metadata: authors: 0: author: Bastos,Paulo S. R.
1: author: Monteiro,Natalia P.
2: author: Straume,Odd Rune
count: World
docna: 0: docna: The Division of Revenues from Unexpected
            Demand Shocks
docty: Policy Research Working Paper
abstracts: cdata!: This paper exploits gaps between
            observed and recently forecasted Gross Domestic Product
            growth in export destinations to estimate the effects of
            unexpected demand shocks on worker compensation. Using
            employer-employee panel data, the paper finds that the
            revenues from these demand shocks are partly transmitted to
            workers in the form of higher average wages, especially
            close to the top of the within-firm wage distribution. These
            wage responses occur both in the form of higher overtime
            payment and base wage increases. The findings also show that
            there are significant increases in bonus-related pay in
            firms managed by high-skilled managers, and the unequal
            average distribution of unexpected revenues is also mainly
            driven by wage effects in the same subset of firms. This
            suggests that the way in which revenues from unexpected
            demand shocks are transmitted to workers is significantly
            related to managerial capabilities.
display_title: The Division of Revenues from
            Unexpected Demand Shocks
listing_relative_url: /research/2024/09/34394095/division-revenues-unexpected-demand-shocks
topicv3: Economic Growth m1326241 471,Trade m1326924 698,Jobs m1503931 2670
publishtoextweb_dt: 2024-09-23T00:00:00Z
dois: 10.1596/1813-9450-10925
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 74
author_keys: 0: author_key: 1210378
1: author_key: NA
2: author_key: NA
model_extractions: Policy Research Working Paper 10925

# The Division of Revenues from Unexpected Demand Shocks

_Paulo Bastos Natalia P. Monteiro Odd Rune Straume_ Development Economics Development Research Group September 2024
Policy Research Working Paper 10925

# **Abstract**

This paper exploits gaps between observed and recently forecasted Gross Domestic Product growth in export destinations to estimate the effects of unexpected demand shocks on worker compensation. Using employer-employee panel data, the paper finds that the revenues from these demand shocks are partly transmitted to workers in the form of higher average wages, especially close to the top of the within-firm wage distribution. These wage responses occur both in the form of higher overtime payment and base wage increases. The findings also show that there are significant increases in bonus-related pay in firms managed by highskilled managers, and the unequal average distribution of unexpected revenues is also mainly driven by wage effects in the same subset of firms. This suggests that the way in which revenues from unexpected demand shocks are transmitted to workers is significantly related to managerial capabilities.

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 authors may be contacted at pbastos@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
# **The Division of Revenues from Unexpected Demand**

# **Shocks**<sup>_∗_</sup>

Paulo Bastos<sup>_†_</sup> Natalia P. Monteiro<sup>_‡_</sup> Odd Rune Straume<sup>_§_</sup>

**Keywords** : Unexpected demand shocks, firm performance, wages, rent sharing, managers. **JEL Classification** : J2, J6, F16, F66

> _∗_ This paper is financed by National Funds of the FCT – Portuguese Foundation for Science and Technology within the project UIDB/03182/2020. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, those of the Executive Directors of the World Bank, or those of the governments they represent. We remain responsible for any errors.

> _†_ Development Research Group, World Bank; CEPR; and REM/UECE, pbastos@worldbank.org.

> _‡_ Department of Economics/NIPE, University of Minho, n.monteiro@eeg.uminho.pt

> _§_ Department of Economics/NIPE, University of Minho, o.r.straume@eeg.uminho.pt
# **1 Introduction**

Understanding the mechanisms by which firm-level revenue shocks impact wage setting is important for explaining the drivers of wage differentials and wage premia across firms and workers, how firms adjust their wage policies to absorb shocks, as well as the role played by wage rigidities in the international transmission of business cycles. Naturally, appreciation is growing in a number of fields—macroeconomics, labor, industrial organization, as well as international trade—for careful empirical work seeking to improve our understanding of these mechanisms.<sup>1</sup> However, research has faced two important challenges. First, it has been difficult to quantify in a systematic but precise way the unexpected component of demand shocks at the firm-level, while distinguishing it from anticipated changes in market conditions. This distinction is important for establishing a causal link between changes in demand and changes in wages. Second, studying the mechanisms by which these unexpected demand shocks impact wage setting requires detailed information on the components of worker compensation.

In this paper, we estimate the effects of unexpected demand shocks on the components of worker compensation. We also explore if and how the wage effects of such shocks are related to managerial skill. We propose a new methodology to identify the unexpected component of demand shocks at the firm-level, exploiting forecast errors in the GDP growth of export markets. In each destination, the unexpected component of demand shocks is measured as the difference between the GDP growth actually observed and recent forecasts of the International Monetary Fund. We then aggregate these shocks at the firm-year level, weighting by the initial share of destinations in firms’ total sales. Since firms initially served different destinations to a varying degree, they were differentially exposed to these unexpected demand shocks across markets. We show that these unexpected shocks tend to be uncorrelated with initial firm attributes, and account for firm fixed effects throughout the estimation.

The empirical analysis draws on an unusually rich collection of data sets for the population of private sector firms operating in Portugal during 2006-2018. We combine a yearly firm census with information on export transactions and employer-employee panel data. We also use an auxiliary data set from a management survey for 2016, covering a subset of these firms, in order to establish a correlation between managerial skill and observable management practices. The employer-employee data allow us to distinguish between effects on each of the various components of worker compensation, including base wages, overtime pay, and other regular and

> 1Recent contributions to this literature, reviewed in more detail below, include Card, Cardoso, Heining, and Kline (2018), Kline, Petkova, Williams, and Zidar (2019), Aghion, Akcigit, Hyytinen, and Toivanen (2018), Grigsby, Hurst, and Yildirmaz (2021), and Fr ́ıas, Kaplan, Verhoogen, and Alfaro-Serrano (2024).

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irregular components of compensation. In addition, the information on schooling and detailed occupation makes it possible to examine heterogeneity in the wage effects of revenue shocks across workers in different positions of the wage distribution, and how these effects are shaped by the skills of top executives.

In the empirical analysis, we find that unexpected demand shocks have a significant impact on sales, employment, investment and average wages. Using employer-employee panel data, we find that firm revenue windfalls, in the form of unexpected demand shocks, are partly transmitted to workers in the form of higher average wages, but in a highly unequal way, with most of the wage increases occurring close to the top of the within-firm wage distribution. Interestingly, firm adjustments are much larger for negative demand shocks. More specifically, a demand increase leads to higher sales, exports, investment, employment and wages when actual demand falls short of expectations, while we do not find similar statistically significant responses to demand increases above the expected level. This suggests the presence of short-run capacity constraints that make output harder to adjust upwards than downwards, and our results are consistent with firms setting short-run capacity according to expected demand.

Although parts of the aforementioned wage increases consist of increased overtime payments in exchange for more hours worked, we also find that unexpected demand shocks lead to significant increases in base wages and other wage components. This suggests that that firms tend to share the unexpected changes in revenue with their workers, which is inconsistent with perfectly competitive labor markets, and also suggests that firms have some degree of product market power. Our analysis reveals indications of several different channels of rent sharing. First, we find that increases in base wages are much stronger for firms with firm-level (as opposed to more centralized) wage agreements, which we take as an indication of collective bargaining power. Second, we also find somewhat stronger and more significant wage effects for longer-tenured workers, which might be caused by hiring or training costs that give individual bargaining power to this type of workers. Finally, we also find evidence of explicit profit-sharing arrangements whereby revenue windfalls are transmitted to workers through increases in performance-based payments. However, this seems to occur only in firms with high-skilled managers. This category of firms is also found to be the main driver of the aforementioned unequal rent distribution across workers within the firm, thus highlighting the importance of managerial skill.

In addition to the literature mentioned above, this paper relates to several strands of existing research. A large body of literature seeks to quantify and explain wage differentials across firms.<sup>2</sup>

> 2Influential contributions include, among many others, Abowd, Kramarz, and Margolis (1999) (henceforth AKM), Card, Heining, and Kline (2013), Card, Cardoso, and Kline (2015) and Song, Price, Guvenen, Bloom, and von Wachter (2019).

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In a recent review of this literature, Card, Cardoso, Heining, and Kline (2018) conclude that more research is needed applying (quasi)-experimental research designs that convincingly tease out the mechanisms through which firm shocks are transmitted to workers. A few recent papers have contributed to fill this gap. Exploiting an unexpected devaluation of the Mexican currency, Fr ́ıas, Kaplan, Verhoogen, and Alfaro-Serrano (2024) carefully estimate how differential export shocks across firms impact wage premia and worker composition.<sup>3</sup> The results reveal that exports have a significant positive effect on wage premia, and that the effect on wage premia accounts for essentially all of the medium-term effect of exporting on plant-average wages.<sup>4</sup> Kline, Petkova, Williams, and Zidar (2019) estimate the effects of innovation on rent-sharing using employer-employee data and rich patent information for the United States. Under the identifying assumption that the U.S. Patent Office’s initial decision on a patent application is as good as random (conditional on observable attributes of the application and the firm), the authors find that each patent-induced additional dollar of operating surplus yields a 29-cent rise in a firm’s wage bill. Also focusing on patents, but using Finish data and a matching estimator, Aghion, Akcigit, Hyytinen, and Toivanen (2018) find that patents are positively associated with earnings of the co-workers of inventors.

Our paper makes several contributions to this literature. First, the employer-employee data we use make it possible to examine effects of unexpected demand shocks on the various components of worker compensation (bases wages, overtime and other components of pay). Recent work by Grigsby, Hurst, and Yildirmaz (2021) convincingly argues that this distinction is important for examining and understanding the extent of wage rigidity over the business cycle.<sup>5</sup> Second, we exploit the role of top managers’ skills—which we show are correlated with management practices observed in data—in shaping these effects. Although a growing body of evidence reveals that manager attributes are important for management practices and firm performance (Bertrand and Mullainathan, 2003; Bertrand and Schoar, 2003; Bastos and Monteiro, 2011; Bender, Bloom, Card, Van-Reenen, and Wolter, 2018), there is little evidence on whether and how managers matter for how exogenous firm shocks are transmitted to workers. Finally, the

> 3The broader literature on firm and labor market responses to exchange rate movements includes Revenga (1992), Bertrand (2004), Verhoogen (2008), Brambilla, Lederman, and Porto (2012), Amiti, Itskhoki, and Konings (2014) and Bastos, Silva, and Verhoogen (2018), among others. Most closely related to this paper, Araujo and Paz (2014) and Macis and Schivardi (2016) use unexpected devaluations of the Italian and Brazilian currencies, respectively, to examine effects on rent sharing and workforce composition.

