rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 summarizes the three previous studies that shed light on the issue of whether progress toward the health MDGs has been pro-poor at the country level. The studies vary in the time period under analysis, and in the number of countries studied. Two use only the Demographic and Health Survey (DHS), while one uses both the DHS and the Multiple Indicator Cluster Survey (MICS). One study focuses on just one MDG and one official indicator, another looks at two MDG and one official indicator of each, and a third looks at three MDGs and six official indicators.

In the absence of consumption data (the preferred measure of household living standards in developing countries (Deaton 1997)), all three studies distinguish between poor and betteroff households using the ‘wealth index’ developed by Filmer and Pritchett (2001) and included in each public release DHS and MICS dataset.<sup>2</sup> Two of the previous three studies address the inequality issue by looking at changes in the ratio of the outcome among the poorest quintile to the outcome among the richest quintile. The other uses the concentration index (Kakwani, Wagstaff and van Doorslaer 1997), analogous to the Gini coefficient except that when looking at inequality in, say, underweight, individuals are ranked by their household’s score on the wealth index, rather than by underweight. Both approaches speak to the issue of relative – rather than absolute – inequality, and shed light on whether progress has been pro-poor in a relative sense (in which case relative inequality is said to have fallen).

The findings of the three previous studies are mixed. Looking at under-five mortality (U5MR) between 1990 and 2001, Moser et al. (2005) find no clear trend , while Suzuki et al.

> 2 This uses principal components analysis to construct a weighted average of a variety of indicators capturing the ownership of assets (e.g. car and television), and the characteristics of the family’s home (e.g. type of floor and roof). Subsequent to the work of Gwatkin et al. (2000; 2007), who published tabulations by ‘wealth quintile’ of a large battery of health, nutrition and population indicators for all DHS surveys, the organization responsible for the DHS (then Macro International) decided to include the Filmer-Pritchett wealth index in each public-release DHS dataset; UNICEF, which is responsible for the MICS, subsequently decided to the same with the MICS.

3
31 take care of the occupational pension schemes for smaller companies. There are no formal restrictions with respect to the design of such additional benefits.

There are no rules for the adjustment of the conversion factor for early retirement benefits in the BVG law. So pension funds are free to offer any early retirement conditions without legal restrictions. In theory, it would be possible for a pension provider to use a very low conversion factor for all retirement ages below the statutory retirement age, though we know of no cases in which this had been done. Common practice is to reduce the conversion factor by 0.2 percent for every year of anticipation of benefits<sup>29</sup> . However, there are again large differences in the early retirement plans across pension funds.

# **3.2 Minimum Interest Rate Requirement under BVG**

The accumulated old–age balances in the second pillar enjoy a guaranteed minimum interest rate. This minimum rate of return is periodically reassessed by the Swiss Federal Council. Table 8 shows the evolution of the minimum interest rate (MIR). It remained constant at 4 percent for 17 years, until the end of 2002. Due to the decline of capital market returns, the previous rate was no longer sustainable and was therefore adjusted by the Swiss Federal Council. Currently, the rate is fixed at 2.5 percent.

**Table** **<u>8  Evolution BVG Minimum Interest Rate</u>**

||1985 - 2002|2003|2004|2005|
|---|---|---|---|---|
|Min. interest rate|4%|3.25%|2.25%|2.5%|

Source: Bundesamt für Sozialversicherungen The minimum interest rate is supposed to reflect market conditions from a relatively risk–averse perspective. If the minimum requirement is set too high, portfolio managers have to take excessive risks, for higher expected returns are only attainable with a riskier portfolio. In order to avoid such an undesirable behavior and to protect the insured persons effectively, the MIR should be linked in some way to the risk–free rate of return on financial assets.

To illustrate the evolution of market interest rates with respect to the minimum interest rate, Figure 13 shows the MIR and a Swiss zero coupon government bond with duration of 10 years<sup>30</sup> . Until 1996, the yield on fixed income investments with a duration of 10 years, represented here by the Swiss zero coupon government bond, was much higher than the minimum interest rate under the BVG. Thereafter, it was the other way round until the minimum interest rate requirement was adjusted downwards.

