metadata: authors: 0: author: Chan,Rosanna
count: Bangladesh
docna: 0: docna: Why liquidity matters to the export decision
            of the firm
docty: Policy Research Working Paper
subtopic: Investment and Investment Climate,Health Care Services Industry,International Trade and Trade Rules,Trade and Services,Transport Services
teratopic: Social Protections and Labor,Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: Under financial constraints, exporting
            may have less to do with productivity and more to do with
            financial resources. The established relationship between
            exporting and productivity would differ when examined
            through the lens of the working capital needs of the firm.
            The hypothesis that working capital matters in the
            firm's exporting decision is explored in two ways:
            first, by articulating a dynamic working capital model of
            the firm that incorporates the firm's export decision.
            Secondly, by testing the hypothesis empirically using a
            unique firm level dataset from Bangladesh, where issues of
            financial constraints are particularly acute. The model
            shows that productivity determines export status of the firm
            as long as it is not under financial constraints. However,
            under financial constraints, export status is less dependent
            on productivity and more dependent on the availability of
            working capital. Empirical results support the model's
            prediction. The relationship between exporting time and the
            need for greater liquidity is also borne out empirically as
            shown by a positive and significant correlation between the
            amount of working capital and the distance of export
            destination. An important policy implication from the
            analysis is that short term liquidity is critical in
            allowing productive firms to export and that access to
            finance may prevent the benefits of trade liberalization
            within a country to be fully realized.
geo_reg_key: 517198,517212,517191
repnme: repnme: Why liquidity matters to the export
            decision of the firm
display_title: Why liquidity matters to the export
            decision of the firm
listing_relative_url: /research/2014/04/19404022/liquidity-matters-export-decision-firm
topicv3: Investment Climate m1326293 405,Transport m1326661 135,Trade m1326924 698,Trade in Services m1326928 780,Health m1504661 3480
publishtoextweb_dt: 2014-04-01T00:00:00Z
historic_topic: Social Protections and Labor,Macroeconomics and Economic Growth,Finance and Financial Sector Development
no_of_pages: 34
author_keys: 0: author_key: 857102
model_extractions: WPS6839 Policy Research Working Paper 6839 Why Liquidity Matters to the Export Decision of the Firm _Rosanna Chan_ The World Bank South Asia Region Finance and Private Sector Unit April 2014
Policy Research Working Paper 6839

# **Abstract**

Under financial constraints, exporting may have less to do with productivity and more to do with financial resources. The established relationship between exporting and productivity would differ when examined through the lens of the working capital needs of the firm. The hypothesis that working capital matters in the firm’s exporting decision is explored in two ways: first, by articulating a dynamic working capital model of the firm that incorporates the firm’s export decision. Secondly, by testing the hypothesis empirically using a unique firm level dataset from Bangladesh, where issues of financial constraints are particularly acute. The model shows that productivity determines export status of the firm as long as it is not under financial constraints. However, under financial constraints, export status is less dependent on productivity and more dependent on the availability of working capital. Empirical results support the model’s prediction. The relationship between exporting time and the need for greater liquidity is also borne out empirically as shown by a positive and significant correlation between the amount of working capital and the distance of export destination. An important policy implication from the analysis is that short term liquidity is critical in allowing productive firms to export and that access to finance may prevent the benefits of trade liberalization within a country to be fully realized.

This paper is a product of the Finance and Private Sector Unit, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at rchan@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
# Why Liquidity Matters to the Export Decision of the Firm

Rosanna Chan<sup>_∗_</sup>

## **1 Introduction**

The growth of exports is a key priority for many developing countries as a means to access foreign earnings and to stimulate economic growth.<sup>1</sup> Yet, among developing countries, access to external finance is a major problem. The World Bank Investment Climate Surveys, covering more than 26,000 firms across 53 developing countries, find that the cost and access to finance is considered by firms to be the top five problems they face ( **?** ). An important question to ask is how do financial constraints affect the export decision of the firm. This question is particularly relevant in light of the 2008 financial crisis, where the freeze on credit coincided with a drop in world exports of more than 30 percent<sup>2</sup> that cannot be entirely explained by the drop in aggregate demand.

> _∗_ Economist, South Asia Finance & Private Sector Development, World Bank. Email rchan@worldbank.org I am grateful to Albert Park, Dan Silverman, Jim Levinsohn, Jan Svejnar, Chris House and Andrei Levchenko for their advice, Mary Hallward-Driemeier at DECRG, World Bank for data and institutional background, Aneeqa Aqeel, Oli Coibon, Jane Dokko, Chris Kurz, Sara Lalumia, Rahul Mukherjee, Joo Youn Park, Bill Powers, Heidi Schierholz, Mine Senses, Lina Walker for their insightful comments and encouragement. I would also like thank seminar participants at the Universities of Michigan, University of Melbourne, Australia National University, University of Wisconsin and the Minnesota International Economic Development Conference for their valuable input. All errors are my own. The views expressed here are those of the author and do not necessarily reflect official views of the World Bank. JEL: D01, D2, D21, D22, D24, D92, F02, F10, F14, F15, F36, O12, G20 Keywords: credit constraints, financial constraints, working capital, firm behavior, international trade, trade costs, transaction costs, entry costs to trade, time, trade barrier. All errors are my own.

> 1See **??**

> 2Quarterly trade data from WTO, compiled from data sourced from IMF, International Financial Statistics; Eurostat, Comext Database; National statistics; Global Trade Atlas.
To understand how financial constraints affect export status, the first step is to ask what are the financing needs of an exporting firm. Take for example, a firm owner who has to decide between selling to domestic or foreign markets. She/he would face export specific costs such as the costs of licensing or getting entry to markets.<sup>3</sup> However, beyond these additional costs, the owner also faces a significant difference in timing between domestic sales and foreign sales. In the case of Bangladesh, exporting to Hamburg can take up to 39 days. With domestic sales, goods would be delivered faster and the payment for the goods received within a shorter time. With foreign sales, although the goods will fetch a higher price, the goods will take longer to deliver and the payment will be received with a longer delay. The data show that most firms receive payment only after their goods have been delivered. Thus, the decision to export involves the owner asking themselves whether they would be able to remain liquid during the period before payment is received. Operating liquidity in financial metric terms is working capital. As such, to address how financial constraints affect the export decision of the firm, it is necessary to understand how working capital affects the export decision of the firm.

This paper makes two contributions to this question. First, a dynamic working capital model of the firm’s export decision is presented. Second, the empirical predictions of the model are tested using a unique panel data set of Bangladeshi firms and robust evidence is found that financial constraints provide a significant barrier to exports.

The theoretical model builds upon the dynamic working capital model of the firm **?** and extends it to the export decision of the firm. At the time of writing, there has not been any existing trade models that have formalized the role of working capital in exporting decisions.

Working capital underlies two fundamental concepts in trade: costs to entry and the role of distance in determining trade volumes. The extra working capital demands from exporting over domestic sales due to shipping time, formalizes the concept of sunk cost or the cost of entry which many trade models are premised upon (see **?????** ). Furthermore, as the amount of working capital directly reflects transportation time, the greater the distance, the greater the working capital demands in exporting. Therefore, the model also speaks to the area of trade research on how distance determines trade volumes as well as on how time can act as a trade barrier (see **???** for research in this area. ).

The main theoretical result is that the export decision of the firm is determined by both productivity and working capital and their relative importance depends on whether the firm is financially constrained or not. The model shows that exporting depends primarily on productivity _if the firm is not financially constrained._ When firms are financially constrained, however, a lack of working capital can severely restrict a firm’s willingness and ability to engage in exports. The implication is that among the population of firms, there will exist low productivity firms that never export regardless of their cash positions, and cash-poor firms that do not export regardless of their productivity levels.

