rafmacalaba/datause-encoder-data huggingface dataset:
80 excessive monetauy growth, high capacity utilization, wage-push and devaluation. Two of these factors, namely, excessive monetay growth and wage-push are relevant in the present context. To ihe extent the fiscal deficits of the publicly-owned electricity sector are monetized, low electricity prices wold lead to high fiscal deficits and thus increased money creation. The effect of an increase in electricity prices would in such a case have a dampening effect on inflation. From 1986 to 1988 deficits of the electricity sector declined firom Colombian Pesos 81.5 to 55.7 billion. The revenue effect of electricity price changes was relatively positive. Assuming the price elasficity of demand for electricity equal to zero, had ihe price refomi not been implemented, se es imated shortfall in government revenues would have been equal to Col. Pesos 184.5 bill. or 4.36 percent of govenmment revenues in the same year.' Table 6.7 summaizes selected econonmc indicators.

_Table 6.7: Colombia: SelekcedMacroeconomc Indicators, 1984-88_

||1984|1985|1986|1987|1988|
|---|---|---|---|---|---|
|GDP ( Peraet change p.a.)|3.4|3.1|5.8|5.3|5.7|
|Currentrevec (C. Pcsosbill.)|1261|1651|2394|3229|4233|
|(Percentchangep.a.)|23|31|41|35|31|
|_Change in Price Index (l4 p.a.)_||||||
|<br>Gracl price level|22.2|24.9|29.2|22.8|27.0|
|<br>CPI|16.1|24.0|18.9|23.3|28.1|
|WPI|18.3|24.9|22.0|25.2|28.3|

Another impact of electricity prices on inflation is the extent to which such an increase triggered wage nfton via the labor unions' attempt to matain real wages. A crucial assunption here is a downward rgidity of real wage. However, there is little evidence to support this hypothesis. On the contray, 1he coraction in real wages observed across 91a sectors in 1985 indicates wage flexibility in ihe Colombian labor zraket Although in 1987 wages had recovered to their 1984 levels, there is little evidence to support the hypothesis of an wage-push led by electricity price increases dwing the period 1986.88. The main reason for inflation in Colombia during 1986.88 was growing fiscal deficits in 1987,

> 47Seem=oc 1.7 frdctbaisrdetWcaca of rew See also, discussion of *cimpac in apter 1.
Roads are archetypal of public economic infrastructure. While telecoms, power and railways are often privately financed, the practical scope for private financing of roads in developing countries has proved to be extremely limited. Yet over recent decades donors have shifted their support from such infrastructure, which was the initial rationale for aid, to social priorities, as exemplified by the Millennium Development Goals. In low-income counties this may have contributed to the deterioration in provision: for example, there is evidence that since the 1980s the African road stock has actually contracted (Teravaninthorn and Raballand 2009).

If poor countries must self-finance much of their road networks, their costs of construction and maintenance become more important. Where costs are unusually high, it is useful to discover why. If the cause of high costs is readily remediable, then it can become an objective of policy. But even if high costs are attributable to factors that are beyond influence, there are important implications. Connectivity is essential for economic development. It enables trade, which in turn enables people to harness the productivity gains that come from specialization and scale. However, the density of a national road network necessary to achieve a given level of connectivity depends upon population dispersion. Connectivity can potentially be increased either by building more roads for a given dispersion, or by encouraging people to relocate into larger settlements. A country in which roads are unalterably very expensive should give greater priority to reducing dispersion. Hence, in studying variation in the unit cost of roads, it is useful to discover both the extent of variation, and the likely reasons for that variation.

Given this research agenda, the contribution of this paper is twofold. First, it provides quantitative evidence on unit costs of road construction and maintenance across a large sample of low and middle income countries. We use a dataset that consists of 3,322 unit costs of work activities across ninety-nine countries obtained from the World Bank (World Bank 2006). To make meaningful comparisons of unit costs of construction data, one needs detailed information on the year and location of the work activity, type of costs (estimated, actual, or contracted) and the specificities of the construction or maintenance activity (what type of road work activity it is). All of these variables are present in our dataset. Second, 2
_INTRODUCT ION_ The World Bank's reviews of railway crlses 2 show very clearly that they do not come about suddenly, nor do they happen by accident. Although the points of emphasis may differ, this conclusion Is Just as true of rallways In the developed as In the developing world. As a broad generalization, railway crises occur because railways have not been encouraged, or allowed, to respond to changes In the economies they serve. Long after major segments of rallway traffic have been captured by competitors which are often privately owned and operated, railways continue to offer services which are not In demand, at prices which are often far below cost, and wlth a quality of service which Is Inferior to the customer's needs. Typically also, as the railway becomes a fiscal drain on an economy already short of resources, longer range maintenance and capital needs are neglected, further diminishing the railway's capabilities as the years pass. The longer the problem continues, the more difficult and expensive It Is to resolve, and the more likely It Is to be *put off until next year".

