rafmacalaba/datause-encoder-data huggingface dataset:
of a functional system. This is particularly true for transportation networks. A government, for example, may consider impleme <mark>nting a new road section designated as an emergency transport route instead of a similar project that serves no emergency relief function. Given the nature of road infrastructure assets as network components, the construction of a new road may also enhance resilience by increasing redundancies in the network that allow access to alternative routes and absorb increased traffic in the case of emergency.</mark> The methodology proposed in this paper is intended to estimate project-level resilience and not system-level resilience, though project functionality is inherently dependent on and related to the asset's relationship to other parts of a system. Further, applied to the transport sector, the goal is to use information that is likely to be available in project feasibility studies for proposed road infrastructure or public data sets to estimate the effect of a disruption on various aspects of functionality. Keeping in line with the nature of the Infrastructure Prioritization Framework (IPF), the analysis at this level is also designed to draw on less complex and less analytically-demanding indicators to make the approach more feasible to apply in information- and capacity-constrained environments.

# 1.4. Dimensions of Functionality

Infrastructure services attend to a wide array of interconnected purposes: they facilitate trade, allow mobility of people and goods, provide access to services, and support a host of other human activities. The relationships between assets in different infrastructure sectors are complex, and due to sectoral variations with respect to physical structures, locations, and degrees of interconnectivity, natural disasters affect infrastructure assets in different ways. The functionality (and potential loss of functionality) of an infrastructure system is difficult to measure for all sectors within a given region. As such, it is presently more meaningful to make judgments of relative resilience within a sector and for similar types of projects. This paper, therefore, focuses specifically on applying concepts of resilience to road infrastructure projects. While measurable aspects of functionality may be directly observable in a non-disrupted (normal) state, the LoF magnitude can only be estimated prior to a disruption. Moreover, the LoF magnitude after a disruption is only partially measurable by examining selected aspects of functionality. For road infrastructure, some of the most important elements of functionality include travel time, road utilization, provision of emergency services, and provision of relief 7
# **<mark>I. Introduction</mark>**

In November 2021, representatives from nations from all over the world attended the COP 26 meetings in Scotland where negotiators sought a pathway forward to achieve a global carbon budget to limit global warming to 1.5 degrees Celsius. Analysts who have studied the necessary carbon emissions reductions for achieving this goal posit that only 11% of the carbon budget is still available (Dasgupta, Lall, and Wheeler 2022).

As observed at the COP 26 meetings, global cooperation to cap greenhouse gas (GHG) emissions faced pushback from many developing countries. A type of bargaining game is now playing out. Many academic economists have argued that there is a global Hicksian Pareto improvement possible if the developing world is compensated for not engaging in fossil fuel intensive economic development (see <mark>Kleinnijenhuis, Adrian and Bolton 2022)</mark> .

Yet, efforts to reduce GHG emissions require incurring upfront costs in return for uncertain future benefits of less climate change risk. The emerging middle class in the developing nations could bear much of these costs as they are now buying and operating energy intensive durables ranging from vehicles to air conditioners to computers. Owners of these durables represent an interest group with a stake in opposing carbon pricing. In the absence of targeted transfers, such a Pigouvian policy would introduce a negative income effect. The French Yellow Vest protests of 2018 may offer a preview of the future in the developing world if the emerging middle class views the low carbon policy agenda to be elitist and to lower their standard of living by raising the prices of energy and limiting their personal freedom.

In this paper, we use several data sets to explore the consumption patterns, carbon emissions and environmental attitudes of people with different incomes and educational attainment levels. First, we use data from the World Bank’s Living Standards Measurement Surveys to estimate durables Engel curves. We provide new evidence on the extensive margin of demand for a variety of carbon intensive durables – cars, motorcycles, air conditioners, refrigerators, computers, televisions, washing machines and cell phones. If a nation’s electricity grid could be cheaply decarbonized then such consumers would not bear the incidence of a carbon tax. Owners of internal combustion engine motorcycles and vehicles would face the carbon tax burden as their asset’s resale value would decline and its operating cost would increase.

