of employment (around 66 percent), its shares of value added (VA), production, and exports are much smaller (in the range of 15-25 percent). The share of its output that is exported is around 10 percent while only some 5 percent of domestic demands are met via imports.

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**Table 2.** Sectoral structure of illustrative SSA country economy (percent).

|Sector|Value<br>added|Production|Employment|Exports|Imports|Exports‐<br>Output<br>ratio|<br>Imports‐<br>Demand<br>ratio|
|---|---|---|---|---|---|---|---|
|Agriculture|24.6|15.9|59.7|21.3|4.6|10.2|4.8|
|Forestry|4.6|3.1|5.4|0.0|0.0|0.1|0.1|
|Fishing|1.3|0.8|0.9|3.9|0.0|33.0|0.4|
|Petroleum and gas|0.1|0.1|0.0|0.0|0.0|0.1|0.1|
|Mining|1.1|1.1|0.2|0.6|1.4|3.5|16.8|
|Food|7.2|11.3|2.9|12.6|7.0|9.0|10.5|
|Beverages|0.9|1.7|0.1|2.9|1.1|13.3|14.3|
|Tobbaco products|0.1|0.2|0.0|0.3|0.2|15.1|39.3|
|Textiles and leather|1.0|1.0|0.2|8.4|4.9|56.4|64.4|
|Wood|0.0|0.5|0.0|0.4|0.2|6.5|5.7|
|Paper|0.1|0.1|0.0|0.5|1.9|30.9|63.7|
|Refined petroleum products|0.0|0.1|0.0|1.0|16.2|97.7|84.7|
|Chemical products|1.1|1.3|0.1|3.1|11.1|17.2|59.2|
|Rubber and plastic|0.2|0.2|0.0|1.0|2.1|33.3|68.8|
|Non‐metalic mineral products|0.8|1.2|0.1|3.1|2.6|20.3|29.4|
|Metal products|0.8|1.3|0.1|5.4|8.2|34.7|59.0|
|Machinery and vehicles|0.1|0.2|0.0|2.7|27.4|98.5|99.9|
|Other manufactures|0.6|1.0|0.1|0.9|1.9|7.3|23.4|
|Electricity and gas|0.5|0.4|0.1|0.5|0.1|10.4|3.8|
|Construction|6.6|11.3|2.0|0.0|0.0|0.0|0.0|
|Trade, hotels and resturants|14.9|17.2|8.2|15.8|2.1|8.3|1.9|
|Transport|2.5|2.3|1.3|2.8|3.9|10.9|21.4|
|Communications|3.7|3.6|0.8|0.0|0.0|0.0|0.0|
|Government|3.5|3.8|3.5|8.1|0.5|19.3|2.2|
|Other services|23.8|20.3|14.1|4.6|2.4|2.1|1.7|
|Total|100.0|100.0|100.0|100.0|100.0|8.1|15.8|

_Note_ : The columns Value added, Production, Employment, Exports, and Imports show the shares (in percent) of each sector in the country totals. The columns Exports-output ratio and Importsdemand show exports and imports as shares (in percent) of total sector output and total domestic demand, respectively.

Table 3 shows the factor shares in total sectoral value added. For example, the table shows that agriculture is relatively intensive in the use of labor; this information will be useful to analyze the results from the CGE simulations.

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**Table 3.** Sectoral factor intensity of illustrative SSA economy (percent)

|Sector|Labor|Prv Capital|Nat Res|Total|
|---|---|---|---|---|
|Agriculture|73.7|15.0|11.3|100.0|
|Forestry|36.0|0.3|63.7|100.0|
|Fishing|21.1|0.2|78.7|100.0|
|Petroleum and gas|27.3|38.1|34.6|100.0|
|Mining|27.3|51.5|21.2|100.0|
|Food|68.9|31.1|0.0|100.0|
|Beverages|23.0|77.0|0.0|100.0|
|Tobacco products|66.1|33.9|0.0|100.0|
|Textiles and leather|39.6|60.4|0.0|100.0|
|Wood|14.6|85.4|0.0|100.0|
|Paper|27.3|72.7|0.0|100.0|
|Refined petroleum products|9.3|90.7|0.0|100.0|
|Chemical products|19.2|80.8|0.0|100.0|
|Rubber and plastic|17.6|82.4|0.0|100.0|
|Non‐metalic mineral products|17.4|82.6|0.0|100.0|
|Metal products|17.9|82.1|0.0|100.0|
|Machinery and vehicles|15.1|84.9|0.0|100.0|
|Other manufactures|30.0|70.0|0.0|100.0|
|Electricity and gas|41.6|58.4|0.0|100.0|
|Construction|50.7|49.3|0.0|100.0|
|Trade, hotels and resturants|49.6|50.4|0.0|100.0|
|Transport|48.8|51.2|0.0|100.0|
|Communications|19.6|80.4|0.0|100.0|
|Government|100.0|0.0|0.0|100.0|
|Other services|52.9|47.1|0.0|100.0|
|Total|55.7|37.3|7.0|100.0|

