metadata: authors: 0: author: Evers,Michael Wayne
1: author: Niemann,Stefan
2: author: Schiffbauer,Marc Tobias
count: World
docna: 0: docna: Inflation, liquidity and innovation
docty: Policy Research Working Paper
subtopic: Economic Growth,Economic Theory & Research,Industrial Economics,Inflation,Employment and Unemployment,Transport Services,Youth and Governance,Social Analysis,National Governance,Government Policies,Quality of Life & Leisure
teratopic: Social Development,Public Sector Development,Industry,Governance,Macroeconomics and Economic Growth,Social Protections and Labor,Transport
abstracts: cdata!: This paper presents a simple model with
            financial frictions where inflation increases the cost faced
            by firms holding liquid assets to hedge risky production
            against expenditure shocks. Inflation tilts firms'
            technology choice away from innovative activities and toward
            safer but return-dominated ones, and therefore reduces
            long-run growth. The theory makes specific predictions about
            how the severity of this adverse effect depends on industry
            characteristics. These predictions are tested with novel
            harmonized firm-level data from 139 developing countries,
            overcoming small sample problems constraining previous work.
            The analysis finds that inflation affects the composition
            but not the overall quantity of investment. A one percentage
            point increase in inflation reduces the establishment-level
            probability of innovation by 4.3 percent but does not affect
            total investment. Moreover, innovating firms display a
            stronger dependence on liquid assets, which, in turn, are
            negatively related to inflation. Generalized
            difference-in-differences estimations corroborate the
            sector-specific predictions of the theoretical model.
repnme: repnme: Inflation, liquidity and innovation
display_title: Inflation, liquidity and innovation
listing_relative_url: /research/2018/05/29882898/inflation-liquidity-innovation
topicv3: Economic Growth m1326241 471,Political Economy of Reform m1326263 836,Transport m1326661 135,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Enterprise Surveys m1504072 2762
publishtoextweb_dt: 2018-05-10T00:00:00Z
historic_topic: Social Development,Public Sector Development,Industry,Governance,Macroeconomics and Economic Growth,Social Protections and Labor,Transport
no_of_pages: 40
author_keys: 0: author_key: 1753528
1: author_key: 1067388
model_extractions: WPS8436 Policy Research Working Paper 8436 Inflation, Liquidity and Innovation _Michael Evers Stefan Niemann Marc Schiffbauer_ Transport and Digital Development Practice May 2018
Policy Research Working Paper 8436

# **Abstract**

This paper presents a simple model with financial frictions where inflation increases the cost faced by firms holding liquid assets to hedge risky production against expenditure shocks. Inflation tilts firms’ technology choice away from innovative activities and toward safer but return-dominated ones, and therefore reduces long-run growth. The theory makes specific predictions about how the severity of this adverse effect depends on industry characteristics. These predictions are tested with novel harmonized firm-level data from 139 developing countries, overcoming small sample problems constraining previous work. The analysis finds that inflation affects the composition but not the overall quantity of investment. A one percentage point increase in inflation reduces the establishment-level probability of innovation by 4.3 percent but does not affect total investment. Moreover, innovating firms display a stronger dependence on liquid assets, which, in turn, are negatively related to inflation. Generalized difference-in-differences estimations corroborate the sector-specific predictions of the theoretical model.

This paper is a product of the Transport and Digital Development Global Practice. 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/research. The authors may be contacted at mschiffbauer@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
# Inflation, liquidity and innovation

Michael Evers<sup>_∗_</sup> Stefan Niemann<sup>_†_</sup> Marc Schiffbauer<sup>_‡_</sup> JEL _classification:_ E22, E31, O16, O31, O42 _Keywords:_ inflation, liquidity, technology choice, innovation, investment

> _∗_ Department of Economics, University of Hohenheim, Germany. E-mail: michael.evers@uni-hohenheim.de

> _†_ Department of Economics, University of Essex, United Kingdom. E-mail: sniem@essex.ac.uk

> _‡_ The World Bank. E-mail: mschiffbauer@worldbank.org
# **1 Introduction**

– There is now a substantial body of evidence indicating that sustained and therefore likely predictable – inflation can have adverse consequences for long-run growth. The theoretical discussion about the negative consequences of inflation has focused on the opportunity cost of holding money, distortions of relative prices and of the tax system, and particularly on the effects on the incentives for investment and saving. However, the empirical support for these ideas and their relevant implications for economic growth is mixed so that there is still no consensus about the causal mechanism underlying the relationship between inflation and growth.

Against this background, our paper makes two contributions. In the first part of the paper, we present a simple model formalizing the idea of a causal effect of inflation on growth, which is transmitted not via the level of aggregate investment, but instead via its composition (technology choice). In the model firms engage in two alternative technologies, basic and advanced. The advanced technology is innovative and yields higher expected returns, but it is subject to idiosyncratic expenditure shocks. By contrast, the basic technology is free from idiosyncratic risk but return-dominated. Firms operating the advanced technology can insure themselves against idiosyncratic risk by holding a precautionary buffer of readily marketable, liquid assets. However, the scope for such insurance is constrained by the limited pledgeability of firms’ income from production. As a consequence, liquidity must be comitted ex-ante and hence carries a liquidity premium. Liquidity provision is therefore costly (Holmstrom and Tirole, 1998), and an increase in the liquidity premium induces firms to economize on its use. Thus, when the terms of hedging advanced production against expenditure shocks become less favorable, firms shift their physical investment towards the basic technology, which leads to less innovation and thereby diminished growth in aggregate productivity and output.

Since the relevant liquidity premium is given by the nominal interest rate, which is linked to the rate of inflation via the Fisher equation, this mechanism implies that increased inflation works like a tax on advanced investment and hence reduces innovation and productivity growth. Moreover, our theory makes predictions about how the severity of this adverse interest rate effect depends on firm or industry characteristics. Specifically, we show that the above liquidity 2
mechanism is particularly relevant in fast growing and volatile industries, and in those with high income pledgeability. Liquidity demand and investment composition in these industries are therefore predicted to be more sensitive to variations in the rate of inflation.

In the second part of the paper, we test these predictions with firm-level data. Examining the microeconomic mechanism and the specific hypotheses of our model requires information on inflation episodes, firms’ investment in advanced technologies (that is, their innovation activities), and the sources of finance available to firms for the working capital expenditure associated with such investment. Empirical work using this information is prone to suffer from small sample problems since inflation only varies at the country-year level and harmonized detailed firm-level panel data are typically only available for few countries or years. This lack of appropriate data has so far limited researchers in studying the impact of inflation on corporate activity, constraining their ability to scrutinize relevant macroeconomic transmission mechanisms on the basis of microeconomic data.

We use novel data from the World Bank Enterprise Surveys (WBES) to address this problem. The WBES provide representative establishment-level data for more than 100,000 establishments that are harmonized across 139 developing countries from 2006-2016. The harmonized WBES data provide substantial variation in firms’ behavior across countries and time periods spanning a significant range of inflation, making it possible to empirically identify the relation between inflation and firms’ innovation and financing activities. The data also include detailed information on establishments’ innovation activity as well as sources to finance working capital and are thus well-suited to test the specific microeconomic mechanism underlying our model. A potential limitation is that the WBES do not provide direct balance sheet information on firms’ short-term liquidity holdings, which we proxy with the extent of working capital finance via internal funds or retained earnings. As a robustness check, we therefore extend our analysis with detailed balance sheet data for U.S. firms from Compustat which include a direct measure of corporate liquidity holdings and also have meaningful variation in inflation rates over the period 1960-2016.

On the basis of the WBES data, we find that inflation reduces the probability of product and process innovation in developing countries but has no effect on total investment. Hence, inflation influences the quality but not the quantity of investment. The impact of inflation on 3
the composition of investment via firms’ technology choice is statistically and economically significant: a one percentage point (pp) increase in inflation reduces the probability of innovation at the establishment level by 4.3%. Consistent with the prediction that firms economize on the use of liquid assets in periods of increased inflation, we find that inflation reduces the share of working capital financed by internal funds or retained earnings, but not the share financed by formal bank loans. Finally, the adverse impact of inflation on innovation increases with establishments’ reliance on internal funds or retained earnings to finance their working capital. The Compustat data, which allow for a tighter measurement of corporate liquidity holdings, further reveal that U.S. firms’ R&D expenditure is increasing with their liquidity holdings: a 1% increase in corporate liquidity holdings is on average associated with a 6% increase in firms’ R&D intensity. Corporate liquidity holdings, in turn, are negatively affected by inflation: a one pp increase in inflation reduces firms’ liquidity holdings by 5.5%. Hence, the adverse effect of inflation on the composition of aggregate investment and its transmission via reduced liquidity holdings is corroborated also in the context of an industrialized economy with developed markets.

