rafmacalaba/datause-encoder-data huggingface dataset:
to increase overall CPS compliance but also to modify the socio-economic composition of the compliers. These demand-side interventions would therefore affect the validity of some of the assumptions on which the identification of the subLATEs relies.

**Bounds** We start our analysis of the subLATE parameters by bounding the _LATEhc_ using the method developed in Section 4.3. This exercise first suggests that children who would have stayed at home absent the CPS construction spent between 3 and 10 months in (pre-)school (Table 10). For exposure, however, the _LATEhc_ is probably closer to the upper bound. Indeed, children who switched from ASP to CPS are _unlikely_ to have experienced a very different level of preschool exposure: we can assume that _a-compliers_ spent about the same time at school irrespective of their treatment status, therefore the overall exposure to any school is likely to be about 10 months, one year after the baseline study. Our results on cognitive measures vary between 0.107 (low bounds for language) and 0.492 sd (upper bound for executive functions), many of them being significant or very close to significance level. The bounds for impact on the main index of cognitive development are 0.138 and 0.462 sd. Bounds for impacts on the socio-emotional index are 0.118 and 0.396 sd, but these are not statistically significant.

We then estimate the narrow bounds using the median of the ASP enrollment’s predicted value as the **_B_** variable. Narrow bounds are as expected tighter (with the overall range between 0.087 and 0.354 sd)—with the magnitude of the upper bounds reduced and the lower bounds almost unaffected. The bounds for the index of cognitive development are 0.138 and 0.345 sd, consistent with moderate-to-large treatment effects. The effect on the socio-emotional index remain statistically insignificant.

Overall, these results confirm that low ITT impacts are consistent with moderateto-large effects on h-compliers.

**Conditional LATE** We next turn to the conditional LATE approach which allows us to estimate both _LATEhc_ and _LATEac_ . In a first step we reject that each set of instruments is weak (the last row of Table 11). This is reassuring as it confirms that the instruments selected do predict ASP enrollment well in the control group (and its substitution to CPS in the treatment group). For each set of instruments, we also run an over-identification test (except for the LASSO which is just-identified): since the instruments are all from the baseline survey and are interacted with the random assignment, they are exogenous and will fail the over-identification test only when treatment effects are not homogeneous. Failing the over-identification test would therefore be an indication that the constant treatment assumption is violated. In most cases, our instruments pass the over-identification test. For both aggregate indexes, the over-identification test is passed for all specifications.

28
<!-- Start of picture text --> Venezuelan Migration Shock, Mg effect<br>Saber 11 Standardized Math Scores<br>n<br>ON<br>6c<br>oS<br>a<br>&<br>6<br>UU<br>®<br>N<br>no)a Oo<br>UC<br>Ssc<br>ne<br>=o<br>o<br>3<br>os<br>2012 2013 2014 2015 2016 2017 2018<br>Year<br>'— Foreign Males +——~ Domestic Males<br>'—— Foreign Females *—~ Domestic Females<br><!-- End of picture text -->
# Figure 8.  Commodity exporters with high and low external debt

