rafmacalaba/data-use-mentions-extended huggingface dataset:
for sustained success. This paper contributes to the literature on the demand side interventions by uncovering the returns from investing in statistical transparency for countries. We focus on economic transparency defined as availability and access of economic information and statistical data (Geraats 2002b).

We explore the relationship between statistical data transparency and sovereign bond spreads while controlling for domestic and external macro-financial variables (classified as pull and push factors). The choice of sovereign bond spreads as the variable of focus is pertinent because of the substantial change in the composition of Sub-Saharan African external debt between 2007 and 2018 (see Figure 1). The share of sovereign bonds and non-Paris Club debt in total outstanding public external debt increased by 18 percentage points and 8 percentage points, respectively. Almost half of the public external debt outstanding and disbursed in 2018 for Sub-Saharan Africa is composed of international bonds and non-Paris Club lending (i.e. 30 percent for bonds and 21 percent for non-Paris club).

This paper improves on the existing literature in three different ways. First, we estimate the impact of public data transparency along with the level of institutional quality and public and external debt. Although greater data transparency alone reduces the external borrowing costs of a country, greater data transparency lowers the external borrowing costs even further when combined with

> 1 Data transparency is not only an issue for low-income countries. It is also problematic in some higher-income countries as well, especially those that do not belong to the Paris Club and therefore are not subject to the standard disclosure requirements, see Horn, Reinhart and Trebesch (2019).

2
that use natural gas for space heating, and only the 2017 and 2018 HBS data. This is because the one-part tariff with stable, uniform prices make it possible for us to compute the quantity consumed from the expenditure and HUS information. The tariff structure remained in place in 2019, but we are not satisfied with the quality of the data from the 2019 HBS at our disposal (the first release by Ukrstat), and for this reason we do not include the 2019 HBS data in our estimation effort.
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- (2) Different treatment of outliers: VV omitted a number of cases with low hourly wages. We have not omitted any cases, but recoded outliers - usually with the largest non-outlying value - after checking for inconsistency with expenditure data.

- (3) Different specification of the earnings equation: Two explanatory variables in the VV regressions appear questionable. First, a cognitive skills variable is included which "is zero if the individual cannot write or read or do arithmetic, and increases with 1 for each of the skills acquired." (p.9) The underlying assumption of equal productivity effect of each of these skills appears most implausible. Moreover, such a variable is likely to introduce collinearity as it has no variance for individuals with a CEPE or above (i.e., the vast majority of employees), who all reported possessing all three skills. Second, VV have proxied total work experience by age minus schooling minus 5. As we explained in the paper, this is inappropriate in the Cote d'Ivoire context. It is preferable to rely on the two direct measures of work experience available in the CILSS (of which VV use only one). Lastly, W's earnings equations do not control for location. Our finditgs indicate that this is an important omission.
# 1. Introduction

This paper explores whether the governing law has any lasting impact on sovereign bond spreads. Foreign currency sovereign bond issuances comprise a significant and vital part of total emerging market (EM) bond issuance. Sovereign bonds are important not only for government finances, but also for providing a benchmark for sub-sovereign borrowings. Conventionally, controlling for bond and issuer characteristics, spreads are expected to be equal across different legal jurisdictions. Differences in spreads, if any, are expected to disappear over time through arbitrage. The main research questions examined in this paper are:

1. What are the stylized facts about sovereign US dollar bonds issued by emerging markets before and after the financial crisis?

2. Are there systematic differences between the spreads of dollar-denominated emerging market sovereign bonds issued under U.K. and U.S. law, after controlling for bond and issuer characteristics?

