rafmacalaba/datause-encoder-data huggingface dataset:
- Juanmarti Mestres, Arnau, Guillem López Casasnovas, and Judit Vall Castelló (2021), “The Deadly Effects of Losing Health Insurance.” _European Economic Review_ 131.

- Kam, Cindy D., and Marc J. Trussler (2017), “At the Nexus of Observational and Experimental Research: Theory, Specification, and Analysis of Experiments with Heterogeneous Treatment Effects.” _Political Behavior_ 39(4): 789–815.

- Laban, Cornelis J. et al. (2004), “Impact of a Long Asylum Procedure on the Prevalence of Psychiatric Disorders in Iraqi Asylum Seekers in The Netherlands.” _Journal of Nervous and Mental Disease_ 192(12): 843–51.

- Leeper, Thomas J., Sara B. Hobolt, and James Tilley (2020), “Measuring Subgroup Preferences in Conjoint Experiments.” _Political Analysis_ 28(2): 207–21.

- Marbach, Moritz, and Guido Ropers (2019), “Not in My Backyard: Do Increases in Immigration Cause Political Violence?” _SSRN Electronic Journal_ . https://papers.ssrn.com/abstract=3310352 (October 20, 2021).

- McLaren, Lauren (2003), “Anti-Immigrant Prejudice in Europe: Contact, Threat Perception, and Preferences for the Exclusion of Migrants.” _Social Forces_ 81(3): 909–36.

- ———. (2015), Immigration and Perceptions of National Political Systems in Europe _Immigration and Perceptions of National Political Systems in Europe_ . Oxford University Press.

- Migración Colombia (2020), “Mas de 1 Millon de Venezolanos Estarian Radicados En Colombia - Migración Colombia.” _Viernes, 03 Abril 2020_ . https://www.migracioncolombia.gov.co/noticias/mas-de-1-millon-825-mil-venezolanosestarian-radicados-en-colombia (April 22, 2021).

- Mutz, Diana C (2011), Population-Based Survey Experiments _Population-Based Survey Experiments_ . Princeton: Princeton University Press.

- Ratkovic, Marc (2021), “Subgroup Analysis: Pitfalls, Promise, and Honesty.” In _Advances in Experimental Political Science_ , eds. James Druckman and Donald P Green. Cambridge University Press, 271–88.

- Rodon, Toni, and Marc Sanjaume-Calvet (2020), “How Fair Is It? An Experimental Study of Perceived Fairness of Distributive Policies.” _Journal of Politics_ 82(1): 384–91.

- Rodrigues Chatruc, Marisol, and Sandra Rozo (2021), _Attitudes Towards Migrants during Crisis Times_ . https://www.iza.org/publications/dp/14319/attitudes-towards-migrantsduring-crisis-times.

- Rozo, Sandra V., and Juan F. Vargas (2021), “Brothers or Invaders? How Crisis-Driven Migrants Shape Voting Behavior.” _Journal of Development Economics_ 150: 102636.

- Ruiz, Isabel, and Carlos Vargas-Silva (2016), “The Labour Market Consequences of Hosting Refugees.” _Journal of Economic Geography_ 16(3): 667–94).

- ———. (2018), “Differences in Labour Market Outcomes between Natives, Refugees and Other Migrants in the UK.” _Journal of Economic Geography_ 18(4): 855–85).

- ———. (2021), “The Impacts of Refugee Repatriation on Receiving Communities.” _Journal of Economic Geography_ 21(2): 169–94.

- Santamaria, Julieth (2020), _“When a Stranger Shall Sojourn with Thee”: The Impact of the Venezuelan Exodus on Colombian Labor Markets_ .

- Scheve, Kenneth F., and Matthew J. Slaughter (2001), “Labor Market Competition and

30
12 network as measured by LN_CONNDENS). It is assumed that the higher the density of water connections, the higher the water losses will be. A denser network tends to be linked with more pressure required in the system to reach the higher floors of buildings. Pressure is probably the most important factor which influences the level of leakage in distribution systems (Skipworth et al., 1999). As a result of the higher pressure, a very dense network of connections is likely to require more maintenance to preserve the network. High connection density also may increase the complexity of managing the service. Connection density and its management falls mostly outside of the control of utility management as it is shaped by urbanization and land settlement patterns of the localities that are served by the utility. In the IBNET sample, small systems mostly located in small towns showed a mean density of water connections of 55 compared to 134 in utilities in large systems, while the variance between utilities can differ widely.

