rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Availability of detailed firm level datasets since the early 1980s has led to discoveries of important regularities in firm and industry evolution. In the international trade literature, research based on these datasets has shown that firms that export, although they are small in number, make a large contribution to economic growth and welfare. Recent surveys by Bernard et al. (2007) and Greenaway and Kneller (2007) show that exporting firms are larger, more productive and they grow faster. Higher performance of exporters and their significant contribution to economic development make it important to determine how the investment climate affects their progress.

Evidence shows that export market entry is associated with significant changes in firm performance around the time at which export sales begin. In the data from Enterprise Surveys<sup>3</sup> employment levels of future-exporters grow by 13%, four times higher than the growth rate of non-exporting firms. Bernard and Jensen (1999) analyze the evolution of future-exporters among the US firms. They find that in addition to being larger in employment, shipments, and labor productivity, future-exporters also grow faster than non-exporters in all three measures. They find that growth premiums between future-exporters and non-exporters are 1.4% per year for employment and 2.4% for shipments. Greenaway and Kneller (2007) summarize a collection of studies that similarly find faster total factor productivity or labor productivity growth of futureexporters relative to non-exporters. Alvarez and Lopez (2005) argue how firms increase their productivities with the explicit purpose of becoming exporters<sup>4</sup> . Using data from Chilean manufacturers they show that future-exporters invest more in physical capital than non-exporters. They find that 1% increase in investment increases the probability of exporting by 0.2%. Overall, all these evidences highlight the changes in firm performance before exporting starts and I argue that any factor that obstructs these changes are likely to discourages firms from exporting.

Faster growing efficient firms self-select themselves into foreign markets. This has been a robust finding in many empirical studies as reviewed in Bernard et al. (2007) and others. However, a sound investment climate is required to complement firm specific, technological, or market driven factors in order the self-selection process to work efficiently. Dollar et al. (2006) show that highly bureaucratic and corrupt governments, inefficient financial services or low quality of infrastructure make it difficult for firms to expand into foreign markets in developing

> 3 This is the main data source used in this paper. It is explained in detail in data description section.

> 4 They call this as conscious self-selection process .

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Luttmer, E. F. (2005) Neighbors as negatives: relative earnings and well-being, Quarterly Journal of Economics, 120(3), 963-1002 Maddala, G.S. (1983). Limited Dependent and Qualitative Variables in Econometrics. Cambridge Univ. Press, Cambridge, UK.

McBride, M. (2001) Relative-income effects on subjective well-being in the cross-section. Journal of Economic Behavior and Organization 45, 251– 278.

Modigliani, F. and Brumberg, R. H. (1954) Utility analysis and the consumption function: an interpretation of cross-section data, in Kenneth K. Kurihara, ed., Post-Keynesian Economics, New Brunswick, NJ. Rutgers University Press, pp 388–436 Marx, K., (1847) Wage Labour and Capital, translated in 1891 by Frederick Engels, chapter on "Relation of Wage-Labour to Capital", available on http://www.marxists.org/archive/marx/works/1847/wage-labour/ch06.htm Oshio, T,, Nozaki, K. and Kobayashi, M. (2011) Relative Income and happiness in Asia: Evidence from nationwide surveys in China, Japan and Korea, Social Indicators Research, 104, 351-367 Persky, J. and Tam, M.Y. (1990) Local status and national social welfare. Journal of Regional Science 30 (2), 229–238.

Persson, M. (1995) Why are taxes so high in egalitarian societies? Scandinavian Journal of Economics, 97, 469-476 Pigou (1920) The Economics of Welfare, MacMillan and Co, London Ravallion, M. and Lokshin, M. (2002) Self-Rated Economic Welfare in Russia, European Economic Review, 46(8): 1453–73.

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10 dependent variable; and fixed effects estimates of the following multivariate specification of Equation (5): in which we reintroduce determinants of growth dropped from equation (4), and add a vector of control variables ωι,τ that impact growth, but not its steady-state rate.