To assess the importance of this mechanism, I use a unique firm-level data set which allows two key aspects of the model to be examined: a) a direct test of the interaction between financial constraints, working capital and productivity in determining the export status of the firm, and b) the relationship between working capital and the export distance that underlies the rationale behind the theoretical model. The 2002 Bangladesh Investment Climate Survey is one of the few panel data sets available that contain firm level financial information, their access to credit and detailed information on exports. The empirical results provide support for the model’s predictions that the effects of working capital and productivity will differ between financially constrained and unconstrained firms. Specifically, for financially constrained firms, productivity matters less than for unconstrained firms while working capital matters much more. The correlation between

> 3Costs to exporting also include shipping and transportation, custom duties and many more. These are discussed in **?** .

2
working capital and export distance is shown empirically to be positive and significant, lending support for the working capital model of the firm’s export decision.

The paper is structured as follows: the next section discusses the relevant literature for both theoretical firm models and empirical work in the area of export and finance<sup>4</sup> , Section 3 motivates the need for the working capital model of export decision by taking a preliminary look at the data to examine the distribution of productivity between exporting and non-exporting firms and also to look at the additional working capital demands that exporting imposes, Section 4 develops the working capital model of the firm’s export decision, Section 5 examines the empirical results and then the conclusion.

# **2 Relevant Literature**

The role of firm productivity as a determining factor for the export status of the firm has been researched extensively both theoretically through the heterogeneous firm models of **??** and empirically in the work of **???** . It is, however, ambiguous as to whether the existence of financial constraints changes the relationship between productivity and exporting.

The literature on the interaction of trade and financial factors has existed for some time(see **?** ), but the emphasis has been on incorporating finance into the theory of comparative advantage to explain trade patterns across countries. Only more recently has the new trade literature begun to consider the role of finance in heterogeneous firm models (see **????** ). Existing models of exporting decisions such as **???** assume a set percentage of borrowing and do not account for the accumulation of internal finance that affect financial constraints and reduce the demand for borrowing. Their models assume that all firms are affected by financial constraints, due to the set borrowing requirement, and therefore financial constraints will _always_ influence exporting decisions, regardless of differences in internal financing between firms. This is a restrictive assumption as it is important to take into account both good financial health, when firms do not require to borrow, and when firms are financially constrained so as to accurately capture the impact of financial constraints on the distribution of firms in the aggregate.

Existing models also assume that allocation of credit is perfectly aligned to productivity. This assumption drives the result of these models: exports with financial constraints simply raises the productivity cutoff across all firms. Existing models assume no time difference between inputs and revenues and as such, the financing of the inputs responds directly to the input choice. This, coupled with the assumption that credit is perfectly aligned with productivity, drives the existing models’ result.<sup>5</sup> However, existing literature has shown that the allocation of credit does not correspond perfectly to productivity (see **??** ). The assumption of direct correspondence is thus too strong as it does not allow for the existence of distortions in the allocation of credit. The assumptions made by existing theoretical models, static borrowing demand and direct correspondence between allocation of credit and productivity, lead to essentially the same result as predicted by **?** with the caveat that financial distortion raises the productivity cut off level across all firms.

The empirical literature using micro level data on the relationship between exporting and finance is limited due to both the availability of firm level data sets as well as the topic being an emerging area of research. Of note, one of the few studies is **?** that uses a large UK panel data set. They show that the financial health of

> 4The literature on the area of trade credit , such as **?????** reviewed in **?** .

> 5The working capital model differs from the existing models in that the firm’s maximization problem at time _t_ is bounded by predetermined internal revenues- that is, revenue is a state variable. Even if credit is perfectly aligned to productivity in the working capital model, this would not detract from the predictions that are driven uniquely by the timing difference between inputs and receipt of revenues.

3
firms positively affects export decisions. Another more recent study, **?** uses cross section of firm level data across 9 developing countries and they find that access to finance is important to the firm’s decision to enter exports. Furthermore, they find that financial constraints create a disconnection between firms’ productivity and their export status: productivity is only a significant determinant of the export decision if the firm has a sufficient access to external finance. While the existing empirical work show that there are significant relationships between exporting and financial factors, neither studies relate these empirical findings to a model that explains the underlying economic relationships that drive these results.

# **3 Why might working capital restrictions affect exporting decisions?**

The Bangladesh Investment Climate Assessment 2002 surveyed 990 firms and collected annual recall data for 2000 and 2001 for key variables, except for access to finance questions that are only reported for 2002. A total of six manufacturing industries are represented: Garments, Textiles, Food, Leather, Electronics and Chemicals. The distribution of firms within each of these industries is representative of the composition of the Bangladeshi economy, with 57 percent of the observations in the garments and textiles industry. The data set contains financial information that allows working capital to be measured at the firm level. Working capital is defined in the data as the sum of inventories, accounts receivable and cash on hand.<sup>6</sup> Working capital can be thought of as current assets or the liquidity of the firm. A unique characteristic of the data set is that it contains country destinations of exports. This allows us to look at whether the distance to country destinations is correlated with working capital at the firm level.

There are three major observations from the data that indicate working capital is particularly important for exporting. First, there are substantial additional costs to exporting in the form of custom duties and procedures. Table 1 shows that average official costs are 84,935 takas (approximately $1,456 USD) and average unofficial costs total 32,895 takas (approximately $564 USD). In light of the average value of exports is only 139,981 takas (approximately $2,399 USD), total custom costs are a substantial cost to firms in Bangladesh. In addition to these costs, exporting firms also hire clearance agents to help get them through customs -around 90% of exporting firms use a clearance agent at an average cost of 0.82% of the value of the freight. These costs plus the time delays in customs would require the firm to have adequate working capital in order to just get their goods through customs.

Second, exporters do not get paid until their goods are delivered to the export destination for a much larger proportion of their sales than non-exporters. Table 2 shows that on average, 89 percent of the sales for exports is paid upon delivery (median is 100 percent) compared to 65.74 percent for non-exporting firms (median 75 percent).<sup>7</sup> Therefore, exporters would require to cover a greater percentage of their costs with their own working capital than non-exporters. Furthermore, the longer the time period is between production and delivery, the greater the amount of working capital is required.

Last, delivery times are longer for exporters than non- exporters. Turnover time differs from industry

> 6Working capital is measured according to the accounting definition: current assets (inventories, accounts receivable, cash and shortterm credit) minus current liabilities (accounts payable and any short-term debt). Note that working capital data is only reported for 2002

> 7A proportion of sales is also bought on credit, and here, non-exporting firms appear to extend more credit than exporting firms. Extending credit to customers would further delay the receipt of revenues from when the cost of production was incurred. On average, amongst those that do extend credit to customers, 43.77 days of credit is extended with a median of 30 days. This is funded mainly from retained earnings ( median of 60%, with the rest from micro lenders or informal lenders). This implies that those firms that extend credit to their customers are doing so because they have enough cash or access to finance. They charge their customers extra for the credit for the delay in the receipt of payment and the delay is not crucial for completing the exchange. Unlike the delays caused by shipping across large geographical distances which are unavoidable and crucial to completing the transaction between the exporter and the customer.

