rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 3: Food and Beverage Consumption Compared to the Distance to the Nearest City**

<!-- Start of picture text --> Overall Quintile1 Quintile2<br>0 20 40 60 80 0 1 2 3 4 5 4 6 8 10 12 14<br>Distance to City (km) Distance to City (km) Distance to City (km)<br>Quintile3 Quintile4 Quintile5<br>13 14 15 16 17 18 19 20 20 22 24 26 28 30 32 30 40 50 60 70 80<br>Distance to City (km) Distance to City (km) Distance to City (km)<br>150 150 150<br>100 100 100<br>50 50 50<br>0 0 0<br>Total Consumption (1,000 Shillings) Total Consumption (1,000 Shillings) Total Consumption (1,000 Shillings)<br>150 150 150<br>100 100 100<br>50 50 50<br>0 0 0<br>Total Consumption (1,000 Shillings) Total Consumption (1,000 Shillings) Total Consumption (1,000 Shillings)<br><!-- End of picture text -->

# **_An analysis based on household surveys_**<sup>**_18_**</sup> **_in three Ugandan districts_**

Table 3 presents some determinants of household income derived from agricultural products sales. The main transport-related findings are of great interest and could seem paradoxical. Indeed, the 2 km distance from a road is not an economic threshold because beyond or above 2 km from a road is never a significant determinant factor of this income. Moreover, bike ownership does not have the expected positive impact on revenues derived from sales of agricultural products<sup>19</sup> (probably due to relatively low value of time<sup>20</sup> ) and road passability also does not have a major impact when it is minimal (because when walking, road passability should be minimal).

**_High yield, high-value crops and selling direct to market is what matters._** What seems to matter for increased income (consumption) is yield, crop type (which means

> 18 For variable definitions, see annex 5. These surveys were conducted in December 2008 and January 2009.

> 19 It may have some impact on non-farming economies but it is not captured in the regression.

> 20 Mainly because the median distance is less than 5 kilometers and the bicycle, when loaded, is pushed.

23
- 20 -

long side of the transaction. Taking this into account, the total instantaneous return from holding the portfolio is: dF - FVdV - SVFVdt We will see that this return is risk-free, and therefore must equal r(F-FvV)dt: dF - FVdV - 6VFVdt - r(F-FvV)dt To obtain an expression for dF, use Ito's Lemma: 2 dF - FVdV + (1/2)FwV(dV) (3) (Higher order terms go to zero.) Substitute (1) for dV, with a - p - 6, and - (dV)' o'V'dt into eqn. (3): dF - (4-S)VFVdt + aVFVdz + (1/21o<sup>2</sup> V<sup>2</sup> FwVdt (4) Now substitute (4) into (2), rearrange terms, and note that all terms in dz cancel out, so the portfolio is indeed risk-free: (1/2)oa2V 2Fw + (r-6)VFV - rF - 0 (5) Eqn. (5) is a differential equation that F(V) must satisfy. In addition, F(V) must satisfy the following boundary conditions: F(0) - 0 (6a) * * F(V)} V - I (6b) FV(V ) - 1 (6c) Condition (6a) says that if V goes to zero, it will stay at zero (an implication of the process (1)), so the option to invest will be of no value. V is the price at which it is optimal to invest, and (6b) just says - that upon investing, the firm receives a net payoff V* I. Condition (6c) is called the "smooth pasting" or "high contact" condition. If F(V) were not continuous and smooth at the critical exercise point V*, one could do better by exercising at a different point.<sup>13</sup> '<sup>3</sup> Dixit (1988) provides a nice heuristic explanation of this condition.
In developing countries, food consumption represents a high share of total consumption, and therefore special interest is placed on proper measurement of food consumption. Being a central welfare marker, proper measurement of consumption - and food consumption in particular, can have far reaching implications for welfare analyses. In this work, we focus on the impact that incorporating FAFH in the measurement of food consumption has on two welfare measures: poverty, understood as a ‘pronounced deprivation in well-being’, and inequality, which relates to the distribution of resources (Haughton and Khandker, 2009).

