From the financial crisis to the real economy: Using firm-level data to identify transmission...

Journal of International Economics 88 (2012) 375–387

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Journal of International Economics

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From the financial crisis to the real economy: Using firm-level data to identifytransmission channels

Stijn Claessens a,b,c, Hui Tong a,⁎, Shang-Jin Wei c,d,e,f

a Research Department, International Monetary Fund, United Statesb University of Amsterdam, The Netherlandsc CEPR, United Kingdomd Columbia University, United Statese Tsinghua University, Chinaf NBER, United States

⁎ Corresponding author.E-mail addresses: [email protected] (S. Claessens)

[email protected] (S.-J. Wei).1 Cetorelli and Goldberg (2011) document a role of glo

Tille (2011) find a role for short-term debt in foreign curre(2010) find greater foreign reserves to be important in al

0022-1996/$ – see front matter © 2012 International Mdoi:10.1016/j.jinteco.2012.02.015

a b s t r a c t
a r t i c l e i n f o
Article history:Received 6 August 2011Received in revised form 28 February 2012Accepted 29 February 2012Available online 8 March 2012

JEL classification:F3

Keywords:Financial crisisReal economyFirm-level dataTransmission channels

Using accounting data for 7722 non-financial firms in 42 countries, we examine how the 2007–2009 crisisaffected firm performance and how various linkages propagated shocks across borders. We isolate and com-pare effects from changes in business cycle, international trade, and external financing conditions, on firms'profits, sales and investment using both sectoral benchmarks and firm-specific sensitivities estimated prior tothe crisis. We find that the crisis had a bigger negative impact on firms with greater sensitivity to businesscycle and trade developments, particularly in countries more open to trade. Interestingly, financial opennessmade limited difference.

© 2012 International Monetary Fund. Published by Elsevier B.V. All rights reserved.

1. Introduction

The 2007–2009 crisis that originated in the United States shockedthe core of the global financial system. It led to a sharp drop in inter-national trade in goods and services to a degree not seen since theend of WWII and triggered a global recession unparalleled since theGreat Depression. A small literature is emerging that studies the(channels of) transmission of the latest crisis across national bordersand the role of country differences in how the economies were affect-ed. The evidence from these studies on the roles of linkages and coun-try differences is mixed. Claessens et al. (2010), Blanchard et al.(2010), and Cetorelli and Goldberg (2011) document evidence thatcountries more integrated with global financial markets sufferedgreater output losses during the crisis.1 In contrast, Rose and Spiegel(2010 and 2011) fail to find strong evidence that country factors,

, [email protected] (H. Tong),

bal banking; Milesi-Ferretti andncy; and Frankel and Saravelosleviating the spillover.

onetary Fund. Published by Elsevier

including bilateral trade and financial linkages with the U.S., are asso-ciated with how the crisis impacted individual countries.2

All these studies rely on aggregatemacro data. Themixed evidenceon the role of specific contagion channels and country factors is per-haps not surprising since macro data reflect the aggregation of multi-ple underlying factors. The crisis likely spread through a combinationof real (e.g., trade) and financial channels, as well as by affecting ex-pectations of consumers and firms, which in turn changed consump-tion and investment behaviors. The existing literature has attemptedto distinguish these channels by including proxies for trade or finan-cial integration (see Rose and Spiegel, 2010, 2011; Milesi-Ferrettiand Lane, 2010). However, these proxies tend to be highly correlatedwith each other and hence do not allow for a clean separation of thedifferent channels. For example, both a reversal of capital flows anda reduction in demand for exports can induce a worsening of corpo-rate sector performance or a contraction in investment.

To make progress, one could employ firm-level micro data. If dif-ferent transmission channels imply different firm-level effects relatedto firm characteristics (e.g., more finance-dependent firms versusmore trade-dependent firms), one has a better chance to isolate and

2 Rose and Spiegel (2011) find few reliable indicators in the pre-crisis data that canhelp explain the incidence of the Great Recession, except that countries with a currentaccount surplus seemed better insulated from slowdowns.

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376 S. Claessens et al. / Journal of International Economics 88 (2012) 375–387

quantify the different channels. Such information is lost in the aggre-gate data. The first firm-level analysis to study how crises (in emerg-ing markets) spread to other markets was conducted by Forbes(2004).3 For the 2008–2009 crisis, micro firm-level evidence is rela-tively scarce, partly because firm-level data for many countries areonly released with long lags.4

One substitute that has been used to date is stock market data, asTong and Wei (2011) do. They report evidence of liquidity crunchesacross emerging economies by showing that the decline in stockprices was more severe for firms that intrinsically are more depen-dent on external finance for working capital over the period fromJuly 2007 to the end of 2008.5 Due to lack of appropriate data at thetime, they were not able to show the impact of the financial crisison actual firm investment and performance.

In this paper, by using actual firm-level balance sheets and incomevariables, and investigating these effects for a large number of coun-tries affected by the crisis, we complement and expand this research.While firm-level data offers richer information than aggregate data,there are caveats to bear in mind. First, since the data cover publiclylisted firms only, we cannot claim that results are representative ofthe whole economy. Second, because firm coverage varies by country,one has to check that the results are not driven by variations in thecountry coverage (which we do).

In the remainder of the paper,we discuss in Section 2 the frameworkthat guides our empirical tests. In Section 3, we describe the sources ofthe data and the definitions of our key variables and in Section 4 presentour empirical results. Finally, in Section 5 we conclude.

2. The framework

Our goal is to use firm-level data to improve our ability to distin-guish different transmission channels through which the financialand economic crisis in the U.S. and other advanced countries affectedthe rest of the world. We examine three possible channels: a businesscycle channel, a trade channel, and a financial channel.

We employ a consistent framework to distinguish the impacts ofthese three channels. To isolate transmission through the businesscycle channel, wemake use of the following idea: if the crisis representsa negative business cycle shock in the respective country, it should bereflected in a relatively worse performance of those firms that aremore business-cycle-sensitive compared to those firms that are lessso. Similarly, if the trade channel is important, it should be reflected ina relatively worse performance of those firms that rely more heavilyon exports compared to those firms that export less. Finally, if a reduc-tion in available credit (a “credit crunch”) plays an important role in thecrisis, it should affect more the performance of those firms that relymore on external finance for investment and working capital relativeto those firms that rely less on external financing.

We cannot use ex post data on trade and finance dependence toanalyze these possible channels. For example, a firm may well reduceits international trade, suggesting that the trade channel is important,but the reason for the reduction in trade could be a lack of working

3 Claessens et al. (2000) investigate how individual East Asian corporations were af-fected by the 97–98 crisis, but their focus was not on spillover channels. In generalthough, studies have used price or aggregate data (see Claessens and Forbes (2001)for an early and Pritsker (forthcoming) for a recent review of the contagion literature).

4 There has been more analysis of the drivers of the recent trade retrenchment, alsousing firm or sector level data; for example, Levchenko et al. (2010), Alessandria,Kaboski and Midrigan (2010), and Bems et al. (2010). Moreover, Duchin et al. (2010)have examined quarterly US investment from Q3, 2007 to Q3, 2008. Internationalfirm-level evidence is still scarce. Bricongne et al. (2009) and Behrens et al. (2010)use firm-level data for France and Belgium to examine the impact of the crisis on firmexports.

5 In terms of transmission mechanisms, Tong and Wei (2011) focus on the composi-tion of a country's pre-crisis capital inflows, and found that stock prices fell significant-ly more for firms in countries with a greater share of short-term capital flows. They didnot explore other channels.

capital, rather than a trade shock. Conversely, a reduction in workingcapital or investment may be the logical response to a reduction in in-ternational trade or domestic activity, and not a reflection of a shockto the supply of external financing.

