rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 17: Volatility of international Interest Rates**

<!-- Start of picture text --> 4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Year<br>1970197219741976197819801982198419861988199019921994199619982000200220042006<br><!-- End of picture text --> Source: author’s own calculations using data from the Federal Reserve Statistical Release. Volatility is measured as the standard deviation of monthly Federal funds effective rate using a five year rolling window.

21
Table 8: Eect of direct SLM training intervention on contact farmers' input use

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Burnt farm plot|0.167|-0.015<br>[0.060]|347|0.037|
|Used natural fertilizer|0.524|0.129<br>[0.067]|343|0.166|
|Used chemical fertilizer|0.071|0.065<br>[0.042]|343|0.037|
|Amount of chemical fertilizer used (kg)|2.681<br>[21.992]|35.227<br>[19.514]|341|0.027|
|Amount of chemical fertilizer used (l)|3.855<br>[27.973]|1.282<br>[5.836]|341|0.033|
|Use herbicides/pesticides/fungicides|0.250|0.022<br>[0.032]|343|0.082|
|Amount of herbicides/pesticides/fungicides used (kg)|0.060<br>[0.361]|0.374<br>[0.223]|341|0.014|
|Amount of herbicides/pesticides/fungicides used (l)|4.367<br>[11.714]|-0.928<br>[1.582]|341|0.048|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013.

Notes: All models include the same explanatory variables as models in Table 4.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat; SLM=sustainable land management.

The main sample has 347 observations. Sample size varies across models due to missing values in the dependent variables.

39
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political corruption. Bhattacharyya and Hodler (2010) use a panel data of countries and find that natural resource abundance is associated with perceived corruption only in countries with a history of nondemocratic rule. They interpret this as evidence that resource rents lead to corruption if the quality of democratic institutions is poor.

Several empirical studies argue that resource abundance hinders democracy, and fosters authoritarian regimes, especially in African countries (Jensen and Wantchekon 2004; Ross 2001; Wantchekon 2002). Early evidence was based on cross-sectional analysis and case studies, and its internal validity is open to debate. A more recent paper (Tsui 2011) uses a more convincing identification strategy exploiting panel data of countries and oil discoveries. Tsui finds that oil discoveries indeed reduce the quality of democratic institutions, but only in already nondemocratic regimes. Oil does not seem to affect institutions in countries with established democracies.

A large body of cross-country evidence points to a positive relation between resource abundance and civil war (Collier, Hoeffler, and Söderbom 2004; Fearon 2005; Fearon and Laitin 2003; Humphreys 2005; Lujala 2010; Ross 2004). This relation seems to be driven by pointsource resources, such as oil, diamonds, and narcotics. These results, however, may not be robust to including country fixed effects, which accounts for several time-invariant unobserved omitted variables. In a recent study, Cotet and Tsui (2013) use a panel data of countries and include country fixed effects. In that case, they fail to find a significant effect of oil discoveries on conflict. Using a similar approach, however, Lei and Michaels (2011) do find a positive relation between oil and conflict. The main difference between these studies is that Lei and Michaels focus on large oil fields while Cotet and Tsui examine all fields, including small ones. This evidence is suggestive of nonlinearities in the relation between resource abundance and conflict.

Finally, the negative relation between resource abundance and growth seems to be present only in countries with already bad institutions. In an influential paper, Mehlum, Moene, and Torvik (2006) show that the resource curse is essentially driven by countries with lowquality institutions. In contrast, in countries with high-quality institutions, resource abundance does not affect growth. Using a panel data set of countries, Collier and Hoeffler (2009) find similar results. In particular, the resource curse seems to be avoided in countries with strong democratic checks and balances. Boschini, Pettersson, and Roine (2007) extend these results by adding differences in the type of resources. They find that the curse is present only in 11
initially remains in the home country (possibly still in the household of his parent) while the migrant is temporarily gone; ( _ii_ ) because they have left children behind in the home country to be looked after by grandparents, possibly after the spouse has joined the migrant at destination;<sup>24</sup> ( _iii_ ) because he and his spouse are from the same home country and thus retain stronger ties to it than if the migrant marries a foreigner; ( _iv_ ) because they can make more of a complete contract with the migrant and his spouse that involves funding migration and getting insurance/remittances in return–whereas if it is with an unmarried migrant, there is a party that then joins the family that is not part of the initial contracting arrangement and could lead to renegotiation. Finally, as argued in section 5.2, it is probably easier for the married migrant to visualize who will also benefit from migration (spouse and children left behind) and than for the unmarried migrant to picture the potential wife and children he might have at destination.

