metadata: authors: 0: author: Sinha,Rishabh
count: Central America
docna: 0: docna: Central America’s Deindustrialization
docty: Policy Research Working Paper
abstracts: cdata!: The paper assembles and harmonizes
            sectoral data from several sources to study the industrial
            trends in six Central American economies. The industrial
            employment share contracted by 2.5 percentage points on
            average over the past two decades. This deindustrialization
            was not trade-driven in which economies substitute domestic
            production of industrial goods via cheaper imports. Instead,
            an increase in barriers restricting the efficient flow of
            labor across sectors drives this decline. Adopting policies
            that target such barriers can potentially deliver
            considerable industrial expansion. But the economic impact
            of this policy is likely to be marginal, with aggregate
            output increasing by 3 percent or less if barriers are
            eliminated. At the same time, this approach also carries
            several risks, and rather than reining in inefficiency might
            introduce new distortions making the economy more
            inefficient. Perhaps a more prudent growth strategy will be
            to concentrate on boosting productivity, which, although
            challenging, has a direct effect on output.
display_title: Central America’s Deindustrialization
listing_relative_url: /research/2022/10/33915570/central-america’s-deindustrialization
topicv3: Growth Monitoring and Promotion m1326325 1444,Structural Policy and Reform m1326450 1130,Trade m1326924 698,Economic Growth Analytics m1583361 467
publishtoextweb_dt: 2022-10-11T00:00:00Z
dois: 10.1596/1813-9450-10203
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1411342
model_extractions: Policy Research Working Paper 10203 Central America’s Deindustrialization _Rishabh Sinha_ Development Economics Development Research Group October 2022
Policy Research Working Paper 10203

# **Abstract**

The paper assembles and harmonizes sectoral data from several sources to study the industrial trends in six Central American economies. The industrial employment share contracted by 2.5 percentage points on average over the past two decades. This deindustrialization was not tradedriven in which economies substitute domestic production of industrial goods via cheaper imports. Instead, an increase in barriers restricting the efficient flow of labor across sectors drives this decline. Adopting policies that target such barriers can potentially deliver considerable industrial expansion. But the economic impact of this policy is likely to be marginal, with aggregate output increasing by 3 percent or less if barriers are eliminated. At the same time, this approach also carries several risks, and rather than reining in inefficiency might introduce new distortions making the economy more inefficient. Perhaps a more prudent growth strategy will be to concentrate on boosting productivity, which, although challenging, has a direct effect on output.

This paper is a product of the Development Research 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://www.worldbank.org/prwp. The author may be contacted at rishabhsinha@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
# Central America’s Deindustrialization

## Rishabh Sinha The World Bank

JEL classification: F11; O11; O14 Keywords: Deindustrialization; Structural transformation; Central America; Growth; Trade; Labor market wedges
# **1   Introduction**

The advent of the Industrial Revolution was a defining moment in the economic history of the world. It ignited a period of Great Divergence in which the industrializing nations broke away from the rest (Clark, 2014). For the most part, this divergence that started centuries ago still defines the fault lines between the rich and the poor economies. It is not so surprising then that industrial expansion is a key component of many growth policies.<sup>1</sup> At the same time, deindustrialization remains a robust feature of economic development (Clark, 1957; Kuznets, 1957; Herrendorf _et al._ , 2014), especially when considering the sector's employment share instead of its output contribution (Rodrik, 2016). Moreover, there is emerging evidence that the onset of deindustrialization has been _premature_ in recent decades (Rodrik, 2016; Felipe _et al._ , 2019). The peak industrial share in the developing economies is lower relative to the early industrializers and materializes at a lower income per capita.

A growing literature is trying to uncover the underlying factors that drive deindustrialization. More crucially, it seeks to quantify the growth implications of deindustrialization and identify policies that can arrest the industry's slide. However, the country-level studies only cover a handful of economies. This analysis requires quality long-term sectoral data, which are not available for many countries. Small and low-income economies are particularly under-represented. Using learnings from other cases to devise policies is of limited use if factors driving deindustrialization vary across countries.

This paper addresses this gap by conducting a country-level analysis of six Central American economies in the last two decades. I piece together data from several sources and harmonize them at the sector level. The raw data require cleaning, and I make several adjustments to ensure quality before using them in analysis. Though value-added shares are indeed a part of the examination, the study concentrates on the dynamics of sectoral employment shares. Two facts guide this choice. First, as already mentioned, deindustrializing trends are more pronounced in employment (Rodrik, 2016). Second, unlike its employment counterpart, the industrial value-added share is only a weak predictor of attaining economic prosperity (Felipe _et al._ , 2019).

> 1 Examples include _Make in India_ and _Made in China 2025_ . However, these large-scale efforts transcend the boundaries of the developing world. The _American Jobs Plan_ seeks to reinvigorate industrial production. The plan deems the sector the _Arsenal of American Prosperity_ that can ‘ _fuel an economic recovery for working families_ (White House, 2021)’.

2
The data shows that deindustrialization has permeated Central America. Except for Panama, the industrial employment shares have contracted in each regional economy, with an average decline of 2.5 percentage points (pp). In the first step of my study, I uncover the fundamental drivers of industrial change in each country. Specifically, I use the sectoral data on employment, value-added, consumption, and trade to identify the importance of three channels in accounting for the dynamics. First, a decline in the industrial labor allocation can be demand-driven, i.e., due to a fall in the industrial share of final consumption. Second, a rise in industrial imports substituting domestic production may lead to trade-induced deindustrialization. Finally, an intensification in the barriers, or labor market _wedges_ (Hsieh & Klenow, 2009), to the movement of resources into the industry can have the same qualitative effect on labor allocation.

