rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Borrowing constraints may have a large impact on an individual’s investment–in tradeable assets and, notably, in education and in other forms of human capital. On the aggregate, borrowing constraints may also have a large impact on asset prices. Several explanations have been given for why borrowing constraints exist, ranging from asymmetric information to legal barriers (for example, Zhang (1997)). In this paper we investigate a different source of borrowing constraints: a psychological aversion to debt on the part of potential borrowers. We define debt aversion as the unwillingness to enter into a financial contract framed as debt relative to a financially equivalent contract framed as an income contingent contract. Such unwillingness is a violation of rational behavior (the agent should be indifferent) and implies that agents could be taking a suboptimal amount of debt.

Investments in education are a natural place to look for debt aversion. Pursuing a higher education degree is one of the largest investment decisions that most individuals face. The investment appears to offer large risk-adjusted returns (Card (1999), Psacharopoulos and Patrinos (2004), and Palacios-Huerta (2003)), so missing the opportunity to pursue a higher education degree can potentially have large welfare consequences. Students also typically need to finance their education, so that borrowing constraints are likely to affect their investment decision. Thus, our analysis focuses on a population where the presence of debt aversion is particularly relevant.

This study takes previous research one step further by taking a finer look at the sources of debt aversion. A student’s aversion could stem from the characteristics of the contract, the framing of the contracts offered to them, or from a negative perception associated with the words “debt” or “loan”. Whereas we do not address an aversion due to the characteristics of the contract, we focus on aversion due to the framing of the contract and due to a negative perception associated with these words. We suggest two measures of debt aversion, one comparable to the previous literature, which encompasses both framing and labeling effects, and another that only captures labeling effects. We denote the latter _labeling debt aversion_ . We report these measures of debt aversion among a diverse population of students who are seeking ways to finance their education. To our knowledge, this is the first study to measure debt aversion among a non-U.S. student and recent graduate population and the first one to disentangle the sources of debt aversion into framing and labeling effects.

We conducted surveys in three Latin American countries: Chile, Colombia and Mexico. People were asked to choose between contracts that were framed as debt or as an income-contingent contract.<sup>1</sup> 1422 persons answered the survey, of which 767 responded

> 1The population consisted of students and alumni who had applied for financial aid through Lumni

2
# Droughts Worsen Air Quality by Shifting Power Generation in Latin America and the Caribbean

Mathilda Eriksson,<sup>_∗_</sup> Alejandro del Valle,<sup>*</sup> and Alejandro de la Fuente<sup>†</sup> JEL Classifications: I1, I3, Q4, Q5, Q53, Q54 Keywords: Drought, Electricity Generation, Air Quality, Health, Poverty, Climate Change

- *Georgia State University, Maurice R. Greenberg School of Risk Sciences, Atlanta, GA.

- †World Bank, Poverty, and Equity Global Practice, Washington, D.C.

Correspondence: meriksson@gsu.edu This is a background paper for the Regional Study on Climate and Poverty in Latin America and the Caribbean supported by the Climate Support facility Whole-of-Economy Program, administered by the World Bank. We want to thank Laura Bakkensen, Judson Boomhower, Dylan Brewer, Olivier Deschenes, Xavier Gine, Matthew Kahn, Namrata Kala, Vikalp Mishra, Seyhun Sakalli, Casey Wichman, Esha Zaveri, and Eric Zou, as well as seminar and conference participants at Georgia State University, Imperial College London, Georgia Institute of Technology, Javeriana University, OECD, and the World Bank for helpful comments.
# **_Table A4.6c Relationship between Task Team Leader (TTL) turnover and IEG outcome rating, weighted by original commitment (in millions, constant 2015 $)_**

||**Depen**<br>|**dent variable: I**<br>|**EG 1-6 outcome**<br>|**rating**<br>|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Dummy for preparation TTL change|-0.049|||-0.0245|
|in the 1st or 2nd ISR|(0.069)|||(0.068)|
|TTL turnover during first half (Number of||-0.253||-0.130|
|TTLs/Number of ISRs)||(0.171)||(0.175)|
|TTL turnover during second half (Number of|||-0.789***|-0.782***|
|TTLs/Number of ISRs)|||(0.227)|(0.226)|
|Constant|2.344***|2.433***|2.867***|2.909***|
||(0.455)|(0.453)|(0.542)|(0.534)|
|Observations|2,763|2,762|2,757|2,761|
|Adjusted R-squared|0.208|0.209|0.218|0.208|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS, weighted by original commitment, and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects.

