rafmacalaba/datause-encoder-data huggingface dataset:
45 by a certain time on the day preceding the gas day. Shippers also nominate capacity at specific intake or offtake points. Nominations of daily volumes can be renewed or changed on a monthly basis and may specify any quantity up to the maximum daily quantity specified in the transportation contract.

- A pipeline company aggregates the gas and capacity nominations and determines whether the pipeline system can match the total nominated capacity and gas volumes. Then it confirms the nominations or asks for adjustments.

- The pipeline deterrnines the schedule of all gas flows into and out of each receipt and delivery point. The flows are scheduled according to priorities determined by the type of transportation contracts involved.

- Shippers inject or withdraw natural gas and are responsible for keeping the difference between actual and nominated gas volumes within the agreed on tolerance levels (5 to 10 percent of nominated volume, but flows should not exceed the maximum daily -

- quantity). Balancing is performed both daily and monthly. Negative imbalances -

- those occurring when a shipper withdraws more gas than it injects are subject to penalties.

The balancing of natural gas flows is aided by the following tools and services:

- _Operational balance agreements._ Under an operational balance agreement, the operators of interconnecting pipelines resolve monthly imbalances among multiple shippers, so that individual shippers do not incur an imbalance penalty. Such agreements promote the integration of pipeline systems because they allow pipeline companies to settle imbalances with other connecting pipeline or distribution companies rather than with each customer.

- _Overrun authorizations._ An overrun authorization allows a shipper to transport more than the maximum daily quantity of natural gas, subject to prior approval by the pipeline company.

- _Penalties._ Penalties are used to discourage shippers from running an imbalance in their shipments. There are three types of penalties:"<sup>8</sup>

-

- * Scheduling variance penalties incurred when the daily flow of natural gas does not match the nominated flow.

-

- * Overrun penalties incurred when the shipper's maximum daily quantity is exceeded.

-

- * Imbalance penalties incurred when the total monthly receipts into the pipeline do not match the total monthly deliveries to the shipper.

- _Market center services_

**IS** For more information on penalties, see U.S. Department of Energy 1994.
analyzes five indicators: access to clean water, sanitation, and electricity; completing sixth grade on time; and attending school from age 10 to 14. The analysis focuses on children because, unlike adults, children cannot be expected to make the efforts needed to access these goods and services, therefore, these five indicators can be considered as proxies for opportunities available to a child. Barros and others (2009), and the updated Barros and others (2010) edition, _Do Our Children Have a Chance?_ , analyze these five indicators for 19 LAC countries using the HOI, exploring both changes over time within countries and comparing changes among countries.

The HOI measures how far a society is from universal provision of basic services and goods, such as sanitation, clean water, education, and the extent to which those goods and services are unevenly distributed.<sup>2</sup> A key feature of HOI is that it not only takes into account the overall coverage rates of these services, but also how _equally_ the coverage is distributed. Coverage is determined by measuring the extent to which those _without_ coverage are concentrated in groups with particular circumstances (for example, economic status, gender, parental education, and ethnicity), which are conditions established at birth. More specifically, _HOI is an inequality-sensitive coverage rate_ that incorporates (i) the average coverage of a good or service, which society accepts should be universal (which implies that the _individual_ is not held responsible for lack of access), and (ii) whether it is allocated according to an equality of opportunity principle.

# **2.2 Unpacking the HOI**

The HOI is defined as the difference between two components: the overall coverage rate of the opportunity ( _C_ ), and a ―penalty‖ for the share of access to opportunities that is allocated in violation of the equality of opportunity principle ( _P_ ). HOI = _C_ – _P_ , which implies that the maximum value HOI for a particular opportunity is the average access (or coverage) rate for that service. It also implies that an HOI of 1 would be possible only when access is _universal_ ( _C_ = 1 and _P_ = 0).

5
avoid out-of-pocket expenditures and thus impoverishment due to healthcare payments. And indeed schemes like _Seguro Popular_ have improved significantly the coverage among the poor and vulnerable. As Figure 13 shows, almost two thirds of the vulnerable were covered by _Seguro Popular_ in 2012. The targeting efficiency of health coverage can still be improved, for instance, the census record of the _Seguro Popular_ was not paired off with _Oportunidades_ , and both programs seem to have to incorporate better tracking of the health conditions of those sectors of the population more prone to experience deteriorating health (i.e. elderly, disabled, chronically ill).