# **2.1 FAFH and Monetary Poverty**

Understanding poverty as pronounced deprivation of well-being, and following the monetary approach, the poor are those individuals whose resources – measured through consumption (or income) - fall below an ‘adequately’ defined threshold. This threshold (the poverty line) is generally set by measuring the cost of acquiring a basket of goods - the cost of basic needs method.<sup>8</sup> Most countries track two levels of poverty: extreme and moderate. Extreme poverty is associated with a poverty line that reflects the cost of acquiring a food basket that satisfies minimum calorie requirements, intending to guarantee minimum nutrition needs. Moderate poverty adds to the basket goods necessary to satisfy other essential needs, such as clothing, health and housing.

To compute the extreme poverty line, the usual approach is to select a representative basket of the consumption patterns of a selected group of the population - the reference population. In some cases the reference population corresponds to the segment of the population whose food consumption is such that the calories consumed are in the neighborhood of the calorie requirements in that setting.<sup>9</sup> In the case of Peru, the reference population is selected as those whose overall consumption levels fall within a somewhat arbitrary distance from the moderate poverty line (on both consumption the preferred welfare indicator in developing countries. While most of this work focuses on consumption, we point to the differences between income and consumption when appropriate.

> 8 Other methods do exist to set an absolute poverty line, for example the food energy intake and subjective evaluations. The first approach is used when no price information is available but suffers from serious flaws. The second approach is rarely used and is not considered a valid replacement of more objective measures. An alternative to an _absolute_ poverty line is a _relative_ poverty line, which establishes deprivation relative to the rest of the population. This method is more common among developed countries.

> 9 Internationally agreed recommendations on calorie requirement are provided by FAO/WHO/ONU, and differentiate by gender, age groups, and physical activity levels.

7
Two statistics, however, show the general tendency for migrants to move up the GNI per capita ladder. First, every element on the diagonal, i.e. every intra-regional migrant link, shows gains. On the diagonal, the sets of receiving countries and the sets of sending countries are identical, so the unweighted difference in GNI per capita between them is zero. Thus, the positive signs reported must reflect the fact that more people move along the paths with increasing GNI per capita than along those with falling GNI per capita. The second statistic is to collapse the whole world into a single entity and calculate the differential. Again, the sets of recipients and senders are identical so the average gain of $9,479 must again reflect a bias in the movement toward those that increase GNI per capita. The increase is large and while GNI per capita will clearly not reflect only returns to labor and while migrants will typically have above-average earning power in their home countries and, these days, probably below-average earning power in their new countries, it suggests large returns to migration.

# **_6._ Conclusion**

In spite of the inherent inaccuracy due to a lack of bilateral data for some countries, the assumptions made and the rough-and-ready methodologies used, the database developed here should provide a reasonably realistic view of current migration patterns. Considering the heterogeneity that exists between the available sources and the various missing components, any undertaking on this subject will inevitably be rather crude. We see no way of improving upon it, however, given current data availability and so we present our series “as is”. We would be delighted to hear of potential extensions or improvements, or, indeed, to see them made by other researchers. To that end, we have tried to highlight the problems encountered and to provide alternative series so that other researchers can build upon our data as they see fit. Despite their shortcomings, we believe that the data will prove valuable in a wide range of applications throughout the social sciences, enhancing both our understanding of global migration and the policy debate surrounding it.

32
||(1)<br>ln(Cons. Num.)|(2)<br>ln(Cons. Quant.)|(3)<br>ln(Cons. Val)|(4)<br>ln(Cons. Cal.)|
|---|---|---|---|---|
|Treatment|0.100|0.195**|0.171*|0.105|
||(0.066)|(0.080)|(0.081)|(0.087)|
|UN Assistance|‐0.028|‐0.065|‐0.152***|‐0.143***|
|**Treatment*UN Assistance**|(0.038)<br>**0.104****|(0.045)<br>**‐0.059**|(0.043)<br>**0.016**|(0.046)<br>**0.011**|
||**(0.051)**|**(0.060)**|**(0.061)**|**(0.064)**|
|Observations|2,204|2,204|2,204|2,204|
|R‐squared|0.38|0.086|0.098|0.108|
|State FE|YES|YES|YES|YES|
|Month FE|YES|YES|YES|YES|
|Controls|YES|YES|YES|YES|
|Interacted Controls|YES|YES|YES|YES|

Robust standard errors in parentheses (White, 1980): *** p<0.01, ** p<0.05, * p<0.1. Column (1) is measured on the household level. Columns (2‐4) refer to per capita OECD adult equivalents.

