rafmacalaba/datause-encoder-data huggingface dataset:
the lowest and highest ranges of the distribution. More significant is the fact that the losses are higher for 50<sup>th</sup> and 90<sup>th</sup> percentiles than for the 10<sup>th</sup> to 50<sup>th</sup> percentiles, indicating that the middle and upper middle classes suffer somewhat higher losses than poorer groups (with the exception of the bottom decile).

Looking at how the impact is distributed _within_ urban and rural areas, there are some important differences between the two areas (Figure 6b). The urban incidence curve shows higher impacts for the bottom 10% of the distribution and lower impacts above the 70<sup>th</sup> percentile, while the rural incidence curve is quite flat, with marginally higher impacts seen only for some of the poorest (below 10<sup>th</sup> percentile) and wealthy (70<sup>th</sup> to 90<sup>th</sup> percentile) groups. It is important to note that the percentiles of expenditures are defined with reference to each area (urban or rural). Since urban areas are better off on the average than rural areas, the k<sup>th</sup> percentile of urban households is better off than (and thus not strictly comparable with) the k<sup>th</sup> percentile of rural households.

These results imply that the urban poor, near‐poor and middle‐class (up to the 70<sup>th</sup> percentile of the urban distribution) are more adversely affected than richer urban households, while the impacts are more evenly distributed among rural households. Notably, the impact is significantly higher among urban households than among rural households for the entire range of the distribution, consistent with earlier results showing that the poverty impact is relatively higher in urban areas (refer to Figure 3).

**Figure 7: Regional growth incidence curve (crisis vs. benchmark, 2010)** Per capita income: Philippines

<!-- Start of picture text --> 0 10 20 30 40 50 60 70 80 90 100<br>Percentile<br>NCR Central Luzon Calabarzon Others<br>-2.00<br>-3.00<br>-4.00<br>-5.00<br>% Change -6.00-7.00<br>-8.00<br>-9.00<br>-10.00<br>-11.00<br><!-- End of picture text --> _Source_ : Own simulations based on FIES (2006) _Note_ : 1) GICs have been smoothed to eliminate sharp kinks in the original curves 2) Percentiles of income on the horizontal axis are applicable to the relevant group (in this case, region) The regional GIC (Figure 7) reinforces our earlier result about urban poverty in the NCR, Central Luzon, and Calabarzon. Especially in NCR, the poorest are by far the worst affected, with the effects sharply declining as one moves to the top of the income distribution. Calabarzon shows a similar trajectory, although there the middle class and the poor appear to face similar impacts, and in Central Luzon, it is primarily the middle class who bear the brunt of the crisis.

19
Table 1: Descriptive statistics

|||UCT|||TUP||
|---|---|---|---|---|---|---|
||Mean<br>(1)|SD<br>(2)|# of obs.<br>(3)|Mean<br>(4)|SD<br>(5)|# of obs.<br>(6)|
|**Panel A: Interventions**|||||||
|Total transfer size (2010 USD PPP)|963|689|14|952|496|11|
|Total transfer size (Years of baseline consumption)|0.62|0.42|13|0.29|0.23|11|
|Baseline annualized consumption (2010 USD PPP)|1573|475|13|4188|2504|11|
|GDP per capita (2010 USD PPP)|2384|2132|14|3464|2340|11|
|Africa|0.93|0.27|14|0.36|0.50|11|
|Transfer duration (Years)|0.66|0.55|14|1.35|0.78|11|
|Year of last transfer|2015.4|3.3|14|2011.5|2.6|11|
|Total intervention cost (2010 USD PPP)|1102|751|14|3315|1778|11|
|Intervention cost per unit of transfer|1.18|0.11|14|3.93|2.04|11|
|**Panel B: Estimates**|||||||
|Efect on annualized consumption (2010 USD PPP)|416|251|18|509|515|20|
|Standard error (2010 USD PPP)<br>|157|86|18|201|125|20|
|Efect on annualized consumption per unit of transfer|0.58|0.57|18|0.50|0.45|20|
|Standard error|0.28|0.30|18|0.21|0.12|20|
|Inverse variance weight|0.06|0.08|18|0.05|0.04|20|
|Years since last transfer|1.5|2.2|18|2.6|2.0|20|
|Sample size|1620|942|18|3127|5411|20|