3
our sample in the 1990s. The reemergence of an active sovereign bond market subsequently encouraged more and more emerging markets to start issuing foreign-currency bonds in London and New York. By the early 2000s, bonds had regained their once dominant position in international sovereign lending. As a result, our sample grows rapidly and reaches a second peak in 2016, with more than 300 foreigncurrency sovereign bonds of 61 countries being actively traded. Further details on the sample and coverage are shown in Appendix B1.

Figure 4: Sovereign bonds in default, 1815-2016 <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>1815 1835 1855 1875 1895 1915 1935 1955 1975 1995 2015<br>Bonds in default (full or partial), share weighted by principal amounts<br>Not in default, share weighted by principal amounts<br>Share in default, in %<br>(weighted by principal amounts)<br><!-- End of picture text --> Figure 4 uses the full sample of bonds in the sample and combines it with the granular default data from Section 3. To compute shares in default, we use the bond’s principal for weighting purposes (issuance amount in USD). In line with Reinhart and Rogoff (2009), we find several main sovereign default waves in the historical data, with more than 40% of external bonds in default in the 1830s, the 1870s, and following the Great Depression. The default wave of the 1980s is not captured in the graph since it involved syndicated bank loans but not bonds. In the modern sample (post-1994), bond default ratios are low in historical comparison, with small peaks during the defaults of Russia in 1998 and Argentina in 2001. From 2006 to 2016, default rates remained close to zero, comparable to the pre-WW1 spell 190013.

# 4.2. Measuring bond returns

The ex-post nominal return for bond _i_ of country _j_ in month _m_ is driven by two main components: price changes and coupon payments. We calculate monthly total bond returns as follows: ii,jj,tt RR =<sup>NNii,jj,mm+ CCii,jj,mm</sup> −1 (2) ii,jj,mm where is the price of bond in month RR and NNii,jj,mm−1 are coupon payments. As is standard practice, coupon payments are considered as accrued interest, meaning that they are equally distributed over the ii,jj,mm ii,jj,mm NN ii mm CC coupon payment period. For example, in the case of quarterly coupons we assume equal payouts in each 20
flict (Deininger and Castagnini 2006; Goldstein and Udry 2008). More importantly, in light of evidence documenting the importance of asset ownership for women’s bargaining power, livelihood opportunities, and intrahousehold allocation of resources towards consumption and investment, low legal rights to women’s property might be at the root of broader patterns of inequality.<sup>3</sup> The idea that an improvement in women’s position relative to men’s is desirable, not only on equity, but also on efficiency grounds, is often advanced as an argument in favor of policies targeted towards women (Duflo 2003). Although the underlying cultural and social dynamics are complex, legal change to improve women’s inheritance rights could potentially provide a low cost means to reduce gender discrimination and improve a wide range of socio-economic outcomes for women. However, empirical study of the topic is rare.<sup>4</sup> Apart from a number of descriptive accounts noting that constitutional amendments to establish equality of women before the law had little impact on real-world outcomes (World Bank 2001), possibly because of low awareness, studies on inheritance rights are few and focus mainly on the distribution of wealth among different descendants (Platteau and 2001, Behrman and Rosenzweig 2004). In a recent study, changes in Muslim family law have been shown to affect household behavior in terms of marriage (Ambrus, Field, and Torero 2009). The fact that modifications in divorce laws in the US had significant impacts on female labor supply, asset accumulation, and domestic violence suggests that exploring the impact of legislative changes on inheritances, especially for women, may be an area where further study could yield significant insights of potentially high policy relevance.

3Women’s ability to control and access resources such as land and independent income has been shown to confer benefits like long term reduction in fertility, higher investment in children’s education health and nutrition, and well-being of future generations. For instance, Thomas (1990) finds that income in the hands of women is associated with larger improvements in child health relative to income in the hands of men. For the link to fertility and child mortality, see Dyson and Moore (1983), Caldwell et al (1983), and Eswaran (2002). A number of studies find that childrens well-being is strongly correlated with womens income relative to mens, where women consistently devote a higher proportion of their income to family needs than do men. Refer to a survey by Strauss, Mwabu and Beegle (2000) for studies that find evidence from Cameroon, India, Kenya, Malawi, and the Dominican Republic.