Across all economies and cultures, this situation Is the result of some or all of the following forces:

- (1) Th.e rallway Is goneraly one of t-e nat,n.s oldet In-atitutions, and Its years of history have endowed It with perceived roles--such as a "public service obligation"--and an associated engineering and production-oriented management culture which are uniquely resistant to change.

- (2) The railway often has the largest single unionized work force In the nation, giving Its workers a great deal of political power which Is used to protect the size of the labor force, even when there Is little productive work to be done.

- (3) Over the years, various classes of passengers (typically commuters and third class Intercity passengers) and shippers (often agricultural Interests and major government-owned mining or Industrial enterprises) have been able to persuade the regulatory authorities of the government to distort the rate structure In their favor. The stated rationale for the Intervention In freight rates Is as predictable ("the nation needs to control freight rates In order to promote exports, or to control Inflation") as the result: nothing positive Is achieved because the resulting deficits are merily shifted from one agency budget to the other, and the management Incentives of both railway and shipper are badly distorted. Regional Interests also believe that the existence of rail service (but not, necessarily, Its use) Is Important elther to maintain the local economy, or to protect the possibility of a desired future development program. Eventually the beneficlaries of the system of cross subsidies come to believe

I
where S<sup>is the share of total subsidy accruing to income group</sup><sup>_i_from travel mode</sup><sup>_j_, xis</sup> ij ij the average monthly expenditure by a household belonging to income group _i_ for travel mode _j_ and ni<sup>= fraction of households in income group</sup><sup>_i._The incidence figures in Table</sup> 11 thus apply to any level of bus and rail subsidies that are a percent of the fare.

**<u>Table 10.  Bus and Rail Expenditures and Subsidies, by Income Group</u>**

|**Income Group**|**Percent of**<br>**Sample**|**Monthl**<br>**Household**<br>**(R**|**y Average**<br>**Expenditure**<br>**s.)**|**Monthly**<br>**Household S**|**Average**<br>**ubsidy (Rs.)**|
|---|---|---|---|---|---|
|||**Bus**|**Rail**|<br>**Bus (29.6%) **|<br> **Rail (1.23%)**|
|<5K|26.6|151.4|89.2|44.8|1.1|
|5001-7500|27.8|195.2|123.6|57.7|1.5|
|7501-10000|21.9|221.5|165.2|65.5|2.0|
|10001-20000|17.8|285.8|226.6|84.5|2.8|
|>20K|5.9|274.6|295.7|81.2|3.6|

**<u>Table 11.  Incidence of Bus and Rail Subsidies, by Income Group</u>**

|**Income Group**|**Percent of**<br>**Sample**|**Percent of T**<br>**Ben**|**otal Subsidy**<br>**efits**|<br>**Percent of H**<br>**Who Receive**|**ouseholds**<br>**Subsidy**|
|---|---|---|---|---|---|
|||**Bus**|**Rail**|**Bus**|**Rail**|
|<5K|26.6|19.1|15.5|90.0|73.9|
|5001-7500|27.8|25.8|22.5|93.6|83.2|
|7501-10000|21.9|23.1|23.8|94.3|90.8|
|10001-20000|17.8|24.2|26.5|92.3|87.6|
|>20K|5.9|7.8|11.6|81.8|79.4|

**<u>Table 12.  Errors of Inclusion and Exclusion of Subsidy</u>**

|**Percentage of Households.....**|**Poor (< 5K)**|**Non-Poor**|
|---|---|---|
|ReceivingBus Subsidy|90.00|92.55|
|Not ReceivingBus Subsidy|10.00|7.45|
|ReceivingRail Subsidy|73.94|86.23|
|Not ReceivingRail Subsidy|26.06|13.77|