_Source_ : Authors’ calculations based on illustrative SSA country SAM.

Compared to a standard CGE application, additional data is needed for the proximity parameter. As indicated in the preceding section, the values for this parameter should measure the extent to which a worker that belongs to activity _a’_ has the capabilities required from workers in a destination activity _a_ ; in other words, it should measure the efficiency of workers from _a’_ in _a_ compared to workers that belong to _a_ . Different approaches are possible and in the concluding section we highlight this as an important area for future research.

For the purposes of this paper, we found it convenient to draw on the PS approach, computing proximities on the basis of revealed comparative advantage 14
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In our application, the proximity indicator is used as a measure of how close the capabilities of workers in a sector are from those required for full efficiency in other sectors; in the PS literature, it is used as a measure of a country’s capabilities, also considering other inputs. The proximity indicator follows the disaggregation of the CGE database. For the commodities that it covers, we use the Comtrade export database. However, proximity data are also needed for sectors not covered by Comtrade. Non-Comtrade commodities are made up of services and a subset of goods (the latter including utilities and construction); all tend to be relatively non-traded. Given that, export data may not exist or be relevant to RCA measurement, we instead use GTAP value-added data. Given that our dataset has a one-to-one mapping between activities (production sectors) and commodities (outputs), it is straightforward to define RCA and proximity indicators using export data for one commodity subset and VA data for another commodity subset.

The computed raw proximity data between all pairs of commodities are shown in Table 4.<sup>17</sup> Further, before the CGE implementation of these raw proximities, scaling was needed to generate empirically valid representations of labor market segmentation.<sup>18</sup> This is part of the broader challenge of testing and improving the validity of CGE models in manners that consider the context of different types of applications.

17 A model with multiple labor categories would require multiple proximity matrices to permit proximity data to differ by labor category (for example, classified by educational attainment, skill, or occupation). For example, IT and human resources staff may have very small distances between sectors; other skilled tasks may be highly specialized. 18 In our application in Section 3, the scaled proximity parameter, ���,�, used in the model is derived from the original raw proximity parameter using the following formula: ���,� = ��� ���� ��� ���� ���,� + � ∙�1 −���,� �, where � is a factor-specific scaling parameter. The higher the value of the latter, the higher the scaled proximity. To ensure that 0 ≤���,� ≤1, the scaling ��� ��� ���� parameter should satisfy the following restriction: −���,� ��1 −���,� �≤� ≤1. Note that �<sup>����</sup> lim→�<sup>���</sup> ���<sup>�,�</sup> + ����� ∙�1 −������,� ��= 1. If the non-scaled proximities are too low, simulated factor reallocation falls short of what is observed in practice; to make reallocation easier, the value of the scaling parameter should be within the following range: 0 < �<sup>����</sup> ≤1. Given current uncertainty about its value, it may be seen as the exogenous shift parameter that is used in the non-base simulations to explore the implications of different degrees of sensitivity of efficiency for potentially relocated labor to proximity.

16
**Table 4.** Proximity between sectors in the illustrative SSA country SAM.