The accounting reveals that shifts in labor market wedges are essential in explaining the region's deindustrialization. The channel causes industrial employment to contract in each regional economy. Its impact on industrial share varies across countries, ranging from a more than 5pp decline in Panama and El Salvador to less than one pp decay in Costa Rica. These wedge-driven contractions are higher than the actual share loss in three of the five economies that have experienced deindustrialization. The channel plays a secondary role in Costa Rica, explaining only 15 percent of the country's massive 6.2 pp drop in industrial share.

Global trade has flourished over the last two decades. Trade, therefore, can potentially explain deindustrialization in countries having a comparative advantage in non-industrial sectors. Instead of engaging in production to satisfy their industrial demand, it might be economical for these economies to import these goods. Surprisingly, I do not find evidence supporting this oft-cited trade-led deindustrialization hypothesis for Central America. Instead of creating headwinds, trade has usually fostered industrial expansion in the region. Nicaragua and El Salvador are the two economies where the channel has induced deindustrialization. Still, its actual impact is benign in both cases.

Finally, changes in final consumption had a varied effect across the region. This channel aided industrialization in four economies, but an allocation of final consumption away from industrial goods led to a labor reallocation away from the sector in Costa Rica and Honduras. Quantitatively, this is the dominant factor that accounts for the deindustrialization in the two countries.

The second part of the paper aims to quantify the aggregate impact of rising wedges. Their presence restricts the efficient flow of resources across sectors and may keep labor trapped in non-industrial 3
sectors. As industrial productivity usually lies considerably above others, wedge-driven deindustrialization might entail significant efficiency costs.

I use a multi-sector equilibrium model to quantify these costs. Consistent with the structural transformation literature, the model features non-homothetic preferences (Kongsamut _et al._ , 2001). The preferences also allow changes in relative prices to influence sectoral consumption allocation by utilizing a non-unitary elasticity of substitution across sectoral goods (Baumol, 1967; Ngai & Pissarides, 2007).

Barring Costa Rica, labor market wedges in the industry have risen in all the economies, affecting deindustrialization. But wedges are not exclusive to the sector. Labor flow into services also faces barriers, and wedges in services are higher than in the industry in four of the six regional economies. Comparing across countries, I find the labor markets in Guatemala and Panama to be most distorted.

I calibrate the model at the country level. This baseline matches the sectoral allocation of value-added and employment. Exogenously changing the wedges in this baseline alters the equilibrium allocation of labor resources, causing the output to change. Any such deviation is due to the variation in wedges. For each country, I measure the output change in the most recent year after I supplant the wedges from the earliest period. Output change is positive for every country except Costa Rica when current wedges in both sectors are replaced with their past values. Still, the implied loss is marginal and ranges from 0.4 percent of current output in El Salvador to around one percent in Panama.

The above findings suggest that deindustrialization has not caused a significant drag on economic growth in Central America. Changes and labor market wedges are the principal driver of industrial contraction. Yet, the output impact they exert is marginal. But can a reindustrialization, led by a reduction in wedges, deliver meaningful efficiency gains in the future?

I consider this scenario in another set of counterfactual exercises. The first exercise corresponds to a sector-neutral policy, i.e., it does not deem a special status to any sector and targets barriers present in both industry and services to drive growth. Eliminating wedges in industry and services causes the industrial employment share to expand by 3.5 pp on average, with modest gains in El Salvador and Honduras (1.3-1.7 pp). Extensions are much higher in Nicaragua (6.6 pp) and Panama (4.4 pp). More importantly, though, the efficiency gains reaped by the economies remain limited. The average output expansion stands a shade below 2 percent, with individual country figures ranging between 0.3-3.2 percent.

4
But industrial (and sectoral) policies, by their very nature, are sector-specific. They seek to address concerns plaguing the industry, with corresponding corrective measures remaining well within the boundaries of the sector. For instance, the Make in India program increased the maximum permissible FDI share in construction and mining to 100 percent while keeping the limits much lower in several other sectors (e.g., insurance, pension, etc.) (Government of India, 2020). Similarly, the American Jobs Plan called for a USD 50 billion investment in semiconductor manufacturing to reduce _barriers to careers in high-innovation sectors_ (White House, 2021). Still, it will be naive to think that capital constraints in India are specific to only a subset of sectors. Or that the semiconductor industry is the only highinnovation sector in the US that experiences the barriers that the American Jobs Plan has under its lens. These sector-focused policies will likely lead to more labor flowing into the respective sectors. On the other hand, their impact on aggregate output is a separate issue altogether.

Relative to a sector-neutral policy, eliminating industrial wedges while keeping services wedges intact generates a greater impetus for industrialization because resources face barriers reducing in a single sector. Correspondingly, a considerable expansion in industrial employment shares follows this selective elimination. The model predicts an average gain of 18 pp in industrial employment shares, multiples higher than the previous case. Each regional economy experiences a double-digit increase. This exercise produces a valuable and somewhat unexpected finding. This selective elimination of industrial wedges often leads an economy to a more distorted state. In other words, a sector-specific policy that targets industrial wedges can create massive reallocation of the workforce towards the sector. But it also runs the risks of increasing allocative inefficiency.

The paper contributes to the deindustrialization literature by conducting a country-based analysis on six Central American economies. These countries are small, with populations ranging between 4–17 million people. Lack of quality data leaves a gap in knowing how deindustrialization manifests itself in such cases. Piecing together and analyzing data on the sectoral composition of employment, output, consumption, and trade from several sources, the investigation complements the previous attempts that focus on output alone (Caceres, 2017; Guisan & Aguayo, 2005, 2015). Crucially, it helps uncover the fundamental drivers of the region's deindustrialization.

