rafmacalaba/data-use-mentions-extended huggingface dataset:
months. To adjust for price variations across time and space, spatial and temporal price deflators are used. Finally, to adjust for variations in household size and composition, consumption expenditure is divided by the officially used adult-equivalent scales, which are based on calorie requirements and vary by age and sex. The national poverty line, which is 7,184 birr per day per adult equivalent, is used. The poverty line is based on a food basket that is required to achieve the minimum daily calorie requirement – 2,200 kilocalories per adult in Ethiopia – and adjusted upwards to include non-food consumption.<sup>14</sup>

# **Descriptive statistics**

A number of household and community (woreda) characteristics are included as explanatory variables in the two-level consumption estimation. The household variables include household size and composition, wealth quintile based on a wealth index created from ownership of selected consumer durables, household head characteristics, main livelihood of the household, a proxy for household capacity to cope with a shock and a dummy for rural households. The woreda level variables include average distance to an all-weather road, proportion of households who are engaged in agriculture, proportion of household heads with at least secondary education and average dietary diversity. The source of data for both household and woreda level variables are the HCES/WMS. While the household characteristics are taken from the surveys directly, woreda level variables are derived by taking the average for those households sampled from each woreda.

Table 1 presents the summary of the explanatory variables. The descriptive statistics show that households are large (with an average household size of 6.4) and most of them (82%) live in rural areas. Close to a quarter of the household heads are female while only 3 percent of the household heads have completed secondary education. The most common livelihood is agriculture while less than 10 percent of the households depend on salaried employment.

> 14 FDRE (2012, 2017); Central Statistical Agency (2018).

12
Regulatory Governance database. The two indicators are therefore closer to measuring items such as whether regulators are independent from any external political influence, have a clear mandate, expertise (i.e. skills) and resources to operate.<sup>10</sup> Several other country-wide sources exist which deal with regulatory governance and transparency in some way or another. However, each of them appears to be somewhat remote with regards to the purpose of categorizing regulatory TFiS measures per our previous discussion above. They are (1) the Transparency International index; (2) World Justice Project that covers areas such as regulatory enforcement, open government and civic participation; (3) Polity IV database and index that measures a country’s democratic institutions; and (4) measures of the Freedom House index that covers more political items rather than regulatory proceedings. They are not taken up in our empirical section.

# **_Modal-specific measures_**

The more modal-specific concepts for TFiS measures that emerged as relevant relate mostly to Modes 1, 3 and 4. Empirical indicators for the latter mode are generally harder to find than empirical indicators of TFiS measures related Modes and 1 and 3. Potential TFiS measures relevant for Mode 1 centered around the issue of cross-border flows of data and the various policies that government have recently implemented to restrict the mobility of data between countries. There are two regulatory databases that 10 Various empirical studies have also used measures from the World Bank’s Governance Indicators database to assess the quality of domestic institutions and regulators and their impact on (services) trade and productivity in services. See for instance Beverelli et al. (2017), van der Marel (2016) and Iootty et al. (2016).

11
values for the fiscal year<br>covered by the Enterprise Survey. In cases<br>where the fiscal year spanned more than one<br>calendar year, the enrollment figure is the<br>weighted average over the calendar years<br>covered, where the weights used are the<br>proportion of firms covered in the calendar<br>year. In the second step, annual values were<br>taken from the first step and averaged over the<br>three years prior to the (final) year covered by<br>the Enterprise Survey. Three years average<br>was taken to avoid too many missing values of<br>primary enrollment rate.<br>Source: World Development Indicators,<br>World Bank.|
|Industry fixed effects|A set of dummy variables indicating the<br>industry to which the firm belongs. Industry<br>grouping here is at the 2-digit ISIC Rev. 3.1|

39
**Figure 2. Share of Students Reporting the Issue Occurs in Every Lesson or in Most Lessons, PISA 2015**

<!-- Start of picture text --> 60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>Students don't listen There is noise and The teacher waits Students cannot Students don't start<br>to what the teacher disorder long for students to work well. working for a long<br>says. quiet down. time after.<br>Tunisia Algeria Jordan Qatar United Arab Emirates Lebanon Non‐MENA<br><!-- End of picture text --> Source: Analysis using PISA 2015.

