rafmacalaba/datause-encoder-data huggingface dataset:
bus subsidies that are about 30 percent larger than their equal share, and about twice the rail subsidy benefits than they would receive under a uniform distribution of the subsidy across income groups. The middle income group, earning 7,500-10,000 Rs. per month receives subsidy benefits from both bus and rail in roughly equal proportion to their share in the population.

If subsidy benefits should be targeted at the poor, then the percentage of the poor not receiving subsidy benefits is an error of exclusion (Foster, 2004) and the percentage of non-poor receiving subsidy benefits would constitute errors of inclusion. For simplicity of calculation, we assume here that all users of public bus and rail receive subsidy benefits. Table 12 shows the errors of inclusion and exclusion for both the rail and bus subsidies. Errors of exclusion are 10 and 26 percent for bus and rail respectively, while errors of inclusion are 93 and 86 percent for bus and rail respectively. In our case, the errors of exclusion for the poor are higher for rail than bus. This reflects the fact that a larger fraction of persons in the lowest income group do not use rail (as opposed to bus) in spite of lower fares for rail.

The low usage of rail by the poorest income group may be attributed to the fact that households in the lowest income group live farther away from rail stations than persons in the higher income groups. However, this in turn reflects the decisions made by households regarding where to live and work. The distribution of commute distances for workers in the < 5K income group lies to the left of the distribution of commute distances for workers in all households (see Figure 3). This may reflect the fact that it is

**Figure 5.  Distribution of One-Way Commute Distances**

<!-- Start of picture text --> All HHs Poor HHs<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>0-1 1-2 2-3 3-5 5-10 10-15 15-20 20-30 >30<br>km<br>%<br><!-- End of picture text --> Source: Baker et al. (2005) 17
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Liberia’s air transport indicators benchmarked against West African neighbors**

||Benin|Cote<br>d’Ivoire|Gambia|Ghana|Guinea|Guinea-<br>Bissau|Liberia|Sierra<br>Leone|
|---|---|---|---|---|---|---|---|---|
|Total seats (millions)|0.42|1.15|0.25|1.88|0.32|0.07|0.16|<br>0.23|
|City pairs served|19|23|7|19|8|2|8|<br>8|
|Percent of seat kilometers flown in older aircraft|7|9|5|3|5|0|67|45|
|International market Herfindahl index (measure<br>of competition)|0.1|0.1|0.1|0.1|0.2|0.5|0.2|0.2|

Source: Gwillliam and others 2009.

Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data

# Water supply and sanitation

The civil conflict in Liberia adversely affected water and sanitation infrastructure in two ways. First, there was direct destruction and disrepair of existing infrastructure, as well as decline of the related institutions. Second, the conflict led to huge shifts of population out of rural areas into the cities and related camps and informal settlements, which put additional strain on urban utilities. The urban growth rate over the period between 1980 and 2000 was 3.8 percent annually. The population of Monrovia alone grew from 0.7 to 1.2 million people during the last decade of the conflict.

## **Achievements**

Relative to benchmark countries Liberia has relatively limited reliance on surface water and a high level of coverage for groundwater. The standardized Demographic and Health Surveys conducted across Africa provide the best basis for making international comparisons (table 5). According to this source, access to private taps and public stand posts in Liberia is just under 3 and 7 percent of the population, respectively, well below what is found in other resource-rich and low-income countries. This appears to be compensated by high access to wells and boreholes, which serve 76 percent of the population, a much higher level than in the peer groups. Less than 15 percent of the population in Liberia is forced to rely on surface water.

**Table 5. Benchmarking water and sanitation indicators**

||Unit|Resource-rich<br>countries|**Liberia**|<br>Low-income<br>countries|
|---|---|---|---|---|
|Access to piped water<sup>*</sup>|% pop|12.0|**2.9**|10.5|
|Access to stand posts<sup>*</sup>|% pop|12.6|**6.8**|16.2|
|Access to wells/boreholes<sup>*</sup>|% pop|49.0|**76.2**|38.3|
|Access to surface water<sup>*</sup>|% pop|23.7|**14.4**|37.4|
|Access to flush toilets<sup>*</sup>|% pop|1.6|**13.4**|4.9|
|Access to improved latrines<sup>*</sup>|% pop|6.4|**14.9**|9.9|
|Access to traditional latrines<sup>*</sup>|% pop|54.8|**15.5**|50.1|
|Open defecation<sup>*</sup>|% pop|27.6|**55.8**|40.3|
|Domestic water consumption|liter/capita/day|90.3|**55.7**|95.7|
|Revenue collection|% sales|<br>69.7|**75.0**|92.7|
|Distribution losses|% production|43.6|**28.8**|34.3|