Our basic empirical strategy therefore is to check whether ex anteclassifications of firms in terms of their intrinsic characteristics –

degree of sensitivity to the business cycle, exposure to trade, and fi-nancial dependence – help to explain changes in their ex post “perfor-mance” (i.e., profits, sales and investments following the crisis). Inour baseline model, we use the approach of relying on the sectorcharacteristics of U.S. firms before the crisis, which are exogenous toour sample of firms (see Rajan and Zingales, 1998), to proxy these in-trinsic characteristics.

To be precise, our empirical specification is given by the followingregression equation:

ΔPerformancei;j;k;t ¼ αþβ � BusinessCycleSensitivityjþγ � TradeSensitivityj þ δ � FinancialDependencejþControli;j;k;t þ εijkt

ð1Þ

where i stands for company, j for sector, k for country and t for time.ΔPerformancei,j,k,t is our measure of the changes in firm-level perfor-mance due to the crisis. For example, we use the change in firms'profit ratio (profits relative to assets), measured as the average ofthe profit ratio for 2008 and 2009 minus the profit ratio in 2007.Using differences in performance has the advantage of controllingfor many firm and country characteristics, such as differences in prof-itability before the crisis. As a start, we assume α, β, γ, and δ to be thesame for all countries in order to estimate average effects.

Propagation of the crisis can depend on not just firm characteris-tics, but also country features. For example, firms in countries moreopen to trade or financial markets could be expected to see theirfirms suffer more from trade or financial shocks. To investigate this,we also explore cross-country heterogeneity in some key dimensions.We do so by including these country features themselves in the re-gressions. Moreover, to explore the channels, we interact firm fea-tures with country features, including country-level of domesticexpenditures, trade openness, exposure to global capital flows, andfinancial development, and then include these interaction terms inthe regressions. For example, we consider the interaction between acountry's degree of international financial integration and itsmanufacturing firms' dependence on external finance.

Our base specification measures a firm's sensitivities to businesscycle and trade and its dependence on external finance usingsector-level information for the United States. As an alternative, weproxy these sensitivities using a firm's own history as realized over2000–2006. The regression specification then becomes:

ΔPerf ormancei;j;k;t ¼ αþβ � BusinessCycleSensitivityiþγ � TradeSensitivityiþδ � FinancialDependencei þ Controli;j;k;t þ εijkt

ð2Þ

where the only difference between this and Eq. (1) is that thesubscripts for the key regressors are now firm- instead of (just)sector-specific. An advantage of this approach is that it incorporatesinformation about heterogeneity across firms within a sector. A dis-advantage is that the firm-specific sensitivity measures can reflectomitted variables and be endogenous to the firm's performance. Torule out some obvious omitted variables, we include individual firmcharacteristics (such as firm size, cash holdings, and profitability),but endogeneity is more difficult to address. Between the two ap-proaches, we therefore place relatively more confidence in the resultsfrom the first specification.

Table 1Number of listed manufacturing firms by country.

Country Obs # Country Obs #

1. Argentina 28 2. Italy 1003. Australia 212 4. Japan 15845. Austria 30 6. Korea (South) 6437. Belgium 48 8. Malaysia 3769. Brazil 111 10. Mexico 4011. Canada 196 12. Netherlands 4913. Chile 41 14. New Zealand 3215. China 866 16. Norway 5317. Colombia 5 18. Pakistan 6619. Czech Republic 2 20. Peru 2221. Denmark 46 22. Philippines 3123. Egypt 41 24. Poland 11425. Finland 60 26. Portugal 1127. France 225 28. Russian Federation 10529. Germany 275 30. South Africa 7231. Greece 91 32. Spain 4333. Hungary 11 34. Sweden 14835. India 995 36. Switzerland 9337. Indonesia 96 38. Thailand 20839. Ireland 12 40. Turkey 12541. Israel 67 42. United Kingdom 349Total 7722

Note: The table lists the number of manufacturing firms in our sample for the year2007. Source: Worldscope.

6 Tong andWei (2008) argue that this index primarily reflects the relative sensitivityof a firm's stock price to an unexpected shock in the business cycle, and is less influ-enced by a firm's sensitivity to financial constraints or uncertainty shocks. First, theyverify that there was a big downward shift in expected aggregate demand, as reflectedby a downward adjustment in the consensus forecast of US GDP growth. Second, be-cause the Federal Reserve took timely actions, both the real interest rate and the TEDspread, after initial spikes, quickly returned to the pre-9/11 level, suggesting the resto-ration of market liquidity. Third, the VIX index, a proxy for the degree of market uncer-tainty, returned to its pre-9/11 level by September 28, 2001. Tong and Wei (2008)therefore conclude that the stock price change from September 10 to 28, 2001, primar-ily reflects the shock in the aggregate business cycle.

7 The data for sector-level exports come from the United Nations Commodity TradeStatistics Database, which records the exports of each 3-digit sector for every country.We sum over countries to derive the global exports for each sector.

377S. Claessens et al. / Journal of International Economics 88 (2012) 375–387

3. Data sources, variables, and basics statistics

We obtain annual data fromWorldscope on the balance sheet, cashflow and income statements for all listed, non-financialmanufacturingcompanies. The data cover 42 advanced countries and emerging mar-kets (note that the U.S. is excluded as it was both the source of the fi-nancial crisis and the country whose data are used to define the sectorcharacteristics used below). The number of listedmanufacturing firmsby country for the last year of the sample (2009) is presented inTable 1. (Our sample period is from 2007 to 2009.) The key dependentvariables are the changes from 2007 to 2008/2009 in three ratios:firm-level profits/assets, sales/assets and investments/assets. Theyare all winsorized at the 1% level to reduce the impact of outliers. Allright-hand-side variables are measured using data prior to 2007.

Fig. 1 plots the density distributions of firm-level profits/assets,sales/assets and investments/assets from 2007 to 2009. The patternsin Fig. 1 are intuitive. For the profit/asset ratio, the curves shift grad-ually to the left over the three years in the sample. Indeed both themean and the median of the profit/asset ratio decline (reported inTable 2a). The left tail also increases over time, indicating an increasein the share of those firms with poor performance when the crisiscame. For the sales/asset ratio, the distribution shifts to the left onlyin 2009, while the curves for 2007 and 2008 track each other quiteclosely (with the 2008 curve being slightly to the right of that for2007). For the capital expenditure/asset ratio, we find that the curvesfor 2007 and 2008 to be quite similar. The 2009 curve clearly shifts tothe left, however, with lower mean and median values, and, interest-ingly, a greater dispersion as well.

Collectively, these charts suggest that sales and investments fellsomewhat later in the crisis phase than profitability did. Fig. 1 alsosuggests that we may find sharper impacts of our explanatory vari-ables considering changes in performance between 2007 and 2009rather than between 2007 and 2008.

i Sector-level and firm-level business cycle sensitivity indexesWe now define our index for a firm's relative sensitivity to thebusiness cycle. As noted, the effect of a crisis on output is likelyto vary by type of product and sector. For example, consumer du-rables are typically more affected than consumer necessities dur-ing a recession. Tong and Wei (2008) develop such a sector-levelsensitivity index using the stock price reactions of US firms to

the September 11, 2001 terrorist attack. They compute the changein log stock price for each US firm between September 10 andSeptember 28, 2001. They then calculate the mean log stockprice change for all firms in each three-digit SIC sector, and useit as a measure of sector-level sensitivity to the business cycle. Ex-cluding financial sector firms, they do this for 361 three-digit levelsectors. This approach assumes that sensitivity to business cycle isan intrinsic property of a sector, and therefore the index derivedfrom the pre-crisis data is applicable to firms in the same sectoracross all countries during the crisis.6

We also develop an indicator for the pre-crisis, firm-specific de-gree of business cycle sensitivity. We construct this as the elastic-ity of firm-specific sales to the country's GDP in the six yearsbefore the crisis, i.e., 2000–2006. More specifically, we regressfor each firm the change in its (log) real sales (in local currency)on the change in the (log) country's real GDP (in local currency)over the period 2000 to 2006, and then use the coefficients asthe firm-level measure of business cycle sensitivity.

