rafmacalaba/datause-encoder-data huggingface dataset:
are considered.

Table 3: Zero-duty MFN tariff lines as a share of all MFN ad-valorem tariff lines, percent

||1996|2001|
|---|---|---|
|Australia|40.8|48.0|
|Canada|32.9|49.6|
|Colombia|1.7|2.8|
|European Union (EU-15)|13.9|18.0|
|Indonesia|19.2|21.9|
|Poland|1.5|5.5|
|Thailand|2.5|3.6|
|United States|20.8|35.2|
|South Africa|40.1|46.4|

_<u>Source:</u>_ <u>Author’s estimates using the UN TRAINS</u> database.

Effectively, zero MFN duties represent an erosion of preferences for PTA partners, who receive no tariff concessions in these tariff lines. The top (dark grey) section of the stacked columns in Figure 2 illustrates the effects of explicitly accounting for zero MFN duties in our sample. For example, removing trade in zero MFN tariff lines from the numerator lowers the share of intraPTA trade in the world total to less than 22 percent, compared with 32 percent of world trade when all trade between PTA partners is included. Large negative effects can also be observed at the regional level, where the left column height (without the top portion) declines significantly for East Asian, African, and Northern importers, but changes relatively little for Latin America, Middle East, and South Asia. For the former regions, the results in Figure 2 imply that the “value” of their intra-regional PTA tariff concessions is eroded considerably by the large share of zero MFN duty lines in their tariff schedules. For example, although 12 percent of intra-regional trade in East Asia takes place between PTA members, only 1 _._ 6 percent of intra-regional trade receives nonzero preference margins. For the Latin America, Middle East and North Africa, and South Asia regions, our findings suggest that intra-regional preferential liberalization has been taking place mostly behind high external tariff walls. With regard to North-South trade, all regions except East and South Asia experience a large erosion of preferential access to their PTA partners in the North as a result of zero MFN tariffs. Although the Asian regions exhibit a less marked decline, recall that the total shares of North-South PTA trade in these regions are very low. This suggests that even for developing countries that have been successful in negotiating PTAs with their important 21
serves. While some schemes serve fewer than 1,000 beneficiaries, others are large enough to serve more than 50,000 beneficiaries. There are 1,034 schemes with less than 1,000 beneficiaries; 600 schemes with 1,000 to 2,000 beneficiaries; and 779 schemes with 2,000 to 5,000 beneficiaries. (The rest of the schemes are larger than these subsets.) According to the survey data, 15.6% of water schemes that are 15 years and under do not possess information on the populations that they serve. As a result, we do not include these schemes when analyzing failure by size. Smaller schemes with fewer than 1,000 beneficiaries account for the single-largest group in all regions except the northwest.

Overall, WASHCOMs, at either the national or regional level, are rare. With the exception of the South East and South South regions, where private consultants dominate, Water Works is the main entity responsible for repairs. Nationally and across all regions, the pump-piped system (PPS) is the preferred way to tap groundwater. Similarly, in most schemes, and across all regions, groundwater depth (that is, meters below ground) is shallow or very shallow. However, the volume of groundwater storage (measured by the depth of stored water in millimeters) is most often medium to high across the nation, though with regional variations. Most storage is of low to low-medium volume in the northern regions, and medium or high in the south. With the exception of the north-central, northwest, and southwest regions, groundwater productivity (measured in liters per second) is medium or high. The availability of spare parts is a major problem in all regions but the southwest: slightly more than 50% of the total observations indicate this, especially in the northern regions. Nearly 831 schemes across the nation reported that they did not know whether spare parts are available.

Two other variables crucial for our analysis, but not presented in Table 1, are the age and functionality status of the schemes in question. The survey data suggest that 54% of the total 5,100 schemes are in use and working;<sup>9</sup> the rest are non-functional. The age of a scheme is calculated as 2015 minus the year of commission, but we could not locate information on the year of commission of 1,282 schemes (25.1% of the total). For the purposes of this functionality analysis, we exclude those schemes

> 9 The numbers of functional and non-functional water schemes in the total sample differ slightly from those presented in Figure 1, which includes only those water schemes for which we have reliable latitude and longitude coordinates.

12
prices). The former technique is more common at the firm level analysis. The latter is widely used at the technology level. The ex-post analysis can directly relate investment on clean technologies with production costs of firm or productivity. The ex-ante analysis focusses on whether investments on clean technologies reduce costs of energy service delivery. For example, if investments in green technologies or renewable energy technologies (e.g., solar, wind, hydro, geothermal) have helped reduce the average electricity supply costs. Similarly, in the transportation sector whether investments in cleaner vehicles, such as electric, hydrogen, and hybrid vehicles, reduce the cost of transportation services. Finally, in the buildings sector (residential or commercial), if the adoption of energy-efficient appliances or devices causes net economic gains to the households or business owners.

