rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Revenues Estimates**

||MK0|MK10|MK20|MK50|MK100|MK300|MK500|
|---|---|---|---|---|---|---|---|
|Price|0|10|20|50|100|300|500|
|Demand vs.MK0<sup>17</sup>|0.00|-0.28|-0.42|-0.61|-0.75|-1.26|-1.34|
|Busrides tomarket town inonemonth|1.35|1.07|0.93|0.74|0.60|0.09|0.01|
|Busrides tomarket townperday|0.023|0.018|0.016|0.013|0.010|0.002|0.000|
|AdultPopulation|1200|1200|1200|1200|1200|1200|1200|
|Total adult market town bus rides per da|y 27.93|22.18|19.29|15.22|12.38|1.96|0.21|
|Total Revenue|0.00|221.76|385.79|761.22|1237.68|587.14|103.45|
|Marginal Per Passenger Revenue|0|10|8.50|24.66|38.50|-332.40|-2337.85|
|AveragePer Passenger Revenue|0|10|20|50|100|300|500|
|Projected Profit (Daily)|-13583|-13296|-13099|-12681|-10082|-10624|-11097|

Notes: Values in Malawi Kwacha. Demand estimates are based on coefficients from unconditional regression of the total phase 2 bus rides on a set of dummies for each price level.

Our findings suggest that subsidizing a daily bus to the market cluster included in this study would be costly. We see that a daily subsidy of MK10,082 (US$67) would be at least needed to achieve a breakeven point for the bus operator (for a roundtrip). This finding is crucial since there is a usual plea to call for competition in transport services in rural areas in order to curb transport prices. This experiment demonstrates that competition, in this case, is impossible to get and, even worse, one operator can never break-even.

The requisite subsidy is due to both supply and demand factors. On the cost side, high fixed costs of bus rental and discontinuities in costs caused by the discrete jump in costs once a bus becomes full drive up the cost of bus operation. On the revenue side, we find that population density and significant price sensitivity drive down aggregate demand for the bus service to the point where the bus route would not be profitable for a private firm<sup>18</sup> .

> 17 Calibration over the full sample.

> 18 This is similar to the findings of Rutter et al. (2000) and Hine and Rutter (2000) with, for instance, an inverse relationship between loading times and population density: in low population density districts, loading times are longer, which is detrimental to the quality of services.

15
and Tanzania (Figure 4).

Table 4 presents the average transportation time ( _Baseline_ ) and the expected change with the implementation of all major transportation infrastructure projects, including road, port, and border improvements (with and without the preference for sea routes) for the countries in the estimation sample.<sup>32</sup> Changes in the average shipping time before and after the implementation of the PIDA projects vary across destination countries. Considering all possible investments, countries in the Middle-East and North African region benefit the most from the investments reducing border and port delays, mostly when shipping to other countries in the regions. Without imposing a preference for maritime routes, the proportional reduction in shipping time in the Africa region ranges from 2.57 percent with countries in East Asia to 19 percent within the Africa region. In general, when assuming a preference for maritime routes, changes in transportation times are more pronounced. The country-level results (shown in Appendix Table B6) exhibit significant heterogeneity, demonstrating why it is crucial to use a network approach to include interconnections between different projects and countries. For each country, transport time varies as much with the infrastructure projects that affect it as with those of neighboring countries traversed by its optimal routes.

The optimal mix of sea and land routes also changes between scenarios. In the baseline, on average, 86% of the total trip length is at sea if maritime routes are preferred, and 51% if no preference is assumed. After all infrastructure investments are made, the shares drop to 84% and 45%, respectively. For certain pairs of countries, there are significant mode shifts. For example, simulated trips between Gabon and Senegal are mostly by sea in the baseline, but switch to land corridors when transport and border investments occur (in the scenario without maritime preference). In fact, the reduction in border waiting times favors land routes, and trips that took place by sea in the baseline then switch to land. On the other hand, trips between Burkina Faso and Algeria are mostly made by land in the baseline, but switch to maritime routes when border times are reduced (in the scenario with maritime preference). Indeed, the reduction of border times between Burkina Faso and its coastal neighbors makes the maritime route more accessible.

In the next section, we present the results of three counterfactual scenarios that examine the combined effects of policy and infrastructure reforms in Africa. These scenarios explore various combinations of the implementation of the African Continental Free Trade Area (AfCFTA) and the Program for Infrastructure Development in Africa (PIDA), and their impact on the continent’s economic development.