_Notes:_ Descriptive statistics for interventions are presented in Panel A, and for their associated estimates in Panel B. Statistics for UCT interventions and associated estimates are in Columns 1 through 3, while statistics for TUP interventions and associated estimates are in Columns 4 through 6. For each variable, Columns 1 and 4 present sample means, Columns 2 and 5 present sample standard deviations, and Columns 3 and 6 present the number of non-missing observations.

— duration, and heterogeneity across estimates in time estimates are on average 1.5 years after the last transfer, with a standard deviation of 2.2 years, providing a range of estimates at both short and long time horizons. We also note that the estimates with tighter confidence intervals typically come from studies with relatively larger cash transfers; we revisit this in our discussion of power in Appendix D.1.

Lastly, we compare characteristics of TUP and UCT interventions and estimates. TUP and UCT interventions provide similar size transfers on average, but differ in a number of important characteristics. TUP interventions are typically implemented with higher consumption households in higher income countries, and are much less likely to be in Africa. Transfers last 16 months on average, twice as long as for UCT interventions. In addition, TUP interventions are typically much more expensive, with additional components comple— mentary to cash transfers the average TUP intervention includes 2.7 units of costs per 13
noting we have also bounded settlement size from below at 5,000. Note to the left in Figure 6b, that for bigger cities, the local slope coefficient would be less in absolute value than the overall -1.20, perhaps better approximating the rank-size rule.

How complete is the LSMS coverage of this hierarchy? Table 3 shows the distribution of cities with their cores and fringes broken out for our countries and for the LSMS sample. The left part of the table tells us that these countries have 167 cities (and fringes), covering 219 cores; and they have 893 LD settlements, apart from rural areas. The right part of the table shows that the LSMS data covers 115 of the 167 cities; but within these cities, only 68 fringe areas are covered. And for LD settlements only 193 of the total 893 are covered. The relatively low count of small places actually surveyed comes from the randomized sampling procedure outlined in Appendix B.

How representative is coverage by the LSMS? Figure 7 compares the size distribution of cities and LD settlements within the sample of countries versus the cities and settlements that are covered by the LSMS. The shapes of distributions of both cities and settlements for the sample are similar to those for the countries overall. The mean and median sizes for each distribution are each marked with dotted lines, with the mean being bigger than the median. For settlements, the means and medians in the LSMS sample are larger than for the country, and the same is the case for cities. This of course is consistent with Table 3.

Next, we ask about characteristics of households in the sample. Table 4 gives characteristics of the LSMS households (top panel) and working people (bottom panel) in the sample by our spatial units. Education of the household head and working-age population decline pretty sharply as we move down the spatial hierarchy. Rural areas, LD settlements, and fringes of cities are much more likely to have the household head or workers in agriculture than the core. Virtually no one anywhere is in manufacturing, the big issue for African cities (Henderson and Kriticos, 2018). Even the proportions in business services, which are potentially tradable across cities, are not that high, at 9% for cities and 2% in rural areas. Business services include the usual business service industries such as real estate and finance but add in high skill workers (like managers) in retail, as well as senior administrators and high skill workers in government. Apart from agriculture even in cities, it seems that most Africans work in low skill retail services and general labor services. However, 16
# TABLE OF CONTENTS

|||Page|
|---|---|---|
|I.|Introduction|1|
|Il.|Methodology<br>and Data|3|
|III.|Basic Variables<br>and<br>Some<br>First Results|7|
|IV.|Macroeconomic<br>Variables<br>and Growth|ll|
||A.<br>Illustrative Overview|il|
||B.<br>Fiscal Policy Indicators|14|
||Cc.<br>TradePolicyIndicators|19|
||<br>D.<br>Monetary and Political Indicators|23|
|Vv.|Sensitivity of the Sensitivity Analysis and Variable Groupings|24|
|VI.|Conclusion|26|
||Bibliography|29|
||Data<br>Set Variables<br>and Sources<br>Tables|32<br>36|