The study carries out an extensive review of existing empirical literature to understand the relationship between green/clean technologies and production costs in general and energy service costs in particular sectors: manufacturing, transportation, electricity, buildings, and agriculture. While retrieving existing studies, multiple databases and numerous keywords are used. The databases include EconLit, Scopus, Web of Science, Google Scholar, JSTOR, and organizational databases such as International Energy Agency and the World Bank Group. It includes peerreviewed journal articles, and academic literature and documents produced by international organizations, research institutions, and government departments. The keywords used are ‘clean energy’, ‘clean energy investment’, ‘clean energy and productivity’, ‘low carbon technologies’, ‘economics of clean energy’, ‘green energy’, ‘green energy technologies’, ‘modern energy’, ‘energy technology and economy’, R&D in energy technologies’, ‘energy and total factor productivity’, ‘clean energy and total factor productivity’.

The findings of the existing studies are mixed and inconclusive. Most studies examining the relationships between green/clean technologies and productivity show a positive relationship. However, the channels through which the investments are translated into productivity gains are not clear. Studies at the sectoral level (e.g., electricity and transportation) do not provide clear evidence of cost reductions in transportation services and electricity supply due to renewable energy technologies and electric or hydrogen vehicles. A similar outcome is observed in the buildings sector. However, investments in energy-efficient technologies, particularly in energyintensive industries reduce production costs or improve productivity.

3
|Quintile_5|5.35|13.63|0.83|0.16|0.03|0<br>20|
|---|---|---|---|---|---|---|
|Total|25.8|66.81|6.09|1.06|0.22|0.01<br>100|

Source: 2007-08 Household Survey; and authors’ calculations. The columns correspond to the number of cars that can be owned by a household (ranges from 0 to 5).

Highly subsidized prices have unsurprisingly led to excessive consumption of energy products in Libya. The household survey data imply that households consume an estimated 1.13 billion liters of gasoline per year, equivalent to about 177 liters per capita (Table 19).<sup>21</sup> To put that into context, we have extracted comparable data from the World Bank database on energy consumption for Libya and other countries in 2010.<sup>22</sup> These data suggest that per capita gasoline consumption in Libya in 2010 was 281 liters (far greater than the household survey data imply) which is also much higher than per-capita consumption in Italy (225 liters) or France (159 liters) for that year, and far higher than the world average (187 liters). Per-capita gasoline consumption in Algeria, another oil producer, is reported at 96 liters in the World Bank’s database. These statistics all point towards significant gasoline overconsumption in Libya. The same conclusion holds when comparing electricity consumption in Libya to that of other countries.

**Table 19: Household Consumption of Energy Products (in millions of units)**

||**Gasoline**<br>**(liter)**|**Diesel**<br>**(liter)**|**Electricity**<br>**(kWh)**|**LPG**<br>**(in Bottle - 15 kg)**|**Kerosene**<br>**(liter)**|
|---|---|---|---|---|---|
|Quintile_1|192.4|4.8|1,496.2|2.8|3.0|
|Quintile_2|232.9|4.9|1,700.9|2.9|5.2|
|Quintile_3|240.6|3.7|1,710.9|2.9|5.7|
|Quintile_4|241.2|4.0|1,654.9|2.8|6.7|
|Quintile_5|223.7|4.7|1,791.3|2.6|6.9|
|Total|1,130.8|22.1|8,354.2|14.0|27.4|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

Figure 5 confirms that the expendiure share of energy products is low for both the poor and rich, although the share is higher for the poor. This is shown by the negative slope of some of the curves depicted in the figure. This is not perceptible for diesel and kerosene partly because these products are consumed in very small quantities but also because these products follow a different pattern across quintiles. The share of kerosene expenditure in total expenditure in particular is very flat across quintiles.

> 21 The authorities had budgeted for 4.47 billion liters of gasoline to be sold on the market in Libya in 2013. 22 Source: http://data.worldbank.org. The data were converted from Kg to liters on the basis that 1 liter of petrol weighs 0.711 kg.

22
# **7. Conclusion**

The original “$1 a day” poverty line aimed to assess poverty in the world as a whole by the standards of what poverty means in the poorest countries. We have revisited this idea armed with a new set of national poverty lines for low- and middle-income countries, drawing on the World Bank’s country-specific _Poverty Assessments_ and the _Poverty Reduction Strategy Papers_ done by the governments of the countries concerned. We have converted these national poverty lines to a common currency using the new set of consumption PPP’s estimated from the 2005 round of price surveys done by the _International Comparison Program_ .