ii Sector-level and firm-level trade sensitivity indexesWe next construct a sector-level measure of sensitivity to trade byregressing the change in the log global exports at the 3-digit sectorlevel over the period 2000–2006 on the change in log global GDP(in US dollars) during the same period.7 We then use the coeffi-cient on global GDP as the sector-level trade sensitivity. Notethat this trade sensitivity index is neither country nor firm specif-ic, similar to the earlier sector index for business cycle sensitivity.We also construct a pre-crisis firm-level measure of its sensitivityto trade shocks. We do this by regressing the annual change of afirm's real sales on the annual percentage change in its homecountry exports over the period 2000 to 2006.

iii Sector- and firm-level financial dependence indexesWe use two measures of a firm's intrinsic dependence on externalfinance: Intrinsic dependence on external finance for investment(DEF_INVj) and Intrinsic dependence on external finance for workingcapital (DEF_WKj). We construct a sector-level approximation of afirm's intrinsic dependence on external finance for capital invest-ment following the methodology developed by Rajan andZingales (1998). Specifically, we define:

Dependence on external finance for investment

¼ capital expenditures� cashflowcapital expenditures

:ð3Þ

Besides capital needed for investment, working capital is requiredfor a firm to operate and to satisfy both short-term debt paymentand ongoing operational expenses, and to allow for trade finance.We follow Raddatz (2006) and construct such a measure of intrin-sic dependence on external finance for working capital using thenotion of “cash conversion cycle”, which is commonly used in fi-nancial analysis to measure the liquidity position of a firm. Thecycle measures the time elapsed from the moment a firm pays

Fig. 1. Density distribution of firm performance around the 2008–09 crisis.


for its inputs to the moment it receives payment for the goods itsells. Specifically, we define:

Dependence on external finance for working capital

¼ 365� inventories� account payablescost of goods sold

þ account receivablestotal sales

� �:

ð4Þ

Following Tong andWei (2011), both sector level indexes are con-structed as follows. First, for each U.S. firm during 1990–2006, wecalculate its dependence on external finance for investment andits cash conversion cycle based on the annual data from CompustatUSA Industrial Annual. Second, we define the sector-level value ofthe two indexes by calculating the median across all firms in thesector (at each SIC 3 digit sector). While the original Rajan andZingales (1998) paper covered only 40 (mainly SIC 2-digit) sec-tors, we expand the coverage to 111 3-digit SIC sectors. Theindex is based on US firms, which are judged to be the least likelyto suffer from financing constraints (during normal times) relativeto firms in other countries, meaning we can reasonably assumethat the same intrinsic external financing dependence applies tofirms in all other countries. This assumption is common in the lit-erature (earlier papers that have used such indexes include

Claessens and Laeven, 2003; Raddatz, 2006; Kroszner et al.,2007). The literature has also confirmed that the rank order of sec-tors in terms of finance dependence ratio is similar in Canada(Rajan and Zingales (1998)).Our alternative measure uses information on the history of a firmduring each year between 2000 and 2006. We define individualfirms' actual use of external finance for working capital and in-vestment (Actual firm use of external finance for investment,ACT_INVi, and Actual firm use of external finance for working capital,ACT_WKi) in a similar way:

ACT INVi ¼ Actual firm use of external finance for investment

¼ capital expenditures� cash flowcapital expenditures

:

ð5Þ

ACT WK ¼ Actual firm use of external finance for working capital

¼ 365� inventories� account payablescost of goods sold

þ account receivablestotal sales

� �

ð6Þ

We calculate at the individual firm level the median of these year-specific ratios, ACT _ INVi or ACT _ WKi. Using these firm-level

Table 2aSummary statistics of firm performance before and during the 2008–09 crisis.

Variable Year Obs Mean Std p25 Median p75 Min Max

Profit/asset 2007 7540 0.097 0.131 0.062 0.108 0.155 −0.555 0.395Profit/asset 2008 7506 0.074 0.146 0.045 0.092 0.143 −0.555 0.395Profit/asset 2009 7147 0.063 0.141 0.030 0.080 0.131 −0.555 0.395Sales/asset 2007 7722 1.019 0.554 0.658 0.933 1.284 0.028 2.964Sales/asset 2008 7721 1.035 0.564 0.665 0.946 1.307 0.028 2.964Sales/asset 2009 7402 0.988 0.551 0.614 0.902 1.255 0.028 2.964Capital expenditure/asset 2007 7606 0.059 0.059 0.019 0.041 0.078 0.000 0.301Capital expenditure/asset 2008 7575 0.059 0.058 0.019 0.041 0.079 0.000 0.301Capital expenditure/asset 2009 7261 0.049 0.052 0.015 0.033 0.063 0.000 0.301

Note: The data are for 7722 listed manufacturing firms from 42 countries. Firm data are winsorized at the 1% level. Source: Worldscope.

Table 2bSummary statistics of key dependent and explanatory variables.

Variable Obs Mean Std p25 Median p75 Min Max

Firm levelChange in profit/asset (%) 7540 −3.09 9.26 −6.27 −2.01 0.86 −38.26 26.68Change in sales/asset (%) 7722 −0.73 22.56 −10.57 0.10 9.85 −82.07 70.73Change in CapEX/asset (%) 7606 −0.57 4.90 −1.99 −0.08 1.31 −19.91 14.74Firm-level business cycle sensitivity 5756 1.93 10.23 −1.79 1.56 5.63 −40.10 47.43Firm-level trade sensitivity 5710 0.48 2.88 −0.47 0.26 1.44 −10.57 12.62Actual firm use of external finance for working capital (ACT_WK, in days) 7257 105.60 58.01 64.50 96.84 134.40 4.40 307.86Actual firm use of external finance for investment (ACT_INV) 6152 −0.31 2.26 −1.17 −0.22 0.51 −8.95 11.81

Sector levelBusiness cycle sensitivity 123 1.56 0.96 1.02 1.43 2.07 −1.06 4.58Trade sensitivity 132 1.32 0.60 0.99 1.29 1.57 −0.64 3.58Dependence on external finance for working capital (DEF_WK , days) 111 91.91 32.71 66.98 88.45 116.06 22.34 158.62Dependence on external finance for investment (DEF_INV ) 100 0.03 0.44 −0.26 0.04 0.33 −0.86 1.13

Country levelFinancial Openness (year 2006) 42 3.47 4.14 1.18 2.07 4.19 0.61 23.81Trade linkage (year 2006) 42 0.02 0.07 −0.02 0.02 0.06 −0.10 0.22Credit over GDP (year 2006) 42 0.87 0.51 0.35 0.87 1.12 0.13 1.86Domestic expenditure over GDP (year 2006) 42 0.97 0.07 0.94 0.97 1.02 0.78 1.09

Note: The data is for 7722 listed manufacturing firms in 42 countries. Key dependent variables are the changes between 2007 and 2008/2009 in the ratios of firm-level profits/as-sets, sales/assets and investments/assets. Source: Worldscope.

Table 2cCorrelation table of key dependent and explanatory variables.