We have received helpful comments from Robert Barro, Surjit Bhalla, Maria Carkovic, David Dollar, Bill Easterly, Dale Jorgenson, Lant Pritchett, Cani Rodrik, Andy Rose, Larry Summers, and seminar participants at Harvard University and the World Bank.
- 24 -

to contain further deterioration of their cashflows. On the whole, however, conversion usually entails heavy outlays, and proper market analyses would be necessary before committing funds. During 1990 **the** average cost of vessel conversion was US$ 10.6 million.

Table 6: TONNAGE PRICE FLUCTUATIONS

|[|T||||||
|---|---|---|---|---|---|---|
|Tonnage Category|1985<br>|j<br>1986<br>|<br>1988|1 1990|1991||<br>1992|
|<br>_NE_<br>!**Tankers**<br>|<br>_WBUILDINGS_|<br> <br>_- Prices in c_|<br>_urrent US$ (_|_million)_||<br>|
|(80,000 dwt)|22.0|18.8|31.5|41.4|43.0|42.0|
|Dry**Bulk**Carriers<br>(30,000 dwt)|11.3|11.7|18.5|23.8|24.1|24.5|
|General Cargo Ships<br>(15,000 dwt)|12.8|14.2|17.3|24.0|24.1|24.2|
|Container Vessels<br>(2,500 TEU)|26.0|27.9|32.0|_51.0_|55.8|59.9|
|_SECON_|_D-HAND TONN_|_AGE - Price_|_s in current_|_US$ (million_|_)_||
|Tankers<br>(80,000 dwt,<br>8-10 years old)|6.5|10.8|17.4|28.7|24.8|16.3|
|Dry Bulk Carriers<br>(30,000 dwt,<br>5-6 years old)|4.7|3.6|10.8|12.7|12.7|14.0|
|General**Cargo Ships**<br>(15,000 dwt,<br>5-6 years old)|3.2|3.3|5.2|10.2|10.2|11.2|
|**Container Vessels**<br>(1,600 TEU,<br>5-6 years old)|14.1|14.0|14.2|22.2|21.3|22.6|
|_DEMIOLITI_|_ON - Tonnage A_|_cquisition C_|_ost in curre_|_nt US$ (milli_|_on)_||
|**Tankers**<br>|||||||
|(80,000 dwt)|1.8|1.9|3.8|4.0|3.0|2.5|
|**Dry Bulk Carriers**<br>(30,000**dwt)**|0.7|0.7|1.3|1.5|1.0|1.0|
|**General Cargo Ships**<br>(15,000**dwt)**<br>**Container Vessels**|0.9|0.8<br>No|1.7<br>demolition sa|1.8<br>les reported|1.3|**1.3**|

Source: Lloyd's **Shipping** Economist. Various Issues. London
34

|**Marg.Propensity toCons.**|<br>**Kil**|**imanjaro**|**Ru**|**vuma**||**Gansu**|**Inne**|**r Mongolia**|
|---|---|---|---|---|---|---|---|---|
||**OLS**|**hh FE**|**OLS**|**hh FE**|**OLS**|**hh FE**|**OLS**|**hh FE**|
|Social expenditures|||||||||
|_Education_|||||||||
|**Earned income**|0.005**|0.002|0.008**|0.004*|0.078***|0.036**|0.029***|0.021**|
||(2.653)|(1.291)|(3.112)|(1.658)|(4.060)|(2.401)|(4.350)|(2.770)|
|**Unearned income**|0.031*|0.025|0.049|0.030|0.065*|0.065|0.012|0.022|
||(1.753)|(1.351)|(1.356)|(0.740)|(1.652)|(1.630)|(0.362)|(0.902)|
|_Health expenditures_|||||||||
|**Earned income**|0.000|-0.001|0.002|0.002|0.010|0.003|-0.001|-0.009|
||(-0.311)|(-0.764)|(1.060)|(0.798)|(0.918)|(0.285)|(-0.126)|(-1.142)|
|**Unearned income**|0.023**|0.009|0.094**|0.083*|0.014|0.021|0.042|0.047|
||(2.245)|(0.586)|(2.422)|(1.904)|(1.023)|(1.440)|(1.247)|(1.624)|
|_Gifts given_|||||||||
|**Earned income**|0.000|0.000|0.002***|0.001**|0.006|-0.001|0.002|0.001|
||(0.853)|(0.322)|(5.080)|(3.146)|(0.886)|(-0.201)|(0.449)|(0.109)|
|**Unearned income**|0.003**|0.000|0.022***|0.022***|0.059**|0.077**|0.320***|0.307***|
||(2.125)|(-0.256)|(4.320)|(3.569)|(2.228)|(2.462)|(3.592)|(3.383)|