Finally, the sector-specific predictions of our theoretical model enable us to apply a generalized difference-in-difference specification as in Rajan and Zingales (1998), which also helps to address any remaining endogeneity concerns. We find that the negative impact of inflation on establishments’ innovation or R&D activity is larger in (i) more volatile sectors, (ii) sectors with higher value added growth, (iii) more R&D intensive sectors, and (iv) sectors with higher asset tangibility. There is thus broad support for the specific mechanism laid out in our theoretical model.

The rest of this paper is organized as follows. Section 2 embeds our paper into the related literature. Section 3 presents the theoretical model. Section 4 discusses the data, the estimation strategy and our empirical findings. The final section concludes.

# **2 Related literature**

There is a vast literature studying the effects of inflation on growth. Theoretical models used to examine the effects of inflation on growth typically combine a variant of an endogenous 4
growth model with the assumption that consumption and capital investment are subject to a cash-in-advance constraint (Stockman, 1981; Abel, 1985). When capital accumulation faces a cash requirement, the long-term capital-to-labor ratio is decreasing in the nominal interest rate, which acts as a tax on capital. As a consequence, aggregate investment should decrease in inflation, but this is not consistent with the empirical evidence. For example, De Gregorio (1993) finds a significant negative correlation between inflation and growth, whereas the relationship between inflation and aggregate investment is negative but not significant. Moreover, cross-country growth regressions reveal that the coefficient of inflation remains virtually unchanged when controlling for investment. Thus, contrary to the theoretical predictions from traditional models, the growth effects of inflation appear to unfold independent from aggregate investment.

Instead, the transmission mechanism from inflation to growth seems to operate via the ‘productivity of investment’. Our model captures this channel in terms of the composition of aggregate investment, that is, in terms of the choice between a basic and an advanced but risky technology,<sup>1</sup> which can be partially insured via corporate liquidity holdings whose costs increase with the rate of inflation. Closely related ideas are pursued in Berentsen, Rojas Breu, and Shi (2012) and Chu and Cozzi (2014). Berentsen, Rojas Breu, and Shi (2012) explore the link between the opportunity cost of holding cash, R&D investment and growth on the basis of a money search model where liquidity is essential for trade to take place in the innovation sector. Similarly, Chu and Cozzi (2014) analyze the effects of inflation on economic growth in a Schumpeterian model with a cash-in-advance requirement on R&D investment.<sup>2</sup> By contrast, in our model, the cost of inflation does not arise from a simple liquidity requirement but from the need for firms to keep a precautionary buffer to self-insure their advanced production against liquidity shocks in the spirit of Holmstrom and Tirole (1998).

This connects our modeling approach to the investment composition effect studied in Aghion, Angeletos, Banerjee, and Manova (2010). Concerned with the relationship between volatility and growth, these authors decompose aggregate investment in order to examine how credit

> 1This obviously resonates with ideas from the literature about occupational choice (Banerjee and Newman, 1993). 2Chu and Lai (2013) examine the growth effects of inflation in a quality ladder model with a money-in-utility specification.

5
constraints affect the cyclical behavior of productivity-enhancing investment. In analogy to the inflation-growth nexus, their cross-country regressions indicate that the observed negative relation between volatility and growth is independent of aggregate investment.<sup>3</sup> However, in the presence of financial constraints the impact of business-cycle shocks on the share of productivityenhancing investment in total private investment is greater for countries facing tighter financial constraints. Aghion, Askenazy, Berman, Cette, and Eymard (2007) corroborate this idea with firm-level evidence about the relationship between credit constraints and firms’ R&D activity over the business cycle. And Aghion, Farhi, and Kharroubi (2012) exploit industry variation in the prevalence of credit and liquidity constraints to establish that R&D expenditure is more affected by countercyclical monetary policy than physical investment.

Indeed, R&D expenditure is a natural proxy for productivity-enhancing investment at the firm-level and, given its limited tangibility, also particularly prone to financial constraints. Building on these facts, our theoretical model and empirical analysis rest on two key relationships. First, that higher inflation works to reduce corporate liquidity holdings; and second, that these liquidity holdings play an essential role in shaping the composition of aggregate investment. The background for most studies of corporate liquidity holdings is the view that external finance is costly and that firms hold liquid assets in order to survive bad times and to have funds readily available if investment opportunities arise. These benefits of corporate liquidity must then be balanced against its cost which materializes in terms of a liquidity premium (Mulligan, 1997). Opler, Pinkowitz, Stulz, and Williamson (1999) examine the determinants and implications of holdings of cash and marketable securities by publicly traded non-financial U.S. firms (1974-1994). They find that (i) firms facing tighter constraints hold a larger share of their total assets in the form of liquid assets, that (ii) firms with strong growth opportunities and riskier cash flows hold particularly high ratios of cash to total non-cash assets,<sup>4</sup> and that (iii) cash holdings increase with the firms’ R&D-to-sales ratio. Moreover, there is evidence that firms build cash buffers mainly from retained earnings and that these reserves are generally not used for planned activities (capital expenditures, acquisition spending or payouts to

> 3 Similar findings are obtained by Fischer (1993), Cukierman, Kalaitzidakis, Summers, and Webb (1993), Ramey and Ramey (1995), Bruno and Easterly (1998) and Easterly (2005).

> 4We see these features – high growth potential and risky cash flow – as the defining characteristics of the advanced technology in our model. The importance of growth opportunities is also emphasized in Almeida, Campello, and Weisbach (2004).

6
shareholders) but instead depleted by (unexpected) operating losses. That is, there is a precautionary motive for holding corporate liquidity to provide insurance against expenditure risk. More recently, Bates, Kahle, and Stulz (2009) document that the average cash-to-assets ratio for U.S. industrial firms has more than doubled between 1980 and 2006 and argue that this increase in cash holdings can be traced back to changing firm characteristics, in particular more risky cash flows and increasing R&D intensity. More broadly, the empirical corporate finance literature provides ample evidence that innovations and R&D are cash-intensive, and that liquidity requirements are more important than for physical investment.<sup>5</sup> On the other hand, the relationship between inflation and corporate liquidity holdings has received less attention by the empirical literature. Our paper thus contributes to the macroeconomic literature on inflation and growth with systematic firm-level evidence concerning both the relation between inflation and corporate liquidity and the mechanism tying liquidity holdings to the composition of investment.

# **3 A simple model**

In the following, we develop a simple model illustrating the causal transmission mechanism from inflation to firms’ decisions about liquidity holdings and technology adoption.<sup>6</sup> The model is partial equilibrium in nature and takes inflation as exogenously given. The Fisher equation then relates the nominal interest rate to the (expected) rate of inflation _π_ , where _β_<sup>_−_1</sup> measures the gross real interest rate, which we normalize to one. More generally, interest rates and inflation are endogenously determined in the general equilibrium of a fullydynamic, monetary economy nesting the environment laid out below. However, the details

> 5The literature uncovers a significant relationship between R&D expenditure and either cash flow or corporate liquidity (Himmelberg and Petersen, 1994; Brown and Petersen, 2011; He and Wintoki, 2016), and that this relationship is more robust and pronounced than for physical investment (Brown, Fazzari, and Petersen, 2009; Brown and Petersen, 2009). Moreover, firms tend to smooth R&D expenditure by maintaining a buffer stock of liquidity in the form of cash reserves (Brown, Martinsson, and Petersen, 2012).

> 6The model’s underlying structure shares some similarities with Brutti (2011), but it has a quite different focus and introduces ex-ante heterogeneity and technology choice.