# **_Table A4.6d Relationship between Task Team Leader (TTL) turnover and IEG outcome rating, mean imputation for missingness_**

||**Depen**<br>|**dent variable: I**<br>|**EG 1-6 outcome**<br>|**rating**<br>|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Dummy for preparation TTL change|-0.055|||-0.028|
|in the 1st or 2nd ISR|(0.044)|||(0.045)|
|TTL turnover during first half (Number of||-0.418***||-0.336**|
|TTLs/Number of ISRs)||(0.153)||(0.159)|
|TTL turnover during second half (Number of|||-0.587***|-0.548***|
|TTLs/Number of ISRs)|||(0.169)|(0.169)|
|Constant|2.088***|2.248***|2.504***|2.644***|
||(0.333)|(0.334)|(0.359)|(0.352)|
|Observations|2,763|2,762|2,757|2,761|
|Adjusted R-squared|0.208|0.209|0.218|0.208|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS, and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects. Mean imputation used for missing values of TTL turnover measures.

55
In (2),  _f_ is the heterogeneity or (taste dispersion) coefficient of group _f_ and is inversely proportional to the identical variance of the idiosyncratic utilities _u f_ 1, _u f_ 2, _u f_ 3.

From (2) we can also calculate that the elasticity of choosing mode _m_ with respect to monetary travel cost _gm_ and travel time _Gm_ are respectively: Taking the auto mode ( _m=3_ ) as an example, and keeping  the same by _f_ , because the _f_ probability of choosing auto<sup>_P_</sup> _f_ 3<sup>increases with</sup><sup>_f,_the monetary cost elasticity decreases with</sup> income (i.e. lower income groups should be more sensitive to monetary cost since they have lower budgets) while because the wage rate rises with _f_ (higher income groups should be more sensitive to travel time, since they have higher values of time).

The person trips by mode are calculated as,

> In (4), _N f_ are the total number of person trips per day (commuting and non-commuting) by

income group _f._ These daily person trips remain fixed in the model. Then, the sum of motorized vehicle traffic in car-equivalent units, is obtained as: where  2,  3 are the inverse ratios of vehicle occupancies (car-equivalent buses per bus persontrip, and fractional cars per person-trip by car respectively) which are used to convert person trips to car equivalent vehicular trips after multiplying by _h_ which is the car-equivalent capacity load of a whole bus. _a_ =0.5 is the fixed share of bus in the public transit mode. Given an aggregate road capacity, _Z_ , the congested round trip travel time per trip is then calculated from the Bureau of Public Roads type of congestion function: 6
||Tab|le 4: Extended|networks||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_n_<sup>_c_</sup><br>_pdt_|0.0551***|0.0699***|0.0607***|0.0709***|
||(0.00364)|(0.00624)|(0.00300)|(0.00362)|
|_nfpt_|0.151***|0.151***|0.132***|0.132***|
||(0.00374)|(0.00374)|(0.00289)|(0.00289)|
|_nfdt_|0.0322***|0.0321***|0.0305***|0.0304***|
||(0.00112)|(0.00112)|(0.000916)|(0.000915)|
|_vfpdt_|0.0414***|0.0413***|0.0375***|0.0376***|
||(0.00199)|(0.00198)|(0.00166)|(0.00165)|
|_n_<sup>_c_</sup><br>_dt_|-0.0170**|-0.0178**|0.00101|0.00175|
||(0.00860)|(0.00858)|(0.00585)|(0.00585)|
|HS4|0.0113***|0.0170***|||
||(0.00285)|(0.00330)|||
|HS4 _×npdt_||-0.00805***|||
|||(0.00284)|||
|HK|||0.00466|0.0179***|
||||(0.00419)|(0.00483)|
|HK _×npdt_||||-0.0241***<br>(0.00553)|
|Observations|38451|38451|52212|52212|

Note: Probit estimations, marginal effects reported. Origin-destination, HS2 and year fixed effects. Robust standard errors clustered by origin-destinationproduct.

fect it; interacted, in Column (4), the effect is, again, negative, suggesting some substitutability between the networks of identical and “close” products.

