rafmacalaba/datause-encoder-data huggingface dataset:
Ltd., Bangalore, University of Illinois at Urbana-Champaign, Illinois, Clean Air Task Force, Boston, Entec AG, Hanoi, New Delhi.

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39
(Tumen, 2016). Therefore, we employ the IV strategy used by Del Carpio and Wagner (2015) to estimate the effects of the number of refugees in 2014 on firm entry while controlling for the proximity to the closest border crossing with Syria. We also test the results from the IV estimations with the DD model using data from 2012 and 2013 when the refugee populations were concentrated much closer to the border. A second problem in the DD framework is that the provinces receiving refugees are located in the southeast of Turkey, which is less developed than the western portion. While we limit the control region to eastern Turkey, which is more similar economically to the treatment region, we further test the results from DD models using the synthetic control method of Abadie et al. (2010) to give higher weights to control group provinces that have pre-treatment outcomes closer to those of hosting regions.

The strongest finding of our analysis is that the refugee influx results in an increase in the number of foreign firm entries.While we find positive effects in some estimations, the effect on total firm entry is not statistically signficant in the main IV estimations. We find suggestive evidence pointing towards an increase in the size of agricultural and transportation sectors. There is indication of an increase in total profits and sales in provinces hosting refugees. However, the placebo tests suggest differential trends in the sales and profits in treatment and control regions. Our results complement the results of Del Carpio and Wagner (2015), who find occupational upgrading among formal employees in sectors that benefit from low-skilled informal labor. Refugee labor appears to be a complement to formal labor and capital and a substitute for native informal employment.

Our primary contribution to the broader literature is the empirical evidence on the effect of the refugee inflows on local firm performance and entry, a potential channel discussed by Maystadt and Verwimp (2014) which has received limited empirical attention in the literature.<sup>2</sup> The findings can also help explain the relatively small effects on wages and formal employment in the migration literature. While the influx of refugees reduces the demand for low-skilled labor of the natives, the growth of the number of firms can mitigate this decline. The emerging picture fits well with conventional economic theory; an increase in the supply of low-skilled employed appears to be met with more capital 2
To exclude such firms from the sample, we use the question implemented in both rounds of the COV-ES in El Salvador, asking firms to state why the firms did not receive any support. The question included the following response categories: (i) not aware (21% as of R2), (ii) not eligible (26%), (iii) no need for support (only available as a separate category in R2, 28%), (iv) applied but did not receive it (8%), (v) too difficult to apply (8%), (vi) did not expect to get it (only available as a separate category in R2, 1%), and (vii) other (with the ability to specify given in R2, 6%).<sup>9</sup> We use this variable to exclude the firms that respond with category (i), (ii), (iii), or (iv), i.e., the firms that are either not aware, or not eligible, or in no need of support, or have been rejected to receive the support. These categories cover the majority of the control group, so the restricted sample is much smaller. However, we think this restriction alleviates the concerns about the negative selection bias, at least to some extent.

The question on the reason behind not receiving government support was not implemented in either round of the COV-ES in Georgia. In the absence of other alternatives, to supplement the analysis of the Georgia data performed on the full sample, we restrict the sample using the question about the government support itself. Recall that whether the firm received any government support has three response categories: Yes, No but expect to receive it in the next three months, and No. As an alternative control group, we use only the firms that did not yet receive the government support but expect it in the next three months. While this category may well include the firms that do not need this support or are not even eligible for it (since it is firms’ expectations that may not materialize), at least it excludes firms that seem to be outside of the government support process as of the interview, including perhaps due to lack of their awareness, and would at least to some extent help alleviate the negative selection bias.

# **5. Matching Estimation**

This section discusses the matching methods estimation that we used to overcome the selection and endogeneity issues discussed in Section 3.

The goal of matching methods is to create two samples out of Support and No-Support groups that look alike in terms of firm characteristics. The only difference between the two samples

> 9 The remaining 2% said that they do not know the reason.