These factors put together could dominate the lower psychic costs of the unmarried son’ migration and the higher probability to intermarry with a U.S. native that translates into higher earnings at destination (and for a longer duration) compared to the married migrant _i ∈ F_ . Table 1 reveals that this applies to only one out of five male migrants who get married after migration.

# **5.3.1 Descriptive evidence from the ENOE**

We draw on data from the 2005Q1 to the 2018Q4 rounds of the ENOE, a rotating panel survey conducted by the INEGI in Mexico, to examine the probability of migrating to the U.S. for married and unmarried men aged 18 to 40 who co-reside with both parents at baseline. The ENOE, which interviews households for five consecutive quarters, allows observing the occurrence of a migration episode from variations in the household roster across interviews, coupled with questions to remaining household members about the destination of the individual who has left the household. The restriction to individuals who co-reside with both parents is necessary to minimize the risk that the migration episode remains unrecorded because of the dissolution of the household of origin of the migrant (Bertoli and Murard, 2020), and the ensuing household attrition, but it clearly reduces the external validity of our

> 24Bertoli et al. (2023) provide evidence, drawing on the Biannual June Supplement of the CPS, that a substantial share of married couples of Mexican migrants in the U.S. have left one or more of their children behind; this data source does not provide information on the timing of the marriage, but these instances are most likely to relate to couples that got married before migrating.

26
are better at solving some of society’s problems (NWW p. 139). On this account, the stability of those societies seems to rest on individual opinions, rather than on coordinated social practices.

The conceptualization of beliefs as exclusively individualistic, rather than intersubjective as well as individual, has an important analytical consequence for North, Wallis and Weingast: it obscures their explanation of constitutional consensus, which is crucial for open access orders. The control of violence – constitutionalism – depends on what they call a “consensus condition,” which entails that “citizens in open access orders share beliefs about the appropriateness of central tenets of their constitution so that they help police the rules by withdrawing support from officials who seek to violate these tenets” (NWW p. 118). Despite the importance this idea plays in their account, they provide no direct evidence that such a consensus exists in any open access order. They argue that in open access orders the control of government is contestable and subject to “clear and well-understood rules” (NWW p. 111), yet never provide an account of which constitutional rules are perceived to be clear and well-understood. Constitutions are “focal points” that “create common expectations among citizens about the behavior of others” (Weingast 2005), but constitutions contain many provisions, often in conflict. Which constitute the focal points? How would the analyst even demonstrate that such a consensus condition existed in a society? It would be difficult even for a large, expensive opinion survey to characterize the set of conditions that might constitute a fundamental breach of the constitution. One could observe that the society is at peace, but then to infer from this that a consensus exists would be to impute a value to the explanatory variable from the value of the dependent variable. This seems to be what NWW are doing. Wedeen (2002: 718) notes that this is a characteristic problem with rational choice accounts that understand culture to be the convergence of individual beliefs. As she puts it, the rational choice concept of “common knowledge” tends to assume the shared quality or commonness of knowledge rather than to question how – or the extent to which – such understandings are, in fact, tacitly understood or consensually shared. In other words, “common knowledge” derives from a consideration of knowledge that can reasonably be considered common, rather than from a consideration of culture as a manifold outcome of human activity. . . By naturalizing the concept of culture in this way, rational choice theorists forgo the ability to know whether common knowledge actually exists in any particular instance or whether another, unspecified, variable is doing the work of coordinating action. By tracking how common knowledge gets produced, is subject to change, or is implicated in 17
_Table 1.6: Additional Employment Probability for the DTS, General Average HS Grade - Probit Estimate on Employment Right after TVET_