4
<u>Table 1: Costs to Customs</u>

|2002 Bangladesh ICA|Mean|Standard Deviation|
|---|---|---|
|Average days to clear customs|8.87|10.69|
|Maximum number of days to clear customs|14.10|13.76|
|Offcial costs (takas)|84935|440869|
|(USD)|1456|7556|
|Unoffcial costs (takas)|32895|221242|
|(USD)|564|3792|
|Value of exports (takas)|139981|321491|
|(USD)|2399|5510|
|Percent that use clearing agent|89||
|Cost of agent as % of the value of freight|0.8132|3.109|

_Source:_ Bangladesh 2002 Investment Climate Assessment Survey, World Bank Note: The exchange rate in 2002 average is 0.017137961 takas=1 USD Table 2: Timing of payment for sales

|Mean|% of Sales paid|% Sales paid at|% Sales bought|Number|
|---|---|---|---|---|
||before delivery|delivery|on credit|of Obs|
|Non-Exporting|5.57|65.74|28.43|561|
|Firms|||||
|Exporting|2.87|89.42|7.66|421|
|Firms|||||
|Total|4.41|75.89|19.53|982|

_Source:_ Bangladesh 2002 Investment Climate Assessment Survey, World Bank to industry. It ranges from a month in garments to 4 months for shrimp farming ( **?** ). For firms that export, clearing customs, transiting to overseas destination and clearing foreign customs all add to the amount of time firms have to wait before receiving payment for their goods. The time to clear domestic customs is on average around 9 days, with a maximum of 14 according to the 2002 Bangladesh ICA (see Table 1). For transiting time, Bangladesh exports are shipped using international shipping lines that run on regular schedules. The time in transit consists of: overland to port, a feeder journey from Chittagong or Dhaka to Singapore to meet up with the international shipping line, and then the destination port to customer location. According to **?** , the greatest delays are caused in the exchange from feeder to mainline vessel as schedules may not be synchronized and containers may wait in the transshipment port for several days. His estimate of ocean transit times from Bangladesh, using a variety of shipping companies, is between 25-35 days. Figure 1 shows the transit times for APL shipping (the world’s sixth largest container transportation and shipping company) which corroborates the **?** estimate. Without even counting for time needed to clear foreign customs and inland delivery, selling to European markets from Bangladesh can add an extra month to the product cycle. Considering that production of garments only requires about a month to process, exporting essentially would double the amount of working capital required.

Among exporting firms, Figure 2 shows that the amount of working capital is increasing with the distance of export destination. This relationship is particularly stark with Europe and the EU - destinations that have the longest shipping transit times from Bangladesh. This suggests that if firms require additional working capital the longer the transit time or the greater the distance of the export destination.

5
<!-- Start of picture text --> 29023028 «11 «113:«17) «4 «> Chittagong)? 5 «12 20 4 O23 Mw OW<br>Ci ie a i a oh www ww ON<br>Hittg & : & :<br>peeeFag & :<br><!-- End of picture text --> <!-- Start of picture text --> Garment Textile<br>3”<br>°<br>Asia Japan EU EuroptmericasUSA Asia Japan EU EuroptmericasUSA<br><!-- End of picture text -->
Figure 3: Time line for Production and Receipt of Revenue <!-- Start of picture text --> t-(1+S) ... t-1 t t+1 ... t+(1+S)<br>Receive domestic sales (t- Receive domestic sales Receive domestic sales<br>1) shipped at t (t+S)<br>Receive export sales (1+S)) (t- Receive export sales (t-S) Receive export sales shipped at t<br>Discover price Pt Discover price Pt+1<br>Determined domestic Determine domestic Determine domestic<br>sales( t-1) sales t sales t+1<br>Determined export Determine export<br>sales (t-(1+S)) Determine export sales t sales t+1<br><!-- End of picture text --> _Note:_ Price _Pt_ represents both domestic price _P_<sup>_D_</sup> and foreign price _P_<sup>_F_</sup> .

production decisions based on the uncertainty of output and prices in the domestic and foreign markets.<sup>9</sup> The firm also has access to financial services where it can borrow or save, _b_ , at interest rate _r_ .<sup>10</sup> The standard assumptions apply where wages, _w_ , the price of capital, _p_<sup>_k_</sup> , and the interest rate, _r_ are assumed to be = <u>1</u> exogenous and non time varying. The discount factor given by _β_ (1+ _r_ )<sup>and the depreciation rate of capital</sup> given by _δ_ . Entry to export markets requires a fixed cost, _f_ , each period, which can be viewed as a license, permit or agent’s fee to export.

To incorporate the need for working capital, time delays between production and receipt of revenue are introduced. The time delays are as follows: selling to domestic markets requires one period to receive the revenue and selling to international markets requires _S_ periods in addition to the one period delay that is normally incurred through domestic sales. That is, exporting requires additional time for products to be shipped and received overseas over that of domestic sales. Figure 3 shows the timing of production and receipt of revenue. At each period, the firm receives the revenue from past sales: past sales includes any domestic sales from period ( _t −_ 1) and any export sales from period ( _t −_ (1 + _S_ )). At each period, the firm also decides on production to be sold domestically and/or exported - the revenue from which will be received either at the next period ( _t_ +1) or at _t_ +(1+ _S_ ) periods later. All costs of production are incurred at the time of production, regardless of whether products are going to export or domestic markets, but the production choice affects the transitional equation at the present period and at (1 + _S_ ) period when export revenues arrive.

At each period, the firm discovers the prices for output; the domestic price, _Pt_<sup>_D_and the foreign price,</sup><sup>_P_</sup> _t_<sup>_F_.</sup> Both prices are stochastic over time. For simplicity, the present value of the foreign price is assumed to be always greater than domestic price so that there is an incentive for firms to enter exports, _β_<sup>_S_</sup> _P_<sup>_F_</sup> _− P_<sup>_D_</sup> _>_ 0.<sup>11</sup> The availability of export markets can be thought of as an additional mechanism to transfer resources over time: domestic sales get _P_<sup>_D_</sup> and returns are delivered at time _t_ + 1 while international sales get _Pt_<sup>_F_but incurs</sup> a fixed cost and returns are delivered at time _t_ + 1 + _S_ .<sup>12</sup> Intuitively, the extra time required for exports costs

> 9This departs from current literature where firms only realize their productivity after paying a fixed cost **???** . The rationale is that firms are privy to more information about their own operations and how productive they are more so than knowing the price conditions on foreign markets.

> 10Note, this follows the international finance literature in that _b_ represent bond holdings and positive implies savings and negative implies borrowing.

> 11Relaxing this assumption will simply allow more instances where firms will choose not to export because it isn’t as profitable. The objective of this paper is to highlight how financial constraints affect export behavior, and thus it is important for firms to have an incentive to export in the first place.

> 12The firm also has available bonds, that earns interest, _r_ , that is received also at _t_ + 1. It is implicitly assumed that the rate of return from domestic production is greater than the interest rate - or else there is no incentive to produce. In other words, _P_<sup>_D_</sup> is bounded from below such that there is always an incentive to produce.

7
the firm the discount rate _β_<sup>_S_</sup> on revenue which is increasing with time _S_ . The delays imply that at each period, the firm has a stock of wealth made up of revenues that are due from past production and any bond holdings from last period. This stock of wealth is the firm’s working capital, _Xt_ : The cash the firm has at any particular point will not include _all_ export sales pending from all previous periods but only the export sales due at that period. The revenue for export sales is only realized as cash at the period the firm receives it. Working capital is liquidity, and pending payments that are not received cannot be used to pay for inputs.<sup>13</sup> The firm chooses labor, investment and percentage of production to export according to how much working capital it has. Therefore, the firm’s budget constraint is given by: Financial constraints are introduced as a limit on how much the firm can borrow at any particular time. The firm is restricted to borrow only up to a percentage, _φ_ , of it’s total wealth: _bt ≥−φXt_ .<sup>14</sup> Define _π_<sup>_D_</sup> as the current value profit from only domestic sales; _π_<sup>_D_</sup> = _β P_<sup>_D_</sup> _AiF_ ( _K, L_ ) _− C_ ( _K, L, K−_ 1). Likewise, define _π_<sup>_E_</sup> as the current value profit from sales where exports are non-zero; _π_<sup>_E_</sup> = �(1 _− ψ_ ) _β P_<sup>_D_</sup> + _ψβ_<sup>1+</sup><sup>_S_</sup> _P_<sup>_F_�</sup> _AF_ ( _K, L_ ) _−C_ ( _K, L, K−_ 1) _− f_ . Variable costs are denoted by _C_ ( _K, L, K−_ 1) = _wL_ + _p_<sup>_k_</sup> ( _K −_ (1 _− δ_ ) _K−_ 1). The dynamic maximization problem of the firm can be described by the value Following **?** , the borrowing constraint for domestic,(1 + _φ_ ) _X −C_ ( _K, L, K−_ 1) _≥_ 0 and for exports, (1 + _φ_ ) _X − C_ ( _K, L, K−_ 1) _− f ≥_ 0, is embedded in the value function where _ν_ denotes the shadow value of loosening borrowing constraint.

