rafmacalaba/datause-encoder-data huggingface dataset:
cross-sectional logits regressions to estimate whether the percentage of voting rights held by the controlling blockholder affect the probability of listing in the U.S. as a level 2 or level 3 ADR.

The table below adapted from Table III of their paper reports the marginal effects and shows that a one percentage point increase in the control rights held by the controlling blockholder decreases the probability of listing in the U.S. by 0.8%. Further, greater the separation of control rights and cash flow rights or higher the control rights held by the management group and its family, the value of private benefits is much higher to the controlling blockholder and the firm is less likely to be listed on a foreign exchange. . Overall, their results strongly suggest that ownership of control rights and the control wedge are strongly negatively associated with the probability of having a U.S. exchange listing.

The direct empirical evidence on the bonding view however has been mixed. Consistent with the bonding view, Doidge (2004) finds that cross-listed firms have lower voting premia and Reese and Weisbach (2002) show that firms from countries with high shareholder protection list in the U.S. to raise capital while those from weak shareholder protection countries list in the U.S. to bond themselves to better corporate governance. Other studies such as Licht (2003, 2004) and Pinegar and Ravichandran (2003) find no support for bonding. Siegel (2005) also points out that there are limits to the enforcement of U.S. securities laws and finds that cross-listing in the U.S. did not deter Mexican firm insiders from expropriating corporate resources.

Perhaps the piece of evidence that is most at odds with the bonding view pertains to increases in corporate valuation following internationalization. Doidge, Karolyi, and Stulz (2004) find that firms cross-listed in the U.S. have a Tobin‟s q ratio that is 16.5% than the q‟s of domestic firms. However, Gozzi, Levine, and Scmukler (2008) examine the valuation effects associated with internationalization and find no evidence of either an enduring effect on Tobin‟s q or an increase in the value of international firms relative to domestic firms. While they find no support for the bonding view their results are consistent with key predictions from segmentation and the market timing theories. Consistent with segmentation theories, Gozzi et al. (2008) find that firms expand after they internationalize and grow relative to domestic firms that have not lowered their cost of capital. Consistent with market timing, they find that Tobin‟s q rises before internationalization and then quickly returns to the pre-internationalization level. However, they 46
<!-- Start of picture text --> n<br>p= ». Di Si<br>i=1<br>‘ ‘<br>Sit=o0 + T Sit=T Pit=o0 + T Pit=T<br><!-- End of picture text --> i=1 <!-- Start of picture text --> i=1<br><!-- End of picture text --> <!-- Start of picture text --> (<br><!-- End of picture text --> P Pi t=T t=0 i <!-- Start of picture text --> (Change in<br>i outputper |<br>‘__capita (Y/N) __}<br>fLo | dt oe Aggregate<br>' Changes in 1 {Changes in the | ' Changes in the } decomposition<br>aggregateper workeroutput(Y/E) !| I,_employmentrate | ! demographic!strucutre<br>( Within-sector| {Between-secor|<br>| component | |. _component !<br>(“Static=) f Dyanmic%<br>i reallocation | | reallocation ; Sectoral<br>| effect | | effect| decomposition<br>| Roleof 1; Roleofeach: {Roleofeach |! each |<br>Leach sector :|sector=; 'sector_! | sector |<br><!-- End of picture text -->

Source: Based on World Bank (2012).

n Ae = >». Ae; i=1
on public funding, 60 percent of TPOs in high-income countries get 75 to 100 percent of their funding from public sources. In low-income countries it is close to 80 percent of TPOs that get 75 to 100 percent of their funding from public sources. It is 67 and 80 percent of TPOs in lower and upper middle income countries that get 75 to 100 percent of their funding from public sources.

# **5 Adaptation of TPOs during the COVID pandemic**

The evolution of TPOs’ budgets around the COVID pandemic shows some interesting patterns. The top panel of Figure 8 shows the distribution of TPOs’ budgets during the period 2018-2021, and the bottom panel decomposes the distribution by income group.