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||DTS|DTS|DTS|DTS|DTS|DTS|
|Years of schooling|−0.014|−0.013|−0.016|−0.011|−0.016|−0.015|
||(0.012)|(0.013)|(0.012)|(0.013)|(0.012)|(0.013)|
|Standardized HS grade|−0.003|−0.004|−0.010|0.006|−0.008|−0.014|
||(0.017)|(0.019)|(0.025)|(0.021)|(0.018)|(0.029)|
|Work experience|−0.010***|−0.010***|−0.009*|0.010|−0.010***|−0.008*|
||(0.003)|(0.003)|(0.005)|(0.009)|(0.004)|(0.005)|
|Work experience (squared)|0.000***|0.000***|0.000**|−0.001***|0.000***|0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Female|−0.028|−0.042|−0.021|−0.014|−0.045|−0.025|
||(0.040)|(0.037)|(0.041)|(0.043)|(0.035)|(0.044)|
|Mother's educational level|−0.003***|−0.003**|−0.003**|−0.003***|−0.003***|−0.003**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|DTS implementation scheme dummy|||||||
|<br>(1=Block release)||−0.050||||−0.037|
|||(0.057)||||(0.079)|
|Given training plan from TVI (1=yes)||0.031||||0.040|
|||(0.045)||||(0.044)|
|Weeks spent for in-school learning|||0.001|||0.001|
||||(0.002)|||(0.002)|
|Weeks spent for in-company training|||0.001|||0.000|
||||(0.001)|||(0.001)|
|Number of hours per week for in-company|||||||
|<br>training|||0.021**|||0.021***|
||||(0.008)|||(0.006)|
|Number of hours per week for in-company|||||||
|<br>training (squared)|||−0.000**|||−0.000***|
||||(0.000)|||(0.000)|
|How to learn work process - from|||||||
|supervisors||||−0.048||−0.093|
|||||(0.077)||(0.134)|
|How to learn work process - from peers||||−0.074||−0.071|
|||||(0.117)||(0.178)|
|How to learn work process - by yourself||||−0.052||−0.092|
|||||(0.096)||(0.073)|
|How to learn work process - from all||||−0.019||−0.079|
|||||(0.046)||(0.087)|
|Reported to TVI during in-company|||||||
|<br>training (1=yes)|||||−0.015|−0.026|
||||||(0054)|(0052)|
|Times visited TVI teacher during in-|||||.|.|
|company training (1=yes)|||||0.001|0.001|
||||||(0.002)|(0.002)|
|Observations|407|374|374|389|<br>365|<br>365|

Source: _DTS Tracking Survey, 2015, World Bank. World Bank calculations._ Note: _Standard errors in parentheses. * p<0.1; ** p<0.05; *** p<0.01_ 37
in the same year? To answer this question, we matched an annual, district-level time series of TSC latrine construction to individual-level data on children’s cognitive achievement.

Figure 2 depicts the paper’s identification strategy graphically. In order to investigate an effect of early-life sanitation coverage we must match cognitive achievement data from childhood (2007, 2008, and 2009) to data on exposure to sanitation in a child’s first year of life (2001, 2002, and 2003). We will verify that it is from exposure in the first year of life that the sanitation-achievement gradient is steepest, a result consistent with the mechanism of an early-life critical period (Doyle et al., 2009).

# **4.1 Data**

Table 1 presents summary statistics from the two data sources used in this paper: administrative records on TSC implementation, and Pratham’s ASER survey on education outcomes, which includes learning tests.