3. What factors could explain such differences, if any?

Contrary to expectations, an analysis of 435 sovereign U.S. dollar bonds issued by 53 emerging market sovereigns during 1990-2015 reveals that since the global financial crisis in 2008, the governing law seems to affect bond spreads for sub-investment grade bonds. During 2008-2015, the launch spread of sovereign bonds issued under U.K. law has been higher than those issued under U.S. law. While the difference in spreads was not significant for investment grade bonds, it ranged from 130 basis points for BB+ bonds to 175 basis points for B- bonds.<sup>2</sup> Collective action clauses (CACs) that reduce the possibility of minority investors holding out on a debt settlement with a borrower, which were featured originally in U.K. law but not in U.S. law, are a possible source of differences in bond spreads. But empirical evidence from the literature is inconclusive on this point. CACs are now common in bonds issued under both U.K. and U.S. governing law. Another possible source of spread difference
Second, we exploit the large sample sizes to obtain inequality measures by age group and birth cohorts, thus being able to decompose observed trends in age profiles and birth cohort changes. For the five countries under analysis, the observed inequality of opportunity exhibits an inverted U- shaped pattern over the life cycle. Moreover, the most recent age cohorts have experienced a lower IOp, thus appearing as the main beneficiaries of the overall decline in inequality.

Third, the paper proposes a theoretical framework identifying the variables that affect (positively or negatively) inequality of opportunity. The framework is then estimated, and the data confirm the predicted signs. The analysis has focused on the role of three variables: the intergenerational persistence in educational attainment, the return of education, and the networking activity of parents. While the first two variables exhibit a declining trend, which other things constant should produce a decline in IOp, the third one appears to be rising in many countries, thus counteracting the effects of the first two. Consequently, the fair optimism that descriptive statistics suggest with respect to income inequality should be mitigated by paying attention to educational persistence and labor market segmentation.

# **References**

Aaberge, R., M. Mogstad, & Peragine, V. (2011). Measuring Long-term Inequality of Opportunity, _Journal of Public Economics_ , 95(3-4), 193-204.

Black, S., and P. Devereux. (2011). “Recent Developments in Intergenerational Mobility.” _Handbook of Labor Economics_ , 4(B), Ch-16, pages 1487- 1541 Bourguignon F., Ferreira F.H.G., Menéndez, M. (2007). Inequality Of Opportunity In Brazil, _Review of Income and Wealth_ , 53(4), 585-618.

Checchi, D., Peragine, V. (2010). Inequality of opportunity in Italy, _Journal of Economic Inequality_ , 8(4), 429-450.

Deaton, A. (1997). “ _The analysis of household survey.
Nearly half of all foreign tourists are in direct transit and a quarter visits Panama for recreation.

Average expenditures differ significantly across tourist types. According to the national accounts, they range from around US$200 per person per trip for transit tourists to more than three times that amount for recreation tourists (Table 3 and Appendix Table A7). Recent data from TSA surveys are broadly consistent with the national accounts. Average expenditures per tourist are on average US$549 per person per trip compared to the slightly lower average from the national accounts. The latest round of TSA data for tourists in direct transit is not yet available, so the TSA data are representative of recreation, business and other types of tourism and not of direct and indirect transit passengers. Since survey results are regarded as a more accurate measure of tourist expenditures, these findings suggest that the national accounts may overestimate expenditures by recreation tourists and underestimate business tourists’ expenditures.

|**Table 3. Foreign**|**Tourists in Pa**<br>**Data fr**<br>|**nama: Num**<br>**om the Natio**<br>|**ber of Visitors**<br>**nal Accounts (20**<br>|**and Expen**<br>**06)**<br>|**ditures in 2006**<br>**Estimates fr**<br>**Tourism S**<br>|**om TSA**<br>**urveys**<br>|
|---|---|---|---|---|---|---|
||Number of<br>People|Percent of<br>All<br>Tourists|Total<br>Expenditures,<br>US$’000|US$/Trip|
These compare with an unweighted population average individual cost of a nutrient-adequate diet in Malawi calculated by (Herforth et al. 2020) of $1.29/person/day (2011 US$ PPP), which was calculated using different food price and nutrient composition data for global comparability. That paper used the food list and prices from the World Bank’s International Comparison Project and used USDA food composition data (Herforth et al. 2020).
One exception is Santarelli (2011) who studied entrepreneurship in Vietnam using a panel of regional-level data for 61 provinces over the period 2000-2008; among other outcomes, the author found that an innovative climate (proxied by the share of technical/R&D personnel in the province) significantly and positively affects the regional net entry rate. Moving to post-entry performance, in the previously-cited work by Goedhuys and Sleuwaegen (2010), the innovative capability (proxied by a dummy for the introduction of new products) was found to increase a firm’s annual employment growth by 2% on average.