In the top panel the distribution seems to be quite stable across years, but the median budget declined in 2020 as the pandemic emerged. This is only marginally visible from the top panel of Figure 8 because of the scale of the y-axis, but the median budget is around USD 3.2 million before and after the global pandemic lockdowns (2018, 2019, and 2021) and only USD 2.7 million in 2020. The bottom panel shows that this pattern is mainly driven by a drop in the median as well as the entire distribution of TPOs’ budgets in low-income countries. Interestingly, the bottom panel also shows that there was a significant increase in the median TPO budget of high-income countries in 2021 (more than 50 percent), suggesting an intensified effort by TPOs in high-income countries as lockdown measures eased. Upper-middle-income countries also followed with a 35 percent increase in the median budget. However, the same was not observed in low-income and lower-middle-income countries where the median TPO budget remained at levels similar to those observed in 2020. A potential explanation for these trends is differences in fiscal space. Low-income countries with little fiscal space may have had no choice but to reduce TPO budgets. To explore this, we correlated changes in total government expenditure (using IMF data) with changes in TPO budgets and found a small positive coefficient, but that is not statistically significant, suggesting that this may not be the main rationale behind changes in TPO budgets during the pandemic.

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unintended consequences for financial systems of EMDEs. As one example, new risk weights, differences in risk measurement of parent banks and their subsidiaries, and stricter capital requirements may exacerbate the deleveraging process and disproportionately affect lending operations in EMDEs. However, as the implementation process is still ongoing, full impact assessments are not yet feasible.<sup>27</sup>

- _Higher and better capital:_ From the capital side, higher regulatory capital requirements induce banks to boost and enhance capital for each unit of risk-weighted assets. Basel III― implemented under the Capital Requirements Directive IV in Europe―dictates that banks achieve significantly higher capital levels coming into force in 2013 and be fully phased in by 2019. The latest available estimate of the aggregate capital shortfall is €478.9 billion which is based on December 2011 data and assumes full Basel III implementation per that date.<sup>28</sup>

- _Capital charges for market risk:_ Basel 2.5 has brought higher capital charges for market risks in banks’ trading operations, which may make these assets less attractive investment opportunities and could trigger de-risking and deleveraging of trading books.

- _Liquidity and funding requirements:_ From the liquidity side, banks are required to withstand sustained liquidity drains and seek more stable sources of funding. However, European banks still have an aggregate shortfall of liquid assets of €1.17 trillion and require an additional €1.4 trillion in stable funding.<sup>29</sup> Together, these liquidity requirements might induce banks to reduce assets, particularly long-term assets, if the necessary (longer-term) funding sources are not available or too costly as has been the case during recent funding stress conditions.

<!-- Start of picture text --> IV.3 Banks have been meeting stricter regulatory IV.4 ... which has directly affected credit standards,<br>requirements by both shedding risk-weighted assets particularly for larger firms<br>and raising capital...<br>Impact Basel III and other reg. req. on RWA and capital Impact Basel III and other reg. req. on credit standards<br>Net percentage of banks indicating increase relative to Net percentage of banks indicating tightening relative to<br>previous quarter previous quarter<br>50 40<br>40 35<br>30 30<br>20 25<br>10 20<br>0 15<br>-10 10<br>-20 5<br>-30 0<br>-40 SMEs Large firms Mortgages Other<br>-50 consumer<br>Risk-weighted assets Capital lending<br>2011H1 2011H2 2012H1 2012H2 exp 2011H1 2011H2 2012H1 2012H2 exp<br><!-- End of picture text --> 27 See FSB (June 2012) for a review of the unintended consequences of regulatory reforms on emerging and developing economies.

28 See EBA (September 2012). This figure includes the capital surcharge for systemically important financial institutions and the capital conservation buffer and assumes full Basel III implementation in December 2011. 29 See EBA (September 2012).

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perhaps prove useful in spurring countries into re‐examining and strengthening their excise tax systems; but they do not necessarily provide a good guide to sound policy for any particular country.<sup>9</sup>

# **Administration**

The main administrative problem with excise taxes on alcohol and tobacco is simply to collect the taxes due. This task is relatively simple when the taxes are imposed on large domestic producers and imports. However, the extent to which illicit products, whether smuggled or illegally produced in the country, may be substituted for taxed products needs to be carefully considered when setting excise tax. Some countries have encountered problems with the illicit production of various types of ‘home’ brews and spirits: such production is usually small‐scale in nature but it may also produce deleterious effects on health and even death.<sup>10</sup> In many countries, smuggling across borders has been a persistent problem with both cigarettes and highly taxed alcohol. Countries need to pay attention to the level of prices charged in neighboring countries when they set their excise rates. As Cooper (2007) notes, the ‘affordability’ of products (as measured by the share of income required to purchase a given quantity) may provide a useful measure of the extent to which increasing excise taxes may make administration more difficult by inducing more illicit trading. However, unless demand is completely inelastic and administration is so weak that access to illegal (unregulated) sources of supply is virtually unlimited and costs less than the tax, tax‐induced reductions in ‘affordability’ are most unlikely to result in so much smuggling that neither revenue nor health will improve after a tax increase.