ΔProfit ΔSales ΔCE Business cyclesensitivity

Tradesensitivity

DEF_WK DEF_INV Firm business cyclesensitivity

Firm tradesensitivity

ACT_WK

ΔSales 0.18*ΔCapital expenditure 0.001 −0.03*Business cycle sensitivity −0.04* −0.04* −0.01Trade sensitivity −0.07* −0.05* 0.02* 0.06*DEF_WK −0.02 −0.04* 0.02 0.03* 0.01DEF_INV −0.05* −0.04* 0.01 0.23* 0.24* 0.26*Firm business cycle sensitivity −0.03* −0.04* −0.03 0.04* 0.004 0.01 0.01Firm trade sensitivity −0.03 −0.03* −0.03* 0.01 0.02 0.01 0.01 0.28*ACT_WK 0.002 0.005 −0.003 0.04* 0.01 0.20* 0.03* 0.04* 0.01ACT_INV 0.03* 0.03* −0.03* 0.02 −0.02 0.03* 0.06* −0.01 0.04* 0.09*

Note: *is at the 5% significance level. The data is for 7722 listed manufacturing firms in 42 countries. Change in profit/asset refers to the difference between the profit/asset ratioaveraged over 2008–09 and the profit/asset ratio in 2007. Similar for the changes of sales/asset and capital expenditure (CapEX)/asset. Source: Worldscope.


indicators, we then ask whether firms that used more external fi-nancing prior to the crisis were more affected by the global crisis.Even though the firm-level actual use of external finance may beendogenous to, say, the quality of firms, the measures are pre-determined with respect to the 2008–2009 crisis.

iv Basic statisticsTable 2a provides summary statistics for our general firm perfor-mance measures before and during the 2008–09 crisis. Table 2breports summary statistics for our dependent variables and key

explanatory variables. The statistics confirm the impression gleanedfromFig. 1: there is awide dispersion acrossfirms in performance. In-deed, while many firms' profits weakened, there were firms that ac-tually increased their profitability in spite of the crisis. Similarly,while the sales and capital expenditure to assets ratios generally de-clined, there were exceptions. These variations would allow us toperform meaningful analyses.Table 2c reports the correlations among variables, with an asterisk in-dicating significance at the 5% level. We find that the change in profit

Table 3The impact of crisis on firm performance—sector feature.

(1) (2) (3) (4) (5) (6)

ΔProfit ΔSales ΔCapEx ΔProfit ΔSales ΔCapEx

Business cycle sensitivity −0.456** −1.196*** −0.0835[0.222] [0.451] [0.106]

Trade sensitivity −1.068*** −1.778** 0.125 −1.091*** −1.815** 0.121[0.266] [0.693] [0.0817] [0.304] [0.781] [0.0839]

Dependence on external finance for working capital −0.00406 −0.0213* 0.00183 −0.00379 −0.0206 0.00188(DEP_WK) [0.00509] [0.0129] [0.00193] [0.00556] [0.0140] [0.00189]Dependence on external finance for investment −0.489 −0.398 0.0997 −0.666 −0.884 0.0673(DEF_INV) [0.392] [0.767] [0.165] [0.409] [0.866] [0.165]Constant −0.391 5.750*** −0.809*** −1.057 3.959* −0.931***

[0.745] [2.092] [0.300] [0.715] [2.181] [0.235]Observations 7540 7722 7606 7547 7729 7613R-squared 0.008 0.005 0.001 0.007 0.004 0.000

Note: Key dependent variables are the changes between 2007 and 2008/2009 in the ratios of firm-level profits/assets, sales/assets and investments/assets. Robust standard errors inbrackets, clustered at the 3-digit sector level. ***pb0.01, **pb0.05, *pb0.1.


is significantly negatively associated with more than half of the ex-planatory variables. Of course, these are only pair-wise correlations,without controlling for other factors. We next address more formallythe factors contributing to changes in performance by employingmultivariate regression analyses.

4. Empirical results

i Baseline resultsWe start with our basic regression, which examines how varioussector features relate to changes in firm performance during thecrisis. These results are reported in Table 3. As our explanatoryvariables are at the sector level, we cluster the standard errorsby sector.In Column 1, we look at the impact of the crisis on the changes infirms' profit/asset ratios. We find the change in profits to be morepronounced for those sectors that are intrinsically more sensitiveto business cycle shocks. This result suggests that there was in-deed a significant channel of business cycle sensitivity during thecrisis period as consumers and firms adjusted. The change inprofits is also more pronounced for more trade-sensitive sectors,consistent with the decline in global trade during the crisis period.The coefficients on DEP_WK and DEP_INV are also negative, albeitinsignificant.8

In Column 2 of Table 3, we look at the impact of the crisis on thechange in sales over assets. Similar to profits, sales declined signif-icantly more for those sectors that are more sensitive to eitherbusiness cycles or to trade, consistent with the presence of impor-tant business cycle and trade effects. Sales over assets also de-creased significantly more for those sectors with greater intrinsicneeds for working capital. This result suggests that disruptions tothe supply of working capital related to the global financial crisisled to a reduction in firm-level sales. This finding is consistentwith Tong and Wei (2011), who found that the crisis reducedstock prices significantly more in those sectors with large workingcapital needs.In Column 3, we examine the impact of the crisis on capital invest-ment. Here we find no significant relationships. This may not be asurprise, since investment is notoriously difficult to explain ingeneral as it depends on (volatile) expectations of future profit-ability and is subject to long leads and lags as well as lumpy be-havior.

8 Note that the trade effect could reflect a financing effect if a contraction of trade iscaused by a contraction in trade financing (see Amiti and Weinstein (2009); Paravisiniet al. (2011)).

The business cycle and the trade channels could be related, espe-cially in a country that is highly open to trade. To give the tradechannel the maximum chance to reveal itself, we drop the busi-ness cycle channel in Columns 4 to 6. We find that the coefficientsfor the trade channel in the profits, sales and investment regres-sions remain almost the same as in Columns 1–3, suggesting thatour business cycle and trade sensitivity indexes capture somewhatdifferent aspects.To provide the economic impact of our estimates, we focus on thestatistically significant variables in Columns 1–3. A one standarddeviation increase in business cycle sensitivity, say from a levelin the Surgical and Medical Instruments sector to one in the Con-struction Machinery sector will reduce profits by 0.44%, or 14% ofthe average decline in profit. Meanwhile, a one standard deviationincrease in trade sensitivity will reduce profit by 0.64%, or 21% ofthe average decline in profit. Finally, a one standard deviation in-crease in the intrinsic needs for working capital will reduce salesby 0.70%, or equivalent to the average drop in sales (0.73%).These estimates suggest that the economic impacts of the threechannels are significant.

ii Addressing possible sample selection and other biasesAcross the 42 countries in the sample, the number of firms is un-even. In this subsection, we examine the concern that the varyingcountry coverage may create a bias due to the dominance of somelarge countries with many firms. We do this in two ways. We firstrun a weighted regression, using the same specifications as inTable 3, with the weights equal to the inverse of the square rootof the number of firms in the sample for each country. The weight-ing scheme reduces the dominance of large countries in the esti-mation results. We find that the results become even morepronounced for the business cycle and the trade channels for profitsand sales (Table 4, Columns 1–3). For example, the coefficient on thebusiness cycle sensitivity measure is −1.2 in the profit equationwith theweighted regression, while it was−0.46 in the baseline re-gression. Also, the coefficient for trade sensitivity is−3.3 in the salesequation with weighted regression, while it was−1.78 in the base-line regression. The dependence on external finance for capital in-vestment (DEP_INV) is now significantly negative at the 10% levelfor investment; that is, the crisis tightens financial constraints andreduces available funding for capital investment.As an alternative way to control for uneven sample coverage, we re-strict our sample to the 100 largest listed manufacturing firms ineach country. The results are presented in Columns 4 to 6 of Table 4.Here the sample size drops to about one-third of the number inTable 3. Again, we find the results for both the business cycle andtrade channels to be larger and more significant than those reportedin Table 3. For example, trade sensitivity now has a coefficient of −3.42, while it was −1.78 in the baseline regression. Furthermore,

Table 4The impact of crisis on firm performance—sector feature with weighted regressions.