Note: Total household expenditure on each expenditure category as dependent variable. T-values are in between brackets. All regressions include time varying village fixed effects. Regressions for Kilimanjaro and Ruvuma (Tanzania) also include loans, (lagged) livestock, members of households holding elected office, gender, education and age of household head, household size, dependency ratio and female share of household labor as covariates. Observations with earned income, unearned income, loans, (lagged) livestock in the top 1% are excluded in all regressions for Tanzania. In each regression for Tanzania, observations with the dependent variable in the top 1% are excluded. Regressions for China (Gansu and Inner Mongolia) also include loans, (lagged financial assets), (lagged) value of livestock, whether business household or not, whether household rural cadre, gender, education and age of household head, household size, dependency ratio, female share of household labor, number of disabled adults aged 1860.
HNLSS, by contrast, suggested a poverty rate for 2009/10 of 56.4 percent (more than 10 percentage points higher) corresponding to 89.4 million people.

_Figure 1 Backcasted trend in poverty rates and number of poor at the US$1.90 poverty line, pass-through of 1_

- a) Poverty rate, % (US$1.90) b) Number of poor, millions (US$1.90)

<!-- Start of picture text --> 60.0 95.0<br>56.4<br>89.4<br>90.0<br>55.0<br>85.0<br>50.0<br>78.5<br>80.0<br>46.3<br>45.0<br>42.841.8 39.1 75.0 71.4<br>40.0<br>70.0<br>35.0<br>65.0<br>30.0 60.0<br>Backcast, pass-through=1 PovcalNet Backcast, pass-through=1 PovcalNet<br>Poverty Rate (percetnt)<br>Number of poor(millions)<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> Note: the figure shows the backcasted poverty rates series at the US$1.90 poverty line (panel a). Using household consumption data from the 2018/19 NLSS and sectoral GDP growth rates from the World Bank-MFM-Tool, we backcast household consumption over the previous decade by applying the same growth rate to household consumption and mapping the sectoral information to the household’s head sector of employment. Number of poor (panel b) is estimated using WDI population data.

Using the same methodology and data we can also estimate poverty at the US$3.20 poverty line. This is a relevant poverty measure for Nigeria as it reflects the typical standards of living in lower-middle-income countries (see Jolliffe and Prydz 2016, World Bank 2018, 2020).<sup>17</sup> Using this higher line, the backcasted series is much closer to the 2009/19 HNLSS poverty estimates. Figure 2 shows that the backcasted poverty rate for 2009 at the US$3.20 poverty line is estimated to be 76.5 percent assuming a pass-through rate equal to one between sectoral GDP consumption and household welfare. This estimate is just 3 percentage points lower than poverty rate estimated directly from the 2009/10 HNLSS (see Table _7_ for full backcasted series).

> 17 This follows the World Bank income group classification of economies based on a country’s GNI per capita. The latest thresholds and classification can be found at <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.</u> Nigeria is classified as a lower-middle-income economy since 2009.

12
temporary and less educated inter-provincial migrants are more likely to move into provinces with well-developed rural enterprises. They also find that migrants (temporary migrants especially) are more likely to choose provinces with high levels of foreign capital investment as destinations.