The firm’s maximization problem involves 2 steps due to discontinuity introduced by the fixed cost of exporting. First, given the cash available and output prices, the firm determines the output levels that will maximize profits under domestic sales as well as for export sales. Under export sales, the firm also determines

> 13It is possible that pending payments could be used as collateral to borrow more, but this scenario will be left for further research. The focus of this paper is to look at cash strap firms in developing countries where any kind of credit is difficult to obtain even on cash collateral.

> 14This setup differs from the existing literature in three ways: 1) borrowing is endogenously chosen, 1) the probability of exiting export markets is not assumed a priori, 2) the amount the firm produces for domestic and international markets is chosen endogenously by the firm in response to relative prices and 3)the time required to trade and the resulting liquidity requirements are modeled explicitly. I take out the assumption of monopolistic competition, where exporting firms can influence price levels, and substitute this with financial constraints as this may be a more realistic assumption for firms in developing countries.

8
the optimal amount to export if exporting. Second, the firm then chooses between the two profits to determine whether to export.

# **4.1 Domestic Sales**

The firm’s dynamic problem in the domestic sales case reduces to only two possible solutions, as established in **?** ; the firm is either not financially constrained, in which case it produces at the optimal, or it is constrained, in which case, it produces as much as it can given cash on hand. The first order conditions are repeated below:

## **Unconstrained:**

_L_<sup>_D∗_</sup> and _K_<sup>_D∗_</sup> is the solution to the firm’s maximization problem if and only if: Equation (1) and (2) hold and (1 + _φ_ ) _X − wL_<sup>_D∗_</sup> _− p_<sup>_k_</sup> ( _K_<sup>_D∗_</sup> _−_ (1 _− δ_ ) _K−_ 1) _>_ 0. Otherwise, the solution is given by Equations (3) and (4) below:

## **Constrained:**

Let _V_<sup>_∗D_</sup> ( _Ai_ ) denote the value of the value function of optimal domestic profits as a function of _A_ and let _X_<sup>_D∗_</sup> ( _Ai_ ) denote the minimum cash requirement for unconstrained profits to occur. Note that both profits and cash requirements are increasing with technology, _A_ . Let _V D_ ( _Ai, Xi_ ) denote the value of the value function where profits are at constrained optimal (where _Xi < X_<sup>_D∗_</sup> ( _Ai_ )). It is necessarily so that constrained optimal profits is lower than unconstrained profits: _V_<sup>_∗D_</sup> ( _Ai_ ) _> V D_ ( _Ai, Xi_ ) .

# **4.2 Export Sales**

The firm’s dynamic problem in the export sales case can also be similarly reduced to two scenarios. Let Σ denote the expected sum of all future shadow values up till the period export revenue is received; Σ = _E_ [ _νS−_ 1(1 + _φ_ )]+ _.....E_ [ _ν_<sup>_′_</sup> (1 + _φ_ )]. This can be interpreted as the opportunity cost of liquidity due to exporting. The first order conditions under export sales are given by: 9
<!-- Start of picture text --> — «( ) ( |)<br>— ) ( [-] )<br>— ; ) [—-] |——-]<br>- kK o f Fy ]<br>Hf!<br>I<br>lo<br>_<br>KH} 4<br><!-- End of picture text -->
# **Constrained**

Equation 9 comes from a derivation of the first order condition from exports, given by: 16 Thus the solution to the export maximization problem is as such:

- If there are no binding future constraints for all future periods up to (1 + _S_ ), i.e.Σ = 0, then the marginal value of increasing export sales is strictly positive. Exports in this case will be the maximum amount possible which is 100 percent; _ψ_ = 1.

- If there are binding future constraints in the period up to (1 + _S_ ), the extent that financial constraints are binding during that time will determine the amount of exports. The choice of _ψ_ affects the size of Σ, the shadow values _ν_ and _ν_<sup>_′_</sup> as well as _E_ � _∂∂VX_<sup>_′′_</sup> �. The choice of labor, capital and export percentage will be determined where all three equations are satisfied. Exports in this case can take on values between 0 and 100 percent due liquidity constraints.

Let _V_<sup>_∗E_</sup> ( _Ai_ ) denote the value of the value function of optimal domestic profits as a function of firm specific technology _Ai_ and let _X_<sup>_E∗_</sup> ( _Ai_ ) denote the minimum cash requirement for unconstrained profits to occur. Additionally, let _V E_ ( _Ai, Xi_ ) denote the value of the value function where profits are at constrained optimal (where _Xi < X_<sup>_E∗_</sup> ( _Ai_ )). It is necessarily so that constrained optimal profits are lower than unconstrained profits: _V_<sup>_∗E_</sup> ( _Ai_ ) _> V E_ ( _Ai, Xi_ ) The cash requirement, _X_<sup>_E∗_</sup> ( _Ai_ ), to satisfy Σ = 0 can be calculated by iterating backwards the budget constraint from the period when export revenue is received. At that period, cash on hand must be greater than expected cost. ie _Xt_ + _s >_ (1+1 _φ_ )<sup>_Et_[</sup><sup>_Costt_+</sup><sup>_s_] for constraints to not bind.Likewise for the period before that, and</sup> so on and so forth. The minimum cash required for constraints to not bind for each period thus can be given as:<sup>17</sup>

> 16This utilizes a backward iteration of _∂∂VX_<sup>=</sup><sup>_E_</sup> <u>�</u> _∂∂VX_<sup>_′′_</sup> � + _ν_ (1 + _φ_ ) that allows us to summarize the change in the value function at time (1 + _S_ ) in terms of the history of shadow values leading up to it: _E_ � _∂∂VX_<sup>11++</sup><sup>_SS_</sup> � = _E_ � _∂∂VX_<sup>_′′_</sup> � _−_ Σ.

> 17The cash threshold is defined for where the firm also expects to export in the future. An alternative scenario is that, due to

11
Let _C_<sup>_F_</sup> denotes the cost to produce optimally at foreign prices.

The characteristics of the cash threshold as defined by Equation 13 is intuitive as all costs enter positively; an increase in production costs and fixed costs will raise the cash threshold. Revenues enter negatively; if foreign price, _P_<sup>_F_</sup> , increases, the amount of cash required to export will decline. _ω_ denotes the current value of foreign sales revenue that is going to be delivered in the future, between _t_ = 0 and _t_ = (1 + _S_ ), from past production. If the firm has never exported before then _ω_ = 0. If the firm has exported before (and the greater the value of _ω_ is), the lower the initial amount of cash required to export. This captures how firms are more likely to export, if they have exported before. The motivation here, however is not a learning story as is often forwarded by existing trade story (ie **?** ), but one of liquidity and the timing of when cash arrives - firms that have exported before will have more liquidity to export in the present period.

The cash requirements also show that in addition to the fixed cost _f_ required to export, the firm requires additional liquidity to cover the longer delays in exporting. The term _Et_ � _C_<sup>_F_</sup> + _f_ ��∑<sup>_S_</sup> _s_ =1<sup>_β s_�</sup> is the cost of production for exporting sales during the interim. The greater the distance, _S_ , the greater the cash required for the firm to not run into binding liquidity constraints. Thus it captures the role of distance in exporting and relates this to the liquidity demands. The borrowing allowance _φ_ also affects the cash threshold: if it decreases, the threshold also increases as firms cannot rely on external borrowing to finance production but need to finance internally.

The cash threshold is an increasing function of firm productivity _Ai_ as cost of production, _C_ , increases with inputs _L_ and _K_ which themselves are increasing functions of productivity. Intuitively, greater productivity increases the level that optimal scale of production is reached.