Weighted regressions Top 100 firms

(1) (2) (3) (1) (2) (3)ΔProfit ΔSales ΔCapEx ΔProfit ΔSales ΔCapEx

Business cycle sensitivity −1.219** −1.479** −0.153 −0.948*** −2.475*** −0.156[0.577] [0.638] [0.219] [0.316] [0.791] [0.111]

Trade sensitivity −1.154** −3.250*** −0.151 −1.354*** −3.421*** 0.107[0.516] [0.885] [0.296] [0.276] [1.127] [0.108]

Dependence on external finance for working capital 0.00363 0.00299 0.00680 −0.00351 −0.0311* 0.00167(DEP_WK) [0.0133] [0.0311] [0.00418] [0.00796] [0.0172] [0.00241]Dependence on external finance for investment −0.459 −2.411 −0.609* −0.212 1.506 −0.109(DEP_INV) [0.967] [2.263] [0.355] [0.507] [1.339] [0.253]Constant −0.761 4.695 −0.669 −0.0270 9.397*** −0.634*

[1.263] [3.538] [0.550] [1.079] [2.917] [0.320]Observations 7540 7722 7606 2635 2703 2666R-squared 0.015 0.014 0.006 0.018 0.016 0.001



dependence forworking capital (DEP_WK)nowbecomes significantlynegative in the sales equation, suggesting that the crisis reduces theavailability of working capital and thereby decreases firms' sales.Our sample of listed firms could have a built-in survivorship bias—those firms that experienced the biggest declines in profitability dur-ing the crisis may have exited the sample. So the true decline in firmperformance may be greater than what our statistics capture.9 Wetherefore control for survivorship bias by running a Heckman selec-tion model, which has a selection equation and an outcome equation.In the selection equation, we include all the explanatory variablesfrom the outcome equation (i.e., the explanatory variables inTable 3), as well as the firm's O-score measured as of 2007. The O-score is developed by Ohlson (1980) and measures the likelihood ofa firm's bankruptcy, with a higher O-score indicating a higher likeli-hoodof bankruptcy.10 In Table 5,we report the results for the outcomemodel in Columns 1, 3, and 5 and for the one-step Heckman selectionmodel in Columns 2, 4 and 6. The selection equations confirm thatfirmswith a higher O-scoreweremore likely not included in our sam-ple by 2009. After controlling for this selection effect (or survivorshipbias), the coefficients on business cycle and trade sensitivities in theoutcome equations are still negative and statistically significant, andsimilar to those of Table 3. In other words, those firms most sensitiveto a business cycle shock or a trade shock experience a greater declinein profit and sales. Moreover, the coefficient for working capital needs(DEP_WK) remains significantly negative in the sales equation. 11

9 Worldscope drops a company if it becomes privately held, merged, liquidated, orotherwise inactive.10 The O-Score combines nine accounting ratios into a single statistic:

O� Score ¼−1:32−0:41Sizeþ 6:03TotalLiabilityTotalAsset

−1:43WorkingCapital

TotalAsset

þ0:08CurrentLiabilitiesCurrentAsset

−2:37NetIncomeTotalAsset

−1:83FFO

TotalLiabilities

þ0:285F−1:72G−0:52H

where Size is the log of total asset divided by the GDP deflator; FFO means pre-tax in-come plus depreciation and amortization; F is a dummy equal to one if cumulative netincome over the previous two years is negative; G is a dummy equal to one if owners'equity is negative; and H is the change in net income.11 There might also have been some anticipation of the crisis, but this is unlikely toqualitatively alter our results either for two reasons. First, any anticipation of the crisiswould presumably lead a firm to be better prepared for the crisis, and it thereforeneeds to respond less to the crisis when the crisis actually comes. This would meanthat the anticipation effects would lead to a downward bias in the size of the estimatedresponse to crisis, biasing our results toward finding no effect. Second, in later robust-ness checks (Table 8), we include as an additional control variable the firm-level cashholdings in year 2006, where cash holding is meant to capture a firm's degree of pre-paredness as reflected in its precautionary savings. Reassuringly, our results onbusiness-cycle and trade sensitivities carry through.

iii Country featuresWe next investigate the role of country factors. To examine thedifferential effects of the crisis across countries we include the fol-lowing country characteristics: the share of domestic expenditurein total activity (defined as the sum of consumer expenditures, in-vestment and government expenditure over GDP), trade linkage(defined as exports minus imports over GDP), financial openness(defined as total international assets plus liabilities over GDP),and financial development (defined as credit to private sectorover GDP). Note that, by definition, the first two variables, theshares of domestic expenditures and net trade in GDP, sum up toone. Country-level domestic expenditure share is interacted withsector-level business cycle sensitivity; country-level trade linkageis interacted with sector-level trade sensitivity; and country-levelfinancial openness and development are interacted with bothDEP_WK and DEP_INV. These country features are all measuredas of 2006, i.e., prior to the crisis, and hence do not vary duringthe sample period.The results are reported in Table 6. In Columns 1–3, we do not in-clude any country or sector fixed effects, while in Columns 4–6, weinclude both sector and country fixed effects (and thus drop thecountry level variables that are not interacted).Column 1 reports the results for the change in profits. Here wefind a significantly negative coefficient for the interaction term be-tween trade sensitivity and trade linkage, but no significant coeffi-cients for the interaction terms between the other sectorcharacteristics and country features. Column 2 reports the resultsfor the change in sales. Here we find again the interaction betweentrade sensitivity and trade linkage to have a negative and statisti-cally significant coefficient, but no other interactive variable isfound to be statistically significant. Column 3 reports the resultsfor the change in capital expenditures. The only significant coeffi-cient is the interaction between trade sensitivity and trade link-age, with trade-sensitive sectors reducing capital expendituresmore in trade-open countries. The coefficient for general tradelinkage is positive (0.66), but its interpretation has to considerthe interaction effect as well. Overall, the net effect for trade link-age is significantly negative for firms with an average trade sensi-tivity (i.e., −4.84=0.66–4.17∗1.32).In Columns 4–6, we include both country and sector fixed effects.The sector-level trade sensitivity interacted with country-leveltrade linkage always retains its significantly negative coefficientfor all three performance measures. For profit, business cycle sen-sitivity interacted with domestic expenditure has again a negativecoefficient, but now significant at the 10% level. For capital expen-ditures, dependence on external finance for investment interactedwith financial op rowsep="1"enness is again negative, and now

Table 5The impact of crisis on firm performance—the Heckman Selection Model with the O-Score.

(1) (1) (2) (2) (3) (3)

ΔProfit Selection ΔSales Selection ΔCapEx Selection

Business cycle sensitivity −0.586*** 0.0748* −1.284*** 0.0714 −0.0851 0.0817**[0.219] [0.0397] [0.466] [0.0439] [0.124] [0.0399]

Trade sensitivity −1.173*** 0.0242 −1.625*** 0.0441 0.126 0.0423[0.264] [0.0443] [0.625] [0.0681] [0.0958] [0.0598]

Dependence on external finance for working capital −0.00374 −0.000527 −0.0221* −0.000526 0.00255 0.000115(DEP_WK) [0.00530] [0.000884] [0.0133] [0.00116] [0.00201] [0.00108]Dependence on external finance for investment −0.435 0.0493 −0.172 0.0790 0.0727 0.0490(DEP_INV) [0.370] [0.0662] [0.778] [0.0749] [0.179] [0.0785]O-score 2007 −0.00552*** −0.00668** −0.0277***

[0.00142] [0.00280] [0.00372]Constant 0.508 1.498*** 6.420*** 1.634*** −0.914*** 1.407***

[0.764] [0.132] [2.080] [0.172] [0.316] [0.167]Observations 7411 7411 7411 7411 7411 7411



also statistically significant at the 5% level.Collectively, these results differ from Rose and Spiegel (2010,2011), who find little systematic evidence regarding the role ofcross-country linkages during the crisis on outcomes at themacro level. Here we find the openness of the country to tradeand finance to have affected the impact of the crisis on firms inmaterial ways.To gauge economic impacts, we use the coefficients of Columns1–3 in Table 6. We find that for a firm in a sector whose trade sen-sitivity is at the 75th percentile (which is General IndustrialMachinery), an increase in the country trade linkage by one

Table 6The impact of crisis on firm performance—sector and country interaction.