> 29 This number can be derived from an actuarially fair adjustment of benefits of 6.8 percent and a technical interest rate of 4%.

> 30Definitions: The Macaulay Duration is the weighted average maturity of e.g., a bond or series of cash flows received.
Table 2: Share of Im ort Used for Ex orts: b Industr <u>p p y y</u>

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Industries|Chemicals,<br>and Tran|Machinery<br>sportation|Electroni<br>and Optical|cs, Metals<br>Equipment|Furn<br>and|iture<br>Wood|
|Dependent Variable|EXP|EXP|EXP|EXP|EXP|EXP|
|IMP|0.298***|0.199***|0.462***|0.373***|0.425***|0.403***|
||(0.019)|(0.029)|(0.026)|(0.032)|(0.046)|(0.070)|
|Processing_×_IMP|0.664***|0.768***|0.578***|0.691***|0.565***|0.718***|
||(0.027)|(0.045)|(0.038)|(0.062)|(0.071)|(0.123)|
|Observations|91116|37901|124516|48633|27827|3526|
|Adjusted R<sup>2</sup>|0.716|0.739|0.739|0.781|0.723|0.781|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Industries|Miscel|laneous|Plastic,<br>and|Rubber<br>Toys|Tex<br>and A|tiles<br>pparels|
|Dependent Variable|EXP|EXP|EXP|EXP|EXP|EXP|
|IMP|0.272***|0.114**|0.188***|0.165***|0.773***|0.673***|
||(0.037)|(0.050)|(0.018)|(0.033)|(0.061)|(0.058)|
|Processing_×_IMP|0.929***|1.021***|0.892***|0.800***|0.542***|0.462***|
||(0.054)|(0.145)|(0.041)|(0.109)|(0.070)|(0.119)|
|Observations|49708|4820|43356|6279|77819|3178|
|Adjusted R<sup>2</sup>|0.76|0.78|0.8|0.812|0.741|0.773|
|_φ_(I,E,K)|No|Yes|No|Yes|No|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Robust standard errors clustered at the industry-year level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level.

14
Madagascar and Mozambique the year-to-year increase was already large before FPE, and appears to continue after it. It is hard to make out a trend in Kenya due to the year-to-year fluctuations.

We next turn to the year that current grade 4 teachers in the SDI surveys report having begun teaching. It is important to note that the sample of teachers in the SDI reflects only those who have remained as teachers and the results need to be interpreted in that light, in particular because this could induce a “survivorship” bias to the results. If, for example, less-qualified teachers were hired following FPE and teachers with this profile are also more likely to have left the teaching profession by the time of the survey, then we would not be capturing them in the sample. Moreover, it is important to note that the students we test are in grade 4 and so would have been exposed to 3 years of teachers prior to this grade. These teachers may have been hired before or after FPE, meaning that total FPE effects may be hard to detect with knowledge of just the grade 4 teacher’s status. These caveats mean that this analysis should be understood as an exploration of whether the impacts of the FPE scale-up are still present in the long term using this particular window into that exploration.

These data do seem to suggest a noticeable change in the density of grade 4 SDI teachers hired after FPE (Figure 2). While it is only in Tanzania and Uganda that it would appear that this happened exactly in the FPE year, all the countries display a change around that date (although in Togo there are other spikes in hiring that do not correspond to the FPE year). In the spirit of RDD, we test for whether there is a change in the density at the time of FPE by implementing the “test for manipulation” as developed by McCrary (2008).<sup>18</sup> The results are presented graphically in Appendix Figure 1: in all cases the density is indeed higher after FPE. However, the difference is only statistically significant in two of the six countries (Kenya and Tanzania), meaning there is not always a clean break in the series relative to the overall year-to-year volatility in the series.

# 4. Results

> 18 We use the version as implemented by Cattaneo, Jansson and Ma (2020) and Cattaneo, Jansson and Ma (2021). This test is typically implemented in RDD situations in order to test whether the running variable has been “manipulated” in such a way as to move subjects from the non-treated to the treated side of a threshold that determines treatment.  As such, a break is considered evidence of a bad outcome.  In our case, we expect there to be a change in the density if there is a surge in teacher recruitment after FPE.