7
of this nesting model are not essential for our key argument that liquidity provision is costly and priced by the relevant scarcity indicator for nominal funds, that is, the nominal interest rate. In essence, the distortion at work in our model is akin to that arising from a cash-inadvance constraint: Self-insurance for entrepreneurs is possible only via liquid assets. Given their exclusive role, these assets therefore carry a liquidity premium, captured by the fact that liquid assets held by entrepreneurs do not pay interest. Just like in the presence of a cash-inadvance constraint, the nominal interest rate thus distorts the allocation in an otherwise real economy. In view of the generality of this mechanism, it plays out in a variety of nesting model environments.<sup>7</sup> We therefore suppress this supporting structure and instead go on to describe the liquidity problem for our simple model. Notice that this is also in line with our empirical approach, which analyzes disaggregate data to examine the firm-level consequences of inflation.

# **3.1 Economic environment**

Consider an economy that lasts for three periods, _t_ = 0 _,_ 1 _,_ 2, and is populated by a continuum of entrepreneurs and a financial intermediary, both of them risk neutral. For simplicity, there is no discounting. The rate of inflation between date 0 and date 2 is given by _π_ . The financial intermediary has deep pockets and can lend at an opportunity cost equal to the nominal interest rate _R_ . This nominal interest rate is pinned down by the Fisher equation, that is, _R_ = _π_ . Entrepreneurs have no endowment but access to two alternative investment technologies, called _basic_ and _advanced_ . The two technologies are alternative means to produce a single homogenous good and are based on potentially risky investment projects, one per entrepreneur. At date 0, entrepreneurs must choose which technology to operate.

The basic technology is riskless.<sup>8</sup> Investment into a basic project costs one unit in _t_ = 0 and yields a nominal return of (1 + _π_ ) _B_ in _t_ = 2. Expressed in date 0 units, the basic technology’s real return is thus _B_ . We assume _B >_ 1, so date 0 investment is profitable.

> 7In an earlier working paper version of this paper (Evers, Niemann, and Schiffbauer, 2007), we describe a complete model embedding a contracting problem about scarce liquidity into a dynamic monetary framework with an explicit endogenous growth mechanism. Fully-developed general equilibrium environments are also considered in Berentsen, Rojas Breu, and Shi (2012) and Chu and Cozzi (2014).

> 8The key difference between basic and advanced projects is that the former are free from idiosyncratic risk, while the latter are not. The model can be easily generalized to include also aggregate risk, which, however, plays no essential role as long as it affects both technologies symmetrically. We therefore abstract from aggregate risk in order to illustrate the economic mechanism in the simplest possible environment.

8
The advanced technology is subject to idiosyncratic risk. Investment into advanced project _j_ costs one unit in _t_ = 0 and yields a nominal return of (1 + _π_ ) _α_<sup>_j_</sup> in _t_ = 2. Expressed in date 0 units, the advanced technology’s return thus is _α_<sup>_j_</sup> , where _α_<sup>_j_</sup> denotes the idiosyncratic shock realized in _t_ = 1. The idiosyncratic shock _α_<sup>_j_</sup> is private information and takes values _α_<sup>_j_</sup> = _A_<sup>_j_</sup> _>_ 0 with probability _ωl_ (lucky) and _α_<sup>_j_</sup> = 0 with probability _ωu_ = 1 _− ωl_ (unlucky). Unlucky projects admit an additional investment with variable size _i_ which returns _ρi_ in _t_ = 2, both expressed in real terms. Assuming _ωlA_<sup>_j_</sup> _>_ 1 for all _j_ and _ρ >_ 1 ensures both date 0 investment and date 1 reinvestment are profitable. Notice, however, that the idiosyncratic expenditure shock _α_<sup>_j_</sup> introduces an element of ex-ante heterogeneity across advanced projects. This heterogeneity will be seen to drive the entrepreneurial choice between the basic and the advanced technology.

Regardless of which technology they operate, entrepreneurs have limited access to credit. Specifically, we assume entrepreneurs can pledge only a fraction _γ <_ 1 of their project’s expected revenues. Moreover, we make the following assumption on model parameters.

# **Assumption 1.** _Assume γB >_ 1 _, γωlA_<sup>_j_</sup> _>_ 1 _for all j and γρ <_ 1 _._

Accordingly, date 0 lending is profitable for the financial intermediary, but date 1 lending, which is relevant only for advanced projects, is not. The advanced technology is therefore associated with a demand for liquidity in the spirit of Holmstrom and Tirole (1998): Unlucky advanced entrepreneurs cannot borrow on the spot market to finance additional investment. Instead, liquidity must be committed already at the initial investment stage (date 0). Moreover, also contingent contracts with the financial intermediary are not available since the idiosyncratic expenditure shock is private information.<sup>9</sup> The limited pledgeability of income and the unavailability of contingent financial contracts together imply that advanced entrepreneurs need to precaution for their date 1 investment needs, and they can do so only by saving a buffer stock of liquid funds. This liquidity must be obtained at date 0 from the financial intermediary who refinances at cost _R_ .<sup>10</sup>

> 9A contingent contract would stipulate a positive transfer in the case of an adverse expenditure shock and a negative transfer in the alternative case. For details, see Holmstrom and Tirole (1998).

> 10Since the idiosyncratic expenditure shocks are private information, there is no scope for liquidity pooling via the financial intermediary.

9
# **3.2 Equilibrium**

At date _t_ = 0, entrepreneurs face the choice between the two alternative technologies. If entrepreneur _j_ operates the basic technology, she borrows an amount _b_<sup>_j_</sup> from the financial intermediary to finance her initial unit investment subject to the date 0 borrowing constraint (1 + _R_ ) _b_<sup>_j_</sup> _≤ γ_ (1 + _π_ ) _B_ , or in real terms, Given our assumption that _γB >_ 1, the borrowing constraint is not binding and _b_<sup>_j_</sup> = 1. Nominal income from the basic technology net of borrowing costs then is (1 + _π_ ) _B −_ (1 + _R_ ) _b_<sup>_j_</sup> , or in real terms, If entrepreneur _j_ instead operates the advanced technology, she borrows an amount _d_<sup>_j_</sup> from the financial intermediary to finance her initial unit investment and her liquidity holdings _l_<sup>_j_</sup> used to self-insure against subsequent expenditure shocks. This is done so as to maximize the expected profit (1 + _π_ ) � _ωlyl_<sup>_j_+ (1</sup><sup>_−ωl_)</sup><sup>_y_</sup> _u_<sup>_j_</sup> � _−_ (1 + _R_ ) _d_<sup>_j_</sup> , or in real terms, where These expressions make the costs of holding liquidity for self-insurance evident. Liquidity must be obtained at cost (1 + _R_ ) at date 0 and then kept until date 1 when the expenditure shock materializes. The real return to a unit of idle liquidity is thus given by 1 _/_ (1 + _R_ ). Notice also that expression (6) implicitly imposes that, on date 1, unlucky entrepreneurs fully reinvest into their project, that is, _i_<sup>_j_</sup> = _l_<sup>_j_</sup> _/_ (1 + _R_ ). This is without loss of generality since reinvestment at 10
date 1 has strictly positive returns. The date 0 budget constraint facing entrepreneur _j_ is whereby the limited pledgeability of revenues from production constrains date 0 borrowing to (1 + _R_ ) _d_<sup>_j_</sup> _≤ γ_ (1 + _π_ ) � _ωlyl_<sup>_j_+ (1</sup><sup>_−ωl_)</sup><sup>_y_</sup> _u_<sup>_j_</sup> �, or in real terms, Maximizing (4) subject to (7) and (8) yields a corner solution where the borrowing constraint is binding, provided the expected return on liquidity held by the entrepreneur is higher than the cost of borrowing. We assume this condition to be satisfied.

**Assumption 2.** _Assume ρ_  ̄ _≡_ [ _ωl_ + (1 _− ωl_ ) _ρ_ ] _>_ (1 + _R_ ) _._

The entrepreneur’s date 0 borrowing and savings then satisfy and Given the cost of self-insurance, the liquidity demand of entrepreneur _j_ is decreasing in the _<u>∂l</u>_<sup>_j_</sup> nominal interest rate, _∂_ (1+ _R_ )<sup>_<_0.11Substituting(10)backinto(4),expectedincomefromthe</sup> advanced technology is Importantly, given that the scope for self-insurance via liquidity holdings is distorted by the nominal interest rate, also the expected income from the advanced technology is decreasing in the nominal interest rate. This is because higher interest rates induce entrepreneurs to hold 11 _<u>∂l</u>_<sup>_j_</sup> Formally, _∂_ (1+ _R_ )<sup>_<_0as,byAssumption1,</sup><sup>_γωlAj>_1.</sup> 11
less liquidity, which, in turn, reduces the date 1 reinvestment following an expenditure shock.