In conclusion, in this subsection we evaluated the possibility that our results could be driven not by the “synergy” effects due to the presence of companies exporting same HS6 products to same destination but rather by some “broader” extended networks. In the light of recent work of Hidalgo et al (2007) we could be concerned that a key omitted variable driving our results is indeed the existence of some broader “institutional capabilities”. For this reason, we added to our baseline specifications two new variable capturing these potential “institutional capabilities” and found that, while indeed 24
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# Table 4

|||Regr|ession Result<br>Advantage In|s for All Deve<br>dices Against<br>(t values sho|loped Count<br>Selected Ex<br>wn in paren|ries Revealed C<br>planatory Vari<br>thesis)|omparative<br>ables|||
|---|---|---|---|---|---|---|---|---|---|
|||||_______<br>__In|dependent Va|riables||||
|Dependent<br>Variable|Constant|Labor<br>intensity|Consumer<br>good ratio|Average per<br>capita wage|Resource<br>dummy|Capital per<br>worker|Scale<br>variable|First trade<br>date|R2|
|RCA1/|2.045<br>(10.31)|-0.018<br>(6.06)*|--|--|--|--|--|--|0.236|
|RCA|1.659<br>(4.48)|-0.010<br>(3.47)'|0.002<br>(5.36)*|--|--|--|--|--|0.386|
|RCA|5.414<br>(12.17)|-0.002<br>(0.47)|--|-0.0007<br>(6.49)|--|--|--|--|0.437|
|RCA|4.659<br>(6.26)|-0.004<br>(2.41)'|0.0D08<br>(1.71)|-0.0005<br>(4.05)*|-1.577<br>(4.27)*|--|--|--|0.525|
|RCA|4.719<br>(6.26)|-0.005<br>(2.45)'|0.0008<br>(1,73)|-0.0006<br>(4.05)'|-1.593<br>(4.27)'|0.00003<br>(0.56)|--|--|0.527|
|RCA|4.612<br>(6.17)|-0.005<br>(2.36)*|0.0007<br>(1.55)|-0.0005<br>(3.85)'|-1.621<br>(4.34)'|--|-0.001|--|0.528|
|RCA|42.156|-0.004|0.0007|-0.0006|-1.622|--|-|0.019|0.530|
||(1.15)|(1.24)|(1.49)|(4.09)|(4.36)|||(1.02)||

1/ In a double log form this regression produces a somewhat better fit as the coefficient of determination Increases<sup>to 28 per</sup> cent.
3

# **The credibility of pre-electoral promises and government performance**

The extreme heterogeneity of democratic performance is easily demonstrated. The rule of law in half of all countries exhibiting either checks and balances or competitive elections in the 1990s was the same or worse as in the median country lacking either one or the other.<sup>1</sup> Comparisons using corruption indicators yield similar results, as do comparisons using objective indicators of performance. More than 20 percent of countries exhibiting strong checks and balances and competitive elections exhibit worse gross secondary school enrollment than the median country that does not. Lindert (2003) documents this as well, showing that “full” democracies are more likely than “elite” democracies to provide primary education. Baum and Lake (2003) find no relationship between democracy and female secondary school enrollment. The sources of this varied performance remain a continuing puzzle of growing importance, since the number of countries holding competitive elections has doubled, from 53 to 101 between 1985 and 2000, and the number exhibiting some checks and balances has risen from 62 to 112.<sup>2</sup> What explains the heterogeneity of democratic performance? Keefer and Vlaicu (2004) point to the credibility of pre-electoral political promises. Most models assume either that all pre-electoral promises are credible to all voters, or none are (see Persson and Tabellini 2000 for a review). There are, however, scarcely any democracies in which these polar cases apply. Keefer and Vlaicu (2004) argue that when political competitors are less able to make credible promises to voters regarding public good provision and economic performance generally, they attempt either to build their credibility among smaller groups of

> 1 The rule of law measure is from Political Risk Services’ _International Country Risk Guide_ and the measures of checks and balances and competitive elections from the _Database on Political Institutions_ .  These are discussed below.