11
which development is proscribed. Robustness-based approaches can embody mini-max regrets as a performance measure but do not need to; in multiple-criteria applications, the user can specify the desired trade-off between giving up net-benefits in “average” outcomes versus mitigating large losses in “adverse” outcomes.

Neither RDM nor a safe minimum standard approach provides a sharp answer to a go-no question. Rather, they would present lessors and lessees with a range of possible outcomes for the share of mining profits received and levels of risks needing to be borne, thus allowing judgements to be shaped about acceptable performance. As noted, these approaches also can help identify which unknowns could have the biggest impacts on outcomes.

Although there are large uncertainties about future demands for minerals, and deep uncertainties about DSM technologies and costs, our reading of the literature (including the EU-sponsored BCA) is that there is enough information available for some form of RDM, possibly incorporating real options analysis. Initially, the analysis would have to be highly stylized. Nevertheless, by explicitly considering different scales and timing of proposed seabed mining operations as alternatives, valuable information could be provided about the minimum scale of investment needed to return a profit, and the sensitivity of economic returns to different key assumptions about technology cost and productivity.

In principle, the analysis of costs, returns, and environmental impacts should be treated jointly in benefit-cost analysis. However, knowledge about the environmental effects of DSM at this time is more fragmentary than is the understanding of technology and market risks. In concept, RDM might be used in these circumstances to conduct an integrated analysis of economic and environmental impacts, relying on expert opinion and stakeholder judgements to roughly characterize potential environmental outcomes, and to explore “what if” scenarios that try to ascertain how large economic gains might have to be in order for different levels of environmental hazard to be accepted. Whether the information provided by this approach is worth the cost is an open question.

Overall, a searching examination of the economic and environmental uncertainties associated with seabed mining projects is an important requirement for precaution in the development of the DSM industry. Environmental uncertainties are the subject of a regulatory program for international waters, but there is room for additional attention on the economic side. This paper has suggested evaluation approaches that can address deep uncertainties within a benefit-cost framework of DSM projects, providing an element of precaution in the assessment of decisions about seabed mining.

21
References to Maddison (1987) and Siegel (1992) regarding long-term real returns to capital and labor are made in Issues Brief 2. Annex Table 10 is an adaptation of data from those two sources. It illustrates that for selected OECD countries, 1950-1973 was a period of atypically high productivity growth, and over the long run the rate of return on long term bonds has exceeded the rate of wage growth in the US and the UK. Rates of return to U.S. stocks and bonds shown in Issue Brief Figure 9.1 are taken from Ibbotson and Sinquefield (1989) and represent real total returns.

# **Annex Table 10 Growth in Labor Productivity (GDP per hour Worked) and Returns to Capital, 1870-1984 (average annual compound growth rates)**

||1870-1913|1870-1950|1950-1973|1973-1984|1870-1984|Real<br>Retums to<br>Long-term<br>bonds<br>1800-1990|
|---|---|---|---|---|---|---|
|France|1.7|2.0|_5.1_|3.4|2.6|-|
|Germany|1.9|1.0|6.0|3.0|2.5|-|
|Japan|1.8|1.7|7.7|3.2|3.1|-|
|Netherlands|1.2|1.7|4.4|1.9|2.1|-|
|United Kingdom|1.2|1.6|3.2|2.4|1.8|2.7|
|United States|2.0|2.4|2.5|1.0|2.1|3.4|

Sources: Maddison, A. "Growth and Slowdown in Advanced Capitalist Economies: Techniques of Quantitative Assessment, Journal of Economic Literature, Vol. XXV, June 1987, pp. 649-698; Siegel, Jeremy "The Real Rate of Interest from 1800-1990: A Study of the U.S. and U.K"., Journal of Monetary Economics 29, 1992, pp. 227-252.

Rates of return per year of education appear in Issue Brief 2 and are Mincerian rates of return (MRR) taken from Psacharopoulos (1993) for each country or region. Regional returns to secondary and higher education are taken from the same source. This study was a review of scores of empirical studies of returns to education. For the individual country returns shown in Issue Brief Table 2.4, Table II shows the original sources of the Mincerian rates of return along with the year corresponding to the data used in each study: 58
and wealth. For the variable CONTRIBUTION1 we add a third model that includes BELIEF1 as an independent variable, because individual cooperation possibly is conditional on the expected contributions of others in the group.