Along these lines we take several steps to ensure the validity of our estimates. First, to properly determine POSTLIB we need to ensure the correct identification of one trade liberalization episode per country. A major consideration here though is that most countries have undergone more than one liberalization program; some of them were reversed. For an event study it is obvious that reversed reforms should be excluded. The SW methodology, used also in WW, selects as year of reform the one in which the trade regime first meets its five well known criteria without any consecutive reversal. Although classifying countries as open or closed to trade based on the SW criteria makes sense in crosscountry regressions, its application for fixed effects estimations is not optimal. Growth may have increased years before the SW criteria was met as a result of earlier reforms, and such acceleration would be considered part of the pre liberalization years. Most importantly, under these criteria, several countries that undertook strong trade liberalization (e.g. China, Croatia, India) are wrongly considered unreformed.

The exact timing of the major trade reform would always be debatable, but for most countries there is a consensus by economists with some knowledge of the countries in question. In others, the dates are rather close. Only in few cases, the years do vary by a large margin. Our list of liberalization episodes is taken from the World Bank’s Trade Assistance Evaluation (IEG, 2006) used also in Jinjarak et al (forthcoming). It includes the most significant trade reform of each country, as determined through a
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government policymakers to use formal WTO dispute settlement on its behalf.<sup>5</sup> Selfenforcement of any foreign market entails a cost. From an economic modelling perspective, this foreign-market specific cost is similar (though in addition) to what an exporting firm must spend to adapt and market its product to new foreign consumers, or to establish a new network to deliver it to them. The main difference between the cost of self-enforcement and these other costs of supplying to a foreign market is that the latter are entirely borne by the exporter, whereas self-enforcement costs are sometimes ‘shared’ because of the positive externalities generated. Because trade policies are typically quite blunt, the effort of keeping a foreign market open to one exporter has implications for other exporters as well. This possibility for sharing both creates an additional set of complications and generates a role for the public sector.<sup>6</sup> For an exporter to use WTO dispute settlement to reopen a market that has been closed with a WTO-violating policy, the first step is to generate technical information on the cause of lost foreign market access, compiled from economic, legal and political 5 This discussion is admittedly partial equilibrium in nature as it completely abstracts from other costs impeding the firm from engaging its government in WTO dispute settlement. Many of these other impediments are noteworthy; they are addressed more fully in Bown (2009a). The idea here is to focus entirely on one particularly critical problem facing developing country exporters in the WTO system, and that is the lack of economic, legal, and political information that they need to even _begin_ consideration of whether to engage in WTO dispute settlement.

6 An alternative model (and motivation) for which monitoring might be useful is if it provides additional information _within_ the policy-imposing country. For example, the provision of new information on the expected domestic cost of a particular welfare-reducing policy implemented by one government ministry could engage other ministries which may have access to more efficient policy instruments.

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Table 5: Willingness to report versus group composition

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||Midline|Survey|||Endli|ne Survey||
||Stress|high|Physical|punishment|Stress|high|Physical p|unishment|
||||is ef|fective|||is eff|ective|
|Phone survey|-0.202***|-0.249***|0.014|0.052|-0.052|-0.049|0.038|0.057|
||(0.065)|(0.073)|(0.067)|(0.073)|(0.050)|(0.048)|(0.063)|(0.061)|
|Mean of Dep. Var (WhatsApp)|0.584|0.618|0.539|0.500|0.202|0.202|0.341|0.341|
|R-squared|0.154|0.216|0.087|0.266|0.136|0.195|0.142|0.204|
|Observations|304|207|304|207|265|265|265|265|
|Controls (Age,Sex)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dep. Var Baseline Level|No|Yes|No|Yes|No|Yes|No|Yes|

