rafmacalaba/datause-encoder-data huggingface dataset:
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# Table 10. Average Occupancy of Vehicles in Accra, by 'ype of Vehicle, 1988

||Vehicle<br>trips|Percent|Average<br>occupancy|
|---|---|---|---|
|Cars|15,275|40.8|2.2|
|Taxis|11,951|31.9|3.7|
|Light buses|5,376|14.4|17.2|
|Heavy buses|645|1.7|33.6|
|<br>Light trucks (incl. vans)|3,416|9.1|4.0|
|Medium trucks|515|1.4|4.5|
|Heavy trucks|235|0.6|7.3|
|Total|37,433|100.0|5.6|

Source: DeLeuw-Cather Int., O-D Survey (1988, p. IV-14).

# C. Road-User Costs (RUC)

The final steps in the computation are the estimation of the externalities associated with each type of vehicle and the weighting of these externalities, given the vehicles' different distribution of travel between urban and interurban roads. The estimate of the externality imposed by the different types of vehicles depends primarily on the evaluation of the damage they inflict on paved and unpaved roads. The recent GHA survey indicates that the number of ESAL per vehicle is 0.8 for buses, 1.1 for medium trucks, and 4.0 for heavy trucks. Earlier estimates were much higher. Adapting SWKP estimates, the weights are 1.5, 2.5 and 12 (Newbery's weights were 0.5, 0.6 and 5.5). Furthermore, some studies assume that the damage inflicted on unpaved roads is proportionate to the number of axles (i.e., 2, 3, and 4.15 for light, medium and heavy trucks), whereas others assume that the damaging effect of the axle of a heavy truck is three times that of other vehicles.

The two sets of assumptions are incorporated in the upper and lower panels of Table 11, which presents the different externalities by type of vehicle. To compute these externalities it is assumed that RDE per km driven on paved roads is CO.9 per ESAL-km, and for unpaved roads ¢0.23 per axle-km (C0. 155 for the second panel). The MCC are C0.03 per PCE-km on interurban roads and CO.3 on urban roads.

To compute the average road damage externality it is assumed that the shares of travel on paved and unpaved roads for each type of vehicle are 75:25, respectively (based on Table 5). To compute the average congestion costs one needs the shares of travel on urban and
Schroeder 1983: 116). Therefore, according to the IADB (1994: 180), local governments must raise their own revenues, because “if they do not, the whole rationale for improved economic efficiency and enhanced governance is in jeopardy.” Jones and Stuart (1983: 94) summarize the claim in favor of local revenue generation in the following way: To achieve genuine local accountability, the local government financial system should ensure that local authorities draw the buck of their income from their own local taxpayers and voters, and that the latter are aware that they are paying their taxes to support local government services.

Despite these claims, it is not clear how the relationship between revenue sources and the level of local accountability plays out. Actually, other researchers argue that the link between local revenue generation and accountability is highly contested. This is particularly true because local governments are always reliant, at least to some degree, on intergovernmental transfers and other non-local sources of revenue (Gloppen and Rakner 2002, Devas 2002). In order to explain this relationship, Moore (1998) developed the concept of “earned” income. Such concept proposes that income is "earned" according to the level of effort states exert in working with citizens. States earn income to the extent that they (i) deploy an extensive organization to collect it, and (ii) provide reciprocal services to citizens. The more a state earns its income through a bureaucratic apparatus for tax collection, the more it needs to enter into reciprocal agreements with citizens about provision of services and representation –in exchange for tax contributions. The greater the dependence of governments on “earned” income, the more likely state-citizen relations are to be characterized by accountability and responsiveness.

Moore applies the concept of “earned income" at the national level, but there is no reason why it cannot be used at the local level. In fact, Fjeldstat (2002) argues that since local authorities interact more closely with citizens than other organs of the state apparatus, local government revenues may be considered a more relevant testing ground of the concept of "earned income" than the central government.<sup>9</sup> Traditionally, local public finance identifies three main sources of local revenues:

- own-source revenues

- intergovernmental transfers

- local borrowing.

In addition, local governments in poor countries have two other sources of revenue:<sup>10</sup>

- international aid, and

- locally generated community contributions.

> 9 One potential problem with establishing a link between accountability and "earned income" at the local level in developing countries is that the number of payers of property taxes –the main local tax– is usually limited. Therefore, the argument fails to account for all those citizens that do not contribute to the financing of local public services, but that benefit from their provision.

> 10 These two additional sources of local revenue do not commonly form part of local budgets, so they are often excluded from studies on local public finances.

