rafmacalaba/data-use-mentions-extended huggingface dataset:
high traffic (and high pollution). In Table A2, we show that we reach similar conclusions if we replace Median Road in the grid cell, with average residual pollution in the cell. This confirms that there are tangible benefits of being located in more polluted areas.<sup>37</sup>

**Road traffic provides access to customers.** Our final step is to provide support for interpreting the positive effect of being located in polluted areas as being generated by better access to customers, hence higher demand. To shed light on this, we leverage our firm survey, where we collected detailed information on how firms access customers. Panel A of Table 4 shows that marketing activities are extremely limited: only 7% of managers spend any money on marketing, and when asked about strategies they adopt to communicate the quality of their products to customers, about 60% say that they just talk to the customers directly, and 22% report engaging in no strategies at all to market their products. In addition, sales are

> 36This information is available for those firm owners who rent the business premises (rather than owning or using them free of charge), which is about 2/3 of the sample.

> 37The number of observations is lower in Table A2 than Table 3 because, as described in Section 4.2, information on pollution is available in 32 of our 52 sampled sub-counties, while road size is available in all sub-counties.

23
, and may face more stringent labor-market barriers. In other words, this group has relatively better housing conditions, but may lack short-term resources that reduce their consumption expenditure.

> 22 In summary, expenditure in these three categories is computed based on the quantities and prices of a selected list of items in each category. See more details about the computation of the consumption aggregate in Appendix A of the Somali Poverty Profile (Pape et al, 2017). A similar procedure was followed in the other countries of analysis.

26
Policy Research Working Paper 9457

# **Abstract**

Financial repression resurfaced in the wake of the global financial crisis and might become a common feature in the post Covid-19 world. To advance knowledge and inform policy advice, this paper presents a new database on interest rate controls, a popular form of financial repression, based on a survey of 108 countries, representing 88 percent of global gross domestic product. The data cover such aspects of interest rate controls as types of controls, legal basis, intended objectives, methodologies, and enforcement rules. In an attempt to provide a meaningful characterization of the data, the paper also provides a preliminarily estimate of the degree of bindingness of the interest rate control regime in a country and presents simple correlations with other financial repression policies.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at pcalice@worldbank.org; fdiazkalan@worldbank.org; and omasetti@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
|51|27|
|Croatia|66|67|33|
|Macedonia|62|21|29|
|Serbia|67|66|29|
|Turkey|56|68|38|
|_EU neighborhood_|_39_|_37_|_22_|
|Armenia|43|44|21|
|Georgia|41|35|26|
|Moldova|40|26|21|
|Ukraine|32|44|18|
|**Total**|**50**|**46**|**25**|

Source: BEEPS survey and authors‟ calculation. Note: This table reports means for each indicator of credit use by country. Observations are weighted to account for stratification of the sample with respect to industry (manufacturing, retail and other services) as well as frim size. Bank loan is 1 if the firm currently has a loan or a credit line from a bank and 0 otherwise. Overdraft is 1 if the firm currently has an overdraft facility from a bank and 0 otherwise. Leverage is the share of firm investment in 2007 which was financed by bank credit.

Which types of firms in Emerging Europe are indebted to banks? Panel A of Table 4.2 shows that the **incidence of bank credit** is related to firm size, financial transparency and economic activity. In line with existing evidence on information asymmetries and credit access (see e.g. Brown et al. 2009), small firms (those which have less than 50 employees), young firms (less than 15 years old) and firms which are not financially transparent (i.e. do not have their accounts audited) are less likely to have bank credit. Firms with export activities are also much more likely to have bank credit than firms which sell purely to their domestic market. The effects of firm size, financial transparency and export orientation are also economically important: Roughly 60% of large, audited and exporting firms have
hedge against the Nordhaus border taxes. The drop of GDP and consumption as a result of external trade sanctions is similar no matter what diversification path they were on (figure 5). Gradually, however, these strategies combined ensure the fastest increase in the rate of growth of GDP and consumption compared with alternatives. By 2050, the combination of asset diversification and cooperation results in households’ consumption and GDP being around 9 and 5 percent higher respectively than under free-riding and traditional diversification scenarios. Asset diversification reduces GHG emissions slightly below traditional diversification for any cooperative policy choices, but only together with domestic cooperative carbon taxes, asset diversification also reduces emissions against BAU (figure 3).