The length of the water network (LN_LENGTHWAT) is another physical characteristic of the water system. The length of the network should be positively related to water losses, as a larger network is more costly to maintain in order to preserve the functioning of the water network. A larger network will often also transport water over larger distances and hence has to deal with higher costs.

The share of energy costs as part of total operation and maintenance costs (LN_ENERGY_SHARE) is used as a proxy for the type of adduction of the distribution system. The more pumping a system needs the higher the share of energy in the total costs will be. We assume that the more pumping is needed in a system, the higher the likelihood that water losses are higher.

The presence of a dummy variable measuring universal water coverage (WATCOVDUMMY) is used to explain the physical characteristics of the water losses. It is assumed that universal coverage has a negative effect on water losses. Universal coverage has a positive effect on using more of the existing production capacity. Water systems tend to be designed with a horizon of several decades in mind and hence tend to have significant excess capacity in less mature water systems; the level of excess capacity will depend on the water supply design standards applied in the country. Hence, once access to water supply services improves (in terms of people and
practically disappeared by the end of the period, due in part to AFP decisions to eliminate flat rate commissions.

Due to the lack of a long time series, data from several other countries were not included in the report but tend to support its conclusions vis a vis private vs public management. Brazilian rates of return for private occupational pension schemes were reported in World Bank (1994a) at 10.6% in real terms between 1986 and 1990. Rates of return in Peru's scheme ranged from 8-10 percent in real terms in its first year of operation according to data provided by the Superintendency of Pensions in Lima. AFPs operating on a voluntary basis in Costa Rica in the early 1990s reported similar rates of return.

# C. Administrative **Costs**

Data on administrative costs of privately-managed schemes and occupational pension schemes (including public or quasi-public schemes) were not available except in the case of the United States and Chile. For the U.S., occupational plan costs which appear in Figure 5.2 in Issue Brief 5, data were taken from Turner and Beller (1989) which in turn are based on Labor Department survey data. While the figure shows that costs/assets are lower the greater the assets of the plan, other factors not shown here may also influence this indicator. For example, other things constant, defined-contribution plans seem to have a cost advantage over defined-benefit plans, especially small plans, as the latter require complex actuarial calculations as a fixed cost.

Administrative costs in the reformed Chilean AFP system are currently the subject of much controversy in the field of pension economics. Data on AFP administrative costs and the components of these costs are analyzed in great detail in Acuna and Iglesias (1992), Valdes-Prieto (1994) and Abuhadba (1994). Some of the conceptual issues involved in comparisons of the Chilean system with public systems are discussed in James and Palacios (1995).

49
# **IV. Products with the Largest Estimated Losses**

Once the mirror gap (trade gap, indicator 1) is calculated and the effective import tax rate<sup>15</sup> has been determined, estimates of revenue losses may be computed. For the sake of brevity, only the main positive and negative gaps by HS2 chapter are presented. Whereas a positive “X-M” gap suggests an undervaluation, a negative gap is indicative of misclassification. Overall losses are estimated at around US$96 million, or 30 percent of non-oil revenues collected.<sup>16</sup> Estimates indicate that valuation fraud (undervaluation) and tariff misclassification accounted, in 2014, for a loss of revenue of US$53.7 million and US$42.4 million, respectively.

# **<u>A. Undervaluation</u>**

Table 2 lists the sectors with the largest estimated losses. Considering only the five sectors with the highest mirror gap, the “X-M” discrepancy is US$242 million ( _cf_ Table 2).