# **<u>4.2.6 Escape from unemployment</u>**

As far as unemployment (or the fear of becoming unemployed, see Section 3.1) is concerned, the literature points out two stylized facts: 1) those who have entered selfemployment from unemployment exit to a higher extent than those who have entered from paid employment (see Carrasco, 1999; Pfeiffer and Reize, 2000; for a slightly more optimistic evidence, Caliendo and Kritikos, 2010); 2) new founders who were formerly unemployed have on average lower economic outcomes and a lower propensity to contribute positively to job creation. For instance, in the previously-cited paper by Arrighetti and Vivarelli (1999), the authors found that defensive motivations such as concern about future career developments and the fear of becoming unemployed were predictors of a below-the-average post-entry evolution (ibidem, p. 936). By the same token, Andersson and Wadensjö, (2007), using a large sample of Swedish-born men who were self-employed in the period 1999-2002 and who were either wage-earners, unemployed or inactive in 1998, showed that those who were previously unemployed systematically had lower incomes compared to those who were previously wage earners; moreover, they also found that income from self-employment declines with the number of days spent in unemployment and that previously-unemployed entrepreneurs are significantly more likely to be ‘solo’ entrepreneurs, _i.e._ to have no employees
bias is difficult to determine–there are arguments that more altruistic doctors or those building up a reputation locate in the public sector, which biases the estimate towards zero (see for instance, Chawla 1997). On the other hand, doctors with higher costs of effort may locate in the public sector, biasing the coefficient upwards.

# 4.1 Interpreting the Relation between Effort and Competence: The Structure of Sorting

To understand how patients match to doctors, we matched household visits to doctors in the parallel household survey to providers in the doctor survey. We completed this matching for all visits over a 2 year period, during which households were administered surveys on a weekly basis for 35 weeks. This data yielded close to 4,000 doctor visits; in 65 percent of these visits, we were able to match the provider visited to a practitioner in our study (recall the sampling scheme gave greater weight to doctors who were visited more). Since all households were administered a consumption-expenditure survey, we then computed the "average income of a patient" as the average income of households matched to providers, weighted by the number of visits over the 2-year period. By construction, the sample of practitioners also includes those who were never visited.

> 19 Our attempt to address the matching issue differentiates this paper from Leonard’s (2005) work. While Leonard (2005) tries to address the selection issue, he does not present a synthesis of how the market operates in equilibrium; as such, it is hard to tell what portion of his results are driven by the selective sorting of patients to doctors. This problem be partially solved with information on patient characteristics. Given the particular environment (crowded urban streets and clinics that consist of a single room open to the outside) both patients and doctors were reluctant to give permission for this exercise and we explore other options for identi fi cation purposes.

13
Table 1 depicts descriptive statistics for selected variables at baseline using data from the balanced panel of 2,382 households, including tests for covariate balancing between the three treatment groups to verify the success of the randomization process.<sup>12</sup> Overall, households in the three groups seem largely balanced on a series of socio-demographic and economic indicators. Yet, they exhibit a few differences regarding agricultural production-related characteristics, which need to be considered in the outcome estimation framework. Moreover, ISFM practices are not necessarily new to farmers, since some were used prior to the interventions, though mostly to a modest extent.