An equal distribution of subsidy benefits implies that the percentage of subsidy benefit received by an income category equals its share in the population. Our findings indicate that while the poorest households constitute 27 percent of total households in the sample, they receive only 19 and 15.5 percent, respectively, of bus and rail subsidies. The wealthiest households, who constitute less than 6 percent of the total sample, receive 15
**Figure 8: Distribution of quasi-fiscal deficits between consumer categories**

<!-- Start of picture text --> 0.1%<br>2.3%<br>6.4%<br>Residential<br>Commercial<br>41.8% Industrial<br>26.4%<br>Government<br>Export<br>Other<br>22.9%<br><!-- End of picture text --> _Source_ : World Bank staff calculations based on utility data.

# 5.3 Quasi-fiscal deficits under benchmark performance

As explained in section 2, benchmark performance is defined as 100 percent bill collection rate, 10 percent T&D losses, and staffing at benchmark levels. A breakdown is provided in Table 8.

**Table 8: Quasi-fiscal deficits under benchmark performance**

|**Country**|**Absolute QFD under**<br>**benchmark performance**<br>**(current US$ million)**|**Relative QFD unde**<br>**(% current GDP)**|**r benchmark perfor**<br>**(% revenues)**|**mance**<br>**(% cash collected)**|
|---|---|---|---|---|
|Benin|-$27|-0.3|-13|-13|
|Botswana|$442|3.0|158|159|
|Burkina Faso|$60|0.5|23|23|
|Burundi|$14|0.4|72|97|
|Cameroon|-$8|0.0|-2|-2|
|Cape Verde|-$5|-0.3|-6|-7|
|Central African Republic|-$2|-0.1|-21|-27|
|Comoros|$5|0.8|46|80|
|Congo,Rep.|-$13|-0.1|-13|-17|
|Côte d’Ivoire|$237|0.7|27|32|
|Ethiopia|$402|1.1|223|258|
|Gabon|-$40|-0.2|-12|-12|
|Gambia,The|$11|1.3|26|35|
|Ghana|-$70|-0.2|-7|-8|
|Guinea|$57|0.9|109|156|
|Kenya|$240|0.4|20|20|
|Lesotho|$3|0.1|6|7|
|Liberia|$0|0.0|0|0|
|Madagascar|$43|0.4|28|46|
|Malawi|$50|1.1|37|40|
|Mali|$72|0.6|29|29|
|Mauritania|$21|0.4|20|25|

39
known as the Central Line are shown in green in Figure 1. The fifth corridor, also operated by the Central Railway, and known as the Harbor Line, appears in blue.

Train service runs from 4:00 am to 1:00 am at intervals of 3 minutes during peak hours and between 5 to 10 minutes during other hours of operation (except late at night, when the headway is about 20-30 minutes). There are also fast trains that do not stop at all stations. The suburban services are currently run by electrical multiple units (EMUs) which are operated using a 1500 Volt DC power supply using overhead centenary. The Western and Central Railways together operate about 2,067 daily one-way trips using 184 rakes (train sets) of 9-cars and 12-cars. The 12-car EMU rakes are utilized mostly during peak rush hours.

Passengers have an option of traveling in first-class or second-class compartments. Women and men travel in separate carriages. Due to high demand, there is serious overcrowding in second-class compartments, with as many as 5,000 passengers traveling per 9-car train during peak rush hours.<sup>8</sup> This can result in 14-16 standing passengers per square meter of floor space.

The two corridors of the Western Railway and three corridors of the Central Railway carried approximately 6.5 million passengers each day in 2004-2005 and 6.2 million in 2003-2004 (see Table 8).<sup>9</sup> Revenues and costs for suburban rail operations in Mumbai in 2005-2006 (the same period used for computing bus subsidies), appear in Table 3. According to official figures, suburban rail operations posted a loss of Rs. 123.8 million, implying a subsidy of 1.2% of fares.