7
aggregate time series or cross-sectional regressions have the potential to reveal the more subtle indirect effects of demographic change, this approach brings its own problems. The implementation inevitably raises concerns about the possible endogeneity of demographic variables and what control variables to use. Schultz (2004) raises a number of concerns about the robustness of the existing cross-country evidence on the demographic determinants of aggregate savings. The robustness of the recent findings from cross-country growth regressions that a higher population growth rate lowers the rate of GDP growth can also be questioned in the light of the results of Sala-I-Martin et al. (2004).<sup>2</sup> As this paper will show, the contribution of demographic changes to aggregate poverty measures is non-parametrically identified. The calculations presented in this paper quantify the first-order poverty impacts of selective mortality and fertility without a regression model. The calculations suggest that the annual impacts of the demographic differentials on poverty account for a sizeable share of the trend rate of “$1 a day” poverty reduction in the developing world. At the same time, the results point to the need for better data on socio-economic differentials in mortality and fertility, and for further research on the social and economic processes linking demographic changes to the evolution of poverty measures.

However, the approach taken here shares a limitation of the cross-country regressions cited above, namely that it does not tell us that any specific demographic policy (such as family planning or promoting female education) will be effective against poverty. Nor do the findings of this paper even establish that policies that operate via demographic variables are any more important than other policies. Promoting economic growth and/or improved distribution from the point of view of the poor may simultaneously reduce fertility and poverty.

> 2 In a meta-study of all the cross-country growth regressions with an average of seven regressors (chosen from 67 candidates drawn from the literature on cross-country growth regressions) Sala-I-Martin et al (2004) report that the rate of population growth is only significant in one fifth of cases.

4
than their population share are more likely to vote for the political parties closest to the most recent non-democratic regime. We identify these parties based on the information provided by the Economist Intelligence Unit’s country reports and other national sources. For each state or region, we compute the vote shares that these political parties received in the first lower house election after transition to democracy.

We collected data for 118 regions in six Latin America countries that have transitioned to democracy since the 1980s and that have political groups close to the previous non-democratic regime (Bolivia, Brazil, Chile, Mexico, Paraguay, and Uruguay)<sup>18</sup> . The model we estimate is:

> where for each country _i_ ,<sup>_s_</sup> _ij_<sup>is the share of votes in the elections for the lower house going to the</sup> parties close to the pre-democracy regime in region _j, rep ij_ is the log of the measure of district _j’_ s over or under-representation described in Section 3.1, _xij_ a set of climate (rainfall and temperature) and geography (elevation and a landlocked dummy) controls, and the<sup></sup> _i_<sup>’s are a full</sup> set of country fixed effects.

The results in Columns 1 and 2 of Table 4 document that overrepresented electoral regions are more likely to vote for representatives belonging to the political parties close to former non–democratic regimes in the first election after transitioning to democracy. Our estimates imply that a one-standard deviation increase in the log of overrepresentation is associated with an increase of between 6.4 and 10 percent in the vote share going to parties close

> 18 We consider the following years for the low chamber elections and the following parties to be closest to the former non-democratic regime:

- Bolivia, 1989, the A.D.N. party.

- Brazil, 1990, the P.D.S. party.

- Chile, 1989, the Alianza coalition.

- Mexico, 2000, P.R.I.

- Paraguay, 1996, the Colorado party.

- Uruguay, 1984, the Colorado party.

> The case of Peru is also interesting, but we do not include it here because, since the 1991 reform, Peru has a lower chamber with only one nationwide electoral district and therefore it is not possible to compute the degree of overrepresentation for each region. This reform was implemented during the Fujimori dictatorship, and in the 1990 election the degree of over-representation was negatively correlated with the percentage of support for Cambio 90, the political group closest to Fujimori in the 1990 elections. Therefore, the 1991 Fujimori reform can also be explained with our theory.

12
|**3**|
|---|
|**...**<br>**...**|

# **4.4.7. What works and researches are necessary for obtaining results that can have practical application?**

Please, indicate, if it is possible, time, financing rate, organizations that are necessary to carry out the works.

# **Possible consequences after industrial application of research results.**

(how does realization of the project results interact with existent technological processes; is essential modification of existent technological processes required?)

# **4.5. INTELLECTUAL PROPERTY 4.5.1. Forms of legal assistance of intellectual property the project has been started**

**with** Invention Trade model Production piece, industrial standard Trade mark Scientific publication Software Database Topology of integrated circuit KNOW-HOW

# **4.5.2. Objects of intellectual property that are to be legalized on completion of the**

**project**

Invention Trade model Production piece, industrial standard Trade mark Scientific publication Software Database Topology of integrated circuit

**4.5.3. Owner of intellectual property on the project. What are those rights based on?**

# **4.5.4. Publications representing works on the project.**

**4.5.5. What exhibitions, conferences with materials on the project theme have you taken part in?**

86
differences in technological development, access to modern inputs and irrigation, and farm management practices. Table 1 shows large differences in yields across major producing countries. Groundnut yields are highest in the United States and China, and lowest in SSA (except South Africa) and India. Yet between the US and China, important differences exist in farm practices. While in the U.S. production is highly mechanized, the high yields in China reflect high labor-intensive farming practices in small plots of land and intensive use of draft animal (Colby et al. 1992). The low yields in Africa and India are the result of limited use of modern inputs including high-yielding seed varieties and high dependence of production upon rainfall.