> 32Appendix Table B7 details the incoming and outgoing transport shocks for each country in the estimation sample. Appendix Table B5 provides regional transport statistics for all African economies.

16
information in 2012 for the six ASEAN countries covered in the original 2008 survey and to collecting information for the four ASEAN countries not previously covered.<sup>2</sup> The ASEAN survey in 2012 focused, as did the earlier surveys, on policies that affect international trade in services – defined, as is now customary, to include the supply of a service through cross-border delivery, consumption abroad, by establishing a commercial presence, or by the presence of a natural person. The perspective is one of a foreign supplier who wishes to provide services to a particular country, and we focus mainly on policy measures that discriminate against foreign services or service providers.

The 2012 surveys updated the policy information obtained from the previous surveys of 2008 for Thailand, Malaysia, Vietnam, Philippines, Cambodia, and Indonesia, and collected information for the first time for Singapore, Brunei, Myanmar, and Lao PDR.<sup>3</sup> The data collection process follows the same method used by the Services Trade Restrictions Database but with a few changes. First, some new sectors and modes have been added to the questionnaire to reflect the regional liberalization priorities of ASEAN countries.<sup>4</sup> These include education, medical, architecture, engineering, and management consulting, as well as the cross border mode in road transportation. Second, the questionnaire is designed to identify differences in intra-ASEAN and extra-ASEAN policy regime in services, and in particular, instances of regional liberalization and preferences. Third, we examine more closely than in earlier surveys whether there is in fact a regulation or policy in place for each specific subsector mode to take into account the conditions in countries like the Lao People’s Democratic Republic and Myanmar. We also assess the implications of the absence of explicit regulation or policy, such as whether there are any implicit limits on the number of licenses allocated.

The seven major services sectors are disaggregated into further subsector-modes (as shown in Annex Table A5<sup>5</sup> ): financial (banking and insurance), education (higher education), medical

> 2 Policy data collected via surveys for ASEAN Member States is not yet publicly available.

> 3 The Brunei survey has been delayed and we do not yet have adequate information on its policies. 4 The choice of sectors in the original Database was based primarily on our assessment of their economic importance from a development perspective and on the existence of meaningful restrictions on services trade.

> 5  Regarding policies governing cross-border (mode 1) trade in international air passenger transportation services, we draw on the WTO’s QUASAR database since it represents the most comprehensive source currently available on bilateral air services agreements, covering over 2000 agreements.

5
14 percent of all Russian banks have been admitted under the deposit insurance system and that the number looks set to grow further. International experience provides ample evidence that extending deposit insurance to weak institutions increases moral hazard risks, provides cross-subsidies to weaker institutions, and raises contingent fiscal liabilities by making institutional failures more likely, as risk-taking appetite increases.<sup>28</sup> It is therefore not surprising that one of the participating banks was already subject to a recent intervention by the CBR.

<!-- Start of picture text --> Fig. 5: Evolution of bank participation in the Deposit Insurance System<br>3000 923 931<br>824 830<br>2500<br>2000<br>1500<br>381<br>1000 265<br>125<br>500<br>48<br>0<br>10/01/2004 11/01/2004 12/01/2004 01/01/2005 04/01/2005 07/01/2005 10/01/2005 01/01/2006<br>volume of deposits number of banks<br>Volume of deposits Number of banks<br><!-- End of picture text --> <!-- Start of picture text --> Source: DIA<br><!-- End of picture text --> Equally troubling are recent proposals being considered by the Duma to gradually increase the coverage ceiling from RUR 100,000 to RUR 300,000 until 2007 and up to RUR 600,000 in the mid-term.<sup>29</sup> Interestingly, these efforts are spearheaded by the DIA with the objective of raising the coverage levels as percentage of GDP under the Russian scheme to international levels and promote more savings. As shown in Figure 6, 98.5 percent of Russian deposits are currently under the RUR 100,000 limit and therefore insured. As mentioned earlier, Russian depositors already have the opportunity to place their funds in public banks, in particular Sberbank and Vneschtorgbank, and enjoy 100 percent coverage of their deposits. It is questionable whether public trust in private banks and therefore their attractiveness will improve with higher coverage, given their still unresolved institutional problems and the limited enforcement power and capabilities of the supervisors.

By increasing the coverage, Russian policy makers will most likely increase the (explicit) fiscal contingencies without any major positive impact. Here again, the political economy factor appears to be leading to a sub-optimal outcome. As Laffont and Tirole (1993) indicate, the multiple principal agent problem also extends to bureaucracies and can lead government agencies to seek undesirable outcomes.