> 2 Based on World Development Indicators PPP adjusted income per capita, and using the Database on Political Institutions variables _Executive Index of Electoral Competition_ ( _EIEC_ ) and _Legislative Index of Electoral Competition_ ( _LIEC_ ), and _Tenure of System_ ( _tensys_ ) which are explained in more detail below.
immediately around you. That gives you one perspective on what is happening. But if you leave the dance floor and get up on the balcony, you can see everything that is going on; i.e., who is dancing and who is not, how the music affects different dancers, where dancers are on the floor, etc. (Heifetz and Linsky, 2002, pp. 51-74) Getting on the balcony incorporates the emotional intelligence skills of social awareness, self-awareness, and self-management (Goleman et al., 2002, pp. 39-50). Social awareness involves sensing others’ emotions, perspectives and motives and the political and personal relationships among the persons relevant to the situation. By stepping back from the fray and asking questions such as “Who cares about the actions I am taking?” “What seems to happening beyond my vision?” “Why are some people engaged and why are others not engaged?” and “What seems to energize particular people and what seems to lead to resistance?” can help the regulator understand what is happening with others. Self-awareness involves reading one’s own feelings and understanding personal strengths and weaknesses. One former regulator said she used to employ what she called the “smell test,” which meant that she would seriously question proposals that didn’t feel right even if she could not pinpoint the problem. Selfmanagement includes controlling one’s own responses, displaying integrity, adapting to situations, and acting on opportunities. Exercising self-awareness and self-management can be some of the more difficult things to do in a leadership role, but they are fundamental to getting on the balcony (Heifetz and Linsky, 2002; Laurie, 2000; Goleman, 1994; Cooper and Sawaf, 1997).

I failed to get on the balcony when working for the regulatory agency in Iowa. The IUB had a long tradition of using fully distributed cost studies in the 1980s, but increasing competition at the fringes of the local telephone monopoly and rapid changes in technology made this policy problematic. Because cost analysis was one of my areas of expertise, the Iowa commissioners asked me to direct a process to establish new costing rules. I attacked this as a technical challenge and wrote a staff proposal that correctly applied the most current economic theories. I then invited industry economists to join me in a series of workshops to refine the proposal.

The workshops were a disaster. Economists from the industry derailed the first two workshops by arguing about pricing issues rather than costing rules. I was worried 9
||(1)<br>2001-2004|(2)<br>2005-2007|(3)<br>Domestic|(4)<br>Foreign|(5)<br>Small|(6)<br>Large|
|---|---|---|---|---|---|---|
|Serv. lib.|0.146<sup>_∗∗∗_</sup>|0.115<sup>_∗∗∗_</sup>|0.131<sup>_∗∗∗_</sup>|0.151|0.167<sup>_∗∗∗_</sup>|0.102<sup>_∗∗∗_</sup>|
||(0.021)|(0.028)|(0.016)|(0.082)|(0.024)|(0.019)|
|Exporter|0.113<sup>_∗∗∗_</sup>|0.031|0.084<sup>_∗∗∗_</sup>|0.125<sup>_∗_</sup>|0.086|0.075<sup>_∗∗∗_</sup>|
||(0.016)|(0.021)|(0.012)|(0.055)|(0.044)|(0.011)|
|Serv. Lib. _×_ Exporter|0.018|0.043|0.043|-0.056|0.145|0.019|
||(0.035)|(0.049)|(0.029)|(0.082)|(0.094)|(0.024)|
|Firms|8813|8174|10444|948|5752|7290|
|N|25710|14730|37418|3022|12041|28399|
|_R_<sup>2</sup>|0.60|0.74|0.64|0.76|0.60|0.69|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Notes: Dependent variable is ln( _TFP_ ) estimated by Olley-Pakes procedure. Robust standard errors are reported in parenthesis. Each estimation is performed with year dummies, industry-time cross-effects, and firm fixed effects, which are not reported for brevity. Column (1) is estimated for 2001-2004. Column (2) is estimated for 2005-2007. Column (3) is estimated for domestic firms with share of FDI less than 10 percent. Column (4) is estimated for foreign-owned firms, with share of FDI above 10 percent. Column (5) is estimated for firms with employment below 50 workers. Column (6) is estimated for firms with employment 50 workers and above Table 5: Results for different sub-samples