**Figure 13: Coverage of** Seguro Popular **; 2006-2012** _Percentage of Population_

<!-- Start of picture text --> 90.0%<br>80.0%<br>70.0%<br>60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>Poor Vulnerable Middle Clas Upper Class Total<br>2006 2008 2010 2012<br>82.4%<br>67.9%<br>63.1%<br>55.3%<br>48.3% 46.2%<br>42.8%<br>26.1% 28.6% 29.9% 27.2%<br>19.2%<br>13.8% 13.6%<br>11.0%<br>7.1%<br>3.6% 1.2% 2.3% 3.0%<br><!-- End of picture text --> _Source:_ Author’s estimations based on ENIGH 2006-2012 Business bankruptcy or unemployment within the vulnerable population fared the highest across socioeconomic groups in the MxFLS. This high exposure is compounded by the fact that vulnerable groups have little access to credit sources captured by our study survey. The unemployment insurance reform recently proposed by the government of Mexico may eventually address part of this failure, but at the moment lacks a robust analysis on its short- and medium-term effects on welfare. In general, this insurance scheme allows workers to access up to six monthly payments in the event of losing their jobs, with an amount being paid as follows: the first payment equals 60 percent of the average worker’s wage during the past 24 months; the second equals 50 percent of the same average; and the last four payments are equivalent to 40 percent. This scheme seems to establish a new social right for workers; however, while three quarters of the vulnerable are engaged in wage activities, it is very likely that many reside in the informal sector (more than half of the salaried workers reported no benefits and a third reported no contract) and thus remain ineligible for unemployment insurance.

Earlier sections show that weather risks prevail in Mexico, and these can drive people into poverty. The agricultural sector is particularly susceptible to climatic conditions given that most farmers remain locked in low productivity rain fed agriculture (three-quarters of the area cultivated in Mexico is rain fed). The vulnerable are mainly located in urban areas and engaged in wage activities, but many remain involved in agriculture (which is the fourth activity for this group after services, retail trade and manufacturing) and may experience poverty if they face weather risks without having access to insurance and credit.

26
> - 18 -

Table 2. 79-Country Sample: Ratios of Military Spending to GDP for Various Country Groups and Time Periods 1/

||Period av<br>|erages, in perce<br>|nt<br>|Comparison of chang|es (Period 11 minus Period I)|
|---|---|---|---|---|---|
||Full Period<br>(1972-90)|Period I<br>(1972-85)|Period 11<br>(1986-90)|Our 79-Country<br>Sample|Hewitt's Sample|
|Full Sample||||||
|Weighted Average<br>|3.80<br>|3.89<br>|3.59<br>|-0.30<br>|-0.35|
|Simple Average<br>|3.35<br>|3.44<br>|3.08<br>|-0.36|..|
|(Standard deviation)|(0.21)|(0.12)|(0.21)|.||
|Industrial Countes||||||
|Weighted Avcrage<br>|3.90|3.99|3.71|-0.28|-0.27|
|Simple Average<br>|3.01<br>|3.07<br>|2.85<br>|-0.22|...|
|(Standard deviation)|(0.13)|(0.07)|(0.12)|...|..|
|Developing<br>Countries||||||
|Weighted Average<br>|3.10<br>|3.14<br>|2.80|-0.34|-1.28|
|Simple Average<br>|3.48<br>|3.58<br>|3.17<br>|-0.41|...|
|(Standard deviation)|(0.26)|(0.16)|(0.24)|...||
|Regional Grouaping||||||
|Asian Developing||||||
|WeightedAverage<br>|3.90|3.94|3.75|-0.19|-2.47|
|Simple Averge<br>|3.66<br>|3.64<br>|3.71|0.07|...|
|(Standard deviation)|(0.28)|(0.28)|(0.33)|...|...|
|Middle East||||||
|Weighted Average<br>|6.80<br>|6.93<br>|6.49<br>|-0.44|-1.30|
|Simple Average<br>|10.70<br>|11.24<br>|9.21|-2.03||
|(Standarddeviation)|(1.35)|(1.07)|(0.82)|...|...|
|North Africa||||||
|Weighted Average<br>|6.50<br>|7.59<br>|3.38|-4.21|-3.52|
|Simple Average<br>|6.06<br>|6.72<br>|4.19<br>|-2.53|...|
|(Standard deviation)|(2.27)|(2.29)|(0.48)|...||
|Sub-Saharan Africa||||||
|Weighted Average<br>|2.70|2.83|2.52|-0.31|0.30|
|Simple Average<br>|2.70|2.75|2.58|-0.17|...|
|(Standard deviation)|(0.24)|(0.26)|(0.12)|...||
|Western Hemisphere||||||
|Weighted Average<br>|2.20<br>|2.28<br>|1.80<br>|-0.48<br>|-0.22|
|Simple Average<br>(Standard deviation)|2.43<br>(0.40)|2.49<br>(0.44)|2.26<br>(0.25)|-0.23|...|