> 28 See Kane / Demirguc-Kunt (2001)

> 29 See Financial Izvestia (2005)
rate the business relationships of PEPs. The dataset has eight categories of PEPs. These comprise individuals in International and Regional Organizations, National, Sub-national and Local Government, State-Owned Enterprises and State-Invested Enterprises, as well as Non-Governmental Organizations. They include all senior political and government leaders and officials but exclude lower level officials. In addition, based on their connection to the primary PEP, links with direct family members and close business associates or advisers are included.

Detailed information for each PEP has been compiled from several sources, including from sanctions, regulatory and legal lists. In addition, a variety of media sources have been used to identify individuals and entities not found on official lists. Consequently, the dataset contains a list of names, aliases and other individual details and evidence on the positions that an individual has held. Each identified individual’s links to either a political party, other PEPs or to specific companies or financial institutions is noted, as are links to sources.

For the purposes of our analysis, the data are organized around five main categories. The first two include PEPs, i.e., persons, while the other three comprise entities:

-

- (a) Political Individuals persons currently holding or having held a political position, including in a political party or having been elected,

- (b) Other Individuals – any person appointed (as opposed to elected) to a PEP position or appointed to a government position, as well as immediate relatives or close associates of primary PEPs,

-

- (c) Political Party any registered and active political party,

- (d) State-Owned or Invested Enterprises and,

- (e) Private companies or financial institutions.

_PEPData_ also includes links between all of these five categories. This allows us to go beyond the existing literature. The latter has focussed on having, or not having a (0-1 binary-type) link between the first two ((a) and (b)) categories and the latter three ((c), (d) and (e)). However, as our analysis shows, there is a web of relationships, direct and indirect, that matter and which are not captured by this approach.

We additionally link individual PEPs in _PEPData_ to firm level information. This is done in the following manner. The first step involves taking the name of the PEP and the explicit link to a company that is directly specified in _PEPData_ . We then associate through the name of that company with firm-specific financials and other information (e.g., on employment) contained in the Bureau van Dijk’s _Orbis_ dataset. Since firm names are 6
**Table 1. Variables, data needs and sources for economic assessment of HAIs**

|**Cost variable**|**Data needs**|**Data value or range**<br>**across countries (range**<br>**for sensitivit analsis)**|**Data source**|
|---|---|---|---|
|**Healthcare cost-of-illne**|**ss**|**y y**||
|Annual<br>healthcare<br>associated infections|Average % of admissions<br>infected with HAI|12.76% (6.4% - 25.6%)<br>used in 8 countries|Abubakar et al (2022)|
|(HAIs)||17.0% (8.5% - 34.0%) used<br>in Ethiopia|Alemu et al (2020)|
||Proportion of HAIs that<br>are AMR|50% (25% to 70%)<sup>2</sup>|Kariuki et al (2022),<br>Murrayet al(2022)|
||No. of annual hospital<br>admissions|2.5 – 11.2 admissions per<br>100population<sup>3</sup>|Ministry of Health|
|Additional<br>cost<br>of<br>treating a patient with<br>an HAI<sup>4</sup>|Average hospital cost per<br>day (‘hotel’ cost)|I: US$5.98-US$55.6<br>II: US$9.6-US$54.06<br>III: US$17.63-US$206.4<sup>5</sup>|IHME (2014, 2015)|
||Cost<br>of<br>drugs<br>and<br>laboratory tests per HAI|I: US$11.8 to US$49.0<sup>6</sup><br>II: US$28.2-US$117.6<sup>7</sup>|Gidey et al (2023)<br>Aerts et al (2022)|
||(no AMR)|III: US$129.5-US$539.5<sup>8</sup>|Bocoum et al(2019)|
||Cost<br>of<br>drugs<br>and<br>laboratory tests per HAI<br>with AMR|Double the costs of HAI<br>(above) for each level of<br>care|Assumption|
||Additional length of stay<br>HAI(no AMR)|5 days (3 to 10 days)<sup>9</sup>|Fenny et al (2020)|
||Additional length of stay|10 days (6 to 20 days)<sup>10</sup>|Otieku et al (2023)|
||HAI with AMR|||
|**Productivity cost-of-illn**|**ess**|||
|Annual HAIs|Same as Healthcare cost|||
|Additional days for<br>recoveryafter HAI|Additional recovery time<br>after havingHAI|2 days|Assumption|
|Loss in value-added of<br>workers|Productivity loss (agric-<br>ultural value-added)|US$2.1 to US$22.0 per<br>day (US$9 to US$284 per<br>dayin industry)<sup>11</sup>|World Bank statistics|
||Proportion<br>of<br>HAI<br>patients that are adults|60%|Assumption|
||Labor force participation<br>rate|28.3% to 83.6% (female)<br>50.8% to 89.2%(male)<sup>12</sup>|ILOSTAT (2023)|
||Proportion<br>of<br>HAIs|60%|Assumption|
||suffered bywomen|||
|Loss of time spent in<br>non-productive|Average<br>daily<br>value<br>(opportunity cost)|30% of the agricultural<br>value-added|Assumption|
|<br>activities||||
|**Mortality cost-of-illnes**||||
|<br>Annual HAIs|<br>Same as Healthcare cost|||
|Case fatality rate|Case fatality rate HAI (no<br>AMR)|6.9% (4% to 15%)<sup>12</sup>|Gidey et al (2023)<br>and 6 other studies –<br>see Table 9|
||Case fatality rate HAI<br>AMR|13.8% (8% to 30%)|Serra-Burriel et al<br>(2020)|
|Value of a premature<br>death|Value of a statistical life|US$4,113 to US$38,719<br>(US$18,706<br>to<br>US$112,449)<sup>13</sup>|Calculation based on<br>Banzhaf (2022)|