In the absence of country-specific studies, these economies can only follow the policy prescriptions from studies focusing on other countries, which may or may not apply to them. The inappropriateness of this approach becomes evident when one considers the trade-deindustrialization link. Recent studies have shown that trade can be a vital force driving deindustrialization (Pierce & Schott, 2016; 5
Rodrik, 2016). The paper adds to this literature by showing that the trade channel cannot explain Central America's deindustrialization. If anything, shifts in trading patterns have fostered industrial expansion in recent decades. Hence, protectionist policies to counter deindustrialization will most likely fail. Industrial activity might even diminish if the trading partners reciprocate these measures.

The rest of the paper is organized as follows. In the next section, I outline the accounting methodology, followed by a discussion of the data used in the analysis. Then, I present the results of the accounting exercise, highlighting the relative importance of the different fundamental drivers of deindustrialization. The penultimate section introduces the resource allocation model, which I use to quantify the aggregate implications of labor market wedges. I conclude the paper by discussing some policy implications.

# **2   Accounting for deindustrialization**

I adopt an accounting approach that helps evaluate the relative importance of three channels– domestic consumption, trade, and labor market wedges. The framework follows from the openeconomy methodology introduced in Uy _et al._ (2013) to study the sectoral composition of value-added, and later extended in Sinha (2019) to bring employment shares under the lens. The accounting framework includes an arbitrary number of countries. Let represent the labor endowment in country . The production follows a standard tiered structure in which three composite pp CC LL sectors (agriculture , industry , services ) produce a sectoral good using tradeable varieties pp (Eaton & Kortum, 2002). The composite sectoral good produced by sector in (aa) (mm) (ss) country is either used for consumption or utilized as an intermediate to produce a tradeable jjpp QQ jj (jj∈{aa, mm, ss}) variety jjpp pp CC 6
(Xx) +) (1-6 )u > + (P ) (P ) ( ) (N ) ¢ ) (=e ~~(—)~~ ? Fe 1-(1-B ) -(1-8 yu ~~—~~ -(1-B )u ~~—~~ (é +i ) > |- -(1-B ju ~~—~~ 1-(1-B un “(1-p \u ~~_|~~ “ a (5) -(1-B6 ju ~~—~~ - -8 ~~—~~ 1-(1-8) a
sectors and restricts the equalization of labor productivity (nominal value-added per worker) across sectors. Introducing time-scripts, the labor allocation p ]′ can be written as a function of final consumption shares p , trade shares pLL, and productivity gaps = [llaappaa, llmmppaa, llssppaa p p p p p CC<sup>�</sup> � (7)NN<sup>�</sup> TT where and denote the time-invariant matrices of labor shares pp pp and sectoral linkages .<sup>3</sup> LL = FF�CC<sup>�</sup> , NN<sup>�</sup> , TT �BB , MM Therefore, function pp pp which combines equations (5) and (6) helps solve for the resulting labor jjpp jjkkpp BB MM ββ μμ allocation when one or more vectors among FF(. ) p and p are counterfactually altered. jjpp CC<sup>�</sup> , NN<sup>�</sup> TT

# **3   Data**

The quantitative application of equation (7) requires time-series data on four variables– employment shares p , final consumption shares p , trade shares p and labor market wedges p . Equation (6) allows the construction of LL p using CC<sup>�</sup> p and p . Hence, in essence, the analysis hinges on the NN<sup>�</sup> TT availability of p , p , p , and TT p . LL VV<sup>�</sup> In addition, I also need the estimates of time-invariant labor share vector LL CC<sup>�</sup> NN<sup>�</sup> VV<sup>�</sup> and intermediate share matrix . Equation (7) allows quantifying the impact of over-time changes in pp and on BB sectoral allocation. However, the unavailability of these data for Central American economies hinders pp pp pp MM BB MM this investigation.

I source the required data from multiple sources. However, the raw data appear spurious for some country-years. Perhaps, for this reason, the literature has not undertaken a long-term sectoral analysis of the Central American economies, especially when it comes to labor allocation. I make several adjustments to the raw data before using them. The data appendix contains the details of these adjustments. Still, I note the nature of adjustments briefly as I provide a summary of each series. **Employment shares (** p **):** The employment data are taken from the International Labor Organization’s (ILO) Key Indicators of the Labor Market database (ILO, 2018) that reports LL employment by sector. The database classifies total employment into six sectors – agriculture, manufacturing, construction, mining and utilities, trade and transportation (includes personal &

> 3 Sinha (2019) contains the detailed derivations of the accounting equations.

8
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**Consumption and trade shares (** p p **):** The National Accounts Main Aggregates database of the United Nations Statistics Division (UNSD) (UN, 2019) contains long-term data on final CC<sup>�</sup> , NN<sup>�</sup> consumption, exports, and imports at the economy-wide level. The sectoral splits of any of the three variables are, however, not available. On the other hand, the UNSD database does report the sectoral composition of value-added. Hence, given the values of other variables in equation (5), it is possible to infer the sectoral composition of final consumption p . To execute this strategy, I first aggregate the value-added series to the three sector level using the CC<sup>�</sup>