> 4While the status of women in developing countries remains an important contemporary issue, there were historical implications of the inheritance legislation. For instance, it is frequently argued that the French birth rate dropped very rapidly in the 19th century following the revolutionary change and Napoleonic affirmation in the inheritance laws from primogeniture to equal division of estates amongst all children (Garner 1914)).

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**<u>Table 5. Instrumental Variable (IV) regression results</u>**

|<br>Dependent var.|<br>ln_W_||<br>_D_||ln_W_||_D_||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|_D_|1.469|(0.568) ***|||1.282|(0.491) ***|||
|ln_TAXIBE_|||-0.026|(0.013) **|||-0.028|(0.012) **|
|ln_SUBUBUS_|||0.008|(0.014)|||0.015|(0.013)|
|ln_MINCBD_|||0.036|(0.061)|||0.015|(0.037)|
|ln_RENT_|||0.011|(0.004) ***|||0.012|(0.004) ***|
|ln_AGE_|0.298|(0.119) **|-0.110|(0.048) **|0.268|(0.110) **|-0.113|(0.048) **|
|_MALE_|0.535|(0.070) ***|-0.050|(0.030) *|0.529|(0.066) ***|-0.050|(0.030) *|
|_PRIM_|0.132|(0.104)|0.080|(0.042) *|0.135|(0.101)|0.085|(0.042) **|
|_SECO_|0.149|(0.142)|0.180|(0.048) ***|<br>0.183|(0.130)|0.186|(0.048) ***|
|_TERT_|0.445|(0.172) ***|<br>0.224|(0.050) ***|<br>0.488|(0.158) ***|0.226|(0.049) ***|
|_Q1_|-0.549|(0.192) ***|-0.213|(0.068) ***|-0.570|(0.174) ***|-0.197|(0.066) ***|
|_Q2_|-0.445|(0.161) ***|-0.133|(0.064) **|-0.441|(0.149) ***|-0.114|(0.065) *|
|_Q3_|-0.511|(0.137) ***|-0.020|(0.065)<br>|<sup>-0.490</sup>|<sup>(0.126) ***</sup>|<sup>-0.003</sup>|<sup>(0.064)</sup>|
|_Q4_|-0.413|(0.127) ***|<br>0.003|(0.063)<br>|<sup>-0.408</sup>|<sup>(0.120) ***</sup>|0.009|(0.062)|
|ln_HHS_|-0.034|(0.083)<br>|<sup>-0.038</sup>|<sup>(0.041)</sup><br>|<sup>-0.030</sup>|<sup>(0.080)</sup><br>|<sup>-0.033</sup>|<sup>(0.040)</sup>|
|ln_MINCSB_|-0.015|(0.033)|0.008|(0.016)<br>|<sup>-0.015</sup>|<sup>(0.029)</sup>|0.007|(0.016)|
|ln_ROOM_|0.129|(0.088)|0.065|(0.040) *|0.155|(0.078) **|0.070|(0.039) *|
|_HOME_|-0.034|(0.078)|0.032|(0.041)|-0.049|(0.072)|0.035|(0.041)|
|_POWR_|0.229|(0.140) *|-0.036|(0.069)|0.221|(0.133) *|-0.032|(0.068)|
|_TAPW_|0.239|(0.143) *|-0.055|(0.068)|0.222|(0.129) *|-0.055|(0.068)|
|_TOIL_|0.032|(0.093)<br>|<sup>-0.016</sup>|<sup>(0.044)</sup>|0.023|(0.087)<br>|<sup>-0.020</sup>|<sup>(0.044)</sup>|
|Constant|10.279|(0.601)***|0.671|(0.247)***|10.444|(0.522)***|0.651|(0.236)***|
|Obs.|858||||858||||
|No. of district|8|||8|0|||0|
|dummies|||||||||
|Wald chi2|378.85||||393.94||||
|Endogeneity test:|||||||||
|C statistic chi2|8.579|***|||7.705|***|||
|Overidentifying rest|riction test:||||||||
|Hansen's J stat.|4.48||||5.70||||

38. One may also be worried that the wage equations are different between the treated (i.e., commuters) and the untreated (i.e., noncommuters). The above treatment effects model implicitly assumes that they are the same. With the two groups differentiated, the treatment effects model can be rewritten by this:
interest in joining a political party, which represents less of a concrete action and more of an expression of partisan political interest.

