rafmacalaba/datause-encoder-data huggingface dataset:
where if for<sup>_v_</sup> _i_<sup>is the unobserved component for firm i,</sup> and if . Standard results imply that the survival function for<sup>_T_</sup> _i_<sup>can be written</sup> as: Therefore the contribution to the log-likelihood function from item i can be written as: where<sup></sup> _i_<sup>is a dummy variable which equals 1 for completed spells and 0 for (right) censored</sup> events.

# <u>5.2.3 Results</u>

Table 1 presents summary statistics of our explanatory variables as well as the age of exiting firms. During the time period under consideration, we find that firm age upon exit is close to 7 years on average; firms in the retail/ wholesale trade sector have the lowest average age upon exit of 6.4 years, while service sector firms exhibit the highest average age at exit of 7.2 years. For comparison purposes, we find the age upon exit of firms in Cote d‟Ivoire is similar to that found by Shiferaw‟s (2009) study on firms in Ethiopia‟s manufacturing sector. The majority of firms (90%) are located in the Abidjan region, which is the commercial and economic capital of the country, comprising some 50% of the country‟s non-agricultural population and generating 70% of all municipal revenue during the period.<sup>20</sup> As discussed earlier, 18% of firms in our sample operate in the manufacturing sector, 34% in the trade sector, and 49% in the service sector. We categorize firms by their current employment size: micro (40%), small (40%), medium (12%), and large (8%). Finally, we include real annual GDP growth, which averaged 1.5% over the sample period. We use leading GDP growth in all of our analysis, GDPt+1, to address endogeneity concerns arising from the largest

> 20 Tuinder (1978).

18
Yi(Post) = α+ Yi(Baseline) + Yi(February) + γ1. Grantsi + γ2. Any BDSi where Yi(Post) is the stacked business or individual outcome of interest in the post-COVID period, with flexible controls for Yi(Baseline) and Yi(Febraury) – the dependent variable values at baseline and February, respectively. ∑m βm are the coefficients on stratification variables including gender and county, as well as additional controls for economic sector. ∑Roundt are the post-COVID survey round dummies for rounds 2 and 3. γ1, γ2, and γ3 are the coefficients of interest representing the causal impact of each corresponding treatment _._ Standard errors are clustered at the individual level.

# **<mark>5.2. Causal Evidence on Mitigation</mark>**

Tables 2-5 present causal impacts on outcomes related to income generation, business performance, business operations, and confidence and life satisfaction, respectively. All even numbered columns present the results from specification (1), while all odd numbered columns present results from a simpler specification where all treatments are pooled into an indicator variable for being assigned any treatment.

Table 2 focuses on income generation. Column (1) shows that compared to the control group average of 34% business ownership in the post-crisis period, entrepreneurs assigned any treatment are 68% more likely to have an operational business – an effect that is significant at the 1% level. Column (2) disaggregates this effect by individual treatment status and finds large and statistically significant effects for the “Grants” and “Grants and Any BDS” arms, both showing a two-fold increases in the likelihood of operating a business compared to the control group, but no significant effects for the “Any BDS” arm. Even the magnitude of the “Any BDS” coefficient is close to zero. Owing to this last finding, the two treatment arms containing grants show significantly higher coefficients than the “Any BDS” arm, evident in the F-test p-values at the bottom of the table. At the same time, the two arms containing grants are statistically indistinguishable from each other.

This trend of the “Any BDS” arm not showing significant impacts continues across other outcomes in Table 2 as well as for outcomes across other tables. As explained previously in section 3, the BDS curriculum was not focused on pandemic resistance and the follow-up period is incredibly short with some overlap between when the BDS components were released and the timing of the follow-up surveys. Appendix Table 2 conducts round-by-round analysis with a more flexible treatment variable that changes across rounds based on which components of BDS were available at the time. However, the results are still statistically insignificant and small in magnitude. Ultimately, any potential impacts of BDS are likely to 12
the project was launched. _Cj,i_ includes the growth rate, which is expected to positively influence the probability of success of the project, as well as per capita GDP level. We also control for the size of the country (population) and the political, financial and economic risk as measured by the International Country Risk Guide<sup>4</sup> ; a project implemented in a more stable environment is expected to have more chance of being successful.

Finally, _Warj,i_ , is a set of variables related to the history of the conflict in country _i_ . Of course the history of conflict varies according to the time period and again, two projects occurring in the same country but in different decades may end up with quite different conflict history.