# **4.3 Exporting Decision**

The exporting decision rests on choosing between domestic sales or export sales depending on which value stream is greater. The value streams depend on the state variables _Ai_ and _Xi_ .<sup>18</sup> First, looking at technology _Ai_ , let’s assume that the firm’s cash level _Xi_ is greater than the minimum cash requirement to export, _X_<sup>_E∗_</sup> ( _Ai_ ). The firm picks the maximum of the two unconstrained optimal value streams: _V_<sup>_∗D_</sup> ( _Ai_ ) or _V_<sup>_∗E_</sup> ( _Ai_ ) . As cash is not a binding constraint both at the present time period and in the future, the firm need only compare current profits: _π_<sup>_D∗_</sup> = _β P_<sup>_D_</sup> _AiQ_<sup>_D∗_</sup> _−C_<sup>_D∗_</sup> to _π_<sup>_E∗_</sup> = _β_<sup>1+</sup><sup>_S_</sup> _P_<sup>_F_</sup> _AiQ_<sup>_E∗_</sup> _−C_<sup>_E∗_</sup> _− f_ . The

> higher than mean expected price realizations, the firm is able to export once but expects to return to domestic sales only as prices return to expected mean. In this scenario, the cash threshold , _XFD_<sup>_E∗_(</sup><sup>_Ai_),willbelower.</sup><sup>_X_</sup> _FD_<sup>_E∗_(</sup><sup>_Ai_) =</sup><sup>_CF_+</sup><sup>_f−β_1+</sup><sup>_SP_</sup> _t_<sup>_FAiF_(</sup><sup>_K,L_)</sup><sup>_−_</sup> _Et_ � _β Pt_<sup>_D_</sup> +1<sup>_AiF_(</sup><sup>_K,L_)</sup><sup>_−CD_��</sup> ∑<sup>_S_</sup> _s_ =1<sup>_β s_�</sup> + _Et_ � _C_<sup>_D_�</sup> (1+1 _φ_ )<sup>_β_1+</sup><sup>_S −ω_</sup>

> Price expectations are iid such that: _Et_ [ _Pt_ +1] = _Et_ [ _Pt_ +2] = _..._ = _Et_ [ _Pt_ + _T_ ]. Expected costs at time _t_ are the same: _Et_ � _Ct_<sup>_F_</sup> +1� = _Et_ � _Ct_<sup>_F_</sup> +2� = _..._ = _Et_ � _Ct_<sup>_F_</sup> + _T_ � where _C_<sup>_F_</sup> denotes the cost to produce optimally at foreign prices and _C_<sup>_D_</sup> the cost at domestic prices.

> 18Recall that all firms are assumed to face the same prices, wages and interest rates.

12
productivity threshold, _A_<sup> ̄</sup> , is defined where _π_<sup>_D∗_</sup> = _π_<sup>_E∗_</sup> : The productivity threshold exhibits some intuitive characteristics in terms of it’s parameters. An increase in the foreign price _P_<sup>_F_</sup> or a decrease in the domestic price, _P_<sup>_D_</sup> , lowers the threshold as this causes exporting to be more profitable relative to domestic sales. Likewise, an increase in the fixed cost, _f_ , raises the productivity bar for firms to be profitable exporting over domestic sales. An increase in the delay, _S_ , such as through shipping distance, increases the threshold through the discount rate.

The productivity threshold is important in that it defines the minimum technology level required for the firm to be profitable in exporting. It would not be profitable for a firm with technology below the threshold to export, regardless of it’s cash position. This threshold holds even under financial constraints as a firm that is not profitable exporting without constraints, will not be profitable exporting _with_ constraints.

However, productivity alone does not determine whether the firm exports. While the productivity threshold separates firms with the ability to export from those that cannot, whether the firm has the means to do so will depend on the level of cash, _Xi_ . Clearly, if the firm meets both productivity and cash requirements, _Ai > A_<sup> ̄</sup> and _Xi > X_<sup>_E∗_</sup> ( _Ai_ ), the firm will export. Also, if the firm does not meet productivity requirements, _Ai < A_<sup> ̄</sup> , then the firm will not export regardless of the cash position, _Xi_ . Therefore, out of the set of solutions dependent on the state variables _Ai_ and _Xi_ , the situation where the firm meets productivity requirements, _Ai > A_<sup> ̄</sup> , but does not have the cash, _Xi < X_<sup>_E∗_</sup> ( _Ai_ ) , is the only combination left to fully map out the solution.

Previously, in the sections on domestic sales and export sales, the minimum cash levels required for unconstrained optimal were defined as _X_<sup>_D∗_</sup> ( _Ai_ ) and _X_<sup>_E∗_</sup> ( _Ai_ ) respectively. If the firm has cash levels below the minimum domestic “unconstrained optimal”, that is _Xi < X_<sup>_D∗_</sup> ( _Ai_ ), then the firm would necessarily not choose to export as the value of exporting is lower than domestic sales, _V D_ ( _Ai, Xi_ ) _> V E_ ( _Ai, Xi_ ) . This will hold regardless of productivity levels, _Ai_ . Intuitively, if the firm cannot afford to produce at optimal for domestic markets, it would not produce for export markets where there are even greater demands on liquidity due to fixed cost of exporting and the longer delays in the receipt of revenues, even if _Ai > A_<sup> ̄</sup> .

Take the situation when the firm’s cash level is above the minimum domestic unconstrained optimal but below the minimum export unconstrained optimal, that is _X_<sup>_D∗_</sup> ( _Ai_ ) _< Xi < X_<sup>_E∗_</sup> ( _Ai_ ) and _Ai > A_<sup> ̄</sup> . In this case, the firm has more than enough liquidity to sell at domestic optimal, but not enough to sell at the export unconstrained optimal. The firm needs to compare between unconstrained optimal domestic sales, _V_<sup>_D∗_</sup> ( _Ai, Xi_ ), with constrained export sales, _V E_ ( _Ai, Xi_ ), where export percent is less than 100 percent. This situation gives rise to two interesting outcomes: either a) the firm chooses to export, but it will export at less than 100 percent which is unusual to observe when there is a fixed cost to export, or b) the firm chooses to sell to domestic markets even though it meets productivity requirements.

The export decision mapped accordingly to the interaction between the two state variables _Ai_ and _Xi_ can be summarized by Figure 4. All firms below the productivity threshold needed to be profitable in export sales do not export regardless of cash levels; that is, if _Ai < A_ , then exports will equal zero, _ψ_ = 0, for all values of _Xi_ (Areas IV, V and VI in Figure 4). All firms that above the productivity threshold, and are not financially constrained to export, will export 100 percent; that is, if _Ai > A_<sup> ̄</sup> and _Xi > X_<sup>_E∗_</sup> ( _Ai_ ), then exports will equal one, _ψ_ = 1 (Area III in Figure 4). Area I in Figure 4 demarcate values of cash that constrain firms from producing at domestic unconstrained optimal levels and thus will not export despite productivity levels that are above the threshold; if _Ai > A_<sup> ̄</sup> and _Xi < X_<sup>_D∗_</sup> ( _Ai_ ), then exports will equal zero, _ψ_ = 0. Area II demarcate values of 13
<!-- Start of picture text --> 4 xTM(A) x*(A)<br>A Financially Constraned Not Financially Constraned Not Financially<br>_...f | ______Constrainal<br>| Do nestic Sales, NoExport wy =0<br>IV Vv VI<br>x”(A) x*(A) Xx;<br><!-- End of picture text -->
The results of the export working capital model of the firm show that the export decision of the firm is determined by both productivity and working capital. The model exhibits the characteristic results of **?** in heterogeneous firm models of export, where exporting depends on productivity but also highlights the fact that this only occurs if the firm is not financially constrained. The working capital model shows that under financial constraints, the lack of working capital can yield different export outcomes even if the firm is productive enough to export. Liquidity constraints can constrain export potential firms to not export and/or export less than 100 percent. Therefore, the correspondence between productivity and exporting is much weaker.