(1) (2)

ΔProfit ΔSales

Domestic expenditure∗Business cycle sensitivity −3.431 6.651[2.351] [6.562]

Trade linkage∗Trade sensitivity −9.139** −18.05**[3.548] [8.999]

Financial openness ∗DEP_WK 0.00175 −0.00651[0.00223] [0.00446]

Financial openness ∗DEP_INV 0.0424 0.593[0.162] [0.362]

Credit/GDP∗DEP_WK −0.00152 0.0357[0.0139] [0.0347]

Credit/GDP∗DEP_INV 0.353 −1.385[1.139] [2.709]

Business cycle sensitivity 2.883 −7.720[2.273] [6.410]

Trade sensitivity −0.920*** −1.132[0.296] [0.713]

Dependence on external finance for −0.00670 −0.0417working capital (DEP_WK) [0.0110] [0.0285]Dependence on external finance for −0.920 −0.795investment (DEF_INV) [0.913] [2.223]Financial openness −0.400** 0.155

[0.192] [0.447]Credit/GDP 0.407 2.526

[1.281] [3.431]Domestic expenditure/GDP −3.620 −35.58

[13.50] [29.69]Trade linkage 6.764 −2.277

[13.69] [30.91]Constant 3.639 37.82

[13.37] [29.61]Sector fixed effects No NoCountry fixed effects No NoObservations 7540 7722R-squared 0.013 0.012

Note: Key dependent variables are the changes between 2007 and 2008/2009 in the ratios ofbrackets, clustered at the country-sector level. ***pb0.01, **pb0.05, *pb0.1.

standard deviation (e.g., from France to Brazil) will reduce a firm'sprofit ratio by 0.53%, or about 18% of the average drop in the profitratio over the crisis period. The same increase in trade linkagewould reduce the sales to asset ratio by 2.14%, which actually ex-ceeds the average decline of sales of 0.73%. Furthermore, the sameincrease in trade linkage would reduce the capital expenditures toasset ratio by 0.41%, which is large compared to the average de-cline of capital investment, 0.57%. Overall, Table 6 suggests thatexposure to international trade was a statistically and economical-ly important channel in the global transmission of the crisis tofirms.

(3) (4) (5) (6)

ΔCapEx ΔProfit ΔSales ΔCapEx

0.0651 −4.140* 4.208 0.439[1.301] [2.160] [6.383] [1.198]−4.172*** −8.049*** −13.23* −3.874***[1.497] [2.496] [7.309] [1.349]0.000668 0.000914 −0.00653 0.000954[0.000746] [0.00234] [0.00450] [0.000752]−0.0963 0.0306 0.438 −0.136**[0.0614] [0.166] [0.342] [0.0652]0.000226 −0.00134 0.0201 −0.00480[0.00674] [0.0137] [0.0334] [0.00639]0.651 0.443 −1.397 0.632[0.611] [1.034] [2.525] [0.533]−0.141[1.234]0.259**[0.130]−0.000740[0.00596]−0.307[0.590]−0.105[0.0747]0.863[0.656]−5.656[5.458]0.663[5.601]4.144[5.396]No Yes Yes YesNo Yes Yes Yes7606 7540 7722 76060.005 0.052 0.060 0.031

firm-level profits/assets, sales/assets and investments/assets. Robust standard errors in

Table 7The impact of crisis on firm performance—role of monetary and fiscal stimulus.

(1) (2) (3) (4) (5) (6)


Fiscal stimulus∗Business cycle sensitivity 0.332*** 0.252 −0.0643 0.317*** 0.240 −0.0680[0.115] [0.344] [0.0551] [0.116] [0.354] [0.0569]

Change in ST interest rate∗DEP_WK 0.00490** 0.00440 0.000926[0.00187] [0.00523] [0.00135]

Change in ST interest rate∗DEP_WK 0.159 0.307 −0.0138[0.156] [0.367] [0.134]

Change in money base∗DEP_WK −0.004 −0.014 0.001[0.006] [0.016] [0.002]

Change in money base∗DEP_INV 0.852** 2.680*** −0.131[0.398] [0.918] [0.153]

Country fixed effects Yes Yes Yes Yes Yes YesSector fixed effects Yes Yes Yes Yes Yes YesObservations 7540 7722 7606 7540 7722 7606R-squared 0.053 0.059 0.030 0.052 0.060 0.030

Note: Key dependent variables are the changes between 2007 and 2008/2009 in the ratios of firm-level profits/assets, sales/assets and investments/assets. DEP_WK is the intrinsicdependence on external finance for working capital; while DEF_INV is the intrinsic dependence on external finance for investment. Robust standard errors in brackets, clustered atthe 3-digit sector level. ***pb0.01, **pb0.05, *pb0.1.


iv Policy measuresCountries took many measures aimed at mitigating the effects ofthe crisis on their economies. These measures varied from mone-tary easing and fiscal stimulus, to financial sector interventions,such as liquidity support, recapitalization and guarantees, and di-rect support to the real sectors, as for trade finance and SMEs (andsometimes large corporations). These policy measures could havemitigated the impact of the crisis and thereby also reduced the im-portance of spillovers in our regressions. In the extreme case, if af-fected countries adopted fully effective counter-cyclical policies,then there may not be much connection between country featuresand the magnitude of spillovers. Policies may also have aimed tomitigate the impacts of specific channels, such as the financialshock or the business cycle shock. It is thus interesting, also froma public policy point of view, to investigate the effects of these pol-icies on firm performance through specific channels.In Table 7, we specifically examine whether countries' fiscal andmonetary stimulus mitigated the impact of the crisis in generaland affected the severity of the transmission through the businesscycle and financing channels in particular. (We do not include thetrade channel here as that is not directly affected by these policymeasures.) We now include country and sector fixed effects asgeneral controls (this also means we can no longer identify thebusiness cycle, trade and finance channels on their own). We in-teract fiscal stimulus, measured by the size of discretionary fiscalstimulus as a percent of GDP announced between September2008 and March 2009, with our business cycle sensitivities. Ourfirst measure of monetary stimulus is proxied by the change innominal short-term interest rates from September 2008 toMarch 2009, as also used by Laeven and Valencia (2011). We in-teract this measure with DEP_INV and DEP_WK to examine theimpact of monetary stimulus on profits, sales, and investmentthrough the financing channel. We do not include monetary stim-ulus on its own as it is captured by the country fixed effects. Boththe fiscal and monetary stimulus might be endogenous as theycould be driven by the severity of the shocks and the depth ofthe recession within the country. Since this would bias the coeffi-cients toward zero, to the extent that we find statistically signifi-cant effects, we can reasonably argue that the stimulus played apositive role.Our measure of fiscal stimulus interacted with business cycle sen-sitivity has a positive significant coefficient in the case of profits,but is insignificant for sales and investment (Table 7, Columns1–3). The interaction of monetary stimulus with DEP_WK is posi-tive for the changes in profits, sales and investment and significant

at the 5% level in case of profit. In Columns 4–6, we use anothermeasure of monetary policy: the change in the money base overGDP from September 2008 to March 2009. We find again thatmonetary policy stimulus interacted with DEP_INV has a signifi-cantly positive coefficient in case of profits. Now the coefficientin the sales equation is also significant, but that in the investmentequation remains insignificant. Overall, we find evidence of somepositive impacts of fiscal stimulus through the business cyclechannel and of strong positive impacts of monetary stimulusthrough the financial channel.