Asset diversification is also a robust economic strategy. “Robust” means that in the long run, asset diversification outperforms traditional diversification by all macro-economic indicators and under all external policies that have been simulated here. With or without cooperation, it robustly offers higher long-term consumption and GDP for the FFDCs as the world moves more rapidly toward decarbonization. It performs better than alternative economic strategies by two decision criteria. By the _minimax (least-regret)_ criterion, asset diversification minimizes the possible losses of consumption in the worst-case scenario, although it does not prevent consumption from being lower than in BAU. Asset diversification also maximizes possible consumption gains in the bestcase scenario, in which FFDCs implement cooperative domestic climate policies through wellhead taxes, meeting the _maximax (highest gain)_ criterion. The only no-regret long-term strategy for FFDC consumers is cooperative climate action with internationally agreed-upon wellhead taxes.

_Figure 10: Long-Term Consumption in Fossil Fuel–Dependent Countries (% difference from BAU)_ <!-- Start of picture text --> 3.00<br>2.00<br>1.00<br>0.00<br>-1.00<br>-2.00<br>-3.00<br>-4.00<br>-5.00<br>-6.00<
(10%). The total scores of each of the three judges was then averaged to get a final score for each firm.

Our main follow-up data measure impacts approximately one year after the program started. We conducted a follow-up survey, which was answered by 180 firms (80%), with 87 control (77.7%) and 93 treated firms (82.3%) answering. This is supplemented by data from the traction sheets for an additional 14 firms, to give some follow-up data for 194 firms (86% of firms, 84% of control and 88% of treatment). Some of the firms only supplied partial follow-up data, resulting in item non-response on some of the financial measures, including our key outcome of export sales. However, Appendix B shows that we cannot reject equality of response rates by treatment status, and that the set of firms responding to the endline survey are balanced on baseline observables, as are those who supply export data.

This follow-up survey contains data on our main outcomes of interest for measuring whether the program enabled firms to gain more customers, increase revenues, and expand exports. We investigated whether administrative data sources could be used to provide more information on these outcomes. However, with firms from six different countries, it was not possible to link these firms to administrative tax data that could provide information on revenues. Export data, such as that included in the World Bank’s Exporter Dynamics Database, is not currently available for these countries in our follow-up period, is provided in an anonymized format, and most importantly, comes from customs data that only captures goods crossing borders, and thus would miss the service exports that the majority of firms in our sample specialize in. Hence survey data provide the only sales and export data available.

Our final source of follow-up data comes from a detailed scoring exercise we undertook on the digital presence of firms in December 2021, also one year after the program started (with a more basic version collected at
goals of the system if the online vote empowered traditionally advantaged demographic groups at the expense of the disadvantaged groups the PB process aimed to help.<sup>1</sup> Several studies have examined the Rio Grande do Sul PB vote, with studies covering the 2012 (Spada _et al._ , 2016) and 2014 (Halkin, Sjoberg and Mellon, 2017; Mellon, Peixoto and Sjoberg, 2017; Peixoto, Sjoberg and Mellon, 2017) votes. These studies allow us to track whether inequalities in Internet access and participation affect demands made through the PB process, and the outcomes of the PB process – that is, which investment projects get finally selected for implementation.

# **Digital divide**

Using the framework above, we first examine whether there is a digital divide in Brazil. Internet access in Brazil was 54% in 2015 (Statista, 2019), with notable demographic gaps in access. Using the 2005 census, Olinto (2009) found substantial education (more educated have greater access), age (younger people have greater access) and racial (white people have much greater access) gaps in Internet access but modest gender (men have slightly greater access) divides.

# **Digital divide → participant profile**

Both Spada et al. (2016) and Mellon, Peixoto and Sjoberg (2017) concluded that online voting’s availability substantially changed the electorate rather than merely providing a convenient option for voters who would have participated anyway. This means the online electorate’s composition affects the overall composition of participants.

The Internet access divide among Brazilians creates a potential for inequality, but this does not entail that participatory inequality will occur along the same lines. In this case, however, Mellon, Peixoto and Sjoberg (2017) show the differences between the online and offline voters in the 2014 PB vote (using online and offline exit polls) closely track the digital divide. They found online voters skewed younger than both offline voters and the general population, and
|Percent of governing body that belongs to: current students|0.00206***<br>(0.000635)||(0.0396)||
|---|---|---|---|---|
|General or specific knowledge test is an admission requirement|0.0339<br>(0.0260)|0.0249<br>(0.0253)|4.516*<br>(2.521)|0.163<br>(0.178)|
|Interview is an admission requirement||0.0478***<br>(0.0158)|4.977***<br>(1.477)|0.337***<br>(0.110)|
|Min. scores in HS GPA or national entry test is an admission<br>requirement|-0.0141<br>(0.0251)||||
|Student body, program, and HEI characteristics (PCA scores)|✓|✓|✓|✓|
|Noise controls, state- and field-fixed effects|✓|✓|✓|✓|
|Graduation year fixed effect||✓|✓|✓|
|Observations|22,663|7,177|6,827|7,089|
|R-squared|0.126|0.122|0.109|0.135|
|Mean dependent variable|0.303|0.707|53.52|4.791|
|Average wage (USD 2017 PPP)||||715.62|
|Adj R squared|0.124|0.117|0.103|0.131|