> 4A related strand of work examines the relationship between exports and wages, but does not exploit quasiexperimental variation in exports, including Schank, Schnabel, and Wagner (2007), Munch and Skaksen (2008), Baumgarten (2013), Irarrazabal, Moxnes, and Ulltveit-Moe (2013), Helpman, Itskhoki, Muendler, and Redding (2017).

> 5Using administrative payroll data from the largest U.S. payroll processing company, they provide descriptive evidence that firms use base wages to cyclically adjust the marginal cost of their workers, although about one third of workers receive no base wage change year over year.

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methodology we propose for identifying firm-level demand shocks has some advantages relative to the shocks previously exploited. Although exchange rate movements and innovations leading to patents are difficult to forecast, variation over time is likely to reflect in part economic fundamentals and policy choices. These developments can be monitored and analyzed by firms, which may therefore partly respond in anticipation.<sup>6</sup> Identifying the effects of unexpected exchange rate movements is further complicated by the fact that they are subject to incomplete passthrough, which may be influenced by the currency in which trade transactions are denominated and vary across firms and markets (Amiti, Itskhoki, and Konings, 2014; Gopinath, Boz, Diez, Gourinchas, and Plagborg-Moller, 2020).<sup>7</sup> Unexpected GDP shocks in destinations are arguably less subject to this concern.<sup>8</sup> Our paper further speaks to a recent strand of work highlighting the role of internationally active firms in the international transmission of business cycles. Using French firm-level data, di Giovanni, Levchenko, and Mejean (2018) show that trade linkages with a foreign country are associated with a significantly higher correlation between a firm and that foreign country, which has significant macro implications. In related work, di Giovanni, Levchenko, and Mejean (2020) document that larger French firms are significantly more sensitive to foreign GDP growth. Using a quantitative model, they find that this granularity accounts for 40 to 85% of the impact of foreign fluctuations on French GDP. Focusing on exporting firms, we contribute to this literature by estimating the response of firm-level sales, employment, investment and wages to both expected and unexpected fluctuations in foreign GDP growth. In addition, we use employeremployee data to estimate how each of these shocks impacts the different components of worker compensation within and across heterogeneous exporters. As Grigsby, Hurst, and Yildirmaz (2021) emphasize, measuring this nominal wage adjustment in micro data is key for disciplining macroeconomic theories of nominal wage rigidity.<sup>9</sup>

> 6Since there are no official forecasts for bilateral exchange rates, it is often difficult to isolate the unexpected component of exchange rate shocks.

> 7Furthermore, exchange rate movements may impact not only firm revenues in export destinations, but also the prices of materials, components and technologies sourced from those markets (which may complement or replace workers). It has been difficult to fully distinguish between these effects. Unexpected demand shocks in destinations are arguably more likely to impact firm performance primarily through exports.

> 8In an important contribution, Hummels, Jorgensen, Munch, and Xiang (2014) examine effects of offshoring and external demand shocks on wages, using employer-employee data from Denmark. Identification of demand shocks exploits variation in firm-specific weighted averages of imports of particular goods by the firm’s trading partners, using the firm’s initial shares as weights. We innovate by isolating the unexpected (and idiosyncratic) component of demand shocks in destinations. In addition, we separately identify effects on the different components of pay, show that wage gains accrue mainly to top earners, and to firms initially led by highly skilled top executives.

> 9In concurrent work, Garin and Silv ́erio (2024) examine the effects of idiosyncratic demand shocks associated with the 2008 global recession on wage setting of Portuguese exporters, and find evidence of significant wage adjustments within firms. The methodology we propose for systematically isolating the unexpected component of macroeconomic demand shocks in destinations can be applied more generally to assess the effects of unforecasted (positive and negative) fluctuations in GDP growth. By design, our approach is arguably less likely to capture

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**Roadmap** The paper is organized as follows. Section 2 describes the data, before Section 3 presents the method for identifying the unexpected component of demand shocks at the firmlevel. Section 4 describes the econometric model, while 5 reports the corresponding empirical results. Section 6 concludes the paper.

# **2 Data**

The empirical analysis in this paper combines and examines several sources of panel data from Portugal spanning the period 2006-2018. We provide a brief description of each data source in this section and give further details in Appendix A.3.

**Employer-employee data** : _Quadros de Pessoal (QP)_ [Personnel Records] is a high-quality compulsory census run by the Ministry of Employment covering the population of firms with wage earners in manufacturing and services. Each firm is required by law to provide information on an annual basis about its characteristics and those of each individual that comprises its workforce. Firm-level information includes annual sales, number of employees, industry code, geographical location, date of constitution and share of capital that is foreign-owned. The set of worker characteristics includes wages (monthly base wage, overtime pay, and other components of pay), gender, age, schooling, date of starting, detailed occupation and hours worked. A worker may also be matched to the firm in which he is employed. Extensive checks have been performed to guarantee the accuracy of worker and firm data. After these checks, we kept for analysis full-time wage earners working at least 100 hours a month, and aged between 20 and 60 years old.

**Firm census** : Using common unique firm identifiers, we supplement the firm-year data from QP with information from _Sistema de Contas Integradas das Empresas (SCIE)_ [Enterprise Integrated Accounts System], a yearly census of firms run by National Statistics Institute (INE). The main objective of SCIE is to characterize the economic and financial behaviour of firms. This data set includes information on total sales, investment, employment, value added, wage bill, industry, location, among several other variables.

**International trade statistics** : We merge the above data sets with yearly data on firms’ export transactions from _Estat ́ısticas do Com ́ercio Internacional (ECI)_ [Foreign Trade Statistics] from INE. This is the country’s official information source on imports and exports. It comprises

> common shocks associated with a single global recession (e.g. financial factors), which may plausibly have heterogeneous impacts across firms. The focus of our analysis is also distinct in that we separately identify the effects of these unexpected shocks on the different components of pay, show that wage effects apply mainly to top earners, and to firms initially led by highly skilled top executives. This enable us to make further progress in identifying specific mechanisms whereby firm-specific unexpected demand shocks impact wage setting.

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the export flows of virtually all exporting firms, and provides detailed information on the product exported, the destination market, and the value and quantity exported. Export values in these data are free-on-board, thus excluding any duties or shipping charges.

**Management practices survey** : We further use data from _Inqu ́erito `as Pr ́aticas de Gest ̃ao (IPG)_ [Management Practices Survey] for 2016. IPG is a non-periodical survey conducted by INE, which collects information on the perceptions of top executives about the management practices of their firms. The 2016 survey was the first and only of its kind collected in Portugal. It seeks to evaluate the importance of management practices for firm productivity, as well as other key indicators that make it possible to evaluate differences in productivity between Portuguese firms. IPG employed a stratified sample of firms operating in Portugal covering the whole non-financial private sector in 2016, excluding micro firms (with fewer than five employees). The sample is representative by sector (20 sectors corresponding of aggregations of the 2-digit level of the CAE), firm size and age, as well as belonging (or not) to a conglomerate. The IPG survey includes questions seeking to evaluate management practices in three main areas: (1) Strategy, monitoring and information; (2) Human Resources; and (3) Management and social responsibility systems. We selected 18 variables that are closely related to those adopted in Bloom and Reenen (2007). Following their approach, our measure of management quality was constructed by z-scoring (normalizing to mean 0, standard deviation 1) the 18 individual questions in IPG, taking averages, and then z-scoring the average. This process yields a management practice score with mean 0 and standard deviation 1.

**Actual and forecasted GDP growth** : We further use yearly information on actual and recently forecasted GDP growth from the World Economic Outlook (WEO) of the International Monetary Fund (IMF). WEO is usually published twice a year (in April and September/October). It presents IMF staff economists’ analyses of global economic developments during the near and medium term. Every April and October, the WEO provides year-ahead and current-year GDP growth forecasts. We refer to the year for which the forecast is being made as the target year. Forecasts made in the Fall WEO before the target year are called year-ahead forecasts and those made during the Spring target year are called current-year forecasts. During our sample period, forecast data are available for 195 countries. As shown by An, Jalles, and Loungani (2018), IMF growth forecasts are virtually identical to other growth forecasts from the private sector, and can therefore be regarded as consensus forecasts from a wide variety of market analysts from both the official and private sectors. After merging these IMF data with ECI we were left with 174 destinations, which account for 99.7% of all exports in 2006. Table A2 reports the export shares to the main destinations in 2006, both in the full ECI data and in

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the estimation sample.

# **3 Methodology for identifying unexpected revenue shocks**

In this section, we propose a new methodology to identify the unexpected component of demand shocks at the firm-level, which exploits forecast errors in the GDP growth of export markets. In each destination, the unexpected component of demand shocks is measured as the difference between the GDP growth actually observed and the current-year growth forecast for that country published in the Spring edition of the World Economic Outlook of the International Monetary Fund. Specifically, the forecast error for a destination-year is defined as: where _FEdt_ denotes the forecast error for destination _d_ in year _t_ , _Gdt_ denotes the GDP growth rate of destination _d_ in year _t_ and _FGdt_ denotes the current-year growth forecast for country _d_ in year _t_ . We then aggregate these destination-year forecast errors at the firm-year level, weighting by the share of destinations in total sales of firm _i_ in the initial year: where _sd_ 0 is the share of exports to destination _d_ in total sales of firm _i_ in 2006 (the first year of our data) and _D_ is the set of destinations for which data on growth forecasts are available. Since firms initially served different destinations to a varying degree, they were differentially exposed to these unexpected demand shocks across markets. Using the same weights, we also aggregate the forecast growth (WFG) at the firm-year level.