Comparing (3) and (11), entrepreneur _j_ adopts the advanced technology if _Eyadvanced_<sup>_j≥y_</sup> _basic_<sup>_j_,</sup> or equivalently, Hence, entrepreneurs whose upside risk _A_<sup>_j_</sup> of operating an advanced project is sufficiently strong self-select into the advanced technology. By contrast, those with lower _A_<sup>_j_</sup> prefer the basic technology. Condition (12) therefore defines the threshold level for _A_<sup>_j_</sup> in terms of model parameters and the exogenous interest rate _R_ . The right-hand side of condition (12) is increasing in the nominal interest rate.<sup>12</sup> That is, in environments with higher inflation and nominal interest rates there are tighter conditions for adopting the advanced over the basic technology.

# **3.3 Empirical predictions**

Since the income from the basic technology _ybasic_<sup>_j_isinvarianttovariationsinparametersother</sup> than _B_ , it follows that the inequality condition (12) for adoption of the advanced technology is driven by variations in the expected income from the advanced technology _Eyadvanced_<sup>_j_given</sup> by equation (11). Based on the comparative statics for _Eyadvanced_<sup>_j_,weobtainthefollowing</sup> empirical predictions.

**Proposition 1.** _In the economy under consideration, there is an adverse interest rate effect:_

- _(i) An increase in the nominal interest rate makes the advanced technology less attractive,_

_and will therefore tilt the composition of investment towards the basic technology._

-  ̄

- _(ii) Keeping the expected return ρ ≡_ [ _ωl_ +(1 _− ωl_ ) _ρ_ ] _on liquidity fixed, the adverse interest rate effect is more pronounced for entrepreneurs whose advanced projects have more upside_

> 12Formally, this result obtains as, by Assumption 1, _γB >_ 1.

12
_potential ωlA_<sup>_j_</sup> _,_

- _(iii) Keeping upside potential ωlA_<sup>_j_</sup> _fixed, the adverse interest rate effect is more pronounced for entrepreneurs whose advanced projects are more risky in the sense of a mean-preserving spread via an increase in A_<sup>_j_</sup> _and a decrease in ωl such that ρ_  ̄ _≡_ [ _ωl_ + (1 _− ωl_ ) _ρ_ ] _increases,_

- _(iv) The adverse interest rate effect is more pronounced for entrepreneurs with higher pledgeable income γ,_

The intuition behind the adverse interest rate effect established in part (i) of Proposition 1 is straightforward. Unlike the basic technology, the advanced technology is subject to idiosyncratic risk, which can be partially insured through a liquidity buffer. However, holding liquidity is subject to an opportunity cost given by the nominal interest rate. Higher interest rates therefore decrease the demand for liquidity,<sup>13</sup> and in consequence also the relative attractiveness of advanced projects. Hence, condition (12) becomes tighter, and the composition of investment is tilted towards the basic technology. That is, the adverse interest rate effect manifests in terms of an investment composition effect.

The following three parts of Proposition 1 establish that the relative attractiveness of advanced projects is more sensitive to variations in the nominal interest rate when they are potentially more profitable (part (ii)), more volatile (part (iii)), or characterized by higher pledgeable income (part (iv)). The underlying mechanism rests on the fact that higher expected profitability _ωlA_<sup>_j_</sup> or higher pledgeable income _γ_ allows for a higher debt capacity, see (8). Given the unit investment scale, an increasing share of the initial debt is held as liquidity, see (7).<sup>14</sup> The role of

> 13Appendix A.1 formally derives the comparative static effects on the demand for liquidity in (10).

> 14The model can be generalized to allow for variable investment scale at date 0. Given the linear specification

13
liquidity for self-insurance and its dependence on the interest rate are thus more relevant when the advanced technology is more profitable or pledgeable. The same logic also applies when the advanced technology is more risky. This is because the mean-preserving spread described in part (iii) works to increase the expected return to liquidity _ρ_ ̄ _≡_ [ _ωl_ + (1 _− ωl_ ) _ρ_ ].

Notice that parts (ii) to (iv) above also allow a direct interpretation in terms of empirically observed outcomes. Industries where _ωlA_<sup>_j_</sup> , _ρ_ ̄ or _γ_ is higher will be more exposed to the advanced technology. This technology allows for higher expected returns, but is also more volatile due to the presence of idiosyncratic liquidity risk. The empirical hypothesis therefore is that the liquidity mechanism should be particularly relevant in fast growing and volatile industries, and in those with high income pledgeability.

# **4 Empirical analysis**

In this section, we use firm-level panel data from the World Bank Enterprise Surveys (WBES) to examine the model’s key predictions regarding the effects of inflation on corporate innovation and financing activity. To that end, the WBES are particularly useful as they provide harmonized cross-country data for a large number of developing countries with significant variation in

## **4.1 Data**

**_World Bank Enterprise Surveys._** The WBES are designed to provide internationally comparable establishment-level data for a large number of developing countries that are representative for the formal private sector economy (that is, establishments with at least five employees) for each country-year episode. They include information on innovation activities, sources of finance, and other key characteristics for over 100,000 establishments in 139 developing countries between 2006 and 2016. The harmonized surveys are conducted about every three years in each country. The surveys provide sampling weights for each country-year episode so that the weighted estimations are representative at the country level. Appendix A.3 provides of technology, the analysis is then per unit of initial investment, with unchanged comparative statics.

14
additional details on the survey design and sampling.<sup>15</sup> The harmonized WBES data capture substantial variation in firms’ behavior across countries and time periods spanning a significant range of financial development and inflation, making it possible to empirically identify the relation between inflation and firms’ innovation and financing activities. The data are thus well-suited to test the specific microeconomic mechanism underlying our model (cf. Proposition 1).

We proxy firms’ investment in advanced technologies with their innovation activity. The surveys distinguish between different dimensions of innovation, that is, between product and process innovation. Both are relevant for our analysis as they are expected to be more risky than conventional investments and to increase future productivity, and ultimately growth. We thus define three variables to measure innovation, indicating whether an establishment introduced a new product, a new production process, or both. Specifically, innovation is measured by a dummy variable equal to one if the establishment either introduced a new product or a new production process in the last three years and zero if not; analogous variables are defined for product and process innovation, separately.

The surveys also provide information on establishments’ sources of finance for their working capital expenditure. This is relevant since the idiosyncratic shock associated with the advanced technology (e.g., an unexpected cost overrun when introducing a new product or process) is captured as part of establishments’ working capital expenditure, which is financed via corporate liquidity in the form of internal funds or retained earnings. Specifically, we exploit the following survey question:

- _Over the last fiscal year, please estimate the proportion of this establishment’s working capital that was financed from each of the following sources? Internal funds/Retained earnings; Banks; Other._<sup>16</sup>

The dependence on internal funds/retained earnings to finance working capital expenditure can be regarded as a close counterpart to the requirement of using corporate liquidity holdings to meet unforseen expenditure needs associated with the advanced technology formulated in our

> 15The harmonized WBES data for all countries are available online at `https://www.enterprisesurveys. org/portal/index.aspx\#/library?dataset=Combined` .

> 16The category ‘other’ financing sources includes non-bank financial institutions, supplier and customer credit, friends and relatives.

15
model. By contrast, bank finance is less likely to be available for this purpose due to the limited pledgeability of income and the non-contingent nature of debt contracts for risky investment, especially in developing countries where financial markets are less developed. We thus expect that establishments that have a higher share of retained earnings in financing their working capital are more likely to innovate.<sup>17</sup> Moreover, we use total investment to examine the impact of inflation and corporate liquidity holdings on establishments’ overall investment. We also include the following establishmentlevel control variables: Establishment size, measured by the log of the number of employees; the establishments’ age; a dummy variable indicating if it is a subsidiary of a larger firm having access to intra-firm credit; and a dummy variable indicating if it has any formal bank loan (that is, different from credit lines for working capital).