Nonetheless, no country can set its tax rates in isolation from those applied in other jurisdictions to which the potential domestic consumers have access. In the European Union (EU), as in North America, there is considerable evidence that both legal cross‐border shopping and smuggling and other forms of tax evasion are encouraged by sizable differences in tax burdens. As mentioned earlier, the EU has established a uniform base for tobacco taxes. It has also established a basic ‘floor’ rate for both alcohol and tobacco taxes. Floor prices may serve to check on the downward pressure that the cross‐border option would otherwise exert on the domestic rates imposed on these goods, especially if the floors were high enough to reflect the (average) external costs of their consumption. However, the present EU floors appear not only too low for this purpose but have proved almost impossible to alter (Crawford,

> 9 For a related argument, see International Tax and Investment Center (2014).

> 10 Bird and Wallace (2010) note that in some instances home production may have some cultural significance as well as effects on employment that need to be taken into account.

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Column 2 shows that exporters have a limited employment response to increases in exports, which is also consistent with the model presented in Appendix Figure A.2. As in Table 5.2, the other explanatory variables associated with firm-level technology correlate with exports in the cross-section, but not in the time-series. This is because technology determines export status more than exporters adjust employment in response to rising exports.

**Table 5.3: Estimated Results of Employment and Export Sales**

|**Dependent Variable: To**|**tal Employment**<sup>1/</sup><br>1|2|
|---|---|---|
|VARIABLES|Between|Within|
|Export sales<sup>1/</sup>|0.083***|0.017*|
||-3.15|-1.87|
|Value of machinery<sup>1/</sup>|0.043|0.040***|
||-1.4|-3.18|
|Cost per worker<sup>1/</sup>|-0.036|-0.088**|
||(-0.32)|(-2.19)|
|Firms' age<sup>1/</sup>|0.502**|-0.133|
||-2.04|(-1.36)|
|Foreign ownership (yes=1)|0.129|0.181|
||-0.29|-1.07|
|Registered at the beginning (yes=1)|-0.021|-0.096|
||(-0.04)|(-0.50)|
|Foreign technology (yes=1)|0.896**|0.023|
||-2.16|-0.12|
|Imported inputs (yes=1)|-0.19|-0.214|
||(-0.46)|(-1.30)|
|Female intensity (yes=1)|-1.057***|0.034|
||(-2.74)|-0.33|
|Constant||-0.015|
|||(-0.16)|
|Year Dummies|No|Yes|
|Region Dummies|Yes|No|
|Sector Dummies|Yes|No|
|Observations|252|252|
|R-squared|0.503|0.153|
|Number of panelid|126|126|
|t-statistics in parentheses|||
|*** p<0.01, ** p<0.05, * p<0.1|||
|Note: Within refers to Fixed Effects.|||
|1/Measured in logarithms.|||

30 | P a g e
# Table 2: Salary Compressiona in Selected African Countries, 1970-1983

|Country/Level|1970|1975|1980|1983|Total Percentage<br>Change in Salary<br>Compression,1975-83|
|---|---|---|---|---|---|
|Malawi<br>Civil Service:<br>Undersecretary/unskill|ed<br>na.|33.0|30.0|29.8|-9.7%|
|NiReria<br>Civil Service:<br>Permanent Sec-retaryi<br>maskillet|2 1 9 <sup>b</sup>|17.6|15.4|9.2|-47.7%|
|Sudan||||||
|Civil<br>Service:<br>Deputy Undersecretary/<br>unskilled|13.0|13.1|10.8|9.3|-29.0Z|
|Zambia<br>Civil Service:<br>Undersecretary/lowest<br>salaried employee|19.2c|14.5|10.3|6.9|-52.4%|

a. Salary compression is measured as the ratio of basic starting salaried of the highest to the lowest skill categories for which data are available.

b. 1972 c. 1971 Source: Lindauer, Meesook and Suebsaeng (1987); Table 5.