By its construction, the weighted forecast error constitutes an unexpected shock in export demand and is therefore plausibly exogenous to the firm. However, one potential concern is that different firms might select themselves into different export environments in a way that make them differentially exposed to forecast errors. For example, growth-seeking firms might choose to operate more in export markets where the expected growth is higher but also more uncertain, thus exposing themselves to higher forecast errors. In order to check for any indications of potential selection effects of this kind, we present a series of scatter plots showing the correlation between weighted forecast errors and each of a set of baseline firm characteristics (sales, exports, export share, employment, investments in tangible and intangible assets, value added, value added per worker and average worker pay) based on observations in 2006 (or in the 7
first year of observation in case of firms that entered the market later than 2006). These plots are displayed in Figure A1 in the Appendix and do not reveal any noticeable correlation between weighted forecast errors and any of the baseline firm characteristics. We take this as a reassuring indication that our methodology for defining unexpected demand shocks is not compromised by selection

# **4 Econometric method**

We now describe the econometric strategy for examining the effects of unexpected demand shocks on firm performance and worker compensation. Our baseline specification is: where _Yip_ denotes the log of the outcome variable of interest in firm _i_ in period _p_ ; _WFEip_ is the weighted forecast error in firm _i_ in period _p_ ; _WFGip_ is the weighted forecast growth in firm _i_ in period _p_ ; _ηXSi_ 0 _∗ ιp_ is the interaction between the initial export share of sales of firm _i_ and a period dummy; _ζi_ is a firm fixed effect; _γjp_ denotes an industry-period effect; _τrp_ denotes a region-period effect; and _εip_ is the error term.

Following Fr ́ıas, Kaplan, Verhoogen, and Alfaro-Serrano (2024), we take 3-year period averages of the corresponding firm-year variables. This makes it possible to obtain a measure of unobserved worker skills by estimating person effects (using AKM regressions) within each 3-year period, and to examine the medium-term effects of more permanent unexpected demand shocks. Furthermore, the period definition of all the independent variables is lagged one year compared to the period definition of the dependent variable _Y_ .<sup>10</sup> This enables us to capture potential lagged responses to unexpected shocks. Allowing for potentially lagged responses to more permanent unexpected shocks is especially important for wage determination, as wage negotiations tend to occur at a particular point in the calendar year.

The ∆operator denotes the linear change of a variable between each period _p_ and period _p −_ 1 throughout the paper. Since the firm-level weights in (2) are fixed in the initial period, variation over time in the forecast error and forecast growth stems only from variation over time in actual and forecasted GDP growth across destinations. This is therefore a shift-share or Bartik-type setting in which aggregate shocks are combined with measures of shock exposure. In our application, the sum of the exposure weights across destinations is different from 1 and

> 10If period _p_ is defined for the independent variables as the three years from _t_ to _t_ + 2, the dependent variable _Yip_ measures the (log of the) outcome variable of interest averaged over the years _t_ + 1 to _t_ + 3.

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varies across firms, which corresponds to the incomplete shift-share case described in Borusyak, Hull, and Jaravel (2022). We therefore adopt their proposed correction, by controlling for the interaction between this sum (the export share of sales of the firm in the initial period) and a period dummy in all our regressions, defined as _XSi_ 0 _∗ ιp_ .

The firm fixed effects account for the role of any time-invariant (observable or unobservable) firm attribute. The industry-period effects absorb common shocks to all firms in an industry in each period, while the region-period effects capture the impacts of common shocks across firms operating in the same region in a given period. We report standard errors clustered by firm. Since the forecast error is defined as the difference between actual and forecasted GDP growth in export destinations, in order to isolate its effect on the dependent variable we need to control for the weighted forecast growth, which would be expected to have a direct impact on firm-level outcomes. Our identification assumption is that after controlling for the weighted forecast growth and these fixed effects, subsequent variations in weighted forecast errors are uncorrelated with firm-specific shocks to firm performance and worker compensation.<sup>11</sup>

# **5 Results**

## **5.1 Summary statistics**

Before turning to the econometric analysis, we report descriptive statistics on several variables underlying our empirical strategy. Our firm-level baseline estimation sample is composed of manufacturing firms that exported in the initial year, and for which it is possible to link information from all the data sets described above (except the IPG survey, which is available only for a subset of firms in 2016). Table 1 reports summary statistics on firms from the estimation sample, both in levels and in changes. These statistics reveal that there exists considerable variation across firms and over time with regard to the weighted average of actual and forecast growth. Table A1 in the Appendix provides summary statistics on firms in the estimation sample for each 3-year period considered in the econometric analysis. Once again, these descriptive statistics show considerable variation across firms in the main variables of interest, both within and across periods. In Table A2 in the Appendix we also show summary statistics for firm age,

> 11Since isolating the effects of the weighted forecast error on firm-level exports and sales requires controlling for the weighted forecast growth, our empirical methodology is not amenable to an instrumental variable approach. If both variables were included in the first stage in order to predict changes in firm-level exports or sales, in the second stage we could only identify the total effect of both expected and unexpected GDP growth in export destinations on wage setting. We therefore adopt a more reduced-form approach in which both the weighted forecast errors and weighted forecast growth are regressed directly on wages, which allows us to isolate the effect of the unexpected component of demand shocks across destinations on wage setting.

9
location and corporate structure. Unsurprisingly, most of the observations (more than 90 percent) are recorded in the three most populous regions in Portugal (Norte, Centro and Lisboa), and the overall degree of domestic firm ownership is relatively high (almost 90 percent). Our data refer exclusively to economic activity that takes place in Portugal, which constitutes all economic activity for the vast majority of firms. There are some firms in our data that also have plants located abroad, but these are very few.<sup>12</sup>

## [ Table 1 here ]

Table A3 in the Appendix reports key moments on the distribution of export destinations of Portuguese manufacturing firms in 2006, both in the full customs data and in our estimation sample. The main export destinations are other EU member states that are part of the eurozone (Spain, Germany, France), but also include countries outside the eurozone and/or the EU, notably the United Kingdom, United States, Angola and Singapore. For all destinations, export shares in the estimation sample are remarkably similar to those in the full customs data.

Figure A2 in the Appendix shows the variation of forecasted and actual GDP growth in each of the top 18 destinations for Portuguese exports. We observe significant variation across destinations with regard to both these variables. Since firms initially served different destinations to a varying degree, they were differentially exposed to these unexpected demand shocks across markets.

Figure 1 shows that there exists significant variation across firms in the estimation sample with regard to weighted actual and forecast growth. The range of forecast and actual growth is often greater than 10 percentage points, which is considerably higher than the averages for both these variables. The range of forecast growth is especially wide (and considerably larger than that of actual growth) in the initial years of the sample period, but remains sizeable over the whole period.

## [ Figure 1 here ]

# **5.2 Average effects on firm performance and worker compensation**

We now turn to the main focus of the empirical analysis: the impacts of unexpected demand shocks on worker compensation. Table 2 reports the point estimates on the effects of the weighted forecast error and weighted forecast growth on various measures of firm performance and average labor costs, using data from SCIE. Columns (1) and (2) reveal that unexpected demand shocks

> 12A total of 18 firms, four of which are foreign owned, have plants located outside Portugal.

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in export destinations lead to increased exports and sales, with the former showing stronger responses than the latter. The stronger effects on exports would be expected given the source of variation we are exploiting: unforeseen growth shocks in export destinations. We take these results as reassuring confirmation that our strategy for identifying unexpected export shocks is valid. We also observe that the magnitude of the effects of the forecast error on both exports and sales is somewhat larger than that of the forecast growth. In Table A4 of the appendix, we split firm-level export values into averages export quantities (in kilograms) and average export prices per kilogram exported. The results reveal that the effect of the demand shock on exports is entirely a quantity effect, so our estimates should be interpreted with this evidence in mind.

Columns (3) to (8) of Table 2 also show significant effects on investment (in both tangible and intangible assets), employment, value added, value added per worker, and average labor costs. For all these variables, the effects of the weighted forecast error are consistently stronger than those of the weighted forecast growth.

# [ Table 2 here ]

In Table 3, we use the employer-employee data to examine the effects on the various components of worker compensation and on the skill composition of the workforce. Regarding worker compensation, the total monthly wage consists of three components: _base wage_ , _overtime pay_ , and _other pay_ (e.g., various types of bonuses). We also have information on the number of hours worked per month, which allows us to calculate the total hourly wage. We report effects on firm-level averages of each of these variables.

# [ Table 3 here ]

The estimates in columns (1) and (2) of Table 3 indicate positive and significant effects of the forecast error on both monthly and hourly wages. Columns (3)-(4) show that this positive average effect is driven by significant increases in both base wages and overtime pay. The _other pay_ coefficient in column (5) also has a sizeable positive point estimate, though the effect on this residual wage component is not statistically significant. In line with the employment responses documented in Table 2, column (6) shows a positive impact on total hours worked.

We also investigate whether an unexpected demand shock has any effects on workers’ skill composition. We measure skill composition in two different ways: (i) the share of workers with a degree, and (ii) average person effects estimated through AKM models. The results are somewhat mixed. While the estimate in column (7) does not show any significant impact on the share of workers with a degree, the estimate in column (8) suggests a significantly negative 11
composition effect, indicating that a positive demand shock leads to a reduction in average worker skills. However, the estimation of person effects leads to a sizable reduction in the number of observations, and hence some caution is warranted in interpreting this result.

Overall, our results suggest that firms respond to an unexpected demand shock partly by asking existing workers to work longer hours and partly by employing more workers.<sup>13</sup> On average, workers benefit from such a shock in the form of higher wage payments. Whereas part of these wage increases accrue in the form of higher overtime payments, the positive and significant effects on average base wages, despite some indication of reduced average worker skills, suggest that the unexpected increase in revenue due to higher demand is partly transmitted to workers through permanent rises in their compensation.