14
# **APPENDIX C. OTHER TABLES**

# **Table C.1 Teacher Effects**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Experience Rate|0.022***|0.026***|0.024***|0.024***|
||(4.184)|(4.755)|(3.572)|(3.562)|
|Max(0, Peer Returns)|0.014**|-0.016*|0.027***|0.026**|
||(1.963)|(-1.776)|(2.772)|(2.542)|
|Min(0, Peer Returns)|0.001|0.001|0.006|0.006|
||(0.322)|(0.175)|(1.027)|(1.086)|
|Peer Volatility|-0.002|0.010|-0.002|-0.002|
||(-0.388)|(1.544)|(-0.229)|(-0.203)|
|Peer Volatility x Max(0, Peer Returns)||0.034***<br>(4.084)|||
|Peer Volatility x Min(0, Peer Returns)||-0.000<br>(-0.008)|||
|Teacher Experience (hours)|0.019|0.016|0.019|0.020|
||(1.417)|(1.231)|(0.914)|(0.948)|
|Max(0, Teacher Returns)|||-0.011|-0.017|
||||(-1.372)|(-1.609)|
|Min(0, Teacher Returns)|||-0.003|-0.002|
||||(-0.359)|(-0.238)|
|Max(0, Peer Returns) x Max(0, Teacher Returns)||||-0.012<br>(-0.518)|
|Min(0, Peer Returns) x Min(0, Teacher Returns)||||-0.001<br>(-0.190)|
|Observations|12,113|12,113|6,822|6,822|
|R-squared|0.101|0.102|0.096|0.096|
|Teacher FE|Yes|Yes|Yes|Yes|

This table shows the results of regressions on market participation. For market participation, the dependent variable is set to one for inexperienced students who made at least one stock purchase in the following 12 months after the completion of a course and is zero otherwise. Experience Rate is defined as the percentage of students in a class with trading background (i.e. students with at least one stock purchase 1 year prior to the begin of the course). Peer Returns is the average returns calculated in a 6-month window among students with trading background in a given course. Teacher returns are the portfolio returns of the class instructor in a 6-month window prior to the beginning of the training. Control variables are described in the text. The OLS regressions include instructor, time (year-month), city, and course curriculum fixed effects. T-statistics in parentheses, calculated from clustering standard errors at the course level. * denotes significance at the 10% level; ** at 5%; and *** at 1%.

59
19 payments are received in the United States, federal courts could establish jurisdiction. Eventual enforcement suits, which are related to, but not arising directly from, the bond contracts, could take place in diverse jurisdictions depending on the presence of assets, the ability to enforce judgments, and sovereign immunity laws (the execution or enforcement stage). This section tries to highlight potential controversies in the order in which they are likely to appear for suits in the U.S. and U.K.

# a. Getting Russia _into_ court: Jurisdictional considerations

There are two main issues to deal with here: serving the suit on the debtor and establishing jurisdiction (i.e. showing that the court can hear and adjudicate the suit). Russian Eurobond contracts do not appoint an agent to receive service in case of a dispute, nor submit to the jurisdiction of a court. But these issues are surmountable. The lack of an immunity waiver does not make Russia impervious to suits, as there are mechanisms under the laws of both the U.S. and the U.K. to serve and sue a foreign sovereign.<sup>53</sup> i. Service of process The U.S. Foreign Sovereign Immunities Act lays down two procedures that could be used here.<sup>54</sup> First, a creditor can send the summons, complaint and notice of suit with translations to the head of the debtor’s ministry of foreign affairs via mail requiring a signed receipt.<sup>55</sup> The U.S. Supreme Court recently confirmed the appropriateness of this procedure.<sup>56</sup> If that method does not

> 53 _Cf. contra_ J. Newman, _<u>If Putin Owes You Money, Good Luck Collecting It</u>_ <u>, The Wall Street Journal,</u> March 9, 2022. _See_ Foreign Sovereign Immunities Act (1976) for the U.S. and State Immunity Act of 1978 for the U.K. 54 28 U.S.C. Sec. 1608(a)(3).

> 55 28 U.S.C. Sec. 1608(a)(3).