||1<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9<br>10<br>11<br>12<br>13<br>14<br>15<br>16<br>17<br>18<br>19<br>20<br>21<br>22<br>23<br>24<br>25|
|---|---|
|1 Agriculture|1.00 0.48 0.49 0.23 0.46 0.80 0.46 0.36 0.44 0.39 0.23 0.25 0.20 0.26 0.31 0.43 0.10 0.16 0.48 0.46 0.64 0.57 0.46 0.26 0.23|
|2 Forestry|0.48 1.00 0.48 0.14 0.27 0.52 0.45 0.39 0.50 0.59 0.34 0.30 0.25 0.41 0.43 0.42 0.18 0.25 0.47 0.49 0.48 0.51 0.50 0.16 0.25|
|3 Fishing|0.49 0.48 1.00 0.22 0.39 0.51 0.54 0.49 0.46 0.37 0.29 0.22 0.24 0.27 0.34 0.31 0.17 0.27 0.34 0.43 0.46 0.32 0.40 0.32 0.32|
|4 Petroleum and gas|0.23 0.14 0.22 1.00 0.26 0.16 0.08 0.11 0.15 0.25 0.11 0.41 0.08 0.03 0.14 0.13 0.08 0.04 0.26 0.22 0.27 0.22 0.21 0.15 0.07|
|5 Mining|0.46 0.27 0.39 0.26 1.00 0.38 0.35 0.29 0.31 0.35 0.23 0.31 0.20 0.09 0.22 0.49 0.03 0.26 0.32 0.33 0.37 0.36 0.40 0.37 0.20|
|6 Food|0.80 0.52 0.51 0.16 0.38 1.00 0.52 0.43 0.49 0.44 0.26 0.30 0.25 0.33 0.39 0.36 0.10 0.26 0.49 0.51 0.56 0.57 0.46 0.28 0.25|
|7 Beverages|0.46 0.45 0.54 0.08 0.35 0.52 1.00 0.48 0.45 0.43 0.40 0.25 0.25 0.40 0.50 0.42 0.18 0.30 0.32 0.49 0.38 0.39 0.50 0.33 0.50|
|8 Tobacco products|0.36 0.39 0.49 0.11 0.29 0.43 0.48 1.00 0.51 0.48 0.26 0.21 0.26 0.42 0.45 0.31 0.18 0.45 0.28 0.39 0.37 0.32 0.44 0.24 0.34|
|9 Textiles and leather|0.44 0.50 0.46 0.15 0.31 0.49 0.45 0.51 1.00 0.38 0.21 0.28 0.21 0.38 0.49 0.38 0.08 0.36 0.36 0.39 0.40 0.44 0.38 0.13 0.18|
|10 Wood|0.39 0.59 0.37 0.25 0.35 0.44 0.43 0.48 0.38 1.00 0.43 0.28 0.10 0.40 0.45 0.44 0.25 0.30 0.47 0.45 0.37 0.46 0.44 0.23 0.25|
|11 Paper|0.23 0.34 0.29 0.11 0.23 0.26 0.40 0.26 0.21 0.43 1.00 0.18 0.22 0.48 0.43 0.36 0.41 0.26 0.26 0.33 0.23 0.27 0.33 0.37 0.55|
|12 Refined petroleum products|0.25 0.30 0.22 0.41 0.31 0.30 0.25 0.21 0.28 0.28 0.18 1.00 0.26 0.15 0.30 0.31 0.12 0.21 0.36 0.39 0.19 0.37 0.38 0.24 0.18|
|13 Chemical products|0.20 0.25 0.24 0.08 0.20 0.25 0.25 0.26 0.21 0.10 0.22 0.26 1.00 0.33 0.30 0.16 0.26 0.30 0.11 0.27 0.15 0.19 0.25 0.38 0.45|
|14 Rubber and plastic|0.26 0.41 0.27 0.03 0.09 0.33 0.40 0.42 0.38 0.40 0.48 0.15 0.33 1.00 0.62 0.33 0.48 0.45 0.26 0.39 0.21 0.34 0.40 0.30 0.52|
|15 Non‐metalic mineral product|s 0.31 0.43 0.34 0.14 0.22 0.39 0.50 0.45 0.49 0.45 0.43 0.30 0.30 0.62 1.00 0.51 0.30 0.35 0.40 0.49 0.27 0.42 0.44 0.24 0.38|
|16 Metal products|0.43 0.42 0.31 0.13 0.49 0.36 0.42 0.31 0.38 0.44 0.36 0.31 0.16 0.33 0.51 1.00 0.24 0.31 0.53 0.53 0.35 0.49 0.50 0.20 0.29|
|17 Machinery and vehicles|0.10 0.18 0.17 0.08 0.03 0.10 0.18 0.18 0.08 0.25 0.41 0.12 0.26 0.48 0.30 0.24 1.00 0.33 0.21 0.22 0.17 0.22 0.23 0.27 0.41|
|18 Other manufactures|0.16 0.25 0.27 0.04 0.26 0.26 0.30 0.45 0.36 0.30 0.26 0.21 0.30 0.45 0.35 0.31 0.33 1.00 0.26 0.31 0.17 0.29 0.27 0.22 0.31|
|19 Electricity and gas|0.48 0.47 0.34 0.26 0.32 0.49 0.32 0.28 0.36 0.47 0.26 0.36 0.11 0.26 0.40 0.53 0.21 0.26 1.00 0.47 0.45 0.56 0.42 0.19 0.15|
|20 Construction|0.46 0.49 0.43 0.22 0.33 0.51 0.49 0.39 0.39 0.45 0.33 0.39 0.27 0.39 0.49 0.53 0.22 0.31 0.47 1.00 0.29 0.49 0.51 0.29 0.39|
|21 Trade, hotels and resturants|0.64 0.48 0.46 0.27 0.37 0.56 0.38 0.37 0.40 0.37 0.23 0.19 0.15 0.21 0.27 0.35 0.17 0.17 0.45 0.29 1.00 0.49 0.38 0.19 0.19|
|22 Transport|0.57 0.51 0.32 0.22 0.36 0.57 0.39 0.32 0.44 0.46 0.27 0.37 0.19 0.34 0.42 0.49 0.22 0.29 0.56 0.49 0.49 1.00 0.47 0.20 0.19|
|23 Communications|0.46 0.50 0.40 0.21 0.40 0.46 0.50 0.44 0.38 0.44 0.33 0.38 0.25 0.40 0.44 0.50 0.23 0.27 0.42 0.51 0.38 0.47 1.00 0.35 0.40|
|24 Government|0.26 0.16 0.32 0.15 0.37 0.28 0.33 0.24 0.13 0.23 0.37 0.24 0.38 0.30 0.24 0.20 0.27 0.22 0.19 0.29 0.19 0.20 0.35 1.00 0.48|
|25 Other services|0.23 0.25 0.32 0.07 0.20 0.25 0.50 0.34 0.18 0.25 0.55 0.18 0.45 0.52 0.38 0.29 0.41 0.31 0.15 0.39 0.19 0.19 0.40 0.48 1.00|