As previously explained, our identification strategy relies on creating a measure of the unexpected component of demand shocks at the firm-level, in order to establish a causal link between changes in revenue and changes in wages. The importance of this strategy can be assessed by comparing our estimates in Tables 2 and 3 with the corresponding estimates from a regression where we (na ̈ıvely) measure the demand shock by the _actual_ GDP growth in export destinations. Such estimates are presented in Tables A5 and A6, respectively, in the Appendix. We see that these estimates are consistently smaller in magnitude than the corresponding estimates in Table 2 and 3. Although an increase in actual GDP growth in export destinations has a significantly positive effect on sales, exports and employment, the magnitudes of these effects are between 38 and 66 percent smaller. Furthermore, the estimated coefficients for monthly, hourly and base wages are not only much smaller in magnitude, but they are also statistically insignificant. This suggests that an analysis using actual GDP growth instead of GDP forecast errors as the main explanatory variable would strongly underestimate the wage effects of changes in demand.

# **5.3 The division of revenues from unexpected demand shocks**

We proceed by examining whether the division of unexpected demand-induced revenues inside the firm accrues disproportionately to some groups of the workforce. In particular, we verify if the demand shocks benefit mainly the firms’ high earners, thereby contributing to increased intra-firm wage inequality, or if the increased revenues are more evenly distributed among the entire workforce.

## [ Table 4 here ]

> 13The result in Column (5) in Table 2 shows that a positive demand shock has a significantly positive effect on the number of workers in the firm, whereas the result in Column (6) in Table 3 shows that such a demand shock also significantly increases the average number of hours worked per worker.

12
In Table 4 we show the estimated effects of unexpected demand shocks on the average monthly wages of high versus low earners within each firm, using three different earnings thresholds. Whereas the coefficients are statistically significant for both groups of workers, the magnitudes of these estimates display a very clear and consistent pattern. First, the estimated effect is consistently larger for the high earners than for the rest of the workforce. Furthermore, the estimated wage effect for the high earners, and the difference in the effects for high and low earners, are both monotonically increasing in magnitude as we move the high earner threshold further towards the top end of the distribution. This suggests that, on average, the division of revenues from unexpected demand shocks is quite unequal and disproportionately benefitting the firms’ top earners.

# **5.4 The importance of heterogeneity in managerial skill**

We now examine if and how the distribution of rents generated by these unexpected shocks vary systematically with the skill-level of the firms’ top managers (measured at the beginning of the sample period), which proxies for the exogenous heterogeneity in firm capabilities that features in Melitz (2003)-type trade models. The underlying assumption (which we explore and discuss below) is that differences in managerial skill are systematically linked with the adoption of different management practices, which in turn affect worker behavior and effort. For management practices that are likely to interact with worker behavior (e.g., monitoring, goal setting, and incentive schemes), it seems reasonable to assume that the effects of such practices depend on the characteristics of the workforce. Thus, we would expect that the adoption of different management practices is systematically related to differences in both the skill composition of the firm’s workforce and the structure of its pay system. Indeed, a growing body of evidence suggests that the skills of top executives are important for management practices, employee selection and firm performance (Bertrand and Mullainathan, 2003; Bertrand and Schoar, 2003; Bastos and Monteiro, 2011; Bender, Bloom, Card, Van-Reenen, and Wolter, 2018). However, there is little evidence on whether and how managers matter for how revenues from exogenous demand shocks are transmitted to workers.

## **5.4.1 Measuring managerial practices and managerial skills**

Following Bender, Bloom, Card, Van-Reenen, and Wolter (2018), we first use the 2016 management survey to compute firm-level management _z-scores_ —an index of adoption of advanced management practices. We then link these data to the other data sets on workers and firms. This allows us to relate measured management quality to worker and firm observables, including 13
worker pay at previous employers in 2011-2016, which we use to estimate worker effects in order to infer ability. The worker effects allow us to measure the quality of workers’ skills at each firm as well as the relative quality of top managers versus other workers.

Table A7 in the Appendix provides descriptive statistics on worker and firm attributes for firms with management z-scores above and below the median. To proxy for worker ability, we consider estimates of individual effects from AKM models using data for the period 2011-2016. We use a similar approach to estimate managers’ ability. Furthermore, we consider a direct measure of firm management skills, namely the share of a firm’s managers holding a university degree.

Managers are identified in two alternative ways. Our main approach is to identify managers directly by _occupational category_ in the data (using the categories CEO and other top managers). Although allowing for more precise identification, the downside of this approach is a loss of observations due to missing data. We therefore complement it by an alternative identification strategy which is in line with previous literature (Juhn, McCue, Monti, and Pierce, 2018), namely to identify managers by their position in the firm’s wage distribution. In this vein, we classify the _five percent highest earners_ in the firm as top managers.<sup>14</sup> The set of firm-level attributes is composed of firm size, the share of foreign and state capital, firm age, the percentage of female employees, export status and export share. The summary statistics reported in Table A7 reveal that firms with higher management z-scores tend to be larger, to have a greater share of foreign and privately-owned capital, and are slightly more likely to be exporters and tend to employ a larger share of female workers. Turning to worker and manager attributes, the statistics reveal that firms with above-median z-scores tend to have a higher share of workers and managers with a degree, as well as larger average estimates of employee and managers’ ability (as revealed by person effects from AKM models). The relationship between z-scores and observable manager characteristics is further explored in Figure A3 in the Appendix. In this figure we show the distribution of z-scores across firms with high- and low-skilled managers, respectively, where the former (latter) are defined as firms with a share of managers with a degree above (below) the median. These distributions are shown when managers are identified according to occupational category (Panel A) and when managers are identified as the top five percent earners in the firm (Panel B). In both cases, we see that the z-score distribution of firms with high-skilled managers lies systematically to the right of the corresponding distribution of firms with low-skilled managers. This pattern is

> 14We have also used a stricter threshold of one percent highest earners. The results are very similar and are available upon request.

14
generally consistent with the evidence in Bender, Bloom, Card, Van-Reenen, and Wolter (2018) for Germany, and suggests that observed skills of top executives are systematically associated with advanced management practices.

# **5.4.2 Managerial skill and the division of revenue shocks**

Having shown that management z-scores tend to be higher among firms with a greater share of highly-skilled top executives, we now examine whether and how managers matter for the distribution of exogenous shifts in revenue. To do so, we split our estimation sample according to whether the proportion of managers with a degree was above or below the median in 2006 (or in the first year of observation in case of firms that entered the market later than 2006).<sup>15</sup><sup>_,_16</sup> Tables A8 and A9 in the Appendix provide some summary statistics for each of the two subsamples, with the sample split based on a definition of top managers according to occupational category in Table A8 and as the 5 percent highest earners in Table A9. In either case, we see that, on average, firms with high-skilled managers are larger, as measured by sales and employment, and also export and invest more, than firms with low-skilled managers. The former category of firms also tend to employ more skilled workers and pay them better along all dimensions of worker compensation.

When estimating (3) on each of the two subsamples, the results reported in Tables A10 and A11 in the Appendix show that the baseline results on the effects of unexpected demand shocks on firm performance generally apply to both subsamples. Regardless of the method used for identifying top managers, the forecast error has a significant effect on exports and sales for both categories of firms, which suggests that our strategy for identifying unexpected demand shocks is valid also for these more restricted subsamples.

Having established the general validity of our identification strategy, our main objective in this part of the analysis is to re-estimate the results in Table 4 using the above explained sample partition, in order to examine whether and how the within-firm distribution of the revenues from unexpected demand shocks is related to managerial skills. The resulting estimates for the two alternative sample partitions are shown in Tables 5 and 6, respectively.

> 15When identifying managers by occupational category, the classification of firms as being managed by highskilled vs low-skilled managers is made according to the first year in which a managerial occupation is observed.

> 16Whether we identify managers by occupational category or by their position in the wage distribution, the share of managers with a degree is zero for more than half of the firms in our sample in 2006, and is thus zero also for the median firm. This implies that all firms in which no manager has a degree are classified as firms with low-skilled managers, whereas all firms in which at least one manager has a university degree are classified as firms with high-skilled managers.

15
In the case where managers are identified according to occupational category (Table 5), the results are quite striking. In firms with high-skilled managers, we find a strong and significant effect of an unexpected demand shock on the wages of the top earners in the firm, but no significant effect on the wages of the remaining workforce (with point estimates much closer to zero). Thus, for this subset of firms, the wage effects appear to be even much more unequal than what is reported in Table 4 using the full sample of firms. For the remaining subset of firms, however, the picture is very different. In firms with low-skilled managers, there are no indications of the top earners benefiting more from an unexpected demand shock than the remaining workers in the firm. In fact, all the point estimates are negative, and more so for the top earners, though none of these effects are statistically significant.

If we instead identify managers by their position in the wage distribution (Table 6), the point estimates are once more much larger for high earners than for the remaining workforce in firms with high-skilled managers, while these differences are much smaller in firms with low-skilled managers. However, the main difference from Table 5 is that the wage effects in the former category of firms are now much less precisely estimated and thus not significantly different from zero. Nevertheless, when seen in conjunction, the results in Tables 5 and 6 give some indications that the distribution of revenues from unexpected demand shocks is related to managerial skills, with the overall unequal distribution of rents seemingly being driven by firms with high-skilled managers, where unexpected demand shocks to a much larger extent benefits workers close to the top end of the earnings distribution.

In order to further explore the role of managerial skill in the division of the revenues from unexpected demand shocks, we decompose the total wage effects reported in Tables 5 and 6, showing the effects on each wage component for different types of workers (top earners versus the remaining workers) across the two categories of firms (managed by high-skilled versus lowskilled managers). These results are displayed in Table 7 (where managers defined according to occupational category) and Table 8 (where managers are defined as the top five percent earners). We show these results for the intermediate wage distribution threshold (top 15 percent earners versus the rest of the workforce), but the estimates are fairly similar if we use different thresholds.