We find that, across countries, national poverty lines tend to rise with mean consumption above a critical level, but that the relationship is quite flat below that level. This pattern in the data is consistent with our interpretation of a national poverty line as a “social subjective poverty line,” defined as the level of consumption below which people in that country tend to think they are poor, and above which they do not.

We find that marked upward revisions to the PPPs of the poorest countries are implied by the results of the 2005 round of the ICP of price surveys. As a consequence, simply updating the old international poverty line for inflation in the US gives a poverty line that is well above the lines found amongst the poorest countries at 2005 PPP. To be consistent with the poverty lines found in poor countries the international poverty line in real $’s has to be revised downwards given the upward revisions to the purchasing power parities of poor countries. We propose instead a new international poverty line of $1.25 a day for 2005 (equivalent to $1.00 a day in 1996 US prices), which is the mean of the lines found in the poorest 15 countries in terms of consumption per capita. The fact that this has a lower real value in the US than our old “$1 a day” line does not mean that global poverty measures will also be revised downwards, given that the purchasing power of household incomes in poor countries is also lower with the new PPPs. On balance the global poverty count will tend to rise, given that larger proportionate revisions to the PPPs are implied for poorer countries.

Our results suggest that relative poverty is a more important concern than was the case 20 years ago. More countries are found in the region where the poverty line rises with mean consumption. Across our sample of developing countries, the overall elasticity of the poverty line to mean consumption is around 0.7—close to the values found for developed countries. We have also proposed a parsimonious schedule of relative poverty lines, consistent with our data on 23
FIGURE 3 HUNGARY: **CONSUMER** PRICE INDEX **(Simulated values** under altemative stabilization **strategies)** <!-- Start of picture text --> 240,<br>220 4 20 W ~~~~~~~~~~~~Simulation 4/<br>200<br>1801<br>160- Simulation 2<br>140 1<br>120j<br>-<br>100 '!'<br>-1991.01 1991.04 1991.07 1991.10 1992.01 1992.04 1992.07 1992.10<br><!-- End of picture text --> 30
**Figure 2. Minimum-to-median and minimum-to-mean wage ratio, Romania and selected OECD countries a. Romania, 2007-2023**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023<br>MW, as % of Mean MW, as % of Median<br>Percent<br><!-- End of picture text -->

# **b. OECD countries and selected non-OECD countries, 2023**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>MW as a % of mean wage MW as a of median wage<br>Percent<br>Colombia Costa Rica Chile Mexico Portugal New Zealand Türkiye Slovenia France Korea United Kingdom Luxembourg Romania Australia Poland Spain Germany Slovak Republic Israel Canada Bulgaria Greece Netherlands Belgium Ireland Hungary Lithuania Japan Croatia Czechia Estonia Latvia United States<br><!-- End of picture text --> _Source: Own estimates based on OECD data, available here: OECD data explorer Note: This indicator represents the_ _<mark>ratio</mark>_ <mark>of</mark> _<mark>minimum wages in relation to the me</mark> an and_ _<mark>median earnings of full-time employees. Using median earnings, as opposed to mean earnings, offers a more accurate basis for international comparisons by accounting for variations in earnings dispersion across different countries. The median is also less affected by extreme values (outliers) in the data compared to the mean. Outliers, which can be unusually high or low values, have a greater impact on the mean, potentially skewing the average. T</mark> he median wage is defined as follows: 50 percent of workers receive a wage below the minimum wage. Certain countries establish varied rates, varying across sectors, regions, occupations, and age groups, and this statistic can hide these differences within countries. Non-OECD countries include Croatia, Romania and Bulgaria._

**The empirical evidence on the impacts of the minimum wage on labor market outcomes and poverty is mixed and contended.**

**As economic theory outlines, in a labor market governed by perfect competition, an increase in the minimum wage is expected to trigger job cuts within the subset of workers earning that wage (as**

6
exploring potential causes of bias will require household data, our data can help in investigating whether, since LARP ended, the bias had eased in locations that had been exposed to systematic registration earlier. Table A4 shows this was not the case; the estimated coefficient on (NASpP # SyD) is insignificant.

In addition to providing substantive insights, Table 6 illustrates the danger that improper specification— either by lumping sporadic and systematic processes together or by omitting relevant policy reforms— will lead to faulty conclusions. Panel A col. 3 shows that, even if policy reforms are properly specified, aggregating systematic and sporadic initial-registration modes results in biased coefficients; in our case, the estimated impacts of policy reform are biased upward and those of registration downward, resulting in a highly significant negative point estimate of the coefficient on initial registration. The fact that omission of the 2010 reforms would bias estimated effects of 2006 reforms upward, mistakenly attributing the impact of the former to the latter, reinforces the need for specifications to be informed by implementation details.