The non‐IDP subsample offers an interesting opportunity to assess the robustness of the results. Constraining the sample only on non‐IDPs, we can estimate our results analogous to Table 3. The pattern of positive and significant treatment coefficients in the lower quantiles vanishes, except for Column (1). This could be interpreted as evidence that the light‐touch method applied are more efficient for the vulnerable IDP population, which has higher incentives to indicate need than the non‐ IDPs. This would be in line with previous studies (e.g., Cilliers, Dube, & Siddiqi, 2015) suggesting a high degree of social desirability bias in the setting of foreign assistance. Specifically, the populations exposed to development aid, in our setting the IDPs, would be more likely to provide socially desirable answers to signal their “worthiness” for assistance. This corresponds to Table 5, providing some weak evidence that the primes are more effective for respondents relying on UN aid. It would be of particular interest to examine those heterogenous effects based on more fine‐grained data on neediness and degree of aid reliance of recipients. For this purpose, however, a “true” benchmark would be needed. As administrative data is non‐existent or of poor quality, an alternative for future research might be to build on measures from qualitative work as suggested by Blattman, Jamison, Koroknay‐Palicz, Rodrigues, & Sheridan (2016). Moreover, one should be careful to draw too strong conclusions from these results as the number of observations is limited in this comparatively small sub‐sample.

_Table 6: Quantile Regressions – reduced sample (only non‐IDPs)._

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Outcome Variables|ln(Cons. Num.)|ln(Cons. Quant.)|ln(Cons. Val.)|ln(Cons. Cal.)|
|Q0.1|‐0.027|‐0.069|‐0.026|0.032|

16
# Acronyms and abbreviations

|Addis Ababa Water and Sewerage Authority|AWSA|
|---|---|
|Africa Infrastructure Country Diagnostic|AICD|
|Blantyre Water Board|BWB|
|Central Region Water Board|CRWB|
|Chad Water and Electric Society|STEE|
|Company of Electricity and Water|ELECTRA|
|Dar es Salaam Water and Sewerage Corporation|DAWASCO|
|Democratic Republic of Congo|DRC|
|Dodoma Urban Water & Sewerage|DUWASA|
|Federal Capital Territory Water Board|FCTWB|
|Ghana Water Company Limited|GWCL|
|Global Water Intelligence|GWI|
|gross national income|GNI|
|increasing block tariff|IBT|
|International Benchmarking Initiative|IBNET|
|the Kisumu Water and Sewerage Company Limited|KIWASCO|
|Latin America and Caribbean|LAC|
|Lilongwe Water Board|LWB|
|long-run marginal cost|LRMC|
|low-income country|LIC|
|Lusaka Water and Sewerage Company|LWSC|
|middle-income country|MIC|
|Millennium Development Goals|MDG|
|Municipality Water Services Agencies|MWSA|
|National Water and Wastewater Office of Burkina Faso|ONEA|
|National Water Company of Benin|SONEB|
|National Water Corporation|NWC|
|National Water and Sewerage Corporation|NWSC|
|Network for Distribution of Water and Electricity|REGIDESO|
|operations and maintenance|O&M|
|Organisation for Economic Co-operation and Development|OECD|
|Poverty and social impact analysis|PSIA|
|Sénégalaise des Eaux|SDE|
|Senegal National Sanitation Agency|ONAS|
|Society for the Development of Employment in Industry and Services|SODESI|
|Kaduna Water Board|Kaduna WB|
|Water Electricity Malagasy Company|JIRAMA|
|Water Supply and Sanitation|WSS|
|World Health Organisation|WHO|
|Water Utility Partnership|WUP|