<!-- Start of picture text --> Low External Debt High External Debt<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>CE with high external debt = 0.48***<br>CE with low external debt = 0.48***<br>-0.4<br>-0.6<br>AzerbaijanYemen Côte d'IvoireUkraine<br>Rwanda Congo, Rep.Timor-Leste NicaraguaEcuador GuyanaLiberia Argentina<br>NigerBolivia KenyaZimbabwe FijiAngola<br>Brazil<br>Sudan SenegalMali Burkina Faso<br>Ghana Burundi Benin Turkmenistan MozambiqueChad Comoros The Gambia Colombia Tanzania Central African RepublicGabon EthiopiaMongolia<br>AlgeriaUzbekistan MyanmarSouth Africa ParaguayTajikistan<br>Iran, Rep. of Madagascar<br>Kyrgyz RepublicRussian Federation Papua New GuineaLao P.D.R. Cabo Verde UgandaCameroon Indonesia Honduras Togo<br>Congo, Dem. Rep.Belize<br>NigeriaPeru Solomon Islands<br>Armenia<br>Sierra Leone Kazakhstan Guinea-Bissau Mauritania Costa Rica Botswana<br>Guinea<br>Zambia<br>Malawi<br>Guatemala<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real<br>São Tomé and Príncipe<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high and low external debt, defined as external debt, as percentage of GNI, above (below) the sample median. The difference between the two averages is not significant. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on International Debt Statistics (World Bank) and WEO (IMF) data.
and for certain countries, notably the East Asian countries, an economic crisis (during 1997-98). Bankscope data refer to the set of large commercial banks in each country, for which accounting data are believed to be of better quality. Where possible we also use consolidated balance sheets data. We include in our sample the countries that had over the samnple period at least three commercial banks recorded in the Bankscope database. We have then eliminated the banks that over the sample period had less than three consecutive years of balance sheet observations, in order to control for the consistency and quality of bank reporting. Finally, in order to minimize the effects of measurement errors and outliers we have filtered out the bank/year observations that exhibited one of the following features:

- a ratio of loan loss provisions over total assets greater than 10%;

- a ratio of eamings before tax and provisions over total assets larger than 10%;

- a ratio of total loans over total assets smaller than 10 % or larger than 90%;

- a growth rate of bank loans in real terms larger than 50% in absolute value.

# The resulting sample includes 37 countries3, with a total of 1,205 banks.

Table 1 provides some descriptive statistics about the variables in our estimation sample. The ratio of loan loss provisions to total assets equals 0.56 per cent on average (with a standard deviation of 0.84 per cent) and the ratio of earnings before taxes and provisions to total assets equals 1.6 per cent (with a standard deviation of 1.2 per cent). Loans represent 57.8 per cent of banks' asset portfolios on average and the average real rate of loan growth is equal to 4.2 per cent. The average real per capita GDP of the

> 3The final sample of countries is: Australia, Belgium, Canada, Chile, Colombia, Denmark, Finland, France, Germany, Greece, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines, Portugal, Singapore, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, United Kingdom, United States, and Uruguay.

11
oi:1( Y RLSE&ARCH- WORKING P aFR 21 66

# Summary fi'ndilngs

r:siPcemt _Albcrro_ Fujimlori created the Peruvian Social FLlind (FONCODES) in I 99 1 with the stated objectives of gencratilcnioptoviient helping to alleviate poverty, and i.mrprovmgo access to social services.

ScLhadY USes province-level data on monthly cxpenleturcs, socioeconomic indicators, and electoral outcomes to analvze political influences on the timing an **g' ographic** distribut;on of FONCODES expenditures between 1991 and 1995.

***** FONCODES projects were directed at poor provinces, as well as provinces in which the mar-ginal political impact of expenditures was likely to be greatest.

The results are robust to many specifications and controls. The Peruvian data thus support predictions made in the literature on political business cycles as well as the literature on political influences on the allocation of discretionary funds.

He finds that: CONCODDES expenditures mcreased significantly .-efoTe electiotns.

- This -pper - a product of the Poverty Division, Poverty Reduction and Economic Management Network is part of a laracr I **_rfo;r_** im the netwvork to understand the functioning and impact of social funds. Copies of the paper are available free **fion'** the World Bank, 1818 H Street N-W, Washington, DC 20433. Please contact Norbert Schady, room MC4-629, t eph oru e 202-45 8-8247, fax 202-222-3283, Internet address nschady(@worldbank.org. Policy Research Working Papers **ale** a,)0 potsed on the Web at http:/","ww.worldbank.org/html/dec/Publications/Workpapers/home.html. August 1999. (30 p i"'s?