# [ Table 7 and 8 here ]

Although there are some differences in the estimates depending on the strategy for identifying top managers, the results in Tables 7 and 8 suggest once more that managerial skill might play a key role for the way in which revenues from unanticipated demand shocks are transmitted to workers. In firms with high-skilled managers, there is little or no evidence that rents are dis16
tributed via increases in base wages. Instead, wage increases apparently occur through increases in overtime and/or other pay. In particular, an increase in other pay, which includes various types of bonuses, is a consistent finding for this category of firms, regardless of how managers are identified. In contrast, there is no indication that this type of pay increases plays an important role in firms with low-skilled managers. For these firms, the significant wage increases seem to come in the form of either overtime payment or higher base wages.

Overall, these results suggest that high-skilled managers to some extent adopt different remuneration schemes for workers than what low-skilled managers do, and that the way in which revenue windfalls are distributed to workers is therefore partly related to managerial skill.

# **5.5 Discussion**

The main result of our analysis is that revenues from unexpected demand shocks are partly transmitted to workers in the form of higher average wages, but seemingly in a highly unequal way, with most of the wage increases occurring close to the top of the within-firm wage distribution. A first basic observation is that this result is inconsistent with the notion of perfectly competitive labor markets. If firms are wage takers in a market where wages reflect workers’ skills, unexpected firm-specific demand shocks would affect wages only if they lead to changes in the skill composition of the firm’s workforce. Since we find no evidence of an increase in average worker skills in response to a demand shock (on the contrary, we find some indications of the opposite), our results suggest that wages to some extent reflect some kind of rent sharing between and their workers.

The evidence of rent sharing in wage determination found in our analysis adds to an already sizeable empirical rent-sharing literature (see Card, Cardoso, Heining, and Kline (2018), for an overview). The exact mechanisms by which rents are shared with workers are potentially numerous, though, and not necessarily mutually exclusive. First, rent sharing could be a result of worker bargaining power, either collectively or individually. Collective bargaining power results from the presence and influence of trade unions in wage determination, while individual bargaining power could result from labor market frictions created by hiring and/or training costs, which create rents that can be captured by incumbent workers (see, e.g., Kline, Petkova, Williams, and Zidar (2019)). Second, rent sharing could also be the result of explicit profitsharing arrangements, for example in the form of performance-based pay contracts, which may be used to increase productivity (Lazear, 1986, 2000). Third, wage determination might partly result from firms’ incentives to induce the desired amount of effort from its workers, in line with the _fair wage_ hypothesis of Akerlof and Yellen (1990). If workers’ notion of a fair wage 17
is based on an _internal_ reference which reflects the firm’s ability to pay, such as revenues per worker, the revenue windfall from an unexpected demand shock would be partly transmitted to workers through an increase in what is considered to be a fair wage.<sup>17</sup> Fourth, rent sharing could be due to firms having some monopsony power in the labor market, for example because of market concentration on the demand side or heterogeneous job preferences on the supply side (Manning, 2021). If each firm faces an upward sloping labor supply curve, the revenues from an unexpected demand shock will be (partly) transmitted to workers in the form of higher wages via higher labor demand.

We would argue that each of the above suggested mechanisms is plausible in our context, despite the importance of industry-level collective bargaining in Portugal. Although trade union density is very low, a high share of wage contracts are determined by collective agreements at industry level, which might suggest that there is limited room for wage adjustments in response to firm-specific shocks.<sup>18</sup> However, it is worth emphasizing that wage determination in Portugal is characterized by a two-tiered wage setting system where firm-specific arrangements result in a mark-up, often of considerable magnitude, on top of the bargained wage floor.<sup>19</sup> Thus, although the presence of collective bargaining might result in some downward wage rigidity, the two-tiered wage setting system still leaves considerable room for firm-specific adjustments to firm-specific shocks.

The different suggested mechanisms are also, to some extent, related to different wage components. In particular, explicit profit-sharing agreements in the form of performance-based pay would be captured by the wage component labeled _other pay_ , which includes various types of bonus payments. In our analysis we do find some evidence that this is a relevant mechanism for rent sharing, but also that the existence of this mechanism crucially relies on managerial skill. More specifically, the previously discussed results in Table 7 and 8 suggest that firms with high-skilled managers to a larger extent adopt performance-based payment schemes and that this is a significant channel for rent distribution in such firms.

In the following we exploit our data to look for indications of the relevance of some of the other rent sharing mechanisms mentioned above, and which are not necessarily related to managerial skill. In particular, we examine whether our results could be explained by individual

> 17For fair wage models based on a firm-internal point of reference, see, e.g., Danthine and Kurman (2006), Bastos, Monteiro, and Straume (2009) and Egger and Kreickemeier (2009).

> 18Portugal and Vilares (2013) report a union coverage rate of more than 90 percent despite a union density rate of only 11 percent.

> 19See Cardoso and Portugal (2005) and Bastos, Monteiro, and Straume (2009). In the latter study, using data for the period 1991 to 2000, wages are found to be more than 25 percent higher than the bargained wage floor, on average.

18
or collective bargaining power. If rent sharing is caused by hiring or training costs that gives individual bargaining power to longer-tenured workers, we would expect that the wage effects of a demand shock are stronger for such workers than for newly hired workers. This hypothesis is explored in Table 9, where we show the decomposed wage effects across these two categories of workers, and where we have classified newly hired workers as workers with less than four years of tenure in the<sup>20</sup>

# [ Table 9 ]

The results in Table 9 show that the wage effects (which all have positive point estimates) tend to be statistically significant for longer-tenured workers but not for the newly hired ones, and the point estimate for the base wage increase is also larger in magnitude for longer-tenured workers. These results give some support to the hypothesis that rent sharing is related to worker tenure.

An alternative hypothesis is that rent sharing is caused by collective bargaining power. This hypothesis can be pursued by exploiting some heterogeneity in the type of wage agreements that exist in our data. Whereas most of the collective wage agreements in Portugal are made at industry or sectorial level, as previously mentioned, there is also a small prevalence of firm-level wage agreements. If trade unions are able to extract some of the rents related to _firm-specific_ demand shocks, we would expect that the resulting wage effects are stronger for workers whose wage contracts are bargained at firm level, all else equal. We explore this hypothesis in Table 10, where we show the estimated wage effects for workers with firm-specific collective agreements relative to workers whose wage contracts result from collective bargaining at a more centralized level.<sup>21</sup>

# [ Table 10 here ]

Interestingly, despite a very low number of observations in the group of firms with firmspecific collective agreements, we see that the effect of a demand shock on base wages is statistically significant and much larger in magnitude for this category of firms than for firms whose wages are bargained at a more centralized level, which we take as an indication of collective bargaining power. It is also worth noticing the large and significant effect of an unexpected demand shock on _other pay_ in firms with firm-level wage agreements, which, besides indicating collective bargaining power, suggests that performance-based pay is imbedded in the wage contracts in this type of firms.

> 20A similar definition is used by Kline, Petkova, Williams, and Zidar (2019).

> 21According to our classification, firms with firm-level wage agreements are those in which at least 50 percent of the workers have this type of wage contracts.

19
# **5.6 Extensions**

We now extend our main analysis by exploring two different aspects of the demand shocks that might be potentially relevant, namely the _persistence_ and the _sign_ of the shocks. In doing so, we restrict attention to our main results presented in Tables 2 and 3.

## **5.6.1 Persistent versus transitory shocks**

How strongly firms respond to a demand shock will likely depend on the persistence of the shock, particularly in the presence of non-trivial adjustment costs. We explore the importance of shock persistence by estimating a version of (3) where we interact the forecast error and forecast growth variables with dummy variables indicating whether a given firm-period observation is classified as a _persistent shock_ or not. We define a persistent shock as a firm-period observation for which the three yearly forecast errors satisfy the following two conditions: (i) the forecast error has the same sign in at least two consecutive years, and (ii) the sign of the errors referred to in (i) is equal to the sign of the 3-year period average.

The results are presented in Tables 11 and 12 and show a very clear and consistent pattern. Unexpected demand shocks have significantly positive effects on sales, exports, investments, employment and wages only when these shocks are persistent (according to our definition). The importance of shock persistence is particularly pronounced for the wage effects, as we find that persistent demand shocks have significantly positive effects on monthly, hourly and base wages, with coefficient estimates that are markedly larger in magnitude than the baseline estimates reported in Table 3. Overall, these results are quite intuitive, and the consistency between the results in Table 11 and Table 12 is reassuring.

## **5.6.2 Positive versus negative demand shocks**

We also explore whether firms respond differently to positive and negative demand shocks, as defined by the sign of the 3-year period average of the forecast error. We do so by estimating a version of (3) where we interact the forecast error and forecast growth variables with dummy variables indicating whether the 3-year period average of the forecast error is positive or negative. The resulting coefficient estimates are displayed in Tables 13 and 14.

20
These results show a quite consistent pattern, as most of the statistically significant effects are due to variations in the _negative_ demand shocks. In other words, variations in demand have statistically significant effects on sales, exports, investments, employment and wages only if actual demand falls short of expectations. On the other hand, variations in demand above the expected level have few significant effects on our dependent variables. These results strongly suggest the presence of short-run capacity constraints that make it harder to adjust output upwards than downwards. One plausible interpretation is that firms set their short-run capacity levels according to expected demand, which allows for short-run adjustments to demand fluctuations below, but not above, the forecasted level.

# **5.7 Robustness**

In the following we present some robustness checks to the baseline estimates, once more focusing on the results in Tables 2 and 3.

## **5.7.1 Alternative forecast error weights**

In our main analysis we have identified the unexpected demand shocks by creating a weighted forecast error variable where destination-year forecast errors at the firm-year level are weighted by the share of destinations in total sales (in the initial year). By using weights that are based on total sales, we implicitly assume that export-related demand shocks have a larger impact on more export-intensive firms, all else equal. Although this seems like a highly reasonable assumption, our approach might be less appropriate if firms make separate strategic decisions (regarding wage contracts, hiring decisions, investment, etc.) for the export-oriented part of the business. Thus, as a robustness check, we have redone our baseline analysis using alternative forecast error weights, where destination-year forecast errors are weighted by the share of destinations in total exports (again in the initial year). The results from these alternative estimations are presented in Tables A12 and A13 in the Appendix.