**Table 2: Undervaluation: Estimated Losses of Revenue by Sector**

|Sector|Estimated losses<br>(US$ millions)|Estimated losses (%<br>of non-oil revenues)|
|---|---|---|
|Textiles,footwear,and leathergoods|25.4|7.6|
|Machinery, electrical appliances, and<br>equipment|11.1|3.3|
|Furniture and furnishings|7.6|2.3|
|Animals or vegetable fats and oils|5.2|1.6|
|Glass andglassware|4.4|1.3|

Since it is extremely difficult to assess the value of textile goods,<sup>17</sup> it is not surprising that our estimates outline that imports of textiles, footwear, or leather goods are mis-declared. Two products—clothing for women and girls (HS4 digit code: 6104) and footwear (HS4 digit code: 6402)—are the source of more than two-thirds of losses (67 percent). Table 12, appendix A presents the main positive gaps (and associated losses) by HS4 digit.

As indicator 5 (the ratio of density values) is very low for electrical appliances and furniture, it suggests that undervaluation is, in this sector, widespread. Undervaluation is estimated at

> 15 The effective import taxation rate is calculated on the basis of the tax value (customs duties and VAT) paid at the border. Since Madagascar customs did not collect all levied taxes at the border (notably, excise duties up to 2015) we are currently unable to accurately calculate the effective taxation rate. This is one of the factors limiting the estimation of losses of tax revenue and hampering measures to correct misclassifications. Furthermore, the estimates are a lower-bound.

> 16 As the main oil producing countries (United Arab Emirates, Bahrain, and Saudi Arabia) do not submit their export figures to the UN Division of Statistics, oil exports (HS2 chapter 27) are not included in the study.

> 17 Rauch classification (1999) has listed clothing under “differentiated products.” Assessing the value accurately is thus an extremely difficult task.

8
A fourth inference from the theoretical framework is that consumption volatility can be expected to be smaller in developed countries with developed risk-reduction mechanisms (risk identification, risk mitigation and risk preparedness) (see Chapter 2). Graph 1.5 illustrates the inverse relationship between consumption volatility and level of economic development. The level of economic development by itself accounts for about one-third of the observed variation in consumption volatility across countries, which implies that it is also an important factor in explaining the volatility of consumption (Table 1.2).

<!-- Start of picture text --> Graph 1.5: Consumption Volatility and Per Capita Income<br>20 _ ... _<br>0 Canbbea<br>+ Otbe.,<br>0<br>t6<br>14 +<br>12 + .<br>+<br>o + 00 c<br>O 9 +++ 0 +0<br>*5 + 0 - +' + +<br>4 ~~~~+<br>'4 + + +<br>0 ~~~~+ + + + + ++<br>3.00 000 I 0 15,001 2000 25.000 30.00 35. 40<br>-2<br>Per Capita GDP (US$, PPP adjusted)<br>Some SIMA database (100 countries included):-<br><!-- End of picture text --> A fifth inference is that smaller economies are expected to suffer from higher consunption volatility than larger economies. Smaller economies are less able to take advantage of economies of scale because their production is not diversified. As in the case of the Caribbean region, their size makes them vulnerable to the impact of hurricanes and other natural disasters. Also, large industrial economies may not suffer from significant output losses because regional or asymmetric shocks from disasters may be absorbed by economic activities in other regions and transfers across regions. This fact is summarized in Graph 1.6 which plots the volatility of per capita consumption against country size (as measured by the logarithm of the population). Country size by itself accounts for about one-third of the observed variation in consumption volatility across countries, which implies that it is also a main factor which explains the volatility of consumption (Table 1.2).

13
**Figure 16: Correlation between openness to FDI and FDI inflows per capita**

<!-- Start of picture text --> 1,000<br>800<br>600<br>400<br>200<br>0<br>30 40 50 60 70 80 90 100<br>Average openness to FDI across sectors<br>FDI inflows per capita, 2007-11 average (USD)<br><!-- End of picture text --> _Note_ : Economies with FDI inflows per capita above US$ 1,000 are dropped as outliers. The Pearson correlation coefficient is 0.193, and this correlation is statistically significant at the 10% level. If all economies are included, the correlation remains positive, but is no longer statistically significant. _Source_ : UNCTADstat, _FDI Regulations_ database 2012 Furthermore, simple correlation analysis shows a positive (0.348) and statistically significant association between the number of days to establish a foreign firm and data collected by Enterprise Surveys measuring the percentage of firms identifying business licensing and permits as a major constraint. Economies in which the process of starting a foreign business is longer as per our findings are also the economies in which investors report business licensing as a major constraint. In Armenia for example, where it only takes 9 days to complete the startup process, only 2.6% of foreign firms identified business licensing and permits as major constraint.