Source: Hewitt (1992).

I/ Sec Appendix lB. for lists of countries included in each grouping for both Hewitt's and our sample.
22

# **V. Recent Papers on Capital Flight**

Cuddington, John T., 1986, "Capital flight: Estimates, issues, and explanations," Studies in International Finance, no. 58, Princeton University Department of Economics, Princeton, NJ.

Chang, P. H. Kevin and Robert E. Cutinby, 1991, "Capital Flight in Sub-Saharan African Countries," in Ishrat Husain and John Underwood, eds, Africaii External Finance in the 1990s, Washington, DC, World Bank, 162-184.

Chang, P. H. Kevin, Stijn Claessens and Robert E. Cumby, 1993, "Conceptual and Methodological Issues in the Measurement of Capital Flight," forthcoming in a volume edited by Benu Varman-Schneider, Christian-Albraechts-Universitat, Kiel.

Cumby, Robert E. and Richard M. Levich, 1987, "On the Definition and Magnitude of Recent Capital Flight," in Donald R. Lessard and John Williamson, eds, Capital Flight and Third World Debt, Washington, DC, Institute for International Economics, 27-67.

Dooley, Michael P., William Helkie, Ralph Tryon, and John Underwood, 1986, "An analysis of external debt positions of eight developing countries through 1990," Journal of Development Economics, Vol 21, no. 2, 283-318.

Dooley, Michael P., 1986, "Country-specific risk premiums, capital flight and net investment income payments in selected developing countries," International Monetary Fund, mimeo.

Dooley, Michael P., 1987, "Comment," in Donald R. Lessard and John Williamson, eds, Capital Flight and Third World Debt, Washington, DC, Institute for International Economics, 79-81.

Dooley, Michael P., 1988, "Capital flight: A response to differences in financial risks,' International Monetary Fund Staff Papers, 35, September, 422-436.

Eggerstedt, Harald, Rebecca Brideau Hall, and Sweder van Wijnbergen, 1993, "Measuring Capital Flight: A Case Study of Mexico," Working Paper, No. 1121, The World Bank.

Erbe, Suzanne, 1985, "The flight of capital from developing countries," Intereconomic.s, November/December, 268-275.

Gajdeczka, Przemyslaw, 1989, "Financial Flows to Developing Countries," Ouarterly Review, World Bank, March.
# Acknowledgments

Alan D. Lopez conrtribted the base data and his Isights, critquing the argument and suggeswt imptant poinb. Expert assesmet for specific diseases or comments on the regression estimates were provided by David Dunlop, Richard Feachem, B. Robert Greenberg, Scott B. _Haistead,_ Dean T. Jamison, Berrihard Liese, Jose Martinez, Alberto Tones, and Patck Vaughn, as well as by other participants at meetings for the World Bank health sector priorties review. However, they are not responsible for the specific figures reported here, which have been reVsed for consistency with the present hamework.

IV
i.e., it measures the difference in GDP growth before and after the GFC in states with a given characteristic, minus the difference before and after the GFC in states without the characteristic.

