rafmacalaba/datause-encoder-data huggingface dataset:
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and 54 _._ 7%, respectively) but NE students participate less (47%). Average monthly wages for BP students are about 23% higher than for SC students and almost twice as high as for NE students.

Table 2 presents average characteristics of students’ high school municipalities. Relative to SC and BP students, on average NE students live in municipalities that are smaller and less urban, with lower GDP and higher homicide rates. SC students, in turn, come from larger, denser, slightly more urban, and higher-GDP municipalities than BP students. As explained below, these patterns are related to the geographic location of SC HEIs.

# **3.2 Identifying variation**

To capture variation in local supply of higher education, we use SNIES data from 2004 (again, one year prior to high school graduation in our sample). We first identify a student’s high school location as given by its administrative division or municipality and find the coordinates for its geographic centroid. We then construct the binary variables _Zj_ and _Hj_ , where _j_ denotes the municipality of interest. _Zj_ is equal to one if there is at least one SC HEI within a 10-km radius around municipality _j_ ’s centroid, and zero otherwise. _Hj_ is equal to one if there is at least one HEI offering bachelor’s (and perhaps SC) degrees–namely, at least one HEI belonging to the "Other HEIs" category.<sup>9</sup> As expected, the local supply of HEIs is correlated with students’ enrollment choices (bottom panel of Table 2). We observe that 73 _._ 7% of SC students went to high school in a municipality where there was at least one SC HEI, but this share drops to 64 _._ 6% and 56 _._ 2% for BP and NE students, respectively. In contrast, approximately the same share (84%) of SC and BP students attended high school in municipalities where other HEIs were available. This is because, as of 2005, institutions providing bachelor’s programs (captured by _H_ = 1) were already widespread across cities of all sizes but SC HEIs (captured by _Z_ = 1) were mostly present in large and medium-sized cities. Indeed, 77 _._ 5% of all students attended high school in municipalities with institutions providing bachelor’s programs but only 60% did so in municipalities with SC HEIs.

Each column of Table 3 denotes a combination of the binary variables of higher education local supply, _Z_ and _H_ . While more than half of students in our sample lived in municipalities with both HEI types, 20% lived in municipalities without any higher education supply, and three percent in municipalities with only SC HEIs. The availability of SC HEIs is related to SC enrollment, as the share of SC students is higher in municipalities including an SC HEI than in others, regardless of the supply of other HEIs (8.8%

> 9Neither _H_ nor _Z_ include SENA, which is not an HEI. We focus exclusively on in-person programs and exclude distance or virtual programs.

9
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

center in access in population density, access to services and structure of economic activity. Classification result is presented in Table 4.

We convert the number of residents to the ratio to the baseline to examine changes from the normal period. The average of the first two weeks of March is used as the baseline. The ratio is scaled using Indicator 3 at the national level to mitigate the impact of fluctuations in the number of active subscribers. Indicator 3 at the administrative unit level is not used as it can introduce certain urban-rural biases; Indicator 3 overestimates the number of active subscribers in urban areas where cell-tower density is higher and people are relatively mobile compared to rural areas. Though this process helps mitigate the impact of the fluctuations on Indicator 6 to a certain extent, Indicator 3 cannot sufficiently address the impact. For instance, when the significance of population decreases at the district level is greater than that of active subscribers at the national level, the value of Indicator 6 computed as the ratio to the baseline cannot not sufficiently inflated.

<mark>Table 4.</mark> Classification of 45 districts

||Urban LGAs|Rural LGAs|
|---|---|---|
|Capital districts|3|5|
|Non-capital districts|19|18|

Source: Authors’ calculations.

Figure 5 shows an increased number of people moving to non-capital districts in rural LGAs in the last weeks of March as the State of Emergency was extended. It uses the number of residents for the four groups, which is calculated as the ratio to the baseline and adjusted using indicator 3. In contrast, districts in urban LGAs and LGA-capital districts show decreasing trends. This suggests that many people in urban areas shifted to rural areas as a result of the lockdown, returning to their hometown in rural areas because of decreased job opportunities in urban areas. During Ramadan, there was a brief spike of activity pronounced in urban LGAs. This reflects mobility patterns seen elsewhere, with an initial burst of out-migration from urban to rural areas, and a gradual trickle back as restrictions were lifted.