18
**A** clear consensus has emerged in recent years that, in contrast to developed countries, one of the principal constraints on 1nsvestment in developing countries is the quantity, rather than the cost, of credit. The rates of t.-turn on investment in these countries typicslly tend to be quite high, whereas real interest rates on loans are kept low by goverrments for a variety of reasons. In such circumstances the investor cannot he expected to equate the current marginal product of capital to its se_-vice cost. Indeed, because the total amount of financing is limited and the price mechanism is not allowed to operate smoothly, it would seem legitimate to argue that the private investor in a developing country is generally restricted by the level of available bank credit.8 An increase in real credit to the private sector encourages real ,civate investment as is confirmed by several empirical studies (Fry 1980, Tybout 1984, Blejer and Khan 1984).

In most developing countries, both the Availability and the real price of foreign exchange affect private investment. This is because imports are often rationed and the cost of investment is influenced by the exchange zate and the uncertainty of access to foreign exchange.9 If foreign exchange for imports is not available on demand at the prevailing exchange rate because of import rationing, thean access to imports at a given exchange rate becomes $The rudimentary nature of capital markets in developing countries limits the financ.ing of private investmen **t** o the use of retained profits and bank credit. Of these, the flow of bank credit to the private sector is likely to be qu&ntitatively the most important. 9Several studies of developing countries have found that imports vary with the restrictiveness of import licensing which in turn varies with changes in stock of foreign exchange reserves (Hemphill 1980, Moran 1989). There is evidence that decisions of Government of Kenya on the restrictive-less of licensing is based on the size of reserves. For example in 1982/83 Kenyan government used an explicit "trigger mechanism" for moving items from restricted to unrestricted list. Movement of such items were triggered on reserve increases above 1.5 months import coverage.

9
Figure 10 The distribution of workers by the size of staff reduction they were effected by (June-Dec. 1992 and 1993) <!-- Start of picture text --> Y-uiz of layoff<br>600<br>00 t 2<br>400<br>(i1<br>300<br>200<br>1~~~~~~~~~~ " '<br>0 10 20 30 40 50 60 70 80 90 100<br>pet effected by lay-of fsmall.r than Y<br>-1992 -~1993<br><!-- End of picture text --> LMFS waves 211992 and 211993. RostrICted to firms reducing their staff.

14
variables are included (hence, excluding 2007-2008 datasets because of the lack of the distance variables).

Columns (3) and (4) report the probability of working in seasonal employment given different regional road quality leading to towns and markets. Respondents who report average and poor road quality in their regions are, respectively, 1,5 and 5.2 percentage points less likely to work in seasonal employment. Considering that 66% of the respondents employed in agriculture report working whole year round and only 1,3% of them or 370 people report having a second job, this could mean that people living in areas with poor roads are less likely to take seasonal jobs during low agricultural seasons. Lastly, column (5) shows that households are less likely to report any household member working outside of the village if they report having poor or average road quality leading to towns and markets.<sup>17</sup> Table 3 reports the relationship between hours worked, agricultural employment, and reported road quality. The first column shows that people who work in agriculture tend to work less, estimated at 16.5 less hours worked the week before the survey, holding all other variables constant and controlling for the survey month. Descriptive statistics show that non-agricultural workers had worked on average 23 hours the week before the interview, while the non-agricultural workers had worked on average 41 hours. This result is in line with the study done by McCullough (2017). By analyzing the Living Standards Measurement Study Integrated Surveys on Agriculture (LSMS-ISA) for four African countries - Ethiopia, Malawi, Tanzania and Uganda - she found that each agricultural worker was working on average 700 hours per year compared to the 1850 hours per non-agricultural worker. Our estimates are higher than those of McCullough (2017) but still show significantly large differences between the working hours of agricultural and non-agricultural workers. Column (2) shows that individuals reporting average or poor quality of roads leading to towns and markets, work slightly less than people reporting having good regional road quality. Column (3) shows how the inclusion of the interaction terms affects the results. Respondents working in agriculture who report average quality of regional roads are estimated to have worked 1.7 hours less the previous week than people reporting good roads. Interestingly, people employed in agriculture who report poor regional roads

> 17Column (5) reports the analysis at the household level.