# **Services liberalization before and after 2005**

We split the sample into two sub-periods – 2001-2004 and 2005-2007 – to control for possible effect of the political regime switch, because Ukrainian governments before and after the Orange revolution of 2005 represented interests of different financial and industrial groups. Also, there was a constitutional reform that shifted political power from the president to the Parliament. The first period was characterized by a more coordinated legislative effort between the president and the Parliament, but the privatization process was non-transparent, resulting in poor investment climate<sup>8</sup> . In addition, the government has been indecisive on the integration strategy for the country. There was a discussion on benefits of EU vs. Commonwealth of Independent States (CIS) integration. The second period was characterized by the surge of FDI due to improvements in investment climate and clearly-stated strategy

> 8Privatization of the Kryvorozhstal, Ukraine’s largest and most modern steelworks, illustrates the irregularities in pre-2005 privatization procedures. In 2004 it has been privatized for 800 million US dollars by Ukrainian oligarchs Akhmetov and Pinchuk in an auction that left out international bidders due to highly protectionist conditions of the tender. In 2005, the steelworks has been re-privatized by Arcelor Mittal for 4.8 billion US dollars.

24
driving the differences between the internet and mobile phone surveys.

We investigate the potential of non-response driving the results in two ways. First, we compare the internet surveys to pre-conflict and other post-conflict data sources to ensure that the surveys are detecting at least some of the large changes in well-being that have been independently reported by other data sources. The internet surveys corroborate a number of these independently reported patterns. Food insecurity drastically rose following the start of the conflict from approximately 10 percent of the population to between 40 and 60 percent of the population during the time period under analysis (e.g., IPC 2015; Favari et al. 2022), and 58.5 percent of internet respondents report having had trouble purchasing food in the market in the month before the survey<sup>19</sup> ; underemployment drastically escalated following the start of the conflict with the majority of households critically relying on humanitarian assistance as the primary source to afford necessary purchases (e.g., IPC 2017; IPC 2018; WFP 2019; Favari et al. 2022), and only 18 percent of internet respondents reported working three or more weeks in the month before the survey; and violence has drastically escalated since the start of the conflict and affected more regions of the country by the time period under analysis here than in the first months of the escalation (e.g., Tandon and Vishwanath 2020; OCHA 2021), and 77 percent of respondents in the internet survey either witnessed violent incidents themselves or know someone who has witnessed violence. Combined, these results are consistent with sample selection not precluding the internet surveys from detecting many of the large changes that have occurred since the start of the Second, in the basic empirical strategy that we present in the next section, we also investigate whether differences in unobservable sample selection between internet and mobile phone surveys make it too difficult to compare the two modalities. Specifically, we compare the difference between internet and mobile phone surveys in sensitive questions to the difference between non-sensitive questions where there is less motivation to answer conducted, benchmark response rates in such surveys are 15 - 20 percent (e.g., World Bank 2020), which are levels at which the same non-response issues are critical for mobile phone surveys as well. 19The question distinguished whether food was available for purchase in the market. In total, 50.5 percent of respondents had trouble purchasing food in the past month even though food was available to purchase, 7.9 percent had trouble purchasing food because food was not available to purchase, and 41.5 percent did not have trouble purchasing food from the market in the past month.

15