# _Source_ : Authors’ calculations based on Comtrade and GTAP 8 data.

In addition to the SAM and proximity data, our CGE model also requires (a) base-year estimates for sectoral employment levels, and (b) a set of elasticities (for production, consumption and trade). In order to estimate sectoral employment, we combined population data with estimates for sectoral employment shares in broad sectoral categories from World Bank (2016). In turn, elasticities were given a value based on the available evidence for comparable countries. For elasticities, the following values were used: (a) the elasticity of substitution among factors is in the 0.2–1.15 range, relatively low for primary sectors and relatively high for manufactures and services (see Narayanan et al. 2012); (b) the expenditure elasticities for household consumption were obtained from Seale et al. (2003); and (c) trade elasticities are 4 for both Armington and CET elasticities.<sup>19</sup> Given the uncertainty with respect to our elasticity values, we conducted a systematic sensitivity analysis of our simulation results with respect to their values; it indicated that the results presented here are robust.<sup>20</sup> 19 These CET and Armington elasticities may seem high. However, the size of responses to trade-related shocks depend not only on these trade elasticities but also on other aspects of the model, including the size of production responses which, in their turn, depend on (a) the mobility of labor and other factors; and (b) elasticities of factor substitution. Thus, a given trade-related shock may generate similar trade and production responses with a combination of high factor mobility and low trade elasticities or, alternatively, low factor mobility and high trade elasticities.

20 The results from the systematic sensitivity are downloadable as supplementary material from www.jgea.org/resources/jgea/ojs/index.php/jgea/article/view/39.

17
# **3. Simulations**

# _3.1. Scenarios_

This section presents the simulations and analyzes their results. The simulations consist of a base solution (which replicates the base-year economy) and a set of non-base scenarios. All scenarios are run under the assumption of fixed total employment. At the macro level, our CGE model – like others – requires the specification of the equilibrating mechanisms (“closures”) for three macroeconomic balances: government, savings-investment, and the balance of payments. Unless specified otherwise, in all simulations the following macroeconomic closure rules are applied: (a) in order to ensure that the simulations are budget neutral, changes in income tax rates on households clear the government budget (i.e., compared to base values, no other changes in taxes or other revenue sources, domestic or foreign, are permitted); (b) in order to ensure that the simulations are neutral in terms of changes in country net foreign assets, foreign savings (the current account deficit) is fixed in foreign currency, an outcome that is achieved through changes in the real exchange rate; and (c) in order to ensure neutrality across the simulations in terms of investing for the future, real investment is fixed; as a result of this and given the fact that real government consumption also is fixed, change in real private consumption may be used to measure changes in aggregate welfare. With regard to non-labor factors, for all simulations we assume that they are sector-specific (no mobility between sectors).