_Source_ : Own estimations using individual-level data from administrative sources and program-level data from the WBSCPS for Brazil. _Notes_ : This table shows coefficients from the (second stage)
In this paper, we have studied institutional and other macro determinants of prevalence of informal dependent employment, as well as informal self-employment in European countries. To our best knowledge, this is the first multi-country study which conducts such an analysis for Europe using direct survey data on work without legal contract rather than proxy measures of the size of informal dependent workforce. Accounting for joint determination of informal wage employment, informal self-employment, and unemployment is another distinctive feature of our analysis.

We have conducted analysis separately for three country groups: Eastern and Southern Europe, Western European and Nordic countries, and Europe at large. Table 10 summarizes the directions of the effects across country groups, two types of informal employment, and specifications. A plus sign in a row titled _X_ and a column titled _T_ for country group _G_ indicates that variable _X_ has a positive and significant effect on the share of informal employment of type _T_ in country group _G_ at least in some (typically, in all) specifications reported in that column, and never has a significant negative effect. Minus sign is interpreted in a similar way<sup>30</sup> . Zero refers to a variable insignificant in every specification (within column). In each column, superscripts indicate alternative specifications; entries without superscripts refer to variables common to all specifications<sup>31</sup> .

Many factors appear to work in the expected way (or at least in the same way when expectations are ambiguous) in all three country groups. Consistently with theoretical predictions, quality of _business environment_ (represented by Business Freedom, Investment Freedom, Trade Freedom, and Monetary Freedom) has a significant negative impact on prevalence of both types of informal employment when other factors, including GDP level and growth, are controlled. Noteworthy, the specific product-market-related factors which appear as significant differ between the two country groups: In Eastern and Southern Europe, these are Investment Freedom and Trade Freedom, whilst in Western and Northern Europe - Business Freedom and Monetary Freedom; when all
# **III.2 Methodology**

To look at the question of the effect of trade regulation, infrastructure development, and enterprise characteristics on export share, we use firm-level data for firm i in sector j in country k to estimate the following equation: The dependent variable is exports as percent of sales. Since, as noted previously, many enterprises do not export any part of their production, this variable is censored below at 0. Since a few, but not very many, enterprises export all their production, it is also censored above at 100. The model is estimated as a two-limit Tobit model, using standard maximum likelihood estimation.

One of the questions of interest is how enterprises’ exporting is affected by problems related to trade and customs regulations. The regressions therefore contain variables proxying for the obstacle that trade regulations impose upon the enterprise. The data that we use to measure the burden imposed by trade and customs regulations is the perception-based data described in the previous section.

One concern about perception-based data is that it might not accurately reflect reality. Although this is a concern, it is important to note that if perceptions have no base in reality, we would not expect to find any relationship between perceptions about trade and customs regulations and export decisions (i.e., if perception data was simply white noise, the coefficient on the variable would be statistically insignificant). However, there is some concern about endogeneity—enterprise managers with greater experience with dealing with customs and trade regulations (e.g., exporters) might have different perceptions about these regulations than enterprise managers without the same level of experience. Consequently, export behavior might affect perceptions rather than the reverse. Further, customs and trade regulations are likely to impose a greater burden (and hence actually be a greater obstacle) for exporters than nonexporters. Consistent with this, exporters are more likely to complain about trade and customs 12
is the interaction term significant (at the 10% level) and marginally so for DPCMREL (at the 10.7% level)<sup>4</sup> . The results in Columns (2) and (4) need not be surprising. If indeed connections are important, even mature firms which are also highly dependent on external finance would be able to get access to finance themselves, while lobbying to exclude others.

Second, the results in Tables VII and VIII are robust to omitting outliers. We checked this by running the basic regressions after cutting of the 1% and 5% tails of the distribution of the dependent variable, and using robust estimation techniques like Iteratively Weighted Least Squares regressions and Absolute Least Value regressions<sup>5</sup> .

Third, the results are robust to using an alternative measure of financial development: total domestic credit to GDP. The results are reported in Table X.