v Additional robustness checksSome firms might have anticipated the arrival of the crisis. Toallow for this possibility, we include among the set of explanatoryvariables a number of firm-specific control variables, such asTobin's Q, firm size (total assets in US dollars), cash holding/asset, and profitability (EBIT over assets). All these firm controlsare again measured by their values in the year 2006, so they arepre-determined with respect to the crisis. The idea of this specifi-cation is that, with anticipation for the arrival of a crisis, firmscould adjust these variables prior to the crisis as a precautionarymeasure.In Columns 1–3 of Table 8, we replicate the first three columns ofTable 3. Adding these firm variables does not weaken our resultsfor the three sectoral sensitivities measures and strengthensthem in some cases. For example, in Column 1, studying thechange in the profit ratio, the coefficient for business cycle sensi-tivity is now larger (−0.57) compared to Table 3 (−0.46).In Columns 4–6 of Table 8, we repeat the exercise of Columns 4–6of Table 6 regarding the interaction of country and sector features,but adding now again our firm-specific controls. The interactionterms of sector and country features become slightly more signif-icant. For example, in the profit equation (Column 4), trade link-age interacted with trade sensitivity now has a coefficient of(−9.1) compared with (−8.0) earlier (Table 6, Column 4). Ofthe firm controls themselves, a higher Tobin's Q is significantly as-sociated with lower profit and lower sales, reflecting that firmsthat were valued higher, possibly because of their (perceived)greater growth opportunities, suffered more in the crisis. Howev-er, we need to exercise caution in interpreting these results, asthey may suffer from endogeneity or omitted variables' biases.We next check whether our results are affected by the specific pe-riod over which we conduct the comparison. In Table 9, we repli-cate the setup of Table 3 but study separately the crisis as to itseffects over the 2008 and 2009 subperiods. In Columns 1–3, westudy the change between 2007 and 2008, while in Columns

Table 8The impact of crisis on firm performance—adding firm controls.

(1) (2) (3) (4) (5) (6)


Business cycle sensitivity −0.565** −1.443*** −0.0870[0.216] [0.458] [0.125]

Trade sensitivity −1.098*** −1.117* 0.137[0.257] [0.630] [0.0952]

Dependence on external finance for working capital (DEP_WK) −0.00217 −0.0267** 0.00277[0.00487] [0.0119] [0.00216]

Dependence on external finance for investment (DEF_INV) −0.558 −1.026 0.0166[0.387] [0.806] [0.179]

Domestic expenditure∗Business cycle sensitivity −4.008* 10.53 0.896[2.155] [6.629] [1.205]

Trade openness∗Trade sensitivity −9.127*** −13.98** −3.454***[2.524] [7.050] [1.262]

Financial Openness∗DEP_WK 0.000872 −0.00644 0.00115*[0.00242] [0.00469] [0.000656]

Financial openness ∗DEP_INV 0.0124 0.200 −0.183***[0.164] [0.356] [0.0606]

Credit/GDP∗DEP_WK −0.00301 0.0189 −0.00403[0.0143] [0.0350] [0.00591]

Credit/GDP∗DEP_RZ 0.779 −0.196 0.869[1.046] [2.645] [0.533]

Tobin-Q (06) −0.694*** −1.348*** −0.0734 −0.607*** −1.029*** −0.0659[0.152] [0.335] [0.0603] [0.156] [0.354] [0.0773]

Firm size (06) −0.0243 0.293* 0.114*** 0.150** 0.0533 0.0321[0.0761] [0.156] [0.0312] [0.0717] [0.176] [0.0364]

Cash holding/assets (06) −3.121** 11.55*** 0.531 −2.593** 9.235*** −0.244[1.249] [2.465] [0.552] [1.207] [2.288] [0.522]

Profitability (06) −8.498*** −20.58*** −1.819*** −9.590*** −19.23*** −1.557***[1.238] [2.629] [0.481] [1.430] [2.737] [0.532]

Constant 1.829 3.690 −2.147***[1.239] [2.925] [0.586]

Sector and country fixed effects No No No Yes Yes YesObservations 6996 7097 7007 6996 7097 7007R-squared 0.034 0.028 0.005 0.080 0.078 0.039

Note: Key dependent variables are the changes between 2007 and 2008/2009 in the ratios of firm-level profits/assets, sales/assets and investments/assets. Robustness standarderrors in brackets. ***pb0.01, **pb0.05, *pb0.1.


4–6, we look at the change between 2007 and 2009. Since thechange in firm performance was stronger in 2009 (as also sug-gested by Fig. 1), we can expect to find greater evidence of the ef-fects of channels during the second subperiod.We largely confirm the role of the various channels and countryfactors. For the change in profit in 2008 compared to 2007 (Col-umn 1), we find no significant result for the sectoral variables interms of the business cycle, trade and financial channels. In con-trast, for the change in profits between 2007 and 2009 (Column4), coefficients are negative for all three channels and significantfor the business cycle and trade channels. For the change of salesin 2008 (Column 2), we find business cycle sensitivity to be

Table 9The impact of crisis on firm performance—separating year 2008 and 2009.

(1)

ΔProfit 08

Business cycle sensitivity −0.215[0.251]

Trade sensitivity −0.264[0.335]

Dependence on external finance for working capital (DEP_WK) 0.00487[0.00491]

Dependence on external finance for investment (DEF_INV) −0.141[0.443]

Constant −2.004***[0.700]

Observations 7506R-squared 0.001

Note: Key dependent variables are the changes between 2007 and 2008 (or 2009) in the rerrors in brackets, clustered at the 3-digit sector level. ***pb0.01, **pb0.05, *pb0.1.

negative, but trade sensitivity to be significantly positive. One pos-sibility for the positive result for trade sensitivity could be that weare using annual data on firm performance and may thus miss thedecline in trade in the fourth quarter of 2008. (Note that on an an-nual basis, exports still increased rather than decreased in 2008, asshown at the global level in Fig. 2.) In Column 2, we find a signif-icantly negative coefficient for working capital needs, whichsuggests the presence of some financial constraints already in2008. For sales in 2009 (Column 5), we find that trade sensitivityhas a negative coefficient, significant at the 1% level. Moreover,compared to 2008, business cycle sensitivity has a more pro-nounced impact, which is also statistically more significant.

(2) (3) (4) (5) (6)

ΔSales 08 ΔCapEx08 ΔProfit 09 ΔSales 09 ΔCapEx09

−0.598 −0.0366 −0.948*** −1.629** −0.151[0.464] [0.103] [0.307] [0.727] [0.134]1.288** 0.134 −2.005*** −5.022*** 0.155[0.598] [0.0870] [0.371] [1.134] [0.104]−0.0407*** 0.000498 −0.0102 −0.00702 0.00145[0.0111] [0.00193] [0.00804] [0.0211] [0.00262]0.124 0.0519 −0.717 −1.212 0.157[0.634] [0.145] [0.543] [1.219] [0.212]4.349*** −0.276 1.723 7.676** −1.234***[1.636] [0.294] [1.148] [3.328] [0.396]7721 7575 7147 7402 72610.004 0.000 0.015 0.011 0.001

atios of firm-level profits/assets, sales/assets and investments/assets. Robust standard

-0.1

0

0.1

0.2

0.3

0.4

0.5

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Lo

g D

iffe

ren

ce

Household consumption Government expenditure Net Exports Capital formation GDP

Fig. 2. Trend of global GDP and its components.


Overall, this suggests that, while financing constraints may haveplayed a role already in 2008, the trade and business cycle chan-nels played a more significant role in affecting firm performancein 2009 than in 2008.We next use another proxy for the trade channel, namely the per-centage change in exports between 2007 and 2009 at the 4-digitSIC sector level for each country where the firm is located. Weuse this measure with the caveat that it is measured over thesame period as our dependent variables are, i.e., it is not pre-determined, and hence is more subject to the problem of endo-geneity. On the other hand, it presumably captures the trade chan-nel more directly, as it measures the degree to which exportsdeclined, and could hence serve as a useful check on our earliermeasure of trade sensitivity. In Table 10, we find that thecountry-sector decline of exports from 2007 to 2009 is associatedwith a decline in firm-level profits, sales and investment over thesame period, with the effect being significant for profits. More-over, we continue to find a significantly negative impact of the cri-sis through the business cycle channel on the change in profits andsales, and evidence of a negative effect through the financingchannel with regard to the change in profits.