> _ihc Se ollc Resea,ch_ **_kVorkXkng_** _Paper Series disserninates the findings of work in progress to encourage the exchange of ideas_ **_aboot_** _oIeuesopmZent issiues._ **_Ani_** _objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 'Te s_ **_ape1s_** _coyrr_ **_tibe_** _n,amr,es ot the authors_ **_and_** _should be cited accordingly. The findings, interpretations, and conclosions expressed in this p.per_ **_are eni!reiv_** _those of the authors. They do_ **_vot_** _necessarily represent the viewJ of the World Bank, its Executive Directors,_ **_or_** _the corrI0tr!es_ **_t"ly_** _rresent._

Produced by the Policy Research Dissemination Center
20 Measures of political structure were obtained from various sources. The tax to GDP ratio was obtained from the IMF statistics. We use the rate of urbanization as a measure of the relative importance of urban forrna **l** abor. Ideally, we would also have liked to get a separate measure for the weight of the capitalists in the population. But this is impossible to obtain for most countries. Moreover, the importance and power of the capitalists is not necessarily related to their relative numbers. We assumed that the importance of the capitalists is an endogenous variable which depends upon the political regime and do not include it in the reduced form equation.

We argue that the weight governments give to formal workers and the capitalists as well as their expected longevity (S', _SK_ and 1r) will depend upon the nature of the regime. The type of regime was classified using the indices for political rights (PoL) and civil liberties (civ) compiled by the group Freedom House. These indices range from 1 to 7, with I indicating the most rights and liberties and 7 the least. As defined by Freedom House, "political rights enable people to participate freely in the political process", and "civil liberties are the freedoms to develop views, institutions and personal autonomy apart from the state" (Ryan 1994, p. 671).

In practice, political and civil liberties go hand in hand. Figure 3 presents regional averages for the two Freedom House indices, and shows the high positive correlation between them-for the year 1990, we calculated the correlation coefficient between the two indices to be 0.92. In our regressions, we used the two indices interchangeably, with very little differences in results. The figure also indicates that on average countries in Sub-Saharan Africa and in the
<!-- Start of picture text --> Female Local Bachelors or Greater<br>100% - 100% - 100%-<br>75% - 75% - 75%-<br>50% - 50% - 50%-<br>25% - 25%- 25%-<br>0% - 0%- 0%-<br>' ! ' ! ! f ' ' ' ' ! ' ' ! '<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Some Training Single<br>100% - 100%-<br>75% - 75%-<br>50% - 50%-<br>25% - 25%-<br>0% - 0%-<br>' 1 ' f ! 1 ' ' ' '<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br><!-- End of picture text -->
_11_ _In the 1960s, I remember that In the OECD and here at the World Bank we talked now and then about the point of no return beyond whlch we couldn't go in financing deficits If the situation was unpropitious. It might be wise to take a look at that kind of problem again._ _But the other dimension to sustainabillty relates to the stability of the international system and the degree of risk that creditors are willing to assume, and the assessments they make of the future. Ts it likely that the repayment capacity is going to remain as low as it Is and so forth. This Is where stability in general and institutional arrangements are fundamental to the molding of the expectations that you, I think, quite correctly found so essential to investment._ _I would reverse the order, and say that we are not likely to unwind the imbalances and thereby return to stability. We are going to have to find stability In order to live with the imbalances, not necessarily at the present magnitude, but it is unreasonable to assume that we are going to be able to get rid of them terribly quickly._ _MR. BANETH: Can one live with the imbalances, unwinding or not, and to what extent? Which precedes what? Are there ways of unwinding them which promote stability, and what are those ways?_ _More on current MR. LIPSCHITZ: I found Professor Boltho's presentation account quite fase-inating. The strongest evidence for your [Bolthol being an_ **_imbalances_** _unreconstructed Keynesian was, perhaps, the absence of any intertemporal considerations, intertemporal constraints of any sort, in the discussion. This is unusual on this side of the Atlantic._ _The theme of anything I would say about both the current imbalances and about longer-term questions of growth would be che need to take explicit account of intertemporal constraints. It is quite difficult to disentangle what is going on within the industrial world: two sorts of things are happening in concert. One is clearly a set of imbalances that were generated by unwise government policies, perhaps to a large extent in the United States, which created international imbalances that are unsustainable. (Incidentally, because of the longer-term consequences, the intertemporal constraints, I wouldn't characterize the U.S. policies of late as a Keynesian success story.) We have got that sort of imbalance. The other sort of imbalance is something thac we characterize as incernational imbalance, but at least in a large part, to my mind, it is intertemporal balance. Let me be more specific._ _To the extent that in Japan, and to a lesser extent Germany, one can characterize high national savings ratios as befing in part an anticipation of demographic trends, one can't possibly_