Interestingly, non-capital districts in urban LGAs are distinctive in that the estimated population remained almost unchanged in April and May after an initial decrease in activity, perhaps in anticipation of lockdowns. This might reflect populations living in suburban areas with more stable jobs and established homes, more reliant on local economic drivers rather than civil service and tourism. In contrast, the sharp decrease in capital districts in urban LGAs could represent the behaviors of migrants without stable jobs or homes, who were particularly vulnerable to the economic downturn.

<mark>Figure 5.</mark> Numbers of residents at the district level in urban and rural areas - ratio to the baseline

**14** | P a g e
8 elected governments face greater political costs from allowing insolvent banks to socialize the risks of imprudent lending. Ultimately, elected governments face greater political costs when they attempt to make fiscal transfers to special interests (delinquent borrowers, careless depositors, imprudent bankers) at the expense of citizens generally.

Of course, elections often fail to generate accountability. In 1997 expropriation risk was the same or higher in 35 percent of countries exhibiting competitive elections than in 60 percent of the countries that did not.<sup>1</sup> Although the literature identifies several explanations for this, two are the focus here. First, political competitors strive to gain electoral advantage by making claims to voters about what they will do if they take office. If those promises are not credible, however, they do not affect the election and politicians have no reason to abide by them once they take office. One precondition of credibility is that government actors confront a cost from reneging. Such a cost might come in the form of damaged reputation. A second source of low credibility is voters’ lack of information about government policy actions and their connection to citizen welfare. Without this information, it is not possible for citizens to verify whether politicians have taken the actions that they had promised prior to their election. Absent verifiability, however, political promises are not credible.

The analysis of information in the literature generally takes for granted the credibility of agreements and asks how imperfect voter information distorts outcomes. Besley and Burgess (2004) do this, asking how imperfect voter information about politician type or actions allows worse outcomes than would otherwise prevail. Grossman and Helpman (1996) argue that if voters are uninformed about candidate characteristics, candidates can spend money to persuade the voters of their qualities. As in the credibility story, citizens in

> 1 The expropriation risk measure is from Political Risk Services’ _International Country Risk Guide_ and the measures of competitive elections from the _Database on Political Institutions_ .  These are discussed below.  There was no difference at all between countries with and without competitive elections on another commonly used measure of property rights, the rule of law measure from the same source.
expenditure is positively related with per capita growth rate. It is the share of health expenditure on preventive care and research and development that has growth effects.

In equation _(4.5),_ we disaggregate the education variable into expenditure on (i) administration, management, inspection, operation of pre-primary, primary and secondary education; (ii) of tertiary education; and (iii) other education. As reported in<sup>equation</sup><sup>_(4.5),_this</sup> last component of education expenditure is positively and significantly<sup>relatedwithpercapita</sup> growth rate. This category of spending on education includes subsidiary services<sup>toeducation</sup> (transportation, food, lodging, medical and other such services to<sup>students),programunits</sup> engaged in administering, supporting, or carrying out applied research into<sup>teaching methods and</sup> objectives, into learning theory and curriculum development, etc.<sup>**A**</sup> unit<sup>increasein theshare</sup> of this category of education spending leads to an increases of **0.63** percentage points<sup>in per</sup> capita real **GDP.** The level of education expenditure (measured<sup>**by**percapita realeducation</sup> expenditure) has negative growth effects.

As for the other variables in the regressions, note that the black-market premium<sup>is</sup> negative and statistically significant in almost all the equations. The<sup>signis whatwouldbe</sup> expected: the higher the premium, the more distorted the economy,<sup>theworseitsgrowth</sup> performance. Interestingly, the shock variable is not<sup>statistically significant.It is possible that</sup> most of the contribution of this variable is being picked up **by** the regional dummies,<sup>which are,</sup> for the most part, statistically significant.