## **4.1.1 Independent variable: Total Sanitation Campaign administrative records**

As its key independent variable, this paper uses administrative records on the implementation of the TSC collected at the district level by the government of India. In the period under study, India had about 600 districts, although some had no rural population. An annualfrequency, district-level time series of TSC household latrine construction, collected for administrative purposes, is publicly available on the program’s website at `http://tsc.gov.in/` . These data were reported by block officials to district offices, where they were aggregated into district-level figures that were input into a computer system maintained by the central government.

The TSC data report latrine _counts_ , but districts vary considerably in size. The 5th percentile district in our data had a 2001 rural population of 120,000; the 95th percentile district had a rural population of 3 million. Therefore, as our measure of program intensity, we compute _TSC latrines per capita_ : the number of TSC latrines reported as built in the 10
- Sosa-Padilla, C. and F. Sturzenegger (2022). Does it matter how central banks accumulate reserves? Evidence from sovereign spreads. _Journal of International Economics 140_ , 103711.

- Veldkamp, L. (2023). _Information Choice in Macroeconomics_ . Princeton University Press.

- World Bank (2017). External debt reporting and financial statements. Bank Policy OPS5.09POL.166. Available online: `/https://ppfdocuments.azureedge.net/8fcc4c7a-0e1c47e0-84b3-0950b0fc4d9b.pdf` . Washington, D.C.: World Bank Group.

- World Bank (2021). Debt transparency in developing economies. Available online: `/https://documents.worldbank.org/en/publication/documents-reports/ documentdetail/743881635526394087/debt-transparency-in-developingeconomies` . Washington, D.C.: World Bank Group.

World Bank (2022). _World Development Indicators_ . Washington, D.C.: World Bank Group.

46
population living in cities with one million or more taken from the United Nations’ _World Urbanization Prospects (UNWUP)_ . To check for robustness, the 750,000 cut-off will also be used.<sup>_21_</sup> In the UNWUP, the population data are available every five years. The data for other years are interpolated, assuming a constant growth rate during each 5-year period. Two sources of data are used to calculate the share (in %) of people in agriculture, _sA_ : FAO’s database and the World Bank’s _World Development Indicators_ (WDI). The coverage of FAO’s database is larger than that of WDI, and the FAO data are used whenever they are available. The share of the population engaged in non-farm activities located in the intermediate space or the “middle”, _sN_ , is defined as the residual, i.e. _sN_ ,=100- _sU-sA_ . Given the (deliberately) narrow definition of urban areas (i.e. only the mega cities), _sN_ includes people living and employed in secondary towns as well as those engaged in off-farm employment in rural villages.<sup>22</sup> The number of floods during the spell are taken from the International Emergency Database (EMDAT)<sup>23</sup> and included to control for possible differences in migration patterns to metropoles and the middle in response to natural hazards. The composite polity indicator compiled by the Polity IV project of the Center for Systemic Peace (Marshall, Jaggers, and Gurr) is used to reflect the autocratic/democratic nature of the political regime. The indicator takes on values between -10 (autocratic) and +10 (democratic).<sup>24</sup> The sample is limited to low and middle-income countries according to the World Bank’s classification in 2008 and spans about a quarter of a century, from 1980-2004. The complete list of available poverty spell observations in _Povcal_ at that time consists of 52 countries and 219 country-spell observations. As poverty measures fluctuate substantially in some

> 21 <u>http://esa.un.org/unup/. April, 2008.  In the urban economics literature, urban concentration is often</u> measured by urban primacy—the share of the urban population living in the largest city. This is adequate when most data points are small countries. The approach is too limited when the spells cover many large countries, which have more than one metro area, as is the case here (Henderson, 2010).

> 22 By using the one million or more as cut-off to define a metropolis, measurement challenges in consistently defining rural and urban areas across countries are also circumvented (Ravallion, Chen, Sangraula, 2007). Nonetheless, noise in the data cannot be denied, also when categorizing the population in agricultural and nonagricultural categories, instead of rural and urban. In depth analysis by Christiaensen and Todo (2013) shows that the measurement error is neither systematic nor systematically correlated with changes in poverty.

