rafmacalaba/data-use-mentions-extended huggingface dataset:
Our results also have implications for the growing literature that uses face-to-face IPV data from multiple countries to draw inferences about IPV prevalence, predictors, and causes (Devries et al., 2013; García-Moreno et al., 2013; Cools and Kotsadam, 2017; Jewkes et al., 2017; Alesina, Brioschi and La Ferrara, 2020; Heise and Kotsadam, 2015). Our finding of significant and systematic underreporting for some types of IPV in our two rural African contexts contrasts with findings from Lima, Peru, where researchers found no overall difference between face-to-face and list method prevalence (Agüero and Frisancho, 2017). These divergent results indicate that reporting bias is, unsurprisingly, strongly affected by contextual factors. Given significant, systematic face-to-face misreporting in some contexts but not others, our results imply that caution should be used when aggregating and analyzing face-to-face IPV data from diverse settings, lest context-specific misreporting bias results.

Policy makers and social scientists measuring sensitive topics could use similar randomized survey experiments to compare IPV reporting rates between the list experiment and direct methods to identify levels of stigma, the types of people most likely to misreport, and the risk of generating biased treatment effects. For example, social scientists researching the impact of interventions on IPV might use such an experiment to sign potential reporting bias in their treatment effect estimates. Also, given Demographic and Health Surveys’ large sample sizes and use in multi-country analysis, perhaps such surveys should measure several IPV questions using the list method as well as the face-to-face method. This would allow data users to assess the likely extent of reporting bias by context, allowing them to make adjustments to inferences about true levels and correlates of IPV.<sup>33</sup> Future research could be conducted in other contexts and on other types of violence and sensitive topics to better understand what drives misreporting. Other potential areas for additional research include studying how to best design and administer the list experiment to minimize bias, and developing alternative measurement methods for efficient causal research on IPV which,
# **3 Data and Motivating Findings**

## **3.1 Data**

My primary unit of observation is the urban census tract (Area Geoestadística Básica in the Mexican micro-data). I use a sample of approximately 3,500 census tracts from 116 different neighborhoods and 24 different municipalities, 16 municipalities of which are in Mexico City and 8 of which are adjacent municipalities from the State of Mexico.

The first source of information is standard GIS data on the location of the transportation network and the new transit subway lines. I also use data on roads and highways in Mexico City to calculate commuting times for different transportation modes using the network analysis toolkit from ArcMap. By merging these datasets, I can estimate commuting/trade costs before and after the transit shock using a weighted average of travel times across the different transportation modes.

The second source of data is the Mexican Economic Censuses collected by INEGI. This is an establishment level data set that provides standard information such as sales, value added, number of workers, salaried workers, social security, and other outcomes. This census is carried out every five years starting in 1994. I am able to define the informal sector at the firm level using social security payments as discussed in Section 2. I categorize firms and workers in four different groups based on labor market regulations. I also calibrate wedges for each location and sector using wage bill, sales, and social security payments.<sup>17</sup> The third source of information is the Mexican Population Census. This census is carried out every ten years, and INEGI provided me with the data since 2000. With this information, I am able to calculate the number of informal, formal, and total residents in each location. In 2000, the Population Census also reported other variables such as household income and job characteristics the week before the census interview. Moreover, I use the 2015 Intercensal Survey that provides information on the workplace, residence, and transportation mode of formal
issued by MEHIB may, however, raise its future costs. MEHIB's credit insurance goes to relatively high-risk transitional countries, as part of government policy to support trade with these countries. In its geographical distribution, MEHIB is exposed 45 percent to CIS countries (around HUF 22bn to Russia), which may negatively influence MEHIB's performance in 1998-99. MEHIB suggests a provision of HUF 5.6bn for its possible 1999 claim on the state budget.

_Credit Guarantee Co._ The state reinsures 70 percent of the obligations that emerge from guarantees issued by the Credit Guarantee Co., up to 70 percent of the company's reserves. The legal ceiling on guarantees outstanding is HUF 55bn in 1998. Shareholders of the Credit Guarantee Co. include mainly commercial banks and saving cooperatives. Guarantees issued by the Credit Guarantee Co. partially cover credits mainly to small and medium sized enterprises. The state budget paid relatively modest HUF 326m on guarantees triggered in 1997. In 1999 however, there are expectations of a total call of HUF 1.8bn due to the budget re-insuring a wider range of business activities of the company (risk capital investments) than before.

- 14 MEHIB is also insuring Eximbank's guarantees.

> _15_ Turnover data show a significantly smaller involvement in insuring against political risks. Use of the statutory limit is measured in terms of negotiated deals rather than actually closed contracts.