18
# Identification Properties for Estimating the Impact of Regulation

# on Markups and Productivity<sup>_∗_</sup>

James Sampi<sup>1</sup> , Charl Jooste<sup>2</sup> , and Ekaterina Vostroknutova<sup>3</sup> 1 World Bank, Vrije Universiteit Amsterdam January 25, 2021

**JEL code** : C150, C310, L510, 0470

**Keywords** : Productivity, Markups, Regulation, Quasi-Maximum Likelihood

> _∗_ We are grateful to Chad Syverson, Philippe Aghion, Siem Jan Koopman, and Andrew Harvey for their helpful and constructive comments. We are also grateful to Martin Rama, Jorge Araujo and to the participants of Authors’ Workshop for ”Competition, Productivity, and Growth in Latin America” report at the World Bank, for a helpful discussion. Corresponding authors: jsampibravo@worldbank.org, cjooste@worldbank.org, evostroknutova@worldbank.org.
# **Contents**

|Ov|erview ................................................................................................................................................... ii|
|---|---|
|A.|Country Context ................................................................................................................................ 4|
||Main recent fiscal and debt developments .......................................................................................... 5|
|B.|Macro-Fiscal Framework for 2014-2023 and Debt Sustainability Analysis ...................................... 7|
|F|iscal risks and debt sustainability ........................................................................................................ 9|
|A|detour: stochastic DSA and scenario analysis.................................................................................. 11|
|C.|Public Investment: Growth Dividend and Debt Sustainability ........................................................ 13|
||Quality of Public Investment: Growth Dividend and Fiscal Multipliers .............................................. 17|
|D.|Public Debt Strategies: Cost and Risk of Debt Portfolios ............................................................... 20|
|E.|Conclusions ..................................................................................................................................... 26|
|F.|References ...................................................................................................................................... 27|
|G.|Annexes ........................................................................................................................................... 28|
|A|nnex I: A DSA model for Mauritania. ................................................................................................ 28|
||Debt Dynamics, Financing, and Borrowings ....................................................................................... 29|
||Macroeconomic Dynamics .................................................................................................................. 29|
|A|nnex II. Optimal composition of public debt in Mauritania. ............................................................ 33|
|A|nnex III.Thresholds on Public Debt for Mauritania.......................................................................... 38|

The authors would like to extend their sincere and unreserved thanks to Fernando Blanco, Carlos Cavalcanti, Philip English, Faya Hayati, Mark Thomas – on the World Bank side – and Tarak Jardak and Mercedes Vera-Martin from the International Monetary Fund, for providing invaluable feedback and peer-reviewing this document.

i
Table 1 reports the sample composition of the data available in Worldscope at the time of this report (end of September 2008). There are two main observations from this table. First, Jordan seems to be lagging in terms of timeliness of data reporting. At the end of third quarter of 2008, only 11% of all firms in the database have data for 2007 in Jordan. For other countries this percentage is much larger – 32% for Morocco, 54% for Egypt and over 90% for Israel and Turkey.

**Table 1.  Sample Composition and Timeliness of Data Reporting**

|Country|Number<br>of firms<br>in 2006|Number<br>of firms<br>in 2007|Percent of firms<br>withdatafor 2007|Financials<br>(in 2006)|Percent of<br>sample that are<br>financials (2006)|
|---|---|---|---|---|---|
|Jordan|36|4|11%|18|50%|
|Morocco|22|7|32%|8|36%|
|Egypt|54|29|54%|13|24%|
|Israel|170|160|94%|17|10%|
|Turkey|210|217|103%|40|19%|

Source: Worldscope, September 2008.

A second observation is about the sample composition. In Jordan in 2006, of 36 firms represented in the sample, 18 are classified as financials (including 11 banks and 7 non-bank financials). Thus, 50% of Jordan sample in Worldscope are financial companies. This percentage is much larger than in other countries, it is 36% in Morocco, 25% in Egypt and less than 20% in Israel and Turkey. The Worldscope represents largest listed companies with investor interest (since the customers of worldscope are international investors). It is important to understand further why financial companies in Jordan are overrepresented in the international database, such as Worldscope.

This assessment primarily focuses on the non-financial corporate sector (as financial companies are analyzed elsewhere). Therefore for increased comparability, we limit crosscountry benchmarking to non-financial companies. Table 2 reports key vulnerability ratios for non-financial companies in Jordan. Below we discuss main vulnerability ratios.

High profitability makes the defaults less likely and gives a sense of the overall soundness of the corporate sector. A common measure of profitability is Return on assets, ROA, defined as net income over total assets. It measures the efficiency with which the company is 3
- 59 -

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