Fourth, to check the results we used three different corruption measures. We took an average of the Corruption Perceptions Index in the period 1996-1997<sup>6</sup> from Transparency International. In addition, we adopted the corruption index from Beck et al. (2000), which is an average over the period 1982-1995. Lastly, we used the KKZ index of Kaufmann, Kraay, and Zoido-Lobaton (1999). All indices are ranged on a scale from 1-10, where a higher score indicates less corruption. The number of observations differ however greatly over the specifications, because coverage of corruption indices is in general poor for the 1980s and early 1990s. Tabel XI presents the results.

The interaction terms with the Transparency International index and the KKZ index confirm the results. However, the Beck et al. (2000) indicator does not produce significant results when explaining DESTREL, despite the fact that all corruption measures are highly positively correlated.

21
assumptions about “true” WTP from the identified interval associated with a switch from one option to the other, by selecting either a single location or imposing a distribution of values within this interval. In many cases, either the midpoint or one end point of the interval is used, which results in artificially low variation and may introduce measurement error. In addition, observations that exhibit NSB must either be dropped from the dataset or modified to impose an arbitrary endpoint on the open interval.

The method that most closely respects the structure of the data is the interval regression approach proposed by Andersen et al. (2007), which employs a generalization of the tobit model. However, MSB observations remain a problem; the researcher has to make assumptions about the interval in which the subject’s WTP lies for MSB. Typically, the first and last observed switch are used, ignoring information from in-between switches.

In summary, existing approaches to MPL deal with choice error only incompletely. Moreover, the problem of order bias has been largely overlooked. This may be because in many contexts, uniform bias is not overly distorting: for example, in many applications of MPL, the main interest may be ordinal preferences or a simple sample split (e.g., into a more or less risk averse group). However, in WTP elicitation, the (cardinal) monetary value has meaning: a downward or upward shift of the distribution of elicited WTP due to bias may make the difference between a majority of the population expressing a positive vs. negative valuation for a service or policy.

In the next section, we propose a two-pronged approach to MPL design and estimation that uses randomization to detect order biases and a random utility model that includes fixed effects and order indicators to explicitly account for choice error and bias. In section 3.6 we use our approach to show with data from South Africa that (i) there is significant order bias in MPL data that affects estimated WTP, and (ii) many existing
A possible alternative would be to use an absolute measure of inequality – such as the variance, or the absolute Gini coefficient<sup>9</sup> - which are ordinally invariant in the standardization. The variance of a post-standardized distribution ( ), for example, is a monotonic (linear) function of the pre-standardization variance ( ), and does not depend on any other moment of the prestandardization distribution: The variance is seldom used as an inequality measure because it is scale-dependent: it increases with the mean. It also fails the transfer sensitivity axiom, by placing greater weight on transfers higher up the distribution than to those lower down. While these are not trivial concerns, it appears to us that in the context of distributions of educational achievement, they are less severe than violating either the transfer principle itself (like the percentile based measures) or ordinal invariance in the standardization, which allows an apparently innocuous operation to fundamentally alter distributional rankings. The variance (and the standard deviation, of course) is a meaningful measure of inequality in the precise sense that it satisfies axioms (i)-(iii) above. The variance is also additively decomposable, and shares of the variance obtained from some such decompositions can be shown to be cardinally invariant to standardization, as discussed in the next section. These properties will prove instrumental in adapting an intuitive measure of inequality of opportunity to the context of education.

For these reasons, we adopt the variance and the standard deviation as our basic measures of inequality of educational achievement. Because users of this kind of data are generally more comfortable with the standard deviation than its square, this is the variable we report. Columns 3-11 in Table 1 present the mean and standard deviation (S.D.) of the standardized test scores in reading, math and science, in that order, for all 57 countries in the 2006 PISA surveys. The column immediately to the right of each S.D. column reports its bootstrapped standard error. Among the countries with higher
664|13|685|4|429|0.006|687|7|682|
|18|Nanyang Technological U|Singapore|0%|8|456|17|489|46|541|4|429|0.021|541|16|596|
|18|Nat Cheng-Chi U|Taiwan<br>(China)|41%|17|255|18|468|50|528|4|429|0.023|529|19|562|
|18|Nat Coun of Appl Econ Res|<br>India|17%|6|541|10|616|24|644|4|429|0.011|644|8|671|
|18|<br>Nat Tsing Hua U|Taiwan<br>|0%|5|619|16|504|73|467|4|429|0.033|469|43|388|
|||(China)||||||||||||||
|18|Pontificia U Catolica de Chile|<br>Chile|13%|8|456|16|497|62|496|4|429|0.028|497|24|504|
|18|U Buenos Aires|Argentina|13%|8|456|16|500|70|475|4|429|0.032|476|23|522|
|18|U Philippines|Philippines|18%|11|370|25|398|107|402|4|429|0.049|403|58|331|

Notes: Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case