Driven by a tremendous growth in China, global production of groundnuts exhibited a strong expansion between 1981 and 2000. Shelled groundnut production grew by 34 percent between 1981-85 and 1996-00. Growth in production has however been uneven across countries. As Figure 1 shows, production growth in China has been impressive, following Chinese 1978 market reforms<sup>2</sup> . This country doubled its production between 1992 and 2000. Groundnut production in India grew up from 1987 to 1998, before dropping to its 1970s level of 6 million tons in recent years. Indian production exhibited however important fluctuations throughout this whole period. Production in SSA picked up in the early 1990s, after a long period of decline. Groundnut production in the United States and Argentina stagnated around 2 million and 300,000 metric tons respectively over the last three decades.

<!-- Start of picture text --> Figure 1. Trends in Groundnut Production Among the World's Main Porducers<br>(million metric tons)<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>China SSA Argentina India USA<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> Source: Oil world.

Table 2 compares the economic cost per acre between the U.S. and China, the two countries of comparable yields and that account for about half of the world groundnut production

> 2 China took also advantage of increased use of high yield varieties (HYV) and agricultural inputs (fertilizers, pesticides, insecticides, mechanization, and irrigation) to boost yields and production (Colby et al. 1992).

6
typically exhibit positive skewness, means tend to reflect the wellbeing of those well above the median and are less informative of the cost of basic needs in a society.<sup>23</sup> Moreover, the mean will be more sensitive to surveys missing top incomes, and as such the median is a more robust measure of ‘typical’ incomes. For these reasons, our preferred specification is based on median and not mean consumption level of the country.

The regression of national poverty lines on the median level of a country’s consumption for our full sample has an intercept of 0.98 and for the 2011 sample, this value is 1.01. These values are not statistically significantly different from each other. The slope coefficient for the full sample is 0.53 and that for the 2011 sample is 0.50, and these are similarly not different from each other. That the more recent subsample of lines from circa 2011 results in estimates that are not different from the full sample suggests that the relationship uncovered by the full sample is both relatively stable over time and continues to be reflective of the relationship for some of the most recent poverty lines in our sample.

# _Attributes of the proposed SPLs_

The underlying motivation for using national poverty lines to construct an estimate of an international relative poverty line is based on the notion that this relative line should reflect typical assessments made at the country level on how basic needs change as countries get richer.

That the parameter estimates from both samples round to a line that has an intercept of one dollar and increases at a rate of 50 percent of the median, offers the additional attribute of simplicity in

> 23 Our preference for the median also corresponds with the theoretical analysis of Aaberge and Atkinson (2013) who use the median as a “watershed” point of reference on the consumption (or income or wealth) distribution for identifying the rich and the poor.

16
|Education|‐0.001|0.003*|0.003**|0.011***|
|---|---|---|---|---|
|Wage work|0.001|0.002*|0|0|
|HH business|‐0.024***|‐0.022***|‐0.009***|‐0.005|
|Assets|0.124***|0.114***|0.103***|0.054***|
|Land|0.006*|0.005*|0.007**|0.011***|
|Access local markets|‐0.005**|‐0.004**|‐0.002**|‐0.002|
|Access local roads|0.037***|0.052***|0.028***|0.005|
|**_Returns_**|0.072**|‐0.120***|‐0.106***|‐0.119***|
|Demographic Structure|0.255***|0.064|0.025|0.216***|
|Education|‐0.186***|‐0.017|‐0.003|0.066*|
|Wage work|‐0.003|0.010|0.001|0.012|
|HH business|0.123**|0.162***|0.056|0.077|
|Assets|‐0.266***|‐0.169***|‐0.156***|‐0.244***|
|Land|0.016|0.022**|0.019**|0.035**|
|Access local markets|0.055***|0.049***|0.021**|0.030*|
|Access local roads<br>_Note:_ _Extreme poor_are pop<br>decile;_middle class_are in th<br>_Source:_HBS 2007 and 2011|0.011<br>ulation groups in<br>e fifth decile, and t<br>/12.|0.045***<br>the bottom 10 perc<br>he_richest a_re in the|0.011<br>ent of the distribution<br>top decile.|‐0.022<br>; the_poor_are in the third|

We also apply the unconditional quantile regression decomposition technique to analyze the factors contributing to the variation of consumption between 2008/09 and 2012/13 using NPS data.