# 3.4 Alternative **Specifications and** Samples

Given the surprising nature of these results,<sup>especially thosehavingtodo withcurrent</sup>

**17**
US participants focused on, and looked more quickly at, the focal objects in their visual arena. The East Asians made more quick glances to the background.

The observed physiological differences, too, can be linked to brain activity. In a study using functional magnetic resonance imaging (fMRI), the neural processes that underpin these differences were identified. Compared to East Asian participants, US participants activated more regions in the brain implicated in object processing (Gutchess et al. 2006).

# 5.9.3  The endogeneity of the brain

Thus, the metaphorical distinction often made between hardware (the brain) and software (culture) may be less rigid than the standard vocabulary suggests. The brain itself and the way it operates can be altered by experience. That this is so has long been suspected, but advances in neuroscience are confirming this. Consider cab-driving in London. It requires continuously retrieving episodic memories of a busy complex city, simulating possible routes, and operating on maps. The drivers make constant use of the navigating functions of their hippocampi (Maguire et al. 2006). As a result, the shape of their hippocampi systematically differs from the shape in non-cab drivers. The longer the cab drivers have been driving, the larger the differences are. The act of driving, self-motion from riding in a car, or preexisting individual differences at the time of career choice cannot explain the differences in the hippocampi.

The example of cab drivers sheds light on human malleability more generally. The routines and habits that people engage in are not randomly distributed across the world. Instead, they emerge from historical experience and are organized by social factors. Just as taxi drivers regularly perform certain tasks (retrieving relevant episodic memories, simulating possible routes, and operating on the mental maps), so too do people in different cultures. Sustained engagement in tasks yields culturally patterned neural activities (Kitayama and Park 2010, p 114); the mind can become “re-tooled” (Wilson 2010).

# 5.9.4  Implications for standard economics

The findings that culture and experience affect cognition, individualism, attention, and perception have an immediate economic implication: the standard assumption in economics of intertemporal separability is misleading. What happens at one date affects behavior and perceptions at later dates.

52
the relation between GDP and corporate sales for just the private firms and find a similar pattern as before – corporate sales begin to recover one period after GDP recovery. All our results hold when we restrict the sample to firms with fiscal year ends in December.

Once we disaggregate across countries in Figure 4, we again find a great deal of heterogeneity. In Figure 4A, when we look at the set of countries where the macro data showed a Phoenix Miracle, we find that sales (dotted line) begins to recover simultaneously with GDP (bold line) as in the case of Thailand or one to three periods after GDP recovery as in the case of Argentina, Indonesia, Malaysia, Mexico, and Russia. However the sales and GDP patterns do not appear to be correlated at all in the case of the Non-Miracle countries – Korea and the two Turkey episodes (see Figure 4B). In the case of Korea there is no drop in sales during the crisis period and in the case of Turkey’s two crisis episodes, aggregate sales does not seem to recover even three periods after GDP recovery. While we previously classified Korea and the two Turkey episodes as Non-miracles at the aggregate level since GDP and credit recovered simultaneously, Figure 4B suggests that micro data also point towards them being non-miracles. We discuss this in greater detail in section 3.2.2.

In addition to the robustness checks in Figures 3B-3D, we undertake several other checks to understand whether statistical issues or sample selection issues are driving the discrepancy between recovery in economy-wide GDP versus corporate sales. First, we find that our results are robust to restricting the sample to firms with fiscal year ends in Dec 31<sup>st</sup> ( <15% of our sample of firms have fiscal year ends before Dec 31<sup>st</sup> ) suggesting that our results are not driven by timing issues related to the reporting of balance sheet information.<sup>16</sup> Second, our results are also robust to not restricting the sample to firms with all five years of data on sales or output suggesting that our results are not driven by sample selection issues associated with firms entering and leaving the database.

To explore whether our results are driven by discrepancies in official reported GDP statistics, we look at two other measures - value added in the manufacturing sector from World Development Indicators and electric power consumption. Several economists including Dobozi and Pohl (1995) have argued that electric power consumption data are a more reliable indicator

> 16 We prefer to use annual data instead of quarterly data since the coverage of firms is better in the annual Bloomberg file. However, our results are robust to using quarterly data rather than annual data.