As the idea of the paper is to analyze the success of projects more specifically in post-conflict environment, it is all the more necessary to understand and best catch the history of the war _vis-`a-vis_ the project. Figure 1 illustrates the method of reasoning. From this tree we derived the set of _Warj,i_ variables.

The first step, at the country history level, was to distinguish between the countries that have always been at peace since 1945, like Ghana or Tunisia, and countries that have suffered from a civil war or multiple episodes of war such as Sierra Leone or the Democratic Republic of Congo. The dummy variable AT ~~P~~ EACE is equal to one for the countries that never had a civil war.

The second step consists in focusing on the starting date of the project: Was the country at war or in post-conflict? This leads us to define two different variables: INIT ~~W~~ AR is equal to one for countries that were at war when the project started. Symmetrically, INIT ~~P~~ C is a dummy which is equal to one when the country was in post-conflict when the project started. We define post-conflict as the 15 years which follows the onset of peace.

The third step focuses on the project period. If the country was at war at the beginning of the project, it is necessary to indicate whether the war lasted for the duration of the project or whether peace was settled during the project. We create a dummy variable, WAR ~~P~~ ROJECT which is equal to one if the country was at war during the entire project. On the contrary, if war ended during the project, a dummy RETURN ~~P~~ EACE is equal to one.

> 4The ICRG is a methodology developed by Political Risks Services. The rating comprises 22 variables in three categories: political, financial and economic each divided in series of risk components. The final composite index is a sum of all those risks that ranges from 0 to 100. This index was preferred to the Country Policy and Institutional Assessment which is computed by the World Bank and therefore might raise endogeneity issues.

8
performance. But we examine a different effect. Our study looks at the impact of bailouts on borrowing costs in the credit markets.

Our paper is also related to the empirical literature on moral hazard in financial institutions. While most of that literature examines how the too-big-to-fail status of a bank benefits its stockholders (e.g., Demirgüç-Kunt and Huizinga 2010; Fratianni and Marchionne 2010; Angbazo and Saunders 1997; Black et al. 1997; and O’Hara and Shaw 1990), there have been a few studies that examine the effects on bondholders (Morgan and Stiroh 2005; Penas and Unal 2004; and Flannery and Sorescu 1996). Our study furthers that literature on moral hazard in credit markets.

Academic research has looked at the role of organized labor within the firm, though it has not explored whether government bailouts of unionized firms impact credit markets. Previous research has shown that unionization has an impact on firm operations. For example, labor unions increase worker salaries<sup>80</sup> , reduce firm operating flexibility<sup>81</sup> , reduce firm profitability and equity values<sup>82</sup> , and impact corporate investment decisions.<sup>83</sup> Chen et al. (2010b) finds that firms in more unionized industries have lower bond yields than firms in lesser unionized industries. The authors of that study believe bondholders view unions favorably because they place a check on management, mitigating agency within the firm. But Chen et al. does not examine the role of public support for unionized companies, which is the focus of our paper. Moreover, Chen et al. studies the period 1984-98. In contrast, our paper focuses on the recent financial crisis and the government’s response. As such, we are able to examine whether government bailouts of unionized firms have an impact on bond prices.

# **V. Data & Methodologies**

In this section, we briefly describe the data sources used in this study. Firm-level accounting and price information are obtained from COMPUSTAT and CRSP. Bond trade prices are obtained from the Trade Reporting and Compliance Engine (TRACE)

> 80 _See_ , e.g., Lewis (1986).

> 81 _See_ , e.g., Chen, Kacperczyk, and Ortiz-Molina (2010a).

> 82 _See_ , e.g., Hirsch (1991) and Abowd (1989).

> 83

> _See_ , e.g., Connolly, Hirsch, and Hirschey (1986).

22
Bangladeshi ...rms and their share in EU imports, but reduces the revenues after tari¤s,<sup>49</sup> which results in exit in the long run.<sup>50</sup> Tari¤ revenues rise quite considerably in the short run, but this will only be temporary. Note that the welfare e¤ects in the short run are the opposite of those in the long run as we have turned o¤ the main channel, namely, entry/exit.