> 23 http://www.emdat.be/, June 2009.

> 24 http://www.systemicpeace.org/inscr/p4manualv2010.pdf

18
The immediate outcomes of solar power are study hours of children as well as the morbidity of household members and changes in the decision-making power of women induced by TV use facilitated by solar electricity. We find that evening study hours of both boys and girls have increased as a result of SHS expansion. For example, boys study 8 additional minutes and girls study 7 additional minutes during evening when the household is connected to a solar panel. Solar power is found to reduce the morbidity of household members, especially female members. For example, SHS adoption reduces respiratory disease of women by aged 16 and above by 1.2 percent. This is perhaps possible if solar power is used for cooking that not only reduced the use of fuel wood for cooking, which may lead to decreased incidence of respiratory illness due to less air pollution. Women are also found to exercise some decision-making power, but that is only when a certain amount of solar power is consumed that facilitates running a TV. No wonder it is the consumption of electricity but not simple SHS adoption that matters more to this decision-making power.

The ultimate effect of electricity provided by SHS is on household consumption and income. There are a number of ways electricity through SHS adoption can facilitate growth in income and consumption. First, replacement of kerosene saves money that can be used for consumption of food and non-food items. Second, changes in time use and decision-making power induced by SHS adoption can help reallocate household resources for consumption and production, which increases both income and consumption. Third, solar power might help promote home-based income earning activities that have a direct effect on income, and hence, an indirect effect on consumption. Our results presented in Table 10 suggest that solar power increases per capita food expenditure by 9.3 percent, per capita nonfood expenditure by 4.7 percent and total per capita expenditure by 5.1 percent, either simply because of adoption of the SHS unit, or through longer exposure to solar power or higher amount of electricity consumption from solar units.

How do the welfare effects of SHS adoption compare to similar benefits due to grid electrification? A study using household survey data of 2005 from rural Bangladesh shows that grid connectivity improves household per capita expenditure by 11.3 percent (Khandker, Barnes, and Samad 2012). The same study also finds that the evening study time of boys and girls goes up respectively by about 22 minutes and 13 minutes. That is, the impacts of SHS adoption are similar in nature while being smaller in magnitude. Because of the 25
**Figure 2:** Mozambique’s hidden debt, 2006 – 2014

<!-- Start of picture text --> 8<br>Hidden debt (in 2014):<br>6<br>• USD 1.36bn<br>• 7.7% of GDP<br>4<br>2<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Initially reported external public debt<br>External public debt added through revisions<br>(USD billion)<br>Public external debt stock<br><!-- End of picture text --> _Notes:_ The figure shows the initially and most recently reported public and publicly guaranteed debt stocks for Mozambique between 2006 and 2014 in billion USD. The grey bars show the initially reported debt stocks. The red bars show additional debt stocks added through revisions in subsequent reporting. The data is from our new database (see Section 3 and Appendix A).

Our measurement approach allows to quantify Zambia’s hidden debts by comparing debt figures across different vintages of the World Bank International Debt Statistics (see Figure 4). The reported stock of Zambia’s external public and publicly guaranteed debt for 2021 amounted to USD 12.5 billion when it was initially published in the World Bank’s International Debt Report 2022. In the latest vintage, the same figure is given as USD 15.7 billion, indicating an upward revision of USD 3.2 billion or 14% of GDP.

# **3 Data, measurement and interpretation**

In this section, we introduce our new dataset of debt data revisions and explain how we quantify hidden debt and hidden debt revelations. Throughout this section, we focus on the main principles of data construction and measurement and refer interested readers to Appendix A for further details.

## **3.1 A new database of debt data revisions**

To systematically quantify hidden debt and its revelation, we construct a new and comprehensive database of all editions (i.e., “vintages”) of the World Bank’s International Debt 7