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# **Table 2 GARCH (1, 1) Estimates**

||**Full**<br>**Sample:**<br>_1985‐2015_|**Pre‐**<br>**Boom:**<br>_1985‐2003_|**Post‐**<br>**Boom:**<br>_2004‐15_|**Crash 1:**<br>_11/19/85‐_<br>_04/31/86_|**Crash 2:**<br>_11/09/90‐_<br>_02/22/91_|**Crash 3:**<br>_07/02/08‐_<br>_02/13/09_|**Crash 4:**<br>_10/01/14‐_<br>_01/28/15_|
|---|---|---|---|---|---|---|---|
|**Mean equation**||||||||
|�|0.02|0.01|0.02|‐0.46|‐0.63|‐0.85|‐0.57**|
|�|(0.82)|(0.13)|(0.48)|(0.28)|(0.20)|(1.59)|(2.44)|
|�����|0.00|0.01|0.03|0.07|0.10|0.47*|13.27**|
|�|(0.03)|(0.08)|(1.57)|(0.30)|(0.24)|(1.79)|(2.21)|
|**Variance equation**||||||||
||‐2.58***|‐1.62*|‐3.13***|‐2.13|‐0.46|‐0.08|‐2.58***|
|��|(5.87)|(1.64)|(7.09)|(0.96)|(1.32)|(0.16)|(3.72)|
|�|0.06***|0.10***|0.06***|0.28|0.02|0.07|0.00|
|����|(7.58)|(5.91)|(4.32)|(0.87)|(0.28)|(1.03)|(0.02)|
|�|0.93***|0.01***|0.92***|0.67**|0.69***|0.67***|0.95***|
|����|(111.9)|(85.2)|(53.1)|(1.99)|(8.20)|(4.91)|(56.5)|
|�<br>���������|0.18|0.57|‐0.98|2.75**|1.21***|0.52**|‐4.53|
|���|(1.00)|(0.08)|(1.52)|(2.20)|(3.81)|(6.27)|(0.99)|
|�<br>���������|‐0.53**|0.32|‐0.62***|‐1.08|‐1.20***|‐0.42***|13.9|
|���|(2.42)|(1.60)|(4.07)|(0.50)|(4.53)|(3.38)|(1.30)|
|�<br>�����|‐2.27|10.68|‐0.38|‐210.6|3.41***|‐0.74|6.66***|
|�|(1.16)|(0.68)|(0.19)|(0.60)|(4.39)|(1.02)|(9.12)|
|�<br>�����|16.72**|18.43|‐0.86***|1.48|‐0.30|‐0.42|‐0.14|
|�|(2.16)|(1.20)|(2.99)|(0.46)|(0.25)|(1.07)|(0.02)|
|**Key test statistics**||||||||
|Log‐Likelihood|‐15700|‐9929|‐5760|‐460|‐684|‐619|‐439|
|Box‐Ljung test (1 lag)|38***|40***|36***|29***|22***|248***|7268***|
|Persistence (GARCH)|0.996***|0.994***|0.988***|0.957***|0.713***|0.737***|0.947***|
|Half Life (days)|161.4|116.7|54.4|15.7|2.0|2.3|12.7|
|_Observations_|_7,325_|_4,603_|_2,722_|_250_|_250_|_250_|_250_|

**Notes** : One (*), two (**), and three (***) asterisks denote parameter estimate significant at the 10, 5, and 1, percent levels.