_Notes:_ This table shows the estimated impacts of the phone-based survey relative to WhatsApp-based survey on sensitive outcomes. “Phone survey” is a dummy variable equal to one if the survey was per call and zero if was assigned to the WhatsApp group. The dependent variable in columns (1)-(2) and columns (5)-(6) is a dummy equal to one if self-report of stress was high in the midline and endline survey, respectively; in columns (3)-(4) and columns (7)-(8) is the perception about physical punishment effectiveness in the midline and endline survey, respectively. The control variables include age in years and sex of the respondent. To account for potential time-invariant characteristics within municipalities, we include municipality fixed effects. Robust standard errors are in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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More generally, sectors employ occupations in different proportions, creating a direct link between sectoral and occupational employment transitions. Figure OA.3 in the Online Appendix shows this link by plotting occupational employment shares separately _within_ the manufacturing and service sectors against countries’ GDP per capita. Overall, the service sector uses professional and clerical occupations more intensely at all development levels. The manufacturing sector is highly concentrated in trade and service occupations in poorer countries but diversifies across occupations in richer ones. Gender differences across occupations within these sectors are relatively small in poorer countries but increase in richer ones. The largest gender gap emerges in clerical occupations, where women account for the entire employment increase with development. Men and women both contribute to the rise in professional and managerial jobs in rich countries, but men remain 5-10 p.p. more likely to work in these occupations than women.

# **2.4 Gender Segregation across Sectors and Occupations**

Having documented gender differences in employment choices across sectors and occupations, we now use the Theil Information H-Index to systematically measure gender segregation across occupation-sector pairs in each country-year. Similar to the Gini coefficient, the H-Index measures segregation on a normalized scale between 0 and 1 by using the entropic “distance” between the observed empirical distribution and an ideal gender-equal distribution. A higher value indicates greater segregation, while zero indicates perfect gender equality. The index allows decomposing total gender segregation across occupation-sector pairs into two additive components, which separately measure gender differences _across_ sectors and across occupations _within_ sectors. Appendix A.1 formally defines the index and decomposition.

Figure 3a plots the segregation across occupation-sector pairs against countries’ real GDP per capita, pooling data across all countries and years. The figure shows an inverted U-shape relationship, indicating that gender segregation is lower in poor and rich countries but high in middle-income countries. Figure 3b plots the share of total gender segregation that is explained by differences across occupations _within_ sectors. This component is significantly higher in richer countries, where it accounts for up to 50% of overall segregation, compared to 20% in poorer countries.<sup>10</sup> This finding is consistent

> 10Changes in labor force participation are attributed to across-sector variation, as inactive individuals are assigned to the home sector, which consists of a single occupation. To assess the importance of the extensive employment margin, we also compute the H-index conditional on labor force participation. The results remain similar and the importance of the within-sector variation in richer countries becomes

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- 33 -

This result might at first seem to contradict what the simpler model of Section 3 tells us. Recall that in that model, an increase in 6 reduces the critical value of the Rroject, V*, at which the firm should invest. But although in this model P* is higher when 6 is larger, the corresponding value of the project, V(P ), is lower. This can be seen from Figure 11, which shows P* as a function of a for 6 - .04 and .08, and Figure 12, which shows V(P ). If, say, a is .2 and _6_ is increased from .04 to .08, P* will rise from 23.8 to 29.2, but even at the higher P*, V is lower. Thus V- V(P ) is declining w'th 6, just as in the simpler model.

This model, although still unrealistic in some respects, gets at the essence of how uncertainty over future prices affects the value of a project and the decision to invest in the project. Furthermore, the model has practical application, especially if the project is one that produces a traded commodity, like copper or oil. In that case, a and 6 can be determined directly from futures and spot market data. Also, the model can easily be expanded to allow for fixed costs of temporarily stopping and restarting production, if such costs are important.19

# Alternative Stochastic Processes.

The model can also be adapted to allow for alternative stochastic processes for the price, P, although in most cases numerical methods will then be necessary to obtain a solution. For example, for some commodities there may be reason to believe that P follows a mean-reverting process, such as: dP/P - X(P - P)dt + _odz_ (23) 19See Brennan and Schwartz (1985) for a model of a mining project that does just this.
- [5] Berry, S., & Reiss, P. (2007). Empirical models of entry and market structure. In M. Armstrong & R. Porter (eds.), Handbook of industrial organization, vol. III. Netherlands: Elsevier Science Publishers.

- [6] Bickel, P. (1978). Using residuals robustly I: tests for heteroscedasticity, nonlinearity. Annals of Statistics, 6, 266-291.