To execute this strategy, I first aggregate the value-added series to the three sector level using the earlier taxonomy. Like raw employment data, the value-added sectoral shares computed using the UNSD data also feature spurious jumps. Therefore, I refine the raw series before using it in the analysis (see data appendix for details). Next, I source data on the sectoral exports and imports shares from a different source, which I use to estimate the net export shares p . I exploit the Atlas of Economic Complexity (Center for International Development, 2018) database that reports a country’s exports and imports across 11 NN<sup>�</sup> sectors. Agriculture and services trade data in the database is presented at the aggregate level and is consistent with ISIC Rev. 3.1. The database reports the industrial data disaggregated into nine subsectors, and I sum across them to construct sectoral exports and imports. Once I have the estimates of p and p (and the estimates of and ) from the above steps, I employ equation (5) to recover final consumption shares VV<sup>�</sup> NN<sup>�</sup> p . BBpp MMpp **Labor market wedges (** p **):** Data on value-added shares CC<sup>�</sup> p and employment shares p allows expressing sector 's labor productivity relative to any arbitrary sector . As agriculture is almost always TT VV<sup>�</sup> LL the sector with the lowest value-added per worker, I measure productivity in industry and services ii jj relative to agriculture and set . Specifically, the wedge applicable to sector in period equals the ratio of its value-added per worker aappaa to agricultural labor productivity iippaa . ττ = 0 ∀ pp, tt ττ ii **Labor share of income** ) **and sectoral linkages** iippaa ) **:** I obtain the labor share of value-added aappaa tt ww ww at the sectoral level from the Global Trade Analysis Project (GTAP) database (pp pp Center for Global (BB (MM Trade Analysis, 2015). The GTAP data are available for three overlapping years over which I perform the quantitative analysis using the time-series on employment, consumption, and trade shares. I estimate the labor share of value-added for each of the three years, the average of which serves as the benchmark labor share for the entire period under analysis.

10
Unfortunately, the input-output data for the Central American economies are not usually available. Barring Costa Rica, for which the input-output tables are available in the OECD database (OECD, 2017), I couldn’t find such data for the other countries. To overcome this challenge, I use the average intermediate shares from eight regional countries (including Costa Rica). In the data appendix, I show that these shares of regional economies lie within close range. Therefore, their average provides a reasonable benchmark for the Central American countries.

Table 1 reports the country-wise coverage of cleaned data obtained from various sources. Column (6) shows the effective period of analysis for each country, which corresponds to the overlapping years in columns (2)–(5).

||**T**|**able 1: Countr**<br>**Value**|**y-wise time co**<br>**Trade**|**verage **|**Effective**|
|---|---|---|---|---|---|
|**Series**|**Employment**<br>**shares**|**added**<br>**shares**|**shares**<br>**(aggregate)**|**Sectoral trade**<br>**shares**|**period**<br>**of coverage **|
|||||**Atlas of**||
|**Source**|**ILO**|**UNSD**|**UNSD**|**economic**<br>**complexity**||
|(1)|(2)|(3)|(4)|(5)|(6)|
|Costa Rica|1980-2017|1976-2017|1976-2017|1995-2016|1995-2016|
|Guatemala|1981-2017|2001-2017|2001-2017|1995-2016|2001-2016|
|Honduras|1991-2017|1970-2017|1970-2017|1995-2016|1995-2016|
|Nicaragua|2003-2012|1987-2017|1987-2017|1995-2016|2003-2012|
|Panama|1970-2017|1970-2017|1970-2017|1995-2016|1999-2016*|
|El||||||
|Salvador|1978-2017|1990-2017|1990-2017|1995-2016|1995-2016|

*1995-1998 is dropped for Panama as the imputed agricultural consumption shares using the linkages are negative.

# **4   Accounting for trends in industrial employment**

This section quantifies the relative importance of the three macro variables p p p � in accounting for the change in industrial shares. Analyzing inter-temporal changes is not only crucial �CC<sup>�</sup> , NN<sup>�</sup> , TT for countries that observe quantitative shifts in the sectoral distribution of the workforce. Significant 11
(I ) (4 = F(C

> = [Al

))

|<sup>=F</sup> (C ,N |
share. Such a situation implies that the negative effect of that particular channel is more than offset by the extension caused by the others.<sup>7</sup> Let me first look at the regional averages reported in the last row. I find that changes in the patterns of final consumption and trade created grounds for an industrial expansion. Shifts in trade shares have supported an extension of the industrial employment allocation and imply an average increase of 2pp. However, the amplifying force generated by the two channels is more than offset by that affected by shifts in labor market wedges. The exercise shows that the overtime changes in wedges, on average, shaved off 4pp from the industrial employment share.

Now, let's see how the inference drawn at the regional level compares with the experience of the constituent economies. Consistent with the regional finding, wedges generate contractionary pressure in each country. Still, its impact varies substantially. Implied shrinkage is comparatively much higher in Panama (8.7pp) and is also quantitatively above the regional average in El Salvador (5.2pp). On the other hand, the channel's impact is milder in Costa Rica and Honduras. Like for the region, the channel accounts for more than the total contraction in industrial share in three of the five economies that underwent deindustrialization and can explain 85 percent of the decline in Honduras. In contrast, though aiding the fall, it accounts for less than 15 percent of the 6.2pp decline in Costa Rica.

**Table 2: Actual and implied change in industrial shares**

||||**Actual**||**Implied c**|**hange (pp)**<br>**Final**|**Labor**|
|---|---|---|---|---|---|---|---|
|**Country**|**Initial**<br>**year**|**Final**<br>**year**|<br>**change**<br>**(pp)**|**Final**<br>**cons.**|**Trade**|<br>**cons. &**<br>**trade**|<br>**market**<br>**wedge**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|CRI|1995|2016|-6.52|-8.26|4.46|-6.17|-0.91|
|GTM|2001|2016|-3.18|0.48|1.79|0.41|-3.74|
|HND|1995|2016|-1.57|-2.69|1.17|-0.80|-1.34|
|NIC|2003|2012|-1.81|1.90|-0.51|2.79|-4.14|
|PAN|1999|2016|1.14|13.13|5.58|13.40|-8.73|
|SLV|1995|2016|-3.30|0.51|-0.18|2.05|-5.24|
|Average|||-2.54|0.85|2.05|1.95|-4.02|

Figures in columns (4)-(7) indicate the change in industrial employment shares when the variables pertaining to a factor listed in the column is counterfactually changed to the level observed in the final year, keeping the value of all the other factors fixed at the initial year. All figures in percentage points (pp).