29
from potential technology spillovers. The survey data reports four different types of premises, namely household, non-household with a permanent structure, non-household with a temporary structure, and non-fixed premises. The statistics are summarized in Table 6. Around 40% of informal businesses operate within their household premises. An additional 27% are located in other permanent structures, whereas around 30% have either no premises or some kind of temporary structure.

**Table 6: Summary of Type of Premises: Informal Businesses**

|Type of Premises|Obs|Percent|
|---|---|---|
|Household|415|41.05|
|Non-household with permanent structure|274|27.1|
|Non-household with temporary structure|270|26.71|
|Non-fixedpremises,includinghawkers|52|5.14|
|Total|1,011|100|

Table 7 shows that the effects of distance on digital technology adoption remain negative and statistically significant when the sample is restricted to informal businesses with permanent structures.

**Table 7: Effects of Distance for the Permanent Location Sample**

||(1)<br>PC|(2)<br>Tablet|(3)<br>Phone|(4)<br>MM|(5)<br>Website|
|---|---|---|---|---|---|
|Distance of Informal to Closest Formal Firm|-0.840***|-0.691***|-0.194**|-0.172***|-0.387|
||(0.141)|(0.201)|(0.093)|(0.037)|(0.440)|
|Num. of People Who Worked Last Month|0.404***|0.273|0.068|-
years (close the average mean remaining maturity of bonds and loans of 2.31 and 4.27 years for the firms in our sample, calculated from Capital IQ data), and we assume that firms repay the outstanding debts in equal annual payments, i.e. they repay a third of their debts each year. Using these assumptions, we calculate the mean dMVD/Book assets to be 5.95 basis points for a sample of 111 firms.

We also consider the increase in the market value of equity relative to the sum of the increases in the market values of equity and debt, i.e. dMVE/(dMVE + dMVD) in case both dMVE and dMVD are positive, which is the case for 66 firms. The mean of dMVE/(dMVE + dMVD) is 0.879, i.e. on average 87.9% of the total valuation gains accrue to shareholders. Alternatively, we compute dMVE/(dMVE + dMVD) conditional on the sum of dMVE and dMVD being positive, yielding 84 observations with a mean of 0.812.

We relate the various dependent variables reflecting share price and CDS spread changes to a range of independent variables that capture (i) the likely relative impact of the PEPP on the firm as indicated by the firm’s credit rating and reliance on bond finance, (ii) other firm characteristics that potentially proxy for credit constraints and debt overhang that could be alleviated by the PEPP, (iii) whether a firm belongs to an industry that is highly affected by COVID-19, and, finally, (iv) the strength of pertinent national fiscal policy measures to counter the pandemic that potentially affect the implications of monetary policy for the firm.

The PEPP is restricted to purchasing corporate debt securities that are investment grade, which suggests that corporate issuers with an investment grade rating are more directly affected by the PEPP. To test this, we construct the Investment grade dummy variable, which signals that the firm had a long term
agreements, which the MDGs loosely resemble. Third, it argues that a more psychologically, morally, and politically salient development framework would better promote political mobilization, and provides illustrative examples for how to increase salience.

# **What were the MDGs?**

It may be easier to state what the Millennium Development Goals were not than to characterize what they were. From the perspective of international law, the Declaration that emerged from the Millennium Summit was neither a treaty nor a binding international instrument. Although the Millennium Declaration was a Resolution adopted by heads of state gathered for the UN General Assembly, there was no reference in the Declaration to a ratification process, which accompanies treaties, nor was the document officially listed in the UN Treaty Database. Although parliamentary involvement in the MDG process did take place in a few countries (for instance, in 2005 the Parliament of Mongolia passed a resolution on the MDGs that established a ninth goal on human rights and democratic governance), these did not amount to a formal assumption of internationally recognized obligations.<sup>2</sup> Over the years, through national declarations, the MDGs may arguably have contributed to evolving norms in international customary law (Alston, 2005), but that, if true, is the exception that proves the rule: the MDGs constituted a weak form of legalization.