- [7] Cabral, L. (1995). Sunk costs, ...rm size and ...rm growth. The Journal of Industrial Economics, XLIII (2), 161-172.

- [8] Cabral, L., & Ross, T. (2008). Are sunk costs a barrier to entry? Journal of Economics and Management Strategy, 17 (1), 97-112.

- [9] Ce...s, E., Ciccarelli, M., & Orsenigo, L. (2007). Testing Gibrat’s legacy: a Bayesian approach to study the growth of ...rms. Structural Change and Economic Dynamics, 18 (3), 348-369.

- [10] Chen, J., & Lu, W. (2003). Panel unit root tests of ...rm size and its growth. Applied Econometrics Letters, 10 (6), 343-345.

- [11] Davies, S., & Lyons, B. (1982). Seller concentration: the technological explanation and demand uncertainty. The Economic Journal, 92, 903-919.

- [12] de Wit, G. (2005). Firm size distributions: an overview of steady-state distributions resulting from ...rm dynamics models. International Journal of Industrial Organization, 23, 423-50.

- [13] Del Monte, A., & Papagni, E. (2003). R&D and the growth of ...rms: an empirical analysis of a panel of Italian ...rms. Research Policy, 32 (6), 1003-1014.

- [14] Gabaix, X. (1999). Zipf’s law for cities: an explanation. The Quarterly Journal of Economics,114 (3), 739-767.

- [15] Geroski, P., Lazarova, S., & Walters, F. (2003). Are di¤erences in ...rm size transitory or permanent? Journal of Applied Econometrics, 18 (1), 47-59.

- [16] Gibrat, R. (1931). Les inegalites economiques. Paris: Sirey.

- [17] Ghosal, V. (1996). Does uncertainty in‡uence the number of ...rms in an industry? Economics Letters, 50, 229-236.

- [18] Ghosal, V. (2003a). Impact of uncertainty and sunk costs on ...rm survival and industry dynamics. Wissenschaftszentrum Berlin für Sozialforschung, Markets and Political Economy Working Paper No. SP II 2003-12. Retrieved from http://bibliothek.wzb.eu/pdf/2003/ii03-12.pdf.

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**Figure 2:  The primary structural deficit and the output gap rise and fall together ECA, 2000-2012**

<!-- Start of picture text -->
-.1 -.05 0 .05 .1 .15<br>Output gap as a share of GDP<br>10<br>5<br>0<br>-5<br>-10<br>Primary structural fiscal balance as a share of GDP<br><!-- End of picture text -->

_Source:_ World Bank staff estimates based on government accounts.

The following empirical estimation considers how this stance may have influenced external balances. Table 2 below shows some of the results from the first set of regressions. Almost all regressions exclude the oil exporters.<sup>12</sup> The first column is the simple OLS regression. In the fixed effects regression (3), ECA countries (excluding oil exporters)<sup>13</sup> do not exhibit a consistent pattern in terms of the relationship between external balances and fiscal balances. However, when the group of countries with fixed exchange rates is distinguished from those with flexible rates; the results show different and consistent patterns for the two groups during the period under consideration. In the first specification, there is an interaction term of the primary structural deficit with the exchange rate regime.<sup>1415</sup> The interaction term between the exchange rate regime and the primary structural balance is constructed by multiplying the primary structural balance with a dummy variable that takes the value 1, if the country has a fixed exchange rate system. Changes in the structural primary deficit are always significantly and positively associated with changes in the current account balance, once the interaction term for countries with fixed exchange rate systems is included. Moreover, for almost all specifications, the magnitude is around 0.3. The countries in the sample that have fixed exchange rates show different results: their

> 12 However, the basic results hold and the effects are larger when oil exporters are included.

> 13 Including oil exporters makes the twin deficits stronger; even in this specification, when oil exporters are included, with country and time fixed effects, the primary structural deficit enters with a significant  coefficient. 14 The countries that are denoted as fixed exchange rate countries are the Baltic countries, Bosnia-Herzegovina, Bulgaria, Kosovo, Montenegro, Slovenia and the Slovak Republic (see IMF Annual Report on Exchange Rate Arrangements, Oct, 2012).