## _Table 1 about here_

# Key outcome variables

Since the key concern of this study is the increase in ISFM adoption, we measure how many ISFM practices households adopted in the preceding harvest cycle (2017), and therefore assess the _number of ISFM technologies adopted_ , ranging from zero to five. We use this variable despite the fact that lime is not relevant in one of the regions (and thus, no farmer in Tigray reaches a value of five), but provide robustness checks verifying that implications do not change, if we exclude lime and employ a zero to four measure instead. Since the complementary use of the practices is pivotal to ISFM, the second main outcome is the _integrated adoption of the full ISFM package_ , assessed with a binary variable that measures whether a farmer has used all four quickwin practices in combination on at least one plot. This variable excludes the use of lime, since adoption would otherwise always be zero in Tigray.<sup>13, 14</sup> Furthermore, the adoption of the individual ISFM quickwin components is of interest, which are _compost_ , _blended fertilizer_ ,

> 12 Table 1 shows those variables that are used as additional covariates in the adoption and knowledge regressions, Table S4.1 in Online Appendix S4 presents further balance checks on selected household, farming and community characteristics 13 To check sensitivity of the results with respect to this definition, we
country size using GDP from the Penn World Tables and the real exchange rate. We use the bilateral real exchange rate between France and other countries using producer prices of France and importer countries from the International Financial Statistics (IFS) of the IMF. Finally, to compute the proxy for foreign competition at the extensive and intensive margin, we use the BACI dataset at the HS6 product level by country of origin.<sup>14</sup> In order to keep a constant sample throughout the paper and to establish the stability of the point estimates, we only consider firms that have information on all control variables. In the main specifications, this leaves us with around 4,900 firm-country pairs for the 9 Asian countries in the period 1999-2005, a total of 34,327 observations.

To perform the robustness check exercises, we use two additional firm level datasets. First, to identify French multinational firms, we match our main dataset with firm level dataset on multinational groups located in France from the _Enquete Echanges Internationaux Intra-Groupe_ produced by the French Office of Industrial Studies and Statistics (SESSI). These data provide a good representation of the activity of international groups located in France. They account for around 82 percent of total trade flows by multinationals, and for 55 and 61 percent of total French imports and exports respectively. Second, to add information on firms’ characteristics, we merge our main dataset with the Annual French Business Surveys (EAE), available from INSEE. The EAE survey is conducted every year and provides detailed firm-level information for all French firms with more than 20 employees whose main activity is in manufacturing. This survey allows us to have information on firms’ employment and labor productivity (value added per worker).

# _Market Access: firm level tariff measures_

To identify the impact of Asian’s trade liberalization on firms’ export patterns, we use tariffs at the HS6 product level to construct a firm level measure of tariff which varies by year and country of origin.

11
for example through an additional offtake contract offering to supply resources which are then deployed as collateral for repayment of the sovereign’s loan).

Only 15 of the 30 loans analyzed could be identified on the World Bank’s Debtor Reporting System. Of these 15, 11 loans could be clearly matched and 4 could be partially matched with the reporting provided in the DRS.35 There are several possible reasons for why these debts were missing from the system. Reporting coverages and definitions vary by country, and as discussed above, loans borrowed by SOEs (or joint ventures that involve state participation) may not be centralized and reported to DRS. In addition, some types of financing may have not been treated by the reporting country as a debt instrument, but rather as an advance payment to a supplier. The absence of information on collateral features in the DRS may have also led to misreconciliation, if there were discrepancies in reporting across sources.

Among the loans not found in the DRS, many could be directly verified in government reporting. For example, DRC’s public debt report provides details of Sicomines loan under the heading of external loans with government guarantees. South Sudan and Republic of Congo report intermittently on the debt that they have outstanding with commodities traders. Angola’s 2018 sovereign bond prospectus provides details of Sonangol’s CDB loans.