# **5 Empirics**

This section examines empirical evidence in support of the model. First some stylized facts are presented: 1) the most productive firms do not necessarily self select into exporting and there does not appear to be a productivity cut-off above which all firms export, 2) significant differences exist in physical and working capital between exporters and non- exporters even within the same industry and 3) access to finance differs significantly between exporters and non-exporters and credit does not appear to be allocated according to productivity.

The Bangladeshi data set allows two key aspects of the model to be empirically tested: a) whether the export status of the firm is determined by both productivity and working capital and that their effects depends on whether the financial constraints are present, and b) whether there is a relationship between working capital and the export distance.

## **5.1 Stylized Facts**

### **5.1.1 Productivity of exporters and non-exporters in the data: Self-selection not evident**

Productivity is measured as total factor productivity estimated according to **?** as the method allows for more generally plausible assumptions as to the dynamic data generating process of the inputs used to estimate the production function.<sup>19</sup> A possible caveat to estimated productivity is that estimates of the production function with a large proportion of financially constrained firms could potentially underestimate factor coefficients as firms are not operating at optimal scale. This potentially could bias the size of the TFP estimate but not the distribution of tfp overall. However, the consequences of estimating production functions with firms operating sub-optimally is not clear and is left as possible future research.

The dominant trade theory at the firm level ( **??** ) predicts that, within each industry, the firms at the upper distribution of productivity above a certain cut off will export while those that are below will produce only for the domestic market. Trade liberalization induces a self-selection of the most productive firms into exports. However, this self selection is not evident in the data and it does not appear that export firms are necessarily the most productive. Figure 5 on the next page shows the distribution of productivity for exporters and non exporters in each industry.<sup>20</sup> If there is evidence of self selection, the productivity levels of exporting firms

> 19TFP was also calculated using OLS, RE, FE and using the **?** method. The OLS estimate suffer from endogeneity issues and both fixed effects and random effects estimates relies on strong assumptions on _ωit_ and has not worked well in practice. The LP estimates relies strictly on specific assumptions on the data generating process of the inputs to production that may not hold generally (see **?** ) and is prone to suffer collinearity. See Appendix A.1 on page 25

> 20Furthermore, Table 12 in Appendix A.2 shows that the median productivity level do not substantially differ between exporters and non-exporters and, in fact, shows that mean productivity is higher for domestic firms than for exporting firms in all sectors except for Leather and Chemical.

15
<!-- Start of picture text --> Garment Textile Food<br>o<br>.<br>+ . s .<br>s<br>. r) .<br>o ‘ ry<br>S<br><% |S rrr rrrC—CiSC<br>£ Non Exporters Exporters Non Exporters Exporters Non Exporters Exporters<br>2 Leather Electronic Chemical<br>ge .<br>o e<br>+> + ;<br>“ —{ =<br>=ee .<br>ov<br>Non Exporters Exporters Non Exporters Exporters Non Exporters Exporters<br>Box plots by sector<br><!-- End of picture text -->
|t-stat<br>-2.470<br>0.633<br>-1.329|3.531|-0.469|t-stat<br>-2.173<br>-6.630<br>-3.013<br>-5.156<br>-4.810|t-stat<br>0.520<br>-7.718<br>-5.319<br>-8.790<br>-8.841||
|---|---|---|---|---|---|
|diff<br>-2.014<br>0.09<br>-65.2|0.566|-0.074|diff<br>-2.94<br>-1.32<br>-81.9<br>-1.393<br>-1.181|diff<br>1.41<br>-2.17<br>-476.5<br>-2.315<br>-2.594||
|y sector<br>iles<br>Total<br>9.979<br>11.12<br>355.9|10.33|9.475|ther<br>Total<br>17.38<br>10.92<br>164.8<br>9.985<br>9.413|icals<br>Total<br>20.17<br>10.78<br>241.3<br>9.911<br>9.501|orld Bank|
|xporters b<br>Text<br>Exporters<br>11.53<br>11.05<br>405.7|9.894|9.532|Lea<br>Exporters<br>18.3<br>11.33<br>189.7<br>10.42<br>9.786|Chem<br>Exporters<br>18.97<br>12.58<br>636<br>11.82<br>11.64|nt Survey, W|
|non-exporters and e<br>Non-Exporters<br>9.516<br>11.14<br>340.5|10.46|9.458|Non-Exporters<br>15.36<br>10.01<br>107.8<br>9.027<br>8.605|Non-Exporters<br>20.38<br>10.41<br>159.5<br>9.505<br>9.046|nt Climate Assessme|
|between<br>t-stat<br>-0.302<br>1.830<br>-1.099|-3.759|-1.163|t-stat<br>-4.071<br>-4.223<br>-3.839<br>-7.953<br>-8.876|t-stat<br>0.402<br>-4.311<br>-0.765<br>-6.922<br>-3.850|2 Investme|
|ifferences<br>diff<br>-0.154<br>0.15<br>-52.6|-0.419|-0.133|diff<br>-18.06<br>-1.186<br>-300.5<br>-2.051<br>-2.425|diff<br>1.18<br>-1.532<br>-98<br>-1.234<br>-2.174|ladesh 200|
|ble 3: D<br>Total<br>7.442<br>11.28<br>463.1|9.126|9.087|Total<br>17.74<br>10.04<br>167.3<br>8.715<br>7.983|Total<br>13.15<br>9.719<br>103.5<br>8.17<br>7.943|_ce:_ Bang|
|Ta<br>Exporters<br>7.465<br>11.26<br>471.1|9.19|9.107|Exporters<br>33.37<br>11.07<br>426.9<br>10.49<br>10.08|Exporters<br>12<br>11.21<br>198.8<br>9.371<br>10.06|_Sour_|
|Garments<br>Non-Exporters<br>7.311<br>11.41<br>418.5|8.771|8.974|Food<br>Non-Exporters<br>15.31<br>9.884<br>126.4<br>8.439<br>7.655|Electronics<br>Non-Exporters<br>13.18<br>9.678<br>100.8<br>8.137<br>7.886||
|Age<br>Log Output<br>Number of Workers|Log Physical Capital|<br>Log Working Capital|Age<br>Log Output<br>Number of Workers<br>Log Physical Capital<br>Log Working Capital|Age<br>Log Output<br>Number of Workers<br>Log Physical Capital<br>Log Working Capital||

17
Table 4: Indicators of Financial Constraints

||Non-exporting|Exporting|Total|
|---|---|---|---|
|Access to Finance: Moderate to Severe|0.6643|0.6095|0.6407|
|(2-4) Problem||||
|Access to Finance: Major to Severe|0.4506|0.3619|0.4125|
|(3-4) Problem||||
|Finances for Investment 100%|0.262|0.1887|0.2305|
|internally fnanced<sup>_∗_</sup>||||
|Finances for Working Capital 100%|0.1907|0.1321|0.1655|
|internally fnanced<sup>_∗_</sup>||||
|Overdraft facility or line of credit|0.6715|0.6506|0.6626|
|Ifyes, % currentlyunused|22.31|30.36|25.63|

_Source:_ Bangladesh Investment Climate Assessment survey: 2002, World Bank. Note: All variables are indicator variables except for percent of credit line unused.<sup>_∗_</sup> 100% internally financed and also reported access to finance as a problem.

affect the firm’s decision to enter international markets where the time delays are much longer than domestic sales. Table 4 compares financial indicators between exporting and non-exporting firms. Access to finance is a subjective variable where respondents are asked to rank the problem from 0, being no problem to 4 as a severe problem. There is a smaller percentage of exporters who reported access to finance as a problem than the percentage of non-exporters. Furthermore, non-exporters report greater severity of the problem. Respondents are also asked where they sourced their financing for investment and for working capital and report the percentage from each category.<sup>23</sup> Exporters source a smaller percent of their finances internally for both investment and working capital; indicating that exporters do indeed have better access to credit. The last financial indicator is whether the firm has an overdraft facility or a line of credit available to them, and if yes, to report the percent of the line unused. Here, a larger proportion of non-exporters than exporters have overdraft facilities or credit lines however, out of those that do have credit lines, the average percent of credit lines used by non-exporters is higher. This suggests that non-exporters are closer to their borrowing limit than exporters or that exporters generally do not use this type of credit as much. Overall, these statistics suggest that non-exporters have less access to finance than exporters.