Turning our attention to models of collective action behaviors that support community forestry, the first three dependent variables are dummy variables. We therefore estimate probit models, which assume a normal distribution.<sup>9</sup> MONITORING and NUMTREES_FARM are count data variables. We therefore estimate the model using Poisson regression, but based on goodness-of-fit Χ<sup>2</sup> tests we reject the use of Poisson models<sup>10</sup> in favor of negative binomial count data models. NUMTREES_CF is an interval variable.<sup>11</sup> We therefore estimate the model using interval regression as was done in Bouma et al. (2008).

Unobservable community factors, including the degree of social cohesion, are potential determinants of public good contributions, as well as individuals’ participation in pro-forest collective action. Such unobservable factors are very difficult to measure, but we take account of unobserved linkages between subjects that are part of the same user group by clustering all robust standard errors at the forest user group level.

# **4. Results**

# _4.1 Descriptive Statistics_

Table 1 below presents the key descriptive statistics associated with our sample. These variables are also, as discussed above, used as controls in our regression models. Sixty-three percent of participants were men and the mean age was 39 years. Average family size is 5.5, with

> 9 Logit regression results are similar and are available from the authors.

> 10 Χ2=3686 (prob > Χ2 (312)=0.000) and Χ2=19,932 (prob > Χ2 (312)<0.001).

> 11 Categories are 0 trees; 1 to 3 trees; 4 to 6 trees; 7 to 10 trees; and more than 10 trees planted during the previous month.

16
processing trade with supplied materials is not qualified for export VAT rebates and provides further assurance for our tax evasion story.<sup>38</sup> Finally, as discussed earlier, it should be easier for a production firm to find a domestic trading firm to evade taxes through underreporting domestic selling prices than to find a foreign partner to collude through underreporting export prices. If this is true, then indirect exports will be more likely to be underreported than direct exports when (t-r) is high. As a result, the indirect export share (our dependent variable) will be lower than its true value when (t-r) is high. It can only lead to a negative correlation between indirect export share and (t-r). Given that we have already found a positive and significant correlation between them, the true coefficient should be even more positive and significant.

# **7. Concluding remarks**

We have identified a significant role for domestic middlemen in facilitating tax evasion in Chinese export trade. This supplements the classic roles of middlemen in helping to overcome imperfect information about markets, connecting buyers and sellers, and certifying product quality, etc. We have identified systematic differences in the effectiveness of this evasion strategy for different types of trade, and traders. The use of middlemen is more likely to be associated with tax evasion for differentiated products, which are more difficult for the authorities to assign benchmark prices to for enforcement purposes. This is consistent with other research on tax and tariff evasion in trade, such as the literature on transfer pricing. The association of middlemen with tax evasion is also stronger for state-owned enterprises than for either foreign enterprises or other Chinese domestic enterprises, suggesting that SOEs may have an institutional or political-economy advantage when it comes to tax evasion.

One open research question relates to the relationship between ownership type and tax evasion. The organizational type of the producer may easily be different than the organizational type of the middleman. The official data record the ownership type of the exporter of record, and as we have shown, the largest share of Chinese export middlemen are SOEs. However, for China as for other countries, it is not easy to determine the

> 38 Because export VAT rebate does not apply to export processing zones (EPZs), this can be used to perform another possible falsification exercise. Unfortunately, we do not have the access to the information on EPZs in the customs data.

29
-32 -

**Table 8: Comparing the deployment of increased resources for primary education in pairs of countries at selected levels of per capita GNP, 1993**

|Per capit|a GNP**($)**|Primary schoolin|g in Country A|Resource<br>availabil**i**ty<br>n<br>availabilit in|Percentage<br> <br>resourc|allocation of cou<br>es for primary ed<br>Reuto<br>in|ntry B's extra<br>ucation|
|---|---|---|---|---|---|---|---|
|Country A|Country B|Enrollment rate<br>**(%)**|Pupil-teacher<br>.00<br>rai<br>ratio|y<br>B relative to<br>tainA<br>that in AaI|Expansion<br>ofcvrg<br>of coverage|<br><br>Reducton<br>pupil-teacher<br>ratio|Tn<br>Total|
|200|400|47.0|44.0|1.43|52.7|47.3|100%|
|400|800|56.7|37.2|1.39|47.0|53.0|100 %|
|800|1,500|66.1|31.3|1.32|42.4|57.6|100 %|
|1,500|3,000|74.3|26.7|1.32|39.2|60.8|100 %|
|3,000|10,000|82.7|22.6|1.46|35.9|64.1|100 %|