iv
|Bangladesh: Safe Migration for Bangladeshi Workers|Aneeka Rahman|
|---|---|
|Bangladesh: Safe Migration for Bangladeshi Workers|Aneeka Rahman|
|Bangladesh: Safe Migration for Bangladeshi Workers|Aneeka Rahman|
|Bangladesh: Safe Migration for Bangladeshi Workers|Aneeka Rahman|
|Bangladesh: Safe Migration for Bangladeshi Workers|Aneeka Rahman|
|Strengtheningthe Institutional Capacityof Druk Holdingand Invest|mA.K.M. Abdullah|
|Strengtheningthe Institutional Capacityof Druk Holdingand Invest|mA.K.M. Abdullah|
|Strengtheningthe Institutional Capacityof Druk Holdingand Invest|mA.K.M. Abdullah|
|Nepal: Human Development - Social Protection Pilot Project|Yasuhiko Matsuda|
|Nepal: Human Development - Social Protection Pilot Project|Yasuhiko Matsuda|
|Nepal: Human Development - Social Protection Pilot Project|Yasuhiko Matsuda|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din) <br>|Manav Bhattarai<br>|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Nepal: CommunityAction for Nutrition Project(Sunaula Hazar Din)|Manav Bhattarai|
|Buildingthe capacities ofprocurement institutions to achieve deve|l Jinan Shi|
|Buildingthe capacities ofprocurement institutions to achieve deve|l Jinan Shi|
|Buildingthe capacities ofprocurement institutions to achieve deve|l Jinan Shi|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|GPOBA W3: Kenya Electricity|Sudeshna Ghosh Banerjee|
|Belarus Privatization|Tatiana Segal|
|Belarus Privatization|Tatiana Segal|
|Belarus Privatization|Tatiana Segal|
|Belarus Privatization|Tatiana Segal|
|Belarus Privatization|Tatiana Segal|
|Belarus Privatization|Tatiana Segal|
|Belarus Privatization|Tatiana Segal|
|Belarus Privatization|Tatiana Segal|
As an indicator of urban access, we used arc distances from surveyed PSUs to growth poles. Although this measure of access to urban centers is not subject to endogeneity concerns due to targeted road placement, one may still worry about the measurement errors as its estimation completely ignored transport networks. Thus, as a further check of robustness, we used the travel time to growth poles as an indicator of urban access instead of arc distances. The travel times are estimated as road network travel times with some reasonable estimates of travel speeds on a given type of road using geographic information system (GIS) software. The marginal e ff ects from multinomial logit estimation are reported in the lower panel of Table 4. Comparison of results in Table 4 (lower panel) and in column 2 of Table 3 indicates that use of travel time to growth poles as urban access indicator has resulted in an improvement in the precision of estimates in the case of high return wage employment and self-employment. The overall results regarding the e ff ect of urban access and its interaction with agricultural potential, however, remain qualitatively unchanged.

Finally, we examine if our results regarding access to urban centers and crop suitability carry over to other possible de fi nitions of non-farm activities. Using the occupational classi fi cation of work, we de fi ne two categories of employment. Factory workers are engaged in production work in manufacturing and services workers comprise of all other nonfarm sectors including sales, transport and communication, construction, and so on. These de fi nitions of factory workers (tradeable activity) and services workers (non-tradeable) are comparable to those of Foster and Rosenzweig (2004a). The marginal e ff ects from the multinomial logit estimation are presented in Table 5. The interaction of urban access and crop suitability again has a statistically signi fi cant e ff ect on the probability of being employed as factory and services workers. In the case of factory workers, distance to major cities has a negative sign but is statistically signi fi cant only at the 10 percent level. In the case of service workers, the marginal e ff ect of distance to growth 20
- [32] Jean-Franc ̧ois Maystadt, Giacomo De Luca, Petros G Sekeris, and John Ulimwengu. Mineral resources and conflicts in drc: a case of ecological fallacy? _Oxford Economic Papers_ , page gpt037, 2014.

- [33] Newsweek. How does isis fund its reign of terror?, 6 November 2014. Janine Di Giovanni and Leah Mcgrath Goodman and Damien Sharkov.