The different non-base scenarios and their treatment of labor mobility are shown in Table 5. The non-base simulations explore how the impact of a terms-oftrade shock is influenced by alternative settings for labor proximities. Across all scenarios, we impose a 35 percent increase in the food export price.<sup>21</sup> Given the sectoral structure described in Table 2 (e.g., the food industry has a relatively high export-to-output ratio), this shock serves to illustrate the working of the proposed approach to modeling labor mobility across sectors.

21 In addition, sensitivity tests indicated that our qualitative findings were robust to alternative magnitudes for the shock: specifically, we tested the same five labor proximity settings for increases in the food export price in a range from 10 to 50 percent. The results from these additional scenarios are downloadable as supplementary material from www.jgea.org/resources/jgea/ojs/index.php/jgea/article/view/39.

18
**Table 5.** Definitions of non-base scenarios.

|**Name / degree of labor**<br>**mobility**|**Description**|
|---|---|
|**zero**|sector‐specific labor due to verylowproximities|
|**low**|proximity parameters from Table 4|
|**medium**|proximity scaling* by 0.5, reducing gap between 1 and<br>values in Table 4 by50%.|
|**high**|proximity scaling* by 0.75, reducing gap between 1 and<br>parameters in Table 4 by75%.|
|**perfect**|integrated labor market(allproximities = 1)|

_Note_ : * The scaling factor is defined in footnote 22.

_Source_ : Authors’ elaboration.

# _3.2. Results_

Tables 6-9 and Figures 1 and 2 summarize the results for the simulations, covering both the macro and sector levels. Overall, the results are regular and as expected. At the macro level (Table 6 and Figure 1), the higher the degree of mobility, the stronger the gains. For the zero mobility scenario, the gain in the food export price raises the value of exports, generating a balance of payments surplus and an equilibrating appreciation of the real exchange rate, which reduces the export volume, raises the import volume, and increases absorption, in this case private consumption since the other parts of absorption (investment and government consumption) are fixed in real terms. These changes take place without any changes in employment or production, i.e., given the assumption that non-labor factors are sector-specific, existing output is merely reallocated from exports to domestic sales.

Across all scenarios, the higher the degree of labor mobility, the stronger the gains in absorption and private consumption, indicating that a more flexible economy is better at reallocating resources to gain from (or mitigate losses due to) international price changes.<sup>22</sup> Since it is able to focus on higher-valued exports, a flexible economy is able to maintain external balance with smaller export 22 A higher trade deficit is more favorable in the sense that, for any given level of real GDP, it permits higher level of domestic final demands (absorption). In addition to the terms of trade gain and labor reallocation, in this set of simulations, the measured welfare gain is boosted because the favored sector (food) pays wages that are above the average. This is due to the fact that the equilibrium solutions (reflecting real world observations) retain observed wage gaps (in our model with fixed relative wage differences, a common formulation). Higher than average wages in certain sectors may compensate for a relatively high disutility from work in these sectors; however, this is not reflected in the model or in the welfare measures (based on private consumption) that are presented.

19
quantities and higher import quantities. GDP at factor cost increases slightly for the non-zero-mobility scenarios due to the fact that, in the food sector, which accounts for almost all output and employment increases, marginal productivity of labor (and wages) are above average.<sup>23</sup> For the real exchange rate, the expected result is that higher mobility would reduce the appreciation needed to eliminate the surplus in the current account; this is indeed the result across the simulations with non-zero labor mobility. However, the lowest real appreciation is registered for the simulation with zero mobility; this is due to the fact that, for this simulation, the absence of employment changes prevents output and export increases for the food sector that drastically increase the current account surplus and, given this, the need for real appreciation.