Source: authors' calculation based on data in table 5.

a/ This column corresponds to the right-hand-side of equation (3) and is the product of country B's advantage over A in coverage and in the pupil-teache **r** atio. Note that the data are close to the figures in column 3 of table 2.5, which correspond to the left-hand-side of equation (3).

_Comparing resource availability and deployment in 1993 and 1975._ Do the same patterns in resource availability and deployment characterize richer and poorer countries in an early period? To find out, we compared the same pairs of countries presented above, using data for 1975. We chose this year because it is sufficiently far back to offer a long-term perspective and because data are still available for a sufficiently large number of countries on the key indicators.

The results pertaining to resource availability appear in figure 11, which also includes the data for 1993 as a reference.31 The pattern in 1975 is comparable to that in the later year in that the resources for primary education are always more plentiful in the richer country in each pair. For all income pairs except the first ($200 and $400), the advantage in 1993 slightly exceeds that in 1975.

> **31** Another way to view the differences between 1995 and 1993 is to compare countries at the same level of per capita GNP in the two years; the results appear in appendix A and confirm the pattems discussed here.
# **4.2.3 Social Protection**

Table 4 shows the summary statistics of those enrolled in _Cadastro Unico_<sup>_ ́_</sup> in December 2019 by their nationality. The di↵erences in household characteristics between Venezuelans and Brazilians are posted in column (3) and (6) and the standards errors are clustered at the province level and seems to be stark and statistically significant. Compared to 23 million Brazilian, about 18,000 Venezuelans are registered in the single registry. Venezuelans registering in _Cadastro Unico_<sup>_ ́_</sup> are on average poorer than their Brazilian peers. Average income of registered Venezuelan is R$85, while that of Brazilian is R$307. 72.3% of Venezuelan live in extreme poverty, with an income less than R$89 while 48% of Brazilians registered have an income less than R$89 (Figure 6a). Venezuelans registered are also more educated with 27% having some tertiary education compared to 3% of Brazilians _Unico ́_ haveregisteredhigh schoolhavingdegreestertiarycomparededucation.to 42%Figureof Venezuelans6b shows thatin20% _Cadastro_ of Brazilians _Unico ́_ havingin _Cadastro_ high school degrees. Except in Roraima and Amazonas, registered Venezuelans’ family sizes (2.8) are also slightly bigger than the registered Brazilian family size (2.7). According to Table 4, Venezuelans seem to have more younger children in the age cohort 0 to 5 years than Brazilians. Registered Venezuelan households are also less likely to have female heads and average age of heads are lower than the registered Brazilian households. Conditions of living seems to be almost similar for both Brazilians and Venezuelans, with about 54% reporting that they have simultaneous access to water supply, garbage collection, adequate sanitation and electricity. However, Venezuelan heads of the households are more likely to be employed and be self-employed than Brazilian heads, although the months worked is substantially lower. Overall, the summary statistics reveal that Venezuelans who register for social assistance are poverty-stricken and work in low quality jobs, although they are more educated and the overall results seem to be consistent all across Brazil including Roraima and Amazonas.

<!-- Start of picture text --> Brazilian Venezuelan<br>3.06% 10.45%1.66%0.28%<br>17.74%<br>15.32%<br>48.52%<br>20.91%<br>72.28%<br>9.78%<br><R$89 >=R$89 & <178 Brazilian Venezuelan<br>>=R$178 & <R$522 >=R$522 & <R$1045<br>>=R$1045 Fundamental High School<br>Graphs by nationality College<br>(a) Household Income (b) Education Level<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Figure 6: Fraction of those registered in _Cadastro Unico_<sup>_ ́_</sup> by income brackets, education and nationality 23