> 56 _Republic of Sudan v. Harrison_ , 139 S. Ct. 357 (2019).
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA block prices of more than $1. ELECTRA, ONEA, Walvis Bay, and Windhoek have adopted a last-block tariff of more than $2/m<sup>3</sup> . The fixed charges, which are expected to be paid every month irrespective of consumption, are usually set at less than $4. Of the 20 utilities that enforce a fixed-fee or minimumconsumption charge, about half are set between $2 and $4. Only eThekwini in South Africa charges a fixed fee of more than $4.

**Figure 2A. Price of first block ($/m**<sup>**3**</sup> **) Figure 2B. Price of nth block ($/m**<sup>**3**</sup> **) Figure 2C. Fixed charges ($)**

<!-- Start of picture text --> 12<br>20<br>16<br>18<br>14 10<br>16<br>14 12 8<br>12 10<br>10 8 6<br>8 6<br>6 4 4<br>4<br>2 2<br>2<br>0<br>0 0<br><0.2 >0.2 & <0.3 >0.3 & <0.4 >0.4 <0.4 >0.4 & <0.6 >0.6 & <0.8 >0.8 <1 >1 & <2 >2 & <4 >4<br>Number of Utilities Number of Utilities Number of Utilities<br><!-- End of picture text --> _Source:_ AICD WSS Survey Database, 2007.

# Structure of unmetered water tariffs

In Africa, water metering is surprisingly widespread, with many utilities reporting 100 percent metering in their service areas. Nine utilities have implemented operating meters for all residential and nonresidential connections. For 35 utilities for which detailed information is available, the average metering ratio is 75 percent. Other utilities, including those in Nigeria and Sudan, have almost negligible or no metering. Most others fall somewhere in between. Those households without meters, however, can escape water utilities’ enforcement mechanism, resulting in revenue losses. To capture the cost of unmetered customers, a fixed charge is typically levied on a monthly basis (table 2).

6
65 percent of budgetary resources, of which a significant percentage has been financed by central government grants. There is some evidence that fiscal decentralization can indeed lead to more efficient production of local public goods and more control over programs by beneficiaries. Transfers of resources and authority to local governments are viewed as enhancing the development process. Theoretically, Bardhan (2002) has argued that the allocative benefits of transferring resources and authority from higher tier to lower tier levels of government for the provision of local public goods.

More practically, in an important study on the effect of fiscal decentralization on health outcomes in China using a panel data set with nationwide county-level data, Uchimura and Jutting (2007) find that counties in China in more fiscal decentralized provinces have lower infant mortality rates compared to those counties in which the provincial government retains the main spending authority, if certain conditions are met.<sup>19</sup> In the ultimate incentive system, the central government introduced competition between provincial and local governments for resources and also ensured that the heads of provinces with higher growth rates were more likely to get promotions than those with lower growth rates.<sup>20</sup> In Africa, particularly in many of the Francophone countries, decentralization has not been as effective or as rapid and occasionally, decentralization has meant the transfer of authority without responsibility. In some countries, it has progressed further than others. Overall, Uganda, South Africa, Ethiopia, Madagascar, and Rwanda are all the most advanced in terms of decentralization. However, by global standards, the proportion of public expenditure controlled by local authorities in SSA countries is quite limited, including those with more advanced forms of decentralization. In terms of actual fiscal control, the local governments in most SSA

> 19 Decentralization is of course not a panacea as China's large network of public hospitals, rural clinics, and private facilities are still overburdened with levels of tuberculosis and neonatal health issues that threaten the country’s progress.

> 20  In China, it is important to note that decentralization is more pronounced than would be visible when analyzing fiscal data for the reason that there is a strong reliance on off-budget funds to finance local government.

31
15 collected, then for Brazil‟s rural areas, and finally for the country as a whole. In each case, we present ; ; and .

Second, we report our estimates of the poverty and inequality effects of these price increases, by estimating poverty and inequality in the counterfactual income distributions corresponding to each of the price effects. The counterfactual income distribution corresponding to the net (or overall) price increase effect (and thus derived from equation 10) is given by (11) below.

The component-specific income distributions, corresponding to equations (7) – (9), are defined analogously. For each such distribution an inequality measure can be computed and, given constant real poverty lines, so can poverty indices. The estimates for the poverty and inequality effects of the price increases – and their subcomponents – reported in the next section are obtained in this fashion.