Although there are some differences compared with the baseline results in Tables 2 and 3, these new estimates paint the same overall picture. An unexpected positive demand shock leads to significantly higher sales, exports and employment, and it leads to a significant increase in both monthly and hourly wages. The main difference from the baseline results is the magnitude of these effects, which are generally smaller when using the alternative forecast error weights. These differences in magnitudes are consistent with our original rationale for basing the forecast error weights on total sales. If export-related demand shocks have larger effects on more exportintensive firms, such differences will be downplayed when basing the forecast error weights on 21
total exports instead of total sales, thus reducing the magnitude of the estimated effects of the shocks.

# **5.7.2 Controlling for industry-by-region period effects**

We have also estimated a more restrictive empirical specification where we control for industryby-region period effects in order to take into account that labor markets might be segmented by industry and region. The results from these estimations are reported in Tables A14 and A15 in the Appendix. They show that all estimates are very similar in terms of both precision and magnitude. Thus, our main results are highly robust to this more restrictive empirical specification.

# **5.7.3 Lagged responses**

We also explore if and how our baseline results depend on the decision to include a time lag on the independent variables. In our baseline analysis, the period definition of all the independent variables is lagged one year compared to the period definition of the dependent variable, which allows us to capture lagged responses to demand shocks. However, the length of the adjustment period might differ across different types of variables. For example, while it is reasonable to assume that wage adjustments and investment responses might take some time, sales and export adjustments might happen much faster. In order to investigate this further, we have re-estimated the effects of demand shocks on the various firm performance measures using a model without any time lags. The results are reported in Table A16 in the Appendix. For several of the variables, such as sales, exports and employment, the estimated effects with lags (Table 2) and without lags (Table A16) are qualitatively and quantitatively very similar. On the other hand, the effect on investments ceases to be statistically significant in a model without time lags. Furthermore, the effect on average wages is also smaller in magnitude and less precisely estimated. These results are consistent with our conjecture that wage and investment responses to a demand shock are likely to take longer time, on average, than adjustments on other variables.

# **5.7.4 Excluding the years of the global financial crisis**

A final worry is that our results might be excessively driven by firms’ exceptional adjustments to policies during years of large global recessions, in particular the 2007-2008 global financial crisis which occured during the first of our four 3-year periods (see Table A1). In order to address this potential worry, we have re-estimated our main results (as reported in Table 2 and 3) on a smaller sample where we exclude the first period (2007-2009). These results are 22
reported in Table A17 and A18 in the Appendix. Despite losing more than one third of our observations, unexpected demand shocks still have a significantly positive effect on sales and exports, with coefficient magnitudes that are very similar for exports and even larger for sales. Furthermore, we find significantly positive effects on monthly and hourly wages that are larger than our baseline estimates. Overall, these results suggest that our results are not particularly driven by exceptional adjustments during the global financial crisis.

# **6 Conclusion**

In this paper, we examined the effects of unexpected demand shocks on worker compensation. We proposed a new methodology to identify the unexpected component of demand shocks at the firm-level, exploiting errors in the official forecasts in the GDP growth of export markets. In each destination, the unexpected component of demand shocks was measured as the difference between the GDP growth actually observed and recent forecasts published in the World Economic Outlook of the International Monetary Fund. We then aggregated these shocks at the firm-year level, weighting by the initial share of destinations in firms’ total sales. Since firms initially served different destinations to a varying degree, they were differentially exposed to these unexpected demand shocks across markets.

In the empirical analysis, we found that unexpected demand shocks have significant effects on sales, employment, investment and average wages. Using employer-employee panel data, we reported evidence that revenues from unexpected demand shocks are partly transmitted to workers in the form of higher average wages, but in a highly unequal way, with most of the wage increases occurring close to the top of the within-firm wage distribution. These wage increases, which include an increase in average base wages, occur despite some evidence that the skill composition of the workforce is negatively affected by unexpected demand shocks.

These results suggest that wages to some extent reflect rent sharing between firms and their workers, and our analysis indicates several different channels through which such rent sharing occurs. One channel is collective bargaining power, which is corroborated by our finding that positive base wage responses to demand shocks are much stronger in firms with firm-level wage agreements than in firms that are subject to more centralized bargaining schemes. Another channel is individual bargaining power created by hiring or training costs, which is consistent with our finding that wage responses tend to be stronger for longer-tenured workers. Finally, for a subset of the firms, we also found significant increases in bonus-related wage components in response to an unexpected demand shock, which indicates rent-sharing through the use of 23
performance-based payment schemes. However, this finding is restricted to the subset of firms that are run by high-skilled managers. We also found that the unequal distribution of rents, with the largest wage increases occurring towards the top of the wage distribution, is to a large extent driven by the same subset of firms. Thus, another main finding of our study is that the way in which revenues from unexpected demand shocks are transmitted to workers is significantly related to heterogeneity in firm capabilities, as proxied by managerial skill.

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26
Figure 1: Weighted actual and forecast GDP growth, firm-level data <!-- Start of picture text --> 2006 2010 2014 2018<br>30<br>20<br>10<br>percentage points<br>0<br>-10<br><!-- End of picture text --> Notes: Figure depicts means and ranges of weighted actual GDP growth (in gray) and forecast GDP growth (in black) at the firm-level, using the estimation sample. Means are displayed in lines, while ranges are shown in bars.

27
Table 1: Summary statistics, estimation sample, 2007-2018

|Variables|mean|sd|min|max|mean|sd|min|max|
|---|---|---|---|---|---|---|---|---|
|||Lev|els|||Cha|nges||
|Weighted forecast error|-0.0576|0.7014|-14.3957|5.9409|0.1257|0.9272|-4.8760|14.4744|
|Weighted forecast growth|0.4503|1.4551|-2.8063|25.8987|-0.3424|1.7559|-24.6233|8.0253|
|log sales|14.9961|1.5749|7.9455|22.9203|-0.0742|0.4380|-6.4713|3.3672|
|log exports|12.6600|2.6719|0.0180|22.1646|-0.0878|1.3947|-14.8877|11.7114|
|log (1 + fixed tangible assets)|10.3196|3.4960|0.0000|20.2001|-0.5649|3.1132|-15.6905|15.1426|
|log (1+ intangible assets)|3.2776|4.4074|0.0000|19.5609|-0.0477|4.2399|-17.1895|16.5090|
|log employment|3.2396|1.3249|0.0000|10.1227|-0.0136|0.3178|-4.0431|3.1974|
|log value added|13.5306|1.5294|2.7091|20.9644|-0.0676|0.5215|-9.1936|4.4144|
|log value added per worker|10.2976|0.7017|1.0996|18.4347|-0.0540|0.4551|-9.0113|4.1573|
|log avg worker pay|9.6312|0.4456|6.8120|12.9764|0.0064|0.2032|-2.4518|3.3622|
|log monthly wage|7.0310|0.4080|6.1200|10.1300|0.0183|0.1920|-2.0650|3.1720|
|log hourly wage|1.8790|0.4110|0.9570|5.0310|0.0161|0.1920|-2.0900|3.1690|
|log monthly base wage|6.8110|0.3740|6.1200|9.9280|0.0139|0.1450|-1.5260|2.5270|
|log (1 + overtime pay)|0.7320|1.4420|0.0000|8.1080|0.0235|1.0080|-8.1080|7.4550|
|log (1+ other pay)|5.0070|1.2980|0.0000|9.9510|0.1200|1.1350|-8.3480|8.0050|
|log total hours|8.0160|1.4070|4.9130|14.6700|-0.0049|0.4130|-5.0790|5.8480|
|share with a degree|0.1470|0.1890|0.0000|1.0000|0.0164|0.0943|-1.0000|1.0000|
|N(obs.)||443|98|||22|199||

Notes: Table reports summary statistics on the firm-level data from the estimation sample for 2007-2018, both in levels and in changes. Levels refer to variables averaged over 3-year periods, changes refer to variation between 3-year periods.
Table 2: Effects of forecast errors and forecast growth on firm performance

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log sales|log exports|log (1+ inv.<br>fixed tangible<br>assets)|log (1+ inv.<br>intangible<br>assets)|log<br>employment|log value<br>added|log value<br>added per<br>worker|<br>log avg.<br>worker pay|
|Weighted forecast error|0.0701***|0.1189***|0.1224*|0.1224*|0.0226***|0.0574***|0.0329***|0.0130***|
||(0.0098)|(0.0188)|(0.0669)|(0.0645)|(0.0053)|(0.0097)|(0.0088)|(0.0045)|
|Weighted forecast growth|0.0320***|0.0785***|0.0500|0.0585|0.0092***|0.0248***|0.0138**|0.0048*|
||(0.0054)|(0.0123)|(0.0454)|(0.0454)|(0.0030)|(0.0058)|(0.0056)|(0.0026)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|22199|
|N (firms)|9306|9306|9306|9306|9306|9306|9306|9306|
|Adj. R<sup>2</sup>|0.0719|0.0707|0.0157|0.0131|0.0535|0.0345|0.0142|0.0306|
|RSS|1580|22750|129170|291606|858|2582|2378|465|

Notes: In each column, the dependent variable is the change between the average of each 3-year period. Standard errors are clustered at the firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 3: Effects of forecast errors and forecast growth on worker compensation and worker composition

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log<br>monthly<br>wage|log hourly<br>wage|log<br>monthly<br>base wage|log (1 +<br>overtime<br>pay)|log (1+<br>other pay)|log total<br>hours|share with<br>a degree|person FE|
|Weighted forecast error|0.0090**|0.0085**|0.0060*|0.0328**|0.0210|0.0314***|0.0028|-0.0423**|
||(0.0041)|(0.0041)|(0.0035)|(0.0144)|(0.0186)|(0.0066)|(0.0019)|(0.0167)|
|Weighted forecast growth|0.0033|0.0030|-0.0001|0.0304***|0.0143|0.0108***|0.0002|-0.0131|
||(0.0023)|(0.0024)|(0.0019)|(0.0104)|(0.0130)|(0.0041)|(0.0013)|(0.0105)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE<br>|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|12631|
|N (firms)<br>|9306|9306|9306|9306|9306|9306|9306|6012|
|Adj. R<sup>2</sup>|0.0210|0.0229|0.0556|0.0222|0.00951|0.0601|0.0231|0.0490|
|RSS|472|471|249|15012|16665|1711|105.3|1546|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firmlevel. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 4: Effects of forecast errors and forecast growth on worker compensation: high vs low earners