**WPIPROC/SF:Jsq**
economic impacts, if they exist, may be concentrated on those workers with high skills. In fact, our empirical finding is consistent with this conjecture. To the extent that the future resource exploration involves capital-intensive development, our findings indicate possibly limited roles of offshore oil development on poverty reduction.

Finally, we evaluate the effects of oil discovery at the household and individual level. We apply a difference-in-differences approach to a newly constructed household income data combined with location information of households to analyze the impact of oil discovery on households’ income. This approach allows us to address individual-level impacts of offshore oil discovery, which tend to be masked in most previous studies based on municipality, county or province level data.

The remainder of the paper is organized as follows: Section 2 provides a brief overview of oil in Ghana, Section 3 discusses related theories and channels through which natural resources affect the local economy, Section 4 describes the dataset used for analysis, summary statistics, empirical strategy and a discussion on the validity of the identification strategy. Section 5 reports empirical results, Section 6 discusses alternative mechanisms, and Section 7 concludes.

# **2. An Overview of Oil in Ghana**

Oil exploration in Ghana began in the 19<sup>th</sup> century. The first onshore oil was discovered and drilled in 1896. Following this discovery, exploration activities continued between 1957 and 1966. Offshore oil is more recent, with discovery and drilling taking place in the 1970’s.<sup>3</sup> By 1983, the country had 54 well counts of both onshore and offshore oil, yet production and commercialization were not on a large scale. Crude oil was discovered in the Western region 2007, which is the most significant discovery among all discoveries because it puts Ghana on the forefront of exporting oil in much larger quantities.

The discovery has drawn attention from both within Ghana and international

> 3 Ghana has <mark>3 offshore sedimentary basins and one onshore basin: Tano-Cape Three Point Basin, Saltpond Basin, Accra-Keta Basin and Voltarian Basin.</mark> Current productive oil fields lie within the <mark>Tano-Cape Three</mark> Basin.