9
Zingales (1998; hereafter RZ), which has been used in a number of tests, is innovative and very much applicable for our empirical tests on the effects of competition in the banking system on economic growth in terms of the degree of access to external financing. Next, we provide more detail on the RZ approach.

The RZ model relates the growth in real value added in a sector in a particular country to a number of country- and industry-specific variables. In the case of RZ, the specific test focuses on financial development. The question of RZ is whether financially dependent firms can be expected to grow more in countries with a higher level of financial development. The authors use U.S. firm data as proxy at the industry level to derive the typical external financial dependence for a particular industrial sector. Their presumption is that the well-developed financial markets in the United States should allow U.S. firms to achieve the desired financing for their respective industrial sector. This offers a way of identifying the degree to which industries desire external financing anywhere in the world.<sup>6</sup> The innovation of the RZ approach is in positioning an interaction between a country characteristic (its financial development) and this (benchmark) external financial dependence of a given industry. It then investigates how industrial growth relates to this interaction term, thereby investigating whether industrial sectors that typically use more external financing grow faster in countries with greater financial sector development. This overcomes some of the identification problems encountered in standard cross-country growth regressions. The approach is also less subject to criticism regarding an

> 6 The advantage of this approach is that one does not need information on the actual external financing dependence for industries in different countries. The comparability of such data would be reduced because accounting practices differ around the world.  It does assume that there are technological and economic reasons why some industries depend more on external finance than do others, and that these differences, to a large degree, prevail across countries. This does not mean that the model assumes a sector in two countries with the same degree of financial development to have exactly the same optimal external financing structures. Local conditions, such as growth opportunities, are allowed to differ between countries. The model assumes only that the rank order of optimal external financing needs across industries is similar across countries.

12
menting large gains in consumption and income for displaced migrants who took up the regularization program.

# **II.C Legal service access for migrant children and adolescents in Colombia**

In Colombia, migrant children and adolescents, irrespective of their migration status, are afforded basic access to emergency health services and education, although the latter comes without the possibility of graduation. A summary of the rights available to Venezuelan migrant children and adolescents in Colombia, categorized by their migration 1. status, is presented in detail in Table Children and adolescents with regularized status enjoy full access to all social services. In contrast, undocumented minors encounter barriers, including limitations on obtaining a bachelor’s degree, accessing technical or technological education, affiliating with subsidized public health insurance, and registering for the principal administrative survey (Sisb ́en), which facilitates eligibility for means-tested programs. These disparities in service access could profoundly influence the life chances and well-being of migrant children from varied backgrounds.

Table 1. Legal Rights and Services for Venezuelan Children and Adolescents in Colombia

||Regular|Irregular|
|---|---|---|
|**Education**|||
|Food and school bus service|✓|✓|
|Primary and secondary|✓|✓<sup>_a_</sup>|
|Promotion across levels|✓|✓|
|Bachelor’s degree upon completion|✓|No|
|Technical or technological education|✓<sup>_b_</sup>|No|
|**Health**|||
|Emergency care|✓|✓|
|Public health programs|✓|✓|
|Vaccines|✓|✓|
|Prevention campaigns|✓|✓|
|Affliation subsidized regime|✓|No|
|**SISBEN**|||
|Registry|✓|No|

_Notes:_ (a) Children without a valid ID must register at the local secretary of education prior to enrollment, (b) Adolescents over 14 years old only.

and Guzman (2022).