> 7 For example, changes in wedges caused considerable downward pressure on industrial shares in Panama. Yet, the tailwinds provided by other channels ensured that the country saw a modest net expansion. In other words, if there were no shifts in consumption and trade shares, Panama would have experienced significant deindustrialization.

13
The qualitative impact of shifts in the other two channels is not as consistent. Changes in trading patterns created headwinds in four economies while fostering deindustrialization in Nicaragua and El Salvador. Still, the contractionary pressure it generates is quantitatively benign, with an implied decline of 0.2-0.5pp. On the other hand, when assisting industrial expansion, its average impact stands at 3.2pp, ranging from 4.5pp in Costa Rica to 1.2pp in Honduras.

Final consumption too has a varied qualitative impact across the regional countries. The channel aided industrialization in four economies, but an allocation of final consumption away from industrial goods led to a labor outflow from the sector in Costa Rica and Honduras. The change in consumption patterns implies a decline of 8.3 and 2.7pp for the two economies, respectively. Final consumption is the dominant factor in accounting for deindustrialization in these two countries. The channel's expansionary impact ranges from being relatively small in Guatemala and El Salvador (0.5pp) to being considerably massive in Panama (13.1pp).

In summary, the above analysis yields the following three findings. First, shifts in labor market wedges are critical to understanding the deindustrialization process in Central America. The channel exerted a contractionary force in each regional economy in the sample. Second, the trade channel is not effective in explaining the decline in industrial employment share. Shifts in trading patterns have, in fact, aided expansion in four of the six countries. Even when providing headwinds, its impact is overshadowed by that of wedges. Third, final consumption bears a varied influence across countries. The channel is the principal factor in accounting for the deindustrialization in Costa Rica and Honduras. Still, its evolution over the years has fostered industrial share expansion in other regional economies.

# **5   Aggregate implications of labor market wedges**

The quantitative analysis in the previous section shows that changes in labor market wedges have been a critical force behind the region's deindustrialization. The presence of wedges indicates barriers to the efficient flow of labor, which restricts an equalization of nominal labor productivities across sectors. As these wedges are quantitatively considerable, it is often argued that the efficiency losses they induce are significant too. In this section, I take a deeper look into the evolution of these wedges and use a resource allocation model to quantify their impact on aggregate output.

14
The notations used in this section are consistent with those used previously. A stand-in firm in each sector (in country during period ) produces output employing labor inputs according to a decreasing returns-to-scale technology given by jjppaa jjppaa jj pp tt YY LL j (9) ββ where YYjjppaa = AAjjppaa∈(0,1]LLjjppaa is the labor share of the value-added and is the sector-specific TFP. jjpp jjppaa Consistent with the accounting framework, the sector-specific labor market wedges appear as a tax ββ AA on per-unit labor employed by the stand-in firm (Hsieh & Klenow, 2009). The firm maximizes profit each period taking the wage rate jjppaa as given. Therefore, the firm's problem is ττ ppaa max ww j ≥0<sup>PPjjppaa</sup> vv where is the sectoral price.8 LL jj YYjjppaa −�1 + ττjjppaa�wwppaaLLjjppaa vv jjppaa A representative household characterizes the consumption side. It possesses a unit of labor that it PP supplies inelastically to earn wages each period. The earned wages, together with firm profits, are spent to maximize utility. As the firm's, the household's utility maximization problem is also static. The household preferences are non-homothetic in sectoral goods and follow a CES preference structure (Comin _et al._ , 2021) given below

> 8 I add the super-script to differentiate the value-added price of a sector from the final output price , which I introduced in the accounting framework. jjppaa vv PP

15
The aggregate output in this economy in any period is equal to the aggregate household consumption . Market clearing requires that the aggregate output ppaa in any period equals the YY tt aggregate household consumption ppaa . As the focus of the analysis is on resource allocation, I ppaa CC YY normalize the labor resource to unity (ppaa ). The two exogenous forces that affect aggregate CC output are sectoral TFP and labor market wedges ppaa . LL = 1 ∀ pp, tt jjppaa jjppaa **5.1 Model calibration** AA ττ The preference function in equation (10) contains four elasticities together with country-specific sector-preferences weights Ω . In addition, the model entails three sets of production parameters– , and . The first two vary over sectors, countries, and time, and like in the accounting jjpp exercise, the labor share of value-added is country-specific but time-invariant. jjppaa jjppaa jjpp AA , ττ ββ **Preference parameters ( )** jj jjpp I borrow the estimates of elasticities from Comin et al. σσ, εε , ΩΩ Comin _et al._ (2021), which uses panel data , which uses panel data Comin _et al._ (2021), which uses panel data , which uses panel data consisting of 39 countries. The substitution elasticity equals 0.50, and the sector-specific incomeelasticities and are set to 0.11 and 1.21, respectively. The industrial sector serves as the reference σσ sector for measuring income effects, and its income elasticity is normalized to unity (aa ss = 1).<sup>9</sup> εε εε mm I estimate the country-specific sector weights by matching the sectoral allocation of value-added in εε the initial year for each country. The first-order conditions from the household optimization problem yield the following relationship

> 9 Consistent with the value-added production functions employed in this paper, the elasticity estimates correspond to an estimation based on the value-added by sectors and not their final output.

16
The sectoral labor shares are taken from the GTAP database (Center for Global Trade Analysis, 2015) and are discussed in more detail previously.