**<u>Table 2.  Annual Passenger Volumes and Revenues on Mumbai Suburban Railways</u>**

|||**Wes**<br>|**tern R**<br>|Mum<br>**ailway**<br>|baiSuburb<br>**s**<br>|an Railway<br>|**Cent**<br>|**ral Rai**<br>|**lway**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|**Year**|**No. o**<br>_(i_|**f Passen**<br>_n Million_|**gers**<br>_s)_|**Lead**<br>_(in_|**Earnings**<br>_(Rs._|**No. of**<br>_(in _|**Passenge**<br>_Million)_|**rs**|**Lead**<br>_(in_|**Earnings**<br>_(Rs._|
||**Ist**<br>**Class**|**IInd**<br>**Class**|**Total**|_Km.)_|_Million)_|**Ist Class**|**IInd**<br>**Class**|**Total**|_Km.)_|_Million)_|
|2003-04|109|1,037|1,146|27.1|4,140|80|1,020|1,100|30|4,790|
|2004-05|112|1,043|1,155|29.1|4,270|80|1,080|1,160|30|5,070|

Source: Vivek Sahai (2006)

> 8 The peak rush hours are 7:00 AM –11:00 AM and 4:00 PM- 9:00 PM. http://www.mrvc.indianrail.gov.in/intr.htm

> 9 The figures in Table 2 apply to all the rail lines pictured in Figure 1.  These extend beyond Greater Mumbai to cover the entire Mumbai Metropolitan Region (MMR).

6
# **3 Econometric Strategy for Imputing Missing Data**

Despite the additional data for 66 non-OECD destinations in 2000 and 27 non-OECD destinations in 1990, our bilateral migration matrices remain incomplete. It is important to emphasize that despite lacking data for a fairly large number of destination countries, the raw data that we have collected nevertheless comprise around 85% of the total in 2000 and two-thirds of the total in 1990. The second major contribution of our paper is in imputing, to the greatest extent possible, those cells for which data are still missing, along both the gender and education dimensions. To this end, we develop a three-step estimation procedure, based upon the most up-to-date theoretical and empirical advances in the literature. We subsequently use the resulting parameter estimates from our empirical exercise to predict the bilateral migrant stocks for cells for which we lack data. While no doubt second-best, given the wider paucity of migration data, we deem our methodology worthwhile in the sense that our results, especially when aggregated over regions, still provide superior estimates of the global winners and losers in the global contest for high-skilled migrants (in a static sense) as when compared to their total absence.<sup>8</sup> A fair comparison to our intuitive approach, which is widely accepted in the literature, are global GDP figures, balance of payments components and international capital flow estimates, which for many countries are computed in light of

> 8In other words, given migrant inflow and outflow data disaggregated by skill level are not available for the vast majority of nations globally, the best available (static) estimates, (thus ignoring dynamic, brain gain effects for example), of the winners of losers of the global battle for talent can be made by comparing the total high-skilled stock in a particular country with the total number of skilled emigrants from that country for which global data on potentially all destinations are required. Even if our imputation methods are imperfect, a sensible allocation of these migrants is still superior for informing one as to the overall global situation, in comparison with the total absence of such data.

14
- 14 -

# **<u>Table 3. Number of households surveyed</u>**

||<br>Road contract lot|<br>Survey|<br>year:|||||
|---|---|---|---|---|---|---|---|
|||2012|2013|2014|2015|2016|Total|
|A1|Aposto-Irbamoda|70|64|68|67|66|335|
|A2|Irbamoda-Wadera|61|58|58|57|56|290|
|A3|Wadera-Negele|109|106|106|103|103|527|
|B1|Mekenajo-Ayira|78|77|78|77|77|387|
|B2|Aiyra-Chanka|73|73|73|73|73|365|
|B3|Chanka-Dembidolo|89|86|87|88|87|437|
|C1|Kombolcha-Burka|138|139|139|136|130|682|
|C2|Burka-Mille|102|100|99|97|103|501|
|D1|Ankober-Awash Arba|240|233|227|203|210|1,113|
|Total||960|936|935|901|905|4,637|

The summary statistics indicate that the surveyed households are significantly heterogeneous ( **Table 4** ). The household income from crop production ranges from ETB50 to ETB800,000, with an average of ETB9,700 or about US$450. Recall that per capita GDP in Ethiopia was US$706 in 2016. Livestock seems to be as important as crop production in the survey areas. The average income from livestock is about ETB8,000. Many households earn income from other activities, such as forest product sales and seasonal employment. This reflects our sampling strategy: Half of the survey villages were located close to the RSDP roads. Note that remittance income is excluded from our non-agricultural income variable because it is perhaps less relevant to our research context. In the sample, about half of the farmers participate in transactions to sell their produce at market: The average value of crop sales among those farmers is US$573, with a wide variation from only US$1 to over US$130,000. This implies that, not surprisingly, some larger-volume producers are more likely to participate in market sales and earn more than smallholder farmers.