Labor reallocation to the food sector is the reason behind the absorption gains beyond what is achieved for the zero mobility scenario – the higher the proximity, the stronger the mobility, the reallocation, and the ability of the economy to export at more favorable terms with the benefits appearing in higher imports and absorption. Table 7 shows the shares of the workers initially employed in different sectors that, in response to the shock, move to the food sector. As expected, when labor mobility increases (among other things meaning that other sectors are in closer proximity to the food industry), we see labor moving into the food industry from a larger number of sectors. For example, labor moves from four and ten sectors in the medium and high scenarios, respectively.

From a different angle, Figure 2 displays the percent changes for output, efficient employment, and physical employment in the food sector for the different scenarios.<sup>24</sup> Except for the case with perfect mobility, the quantity of labor in efficiency units that moves to the food sector is smaller than the physical quantity due to the loss in efficiency. The fact that certain sectors are more prone to release labor to food is in part determined by the proximity data – other manufacturing sectors, such as beverages, are relatively close to food. In fact, Table 4 in Section 2.3 shows that beverages is a sector in close proximity to the food processing industry. However, the pattern of labor reallocation is also influenced by other factors, including the extent to which the output of the different sectors is tradable – as a result of the real appreciation, non-food sectors that are relatively tradable lose more than others in terms of relative profitability.

Tables 8 and 9 show the changes in sectoral output and employment (both physical and efficient), respectively. Not surprisingly, the major backward link of 23 Given the current set of assumptions, real GDP at factor cost changes due to (a) losses in productivity when labor moves between sectors; and (b) exogenous wage differentials across sectors. Labor movements between two sectors with a linking proximity of 1 (no productivity loss) and identical wages, do not change GDP.

24 Under perfect mobility, we can compute employment changes but we cannot single out the sectors of origin and destination for labor movements.

20
food is with agriculture. In the base-year dataset the food industry accounts for 27 percent of total demand for agricultural output.<sup>25</sup> As the increase in food production increases with increased mobility, the positive impact on intermediate food demands for agricultural output comes to dominate the negative impact on agricultural exports from appreciation. Interestingly, this input-output relation between agriculture and food dominates the labor reallocation from agriculture to food that would have been expected given the close proximity between them (see Table 4 in Section 2.3).

In Table 9, all contracting activities show the same negative changes in physical and efficiency units since all of the departing workers were assigned to the activities from they depart (their proximities were equal to one, i.e., _a_ = _a_ ’). On the other hand, given that the proximities for movements from contracting to expanding activities here always are less than one, the positive employment changes are always smaller in efficiency units than in physical units.

**Table 6.** Real macro indicators (percent change from base).

|Item|baseyear*|zero|low|medium|high|perfect|
|---|---|---|---|---|---|---|
|Absorption|109.7|0.9|1.4|1.7|1.9|2.0|
|Private consumption|77.1|1.3|2.1|2.5|2.7|2.8|
|Exports|16.0|4.3|4.0|3.9|3.4|2.9|
|Imports|25.7|6.5|7.1|7.7|7.9|8.3|
|GDP at factor cost|91.4|0.0|0.5|0.8|1.0|1.0|
|Real exchange rate|1|‐4.5|‐8.1|‐7.5|‐7.0|‐6.9|

_Note: * In this column, the unit is % of GDP at market prices except for the real exchange rate, which is indexed to 1._ _Source_ : Authors' calculations.

> 25 Other major demands for agricultural output is for household consumption, exports, and for use in the “Trade, hotels and restaurants” sector.

21
**Table 7.** Share of labor initially employed in activity _a_ (column) allocated to food processing (percent).

|**Activity**|**low**|**medium**|**high**|
|---|---|---|---|
|Agriculture|0.4|0.4|0.0|
|Forestry|0.0|0.0|0.0|
|Fishing|0.0|0.0|0.0|
|Petroleum and gas|0.0|0.0|0.0|
|Mining|0.0|0.0|0.7|
|Food|100.0|100.0|100.0|
|Beverages|0.0|3.3|10.5|
|Tobacco products|0.0|7.7|14.4|
|Textiles and leather|0.0|2.7|15.8|
|Wood|0.0|0.0|0.0|
|Paper|0.0|0.0|0.0|
|Refined petroleum products|0.0|0.0|8.7|
|Chemical products|0.0|0.0|0.0|
|Rubber and plastic|0.0|0.0|0.0|
|Non‐metalic mineral products|0.0|0.0|10.2|
|Metal products|0.0|0.0|7.3|
|Machinery and vehicles|0.0|0.0|0.0|
|Other manufactures|0.0|0.0|3.3|
|Electricity and gas|0.0|0.0|0.7|
|Construction|0.0|0.0|0.0|
|Trade, hotels and resturants|0.0|0.0|0.0|
|Transport|0.0|0.0|4.3|
|Communications|0.0|0.0|0.0|
|Government|0.0|0.0|0.0|
|Other services|0.0|0.0|0.0|

_Source_ : Authors' calculations.