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:|||log mont|hly wage|||
|High vs. low earners|high|low|high|low|high|low|
|Definition|5%|95%|15%|85%|25%|75%|
|Weighted forecast error|0.0172**|0.0073**|0.0128**|0.0063**|0.0098*|0.0051*|
||(0.0068)|(0.0030)|(0.0059)|(0.0029)|(0.0055)|(0.0029)|
|Weighted forecast growth|0.0062|0.0015|0.0037|0.0014|0.0021|0.0008|
||(0.0043)|(0.0020)|(0.0036)|(0.0020)|(0.0033)|(0.0021)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|20888|20888|20888|20888|20888|20888|
|N (firms)<br>|8745|8745|8745|8745|8745|8745|
|Adj. R<sup>2</sup>|0.00868|0.0193|0.0110|0.0214|0.0127|0.0223|
|RSS|1602|293|1106|272|908|258|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 5: Effects of forecast errors and forecast growth on worker compensation, high vs low earners, according to managerial skill (managers defined by occupational category)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:|||log mont|hly wage|||
|High vs. low earners|high|low|high|low|high|low|
|Definition|5%|95%|15%|85%|25%|75%|
|A. Firms with high-skilled managers|||||||
|Weighted forecast error|0.0435***|0.0057|0.0359**|0.0067|0.0288**|0.0044|
||(0.0159)|(0.0068)|(0.0139)|(0.0061)|(0.0133)|(0.0061)|
|Weighted forecast growth|0.0191**|0.0011|0.0130*|0.0009|0.0088|-0.0009|
||(0.0083)|(0.0042)|(0.0072)|(0.0043)|(0.0066)|(0.0052)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|6226|6226|6226|6226|6226|6226|
|N (firms)<br>|2739|2739|2739|2739|2739|2739|
|Adj. R<sup>2</sup>|0.0242|0.0366|0.0276|0.0440|0.0293|0.0433|
|RSS|447|80|292|71|236|66|
|B. Firms with low-skilled managers|||||||
|Weighted forecast error|-0.0028|-0.0013|-0.0147|-0.0044|-0.0139|-0.0041|
||(0.0131)|(0.0065)|(0.0111)|(0.0065)|(0.0106)|(0.0065)|
|Weighted forecast growth|0.0040|-0.0034|-0.0044|-0.0034|-0.0039|-0.0028|
||(0.0094)|(0.0040)|(0.0080)|(0.0039)|(0.0077)|(0.0039)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|6478|6478|6478|6478|6478|6478|
|N (firms)<br>|3015|3015|3015|3015|3015|3015|
|Adj. R<sup>2</sup>|0.0383|0.0376|0.0314|0.0419|0.0326|0.0466|
|RSS|406|72|276|66|224|63|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 6: Effects of forecast errors and forecast growth on worker compensation, high vs low earners, according to managerial skill (managers defined as top 5% earners)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:|||log mont|hly wage|||
|High vs. low earners|high|low|high|low|high|low|
|Definition|5%|95%|15%|85%|25%|75%|
|A. Firms with high-skilled managers|||||||
|Weighted forecast error|0.0191|-0.0006|0.0129|-0.0011|0.0049|-0.0027|
||(0.0128)|(0.0051)|(0.0107)|(0.0047)|(0.0098)|(0.0047)|
|Weighted forecast growth|0.0084|-0.0016|0.0028|-0.0022|-0.0018|-0.0041|
||(0.0076)|(0.0032)|(0.0061)|(0.0032)|(0.0054)|(0.0035)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|9299|9299|9299|9299|9299|9299|
|N (firms)<br>|3682|3682|3682|3682|3682|3682|
|Adj. R<sup>2</sup>|0.0213|0.0374|0.0225|0.0407|0.0259|0.0415|
|RSS|72|123|466|113|377|106|
|B. Firms with low-skilled managers|||||||
|Weighted forecast error|0.0154*|0.0105***|0.0121*|0.0095**|0.0118*|0.0086**|
||(0.0080)|(0.0037)|(0.0073)|(0.0038)|(0.0069)|(0.0037)|
|Weighted forecast growth|0.0062|0.0037|0.0050|0.0038|0.0051|0.0039|
||(0.0051)|(0.0026)|(0.0046)|(0.0027)|(0.0043)|(0.0027)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|11585|11585|11585|11585|11585|11585|
|N (firms)<br>|5063|5063|5063|5063|5063|5063|
|Adj. R<sup>2</sup>|0.0200|0.0250|0.0219|0.0262|0.0262|0.0277|
|RSS|845|165|619|154|511|145|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 7: Effects of forecast errors and forecast growth on different wage components, high vs low earners, according to managerial skill (defined by occupational category)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:|log bas|e wage|log overt|ime pay|log ot|her pay|
|High versus low earners<br>Definition|high<br>15%|low<br>85%|high<br>15%|low<br>85%|high<br>15%|low<br>85%|
|A. Firms with high-skilled|managers||||||
|Weighted forecast error|0.0087<br>(0.0120)|-0.0026<br>(0.0051)|0.0292<br>(0.0397)|-0.0093<br>(0.0456)|0.0927<br>(0.0638)|0.0820**<br>(0.0386)|
|Weighted forecast growth|-0.0035<br>(0.0058)|-0.0059*<br>(0.0036)|0.0198<br>(0.0270)|0.0096<br>(0.0301)|-0.0220<br>(0.0514)|0.0257<br>(0.0224)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|6226|6226|6226|6226|6226|6226|
|N (firms)|2739|2739|2739|2739|2739|2739|
|Adj. R<sup>2</sup>|0.0629|0.0886|0.0590|0.0684|0.0271|0.0229|
|RSS|148|38|6480|4630|6604|3038|
|B. Firms with low-skilled m|anagers||||||
|Weighted forecast error|-0.0072|-0.0020|0.1208***|0.0357|-0.0407|0.0263|
||(0.0083)|(0.0051)|(0.0389)|(0.0360)|(0.0579)|(0.0450)|
|Weighted forecast growth|-0.0089<br>(0.0060)|-0.0026<br>(0.0030)|0.1016***<br>(0.0297)|0.0362<br>(0.0262)|-0.0006<br>(0.0476)|-0.0101<br>(0.0282)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|6478|6478|6478|6478|6478|6478|
|N (firms)|3015|3015|3015|3015|3015|3015|
|Adj. R<sup>2</sup>|0.0572|0.0894|0.0419|0.0357|0.0294|0.0298|
|RSS<br>|191<br>|35<br>|5114<br>|4063<br>|7719<br>|3410<br>|

<u>Notes: In each column, the dependent variable is the change between the averages of each 3-year period.</u> Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 8: Effects of forecast errors and forecast growth on different wage components, high vs low earners, according to managerial skill (defined as top 5% earners)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:<br>|log ba|se wage|log over|time pay|log o|ther pay|
|High versus low earners|high|low|high|low|high|low|
|Definition|15%|85%|15%|85%|15%|85%|
|A. Firms with high-skilled|managers||||||
|Weighted forecast error|0.0011<br>(0.0084)|-0.0042<br>(0.0038)|0.1185***<br>(0.0342)|0.0539*<br>(0.0296)|0.0681<br>(0.0478)|0.0608**<br>(0.0298)|
|Weighted forecast growth|-0.0040<br>(0.0044)|-0.0046*<br>(0.0025)|0.0719***<br>(0.0239)|0.0636***<br>(0.0212)|-0.0294<br>(0.0386)|-0.0025<br>(0.0177)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|9299|9299|9299|9299|9299|9299|
|<br>N (firms)|3682|3682|3682|3682|3682|3682|
|Adj. R<sup>2</sup>|0.0323|0.0808|0.0361|0.0404|0.0215|0.0280|
|RSS|272|59|10771|7595|11250|5042|
|B. Firms with low-skilled|managers||||||
|Weighted forecast error|0.0109*|0.0097***|0.0301|0.0308|-0.0175|0.0067|
||(0.0062)|(0.0028)|(0.0225)|(0.0217)|(0.0368)|(0.0286)|
|Weighted forecast growth|0.0012<br>(0.0039)|0.0049**<br>(0.0021)|0.0299*<br>(0.0164)|0.0154<br>(0.0145)|0.0088<br>(0.0262)|0.0038<br>(0.0181)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|11585|11585|11585|11585|11585|11585|
|<br>N (firms)|5063|5063|5063|5063|5063|5063|
|Adj. R<sup>2</sup>|0.0377|0.0719|0.0265|0.0217|0.0164|0.0185|
|RSS|423|85|8736|6446|15292|8820|
|Notes: In each column, the|dependent va|riable is the|change betw|een the averag|es of each|3-year period.|
|Standard errors are clustered <br>significance.|at firm-level.|*10% level of|significance,|**5% level of si|gnificance,|***1% level of|
Table 9: Effects of forecast errors and forecast growth on worker compensation: longer-tenured vs newly hired workers