36 The Sicomines loan stands out as the sole RBL transaction for which the loan contract is publicly available.

The lack of transparency on RBLs has a number of implications. First, these deals are often highly complex and are therefore susceptible to receiving a poor evaluation of the risks that they entail. Second, given limited competition in providing such loans

> 31 https://eiti.org/chad

> 32 https://eiti.org/news/republic-of-congo-from-disclosure-to-diagnostics

> 33 IMF Country Report No.19/286, statement here: <u>https://www.africanews.com/2018/11/28/gecamines-publishes-a-comprehensivereport-in-response-to-outrageous-and-systematic-allegations-of-certain-ngos/</u>
variable bias. The second challenge is potential reverse causality that, all else equal, countries that actively seek a green economy have high acceptance of carbon pricing and are more likely to have related policies implemented. The endogeneity problem may bias the OLS estimates and generate misleading results. Rafaty, Dolphin and Pretis (2020) develop a new database of coverage-weighted carbon prices and employ a generalized panel synthetic control estimator to estimate the average effect of carbon pricing on emissions in 39 (mostly European) countries from 1990-2016. They conclude that the introduction of carbon pricing reduced the growth rate of emissions by 1-2.5 percentage points on average in their sample.

Studies focusing on a particular country use a variety of techniques to deal with identification challenges. Floros and Vlachou (2005) examine the response of manufacturing industries (two-digit level of international standards for industrial classification) to a carbon tax in Greece by employing the two-stage translog cost function to time series data during the 1982–1998 period. They find that the carbon tax reduces CO 2 emissions in manufacturing industries in Greece, in part by reducing demand and in part by causing substitution of petroleum fuels with electricity and substitution of energy with capital. Martin et al. (2014) finds a strong negative impact of a carbon tax on energy intensity and electricity use in manufacturing firms in UK. Aghion et al. (2016) investigate, using patent data from 3,412 automobile firms and individuals between 1965 and 2005 across 80 patent offices, how a carbon tax helps reduce CO 2 emissions in the long run. They find that clean innovation is stimulated by increases in fuel prices, whereas dirty innovation is depressed. They also show strong path dependency, which locks economies into high levels of carbon emissions, even after introducing a mild carbon tax or R&D subsidies for clean technologies.

Much empirical work on the effect of introducing a carbon tax relies on the assumption that consumers or businesses respond to a carbon tax in the same fashion as they would to an equivalent change in the
ing sector is relatively high. The rm's capital wedge in the 90th percentile of the productivity distribution is 4 (U.S. Industry Share) or 3 (Brazilian Firm-Specic Share) times greater than the capital wedge for rms in the 10th percentile. The rm's labor wedge is considerably smaller, varying between 2 (U.S. Industry Share) and 2.75 (Brazilian Firm- Specic Share). De Vries (2014) analyzes the existence of resource misallocation in the Brazilian formal retail sector. The author uses rm-level data from the National Retail Survey (Pesquisa Anual de Comércio, PAC) constructed by Instituto Brasileiro de Geograa e Estatística (IBGE). The author applies the Hsieh and Klenow (2009) methodology to study changes in resource allocation in Brazil's retail sector during the period from 1996 to 2006. The ndings suggest there are large potential output gains from the reallocation of resources to the most ecient retailers. Improvements in resource reallocation may increase TFP levels by a factor of two. However, he does not nd any allocative eciency improvements for the retail sector between 1996 and 2006. Brazil opened up its retail sector in the World Trade Organization's 1995 General Agreement on Trade in Services, and also within MERCOSUR. Furthermore, the participation of foreign capital in Brazilian retail rms was freed from restrictions in the Sixth Constitutional Amendment of 1995. It could thus be expected that these reforms would result in a productive reallocation through the expansion of modern retail chains and the growth of small successful retail businesses. According to De Vries (2008), however, the Brazilian experience suggests a dierent pattern. Retail chains did not replace mom-andpop stores during the period following reforms. The author argues that this may be partly explained by business regulations, such as zoning laws, and diculties in setting up national distribution systems because of the low quantity and quality of rail and road networks. The limited role of reallocation in Brazil's retail sector may