5
Table S8: Predicting other measures of poverty from CDR

|**Model**<br>|_R_<sup>2</sup> **or AUC**|**Top Five Features**|
|---|---|---|
|_Panel A: Predicting belo_|_w poverty line_|_(binary)_|
|Logistic (No Penalty)|0.53|Reporting # Records, Active Days, Active Days Day, Active Days<br>Night, Active Days WD|
|Logistic (L1 Penalty)|0.53|Reporting # Records, Active Days, Active Days Day, Active Days<br>Night, Active Days WD|
|Random Forest|0.56|NOI Out Night Call, BOC Night Call Kurtosis, CD Day Call<br>Skew, Nois Night Call, IT Night Call Kurtosis|
|Gradient Boosting|0.55|IT Night Call Kurtosis, IT Text Max, Radius Gyration WE Night,<br>Entropy Antennas, NOI Out WD Call|
|_Panel B: Predicting con_|_sumption (con_|_tinuous)_|
|Linear Regression|-0.21|% Pareto Recharges WE Night, % Pareto Recharges WE, %<br>Pareto Recharges Night, Entropy Contacts WD Day Text, PPI<br>WE Night Text|
|LASSO Regression|-0.00|Reporting # Records, PPI Text, PPI Day Text, PPI Night Call,<br>PPI Night Text|
|Random Forest|-0.02|Churn Rate Mean, IPC WE Night Call Kurtosis, IT Recharges<br>WE Day Skew, IPC WE Night Call Skew, CD WE Call Median|
|Gradient Boosting|-0.03|CD WD Night Call Skew, IPC WD Day Text Skew, IT WD Night<br>Call Min, IT WD Night Call Max, IT WE Night Call Max|
|_Panel C: Predicting asse_|_t index (conti_|_nuous)_|
|Linear Regression|-0.06|IPC Text Min, IPC WD Text Min, IPC WD Day Text Min, BOC<br>WD Text Min, % Initiated Conversations WD|
|LASSO Regression|0.00|Active Days WE Day, Active Days WD, Active Days WE, Active<br>Days, Active Days WD Day|
|Random Forest|0.00|IT Night Call Skew, IPC Text Min, IT WE Day Call Median, IT<br>WE Call Median, Entropy Contacts WE Night Call|
|Gradient Boosting|-0.02|IT Text Median, Entropy Antennas WE, Entropy Antennas WD<br>Night, Entropy Contacts WE Night Call, IT Recharges Night Min|
|_Panel D: Predicting CW_|_R group (cont_|_inuous)_|
|Linear Regression|0.01|PPI Night Text, IT Recharges Day Skew, IPC WE Call Min,<br>Active Days WE Night, IT Recharges WD Day Skew|
|LASSO Regression|0.05|PPI Night Text, Active Days WE Day, Active Days WE Night,<br>IT Recharges WD Day Skew, IT Recharges Day Skew|
|Random Forest|0.04|# Contacts WE Day Call, Entropy Contacts WD Night Call, IPC<br>Night Call Kurtosis, # Contacts WE Call, IT Call Kurtosis|
|Gradient Boosting|0.03|IT Call Kurtosis, IT Recharges Day Skew, # Contacts WE Day<br>Call, IT Recharges Day Kurtosis, IPC WD Night Call Kurtosis|

_Notes_ : Machine learning results for predicting: (A) Below-poverty-line status, using consumption data and based on Afghanistan’s national poverty line; (B) Total consumption (log-scale); (C) Asset index; and (D) Community Wealth Ranking. Performance is evaluated on the sample of 535 matched households. Binary metrics (A) are evaluated using the mean AUC score over 10-fold cross validation; Continuous metrics (B-D) are evaluated using the mean _R_<sup>2</sup> score over 10-fold cross validation. See Appendix B for details of features.

43
In some countries (such as France) strict prohibitions are in place on the transfer of bank client data to insurers.<sup>11</sup> In other countries, opt-in or opt-out requirements are common.

Key required elements of any data protection regime allowing for data transfer are as follows:

- The consumer is made aware that data are being collected

- The consumer has access to his or her data

- The consumer can correct any errors and

- The consumer has the option of preventing that data from being shared with third parties (the definition of third party may or may not include related group companies).

A recent case review in Hong Kong SAR, China, concluded that where the right to transfer data is not specified in detail in the acknowledgment form, the relevant clauses in supporting material should be listed in the acknowledgement form (Office of the Privacy Commissioner 2011).

_Best practice example—Hong Kong SAR, China._

# **References**

Arena, M. 2008. ―Does Insurance Market Activity Promote Economic Growth? A Cross Country Study for Industrialized and Developing Countries.‖ _Journal of Risk and Insurance_ 75 (4): 921–46.

Davis, S. 2007. _Bancassurance: The Lessons of Global Experience in Banking and Insurance Collaboration._ CITY: VRL Knowledgebank.

Feyen, E., R. R. Lester, and R. de Rezende Rocha. 2011. ―What Drives the Development of the Insurance Sector? An Empirical Analysis Based on a Panel of Developed and Developing Countries.‖ World Bank Policy Research Working Paper 5572, World Bank, Washington, DC.