22
**Table 8.** Real output by sector (percent change from base).

|**Activity**|**base**|**low**|**medium**|**high**|**perfect**|
|---|---|---|---|---|---|
|Agriculture|8,428|‐0.26|‐0.30|‐0.01|0.53|
|Forestry|1,568|0.02|0.11|0.22|0.43|
|Fishing|439|0.00|0.00|0.00|‐0.27|
|Petroleum and gas|37|0.00|0.00|‐0.18|‐1.64|
|Mining|392|0.00|0.00|‐0.20|‐1.13|
|Food|2,460|8.39|12.49|15.15|18.23|
|Beverages|308|0.00|‐0.77|‐2.51|‐3.92|
|Tobbaco products|21|0.00|‐5.17|‐9.71|‐13.08|
|Textiles and leather|333|0.00|‐1.06|‐6.55|‐10.67|
|Wood|7|‐2.97|‐3.46|‐3.31|‐2.79|
|Paper|26|0.00|‐1.03|‐4.14|‐6.93|
|Refined petroleum products|13|0.00|0.00|‐0.84|‐2.70|
|Chemical products|363|0.00|0.00|‐0.31|‐3.08|
|Rubber and plastic|63|0.00|0.00|‐1.41|‐3.61|
|Non‐metalic mineral products|267|0.00|‐0.01|‐1.84|‐3.64|
|Metal products|265|0.00|‐0.38|‐1.94|‐4.36|
|Machinery and vehicles|49|0.00|0.00|‐1.72|‐4.29|
|Other manufactures|207|0.00|0.00|‐1.00|‐4.23|
|Electricity and gas|181|0.00|0.00|‐0.31|‐2.70|
|Construction|2,256|0.06|0.09|0.10|0.12|
|Trade, hotels and resturants|5,119|0.00|0.00|0.00|0.43|
|Transport|847|0.00|0.00|‐2.11|‐4.96|
|Communications|1,284|0.00|0.14|0.00|‐0.79|
|Government|1,216|0.00|0.00|‐1.40|‐6.07|
|Other services|8,180|0.00|0.05|0.49|0.72|

_Note:_ * For the zero mobility scenario, real output does no change from the base year.

_Source_ : Authors' calculations.

23
**Table 9.** Employment by sector in physical and efficiency units (percent change from

base).