# **5. Implications for Research and Policy**

Administrative data allows us to analyze the short- and medium-term effects of a program in Lesotho that combined systematic initial registration of rights (titling) with reforms of regulatory and institutional policies. Based on descriptive evidence that a structural break in the cost of registration coincided with the reforms, as was expected given much of the literature, we were able to document that titling had significant social empowerment effects on female co-ownership and investment. We add to the literature by showing that policy reforms affected operation of land sales and mortgage markets, especially in the medium term and for female co-owners, thus ensuring registry sustainability and translating social into economic empowerment. Beyond the focus on all property in a city rather than just informal settlements or rural areas, a major difference from previous land titling studies is our use of administrative data for a relatively long period both before and after project interventions and changes in policy. Using administrative data as a frame for household surveys to explore the underlying distributional and growth dynamics could be of great interest.

The finding that beyond the short-term effects of systematic initial registration, policy reforms associated with urban titling can activate land sales and mortgage markets raises two questions about design of programs in this area: In most contexts the potential for land and associated mortgage market activity and, if registration is easier, sporadic registration, is higher in cities than in the informal settlements or rural areas that have traditionally been the focus of titling programs. Supporting policy and institutional reforms to make it easier to register property in cities could launch a nationwide strategy while at the same time catalyzing expansion of registry coverage, activation of mortgage markets, and the realization of indirect benefits in terms of land use 18
performance measure that weights cases equally; however, the findings are similar for the “across responses” measure that weights responses equally. A comparison of the empirical cumulative distributions of performance in Figures 1a and 1b, equally weighing cases and responses, respectively, yields three findings. First, the range of observed performance is comparable across all arms. Second, the two incentive arms perform substantively and statistically better than the information arm (Kolmogorov-Smirnov tests _p_ =0.00) largely from a shift in the middle of the distribution. Third, the distributions of the rewards and penalty arms are not economically or statistically significantly We observe the same pattern in the regression results for average effects presented in Table 3. Performance in the information arm is 53 percent and 4.3 and 3.4 pp higher across cases in the rewards and penalties arms, respectively, and approximately 2 pp higher across responses. This suggests that overall, incentives increase average performance by 6 to 8 percent. These results are robust to the linear and interacted inclusion of covariates, specifically gender and job grade (Tables A.10 and A.11). For both measures, the rewards and penalty arms are statistically indistinguishable in terms of impacts on performance, with a _p_ -value of 0.29 reported in the bottom panel of Table 3. Such a lack of difference between rewards and penalties is consistent with an agent who is loss neutral, as discussed in Section 2.<sup>8</sup> As not all the cases are structured in the same way, Columns (3)–(7) of Table 3 report the impact estimates for each case separately. By and large, we find that the rewards and penalties arms outperform the information arm for each case and there is no statistically significant difference between performance in the rewards and penalty arms. The different structure of the assessment across the cases also appears to matter, as the two simple cases have higher impacts of rewards, with 5.3 pp (27 percent) and 11 pp (69 percent), compared with magnitudes of 1 to 3 pp (3 to 5 percent) for the complex cases. Even though the task structure is simple—providing a yes or no answer—it is not necessarily the case that arriving at that answer is trivially easy. Indeed, performance in the information arm is at both its highest and lowest for the two simple cases, at

> 8Table A.4 reports impacts on overall performance measured in z-scores instead of proportion correct. Our PAP, discussed in Appendix C, lists both measures of overall performance, and thus we report both. The results are robust to either way of measuring performance. However, given that the two types of tasks—simple and complex—involve different numbers of actions and thus have significantly different underlying distributional variation, we prefer the proportion of correct responses as an outcome measure.

22
<!-- Start of picture text --> 1980 1990 2000 2010 2020<br>year<br>General Gov. Public Sector<br>Central Gov.<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: ILO Stats The size of public employment, as percentage of the labor force, varies widely across regions. Middle East and North Africa (MENA) shows the highest levels of public employment in the three measures (Figure 6). Europe and Central Asia (ECA) and Sub-Saharan Africa (SSA) also show high levels of public employment compared to other regions. South Asia (SA) and East Asia Pacific (EAP) show lower levels of public employment at all the levels.

The evolution of absolute employment levels over time is volatile and noisy, so we plot the average over each decade to better visualize the trends by region (Figure 7). Most of them have a declining trend, except LAC and MENA, which show the opposite in central and general government levels since the 1990s. These regional trends are only suggestive because the country composition changes over time due to data availability.<sup>5</sup> Figure 6: Public employment (% of Labor Force), by region

> 5Figures 13, 14, and 15 in the Appendix show the three time series of employment by country.

9