**_Other data sources._** We measure inflation by consumer price inflation from the World Bank World Development Indicators (WDI) for all countries and years included in the WBES.<sup>18</sup> We remove inflation outliers by excluding country-year observation with excessive rates above 100% as well as significant deflation episodes with rates below -2% since firms’ incentives to adjust their investment behavior might be driven by different factors during these extreme episodes. Qualitatively, our results remain unchanged if we do not impose any range for inflation or use alternative restrictions (only including inflation episodes above 0% and below 20% or 50%). The average (median) inflation rate in the WBES sample is 7% (5.8%). We also use real GDP per capita in constant US dollars from the WDI as a macro-level control variable. Our empirical specification works with the first lag of both inflation and real GDP per capita since the variables from the WBES refer to the previous year.

Moreover, we use two-digit sector-level information to test the ‘difference-in-difference’ predictions (ii)-(iv) in Proposition 1. Specifically, the sector variables allow us to examine whether the adverse effect of inflation on innovation is more pronounced in more productive sectors, in

> 17While there are several potential financing sources for working capital, we show (cf. Table 1 below) that retained earnings are the predominant form. By contrast, formal bank loans are available only to relatively few firms, and their share in working capital finance is minor.

> 18Data on consumer price inflation are available for all countries and time periods. Alternative measures for the cost of liquidity such as nominal deposit or lending interest rates are available only for about two-thirds of the country-year pairs. Where available, however, these interest rates are highly correlated with our inflation measure.

16
more volatile sectors, in sectors with a higher R&D intensity, and in sectors with a lower capacity to pledge collateral for external financing. Importantly, these sector-specific predictions permit a generalized difference-in-difference specification as in Rajan and Zingales (1998) to address any remaining endogeneity concerns.

We identify firms whose projects have more upside potential in the sense of hypothesis (ii) of Proposition 1 through their economic activity in the U.S., which serves as a benchmark country. This is done by computing the average value added growth across two-digit ISIC Rev 3.1 sectors for all years in the UNIDO (2017) Industrial Statistics Database and, alternatively, by the average R&D intensity of two-digit sectors provided by Samaniego and Sun (2016). Similarly, we record the volatility of economic activity as measured by the standard deviation of value added in two-digit ISIC Rev 3.1 sectors for all years in UNIDO (2017) to identify more risky projects as in hypothesis (iii) of Proposition 1. Finally, we approximate the pledgeable income of entrepreneurs as in hypothesis (iv) of Proposition 1 by the industry-level asset tangibility taken from Samaniego and Sun (2016), which is measured as the ratio of fixed assets to total assets. That is, firms in sectors with higher asset tangibility can pledge more collateral and thus have better access to external

**_Descriptive statistics._** The descriptive statistics, summarized in Table 1 show that about half (53%) of all formal establishments in developing countries engage in some form of innovation activity; 39% introduced a product which was new to the firms’ main market, and 43% introduced a new production process. But only relatively few establishments (35%) have access to formal bank loans. Similarly, the share of bank finance for firms’ working capital expenditure amounts to only 13%. Instead, the predominant form of working capital finance is internal funds/retained earnings; their share accounts for 72%. Moreover, about half (46%) of the establishments report internal funds/retained earnings as their only source of working capital financing, and for 78% they are the primary source (financing more than half of their working capital expenditure). These statistics are consistent with the view that investment in innovation is largely intangible and risky; the associated working capital expenditure is therefore difficult to finance externally and must be covered by corporate liquidity holdings in the form of internal funds/retained earnings.

17
Table 1: Descriptive statistics

||Observations|mean|s.d.|min|max|
|---|---|---|---|---|---|
|**WBES and WDI:**||||||
|Innovation|79,608|.529|.499|0|1|
|New product|79,379|.392|.488|0|1|
|New process|77,742|.434|.496|0|1|
|loan|125,992|.349|.477|0|1|
|share retained earnings in WC|114,174|.718|.339|0|1|
|share bank fnance in WC|114,174|.126|.240|0|1|
|share other fnance in WC|114,174|.149|.255|0|1|
|share of frms for which retained earnings|114,174|.461|0|.461|.461|
|is the only source of WC fnance||||||
|share of frms for which retained earnings|114,174|.775|0|.775|.775|
|exceed half of their WC fnance||||||
|ln(inv)|48,660|13.8|3.32|0|29.0|
|ln(empl)|130,089|3.25|1.39|0|11.1|
|ln(age)|129,046|3.01|.570|0|5.89|
|subsidiary|127,496|.178|.383|0|1|
|infation|129,309|7.02%|68.3|-2%|100%|
|ln(real GDP pc)|133,141|7.93|1.05|5.25|10.9|

# **4.2 Empirical specification**

The data allow us to examine the key relationships in the mechanism linking inflation to technology choice and to test the specific predictions in Proposition 1. In detail, we test the two key components of the model mechanism: First, that firms reduce their liquidity holdings _<u>∂l</u>_<sup>_j_</sup> in periods of higher inflation, _∂_ (1+ _R_ )<sup>_<_0;andsecond,thatfirmsreducetheirinvestmentin</sup> innovation activities in periods of higher inflation (part (i) in Proposition 1).

We consider empirical specifications of the form where _yjsct_ is either given by the innovation dummies or by the share of working capital financing through retained earnings, bank credit, and other sources for firm _j_ in sector _s_ , country _c_ , and year _t_ . _inflct_ measures the level of inflation firm _j_ faces in country _c_ and year _t_ . _Xjsct_ is the matrix of firm-level control variables, _Xct_ the vector of macro-level control variables (real GDP per capita), _Is_ is a vector of two-digit sector fixed effects, _Ic_ is a vector of country fixed effects, and _εjsct_ an independent and identically distributed error.<sup>19</sup> We always cluster the error

> 19Note that we cannot include establishment fixed effects since the panel dimension of the WBES data is not representative at the aggregate level. But the cross-section of establishments is representative for the formal

18
terms at the country-year level so that the standard errors are robust to within-group (serial) correlation.<sup>20</sup> The mix of macro- and microeconomic data allows to examine causality. Specifically, we can exclude the possibility of reverse causality since the innovation decision of a single firm has no significant feedback effect on the aggregate level of inflation. It may of course still be possible that inflation correlates with other country-level variables that influence an establishment’s investment decision. For this reason, we include the following control variables. First, we include country fixed effects to control for any time-invariant aggregate factors that may be correlated with inflation and firms’ incentives to innovate such as countries’ level of institutional and financial development (these move slowly over time and are hence considered quasi-fixed for the WBES time horizon). We thus measure the impact of the within-country variation in inflation. Second, we control for establishment-level variables and sector fixed effects to control for firm characteristics that may be correlated with their innovation decision and aggregate inflation. Third, we control for variation in countries’ economy activities over time.

When the dependent variable is an innovation dummy, we estimate (13) using a probit regression. The main coefficient of interest ( _β_ 1) measures the impact of the within-country variation in inflation on the average innovation activity of establishments operating in the same two-digit sector within this country, after controlling for changes in the establishment-specific control variables as well as for changes in the country’s aggregate economic activity. That is, _β_ 1 measures the effect of a change in a country’s inflation rate on the establishment’s innovation activity in that country. To estimate the impact of inflation on establishments’ financing sources for their working capital, we use a quasi maximum likelihood fractional multinomial logit estimator (fmlogit), which is efficient if the dependent variable ranges between zero and one and consists of multiple shares which add up to one for each observation (Papke and Wooldridge, 1996). The excluded category is given by ‘other’ financing sources.

To test the model-specific predictions (ii)-(iv) in Proposition 1, we additionally consider

> private sector in each country and year so that controlling for country fixed effects implies that we measure to what extent establishments within a given country adjust their innovation activity, on average, if the inflation rate in that country changes over time.

> 20Our standard errors are thus not subject to the Moulton (1990) critique in situations where the dependent variable is less aggregated (firm-level) than the independent one (country-year-level).