In the República Bolivariana de Venezuela, which is on the other extreme and where the establishment process takes on average 325 days to complete, the percentage of firms identifying licensing as a major constraint spikes to 69.6%.

24
- 54 -

parameters to each country (only a single set are used) but from the evaluation of the elasticities at the different shares. If two countries had the same fuel shares then they would have the same elasticities.

TabI2 4.1t Griffin's Estimates of Own Price ElasticLties in the Electricity Sector

||Coal|Gas|Fuel Oil|
|---|---|---|---|
|Canada|-0.66|-0.95|-2.94|
|U.S.A.|-0.66|-0.90|-3.46|
|Japan|-0.55|-2.40|-1.83|
|Austria|-0.79|-0.79|-3.22|
|Belgium|-0.75|-0.83|-3.08|
|Denmark|-0.51|-|-1.79|
|Finland|-0.86|-0.91|-1.99|
|France|-0.74|-0.80|-3.65|
|West Germany<br>|0.39|-1.14|-10.25|
|Greece|-0.93|-|-0.98|
|Ireland|-1.16|-|-0.78|
|Italy|1.01|-1.17|-1.26|
|Netherlands<br>|0.48<br>|-1.65|-2.37<br>|
|Norway|-3.90|-|-0.40|
|Portugal|-0.89|-1.42|-1.27|
|Spain|-0.44|-2.12|-2.45|
|Sweden<br>|-2.36|-4.12|-0.49|
|Switzerland|-|-|-0.34|
|Turkey|-0.69|-1.23|-1.85|
|U.K.|-0.35|-1.82|-3.74|
|O.E.C.D.|-0.57|-0.94|-3.12|

(Blanks indicate that a given country does not use the fuel for electricity generation.) Griffin noted that the own prlce elastlcitLes are conesderably higher than those based just on tLme serLes data for the U.S. His own earlier work had found -0.3 for coal, -0.0 for gas and -0.2 for fuel oil, while Hudson and Jorgenson (19741 had found -0.45 for coal, -0.10 for gas and -0.88 for fuel oll. He interpreted the lower values as being short run (perhaps an adjustment period of only 2 years) while the cross section results were nearer to long run values with a 20 year adjustment pattern. Table 4.2 provides the correspondlng crossprice elasticities which again differ between countries according to their fuel shares.
improvements. Subsequently, special import conditions were laid down in December 2003 and Kenya was added to Part I.<sup>42</sup> Kenyan exports of Nile perch to the EU present a rather complex and multi-layered illustration of the role of hygiene standards and provides a convenient comparison with the experiences of other countries and the efficacy of their attempts to achieve compliance. On the one hand, Kenya had struggled for some time to comply with EU requirements relating to hygiene in the processing and marketing of fish and fishery products. In part this reflects the lack of efforts to up-grade food safety controls in line with the growth in exports over a considerable period of time. On the other, it had faced a period during which exports of Nile perch to the EU were prohibited and otherwise restrictions were applied. Clearly, the two are not unrelated; the fact that the EU had identified deficiencies in the food safety controls applied in Kenya undoubtedly influenced the actions taken by the European Commission. However, Kenya’s efforts were hampered by two events over which it had no control, namely an outbreak of Cholera and an incident of fish poisoning in Uganda.

**<u>Table 8. Problems identified in European Commission inspection, September 1999:</u>**

|**Issue**|**Details**|
|---|---|
|Monitoring samples|•Lack of records, for example on amount of sample in<br>relationto amount of landedfish|
|Surveillance samples|•Sample not sealed for transportation<br>•Lack of documentation on date, amount, kin and<br>sender of sample<br>•Long transportationperiod tolaboratory|
|Laboratory facilities|**Laboratory of Government Chemist**<br>•Poor conditions of hygiene and cleanliness<br>•Potential for cross-contamination of samples<br>•Analytical equipment in poor condition. No<br>registration or maintenance records<br>•Chemicals and standard solutions expired<br>•No quality control system in operation<br>•No accreditation or recognition<br>•Testing methods unsuitable for specific analyses and<br>only of utility for rough screening<br>**Kenya Bureau of Standards**<br>•No registration or maintenance records for equipment<br>•No written operating procedures<br>•No accreditation or recognition<br>•Chemicals and standard solutions expired<br>•Capacity limited in terms of range of active<br>compounds that canbe analysed|

_Source: European Commission (1999)._

> 42 Commission Decision 2004/39/EC; Commission Decision 2004/36/EC.