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4 coordinating tax reforms: can lost tariffs be recouped?

to minimize or evade payments, which if substantial could render trade taxes part of a _first_ -best tax package.

In this paper, I combine trade theory, cross-national evidence, and an in-depth case study of a low-income country using a unique data set on all import transactions at the border in Nepal.<sup>8</sup> I find that low-income countries have had a mixed record of achievement in offsetting reductions in trade tax revenue. This is partly because of their weak enforcement of domestic taxes like Value-Added Tax (VAT). In principle, a strict enforcement of a positive, single-rated VAT with no exemptions is a highly effective form of modern taxation, and can negate substantial losses in tariff revenue. I confirm this by using a partial equilibrium model to simulate reforms using data from Nepal on tariffs and up to _ten_ additional domestic taxes imposed on more than 400,000 import transactions between January 1 and December 31, 2008.<sup>9</sup> The paper proceeds as follows. Section 2 uses panel data from selected lowincome countries to assess whether they have succeeded in replacing trade taxes with domestic sources over a period of 25 years. Given the limitations for countryspecific policy inference from cross-country regressions, sections 3 and 4 cover a country case study. Section 3 begins by adapting conditions for welfare-enhancing tariff cuts to a revenue-enhancing result from a _coordinated_ tariff and tax reform in the presence of an informal sector. Two sets of plausible policy reforms are then simulated: i) different tariff cutting approaches are matched by domestic tax reforms with and without the assumption of a large informal sector; and ii) tariffs and other discriminatory charges on imports from members party to the Agreement on the South Asian Free Trade Area (SAFTA) are eliminated with and without Sensitive Lists that exempt a subset of products from tariff cuts.<sup>10</sup> I check for

> 8 “Border” in this paper refers to a generic port of entry. In many countries, a substantial share of imports arrives by air into cities that may not technically be on the border.

> 9 In 2009-10, 22.5 percent of the government’s tax revenue was generated from tariffs on imports (Government of Nepal 2011).

> 10 Note that tariff cuts often take place as part of a broader package of trade policy reforms. Liberalization of trade policy implies more than tariff cuts, for example, the conversion of quotas into tariffs, elimination of tariff exemptions and trade-related subsidies, reform of state-trading monop-
Table 4: Effects of the share of immigrant classmates in the grade

||Nati|ves’|Nati|ves’|
|---|---|---|---|---|
||languag|e score|math|score|
||(1)|(2)|(3)|(4)|
|**Ttt 1Sh f iit**|-0.776|-0.751|-0.185|-0.191|
|**reamen : are o mmgrans**|(0.697)|(0.769)|(0.701)|(0.706)|
|**Treatment 2:Share of recent immirants**|-3.08***|-2.88***|-1.55|-1.49|
|**g**|(0.981)|(0.977)|(0.913)|(0.906)|
|**Treatment 3:Share of other immirants**|0.145|0.168|0.012|-0.027|
|**g**|(0.688)|(0.676)|(0.691)|(0.653)|
|Grade-level controls||✓||✓|
|Grade effects|✓|✓|✓|✓|
|School-by-year effects|✓|✓|✓|✓|
|Number ofgrade-level observations|12,053|12,053|12,053|12,053|

_Notes._ ***: significant at the 1% level, **: significant at the 5% level, *: significant at the 10% level. Robust standard errors clustered at the school level are reported in parentheses. Each row reports the coefficients from separate regressions of the effect of the corresponding treatment on natives’ average test scores. Controls for grade-level characteristics include: the share of students by level of parental education, the share of female students in the grade, the share of disadvantaged students according to the Dutch weighting system, the share of students that repeated a grade, the average class size in the grade, teacher’s years of experience, the total number of students in the grade and its square. Controls for linear school trends are also included. Estimates that do not control for linear school trends are quantitatively similar and available upon request.

51
# function.'

In this framework, the argument is that increases in public spending on PHC are effective in improving health status, while curative and especially secondary and tertiary curative services are not. This can rationalize an _increase_ in public funds spent on PHC as well as a _reallocation_ of the health budget towards PHC activities.