Documentation costs do not a¤ect marginal costs, but make some ...rms choose not to meet ROOs, reducing their cost and price, and in turn, the price index. As a result, Bangladeshi sales rise, though their revenues (net of tari¤s) fall. With no home yarn requirement more ...rms meet ROOs. As there is no marginal cost disadvantage associated with doing so, their prices fall, reducing the price index, while their sales and revenues rise. With costless preferences, there is an additional e¤ect as all ...rms meet ROOs. In all the experiments, welfare in the EU rises. In Columns 2 and 3 of Table 8, the driving forces are a rise in consumer surplus and tari¤ revenues, while in Columns 4 and 5 welfare rises despite a fall in tari¤ revenues.

Table 8. Short-run equilibrium implications of policy changes.

||Baseline|No preferences|Higher doc. costs<br>No home yarn req.|Costless pref.|
|---|---|---|---|---|
|Tari¤ in EU (tBD;EU)|12%|12%|12%<br>12%|0%|
|Tari¤ in EU, ROO (t<sup>ROO</sup><br>BD;EU<sup>)</sup>|0%|12%|0%<br>0%|0%|
|Cost disadvantage (�)|0.85|1.00|0.85<br>1.00|1.00|
|Relative doc. costs (d<sup>EU</sup>=f)|0.66|0.00|1.32<br>0.66|0.00|
||||Change in mass of ...rms, %||
|Mass of successful exporters to EU|485|0.00%|0.00%<br>-0.21%|-0.21%|
||||Change in demand shock cuto¤, %||
|Demand shock cuto¤ in EU|0.1866|+0.37%|0.00<br>+0.37%|+0.37%|
||||Change in price index in EU, %||
|Aggregate price index in EU|100%|-1.63%|-1.09%<br>-3.67%|-3.68%|
||||Change in tari¤ revenues collected, %||
|Tari¤ revenues in EU|447k|+12,964%|+130%<br>-43.0%|-100%|
||||Change in Bangladeshi exports||
|Bangladeshi exports, RBD;EU|482.3m|+0.97%|+0.01%<br>+4.64%|+4.65%|
||||Change in revenues of Bangladeshi ...rms||
|Revenue of Bangladeshi ...rms|481.8m|-11.06%|-0.11%<br>+4.68%|+4.75%|
||||Change in welfare||
|Change in welfare in EU ($)|—|107,433k|33,712k<br>111,610k|111,627k|

It is worth emphasizing the di¤erence in the long run export e¤ects (both in the US and

> 49The after-tari¤ revenues, rij (') ; are given by (6), and the gross sales of Bangladeshi ...rms can be calculated as rij (') = (1 � tij) :

> 50 Of course, as entry is ...xed, there are no e¤ects on the US market so we ignore it for the time being.

36
Naz, F.; Buisson, M.C. (2015). Multiple actors, conflicting roles and perverse incentives: The case of poor operation and maintenance of coastal polders in Bangladesh. In Proceedings of the CPWF, GBDC, WLE Conference on Revitalizing the Ganges Coastal Zone: Turning Science into Policy and Practices, Dhaka, Bangladesh, 21–23; pp. 147–161.

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Angelucci, Manuela and Giacomo De Giorgi. 2009. “Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles' Consumption?” _The American Economic Review_ 99(1): 486-508.

<mark>Atkin, David and Dave Donaldson. 2015. “Who's Getting Globalized? The Size and Implications of Intra-national Trade Costs.” No. w21439. National Bureau of Economic Research.</mark> <mark>Attanasio, Orazio, Vincenzo Di Maro, Valérie Lechene, and David Phillips. 2013. “Welfare Consequences of Food Prices Increases: Evidence from Rural Mexico.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>104: 136-151.</mark> <mark>Attanasio, Orazio, and Elena Pastorino. 2019. “Nonlinear Pricing in Village Economies.”</mark> _<mark>Econometrica</mark>_ <mark>(forthcoming).</mark> <mark>Baird, Sarah, Francisco Ferreira, Berk Özler and Woolcock, Michael. 2013. “Conditional, Unconditional and Everything in Between: A Systematic Review of the Effects of Cash Transfer Programmes on Schooling Outcomes.”</mark> _<mark>Journal of Development Effectiveness</mark>_ <mark>6(1): 1-43.</mark> <mark>Baten, Joerg and Matthias Blum. 2014. “Why Are You Tall While Others Are Short? Agricultural Production and Other Proximate Determinants of Global Heights.”</mark> _<mark>European Review of Economic History</mark>_ <mark>, 18(2): 144-165.</mark> Beegle, Kathleen, Emanuela Galasso and Jessica Goldberg. 2017. “Direct and Indirect Effects of Malawi’s Public Works Program on Food Security.” _Journal of Development Economics_ 128:1-23.