19
empirical specifications of the form where _sectorvars_ denotes either average value added growth, value added volatility, R&D intensity, or asset tangibility of sector _s_ . _Ict_ are _country ∗year_ fixed effects, which control for changes in any time-varying country-level factors (including countries’ levels of inflation, real GDP per capita, financial development, etc.) and thus reduce the empirical identification exclusively to the variation across sectors in the impact of inflation on innovation.<sup>21</sup> The coefficient of interest ( _β_ 1) is the interaction term between inflation and the relevant sector-specific attribute. The interaction term reflects the idea that, _ceteris paribus_ , the innovation activity in establishments within a sector that is, for instance, more R&D intensive should be more sensitive to variations in inflation than the activity of establishments in sectors that rely less on R&D. Equation (14) is thus a generalized difference-in-difference specification following the strategy of Rajan and Zingales (1998). It tests whether the sector-level effects of inflation on innovation activity are in line with our theoretical predictions, controlling for time-varying establishment-level control variables and for fixed and time-varying unobserved country characteristics. Apart from testing the model-specific hypotheses (ii)-(iv) in Proposition 1, (14) thus also provides a test for causality of the impact of inflation on establishments’ innovation activity.

# **4.3 Empirical results**

The first two columns of Table 2 show that inflation reduces the share of working capital financed by retained earnings relative to ‘other’ financing sources (supplier and customer credit, informal sources), but not the share financed by banks. That is, a one percentage point (pp) higher inflation reduces the share of working capital financed by retained earnings by 3.7 pp. Moreover, bank finance is more important for large firms and those that are subsidiaries of larger conglomerates. The results are consistent with our model prediction that, in periods

> 21 Note that the inclusion of _country ∗ year_ fixed effects also captures the level effect of inflation in 14 and thus cannot be included in 13. The sector fixed effects, in turn, capture the level effects of our sector-specific variables such as sectors’ R&D intensity.

20
of higher inflation, firms reduce their holdings of liquid assets to finance unexpected working capital expenditure (see equation (10)). Column 3 of Table 2 shows that the probability for Table 2: Harmonized firm panel for 139 countries from 2006-16: Inflation reduces firms’ liquidity holdings and innovation

||Retained|Bank|Innovation|New|New|ln(Inv)|
|---|---|---|---|---|---|---|
||earnings|fnance||product|process||
||fmlo|git|probit|(margina|ls)|ols|
|infation|-.037<sup>_∗∗_</sup>|.005|-.043<sup>_∗∗_</sup>|-.029<sup>_∗∗_</sup>|-.030<sup>_∗∗_</sup>|.005|
||(-3.53)|(0.35)|(-3.52)|(-2.22)|(-3.19)|(0.25)|
|ln(empl)|-.041|.202<sup>_∗∗_</sup>|.061<sup>_∗∗_</sup>|.044<sup>_∗∗_</sup>|.062<sup>_∗∗_</sup>|.726<sup>_∗∗_</sup>|
||(-1.46)|(7.61)|(12.6)|(13.1)|(9.01)|(14.7)|
|ln(age)|.014|.040|-.031<sup>_∗∗_</sup>|-.028<sup>_∗∗_</sup>|-.043<sup>_∗∗_</sup>|-.087|
||(0.21)|(.54)|(-3.12)|(-3.29)|(-2.93)|(-1.01)|
|subsidiary|.093|.251<sup>_∗_</sup>|.108<sup>_∗∗_</sup>|.111<sup>_∗∗_</sup>|.081<sup>_∗∗_</sup>|.342<sup>_∗∗_</sup>|
||(0.68)|(1.81)|(3.49)|(3.49)|(4.84|(3.88)|
|ln(real GDP pc)|-1.60<sup>_∗∗_</sup>|-.182|-.625<sup>_∗∗_</sup>|-.529<sup>_∗∗_</sup>|-.969<sup>_∗∗_</sup>|-3.23|
||(-2.74)|(-0.26)|(-3.44)|(-3.08)|(-4.43)|(-1.57)|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms|103,050|103,050|72,643|72,451|70,909|41,155|

World Bank Enterprise Surveys establishment-level data in 139 developing countries from 2006-16. Marginal effects are reported for the Probit estimations. Median inflation: 6%. Heteroscedasticity robust s.e. clustered at country _∗_ year level; t-statistics in parenthesis. **,* significant at 5%, 10%.

establishments in a given country to engage in innovation activities is reduced during episodes of higher inflation. Higher inflation diminishes the probability of both product and process innovation (columns 4-5). The corresponding coefficients are significant at the 5% level. By contrast, inflation has no effect on total investment (column 6), implying that inflation influences the composition but not the overall volume of investment.

The results thus suggest that inflation shifts establishments’ technology choice from advanced to basic processes/products that are less exposed to liquidity risk, confirming hypothesis (i) of Proposition 1 in the model. The marginal effects reported in column 3 of Table 2 imply that a 1 pp increase in inflation reduces the innovation probability by 4.3%. The adverse impact of inflation on firms’ technology choice is thus statistically and economically significant.

21
In Table 3, we analyze how the adverse effect of inflation on innovation depends on establishments’ access to external finance. First, we find that inflation reduces innovation also when Table 3: Harmonized firm panel for 139 countries from 2006-16: Adverse effects of inflation depend on firms’ access to finance

||||Inno|vation|||
|---|---|---|---|---|---|---|
||||RE=100%|RE_>_50%|RE_<_50%|RE_<_5%|
|infation|-.041<sup>_∗∗_</sup>|-.041<sup>_∗∗_</sup>|-.046<sup>_∗∗_</sup>|-.044<sup>_∗∗_</sup>|-.024<sup>_∗∗_</sup>|-.019<sup>_∗∗_</sup>|
||(-3.67)|(-3.56)|(-5.34)|(-3.86)|(-2.07)|(-2.02)|
|loan|.134<sup>_∗∗_</sup>|.156<sup>_∗∗_</sup>|.102<sup>_∗∗_</sup>|.132<sup>_∗∗_</sup>|.109<sup>_∗∗_</sup>|.096<sup>_∗∗_</sup>|
||(5.44)|(5.96)|(4.37)|(5.27)|(2.89)|(2.03)|
|inf_∗_loan||-.005<sup>_∗∗_</sup>|||||
|||(-1.98)|||||
|ln(empl)|.051<sup>_∗∗_</sup>|.051<sup>_∗∗_</sup>|.054<sup>_∗∗_</sup>|.051<sup>_∗∗_</sup>|.050<sup>_∗∗_</sup>|.018|
||(16.2)|(16.9)|(15.4)|(13.4)|(6.80)|(1.04)|
|ln(age)|-.033<sup>_∗∗_</sup>|-.032<sup>_∗∗_</sup>|-.038<sup>_∗∗_</sup>|-.031<sup>_∗∗_</sup>|-.031|.023|
||(-3.58)|(-3.54)|(-3.07)|(-2.01)|(-1.00)|(0.64)|
|subsidiary|.095<sup>_∗∗_</sup>|.094<sup>_∗∗_</sup>|.102<sup>_∗∗_</sup>|.100<sup>_∗∗_</sup>|.082<sup>_∗∗_</sup>|.013|
||(3.00)|(3.02)|(3.05)|(3.29)|(2.18)|(0.20)|
|ln(real GDP pc)|-.663<sup>_∗∗_</sup>|-.666<sup>_∗∗_</sup>|-.398<sup>_∗∗_</sup>|-.570<sup>_∗∗_</sup>|-.739<sup>_∗∗_</sup>|-.046|
||(-3.87)|(-3.83)|(-2.47)|(-3.66)|(-3.21)|(-1.64)|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms|70,499|70,499|33,193|54,826|15,656|4,900|

World Bank Enterprise Surveys establishment-level data for 139 developing countries from 2006-16; marginal effects reported. ”RE _>_ 50”: Share of working capital financed through retained earnings larger than 50%. Heteroscedasticity robust s.e. clustered at country _∗_ year level; t-statistics in parenthesis. **,* significant at 5%, 10%.

– firms have access to formal bank loans. Access to bank loans in general, not necessarily – for financing working capital expenditure has a positive direct effect on the probability that establishments innovate (column 1). However, the negative effect of inflation on innovation is stronger for those establishments that have access to a formal bank loan (column 2). This is intuitive since these establishments are more likely to innovate and thus more exposed to the associated expenditure shocks which need to be need to be precautioned against with a buffer of liquid assets whose opportunity cost increases with inflation. The relevant effects are 22
sizeable. We find that a 1 pp increase in inflation reduces the probability that establishments without access to a formal bank loan innovate by 4.1%; having access to a formal bank loan reduces this probability by an additional 0.5 pp (to 4.6%).