For the crop income equation, five inputs are considered for _Xk_ : _k_ = { _L, R, I, F,P_ }, following the agricultural economics literature (e.g., Gyimah-Brempong, 1987; Bravo-Ortega and Lederman,2004). _L_ denotes the number of labor force in each household, which is assumed to include household members between the ages of 16 to 60. While _R_ denotes rain-fed areas used for crop production, _I_ represents irrigated land areas. Two advanced inputs are included: fertilizer ( _F_ ) and pesticides ( _P_ ).
# **Figure 12. Poverty headcount and shared prosperity in Indonesia in the baseline scenario 2015-2030**

<!-- Start of picture text --> 12a. Headcount ratio of extreme poverty 12b. Percentage of skilled labor to total employment<br><!-- End of picture text --> <!-- Start of picture text --> Indonesia: Poverty Headcount (%)<br>62.8<br>56.8<br>48.8<br>40.0<br>31.4<br>26.6<br>20.5<br>14.5<br>2015 2020 2025 2030<br>$3.20/day $5.50/day<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br><!-- End of picture text --> <!-- Start of picture text --> Indonesia: Skilled labor, % of total employment<br>36.8 38.9 40.9 42.3<br>4.6 4.8 4.9 4.9<br>2015 2020 2025 2030<br>Agriculture Non-Agriculture<br>50<br>40<br>30<br>%<br>20<br>10<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 12c. Average income of the top-60 over bottom-40 12d. Gini coefficient<br><!-- End of picture text --> <!-- Start of picture text --> Indonesia: Incomes of T60/B40 Indonesia: Inequality<br>4.0 44.2<br>4.0<br>43.2<br>3.9<br>42.2<br>3.7 40.6<br>2015 2020 2025 2030 2015 2020 2025 2030<br>4.0<br>44.0<br>3.9 43.0<br>Gini coeff., %<br>42.0<br>3.8<br>41.0<br>3.7<br><!-- End of picture text --> The EU-IDN, FTAAP, and TPP-15 scenarios all lead to further poverty reduction, but also to increased inequality. Figure 13a shows the growth incidence curves for each one of these three scenarios and Figure 14a shows their effects on poverty reduction. The relative size of the macroeconomic effects of the different PTAs is in line with that of both overall growth and poverty reduction. The EU-IDN CEPA yield the largest poverty reduction effect for a line of PPP$5.50 a 25
_Figure 1: Female to male labor force participation ratio_ <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>2018 2019 2020 2021<br>Algeria Bahrain Egypt Iran Jordan<br>Kuwait Lebanon Morocco Oman Qatar<br>Saudi Arabia Tunisia UAE<br><!-- End of picture text --> <!-- Start of picture text --> Algeria<br>Kuwait<br>Saudi Arabia<br><!-- End of picture text --> <!-- Start of picture text --> Bahrain<br>Lebanon<br>Tunisia<br><!-- End of picture text --> <!-- Start of picture text --> Egypt<br>Morocco<br>UAE<br><!-- End of picture text --> Source: Constructed by the authors using World Development Indicators (2022) Around 57% of individuals in the MENA region had access to the internet in 2018. By 2020, this share soared to 76%, higher than the world value of 60%. However, much of the increase was among men. According to the index of the gender gap in internet access provided by the Economic Intelligence Unit (2022), men had higher internet access than women in nine countries of the thirteen included in the region in 2021 (Table 1).

Closing the gender gap in internet access might not be sufficient to ensure that women benefit from the internet in accessing information and economic opportunities. Nonetheless, evidence suggests that it is powerful. As figure 2 illustrates, countries in which the gender gap is in favor of men (where the gender gap in the internet access index has a high positive value), such as Egypt and Morocco, have low female-to-male labor force participation ratios, while the four countries characterized by equal internet access, indicated by a 0 value for the internet gap index, have the highest female-to-male labor force participation ratio. As figure 3 illustrates, inclusive access to the internet with relevant and secure content is associated with a lower gender gap in labor force participation (The Economist Intelligence Unit, 2021b). Countries with high scores on the Internet Inclusion Index (III), which assesses the enabling environment of adoption and beneficial use of the internet, have greater equality in labor force participation rates.

6