||Physical u|nits|||Efficiency|units|||
|---|---|---|---|---|---|---|---|---|
|Activity|low|medium|high|perfect|low|medium|high|perfect|
|Agriculture|‐0.36|‐0.41|‐0.01|0.72|‐0.36|‐0.41|‐0.01|0.72|
|Forestry|0.10|0.37|0.71|1.22|0.06|0.30|0.62|1.22|
|Fishing|0.00|0.00|0.00|‐1.27|0.00|0.00|0.00|‐1.27|
|Petroleum and gas|0.00|0.00|‐0.64|‐5.54|0.00|0.00|‐0.64|‐5.54|
|Mining|0.00|0.00|‐0.72|‐3.89|0.00|0.00|‐0.72|‐3.89|
|Food|15.47|21.19|25.86|27.38|12.38|18.56|22.63|27.38|
|Beverages|0.00|‐3.30|‐10.52|‐16.09|0.00|‐3.30|‐10.52|‐16.09|
|Tobacco products|0.00|‐7.72|‐14.36|‐19.19|0.00|‐7.72|‐14.36|‐19.19|
|Textiles and leather|0.00|‐2.67|‐15.83|‐25.04|0.00|‐2.67|‐15.83|‐25.04|
|Wood|‐18.85|‐21.69|‐20.81|‐17.76|‐18.85|‐21.69|‐20.81|‐17.76|
|Paper|0.00|‐3.75|‐14.48|‐23.41|0.00|‐3.75|‐14.48|‐23.41|
|Refined petroleum products|0.00|0.00|‐8.67|‐25.82|0.00|0.00|‐8.67|‐25.82|
|Chemical products|0.00|0.00|‐1.59|‐15.18|0.00|0.00|‐1.59|‐15.18|
|Rubber and plastic|0.00|0.00|‐7.76|‐19.00|0.00|0.00|‐7.76|‐19.00|
|Non‐metalic mineral products|0.00|‐0.04|‐10.16|‐19.35|0.00|‐0.04|‐10.16|‐19.35|
|Metal products|0.00|‐2.12|‐10.40|‐22.28|0.00|‐2.12|‐10.40|‐22.28|
|Machinery and vehicles|0.00|0.00|‐10.88|‐25.53|0.00|0.00|‐10.88|‐25.53|
|Other manufactures|0.00|0.00|‐3.29|‐13.49|0.00|0.00|‐3.29|‐13.49|
|Electricity and gas|0.00|0.00|‐0.74|‐6.40|0.00|0.00|‐0.74|‐6.40|
|Construction|0.27|0.23|0.23|0.24|0.12|0.18|0.20|0.24|
|Trade, hotels and resturants|0.00|0.00|0.01|0.86|0.00|0.00|0.01|0.86|
|Transport|0.00|0.00|‐4.29|‐9.93|0.00|0.00|‐4.29|‐9.93|
|Communications|0.00|0.96|0.00|‐3.99|0.00|0.72|0.00|‐3.99|
|Government|0.00|0.00|‐1.40|‐6.07|0.00|0.00|‐1.40|‐6.07|
|Other services|0.00|0.11|1.07|1.36|0.00|0.09|0.94|1.36|

_Source_ : Authors' calculations.

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<!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4 zero<br>3<br>low<br>2<br>1 medium<br>0 high<br>perfect<br><!-- End of picture text -->

**Figure 1.** Real macro indicators by scenario (percent change from base).

_Note_ : In this figure the definition of the real exchange rate was changed so that an increase means an appreciation.

_Source_ : Authors' calculations.

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>low medium high perfect<br>Effient employment Physical employment<br>Output<br><!-- End of picture text -->

**Figure 2.** Employment and real output in food processing (percent change from base). _Source_ : Authors' calculations.

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# **4. Conclusions**

In this paper, we present an intuitive entry point to the modeling of labormarket mobility: labor movements and wage differentiation are driven by the extent to which sectors are similar in terms of the capabilities that are required from their workers. It fits naturally in economy-wide models where producer decisions are driven by profit-maximization and labor may be reallocated across sectors. The approach is flexible in that, subject to data constraints, it can replicate any observed pattern of pair-wise sector segmentation, including asymmetric patterns: for example, unskilled worker reallocation from agriculture to construction may be easy whereas reallocation in the opposite direction may be difficult due to the fact that construction workers lack many of the capabilities required in agricultural work.

A research agenda that draws on the proposed approach may address several issues. The top priority may be to empirically estimate proximity parameters, preferably with disaggregations that are closely aligned with the disaggregation of the rest of the application database both with regard to sectors and labor types. Given that mobility is likely to vary across labor types, the payoffs may be high from further disaggregation (for example on the basis of educational attainment or gender). Household and labor market surveys may provide (or be designed to provide) needed data on worker movements and how the wages received by workers employed in a sector differ depending on their sector of origin.

A second direction of research is related to extensions of the approach, each of which would come with its own additional data requirements. Here, top empirical priorities may be to bring unemployment into the model, presumably with sectoral capabilities assigned to the unemployed, and to make the analysis dynamic. In a dynamic setting, it would be necessary to model the evolution of the size of the labor pools, disaggregated by sector and perhaps according to other criteria. In light of learning by doing, it would be natural to expect the labor stocks (classified by assigned capabilities) to grow relatively fast for sectors with increasing shares of employment. It would also be necessary to consider the sectoral capabilities of those who enter to or exit from the labor force.<sup>26</sup> When addressing these issues, it would be crucial to draw on the labor economics literature. The development of dynamic approaches to labor market modeling that 26 Those who enter and leave the labor force may be split into different categories. For example, entrants may be composed of people leaving school, immigrants, and youth reaching labor force age) while those who exit may be made up of those who leave the labor force due to emigration, retirement, and death. Movements into or out of the labor force may also be due to other causes (such as changing gender roles or changing prospects for landing a job).

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