A more direct test of our model predictions is possible by examining the importance of retained earnings for working capital finance (columns 3-6). We find that the adverse impact of inflation on innovation increases with establishments’ reliance on retained earnings to finance their working capital. For establishments that finance less than half of their working capital expenditure through retained earnings (column 5), a 1 pp increase in inflation reduces the probability to innovate by 2.4%. Recall, however, that the majority of firms do not have (sufficient) access to formal bank loans for financing their working capital expenditure and thus need to rely on internal funds or retained earnings. Among the establishments for whom retained earnings are the main source of working capital finance (78% of the sample, column 4), the adverse effect of inflation on innovation increases to 4.4%. And for the establishments that rely exclusively on retained earnings (47% of the sample, column 3), the innovation probability is reduced by 4.6%.

Table 4 provides the results of the generalized difference-in-difference estimation following the approach of Rajan and Zingales (1998). We test for the causal impact of inflation on innovation, exploiting variation in industry characteristics. The results confirm all four model predictions and address any remaining concerns about the endogeneity of country-level inflation for establishments’ innovation decision. In line with Proposition 1, we find that the impact of inflation on establishments’ innovation decision is larger in (i) more volatile sectors, (ii) sectors with higher value added growth, (iii) more R&D intensive sectors, and (iv) sectors with higher asset tangibility. The common ground here is that these sectors are more exposed to advanced but risky technologies so that the liquidity mechanism becomes particularly relevant.

# **4.4 Robustness**

While the WBES include detailed information on firms’ sources for financing their working capital expenditure, it does not provide direct balance sheet information on their short-term liquidity holdings. Balance sheet information on corporate holdings of cash and cash equivalents is, however, necessary to directly test the second key component of the model mechanism, 23
Table 4: Harmonized firm panel for 139 countries from 2006-16: Sector-specific identification of inflation effects on innovation

|||I|nnovation||
|---|---|---|---|---|
||sector volatility|sector growth|sector R&D intensity|sector fxed-assets|
|infation _∗_sector-volatility|-.001<sup>_∗∗_</sup><br>(-2.14)||||
|infation _∗_sector-growth||-.090<sup>_∗∗_</sup>|||
|||(-3.68)|||
|infation _∗_sector-R&D|||-.004<sup>_∗_</sup>||
||||(-1.72)||
|infation _∗_sector-fx-asset||||-.002<sup>_∗∗_</sup>|
|||||(-2.06)|
|loan|.133<sup>_∗∗_</sup>|.132<sup>_∗∗_</sup>|.132<sup>_∗∗_</sup>|.133<sup>_∗∗_</sup>|
||(11.3)|(11.4)|(11.7)|(11.8)|
|ln(empl)|.046<sup>_∗∗_</sup>|.046<sup>_∗∗_</sup>|.062<sup>_∗∗_</sup>|.046<sup>_∗∗_</sup>|
||(10.9)|(11.0)|(11.2)|(11.5)|
|ln(age)|-.024|-.024|-.024|-.024|
||(-1.06)|(-1.08)|(-1.09)|(-1.12)|
|subsidiary|.090<sup>_∗∗_</sup>|.088<sup>_∗∗_</sup>|.089<sup>_∗∗_</sup>|.089<sup>_∗∗_</sup>|
||(3.11)|(3.10)|(3.10)|(3.11)|
|Sector FE|Yes|Yes|Yes|Yes|
|Country_∗_Year FE|Yes|Yes|Yes|Yes|
|Firms|44,842|44,842|44,721|44,721|

World Bank Enterprise Surveys establishment-level data for 139 developing countries from 2006-16; marginal effects reported. Heteroscedasticity robust s.e. clustered at country _∗_ year level; t-statistics in parenthesis. **,* significant at 5%, 10%.

namely that firms that innovate hold more liquid assets to insure against the associated risk (see equation (10)).

To the best of our knowledge, reliable balance sheet information on corporate liquidity holdings is not available for developing countries. We thus complement our analysis of the WBES with balance sheet data for U.S. firms from Compustat. The data provide information about cash holdings, the direct counterpart to the concept of corporate liquidity in our model. The Compustat data are also well suited for our analysis since they have the longest available time period for a panel of firms for a single country, providing sufficient variation in inflation 24
over time; they include all sectors of the U.S. economy from 1960-2016. The data further serve to test the empirical relevance of our mechanism for an industrialized economy with more developed financial markets.<sup>22</sup> The Compustat sample is based on publicly traded firms. While these are relatively few compared to the total number of firms, they include the largest firms in the U.S. economy. In fact, the data account for one-third of total U.S. employment and about 41% of total U.S. sales (De Loecker and Eeckhout, 2017). While detailed measures of innovation in terms of technology choice (product and processs innovation) are not available, Compustat allows to measure the intensive margin of firms’ innovation activity: we proxy firms’ exposure to the risky advanced technology by their R&D intensity as measured by R&D expenses relative to total assets. We also use firms’ total investment and balance sheet information on cash holdings as discussed earlier. Further, we include the stock of total assets and retained earnings as firm-level control variables.

The descriptive statistics are summarized in Table 5. The average (median) inflation rate in the sample is 3.6% (3.0%); and firms’ average (median) share of R&D in total assets amounts to 26% (5%). Importantly, the data reveal that even in countries with relatively developed financial markets such as the U.S. firms hold a large amount of cash, accounting on average for 11% of firms’ total assets (median of 4%). In line with earlier findings (Opler, Pinkowitz, Stulz, and Williamson, 1999), our model suggests that the principal motive for these cash holdings is to provision for liquidity needs arising due to expenditure shocks associated with the advanced technology, that is, with more productive but risky investment.

Table 5: Descriptive statistics

||Observations|mean|s.d.|min|max|
|---|---|---|---|---|---|
|**Compustat and WDI:**||||||
|R&D/assets|125,709|.261|8.59|0|1|
|Corp. liquidity/assets|320,325|.113|.179|0|1|
|Inv/assets|357,136|.641|.230|0|1|
|ln(corp. liquidity)|326,943|2.22|2.05|-6.91|13.0|
|ln(total assets)|382,007|4.77|2.55|0|15.1|
|ln(retained earnings)|237,585|3.54|2.46|-6.91|12.9|
|infation|428,482|3.64%|2.53|-.36%|13.5%|
|ln(real GDP pc)|428,482|6.38|.231|6.16|6.99|

> 22Our Compustat results are also better comparable to those from the empirical corporate finance literature discussed in the Introduction.

25
For the Compustat sample we consider a slightly different empirical specification due to the different nature of the data, where _yjst_ denotes either R&D expenses over total assets or cash holdings over total assets for firm _j_ in sector _s_ , and year _t_ . _Xjst_ is the matrix of firm-level control variables, _Xt_ the vector of macro-level control variables, _Ij_ is a vector of firm fixed effects, and _εjst_ an independent and identically distributed error. The main coefficient of interest ( _β_ 1) measures to what extent a firm in the U.S. adjusts its R&D activity in periods of higher inflation, after controlling for all fixed firm attributes, for changes in the firm-specific control variables, and for changes in aggregate economic activity. We also report the results from the analogous specification to (15) where we control for industry instead of firm fixed effects. In that case, _β_ 1 measures whether establishments operating in the same two-digit industry are, on average, less likely to innovate when rises.

The results, reported in Table 6, show that U.S. firms indeed hold more cash when they invest in R&D.<sup>23</sup> A 1% increase in corporate cash holdings is associated with an increase of 1.5 pp in firms’ share of R&D in total assets (column 1), corresponding to an average increase in firms’ R&D intensity by 6%. The positive correlation between corporate cash holdings and R&D investment is significant across firms operating in the same two-digit sector (column 1) and within firms over time (column 2). The results in columns 3 and 4 further confirm that inflation raises the (opportunity) cost facing firms holding such liquidity. A 1 pp increase in inflation reduces firms’ liquidity holdings by 5.5% (liquidity is reduced by 0.6 pp relative to an average across firms of 11%). Moreover, the results are robust if we restrict the sample to firms that actually report positive R&D investment (column 5). Taken together, columns 1 to 5 of Table 6 confirm our earlier findings from the WBES sample and thus further corroborate the theoretical model’s central predictions.