The results are presented in Figure 11. Similar to the HBS data, NPS reveals a quite significant improvement in household endowments over time; however, in contrast to the HBS results, the improvement of endowments is more marked for the richest population groups. We also observe a quite significant deterioration of returns, particularly for the poorest groups, that have offset the endowments’ improvements, inducing a decline in total consumption. It is important to note that in this decomposition procedure, consumption is adjusted by the (inter‐year) survey price deflators. The use of the CPI for adjusting consumption would have shown a less sharp decline in returns and a slight increase in overall consumption over time; however, the variations across the different deciles would have remained unchanged.

As for HBS, the results in Figure 11 indicate that the increase of the endowments is driven by a significant expansion of asset ownership, mainly transportation and communication means. Educational attainment of household heads has improved as well but to a lesser extent. Access to local markets seems to have improved but only for better‐off households. The NPS findings also indicate a potential decline in households’ engagement in business activities, but the results are not significant.

43
6 65 divided by those aged 20–64) is anticipated to increase from 25 percent today to almost 45 percent in the year 2035<sup>5</sup> . Demographics will also affect the age composition of the electorate. The age of the median voter, for example, will increase to approximately 52 until 2025. The factual veto right of the population will put tight limits on politically feasible reform of the pension system.

**Figure 4  Life Expectancy and Fertility Rates**

<!-- Start of picture text --> 90 3<br>2.5<br>85<br>2<br>80<br>1.5<br>75<br>1<br>70<br>0.5<br>65 0<br> life expectancy at birth, men life expectancy at birth, women<br> fertility rates<br>1950 1960 1970 1980 1990 2000 2010* 2020* 2030 2040* 2050* 2060<br><!-- End of picture text --> Source: Bundesamt für Statistik (2005), p.36 and p. 103

**Figure 5  Projected Dependency Ratios and Labor Force Participation Rates**

<!-- Start of picture text --> 90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>1999 2000 2010 2020 2030 2040 2050 2060<br>>= 65 old (in %)<br>Old-age dependency ratio (in %)<br>labor-force participation rate (in % of total population)<br>labor-force participation rate of 15-64-old ones (in %)<br>Source: Bundesamt für Statistik (2005), p. 100<br><!-- End of picture text --> 5Foreign immigrants, which make up approximately 20 percent of the work force, are net contributors at present. An increase in immigration is not really considered an option due to political resistance and the fact that the fertility rate of second generation immigrants is very close to that of Swiss citizens.
convince in all estimation exercises. Especially in the estimation of the important poverty headcount (FGT0), the multiple imputation techniques are virtually unbiased.

# **Application to Mogadishu**

In late 2014, consumption data using the proposed rapid methodology were collected in Mogadishu using CAPI. The rapid consumption questionnaire did reduce face‐to‐face time considerably. A household visit took about 40 minutes on average (median: 35 minutes) including greeting, household roster and characteristics, consumption module as well as perception questions. Nine out of ten interviews took less than 65 minutes.

After data cleaning and quality procedures, 675 households with consumption data were retained.<sup>10</sup> A welfare model was built to predict missing consumption in optional modules. The welfare model is tested on the core consumption (after removing the core consumption as explanatory variable). The model for food consumption retrieves an R2 of 0.24 while non‐food consumption is modeled with an R2 of 0.16 (see Table 3). It is important to emphasize that these models give a lower bound of the R2 compared to the models used in the prediction as the prediction models include the core consumption as explanatory variable. Given the assessment of the different estimation techniques in the last section, the multivariate normal approximation using multiple imputations is applied to the Mogadishu data set.

For the Mogadishu data set, the assignment of items to modules had to be refined manually.<sup>11</sup> The refinement has minor impact on the share of consumption per module (Table 2). It is peculiar though that the share of consumption per module is very different between Hargeisa and Mogadishu. Using the Hargeisa data set, 91% of food consumption (76% for non‐food consumption) is captured in the core module. In contrast, the core food consumption share is only 64% (for non‐food consumption 62%) in Mogadishu before imputing consumption of non‐assigned modules. Thus, employing a reduced consumption module based on consumption shares identified in Hargeisa would have crudely under‐ estimated consumption in Mogadishu without the possibility to evaluate the inaccuracy. In contrast, the

> 10 While the survey also covered IDP camps, the presented analysis is restricted to households in residential areas excluding IDP camps.

> 11 The manual refinement is necessary to ensure that items like ‘other fruits’ cannot double count types of fruits not assigned to the household. This is implemented by relabeling and manual assignment to modules. In addition, some items grouping several sub‐items were split into single items, which is generally preferable for recall and recording as well as calculation of unit values.

14