<!-- Start of picture text --> 8 —<br><!-- End of picture text --> _— 8 —_
11

## **Table 3. Results of Logit and Probit Regressions**

|**Estimates for graduate (1= graduate, 0= non-g **|**raduate)**||
|---|---|---|
||**Logit**|**Probit**|
|GNI per capita/operational cutoff|0.091***|0.088***|
||(4.94)|(5.14)|
|Log of years over cutoff-capped at 9|0.122***|0.108***|
||(5.10)|(5.58)|
|Log of years above cutoff in excess of 9|−0.033**|−0.035|
||(−2.45)|(−2.44)|
|Creditworthiness|0.006***|0.006***|
||(4.65)|(5.14)|
|Poverty headcount|−0.332|−0.383**|
||(−1.54)|(−1.97)|
|Log of population|0.057***|0.051***|
||(4.91)|(5.13)|
|Log of life expectancy|0.694***|0.682***|
||(3.09)|(2.96)|
|Urban population share|0.422**|0.413**|
||(2.49)|(2.54)|
|Institutional development|0.465*|0.516**|
||(1.80)|(2.31)|
|Resource rents/GDP|−0.017|−0.024|
||(−0.08)|(−0.11)|
|Log of OECD GNI per capita/cutoff|1.383***|1.338***|
||(7.32)|(7.36)|
|Angola dummy|0.126|0.149|
||(1.19)|(1.56)|
|_Number of observations_|_705_|_705_|
|_Number of countries_|_65_|_65_|
|_pseudo-R_<sup>_2_</sup>|_0.78_|_0.78_|
|_Mean dependent variable_|_0.27_|_0.27_|
|_Correctly predicted (%)_|_94.9_|_94.5_|

_Note:_ Summary statistics are displayed in Appendix in Table 4, pre-1999 interactions in Table 5. t-statistics (in parentheses, beneath marginal effects) are adjusted for clustering by country; * indicates significance at the 10 percent level, ** at the 5 percent level, and *** at the 1 percent level for two-tailed tests.

# _Predictive power_

The pseudo-R<sup>2</sup> (or proportionate reduction in the log likelihood) for the logit models used to generate predicted probabilities is 0.78. In ‘predicting’ graduation status of the sample observations, the model correctly classifies 95 percent of them. By contrast, a ‘null model’ would classify only 73 percent correctly (by predicting, in the absence of any information other than the proportion of observations that are graduates, that none of them were).
climate change and natural disasters on poverty often rely on household consumption surveys as a dominant source of data. This is true both for single-country (e.g., Akter and Mallick, 2013; Arouri _et al._ , 2015; Bangalore _et al._ , 2018) and cross-country analyses (Azzarri and Signorelli, 2020; Carter _et al._ , 2007). A notable advantage of most living standards measurement study (LSMS)-type household surveys is that they provide comprehensive data on household consumption levels, enabling researchers to construct poverty indicators that directly align with the study’s objectives (Wong and Brown, 2011; Jakobsen, 2012). In singlecountry analyses, the scope of the survey can be more closely tailored to specific regional or national contexts, providing nuanced understanding of the poverty situation. These studies often employ the headcount poverty rate and the Gini coefficient to measure inequality, which can be estimated using household surveys.<sup>6</sup> While single-country studies often focus on specific events or circumstances within a country, their findings are typically limited in generalizability. In contrast, cross-country studies attempt to overcome this limitation by comparing and contrasting experiences across a range of countries. This broader perspective can shed light on patterns and relationships that might not be apparent within a single-country context, thereby offering findings with greater generalizability. However, assembling comparable data across multiple countries presents a significant challenge due to variations in data collection methods, definitions of key variables, and data quality among different nations. As a result, many studies often rely on standardized databases on poverty and inequality such as the World Bank’s PovcalNet database, later transitioned into the Poverty and Inequality Platform (PIP), which offer estimates for inequality and poverty under various poverty lines or the United Nations University’s World Income

> 6 While most studies primarily rely on the Gini coefficient (Cappelli _et al._ , 2011; Tol, 2021; Paglialunga _et al._ , 2022; Pleninger, 2022), other inequality indexes are also estimated based on household surveys such as income ratios, Theil index, or Palma ratio to analyze inequality in more depth (Keerthiratne and Tol, 2018; Diffenbaugh and Burke, 2019). Beyond income poverty and inequality, recent studies also follow a lack of basic needs approach and examine other welfare aspects including food, education, health care, and social protection (e.g., Paavola, 2017; González _et al._ , 2022; Rana _et al._ , 2023).

11
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