The argument for PHC, however, typically relies almost exclusively on the health production function (perhaps, modified to include accounting costs to generate MICE rankings). However, the actual impact of public spending is the product of all four terms in the above equations: _allocation of the budget, public sector efficacy, market impact on consumer demandfor services,_ and _health impact of services._ If any one of these is low, the total impact will be low.

# **_II) Impact of public spending and PHC on health status_**

Has public spending on health, and more particularly on PHC, promoted good health? We review three strands of evidence: first, country level evidence, second, the impact of facility availability on the health status of individuals, and third, the evaluation of projects and experiments.

Mortality is easy to measure while morbidity is not and (fortunately for people but unfortunately for research) mortality (except among the very young and very old) is rare.

7 One prominent economist when asked what an economist needed to know about the particulars of the production function in order to make sensible policy recommendations, responded "Convexity." 9
<!-- Start of picture text --> b. Saber Pro<br>Other Professionals Teachers<br>0.10<br>0.00<br>Quantitative reasoning Native language (Spanish) Foreign language (English)<br>-0.10<br>-0.20<br>-0.30<br>-0.40<br>-0.50<br>-0.60<br>Source: Authors’ calculations based on ICFES data.<br>Note: Confidence intervals at 95% added.<br>Z-score in Saber Pro<br><!-- End of picture text -->

# **3.** **<mark>Relative learning mobility</mark>**

<mark>Relative learning mobility is assessed by the change in z-scores in quantitative reasoning, native language and foreign language between Saber 11 and Saber PRO. Figure 2 illustrates such relative learning mobility for some selected university programs. The education majors are grouped into four categories according to their closest related emphasis: quantitative reasoning (mathematics, physics, electronics, etc.), native language (Spanish, social sciences, humanities, philology), foreign language (mainly English, although there are students of some other foreign languages), and others (preschool, arts, sports and others).</mark><sup>3</sup> The figure illustrates <mark>that after around five years of college, gaps in quantitative reasoning between school teaching programs and other (selected) academic programs widened considerably.</mark><sup>4</sup> <mark>That is, teachers’ skills in quantitative reasoning either relatively deteriorate or, another way looking at it, do not improve as much as those of other professionals.</mark>

> 3 See the online appendix for information on how we classified each major in education into each of these four categories: https://sites.google.com/site/cfbalcazars/misc

> 4 We selected those programs with the highest changes in their z-scores between Saber 11 and Saber Pro for our illustrative purposes.

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# 6 Education Financing and Impact on Learning

## 6.1 Officials in the EAP region overestimate public funding for education.

Overall, respondents from EAP countries believe that education financing levels are higher than they actually are, see Figure 16. This overestimation of education financing in EAP contrasts with other regions, where officials tend to underestimate education spending relative to actual spending levels. This finding remains consistent even when the education budget figure is taken into account instead of actual spending (World Bank, 2023). When asked about their preferred percentage of the total government budget to be spent on education, EAP respondents consistently provided estimates that exceeded their own perceptions of the current spending share allocated to education.

_Figure 15: Share of public funding for education of total public spending_ <!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>LAO PHL VNM IDN MNG Average Non-EAP<br>EAP<br>Estimated budget share Actual budget share Officials' preferred budget share<br>Share of public spending for education<br><!-- End of picture text --> Sources: CGD–World Bank survey, 2022. Survey respondents were asked two financing questions: (i) Now thinking about the overall government budget, roughly what percentage do you think is spent on education? and (ii) What percentage of the total - government budget do you think should be spent on education? Data on actual budget share from UNESCO for 2021 (http://sdg4 <u>data.uis.unesco.org/).</u> The magnitude of the misperceptions is larger in EAP than in other countries, and in a different direction. On average, the gap between the estimated share of the budget and the actual share of the budget is 4 percentage points in surveyed countries in EAP, while the gap is about 1 percentage point in the other direction in non-EAP countries. As a share of education funding, this gap is very large in real terms, as the average gap amounts to 25 percent of total actual public resources budgeted for education in the surveyed countries of the EAP region.

## 6.2 Officials’ belief in the need for additional public funding aligns with data.

Perceptions of the need for additional spending on education among surveyed officials align with available data on the actual needs of EAP education systems. Public spending for education in the EAP

> 16 Except in Indonesia, which constitutionally mandates 20 percent of the national budget be spent on education.

19