# **5.  Results**

Brazilian inflation started increasing towards the end of 2006 and reached a peak of around 7 percent, in June 2008. It averaged 5.3 percent for the 2007-2008 two-year period. This increase was driven mainly by food prices which rose substantially during 2007 and 2008. Food price inflation peaked in June 2008 at about 18.3%. Price growth for all other categories was roughly constant around 5 percent - or lower, for housing, residential goods and communication items (Figure 1).

Behind this large increase in the average price of food, there was substantial variation across both specific types of foods and regions of the country. Most prices started increasing in 2007. The price of grains (cereals), which grew by 80 percent in the twelve months to July 2008, led the increase, followed by that of tubers and roots (50 percent) and meat (40 percent). The price of drinks and teas showed the lowest growth, with an average of around 5 percent (Figure 2). Even within food groups there was a large variation across different parts of the country. Grain prices, for example, grew by 125 percent in Salvador, but by less than 50 percent in Fortaleza. The price of flour and pasta rose by almost 40 percent in Belém and Salvador compared to about 15 percent in Recife and Fortaleza (Figure 3).
MULTILINGUAL CHILD ASSESSMENT The average age of children in the study was 54.42 months (range 24–83 months) ( **Table 1** ). About one-quarter (27%) of caregivers were illiterate. Maternal and household characteristics were similar to those observed in the representative 2014 Kenya Demographic and Health Survey sample for the study area.

# **Psychometric Properties of the Instruments**

The internal consistency of the vocabulary measures ranged from  = 0.57 to 0.90: Cronbach’s alphas were lowest for the expressive and English receptive tests and highest for the MDAT language test ( **Supplemental Table 1** ). The internal consistency of the receptive vocabulary assessments was higher for Luo (  = 0.78) and Swahili (  = 0.76) than for English (  = 0.57). The IRR of the receptive vocabulary tests was  = 1 for Luo,  = 0.89 for Swahili, and  = 0.95 for English. The internal consistency of the expressive vocabulary test was  = 0.67, while the IRR was  = 0.95. The internal consistency of the MDAT fine motor and language tests was  = 0.94 and  = 0.90, respectively. IRR of the total score for each measure was  = 0.93 for fine motor and  = 0.86 for language.

Correlations among the baseline child development assessments ranged from _r_ = 0.32 to 0.56, and all correlations were statistically significant at the _p_ < 0.001 level ( **Supplemental Table 2** ). The three age-normalized receptive vocabulary scores were all moderately correlated with each other, the expressive vocabulary score, and the MDAT scores, while the expressive vocabulary score was also moderately correlated with both MDAT scores. The MDAT tests had the strongest correlation ( _r_ = 0.56) with each other; among the vocabulary assessments, they had the strongest correlations with Luo vocabulary ( _r_ = 0.48–0.49).

12
Table 5: Corruption

||TI|WGI|WGImed|
|---|---|---|---|
||(1)|(2)|(3)|
|TI Corruption|0.072<sup>_∗∗∗_</sup><br>(0.025)|||
|WGI Corruption||0.111<sup>_∗∗∗_</sup><br>(0.043)||
|WGI Corruption>Median|||0.154<sup>_∗∗∗_</sup><br>(0.051)|
|Log Ruggedness|0.111<sup>_∗∗∗_</sup>|0.123<sup>_∗∗∗_</sup>|0.125<sup>_∗∗∗_</sup>|
||(0.035)|(0.033)|(0.032)|
|Log of Rainfall|-.093|-.081|-.061|
||(0.07)|(0.067)|(0.07)|
|Log dist to coast|-.057|-.052|-.051|
||(0.046)|(0.044)|(0.043)|
|Population Density|0.077<sup>_∗∗∗_</sup>|0.083<sup>_∗∗∗_</sup>|0.074<sup>_∗∗∗_</sup>|
||(0.017)|(0.016)|(0.017)|
|Log of Surface Area|0.076<sup>_∗∗∗_</sup>|0.081<sup>_∗∗∗_</sup>|0.088<sup>_∗∗∗_</sup>|
||(0.02)|(0.02)|(0.02)|
|Length>than 50km|-.103<sup>_∗∗_</sup>|-.107<sup>_∗∗_</sup>|-.110<sup>_∗∗_</sup>|
||(0.042)|(0.043)|(0.043)|
|Estimate|-.023|-.023|-.011|
||(0.058)|(0.058)|(0.057)|
|Contract|-.060|-.058|-.054|
||(0.073)|(0.073)|(0.072)|
|Log of GDP pc (1985)|-.024|-.025|-.007|
||(0.044)|(0.045)|(0.046)|
|Foreign frm or JV|0.247<sup>_∗_</sup>|0.282<sup>_∗∗_</sup>|0.28<sup>_∗∗_</sup>|
||(0.135)|(0.122)|(0.124)|
|World Bank|-.005|0.005|-.017|
||(0.09)|(0.092)|(0.093)|
|Bilateral Donor|0.164|0.185|0.175|
||(0.137)|(0.138)|(0.136)|
|Obs.|3322|3322|3322|
|_R_<sup>2</sup>|0.898|0.897|0.898|