School safety drops sharply in MENA countries around and following the Arab Spring by an estimated average of half a standard deviation. Countries with the lowest levels of school safety, Syria, the Republic of Yemen, Tunisia, Morocco and Egypt, experienced, or are still experiencing, the aftershocks of the 2011 Arab Spring. The situation in Egypt, for instance, is striking. In 2007, the school safety index for Egypt was high—0.25 standard deviation above the world average. Egyptian schools were among the safest, even safer than schools in Canada, which are considered to have a fairly safe learning environment.<sup>4</sup> However, in TIMSS 2015, Egypt ranked among the least safe education systems, with a safety index approximately 1.25 standard deviations below the international average. These results coincide with a one standard deviation drop, over eight years, in the classroom disciplinary climate (see Appendix A, Table 6). In fact, trends in school safety indexes are quite consistent with what is observed when measuring classroom disciplinary climate. For instance, school safety indexes are mirrored in the disciplinary
# **_Have governments complied with the rules?_**

**Consider first compliance** . As noted above, it is possible for rules to influence fiscal policy even if compliance with them is low (and it is also possible for them to have little influence even if compliance is high). The crucial question is the counterfactual one: what would have happened in the absence of the rules? The available data are not sufficient to answer this question. Nonetheless, it is useful to have an idea of the extent of compliance, because a high rate of noncompliance is suggestive of problems. Given the complexity of fiscal rules—including the existence of escape clauses and subtle differences in accounting—it is difficult to provide definitive evidence even about this question.

**A quick review suggests that fiscal rules in the Western Balkans have been followed a little more often than not.** The evidence is set out in Table 3, in which green shading shows compliance with the rules, red shading shows noncompliance, and absence of shading implies that no rule was in force. The table is rough in several ways. Only the debt and overall-deficit rules are considered; no account is taken of rules on current and primary deficits, expenditure, and guarantees. The data on actual debts and deficits also come from the World Bank’s database, and may differ in some respects from the data used domestically to define the debts and deficits that are subject to the rules. Third, no account is taken of escape clauses and other subtleties in the rules. Subject to the provisos mentioned above, the table suggests the following:

- Albania has followed its debt rules in most years, though the original debt rule was repealed as soon as it was breached.

- Kosovo has followed its debt rule every year and its deficit rule most of the time.

- Montenegro has generally not complied with its debt and overall-deficit rules.

- Serbia has usually not complied with its debt rule, but, according to the
positively and significantly associated with the implied privatization sales value.

In an earlier 1999 paper, Wallsten estimated non-sequentialized models for 30 Latin American and African countries. The effect of having a regulator was never significant at the 5% level or better for any of the telecom indicators. However, a variable interacting privatization and regulation was positive and significant for mainline penetration, number of payphones and amount of connection capacity. This model included a variable intended to capture competition in fixed line services. However, the proxy – the number of wireless operators owned by a company other than the incumbent – is a relatively poor one.

# **5.3.3 Gutierrez (2003)**

Gutierrez (2003) concentrates on estimating the impact of regulation on telecom outcomes in 22 Latin American and Caribbean countries. As with the other telecom papers discussed above, he uses panel data techniques which he applies to the experience over the period 1980-1997.

The main importance of this paper is that:

- (a) the regulatory variables used are an <u>index</u> (or sub-indices) derived from a number of characteristics of regulatory governance;

- (b) it reports tests for endogeneity of competition, privatization and regulation; and

- (c) it reports results for static and dynamic panel models as well as testing for fixed versus random effects

The index used in this paper is a 7-item index with each element being given a 16.7% weight. The index is made up entirely of legally defined measures. There are no items on regulatory practice (e.g. turnover of regulatory commissioners) or quality, not least because of the problems of collecting such data for other than a very small sub-sample of countries.

The measures in the index are:

- 1) A Legal factor – whether the telecom regulator (for countries where an independent regulator is present) was established by a primary law or a decree;

- 2) A Separation factor – whether operating and regulatory factors were separated, irrespective
the preceding 30 days (Table 1, Panel C).