We include a range of control variables, including a binary measure of whether respondents left assets in the United States, binary measures indicating whether respondents have at least one child in the United States or in Guatemala, years of education, a binary variable indicating whether the respondent was last apprehended at the border, a log of the number of years in the United States, and employment status. We also include various demographic variables. The variable “Indigenous” refers to whether respondents’ mother tongue is anything besides Spanish. We also include a variable indicating whether respondents have visible tattoos because of affiliations between gangs and tattoos in Guatemala. Finally, we control for survey round. The 1- and 6-month follow-up surveys straddled the onset of the COVID19 pandemic, so this control variable also captures any changes in the dependent variable that may be linked to the pandemic, such as an overall lower interest in (or expectation of) civic engagement.

# **4.2 Randomness of Extortion**

We argue that extortion suffered while migrating is a quasi-random experience. Qualitative evidence concerning migration through Mexico as well as a quantitative analysis of balance within our sample support this argument.

Violence, including extortion, can happen to any migrant regardless of their income. Put simply, as one scholar writes, “there is no subgroup that seems to be particularly at risk among deportees... kidnapping occurs simply because one is a migrant” (Slack et al., 2018, p. 196). Another suggests that individuals of any income can be kidnapped for the purpose of extorting money from their families; the kidnappers “know that their families will send money even if they cannot afford to” (Vogt, 2013, p. 764). Indeed, Vogt (2013) argues that violence along the migration trail is viewed by many migrants as a necessary evil, where its risk is high enough to become expected. Finally, one study of over 12,000 migrants traveling through Mexico to the United States suggests that years of schooling, having children, and having entered the U.S. previously are not correlated with the likelihood of experiencing violence. (Leyva-Flores et al., 2019). Thus, random chance plays a significant role in who experiences victimization during migration.

One potential way in which migrants can reduce the likelihood that they will be victimized is by selecting “good” coyotes. More precisely, migrants may try to work with coyotes who have good reputations in their hometowns. However, migrants do not always have the capacity to gauge the trustworthiness of coyotes because of the networked structure of the 14
downward mobile, 13 percent are upward mobile, and 53 percent are never poor. The method estimates that downward mobile households see their welfare fall by 65 percent on average, while upward mobile households escape poverty with a mean welfare increase of 190 percent. We also predict growth incidence curves and find evidence of a marked pro-poor growth.

This paper contributes to two strands of literature. First, it contributes to the methodological discussion on welfare dynamic prediction methods when panel data are unavailable. The LASSOPMM approach presented in this paper embraces machine learning methods whose primary goal is accurate prediction, makes less restrictive assumptions than previous synthetic panel methods, and provides a simple framework that is easier to replicate in practice. The paper also highlights that the quality of the predictions is directly linked to the quality of the underlying welfare model used. Previous papers present predictions that are statistically indistinguishable from actual household poverty transitions with a relatively low fit. This is not the case for predictions in this paper; the quality of the underlying welfare model is key. Although the paper is restricted by a limited set of harmonized variables, the models we estimate have greater explanatory power compared to previous methods. Second, we contribute to the discussion of how the results from welfare prediction methods can be used to guide policy. The application of LASSO-PMM is carried out on 43 cross section datasets. To our knowledge, no other paper in the literature has used this amount of microdata to estimate welfare dynamics using cross-sections. The results from applying LASSO-PMM to cross-section data yield not only predicted poverty rates in the aggregate, but also the share of individuals who are upward and downward mobile, a key population for targeting anti-poverty policies. The results also provide insight on the magnitude of income changes that drive individuals both into and out of poverty, which is also useful knowledge for development efforts.