# **IV. Data Sources and Descriptive Statistics**

The _Vietnam Migration Survey_ (VMS) 2004 was conducted as an intercensal survey between the 1999 and 2009 censuses by Vietnam’s General Statistics Office (GSO) with the technical support of the United Nations Population Fund (UNFPA) (GSO, 2005). In the VMS, about 5,000 migrants were interviewed in the destination areas which recorded high levels of in-migrants in the 1999 census (GSO, 2005). A migrant is defined as “a person aged 15-59 who had moved to their current district/quarter from another district/quarter during the five years prior to the survey, and who had resided at their current place of residence one month or more” (GSO, 2005, p. 14). The eleven cities and provinces covered by the VMS represent five areas, namely, Hanoi, the Northeast Economic Zone (including Quang Ninh, Hai Phong and Hai Duong), HCHC, the Southeast Industrial Zone (Binh Duong and Dong Nai) and the Central Highlands (Gia Lai, Dac Lac, Dac Nong, and Lam Dong). Most of the migrants who went to the Central Highlands were interviewed mainly to understand the pattern of rural-rural migration as compared to rural-urban migration (GSO, 2005).

On the one hand, since the survey covers only eleven provinces as migrants’ destination areas, and information to construct sampling weights that would adjust for the unequal probability of selection is not available (GSO, 2005), the VMS is not a suitable data source to evaluate the general trend of internal migration in Vietnam. On the other hand, the VMS has several features which make it a suitable source for data to analyze the role of foreign job opportunities in
45

**Box** 3. Estimation of Pollutant Emissions **from** Automobiles and **Buses** in Selected Metropolitan Areas in Developing Countries: Data and Underlying Assumptions

- The number of trips were estimated using the relationship between number of trips and urbanized area population (Equation 1) and urban population data from Table 11.

- Non-motorized trips were assumed to constitute 25 % of all trips made in the metropolitan areas. The estimated percentage of non-motorized tnps may be high for Latin American cities, but is quite representative of Asian cities.

- The modal share of motorized tnps was assumed to be the same as that given in Table 18, although this modal split may not be temporally stable. For example, the share of buses in motorized trips in Calcutta increased from 34% in 1970 to 67% in 1980 whereas the share of buses in motorized trips in Karachi decreased from 63% to 52% during the same period [World Bank 1975, World Bank 1986]. For the purpose of this exercise it was assumed that the modal split would be the same in 2000 as was observed in 1980. With the rapid increase in private vehicle ownership in developing countries, the assumption of a constant modal split may not be valid and may result in a significant underestimate of fuel consumption and also in the aggregate amount of emissions.

- An average trip length of 5 km was used to estimate passenger-kilometers by different modes. In many developing countries, such as in India, a trip length of 3-6 km is quite common [Pendakur 19881.

- The emission factors per passenger-kilometer shown in Table 20 were used to calculate emissions from automobiles, paratransit (minibuses, vans, and combi), and buses.

- Rail emissions were not estimated as reliable data is not available on rail operations. Furthermore, the proportional usage of different energy sources in the generation of power for rail operations in various cities is not known. For example, Bombay uses three sources, coal, natural gas and oil, whereas in Brazil water is the major source of power generation.
government from building such alternative routes in the future, thus siphoning off some of the traffic from the concession. It would be nearly impossible for a government to commit-even when it truly wanted to-to ensuring that no such policy change would occur.

The traditional solution to overcome the obstacles that traffic and regulatory risk pose for a concession programme is to give government income, traffic or debt guarantees. The guarantee scheme adopted in Chile is discussed next.

# **5.1 Minimum Income Guarantees**

There are variations to the minimum income guarantee scheme across concessions. The first two concessions (El Melon Tunnel and the Madera Road) have actual physical traffic volume guarantees. Subsequent concession contracts express the guarantee as a minimum income level. If different types of traffic (cars, buses and trucks) pay different tolls, then it may make a difference which approach is taken.<sup>27</sup> Since the original objective of the guarantee scheme in Chile was to facilitate concessionaires' access to the financial market, a minimum income guarantee seemed preferable.