The hallmarks of a legalized international regime are obligation, precise, and delegation (Abbott, Keohane, Moravcsik, Slaughter, & Snidal, 2000). The “hardest” forms of legalization occur when formal procedures (such as accession to an agreement, the possibility of reservations, provisions for withdrawal) are used to establish obligations, when there are mechanisms for monitoring compliance and penalizing failure to comply, when specific and authoritative organizations are tasked with these compliance monitoring and sanctioning functions, and when the rules that underlie the obligations are precise and

> 2 Manning (2010) notes that at least ten countries added new goals or modified those of the MDGs. Albania, Iraq,
Q1-10 Q3-10 Q1-11 Q3-11<br>Change, USD Billions<br>Change, USD Billions<br>Q1-06 Q3-06 Q1-07 Q3-07 Q1-08 Q3-08 Q1-09 Q3-09 Q1-10 Q3-10 Q1-11 Q3-11<br>Q1-06 Q3-06 Q1-07 Q3-07 Q1-08 Q3-08 Q1-09 Q3-09 Q1-10 Q3-10 Q1-11 Q3-11<br><!-- End of picture text --> Source: BIS, Consolidated Banking Statistics, table 9C.

15 See _The Economist_ , ―Reshaping Banking,‖ April 19, 2012, http://www.economist.com/node/21553015/print.
firms typically pay low wages, and, as a result they are more exposed to increases in the minimum wage. A second attractive feature of the EIS is the broad coverage of sectors of activity: it includes registered private firms in all sectors of activity, apart from the financial sector. We can thus examine the effects of the minimum wage on the formal economy as a whole, and we can examine heterogenous effects across sectors.

> 12 The EIS was constructed by merging various administrative datasets across government entities in an effort led by the Ministry of Industry and Technology. The original sources of the administrative datasets included the Ministry of Industry and Technology, the Ministry of Trade, the Revenue Administration, the Social Security Institution, the Small and Medium Business Development and Support Administration, the Turkish Statistical Institute, the Turkish Patent and Trademark Office, and the Scientific and Technological Research Council of Turkey.

> 13 Turkey has 923 districts.

> 14 Those in the remaining 20 percent are largely agricultural workers who are rarely registered with social security institutions.

9
Policy Research Working Paper 5946

# **Abstract**

This paper examines how different establishments performed during the recent global financial crisis, focusing on the role of foreign ownership. The paper investigates how foreign ownership affected establishments’ responses to negative economic shocks, using a cross-country panel dataset with detailed information on operation, location and industry for more than 12 million establishments from 2005–2008. The evidence shows that multinational subsidiaries on average fared better than local counterfactuals with similar economic characteristics. Among multinational subsidiaries, establishments with stronger production and financial linkages with parent companies showed greater resilience. Finally, in contrast to the crisis period, the impact of foreign ownership and linkages on an establishment’s performance was insignificant in noncrisis years.

This paper is a product of the Trade and Integration Team, Development Research 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 author may be contacted at xchen4@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
true and false choices to assess respondents’ understanding of basic financial concepts as well as their numeracy skills: respondents were required to do simple divisions, to perform basic calculations related to interest rates, to identify the better bargain among two different savings and loan products, and to demonstrate their understanding of how inflation affects their savings. Lastly, one question aimed to evaluate respondents’ know‐how needed to successfully interact with financial institutions (awareness of required documentation for being able to open an account). Aggregating the questions into a single index, we find two things (see Table 8): (i) scores are very similar across all groups and (ii) the aggregate scores are relatively high, with the weighted and arithmetic scores yielding similar results, perhaps reflecting a lack of variation and cognitive separating ability of the set of questions. However, when exploring the covariates associated with these financial literacy scores, we find strong relationships between the overall score and (i) whether business owners had a savings account at baseline and (ii) whether they had any schooling, supporting the assertion that the indices are informative in distinguishing between financial literacy levels, and the similarities in scores across groups reflects balance induced by the randomization.

# **_Perceptions_**

We find increases in self‐reported trust and perceptions of MFBs directly after the screening events; however, when asked the same questions in the follow‐up survey, many of the initial differences reduce or disappear.<sup>12</sup> While males are influenced most strongly by the movie stimulus in the short run, differentials in self‐reported trust only sustain for females in the longer run. Table 9 presents the results from the screening and endline surveys. While the movie on its own has some impact on whether people report that they would trust an MFB to keep their money when people were asked this question at the screening, the presence of _Accion_ seems to have a much larger effect than the movie, and there is no additivity of
2008, and 2009 for decomposition analysis. 2009 is the last year for which all the data necessary for decomposition are available. For oil prices, this study uses the annual average of the prices of three marker crudes: Brent, West Texas Intermediate, and Dubai Fatah. In 2011 U.S. dollars, the prices were US$23.59 a barrel in 1999, US$101.25 in 2008, and US$63.80 in 2009. The price in 1999 was within US$2 a barrel of the decadal average in the 1990, both in 2011 U.S. dollars. The 2008 average price was the closest of all historical prices to the 2011 average of US$104. Therefore, the level of vulnerability in 2008 is likely to be fairly representative of that in 2011, for which data are not yet available. Decomposition analysis is carried out for the change in vulnerability between 1999 and 2008 (first period) and between 1999 and 2009 (second period). During these two periods, oil prices increased by a factor of 4.3 and 2.7 in real terms, respectively; the price in 2009 was lower than that in 2008 by more than one-third.