36
- Visited at least one of its competitor’s businesses to see what products its competitors have available for sale

- Asked existing customers whether there are any other products the customers would like the business to sell or produce

- Talked with at least one former customer to find out why former customers have stopped buying from this business

- Asked a supplier about which products are selling well in this business’ industry

- Attracted customers with a special offer

-

- Advertised in any form (last 6 months)

The _stock score_ ranges from -1 to 2, and it is calculated by subtracting one point

- If the business runs out of stock once a month or more

And adding one point for each of the following that the business has done in the last 3 months

-

- Attempted to negotiate with a supplier for a lower price on raw material

- Compared the prices or quality offered by alternate suppliers or sources of raw materials to the business’ current suppliers or sources of raw material

The _records score_ ranges from 0 to 8, and it is calculated by adding one point for each of the following that the business does

- Keeps written business records

- Records every purchase and sale made by the business

- Able to use records to see how much cash the business has on hand at any point in time

- Uses records regularly to know whether sales of a particular product are increasing or decreasing from one month to another

- Works out the cost to the business of each main product it sells

- Knows  which goods you make the most profit per item selling

- Has a written budget, which states how much is owed each month for rent, electricity, equipment maintenance, transport, advertising, and other indirect costs to business

- Has records documenting that there exists enough money each month after paying business expenses to repay a loan in the hypothetical  situation that this business wants a bank loan

The _financial planning score_ ranges from 0-12, and it is calculated by adding up to three points for each of the following two questions

- How frequently do you review the financial performance of your business and analyze where there are areas for improvement

- How frequently do you compare performance to your target

- Zero points for “Never”

- One point for “Once a year or less”

- Two points for “Two or three times a year”

- Three points for “Monthly or more often”

And adding one point for each of the following that the business has 27
**<u>Table 7: Estimated impacts of changes in neighboring large farm area/distance on smallholders’ yields</u>**

||**Maize**|**Wheat**|**Sorghum**|**Teff**|
|---|---|---|---|---|
|||**Panel A: Dista**|<br>**nce measures**||
|**Distance any farm**|||||
|Distance|-0.00219|0.000202|0.000824|0.00100|
||(0.00169)|(0.00218)|(0.00132)|(0.00160)|
|Distance<sup>2</sup>|-1.34e-06|-9.84e-06|-8.64e-06|-3.14e-05*|
||(1.41e-05)|(1.88e-05)|(9.23e-06)|(1.76e-05)|
|R<sup>2</sup>|0.424|0.363|0.287|0.258|
|**Dist. same crop**|||||
|Distance|-0.00339*|0.00144|-0.000178|-0.00142|
||(0.00178)|(0.00181)|(0.000857)|(0.00121)|
|Distance<sup>2</sup>|9.81e-06|-1.67e-06|2.54e-06|5.49e-06|
||(9.19e-06)|(5.77e-06)|(3.25e-06)|(5.36e-06)|
|R<sup>2</sup>|0.424|0.365|0.287|0.255|
|||**Panel B: Ar**|**ea measures**||
|**Area, all farms**|||||
|0 - 25 km|0.00110***|0.00101|0.000119|0.000856|
||(0.000388)|(0.000615)|(0.000645)|(0.000725)|
|25 - 50 km|-0.000232|-0.000128|0.000103|7.74e-05|
||(0.000201)|(0.000516)|(0.000128)|(0.000324)|
|50 - 100 km|3.64e-05|-0.000327|3.29e-05|-9.32e-05|
||(0.000105)|(0.000231)|(4.95e-05)|(0.000246)|
|R<sup>2</sup>|0.423|0.365|0.287|0.256|
|**Area, same crop**|||||
|<br>0 - 25 km|0.00250***|0.00393***|-0.000447|0.000575|
||(0.000445)|(0.00106)|(0.000976)|(0.00327)|
|25 - 50 km|0.000277|0.00205**|0.000125|0.000738|
||(0.000594)|(0.000841)|(0.000223)|(0.00298)|
|50 - 100 km|<br>-0.000493|<br>0.000574*|<br>7.15e-06|<br>-0.00230**|
||(0.000320)|(0.000299)|(4.60e-05)|(0.00106)|
|R<sup>2</sup>|<br>0.423|<br>0.367|<br>0.287|<br>0.256|
|No. obs.(kebeles)|10,768|5,295|6,973|7,767|

_Note:_ Woreda fixed effects and year trends included throughout. Standard errors clustered at woreda level.

23