In analogy to the WBES results for developing countries, we find that U.S. firms reduce their

> 23Our finding is consistent with Opler et al. (1999) who show that U.S. firms with higher growth opportunities, approximated by their market-to-book value and R&D expenses, hold on average more liquid assets (cash and marketable securities) relative to total assets.

26
Table 6: U.S. long firm-level panel data 1960-2016: Firms adjust liquidity holdings and reduce R&D investment in <u>periods</u> of higher inflation

||||||||||1960-2008|
|---|---|---|---|---|---|---|---|---|---|
||R&D/|assets|Corp.|liquidity/a|ssets|R&D/|assets|inv/assets|R&D/assets|
|infation|||-.006<sup>_∗∗_</sup>|-.003<sup>_∗∗_</sup>|-.003<sup>_∗∗_</sup>|-.003<sup>_∗∗_</sup>|-.001<sup>_∗∗_</sup>|-.001<sup>_∗_</sup>|-.001<sup>_∗∗_</sup>|
||||(-5.97)|(-5.05)|(-3.75)|(-3.17)|(-3.87)|(-1.77)|(-4.86)|
|ln(Corporate liquidity)|.015<sup>_∗∗_</sup>|.001<sup>_∗∗_</sup>||||||||
||(6.39)|(2.84)||||||||
|ln(total assets)|-.027<sup>_∗∗_</sup>|-.017<sup>_∗∗_</sup>|-.018<sup>_∗∗_</sup>|-.015<sup>_∗∗_</sup>|-.003|-.016<sup>_∗∗_</sup>|-.016<sup>_∗∗_</sup>|-.012<sup>_∗∗_</sup>|-.015<sup>_∗∗_</sup>|
||(-4.31)|(-10.4)|(-11.6)|(-7.97)|(-1.22)|(-3.62)|(-9.20)|(-5.01)|(-9.74)|
|ln(retained earnings)|.004<sup>_∗_</sup>|.005<sup>_∗∗_</sup>|.008<sup>_∗∗_</sup>|.006<sup>_∗∗_</sup>|.005<sup>_∗∗_</sup>|.005<sup>_∗∗_</sup>|.005<sup>_∗∗_</sup>|.022<sup>_∗∗_</sup>|.004<sup>_∗∗_</sup>|
||(1.92)|(3.59)|(8.87)|(12.7)|(5.64)|(2.24)|(3.92)|(16.8)|(4.79)|
|ln(real GDP pc)|-.069<sup>_∗∗_</sup>|-.046<sup>_∗∗_</sup>|-.064<sup>_∗∗_</sup>|-.029<sup>_∗∗_</sup>|-.034<sup>_∗∗_</sup>|-.062<sup>_∗∗_</sup>|-.039<sup>_∗∗_</sup>|.143<sup>_∗∗_</sup>|-.040<sup>_∗∗_</sup>|
||(-4.02)|(-9.48)|(-5.50)|(-2.66)|(-2.66)|(-4.60)|(-9.46)|(10.9)|(-9.65)|
|Firm FE|No|Yes|No|Yes|Yes|No|Yes|Yes|Yes|
|Sector FE|Yes|No|Yes|No|No|Yes|No|No|No|
|Obs|62,104|62,104|202,285|202,285|61,691|68,500|68,500|234,610|59,893|

U.S. firm-level balance sheet panel data from Compustat, 1950-2016. Median inflation: 3.0% (average 2.6%). Heteroscedasticity robust s.e. clustered at year level; t-statistics in parenthesis. **,* significant at 5%, 10%.

investment in advanced but risky technologies (as measured by their R&D share) in periods of higher inflation (columns 6-7 of Table 6). The result is robust to variations in sample periods, for instance, when excluding the years after the global financial crisis (column 9). As before, we do not find an equivalent impact of inflation on firms’ total investment (column 8).<sup>24</sup> Finally, the data for U.S. firms also support the theoretical model’s sector-specific predictions in the generalized difference-in-difference estimation. Table 7 presents the results. We find that the negative impact of inflation on U.S. firms’ R&D investment is larger in more volatile sectors, in sectors with higher value added growth, and in more R&D intensive sectors.<sup>25</sup>

> 24The coefficient is marginally significant at the 10% level. However, we find a significant negative impact of inflation on the ratio of R&D over total investment (not reported in Table 6), consistent with the notion that inflation has a stronger impact on R&D than on non-R&D investment.

> 25We do not test the model prediction for sectors with higher asset tangibility since we expect that firms in the U.S. can also leverage their intangible assets (e.g., their brand name) as collateral. We thus predict a meaningful correlation between firms’ access to external finance and their ability to provide collateral exclusively through non-intangible assets only for firms in financially less developed countries. In fact, we find that, in the U.S., R&D intensive two-digit sectors have a lower asset tangibility.

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Table 7: U.S. long firm-level panel data 1960-2016: Sector-specific identification of inflation effects on R&D

|||R&D/asset|s|
|---|---|---|---|
||sector volatility|sector growth|sector R&D intensity|
|infation _∗_sector-volatility|-.001<sup>_∗∗_</sup>|||
||(-2.67)|||
|infation _∗_sector-growth||-.002<sup>_∗∗_</sup>||
|||(-2.86)||
|infation _∗_sector-R&D|||-.001<sup>_∗∗_</sup>|
||||(-5.38)|
|ln(total assets)|-.016<sup>_∗∗_</sup>|-.016<sup>_∗∗_</sup>|-.016<sup>_∗∗_</sup>|
||(-9.79)|(-9.79)|(-9.85)|
|ln(retained earnings)|.004<sup>_∗∗_</sup>|.004<sup>_∗∗_</sup>|.004<sup>_∗∗_</sup>|
||(3.38)|(3.37)|(3.36)|
|Firm FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|Obs|37,949|37,949|37,949|

U.S. firm-level balance sheet panel data from Compustat, 1950-2016. Median inflation: 3.0% (average 2.6%). Heteroscedasticity robust s.e. clustered at year level t-statistics in parenthesis. **,* significant at 5%, 10%.

# **5 Conclusion**

Our paper makes two contributions to the literature concerned with the effects of inflation on innovation, productivity and growth. We first present a simple model proposing a negative causal effect of inflation on growth, which is transmitted not via aggregate investment, but instead via technology choice. Hence, inflation affects the composition of investment, tilting it away from innovative activities towards safer but return-dominated ones. The primary channel along which adverse effects of inflation materialize is therefore not in the volume of aggregate investment, but in the dynamics of aggregate productivity growth.

Our theory is based on the idea of costly liquidity provision in the presence of financial frictions and makes predictions about how the severity of the adverse inflation effect depends on firm (or industry) characteristics. Specifically, we show that the liquidity mechanism is particularly relevant in fast growing and volatile industries, and in those with high income 28
pledgeability. The demand for liquidity and the composition of investment in these industries are therefore predicted to be more sensitive to variations in the rate of inflation.

Second, we test these predictions with firm-level data. We use novel data from the WBES to overcome the small sample problems typically encountered in empirical models that aim to examine the consequences of aggregate macroeconomic conditions (inflation) for individual microeconomic units (firms). For the harmonized panel covering firms in 139 developing countries, we find that inflation shifts the composition but not the overall quantity of investment. The impact of inflation on firms’ technology choice is statistically and economically significant: a 1 pp increase in a given developing country’s rate of inflation reduces the probability that establishments in that country engage in product or process innovation by 4.3%. Inflation also reduces the share of working capital financed by internal funds/retained earnings. And the adverse effects of inflation increase with establishments’ dependence on retained earnings to finance their working capital, consistent with the view that innovation activities are hard to finance externally. Finally, exploiting variation in industry characteristics, our generalized difference-in-difference estimations validate the sector-specific predictions of our theoretical model.

In a further robustness exercise, we consider balance sheet data for U.S. firms which provide direct information about firms’ liquidity holdings. We find that corporate liquidity (cash) holdings are increasing with firms’ R&D investment but that inflation reduces their liquidity holdings. Our findings thus corroborate the key components of the transmission mechanism proposed in our theoretical model also in the context of an advanced economy with developed markets.

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