Hong Kong Office of the Privacy Commissioner. 2011. ―Transfer of Customers’ Personal Data by a Bank to an Insurance Company without Consent.‖ Report No. R11-1696, Office of the Privacy Commissioner, Hong Kong, China.

Lester, R. 2010. ―MENA Flagship Insurance Report.‖ World Bank, Washington, DC. Munir, A. B. 2010. Presentation to MSC Malaysia Personal Data Protection Conference, July.

49
Table 5: Hours Worked (OLS, not conditional on working)

||||Work Withi<br>the|n<br>Market Wor<br>Within the|k<br>Household|<br>Market||Agricultur|e|N|onagricultural a|ctivities|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Total Hours|Wage work|<br>Household|Household|Production|Work|Wage work|Household|All|Wage wor|k Household|All|
||(1)|(2)|(3)|(4)|(5)|(6)|(1)|(2)|(3)|(4)|(5)|(6)|
|Ages 6-15|||||||||||||
|Rice Price|-9.508|-0.516|-9|-5.376|-3.719|-5.895|-0.15|-4.425|-4.578|-0.366|-0.951|-1.317|
||[2.381]**|[0.418]|[2.272]**|[1.479]**|[1.495]**|[1.565]**|[0.383]|[1.417]**|[1.446]**|[0.196]*|[0.573]*|[0.598]**|
|Rice Price*Male|5.015|0.674|4.337|2.057|2.306|2.746|0.103|2.171|2.29|0.571|-0.115|0.457|
||[1.849]**|[0.463]|[1.838]**|[1.516]|[1.276]*|[1.567]*|[0.419]|[1.475]|[1.539]|[0.290]**|[0.623]|[0.669]|
|Observations|9532|9579|9532|9579|9539|9579|9579|9579|9579|9579|9579|9579|
|R-squared|0.45|0.08|0.44|0.36|0.23|0.38|0.08|0.37|0.38|0.05|0.1|0.1|
|Ages 16-24|||||||||||||
|Rice Price|-9.631|3.126|-12.488|-8.758|-3.671|-5.896|0.95|-8.678|-7.751|2.176|-0.08|1.855|
||[4.598]**|[2.462]|[4.837]**|[4.112]**|[1.777]**|[3.826]|[1.172]|[4.114]**|[4.131]*|[2.122]|[1.718]|[2.654]|
|Rice Price*Male|1.117|-2.315|3.125|1.031|1.98|-0.99|0.297|-0.545|-0.229|-2.612|1.579|-0.75|
||[4.214]|[2.735]|[4.754]|[4.090]|[1.919]|[3.874]|[1.294]|[4.131]|[4.073]|[2.415]|[2.293]|[2.903]|
|Observations|6040|6043|6040|6042|6056|6042|6043|6042|6042|6043|6043|6043|
|R-squared|0.29|0.18|0.31|0.28|0.28|0.27|0.16|0.35|0.34|0.17|0.13|0.22|
|Ages 25-64|||||||||||||
|<br>Rice Price|-9.19|3.566|-12.706|-9.551|-3.175|-6.032|1.596|-8.942|-7.347|1.97|-0.609|1.315|
||[4.128]**|[1.094]**|[4.078]**|[3.296]**|[1.682]*|[3.339]*|[0.873]*|[3.332]**|[3.429]**|[0.769]**|[1.228]|[1.345]|
|Rice Price*Male|3.156|0.425|2.759|0.786|1.989|1.178|0.15|0.576|0.723|0.275|0.211|0.454|
||[3.283]|[1.744]|[3.414]|[2.849]|[1.924]|[2.842]|[1.025]|[2.988]|[3.323]|[1.417]|[1.744]|[1.962]|
|Observations|13301|13305|13301|13305|13319|13305|13305|13305|13305|13305|13305|13305|
|R-squared|0.27|0.13|0.26|0.24|0.24|0.27|0.1|0.32|0.29|0.13|0.11|0.19|

Note: See comments to table 3. Number of observations varies because some categories of work have missing observation.s