_Notes_ : Dependent variable is the log of cost per km; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects and region fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

34
with boundary condition lim _V(k(t))e-P'_ = _O_ 7he soludon for r" is given by _Vl(k)_ =log(pk) + **_B'_** p (12) where,

**_B'_** - 1 [cc'- p +klog( -b)] p

NoMe that th tern _log(1-b)_ corresponds to the loss in utility caused by the expectation of capital expropriation. From the F.O.C., consuinption is given by c, = pok. Given optimal consumption, the informal individual capital stock evolves according to _(' p) dt- bkidq1_ (13) Let the measure of infornal people at time _t_ be _(1-I&._ Then aggregate informal capital is _kl= r(1-L)E(k 1_<sup>_Iie4_</sup> When no capital flows to or from the informal secor, the evolution of informal aggregate capital is dictated by the dhange in average capita in the sector. That is, to get the equation of motion for aggregate informal capital **_(w5,_** we take expected values (over individuals) in (13). Since the q processes are uncorreied E(k4/ _ide) =E(ki/ iel) A._ Then, _dk'_ = _(a'-p-Ab)k'dt_ Hence, when no capital flows to or from the infornal sector, the informal capital growth rate ()

-12-
# **A Solow-Swan growth model**

One of the implications of the Solow-Swan growth model (Solow, 1956; Swan, 1956) is that countries with low levels of productivity should catch-up to those at the frontier. In an economy characterized by the standard production function, consisting of technology ( _At_ ), capital ( _Kt_ ) and labor ( _Lt_ ):<sup>20</sup> the rate of growth in the capital stock per worker ( _gk_ ), and therefore output per worker, is decreasing in _kt_ = _Kt/Lt_ (capital per worker). Formally: where s is the fraction of output that is saved, _δ_ denotes the depreciation rate of capital, n is the growth rate of population, and _g_ is the growth rate of technology.<sup>21</sup> Countries with lower initial capital should, therefore, grow faster, converging to the productivity level of high-income economies. However, this is contingent on several assumptions: that there are decreasing returns to capital intensity, saving rates ( _s_ ) are homogenous across economies (Mankiw et al., 1992); and, that technology ( _A_ ) is costless to replicate across borders regardless of country-characteristics.

# **B Data**

Throughout the document, productivity is measured as output per worker, measured at 2010 prices and exchange rates to the US dollar. Labor productivity measured as output per worker can mismeasure output per unit of labor input when workers or employers adjust their working hours. Total hours worked is a more accurate measure of labor input than the number of workers, but data is available for only 30 EMDEs. As a result, sizable changes in hours worked over the business cycle can generate cyclical swings in measured labor productivity per worker.<sup>22</sup> Data on other macroeconomic aggregates such as GDP are from the World Bank’s World Development Indicators (WDI) database, with data on employment from the Conference Board’s Total Economy Database (TED), complemented by data from the International Labour Organization (ILO) where TED data is incomplete. Data are available on a consistent basis since 1970 for a sample consisting of 29 advanced economies and 74 EMDEs. Six oil-exporting EMDEs which had productivity levels above those of the United States in the 1970s are excluded from the statistical analysis of beta and club convergence. For the initial overview of the current distribution of productivity levels, the sample is expanded to 126 EMDEs and 35 advanced economies.

> 20See Romer (2011) and Barro and Sala-i Martin (2004) for a textbook treatment of the Solow-Swan model of growth.

> 21 A zero-saving rate ( _s_ = 0) implies that the capital stock per worker declines at the effective rate ( _δ_ + _n_ ), reflecting both capital depreciation and population increase.

> 22Hours worked per employee can fluctuate over time. For example, in the average OECD country since 1990, average hours per worker has fallen by 6 percent. Within the OECD, average hours per worker ranged from 1,363 in Germany to 2,148 in Mexico in 2015.

25