# **5.2   Aggregate effects of changes in wedges over time**

Before quantifying the aggregate implications of wedges, I briefly show how they have evolved over the years. Have they increased, decreased, or been cyclical? Note that the estimates of take both positive and negative values, both of which imply inefficiency. Therefore, in principle, I need to jjppaa ττ transform the labor market wedges to make them monotonic with inefficiency. However, the 17
empirical estimates of in each Central American economy for the sample years never lie below zero, making such a modification unnecessary.jjppaa<sup>10</sup> ττ The duration over which I can estimate the labor market wedges is longer than the one considered earlier (column (6), Table 1). The reason is that the aggregate analysis abstracts from trade and thus no longer requires data on trade shares (columns (4) and (5), Table 1). As a result, the overlap in columns (2) and (3) of Table 1 provides the period for which I can conduct the aggregate analysis. Figure 2 shows the behavior of wedges in each regional economy over time. The circles in blue and the squares in red correspond to wedges in industry and services, respectively. Recall that the wedges in non-agricultural sectors are estimated relative to those in agriculture . The intertemporal evolution of labor market wedges offers several insights. aappaa �ττ = 0, ∀pp, tt� First, as already noted, the estimates are always greater than zero, implying that labor faces obstacles in moving out of agriculture. But these barriers are not specific to the industrial sector which keeps it from expanding. The channel also constrains the extension of services. In four of the six Central American economies, wedges in the services sector are, in fact, higher than in the industry.

Finally, there is no evidence that the economies are becoming more efficient over time. In four countries, wedges in both sectors in the most recent year are higher relative to the earliest year for which data are available. Industry in Costa Rica and services in Nicaragua offer the only bright spots, where wedges in the final year are below the initial year. Moreover, in many cases, evolution does not follow a secular trend. Panama appears the most striking specimen of this phenomenon. Beginning from the mid-1980s, wedges in both industry and services contracted sharply till the early 2000s. But they rose sharply thereafter to finish above the earlier peaks. These distinct reversals create pivot points that may help uncover the root causes of labor market inefficiencies, including those related to policy and regulations.

> 10 Chen _et al._ (2021) propose one such transformation.

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Second, comparing countries, I find that wedges (in both sectors) are significantly higher in Guatemala and Panama. The labor markets in Costa Rica and Nicaragua are least distorted, even though the period covered for the latter is considerably restricted.

Finally, there is no evidence that the economies are becoming efficient over time. In four countries, wedges in both sectors in the most recent year are higher relative to the earliest year for which data are available. Industry in Costa Rica and services in Nicaragua offers the only bright spot, where wedges in the final year are below the initial year. Moreover, in many cases, evolution does not follow a secular trend. Panama appears the most striking specimen of this phenomenon. Beginning from the mid-1980s, wedges in both industry and services contracted sharply till the early 2000s. But they rose sharply thereafter to finish above the earlier peaks. These distinct reversals create pivot points that may help uncover the root causes of labor market inefficiencies, including those related to policy and regulations.

Now, let me return to the task of quantifying the aggregate impact of changes in labor wedges. The two exogenous elements– TFP � and wedges �, jointly determine the aggregate output of country in year . Let (. ) be the function that maps the two exogenous factors to jjppaa jjppaa . The ppaa �AA �ττ YY quantitative exercise estimates the output variation pp 1 in the most recent year 1 when I replace ppaa pp tt YY YY the level of wedges from those in the earliest year 0 ppaa ΔYY tt <u>p�� jp</u> 1�, � <u>jp</u> 0�� tt = 1 paa1 paa1�� −1 (16) YY AA ττ ppaa A negative ΔYY pp jjppaa1 implies that aggregate output in the most recent year declines if the economy is jjppaa YY ��AA �, �ττ subjected to past wedges, signifying an increase in allocative efficiency. ppaa ΔYY Panel A in Table 3 reports the results of the exercise. Note that the most recent (column (1)) and the earliest year (column (2)) varies across countries due to differences in coverage. First, let's consider column (3), which shows the percent change in output when I replace wedges in both non-agricultural sectors. Except for Costa Rica, all other regional economies experience a positive Δ 1, i.e., a decline in allocative efficiency. This finding is not surprising given a general rise in wedges (Figure 2ppaa ). Still, the aggregate cost of this rise in inefficiency is benign. The counterfactual output increase ranges from 0.44 percent in El Salvador to just above a percent in Panama. At the same time, the efficiency gains in Costa Rica are marginal too. Output declines by about a fifth of a percent when I use wedges from 1980.

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**Table 3: Impact of changes in wedges on aggregate output**