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34
# **<u>Table 1: Levinsohn-Petrin (2003) productivity estimator (dependent</u>**

# **<u>variable: ln TFP)</u>**

||_Coefficients_|_z-statistic_|
|---|---|---|
|_Log of Labour(no. of workers)_|_0.09***_|_24.83_|
|_Log of Capital Stock (tangible fixed_<br>_assets)_|_0.07***_|_8.89_|
|_Log of Materials(cost of goods sold)_|_0.78***_|_39.11_|
|_Wald test of constant returns to_|_scale: Chi2 = 29.89(p_|_= 0.0000)_|

*** indicates significance at the 1 percent level.

**Average productivity of manufacturing firms increased steadily from 2003 to 2008, albeit at an uneven annual growth rate: TFP grew by about 3 percent in 2004, and by nearly 6 percent in 2008 (Figure 1).** This results in an average annual TFP growth of 4 percent over the whole period, similar to what was found in Alam et al (2007) for the period 1999-2003. Contrary to conjectures of some analysts attributing Russia’s growth mostly to high oil prices, there is no sign of an obvious slowdown in TFP growth in Russia’s manufacturing over the entire decade 1999-2008. The result of rising productivity is robust to the effects of “survivorship bias” i.e., both firms within the panel (14,067 firms)  (mainly new and exiting firms) and those outside the panel have seen an increase in TFP, although non-panel firms have experienced a faster growth rate averaging 4.5 percent, compared to 2.7 percent for panel firms (Figure 2).

<!-- Start of picture text --> Figure 1: Average Productivity of Russian Figure 2: Average Productivity - Panel Vs. Non-<br>Manufacturing Firms. Panel Firms<br>Non-Panel Firms Panel Firms<br>Non-Panel (growth rate) Panel (growth rate)<br>TFP Mean Growth Rate Growth<br> Levels Rate % TFP Growth Rate<br>13.5 10%<br>13.5 6%<br>13.0<br>13.0 5% 8%<br>12.5<br>12.5 6%<br>4%<br>12.0 12.0<br>3% 4%<br>11.5 11.5<br>2% 2%<br>11.0 11.0<br>10.5 1% 10.5 0%<br>10.0 0% 10.0 -2%<br>2003 2004 2005 2006 2007 2008 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> 8
Policy Research Working Paper 7230

# **Abstract**

This paper studies the sharp increase in violence experienced in Mexico after 2006, known as “The War on Drugs,” and its effects on human capital accumulation. The upsurge in violence is expected to have direct effects on individuals’ schooling decisions, but not indirect effects, because there was no severe destruction of infrastructure. The fact that the marked increases in violence were concentrated in some municipalities (and not in others) allows for implementation of a fixed-effects methodology to study the effects of violence on educational outcomes. Different from several recent studies that have found significant negative effects of violence on economic outcomes in Mexico, the paper finds evidence that this is not the case, at least for human capital accumulation. The paper uses several sources of data on homicides and educational outcomes and shows that, at most, there are very small effects on total enrollment. These small effects may be driven by some students being displaced from high-violence municipalities to low-violence municipalities; but the education decisions of individuals do not seem to be highly impacted. The analysis discards the possibility that the effects on enrollment of young adults appear small because of a counteracting effect from exworkers returning to school. The results stand in contrast with recent evidence of the negative effects of violence on short-term economic growth, since minimal to null effects on human capital accumulation today should have little to no adverse effects on long-term growth outcomes in Mexico.

This paper is a product of the Poverty Global Practice Group. 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 authors may be contacted at fmarquez@ princeton.edu, fperezar@rand.org, and crodriguezc@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
# **Issuer Composition and Stock Market Growth**

**Djeneba Doumbia**<sup>1</sup> The World Bank

## **Imtiaz Ul Haq**

International Finance Corporation

## **Valentina Saltane**

The World Bank Keywords: stock market, issuer composition, equity issuers, sectoral diversity JEL codes: G15, G18, O16

> 1 We would like to thank Denis Medvedev, Tatiana Didier, Alvaro Enrique Pedraza Morales, and Neil Gregory for providing comments on the paper. We extend special thanks to Paulomi Mehta who supported the work on this paper as a research analyst. The corresponding author can be contacted at ddoumbia@worldbank.org. All remaining errors are our own. The views expressed in the paper are those of the authors and should not be attributed to the World Bank Group, its Board of Directors, or member states.