> 15 Inclusion of oil exporters strengthens the relationship.

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increase in emerging economies. In several emerging economies these shares could be similar or even surpass values recorded before the Asian financial crisis (AFC).

Fourth, policy makers in emerging economies face challenges to contain the risks associated with high nonfinancial corporate debt levels, as well as to limit the potential negative impact of corporate debt defaults. Because capital markets have gained importance in firm financing relative to banks, traditional prudential policies focused on the banking sector might not be as effective as in the past. New policies might be needed to create safe conditions for corporate borrowing in capital markets. Policy makers could also put in place policies to deal with corporate debt distress similar to those aimed at the sovereign and banking sectors.

The rest of the sections are organized as follows. Section 2 discusses the main stylized facts and drivers behind the boom in nonfinancial corporate debt. Section 3 examines the main risks associated with the rise in nonfinancial corporate debt. Section 4 analyzes how a weakening economy or a change in market conditions would impact highly indebted corporations. Section 5 concludes with a discussion on what policies governments could implement to mitigate risks and manage distress in the nonfinancial corporate sector. Section 6 discusses some questions open for future research.

# **2. Rising Nonfinancial Corporate Debt: Stylized Facts and Drivers**

# _2.1. Stylized Facts_

To obtain some basic stylized facts, we compute the levels and evolution of nonfinancial corporate debt by gathering data from the Bank for International Settlement (BIS). The BIS defines debt by the nonfinancial corporate sector as all liabilities (including loans, bonds, and other claims) issued domestically and abroad by firms that produce market goods and nonfinancial services.

These data show that nonfinancial corporate debt around the world multiplied by 1.6 times between 2008 and 2018, increasing from about $45 trillion in 2008 to $71 trillion in 2018, in constant 4
**Poi** F A **RESEARCH** W(ORKING PAI'I **K** 15 27

# **Summary findings**

FrvC Claessens, Burridge, and Blanchet examine flows of foreign direct investilicim t to 46 developing countries to test whethler **SucIh** flows are aLutollonOtLIs or accomimillodating _uis-di-vis_ the current account and othetr capital flows, sinig Granger-causality tests, they find that: Requiremlients to surrender export proceeds to the monetarv authlorities and the existence of special exchanige rates for some capital accouLnt transactions reducc the probability that foreign direct investmient is indepenidenr.

_*_ The more liberal a countrv's forteigo exchange ssten, the more foreigni direct investmiienit is likely to bh independenit or exogenious.

Foreign direct investimient is associated witrh a larger increase in capital formation wvhen it is independenit than when it is Granger-caused bv other capital flows.

This paper -a product of the Debt and International Finance Division, Internationial Economics Departiielt - is part of a larger effort **in** the department to study thEt determinanitsand impact of foreign direct investnment. The studyv wvas funded by the Bank's Researchi Support Budget under the researclh project "Foreign Direct Investment in a Mlacroeconomici Framework" (RPO 678-15). Copies of this paper are available free fromi the World Bank, 1818 H StreetrNWV Washington, DC 20433. Please contact Rose _Vo,_ room N3-046, telephone 202-473-3722, fax 202-522-3277, Internet addreFx hvo l **(t** aworldbank.org. October 1995. (21 pages) _1hc 'olicv Research_ **_Wo(_** _Vrking Paper Series disseminates the_ **_firndings_** _)f fwork in progress to_ **_encourage_** _the exchange of/ ideas_ **_aib,aiii_** _|devLelo pmi')t issules. Anohsibectiu'e of the series is to get the findings out quiclGkly, even/ if the presentaltion(s are less thano hfully p,d5ishe._ 1 _/'t papers earn' the names of the authors and should be_ **_tIsed and_** _cited accordingly. The findings, interpretations, and coniclusitits_ **_ale'_** _thoe authors_ **_own antd_** _should_ **_niot bIe_** _attributed to the World_ **_Bank,_** _its Executive Board of Dir ctors, or anPly of its_ **_iember_** _be_ **_,,tunct_** _tes._

Jiroduicedj by the Policy kRsearclh Disseminiation Center