Table 10The impact of crisis on firm performance—alternative measure of trade channel.

(1) (2) (3)

ΔProfit ΔSales ΔCapEx

Business cycle sensitivity −0.406* −1.146** −0.0638[0.246] [0.477] [0.107]

Trade channel (% change ofcountry-sector exports)

1.353*** 1.127 0.150[0.379] [0.776] [0.195]

Dependence on externalfinance for working capital

−0.00216 −0.0194 0.00181

(DEP_WK) [0.00534] [0.0126] [0.00197]Dependence on external financefor investment

−0.707* −0.906 0.159

(DEP_INV) [0.362] [0.692] [0.163]Constant −2.144*** 2.957* −0.644**

[0.663] [1.557] [0.274]Observations 7552 7735 7619R-squared 0.006 0.004 0.001

Note: Key dependent variables are the changes between 2007 and 2008/2009 in theratios of firm-level profits/assets, sales/assets and investments/assets. Standard errorsin brackets. ***pb0.01, **pb0.05, *pb0.1.

vi Firm-level measure of business cycle, trade and financial channelsIn Table 11, we replace our sector-level measures of real and financialsensitivities with firm-level measures. That is, we use firm-level busi-ness cycle and trade sensitivities, the actual firm-level dependence onexternal finance for investment, and firm-level working capital usage.These variables are measured using pre-crisis firm-level data, 2000 to2006. As noted, relative to sector features derived from US data, thefirm-level measures could be subject to some endogeneity issuesand hence could bias our estimation.In Column 1, we report the results for the change in profits. We findthat the profit rate is significantly lower for more business-cycle-sensitive firms. This is consistent with Table 3 where we usedsector-level business cycle sensitivity. However, the coefficient onACT_INV is now significantly positive, which could reflect the endo-geneity in ACT_INV. In Column 2, we report the results for the changein sales over assets. Again, we find the coefficients on business cycleand trade sensitivity to be negative and statistically significant, similarto the findings based on sectoral measures of sensitivity. In Column 3of Table 11, we report the results for the change in capital expendi-tures. Firms with high trade sensitivity or large ACT_INV prior to thecrisis seem to need to adjust their capital expenditures during the cri-sis significantly more downwards.These findings are intuitive, but differ from the sectoral analysis inTable 3 (where we did not find a significant impact on investment).As a robustness check, in Columns 4 to 6, we focus on the trade chan-nel and do not include the business cycle channel, to avoid their pos-sible joint codetermination affecting our results (the firm-level tradeand business cycle sensitivities have a correlation of 0.28, the highestamong all our measures). Now the trade channel shows a coefficientwith a largermagnitude and significantly negative for all three depen-dent variables. This suggests that someof the business cycle effects op-erate through the trade channel.12

As a robustness test,we repeat the regressions of Table 11by includinginteraction terms between firm-level sensitivities and country

12 The trade channel may reflect other characteristics of firms, such as productivity.Our sectoral trade sensitivity is less subject to the influence of firm characteristics, asit is based on global sector-level exports. Moreover, our baseline results for sectoraltrade sensitivity carry through when we include measures of firm-level quality, suchas Tobin's Q, firm size and profitability (Table 8). Our firm-level trade sensitivity mea-sure might be correlated with firm quality. However, results using firm-level trade sen-sitivity in Table 11 also carry through if we include as additional control variables themeasures of firm-level quality from Table 8 (not reported to save space).

image of Fig.�2

Table 11The impact of crisis on firm performance—firm features.

(1) (2) (3) (4) (5) (6)

Variables ΔProfit ΔSales ΔCapEx ΔProfit ΔSales ΔCapEx

Firm-level business cycle sensitivity −0.0246*** −0.0511** −0.00481[0.00869] [0.0205] [0.00410]

Firm-level trade sensitivity −0.0345 −0.125* −0.0273* −0.0574* −0.174** −0.0348**[0.0308] [0.0727] [0.0145] [0.0295] [0.0696] [0.0139]

Actual firm use of external finance for working capital (ACT_WK) 0.000835 −0.00513 0.000626 0.000548 −0.00569 0.000548[0.00183] [0.00432] [0.000863] [0.00183] [0.00432] [0.000863]

Actual firm use of external finance for investment (ACT_INV) 0.105*** 0.210** −0.0432** 0.108*** 0.217** −0.0430**[0.0401] [0.0941] [0.0190] [0.0401] [0.0940] [0.0190]

Constant −2.891*** 0.166 −0.476*** −2.892*** 0.162 −0.472***[0.228] [0.540] [0.108] [0.228] [0.539] [0.108]

Observations 5808 5915 5868 5812 5919 5872R-squared 0.003 0.003 0.002 0.002 0.002 0.002

Note: Key dependent variables are the changes between 2007 and 2008/2009 in the ratios of firm-level profits/assets, sales/assets and investments/assets. Standard errors inbrackets. ***pb0.01, **pb0.05, *pb0.1


features (not reported). We find for the change in profits, the interac-tion between ACT_INV and financial development (proxied by privatecredit/GDP) to be significantly positive, but not the other interactionterms. For the changes in sales, we do not find any significant interac-tion effects. When we further include country dummies, we findsimilar patterns. The lack of significant results could be due to firm-specific measures being more subject to endogeneity. Our sector-level indexes do not suffer from these endogeneity problems and wethus putmoreweight on sector-based analyses. Nonetheless, it is reas-suring that the analyses with firm-specific indexes confirm the role ofbusiness cycle and trade sensitivity and also find some evidence of afinancial channel.

5. Conclusions

In this paper, we apply a simple and well-establishedmethodolog-ical framework to study the real impacts of the 2008–09 crisis onfirm-level performance and the role of global linkages in transmittingthe crisis. We analyze three channels through which the crisis mayhave affected firms: a business cycle channel, a trade channel, and afinancial channel. To investigate the business and trade channels,we asked the question: if we characterize firms based on their intrin-sic sensitivity to business cycle or trade shocks, do firms with differ-ent scores perform differently during the crisis? Similarly, if wecharacterize firms into different baskets based on their sensitivity toshocks to external financing (in terms of investment and workingcapital needs), does this help to explain the ex-post performance ofthese firms? To investigate the role of global linkages, we includecountry-level trade and financial linkages and their interactionswith the proxies for the business cycle/trade/financial channels intoour regressions.

We examine changes over the crisis period in three measures offirm performance—sales, profits and capital expenditure—for 7722manufacturing firms from 42 countries. We find that, in economicterms, the trade and business cycle channels were the most impor-tant, particularly in 2009. When we examine the role of country-level linkages, including trade and financial linkages, we find thattrade linkages played a significant role in the spillover of crisis,while the evidence for the role of financial linkages is considerablyweaker.

It is important to point out that the current paper is not meant tobe a comprehensive assessment of the welfare effects of global link-ages. To do that, several additional aspects need to be examined, in-cluding how different forms of global linkages affected firm growthrates, investment, and performance during tranquil times. Thiswould be a fruitful topic for future research.

Acknowledgments

We would like to thank the participants in the NBER Global Finan-cial Crisis preconference and conference and especially Charles Engel,Kristin Forbes, Jeffrey Frankel, Linda Tesar, and the referees for veryuseful comments and suggestions, and Mohsan Bilal for excellent re-search assistance. The views expressed in this paper are those of theauthors and do not necessarily represent those of the IMF or IMFpolicy.

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