Vulnerability calculations in this paper use two different data sources for oil production, consumption, and trade:

1. For vulnerability levels and changes without decomposition (figures 3–6), for all countries for which the International Energy Agency (IEA) has data, net imports are computed from the IEA’s oil import and export statistics. For the remaining countries, net
# **Roads and Rural Development in Sub-Saharan Africa**<sup>**1**</sup>

## **Claudia N. Berg, Brian Blankespoor, and Harris Selod**<sup>**2**</sup>

### **Keywords:  Transportation, Market access, Cropland expansion, Africa**

**JEL: R4, O1, Q15**

> 1 The authors thank Rabah Arezki, Punam Chuhan-Pole, Richard Damania, Uwe Deichmann, Siobhan Murray, Sandrine Mesplé-Somps and Gilles Spielvogel for earlier discussions related to this paper, and Rémi Jedwab and Adam Storeygard for making the roads data they have constructed available for calculation of accessibility indexes, as well as Rose Choi for research assistance.  Funding from DFID under the World Bank’s Strategic Research Program ‘Transport Policies for Sustainable and Inclusive Growth’ is gratefully acknowledged. Corresponding author: Harris Selod (email: hselod@worldbank.org, address: 1818 H Street, NW, Washington, D.C. 20433). 2 Berg: Research Department Commodities Unit, International Monetary Fund. Blankespoor and Selod: Development Research Group, The World Bank.
- **International reserves:** in the monthly analysis, the stock of foreign exchange reserves held by the Central Bank of Argentina in US dollars is deflated by US CPI ( _res_ ).<sup>20</sup> In the annual analysis, international reserves are expressed as a percentage of GDP ( _resY_ ).

- **Trade openness:** in the monthly analysis, total trade (exports plus imports) in US dollars is deflated by US CPI ( _trade_ ). In the annual analysis, trade is expressed as a percentage of GDP ( _tradeY_ ).

We also considered a range of other variables that are not in our preferred specification, namely: real interest rate differentials,<sup>21</sup> net foreign assets deflated by US CPI (nfa), government spending deflated by CPI (gov), money supply defined as broad money in circulation deflated by CPI as a measure of monetary policy (m2).<sup>22</sup> The full list of variables considered in our analysis is found in Appendix Table A.1.

# **_Data Adjustments_**

Our analysis deals with potential data mismeasurement, whereby official and private sector inflation and output estimates differed substantially in recent years. Alleged manipulations of depressed inflation and inflated growth figures have undermined the credibility of official statistics and led to widespread reliance on private sector parallel estimates of the inflation rate and output growth. To deal with this, we construct adjusted measures of the REER and productivity differentials.

If domestic prices are indeed underestimated, as is possibly the case for Argentina,<sup>23</sup> the REER would also be underestimated. We therefore complement the analysis by considering a measure of the REER that corrects for this. Data for the monthly real effective exchange rate, starting in 1994, is obtained from the BIS. We adjust this measure for cumulative monthly discrepancies between official (INDEC) and private sector inflation estimates since November 2007 from PriceStats, thereby constructing an
> The explanatory variable 1 _{Near}vjk_ equals 1 for sets that occurred inside a treatment bin in which there was a statistically significant change in juvenile catch (see Figure 7) and equals 0 otherwise. I define 1 _{Near}vjk_ in this way because I am interested in whether the large spillover effects I estimated in Section IV can be explained by the closures policy communicating information about the value of fishing in those places and times. I include the distance of each set to the coast, vessel by season fixed effects, day-of-sample fixed effects, and two-degree grid cell by season fixed effects in order to partially control for differences in the cost of each set (e.g., sets farther from shore require more fuel, all else equal). Dayof-sample fixed effects also control for the international price of fishmeal, which determines the price fishers receive per ton of anchoveta they land. Therefore, the change in tons per set captured by _β_ 1 represents the change in revenue per set from fishing near closures. To the extent that the fixed effects and the distance of each set to the coast control for cost per set, _β_ 1 can also be interpreted as the change in profit per set from fishing near closures.

When I define the near indicator relative to potential closures, I find that sets that happen 27Vessels report catching 0 tons for 11% of sets in the electronic logbook data.

37