Balance tests indicate that treatment and control assignment groups had largely similar baseline characteristics, except for the type of trade they did and their experience with GBV. At a 10% level of significance, there were 5% more exporters among traders randomly selected to receive the training than among those assigned to the control group (Table 2). This could give incentives to traders from the treatment group to cross the border early at unofficial morning times, to avoid meeting with border officials, and sell their products in the neighboring country to avoid paying fees on their return border crossing. However, there was no significant difference in the border crossing times between the assignment groups at baseline (Table 3). While Table 3 shows no significant treatment-control differences in trader knowledge, crossing patterns, or bribes, it does reveal an imbalance in the incidence of GBV. Approximately 19% of traders assigned to the control group

> 15A comparison of this sample with DHS data from DRC and Rwanda suggests that these traders’ households are at a similar welfare level to other urban households along the border (Garc ́ıa Mora and Roshan, 2013).

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Table 3: Exogenously Calibrated Parameters

|Parameter|Value|Target/Reference|
|---|---|---|
|_σ_|5|Simonovska and Waugh(2014)|
|_{βa_,_βm_,_βs}_|_{_0.92, 0.86, 0.95_}_|Share of Energy in Production, 2010|
|_{ωa_,_ωm_,_ωs}_|_{_0.204, 0.272, 0.524_}_|Value Added Shares, 2010|
|_ν_|0.93|Expenditure Share in Electricity, 2008-2009|
|_θ_|_−_0.30|Expenditure Share in Housing, 2002-2009|
|_γ_|0.47|Literature (see the text)|

Calibrated values correspond to Brazilian data, except when taken from the literature.

**Construction of the Road Network of Brazil.** The road network is defined as _R ≡_ ( _E_ , _V_ ) where _E_ is a set of edges or roads, and _V_ is a set of vertices or road junctions. Each microregion is associated with a point in the plane corresponding to its centroid, which we express as the set _M_ . The set of road junctions _V_ and the set of microregions centroids _M_ do not need to coincide, so to construct an adjacency matrix of the network joining _V_ and _M_ we choose to shift points in _M_ to the closest point in _V_ that is still within the microregion. That is, for each _mi_ ( _xi_ , _yi_ ) _∈M_ , we associate a _vj_ ( _xj_ , _yj_ ) _∈V_ such that

**Definition of Microregion Centroids.** Instead of using geographical centroids, we opt to choose a point of economic significance. To do so, we use data from the Brazilian Institute of Geography and Statistics, IBGE ( _Instituto Brasileiro de Geografia e Estatistica_ ) on the registration of selected locations, which offers georeferenced data on cities. A location is
was taken from World Bank 2010; industrial-tariff data is for 2006 and was based on World Bank 2006; residential-tariff data is based on 2011 estimates provided by World Bank staff.

Power outages are a huge problem in Sierra Leone. The country records 46 days of power outages per year, more than four times the level observed in other low-income and fragile states in Africa. Erratic power supply in Sierra Leone has repercussions throughout the economy. Due to frequent power outages, firms lost as much as 7 percent of their sales in 2009, which is greater than in other fragile African states (table 3). Mining companies in the Mano River region reported that power was a serious constraint to their productivity and that they relied on their own generators (World Bank 2010b). Simulations suggest that inadequate power generation corroded as much as 0.10 percent of per capita growth in Sierra Leone over the past decade.

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Direct transfers Indirect taxes Subsidies<br>Education Health Net balance Total Balance<br>Note: The net balance of a household is calculated as the difference between consumable income and market income plus<br>pensions and is equal to all transfers and subsidies received by the household minus taxes paid by the household.<br>Source: World Bank Group staff estimates based on ENPH 2017<br><!-- End of picture text -->

## i. Taxes and social spending

A relevant question for public policy decision-making is whether each of the tax interventions and combinations thereof helps reduce inequality. The Kakwani indicator may be used to answer this question. If the Kakwani indicator is positive, then the intervention under consideration is progressive. In case the coefficient is negative, then it is possible to state that the policy is regressive and therefore contributes to increasing inequality. It is important to analyze each of the policies individually, since, by doing so in the aggregate, it may be possible that the positive effect of another compensates the negative effect of one policy. It should be noted that the progressivity or regressivity of a policy does not imply that it is good or 33
The results are reported in the fourth column of Table 4A. The group variables have the expected signs. Firms acquired by foreigners are more likely to belong to debt-ridden groups and less likely to belong to groups with large assets. This suggests that firms belonging to capital constrained groups are more likely to have foreign ownership.