Table 3 presents the results. Column 1 shows that states with credit growth above median prior to the GFC had on average a 1.45-percentage-point larger decline in GDP growth per year afterwards, compared to states with below median credit growth. Similarly, column 3 shows that the growth slowdown in states in the top tercile of the credit growth distribution was 2.39 percentage points larger than for those in the bottom tercile. The estimates are statistically significant at the 10 and 5 percent level, respectively. As a further robustness check on this result, we estimate equation 4 using the continuous variable measuring credit growth between 2003–04 and 2007–08 as the statelevel characteristic. Our estimates imply that GDP growth reduced by 0.45 percentage points after the GFC for every additional percentage point increase in credit growth (column 5). Similarly, the level of credit dependence of a state’s economy was negatively correlated with the effect of the GFC on growth: column 6 shows that states that had an above median credit-to-GDP ratio in 2000 experienced slower GDP growth after the GFC, compared to states with a below median credit-to-GDP ratio.

**Table 3: Impact of GFC on states with varying credit growth and credit dependence**

||(1)<br>GDP<br>Growth|(2)<br>GDP<br>Growth|(3)<br>GDP<br>Growth|(4)<br>GDP<br>Growth|(5)<br>GDP<br>Growth|(6)<br>GDP<br>Growth|
|---|---|---|---|---|---|---|
|High Credit Growth x Post-|||||||
|GFC (Median)|-1.449*||||||
||(1.854)||||||
|High Credit to GDP x Post-|||||||
|GFC (Median)||-1.26|||||
|||(1.485)|||||
|High Credit Growth x Post-|||||||
|GFC (Tercile)|||-2.39**||||
||||(2.268)||||
|High Credit to GDP x Post-|||||||
|GFC (Tercile)||||-0.620<br>(0.710)|||
|Credit Growth 2003-08 x Post-|||||||
|GFC (Continuous)|||||-0.448**||
||||||<br>(2.584)||
|Credit to GDP x Post-GFC|||||||
|(Continuous)||||||-0.053*|
|||||||(2.026)|
|Observations|320|320|208|224|320|272|

22
and formal tenure modes. The theoretical model exposes the trade-off faced by the homeowner in deciding how much backyard land to rent out: loss of yard space consumption in return for a gain in rental income. Higher rent raises the opportunity cost of own-consumption of yard space (depressing it), but the gain in rental income from higher rent has the opposite effect, leading to an ambiguous net impact of rent on consumption. Under common forms for preferences, however, the homeowner’s own-consumption of yard space falls as land rent increases, causing more land to be rented to backyarders. Better job access, which raises the land rent backyarders are willing to pay, then tends to lead to more backyarding, but it also raises the homeowner’s income net of commuting cost, which leads to less backyarding. Under a natural assumption, the net effect is an increase in backyarding, leading to a predicted positive association between job access and the extent of backyarding. This result matches the usual connection between job access and population density in urban models, but it comes from a subtler combination of This hypothesis is tested by combining a satellite-based count of backyard dwellings per parcel with job-access data, which come from job data at the level of transportation zones together with an origin-destination matrix showing trip times between zones. The empirical results strongly confirm the prediction that better job access increases the extent of backyarding. In addition, the estimated inverse relationship between backyarding and parcel size suggests that lower homeowner income (associated with small parcels) may spur backyarding, as also predicted by the model.

Thus, using information from a number of remarkable data sets, the paper provides unique insights into a land-use practice that is mostly absent in developed Western countries, even though it bears some resemblance to room rentals by homeowners under an Airbnb-style arrangement. An understanding of the forces that drive the backyarding phenomenon, particularly the link to job access, is potentially useful to South African city planners as they attempt to manage the evolution of their urban areas. For example, transport policies that increase job access would lead to more backyarding in the affected areas, while policies that raise employment for homeowners would reduce it. Planners should also recognize that, even though backyarding may be technically illegal and often unsightly, it generates short-run efficiencies 21
number of observations in the cluster. Within cluster covariance is given by _σ_ ˆ _η_<sup>2.Onthediagonal</sup><sup>_σ_ˆ</sup> _e,ch_<sup>2is</sup> added to obtain the observation specific disturbance varaince. The resulting **cluster block** of this _N × N_ matrix is equal to: where **0** is a block of zeroes; the number of columns equals the Ω<sup>ˆ</sup> _c_ from above, and the number of rows to the Ω<sup>ˆ</sup> _c_ next to it.