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. variable:|log monthly<br>wage|log monthly<br>base wage|log (1 +<br>overtime pay)|log (1+ other<br>pay)|
|A. Newly hired workers (less|than 4 years)||||
|Weighted forecast error|0.0091|0.0058|0.0234|0.0233|
||(0.0062)|(0.0063)|(0.0207)|(0.0252)|
|Weighted forecast growth|0.0004|-0.0019|0.0331**|-0.0140|
||(0.0037)|(0.0037)|(0.0148)|(0.0174)|
|Period x region FE|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|
|N (obs.)|18700|18700|18700|18700|
|N (firms)<br>|8149|8149|8149|8149|
|Adj. R<sup>2</sup>|0.0139|0.0248|0.0277|0.0105|
|RSS|920|811|15993|16824|
|B. Longer-tenured workers (a|t least 4 years)||||
|Weighted forecast error|0.0094**|0.0075**|0.0430***|0.0099|
||(0.0046)|(0.0036)|(0.0159)|(0.0232)|
|Weighted forecast growth|0.0036|-0.0001|0.0331***|0.0110|
||(0.0030)|(0.0023)|(0.0113)|(0.0153)|
|Period x region FE|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|
|N (obs.)|21418|21418|21418|21418|
|N (firms)<br>|9019|9019|9019|9019|
|Adj. R<sup>2</sup>|0.0331|0.0618|0.0228|0.0088|
|RSS|582|306|14946|17485|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 10: Effects of forecast errors and forecast growth on worker compensation: firm-level vs more centralised wage agreements

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. variable:<br>|log monthly<br>wage|log monthly<br>base wage|log (1 +<br>overtime pay)|log (1+ other<br>pay)|
|A. Firm-level wage agreements|||||
|Weighted forecast error|0.3924***|0.2185**|-0.6267|0.6367**|
||(0.1029)|(0.0823)|(1.2591)|(0.3091)|
|Weighted forecast growth|0.0722*|0.0429|-0.8687|0.1116|
||(0.0410)|(0.0429)|(0.6234)|(0.0994)|
|Period x region FE|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|
|N (obs.)|137|137|137|137|
|N (firms)<br>|55|55|55|55|
|Adj. R<sup>2</sup>|0.734|0.521|0.564|0.767|
|RSS|0.476|0.385|35.030|4.906|
|B. More centralized wage agreements|||||
|Weighted forecast error|0.0087**|0.0060*|0.0337**|0.0202|
||(0.0041)|(0.0035)|(0.0144)|(0.0186)|
|Weighted forecast growth|0.0032|-0.0001|0.0311***|0.0139|
||(0.0024)|(0.0019)|(0.0104)|(0.0130)|
|Period x region FE|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|
|N (obs.)|22062|22062|22062|22062|
|N (firms)<br>|9256|9256|9256|9256|
|Adj. R<sup>2</sup>|0.0209|0.0552|0.0209|0.00951|
|RSS|469|247|14892|16628|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 11: Effects of forecast errors and forecast growth on firm performance: persistent versus transitory demand shocks

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log sales|log exports|log (1+ inv.<br>fixed tangible<br>assets)|log (1+ inv.<br>intangible<br>assets)|log<br>employment|log value<br>added|log value<br>added per<br>worker|<br>log avg.<br>worker<br>pay|
|Weighted forecast error|||||||||
|- transitory shocks|0.0154|0.1101|-0.0924|0.0618|0.0038|0.0436**|0.0360*|0.0049|
||(0.0153)|(0.0835)|(0.1346)|(0.1712)|(0.0120)|(0.0193)|(0.0195)|(0.0081)|
|- persistent shocks|0.0880***|0.1136***|0.1829**|0.1232|0.0290***|0.0625***|0.0322***|0.0159***|
||(0.0130)|(0.0226)|(0.0902)|(0.0805)|(0.0067)|(0.0126)|(0.0112)|(0.0061)|
|Weighted forecast growth|||||||||
|- transitory shocks|0.0191*|0.0989**|0.0256|0.0961|0.0039|0.0198|0.0135|0.0021|
||(0.0098)|(0.0470)|(0.0812)|(0.1034)|(0.0075)|(0.0126)|(0.0129)|(0.0054)|
|- persistent shocks|0.0391***|0.0750***|0.0722|0.0555|0.0118***|0.0269***|0.0136**|0.0060**|
||(0.0065)|(0.0132)|(0.0533)|(0.0511)|(0.0032)|(0.0067)|(0.0063)|(0.0030)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|22199|
|N (firms)|9306|9306|9306|9306|9306|9306|9306|9306|
|Adj. R<sup>2</sup>|0.0734|0.0707|0.0160|0.0131|0.0538|0.0344|0.0141|0.0307|
|RSS|1577|22748|129122|291592|858|2581|2378|465|

Notes: In each column, the dependent variable is the change between the average of each 3-year period. Standard errors are clustered at the firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 12: Effects of forecast errors and forecast growth on worker compensation and worker composition: persistent versus transitory demand shocks

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log<br>monthly|log hourly<br>wage|log<br>monthly|log (1 +<br>overtime|log (1+<br>other pay)|log total<br>hours|share with<br>a degree|person FE|
||wage||base wage|pay)|||||
|Weighted forecast error|||||||||
|- transitory shocks|0.0100|0.0107|-0.0010|0.0328|0.0516|-0.0134|0.0068|-0.0555**|
||(0.0070)|(0.0070)|(0.0060)|(0.0370)|(0.0448)|(0.0154)|(0.0047)|(0.0245)|
|- persistent shocks|0.0115**|0.0108**|0.0113**|0.0204|0.0011|0.0466***|0.0037|-0.0374|
||(0.0054)|(0.0054)|(0.0046)|(0.0169)|(0.0209)|(0.0081)|(0.0026)|(0.0230)|
|Weighted forecast growth|||||||||
|- transitory shocks|-0.0041|-0.0048|-0.0099***|0.0645***|0.0485*|-0.0013|-0.0046|-0.0114|
||(0.0043)|(0.0042)|(0.0037)|(0.0223)|(0.0279)|(0.0107)|(0.0030)|(0.0155)|
|- persistent shocks|0.0048*|0.0044|0.0025|0.0234**|0.0048|0.0168***|0.0009|-0.0130|
||(0.0028)|(0.0028)|(0.0023)|(0.0111)|(0.0136)|(0.0042)|(0.0014)|(0.0126)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|12631|
|N (firms)<br>|9306|9306|9306|9306|9306|9306|9306|6012|
|Adj. R<sup>2</sup>|0.0213|0.0233|0.0565|0.0224|0.00960|0.0610|0.0243|0.0489|
|RSS|472.1|471.2|248.3|15008|16662|1709|105.2|1546|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 13: Effects of forecast errors and forecast growth on firm performance: positive versus negative demand shocks

|Dep. variable:|log sales|log exports|log (1+ inv.<br>fixed tangible<br>assets)|log (1+ inv.<br>intangible<br>assets)|log<br>employment|log value<br>added|log value<br>added per<br>worker|log avg.<br>worker pay|
|---|---|---|---|---|---|---|---|---|
|Weighted forecast error<br>|||||||||
|- positive shocks|0.0250|0.1333|0.1059|0.0257|0.0061|0.0516**|0.0427*|0.0040|
||(0.0217)|(0.1216)|(0.1546)|(0.1784)|(0.0142)|(0.0217)|(0.0225)|(0.0082)|
|- negative shocks|0.0826***|0.1077***|0.1128|0.1758**|0.0273***|0.0580***|0.0291***|0.0155***|
||(0.0126)|(0.0301)|(0.0858)|(0.0835)|(0.0067)|(0.0121)|(0.0111)|(0.0058)|
|Weighted forecast growth|||||||||
|- positive shocks|0.0321***|0.1030***|0.0977|-0.0308|0.0090*|0.0278***|0.0174**|0.0046|
||(0.0076)|(0.0273)|(0.0654)|(0.0830)|(0.0053)|(0.0087)|(0.0087)|(0.0036)|
|- negative shocks|0.0379***|0.0709***|0.0409|0.0921*|0.0114***|0.0248***|0.0117*|0.0060*|
||(0.0068)|(0.0209)|(0.0554)|(0.0552)|(0.0037)|(0.0070)|(0.0069)|(0.0032)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|22199|
|N (firms)<br>|9306|9306|9306|9306|9306|9306|9306|9306|
|Adj. R<sup>2</sup>|0.0729|0.0708|0.0157|0.0132|0.0537|0.0344|0.0141|0.0307|
|RSS|1578|22747|129156|291569|858|2581|2378|465|

Notes: In each column, the dependent variable is the change between the average of each 3-year period. Standard errors are clustered at the firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
Table 14: Effects of forecast errors and forecast growth on worker compensation and worker composition: positive versus negative demand shocks

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log<br>monthly<br>wage|log<br>hourly<br>wage|log<br>monthly<br>base|log (1 +<br>overtime<br>pay)|log (1+<br>other<br>pay)|log total<br>hours|share<br>with a<br>degree|person<br>FE|
||||wage||||||
|Weighted forecast error|||||||||
|- positive shocks|-0.0013|-0.0014|0.0073|0.0338|-0.0528|0.0138|0.0113**|-0.0244|
|- negative shocks|(0.0097)<br>0.0119** <br>(0.0053)|(0.0098)<br> 0.0115**<br>(0.0053)|(0.0061)<br>0.0059<br>(0.0045)|(0.0394)<br>0.0233<br>(0.0179)|(0.0455)<br>0.0434* <br>(0.0244)|(0.0153)<br> 0.0371***<br>(0.0086)|(0.0052)<br>0.0007<br>(0.0027)|(0.0240)<br>-0.0530**<br>(0.0223)|
|Weighted forecast growth|||||||||
|- positive shocks|0.0031<br>(0.0035)|0.0020<br>(0.0035)|-0.0009<br>(0.0025)|0.0614***<br>(0.0211)|0.0077<br>(0.0214)|0.0081<br>(0.0063)|-0.0006<br>(0.0018)|-0.0016<br>(0.0129)|
|- negative shocks|0.0047<br>(0.0030)|0.0045<br>(0.0030)|-0.0001<br>(0.0024)|0.0230*<br>(0.0120)|0.0255<br>(0.0157)|0.0137*** <br>(0.0051)|-0.0007<br>(0.0016)|-0.0204*<br>(0.0123)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|12631|
|N (firms)|9306|9306|9306|9306|9306|9306|9306|6012|
|Adj. R<sup>2</sup>|0.0211|0.0230|0.0555|0.0225|0.00970|0.0601|0.0237|0.0491|
|RSS|472.2|471.4|248.5|15007|16660|1711|105.3|1545|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.