It is not only important to verify whether exporters have better access to finance than non-exporting firms but also to see whether access is allocated according to firm productivity. As discussed in the introduction, the results of existing theoretical models is driven by a direct correspondence between productivity and the amount of credit the firm has access to. It is this correspondence that supports the self selection of the most productive firms into exports even under financial distortion. Table 5 shows the average productivity according to each financial variable. There is no significant difference in average productivity between firms that report access to finance as a problem and between firms that fund investment wholly from internal finance. Productivity is slightly higher for those who can borrow to fund working capital<sup>24</sup> and those who have overdraft facilities. The difference in productivity however, does not appear large and the size of the standard deviations imply considerable overlap in the TFP distribution between constrained and not constrained categories. Therefore there is no strong evidence to suggest that credit is allocated along productivity lines.

> 23There are 14 categories the respondents are asked to choose to fill in: a. Internal funds or retained earnings b. Local commercial banks (loan, overdraft) c. Foreign-owned commercial banks d. Leasing or hire purchase arrangement e. Government subsidies f. Investment Funds/Special Development Financing/ Or Other State Services g. Trade credit (Supplier or customer credit) h. Credit cards i. Equity (Capital, sales of stock) j. Family, friends k. Informal sources (e.g. money lender) l By selling other assets m. Letters of credit n. Other (specify source):

> 24That is, firms that do not fund working capital wholly internally.

18
Table 5: Productivity by Financial Constrained Indicator Variables Average Productivity

|Indicator variable=|No|Yes|t-stat|
|---|---|---|---|
|Access Mod-Severe|4.212|4.127|1.123|
||(1.15)|(1.068)||
|Access Major-Severe|4.18|4.126|0.7616|
||(1.131)|(1.05)||
|100% Internally fnanced Investment|4.177|4.115|0.7433|
||(1.107)|(1.105)||
|100% Internally fnanced Working Capital|4.193|4.012|1.906|
||(1.108)|(1.09)||
|Overdraft Facility/Credit line available|3.978|4.256|-3.8719|
||(0.9985)|(1.151)||

_Source:_ Bangladesh 2002 Investment Climate Assessment Survey, World Bank

# **5.2 Working Capital and Distance to Export Destination**

The 2002 Bangladesh data set is unique in that the survey asked firms to list their main export destinations and amount exported to each of these destinations for years 2000, 2001 and 2002. From this, a weighted export distance can be calculated for each firm. The availability of data on the distance of the export destination allows a simple test of the relationship between working capital and the export distance by exporting firms. This relationship is a key component of the model: the greater the export distance, the longer the delay in the receipt of payments and the greater the demand for working capital required.<sup>25</sup> The empirical question is whether the amount of working capital, _X_ , is significantly and positively correlated with export distance among exporting firms controlling for factors that also determine the amount of working capital. If working capital does not vary with distance, than this puts into doubt whether the working capital is driven by delays in the receipt of revenues, or is simply a function of costs. The empirical specification is motivated by the determinants for the amount of cash required to export from Equation 13. Working capital demand increases with time delay _S_ , cost of production _Ct_ , expected cost of production _Ct_ +1 (to ensure that costs are covered in the interim between production and when revenue is received upon delivery) and with productivity _A_ . The time delay, _S_ , is proxied by the distance to export destination, _distance_ . Whether the firm will export in the interim periods during the delays also affect how much working capital is needed. Therefore, working capital demands would also increase with expected relative foreign to domestic price, _p_ .

The stock of working capital kept on hand would decrease with supply of liquidity: the amount the firm can borrow which is measured as short-term liabilities, and past export production, proxied by years of exporting, _yearsexporting_ .<sup>26</sup> The empirical specification is as follows: Distance enters the specification as one period ahead as the export distance is realized after the firm

> 25See previous sections as well as refer to Table 2 on page 5 that shows when payments are made and Figure 1 on page 6 for transit times.

> 26 Financial constraint variable was not included as this would restrict the sample to one year, and cannot estimate the relationships. Furthermore, the discount rate _β_ could be proxied by reported interest rates but interest rates were also only available for 2002.

19
Table 6: Working Capital and Distance to Export Destination

|Lagged LogWorkingCapital|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Log weighed distance to export|0.382|0.464*|0.668**|0.739**|
|destination|(0.237)|(0.241)|(0.338)|(0.349)|
|Controls for Cost, Productivity,||Yes||Yes|
|short-term Liabilities, Years|||||
|Exporting, Export Price to|||||
|Domestic Price,|||||
|Interact sectors*Log distance|No|No|Yes|Yes|
|Observations|848|780|848|780|
|R-squared|0.007|0.091|0.017|0.099|
|Number of frms|459|438|459|438|
|Standard errors inparentheses,**|*p<0.01,**|p<0.05,|*p<0.1||

Notes: 2002 ICA World Bank (with recall data from 2000, 2001.). Regressed using fixed effects for the sample exporting firms only. Distance data from Centre d’Etudes Prospectives et d’Informations Internationales (CEPII), at http://www.cepii.fr/anglaisgraph/bdd/distances.htm using distance measured from the capital city. Up to five countries were reported in the survey (only for 2001 and 2002) and the weighted average distance was calculated by the proportion of export revenue of each country. Productivity calculated from structural estimation of the production function (see **?** ). None of the coefficients for the interactive terms for sector and log distance were significant and are not reported. Log Yrs Exporting is averaged across products. Full regression in Appendix 14 in Table 14 makes it’s export decision based on current working capital levels.<sup>27</sup> The destination for exports would systematically differ according to industry, and the interaction between sectors and export distance were also included to improve fit.

The specification was estimated using fixed effects and the results are shown in (the full set of estimates are shown in Appendix 14 in Table 14). Column (1) shows the estimation with no other control variables and there is no significant correlation between working capital and the distance to export destination. Column (2) includes controls for cost, productivity and other variables, and columns (3) and (4) include interaction between sectors and export distance.

The estimated results shows that the correlation between working capital and export distance is positive and significant when relevant controls are included. Furthermore, the coefficients for the control variables: Lagged cost, current cost and productivity are correctly signed and significant (see in full estimate results in Appendix 14 in Table 14). The results show that the size of the estimated coefficient for distance is much larger than the estimated coefficients for cost and productivity. The _distance_ variable may also be proxying for additional transport costs associated with shipping longer distances. However, the significant and positive coefficient suggests that the distance to the export destination could be the largest factor in determining the liquidity needs of the firm, and this could be due to the delays in the receipt of revenues associated with longer shipping times.

# **5.3 Export Status of the Firm**

The model in the Section 4 provides a set of theoretical predictions that could be put to the data: i) when firms are financially constrained, export status is driven by the level of working capital and less by productivity

> 27This specification is an approximation given different industries will have different turnover time. The Bangladesh data has a majority of garment and textile firms reflecting the Bangladesh economy and the specification on the timing of working capital and export distance one year ahead fits this particular data set. Specification using the same time period, i.e. _Xt_ and _distancet_ yields a negative and insignificant coefficient - that supports the specification for distance as a forward variable.