||**iil**||**Chan**<br>**Wed**<br>|**ge in outpu**<br>**ges change**|**t (%)**<br>**d in**|
|---|---|---|---|---|---|
|**Country**|**Inta**<br>**wedges**<br>**from**|**Wedges**<br>**replaced**<br>**from**|**Industry**<br>**&**<br>**services**|**Industry**<br>**only**|**Services**<br>**only**|
||(1)|(2)|(3)|(4)|(5)|
||**Panel**<br>|**A: Longest**<br>|**possible peri**<br>|**od at countr**<br>|**y level**<br>|
|CRI|2017|1980|-0.21|-0.09|-0.07|
|GTM|2017|2001|0.71|0.12|0.48|
|HND|2017|1991|0.85|-0.02|0.72|
|NIC|2012|2003|0.27|0.44|-0.12|
|PAN|2017|1970|1.03|-0.02|0.25|
|SLV|2017|1990|0.44|-0.43|0.45|
|Average|||.|.|.|
||**Pa**|**nel B: Longe**|**r uniform pe**|**riod (1991-2**|**017)**|
|CRI|2017|1991|0.04|0.03|0.00|
|GTM|2017|1991|.|.|.|
|HND|2017|1991|0.85|-0.02|0.72|
|NIC|2017|1991|.|.|.|
|PAN|2017|1991|0.95|-0.35|0.25|
|SLV|2017|1991|0.28|-0.26|0.32|
|Average|||0.53|-0.15|0.32|
||**Pa**|**nel C: Shorte**|**r uniform pe**|**riod (2003-2 **|**012)**|
|CRI|2012|2003|0.06|0.08|-0.05|
|GTM|2012|2003|0.64|0.15|0.43|
|HND|2012|2003|-0.48|-0.03|-0.46|
|NIC|2012|2003|0.27|0.44|-0.12|
|PAN|2012|2003|0.87|-1.97|0.45|
|SLV|2012|2003|-0.08|0.01|-0.09|
|Average<br>|||0.21<br>|-0.22<br>|0.03<br>|
|<br>Columns (3)-(<br>(column (1)) a|5) report the<br>re replaced by|output change<br>those from yea|ΔYpa1(in %) w<br>rt0(column (2|hen wedges in<br>)).|the yeart1|

The setup also allows me to quantify the efficiency implications of changes in the industrial and services wedges individually. Column (4) reports the output change when I alter the former, leaving the latter unchanged at the final year levels. Unlike the combined impact in column (3), the change in industrial wedges implies an output contraction in Honduras, Panama, and El Salvador. Hence, the shift in industrial wedges over time has contributed to output expansion. The individual component 21
is in line with the combined effect in Costa Rica, Guatemala, and Nicaragua. Industrial wedges account for around 15-45 percent of the aggregate change in column (3) for the former two regional economies. In contrast, the counterfactual rise in output in Nicaragua is 1.65 times larger.

Column (5) sheds light on the economic consequences of changes in services wedges. Supplanting past wedges increases output in four of the six regional economies. These changes account for anywhere from 25 percent (in Panama) to almost the total effect (in El Salvador) reported in column (3). The intertemporal evolution of service wedges in Costa Rica and Nicaragua aid economic efficiency 1 < 0� and accounts for a third of the combined effect in the former. The cross-country comparison in ppaa Panel A is somewhat inconsistent due to the variation in periods. is somewhat inconsistent due to the variation in periods. �ΔYY Panel A is somewhat inconsistent due to the variation in periods. is somewhat inconsistent due to the variation in periods. The exercise considers changes in wedges over around half a century in Panama. In sharp contrast, it examines variations over less than a decade in Nicaragua. This enormous gap in duration can produce a corresponding immense gap in wedges across countries if the wedges evolve monotonically. Moreover, the factors that affect the wedges in any year might also have a regional or global component. Because of these two reasons, a cross-country comparison should utilize a uniform period.

I consider two such uniform periods. In Panels B and C, I analyze the changes in labor market wedges during 1991-2017 and 2003-2012, respectively. Both panels have a drawback and an advantage. The former covers a longer horizon (>25 years) at the cost of ignoring Guatemala and Nicaragua, for which data are unavailable. Panel C presents the opposite scrutiny by including all countries but studying evolution over a much shorter period.

Foremost, Panels B and C confirm that the aggregate consequences of changes in labor market wedges are marginal. The maximum output expansion, which signifies a rising inefficiency, is less than a percent (column (3) in Panel B, Panama). Similarly, the maximum contraction remains modest across the two panels and is around 2% of the total output (column (4) in Panel C, Panama).

Next, like in the topmost panel, the overtime variation has usually led to a rise in inefficiency. Output change Delta is positive for all four and four of the six regional economies in Panels B and C, with aggregate costs being most pronounced in Panama.

Finally, the wedges in industry and services often bear qualitatively differential effect, which also changes across the two panels. In Panel B, where I consider a longer horizon, the changes in services wedge always imply an expansion. On the other hand, the aggregate output increases in each country except Costa Rica when I supplant the past industrial wedges keeping those in services fixed at 2017 22
<u>(A__},{e__ 03)</u> of} D <u>{A__},{e__9})</u> { } 3
stands at 3.5 pp, ranging from 1.3 pp in El Salvador to 6.6 pp in Nicaragua. Still, the efficiency gains from this industrialization remain trivial. The average increase is below 2 percent, with the figures for individual countries lying between 0.3 to 3.2 percent.

**Table 4: Aggregate impact of wedge-led industrialization**

|||**Change in**<br>**employm**<br>**(p**|**industrial**<br>**ent share**<br>**p)**|**Change i**<br>**(**|**n output**<br>**%)**|
|---|---|---|---|---|---|
|||**Wedges e**<br>**i**<br>|**liminated**<br>**n**|**Wedges e**<br>**i**<br>|**liminated**<br>**n**|
||**Initial**|**Industry**||**Industry**||
|**Country**|**wedges**<br>**from**|<br>**&**<br>**services**|**Industry**<br>**only**|<br>**&**<br>**services**|**Industry**<br>**only**|
||(1)|(2)|(3)|(4)|(5)|
|CRI|2017|3.24|10.88|0.30|-0.33|
|GTM|2017|3.84|19.25|3.16|-2.37|
|HND|2017|1.65|14.02|2.23|-1.64|
|NIC|2012|6.64|13.47|1.63|0.07|
|PAN|2017|4.42|32.34|2.48|-6.70|
|SLV|2017|1.33|18.31|1.65|-2.59|
|Average<br><br>||3.52<br><br>|18.04<br><br>|1.91<br><br><br>|-2.26<br>|
|Columns (2) an<br>wedges in the<br>ΔYpa1<br>I(in %) ar<br>|d (3) report th<br>yeart1(colum<br>e reported in c<br>|e change in indu<br>n (1)) are elimi<br>olumns (4) and<br>|strial employmen<br>nated. The corres<br>(5), respectively.<br>|t shareΔlpa1<br>I<br> <br>ponding chan<br>|(in pp) when<br>ge in output<br>|

As services wedges also lie above zero, eliminating them causes labor to move into the sector. Some of these resource gains occur at the cost of the industrial sector. Thus, industrial employment can expand more than in column (2) if I only eliminate the industrial wedges and keep those in services fixed. Column (4) reports the industrial share gain from this selective removal of wedges. Industrial employment share grows by about 18 pp on average, more than five times the expansion borne by a non-selective treatment. Each regional economy experiences robust double-digit gain, with Panama outperforming the others.