12
Another way to look at the behavior of the inequality extraction ratio with a changing social minimum is to take the current actual social minima, find out what they implicitly imply about b, and calculate the inequality extraction ratio based on such “real” data (that is, without a prior assumption of a given elasticity). Take the United States in 2012. Its GDP per capita was $43,000 and the federal poverty line for a four-member household was $23,050. On a per capita basis, this gives a poverty line of $5,762 per year. Assuming as before a physiological subsistence of $300 per year, and plugging these actual values into (6), enables us to calculate the implicit elasticity b. It works out as 0.59. Figure 7 shows the inequality extraction ratio for the United States over the period 1965-2012 using this implicit observed elasticity of the social minimum with respect to real income. The pattern of the increase is the same as with b=0.5, but the level of the extraction ratio is higher. In 2012, it is about 3 points higher: 51 percent instead of 48 percent. Of course, in either case, the rise of the extraction ratio during the past 45 years was substantial.

Figure 7. Inequality extraction ratio in the United States, 1967-2012 (calculated using the observed elasticity between the social minimum and GDP per capita, b=0.59) <!-- Start of picture text --> 0.52<br>0.50<br>0.48<br>0.46<br>0.44<br>0.42<br>0.40<br>0.38<br>1967 1972 1977 1982 1987 1992 1997 2002 2007 2012<br>Year<br>ratio<br>Inequality extraction<br><!-- End of picture text --> 15
Table 4: Credit Use Equation

|**_EXPLANATORY VARIABLES_**|**_SML_**_est_|_imator_|**_TLS_**_esti_|_mator_|**Random****_ef_**|**_fect probit_**|
|---|---|---|---|---|---|---|
||_ESTIMATES_|_STD._<br>_ERRORS_|_ESTIMATES_|_STD._<br>_ERRORS_|_ESTIMATES_|_STD._<br>_ERRORS_|
|Intercept|-450.65**|36.94|||||
|Time Trend (T)|-161.94**|13.98|-170.48**|28.97|||
|Log of HH adult population (POP)|113.35**|13.69|112.22**|56.31|0.335365**|0.042109|
|Cattle Stock (1000 Ks)|-37.44**|13.98|-56.11**|29.13|-0.00018**|0.000046|
|Draught Animals & implement (1000 K)|386.62**|56.84|333.18|236.07|0.000976**|0.00014|
|Sex of HH's head (Women=1)|94**-0.3762|4**||||0.065369|
|Primary education|5.80**|1.54|||-0.12828**|0.009673|
|Secondary Education|64.80**|15.93|||0.005|0.057659|
|Distance to Markets|-34.98**|2.94|||0.170135*|0.054838|
|s2|359.20**|19.83|||0.6915|0.0485|
|Mean Log- Likelihood|-1.27||||||
|Number of Observations|9706||9706||9706||

Indicates statistically different from zero at the I0% significance level.

Indicates statistically different from zero at the 5% significance level.
# **_An Empirical Analysis of the Effects of Climate Variables on National Level Economic Growth_**

Casey Brown<sup>1</sup> *, Robyn Meeks<sup>2</sup> , Yonas Ghile<sup>1</sup> , Kenneth Hunu<sup>1</sup> 1 Department of Civil and Environmental Engineering, University of Massachusetts 2 John F. Kennedy School of Government, Harvard University

*Corresponding Author, <u>cbrown@ecs.umass.edu</u>
# **The Distributional Impact of Taxes and Social S endin in Croatia** **<u>p g</u>**

Gabriela Inchauste<sup>†</sup> and Ivica Rubil<sup>♦1</sup> JEL classification: H22, I38, D31 Keywords: fiscal policy, fiscal incidence, social spending, inequality, poverty, taxes, Croatia

> † The World Bank

> ♦ Postdoctoral fellow at the Institute of Economics, Zagreb

> 1 We are indebted to Ivica Urban, Sanja Mađarević-Šujster, Moritz Meyer, Maria Davalos, Karolina Goraus, and Javier Suarez for helpful comments and advice. All remaining errors are our own.