Ωis the variance-covariance matrix of the error vector necessary to estimate equation (1) via GLS and obtain ˆ ˆ _βGLS_ and Var _βGLS_ . The estimates for the GLS detailed by ELL (2003) are: � � and where _W_ is a _N × N_ diagonal matrix of sampling weights. Because _W_ Ω<sup>_−_1</sup> is usually not symmetric,<sup>23</sup> as noted by Haslett et al. (2010), the variance covariance matrix must be adjusted to obtain a symmetric matrix. This is done by obtaining the average of the variance covariance matrix and its transpose (Haslett et al., 2010).

Newer versions of the PovMap software no longer obtain _GLS_ estimates using this approach. Adjustments have been made in order to better incorporate the survey weights into Ω. The newest version of PovMap uses the _GLS_ estimator presented by Huang and Hidiroglou (2003), which is discussed below.

# **3.2 Henderson’s Method III decomposition**

After a decade of use of PovMap, and numerous poverty maps completed by the World Bank using the original ELL small area methodology; the software was updated with Henderson’s Method III (H3) decomposition of the variance components and an update of the _GLS_ with a modification of Huang and Hidiroglou (2003).<sup>24</sup> Obtaining the _GLS_ estimates once again requires the estimation of the variance components, which are

> 23The lack of symmetry is due to the difference in sampling weights between observations.

> 24This is detailed in Van der Weide (2014).

7
calls “symbolic public goods,” are a precursor to the possibility of collective action in community development because they may mitigate the effects of heterogeneity. People who belong to the community abide by the rules of the community both because they internalize its ideology and because they face sanctions if they violate the rules. The incentives work not just as exogenous constraints but as what can be called “constraining preferences” (Rao and Walton 2004). Thus studying the ideology and beliefs underlying collective action is key to understanding how it interacts with inequality and heterogeneity.

Empirical work on heterogeneity and collective action has focused mostly on economic or social (race, caste, ethnicity) heterogeneity. Social heterogeneity is the more difficult concept to measure because it is usually more than a matter of creating an index that weighs the number of identifiable groups in the community by the size of their populations—though most of the economics literature does precisely this.

Alesina and La Ferrara (2000) examine the influence of heterogeneity on participation using survey data on group membership in the United States and data on U.S. localities. They find, after controlling for many individual characteristics, that participation in social activities is significantly lower in more economically unequal or more racially or ethnically fragmented communities. They also show that heterogeneity has the most significant impact on participation in groups where excludability is low and significant interaction among members is necessary. The study attempts to deal with the possible endogeneity of the income inequality measure. It is unclear from their study, however, what the marginal impact of any given heterogeneity measure is. In particular, 38
# **Figure1: Trends in population and working age population in Russia compared to Western and Eastern Europe, 1990-2050**

<!-- Start of picture text --> 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br>Year Year<br>Western Europe Eastern Europe Russia Western Europe Eastern Europe Russia<br>Source: UN World Population Prospects: The 2010 Revision. Projections after 2010, median variant. Source: UN World Population Prospects: The 2010 Revision. Projections after 2010, median variant.<br>110<br>100<br>105 95<br>100 90<br>95 85<br>Population (2010=100) 80<br>90<br>Population Aged 15 to 64 (2010=100) 75<br>85<br><!-- End of picture text -->

**Figure 2: Dependency ratios in Russia compared to Eastern and Western Europe, 1990-2030**

<!-- Start of picture text --> 1990 1995 2000 2005 2010 2015 2020 2025 2030<br>Year<br>Western Europe Eastern Europe Russia<br>Source: UN World Population Prospects: The 2010 Revision. Projections after 2010, median variant.<br>65<br>60<br>55<br>50<br>Dependency Ratio<br>45<br>40<br><!-- End of picture text -->

- When we separate the two _components of the dependency ratio_ , it becomes clear that the changes in the dependency ratio prior and after 2010 are driven by different factors ( **Figure3** ). The decline in the dependency ratio prior to 2010 is linked to the drop in the share of the population aged 0 to 14 relative to the population aged 15 to 64 (young-age dependency ratio). By contrast, the rise in the dependency ratio after 2010 is driven by the rise in the share of the population aged 65 or older relative to the population aged 15 to 64 (old-age dependency ratio). The same is true for Eastern Europe. In Western Europe, in the last two decades, the moderate rise in the old-age dependency ratio was compensated by small decreases in the young-age dependency ratio, whereas the future rise in the dependency ratio is driven, as in Eastern Europe and Russia, by the rise in the old-age dependency ratio.

4