43
26 tax that Is at least paitly experence-rated on a Ul system would reduce its costs and lower unemployment, but that particular experiment has never been attempted.

# Other Economic Impacts

All of the questions discussed In this Subsection involve issues in both benets and inanoing. Moreover, each relates to the potential non-labor market goals of the Ul program that were presented In Section B.

Do Ul benefits smooth consumption? This Issue was for many years analyzed as one of *benefit adequacy,n that Is, were the benefits sufficient to replace a large fraction of the reduction in family income? This gets toward the consumption-smoothing goal that was discussed In Section B, but I is not directly on target. A family might lose income as a result of a spell of unemployment of one family member; but total consumption, including of goods produced in the home, may not change or may drop substantially. To examine the sucCess of Ul in smoothing consumption one must examine consumption directly. The Issue Is the extent to which Ul benefits (and any changes In the shifted tax burden that accompany the payment of higher benefits) loosen the liquidity constraint that may be engendered by the loss of income.

There is a large literature of studies of benefit adequacy. These generally find that other labor-force responses do not suffice to make up the difference between the lost eamings and the Ul benefits received by the unemployed worker. Only one study (Hamormesh, 1982) has examined the extent to which Ul benefits loosen the liquidity constraint facing unemployed American households. The evidence suggests that roughly half of all benefts are spent In a way that suggests the household would face a drop In consumption without them. Applying the resuits to other countries, the longer duration of benefits suggests that they accomplish even more In this regard (since households presumably face a more svere liquidity crunch the longer a member has been unemployed).

This discusslon Ignores the Impact of the provision of publicly-funded Ul benefits on private saving. Without Ul individuals would accumulate precautionary savings to cover spending during
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In keeping with this picture, the final row of Table 3 shows that the median microfinance bank relied mainly on commercial funding and deposits. The median nongovernmental organization, in contrast, turned to non-commercial borrowing and donations with far greater frequency. A more detailed breakdown of the data, given in Table 4, shows that for the 134 NGOs in our sample, 39 percent of funding came from donations, with another 16 percent coming from non-commercial (soft) loans. For the 24 banks in the first row, the two categories contributed just 3 percent to total funding. In contrast, commercial borrowing and deposits combined to give 84 percent of total funds.

# _How robust are the financial data?_

Rather than taking an institution’s statement of profitability at face value, these data have been adjusted to account for hidden subsidies; this is what makes these data especially valuable. But the devil, as is often the case, is in the details. If a socially-motivated lender obtains foreign capital from a social investor at a concessional interest rate of, say, 2 percent a year, the adjustments here account for the fact that the institution would have instead paid a higher interest rate in the capital market (were it instead a fully commercial bank). The difference in interest rates is part of an implicit subsidy. The same holds true for equity shares in the microfinance banks that are held by social investors who do not seek full financial returns.

The idea behind the correction is simple, but implementing it is not. The adjustments made by the MIX organization rest on estimates of the alternative cost of capital that the microlender would have had to pay if it had instead obtained the capital in the market. Is that rate 6 percent? 10 percent? 14 percent? The estimate ought to account for the perceived risks of investing in microfinance institutions, which include the risk that the quality of the loan portfolio might deteriorate (especially given that the portfolio is not backed by collateral), as well as any political risk or exchange rate risk that may affect net returns (investors might also be concerned with liquidity and the possibility that the ability to withdraw funds or sell shares may be limited).

The adjustments implemented by the MIX, the source of the data, use a country’s deposit rate (taken from the International Monetary Fund’s database) as the assumed cost of capital that institutions would have to pay in the absence of subsidized funding. It’s a relatively low cost of 20