In the final column of Table 4A, we include the export share of the firm's output in the regression equation. There are two reasons why export share might positively impact foreign ownership. First, firms with a large export share may benefit more from foreign ownership if foreigners have greater access to global markets. This follows the case in the theoretical section where the foreign parent adds more value to the target than a domestic parent would add. Second, foreigners may have better information about firms which also serve their home market. This is akin to a lower transaction cost because less information about the target needs to be acquired by the foreign parent, which implies that a Korean exporting firm is more likely to be acquired than an otherwise similar Korean firm. We find that firms that export a large share of output are more likely to attract foreign investment.

Next we use the 1999 data to test the robustness of these findings and further distinguish portfolio investors from merger and acquisition activity. First, we examine how firm characteristics affect the likelihood of receiving positive foreign investment, i.e., the dependent variable is 1 if foreign ownership is greater than 0. Second, we estimate the likelihood of receiving foreign portfolio investment, defined as foreign investment between 0 and 10 percent. Third, we use the same binomial variable defined above (foreign investment greater than 10 percent) on the 1999 ownership data. And finally, we evaluate a more restrictive binomial variable, which is 1 only if the firm is known to have been a target of foreign acquisition in the past or has foreign ownership share exceeding 25 percent.

The results are reported in Table 4B. The first column reports the results for the likelihood
ital market imperfections that brings finance misallocation down from the median to the 25<sup>_th_</sup> percentile of the cross-industry distribution of financial misallocation would trigger aggregate productivity gain of 5.2% on average, and an extra 1.2% gain in industries with a higher dependence on external borrowing. We found reassurance for the plausibility of these findings in that they fall in the ballpark of the gains found in macro-development studies under very different quantification strategies.

# **6. Conclusion**

Financial frictions play an important role in determining total factor productivity. In this study, we advance the extant literature by providing new evidence on the global properties of financial misallocation and its ability to account for the misallocation of real resources that is observed in the data.

In light of a promising new methodology to measure finance misallocation and its narrow application to few countries, an important goal of the paper is to assess whether such methodology would deliver reasonable implications for the distribution of finance misallocation across countries with different degrees of financial development. Such an assessment is challenged by the data requirements: one must be able to count with firm-level databases offering a wide international and sectoral coverage, as well as rich information on the firm’s balance sheet. In this paper, we took this challenge confronting the new methodology with the ORBIS database, arguably the only suitable database for the task.

We confirm that the novel measure of finance misallocation proposed in Whited and Zhao (2021) varies systematically with a country’s economic development in the direction that one would expect: finance misallocation is twice as severe in the poorest than in the richest countries in our sample.

We also validate a number of well-rooted conjectures in the literature concerning the distribution of financing costs across firms of different ages and sizes. Based on the theoretical implication that younger and smaller firms are more likely to be financially constrained, we show that this is actually the case under our measure of theory-based 33
completing both training and coaching. The magnitude of

> 9 In October 2010, qualitative data were collected to gather students’, coaches’, and professors’ perceptions on the implementation of the intervention.

> 10 Detailed contact information was collected in the baseline surveys. This included students’ address, email address, landline and cell phone numbers, as well as their parents’ address, landline and cell phone numbers. Most students register at employment offices upon graduation, and contact information (phone numbers and address) from the employment office database was also collected and merged with the data (see World Bank and MFPE, 2012). 11 The attrition rate is lower to the one achieved for other surveys in the country. For instance, the attrition rate for the 2005 Tunisia graduate tracer survey was 11 percent. Oostenbeck et al. (2008) have an attrition rate of 56 percent in their study of entrepreneurship education for university students in the Netherlands.

> 12 Attrition in the full baseline sample was 7.2 percent at follow-up. At baseline, 10.1 percent of applicants could not be reached for the complementary phone survey conducted in January and February 2010. Combined attrition in either this baseline phone survey or the follow-up survey collected in 2011 is 15.9 percent. Attrition in both surveys was 1.4 percent. All of these attrition indicators were balanced across treatment and comparison groups.

> 13 Table 2 is presented for the effective sample used for estimations and composed of the 1,580 students that could be tracked at follow-up. Results are almost identical when using the full baseline sample of 1,702 students.

> 14 For example, participants seemed to be on average older and had more years of experience than non-participants.

11
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