20
(Areas I, II and IV in Figure 4 ) and ii) when firms are not financially constrained (Areas III, V and VI in Figure 4 ), export status of the firm is driven by productivity and changes in working capital does not change the export status of the firm. Thus the main test of the theoretical model is to see whether cash only affects export decisions when firms are financially constrained and at the same time, whether productivity affect export decisions when firms are not financially constrained. Let _fc_ be a dummy that indicates whether the firm is financially constrained, that is whether _Xi < X_<sup> ̄</sup> .<sup>2829</sup> The empirical specification is as follows: The test involves four predictions on the estimated coefficients: a) the interaction between financial constraints and working capital, _β_ 1, is positive and significant, b) the coefficient for working capital only, _β_ 2, should not be significantly different from zero, c) the coefficient for productivity when not constrained, _β_ 4, should be positive and significant and d) the coefficient for productivity under constraints _β_ 3 is the negative of _β_ 4 and significant in order for productivity to not affect exports under financial constraints. That is: The 2002 Bangladesh data set contains information on the percentage of sales that are exported. Out of the 974 firm observations, only 76 firms reported exporting less than 100 percent. The majority of firms report zero exports with about a third of the firms reporting 100 percent exports. The model does not offer any qualifications on how many firms in the population would export less than 100 percent. However, the fact that the model does predict that there will exist firms who will choose to export less than 100 percent, even in the presence of exporting fixed cost, is a unique and important characteristic of the model. The small number of observations of export percentage makes identification difficult and as a result, the dependent variable used is a export status dummy, _E_ , that is equal to one if the firm engaged in any exports.

The financial constraint variable, _fc_ , is a dummy that is equal to one if the firm reports access to finance as a problem (from minor to major severity) and finances working capital entirely from internal finance (that is, does not use any borrowing from any other sources). The rationale behind interacting these two conditions is that firms may report access to finance as a problem when they actually do have access to borrowing. The measure does however, leave out firms that are able to borrow but remain financially constrained due to reaching borrowing limits or quotas. In this sense, it may not capture all possible financially constrained Working capital, _X_ , is measured according to the accounting definition: current assets (inventories, accounts receivable, cash and short-term credit) minus current liabilities (accounts payable and any short-term debt). Productivity, _A_ , is measured as total factor productivity estimated according to **?** and the production function is estimated for each sector.<sup>30</sup> Controls for other variables in the model that were available from

> 28Note that financial access questions are only available for 2002, and thus the number of observations available for estimation is 990.

> 29See **?** for further examination of the financial access variable including sources of variation that determines access to finance.

> 30Estimates using Levinsohn- Petrin (LP2) does not change sign nor significance, but do to some extent change the size of the

21
Table 7: Export Status Estimation Results

|Probit|E|xport Status||
|---|---|---|---|
||(1)|(2)|(3)|
|Financially Constrained|-0.4867***|-0.420***|-0.314|
||(0.0917)|(0.137)|(0.202)|
|FC*Log Working Capital|0.1070***|0.101***|0.0682**|
||(0.0317)|(0.0281)|(0.0298)|
|FC*Productivity|-0.1284|-0.169*|-0.126|
||(0.0937)|(0.0966)|(0.107)|
|Log Working Capital|0.0162|0.0288**|0.0298**|
||(0.0112)|(0.0123)|(0.0124)|
|Productivity|0.3353***|0.355***|0.0621|
||(0.0357)|(0.0477)|(0.0419)|
|Controls|No|Yes|Yes|
|Sector dummies|No|No|Yes|
|Observations|959|936|936|
|Pseudo Rsqr|0.149|0.261|0.391|
|Log pseudolikelihood|-557.6|-472.7|-389.6|

_Source:_ Bangladesh 2002 Investment Climate Assessment Survey, World Bank Notes: Only 2002 data used for estimation, Bootstrap standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Reported marginal effects, Base line for Equation (3) is Garments.

the data set are: export price to domestic price ratio, log labor cost, log weighted input price, age and sector dummies are also included (base sector as Garments).<sup>31</sup> The results of export status is shown in Table 7 (full results in Appendix A.4 in Table 13). Results show that under financial constraints, working capital is significant and positive in determining export status of the firm, and this result remains robust even with additional controls across all three specifications. The effects of working capital, without the interaction with financial constraints, is small or insignificant in determining export status. This suggests that working capital increases the likelihood of a firm exporting mainly when the firm is financially constrained. A 10 percent increase in working capital raises the probability of exporting by over 10 percent for a financially constrained firm but by only about 3 percent for a firm that is not constrained. All estimates pass joint significance tests of _β_ 1 and _β_ 3. A possible explanation as to why working capital for non financially constrained firms remains significant under additional controls, even though the size is small (in regressions (2) and (3)), could be due to the financial constraint measure not capturing all possible firms that are constrained.

The estimates for productivity for all regressions are signed according to the model’s prediction, that is, _β_ 2 is negative and _β_ 4 is positive. However the size and significance of the coefficients vary across the 3 regressions. Joint significance test of _β_ 2 and _β_ 4 can reject the null for specifications (1) and (2). In the specification (3), both coefficients for productivity are jointly insignificant when sector specific dummies are included. This could be due to the small sample size within each sector in the data set. However, in other empirical studies, such as **?** where empirical specifications always include controls for sectors, they too find that the estimated coefficient for productivity is not significant and their sample contains 23,641 observations. This could be due to the fact that there is more variation in productivity between sectors than between firms within a sector, and this may be in part due to how productivity is estimated. Specification (3) show that

> coefficients. See Appendix for productivity measures.

> 31See Appendix A.3 for table of variables used in estimation.

22
within sectors, productivity does not affect export status at all -regardless of whether firms are constrained or not.

In the first and second regression, without sector dummies, the productivity coefficient is large and highly significant, in accordance to theory. The coefficients for the interacted productivity and financial constraint is substantially sized, and only significant in the regression with additional controls. The estimates with additional controls resonates more due to specification and better fit. The results from regression (2) suggest that productivity matter less to the export status of financially constrained firms than unconstrained firms, but the effect does not completely negate the effects of productivity.

Looking at the estimates overall, results provide support for the model’s predictions that the effects of working capital and productivity will differ between financially constrained and unconstrained firms. Intuitively, working capital matter more for financially constrained firms, and productivity matters less when

# **6 Conclusion**

Empirical evidence suggests that, particularly in developing countries, financial factors matter for exporting. Extending firm trade models to include dynamic borrowing constraints points to the importance of working capital, as well as productivity for determining the decision to export, the percentage of output that will be exported and its destination. As such, the working capital model adds an important caveat to the established literature on the relationship between productivity and the export decision of the firm. Testing this model empirically with a unique data set from Bangladesh supports the proposition that working capital and productivity affect export status of the firm and their effects are differentiated by financial constraints. Empirical results also confirm the relationship between working capital and the distance to export destination. The empirical results provide support for the model’s predictions that the effects of working capital and productivity will differ between financially constrained and unconstrained firms. Specifically, for financially constrained firms, productivity matters less than for unconstrained firms while working capital matters much more. The correlation between working capital and export distance is shown empirically to be positive and significant, lending support for the working capital model of the firm’s export decision.

There are many exciting directions future research in this area will need to explore, both at the micro and macro level. On a macro level, the partial equilibrium model of working capital of the firm can be incorporated into general equilibrium models to look at the propagation of the effects of financial constraints across the economy and across countries. Further research and exploration into the dynamic interaction between the financing needs due to time delays and the financial structure of countries has the potential to add insights to international trade patterns. The distortion that financial constraints introduce to the self-selection of the most productive firms into exporting warrants further investigation as to how this may affect overall industry productivity and thereby the aggregate growth of the economy. In terms of theories of development, if the results of the model are drawn analogously for an economy represented by a single firm, this could potentially prescribe a development path where trade liberalization may play a very minor role at low levels of wealth. This may provide further impetus for developing countries to move quickly in the development of functioning financial systems that allocate financial resources closely in line with productivity and the availability of short-term credit and trade credit so that the most productive firms enter exports, and aggregate industry productivity improvements can be reaped from trade liberalization.

On the micro level, the working capital model exhibits distortions to factor ratios (also see **?** ) and this 23
could potentially lead to different sectors that export within an economy than those predicted by comparative advantage trade theories. Of particular significance is the need to develop theoretical models and empirical tools that will allow the analysis of the firm at sub optimal production levels, and this is crucial in the study of developing economies where financial constraints are the norm. The estimation of productivity under these conditions warrants urgent attention.

# **References**

24