Before the bidding stage, MOP sets the total income level it will guarantee for the duration of the concession. This is set to a level equivalent to 70% of the estimated official cost of the project. Except for the first concessions tendered, MOP now gives bidders the flexibility to choose the time profile of the yearly guarantee within a band contained in the Bidding Documents. The bands are set with reference to the expected growth in traffic levels and usually have a maximum of 80% to _85%_ of expected yearly income as a ceiling.

Bidders choose yearly income guarantees subject to the constraint that the present value of the chosen guarantee is equal to the total set by MOP. In other words, each bidder chooses the yearly minimum income guarantee (within the band set by MOP), _MIC,,_ subject to where p = the discount rate (set in the bidding documents).

_(I + OC + MC) =_ the official net present value of the concession. This corresponds to the initial estimate by MOP of the investment, operating expenditure and maintenance cost over the life of the concession.

If in a given year income levels fall below the minimum income set for that year, the government pays the concessionaire the shortfall in revenues.

> 27 If the contract guarantees a minimum total traffic level, then changes in the composition of traffic will affect income flows but would be irrelevan **t** o the guarantee scheme.

29
|||||Table<br>|10:U<br>|niversalS<br>|erviceO<br>|bligatio<br>|nsinLatin|Americ|a<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||5.|o C<br>o<br>3°|USOs|OBLIGATIO|NS||ETAILS|HEME|||F<br>|NDIN<br>USOs|GME<br>FUND|HANI|SM|
||t|HT|CD<br> U~~|a<br>=~~<br>o<br>**cr**<br>~~~~~~|F'.<br>~~~~|c<br>r7<br>~~~02<br>~~~~~<br>|i<br>t,<br>**U,**<br>a<br>.<br>~~~~~<br>|~C<br>|**C**<br>|**0K**<br>**A**<br>TX|**0**<br>**T**<br>||||C|
|Argentina<br>|X<br>|X<br>|X<br>|=<br>||X|X|X||X||X||=|Payorplay<br>|
|Bolivia<br>|X<br>|X<br>|X<br>|X37|||||||||X38||Government|
|Brazil<br>|X<br>|X|X39||||||x40|||X||||
|Chile<br>|X<br>||||X|X||X|||X41||X<br>||Centralgvt|
|Colombia|X|X|||X|||||_|||X<sup>42</sup>||Operatorlevy|
|Ecuador<br>|||X|||||X<br>||||||X<br>Ecuad<br> <br>X|WorldBankTechnical<br>or<br>X X<br>assistance|
|ElSalvador<br>|<br>|_<br>||||||X<br>||||||||
|Dominican<br>Republic<br>|X<br>_43|||||||X|||||X<br>_||2%taxonsubsc.bills,<br>int'lsettlements|
|Guatemala<br>|||_||X|X|X<br>|||X|_||X||Spectrum auctions|
|Honduras<br>||_<br>|||||X|||||||X||
|Peru<br>|X<br>|X<br>|X<br>||X|X||X<br>|||||X<br>|_|Operatorlevy<br>|
|Mexico|X|X|X|||||X|||X|X|||Virtualfund<br>considered|
|Nicaragua<br>||||||||X||X||||X|WorldBankTechnical<br>assistance|
|Venezuela|X|X|X|44|_X_|_X_|_X_|_X_||||||||

37 Cooperatives with rural obligations.

38 Telecom law of 1997 has provisions for using El Fondo Nacional de Desarrollo Regional for funding rural telecoms, no evidence of actual implementation.

> 39 The incumbent operators, no obligations for the mirror license holders.

40 Virtual telephony.

41 To ensure the viability, payphone operators are planning to charge higher interconnection fees.

42 Social Telephony Fund.

43 DGT, Directorate-General of Telecommunications has operated rural telegraph system, but its to be discontinued.

44 Cellular operators are permitted to serve rural areas, and have service expansion requirements.

Source: Estache, Foster and Wodon (2001) and data provided by Juan Navajas, The World Bank