- [34] Now. Where does daesh oil go ? [translated from arabic], 22 December 2015.

- [35] Santiago Saavedra and Mauricio Romero. Local incentives and national tax evasion: The response of illegal mining to a tax reform in colombia. Technical report, Stanford University, 2017.

- [36] Ra ́ul S ́anchez de la Sierra. On the origins of the state: Stationary bandits and taxation in eastern congo. _Working paper, University of California Berkeley_ , 2017.

- [37] Abu Sayyaf. Islamic State, Diwan of Natural Resources, Oil and Gas Division - Oil and Gas Report 003 #82. Seized May 15, 2015 from Abu Sayyaf, Declassified 2017, 2015.

- [38] Louise Shelley. Blood money. _Foreign Affairs_ , 2014.

- [39] Kaifang Shi, Chang Huang, Bailang Yu, Bing Yin, Yixiu Huang, and Jianping Wu. Evaluation of npp-viirs night-time light composite data for extracting built-up urban areas. _Remote Sensing Letters_ , 5(4):358–366, 2014.

- [40] A. Stohl, Z. Klimont, S. Eckhardt, K. Kupiainen, V. P. Shevchenko, V. M. Kopeikin, and A. N. Novigatsky. Black carbon in the arctic: the underestimated role of gas flaring and residential combustion emissions. _Atmospheric Chemistry and Physics_ , 13(17):8833– 8855, 2013.

21
## **Box 2: Expenditure on urban transport in Madrid**

The following table presents the percentage of urban transport expenditure per household income in Madrid. It shows that for all income groups this expenditure is quite low. Although for poorer households this result may be partly due to repressed travel, the figures nonetheless do give the impression that distributional concerns are probably not among the most pressing issue related to public transport fares and reform in Madrid at this level of subsidization. This is not to say that there are no outstanding social or distributive concerns in this city, only that these problems perhaps should be addressed by other policies outside of the transport sector.

|**Monthly income**|**Inhabitants**|**% expenditure on**<br>**food**|**% expenditure in**<br>**urban public**<br>**transport**|
|---|---|---|---|
|<500 €|187,662|16.09%|0.95%|
|500 €-1,000 €|890,371|16.85%|1.07%|
|1,000 €- 1,499 €|1,493,780|17.31%|1.01%|
|1,500 €- 1,999 €|1,269,356|14.66%|1.06%|
|2,000 €- 2,499 €|632,309|14.47%|1.54%|
|2,500 €- 2,999 €|416,801|12.57%|0.95%|
|3,000 €- 4,499 €<br>|601,772|11.36%|0.87%|
|>5,000 € <sup>(a)</sup><br>|123,522<br>|10.49%<br>|0.69%|
|_Notes:_ <sup>(a)</sup>The results for t|his level of income have l|ittle representativeness.||

_Source:_ Vassallo and Pérez de Villar (2007)

# **4. TYPOLOGY OF SUBSIDIES IN THE TRANSPORT SECTOR**

The evidence clearly shows there is a myriad of subsidies being applied to urban public transport. Independently of the reasons that justify these subsidies, a relevant question for a policymaker is, what are the options available when considering, designing or modifying subsidies? Subsidies can be classified along many dimensions according to who receives the financial transfers, the targeting mechanism used to distribute benefits and how they are funded. We will discuss each of these topics in turn.

# **4.1 Who formally receives the subsidy?**

Formally, subsidies can be channeled to transport suppliers (supply side subsidies), or directly to beneficiaries (demand side subsidies). In turn, supply side subsidies can take two forms: infrastructure (or capital) subsidies or subsidies to cover operating costs. In both cases, the objective is to lower the cost of service to final users either by lowering the proportion of costs that must be funded from fares (supply side subsidies) or by lowering the monetary outlays of users (demand side subsidies).

It is important to keep in mind that although the final goal of all these subsidies is to benefit users, in many cases some of the benefits (or costs) accrue to others. This is the case when supply side subsidies are captured by operators in the form of higher costs and inefficiencies. In the case of demand side subsidies, equilibrium changes in the prices of 14