But does this massive industrialization entail equally healthy economic growth? The figures in column (5) imply that it isn't so. Instead of generating efficiency gains, this selective elimination affects a contraction in five of the six economies. This finding contrasts with the previous result in column (4), where each country saw its output grow. The reason is that a selective elimination still leaves the economy distorted. Removing wedges in both industry and services leads to complete efficiency at which the output attains a maximum. But the wedges in either sector do not individually bear an 24
inverse relationship with economic efficiency. In other words, the distorted economy with no industrial wedge is not necessarily efficient compared to an economy with an industrial wedge with the same level of services wedge. As it turns out, in this case, the distorted regional economies with only services wedge are relatively more inefficient than the initial distorted states.

In conclusion, eliminating labor market wedges can lead to quantitative expansion in industrial employment share. However, such industrialization will not generate substantial output growth. Furthermore, selective removal of industrial wedges can propagate inefficiency rather than curtailing it.

# **6   Discussion**

Both developed and developing countries are concerned about industrial progress, and the sector's expansion is often a policy target in many growth plans. The paper compiles data to study the industrial trends in six small Central American economies. It isolates labor market wedges as the dominant channel affecting deindustrialization. The shifts in wedges over the recent decades have increased allocative inefficiency. Therefore, a rollback in wedges to past levels can foster industrial and aggregate growth. So, does it make sense for Central America to adopt a policy that promotes industrialization through reductions in labor market wedges? Unfortunately, there are many risks associated with such an approach.

First, the results in Section 5.3 outline the pitfalls of taking a sector-focused approach that targets industrial barriers to promote industrialization-led growth. While such a policy can cause a considerable expansion in industrial employment, it can also lead the economy to a more distorted state, causing a decline in output. Additionally, the results for Costa Rica and Panama show that this risk remains even when barriers in industry outweigh those in services. Therefore, only targeting barriers faced by the construction and semiconductor sectors in India and the US, respectively, even if they are most adversely affected, does not guarantee a rise in output.

Second, the wedges can originate from several sources, making their identification extremely difficult. This identification problem can make even a sector-neutral approach a risky proposition. For example, consider a sectoral move that involves migrating to a new location. The resource costs of relocation are sometimes sizable (Poncet, 2006; Bayer & Juessen, 2012), and borrowing constraints can dissuade labor from moving (Bryan _et al._ , 2014). Suspecting such a scenario, one can allocate funds for financial market interventions that make borrowing easier. However, it could also be the case that the borrowing constraints are not binding. Instead, some non-monetary factors are in fact responsible for 25
labor immobility.<sup>12</sup> For instance, a move might result in welfare loss stemming from differences in language, culture, laws, etc. (Belot & Everdeen, 2012). Therefore, a misidentification of actual cause might lead to interventions that not only do not reduce barriers but also waste scarce resources. On the other hand, even if the policy successfully carries out the difficult tasks of identifying the sources and implementing necessary interventions, the expected efficiency gains remain meager.

Given the risks associated with the wedge-driven industrialization policies, perhaps a better strategy will be to concentrate on productivity growth, which certainly is not easy to achieve. But given the marginal gains from the barrier mitigating policies that promote industrial expansion, it might be prudent to pursue this difficult task as it has a direct and relatively more significant impact on growth. At the same time, achieving industrial productivity improvements may not always be the most straightforward option. Because productivity in the non-industrial sectors, especially services, lies far behind the global frontier in many countries, it might be easier to achieve productivity growth in such sectors.<sup>13</sup> Finally, marginal changes in aggregate output do not imply that the welfare implications are negligible too. Severe distributional consequences can accompany such benign aggregate effects (Fajgelbaum _et al._ , 2020). Moreover, structural transformation bears a close relationship with regional convergence, income inequality, informality, and business cycles. Thus, a deeper evaluation of welfare implications requires exploring how deindustrialization interacts with these aspects of the economy. Additionally, an industrial decline can lead to political instability and a rise in crime and violence (Rodrik, 2016; Caceres, 2018). Such issues are fundamental for general well-being and need thorough consideration.

> 12 The above two factors are only among a handful of potential causes. A move often damages ties to local social networks, resulting in the loss of the associated benefits (Munshi & Rosenzweig, 2016). To the extent occupational choices extend to sectors, the wedges can arise because of gender-specific barriers faced by women in pursuing certain jobs (Sinha, 2020). A weak institutional environment can also build barriers to an efficient flow of resources (Sinha, 2021). Finally, differences in human capital and hours worked can partially explain these wedges Gollin _et al._ , 2013; Lagakos & Waugh, 2013; Young, 2013).

> 13 Such productivity catch-up may not always lead to industrialization. For example, services productivity in Latin America lies far behind that of the industry, making faster productivity growth in the former relatively easier (Beylis _et al._ , 2020). Though aiding growth, such a development will shift the comparative advantage away from the industry, creating grounds for deindustrialization.

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