rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Lagging Lagging<br>Lagging Leading Leading<br>Leading Contemporaneous Contemporaneous<br>0.0 20.0<br>8.0<br>-2.0 15.0<br>6.0<br>-4.0 10.0<br>4.0<br>-6.0 2.0 5.0<br>0.0 0.0<br>Data Source: Authors’ calculation based on WDI data<br>CA balance(% GDP) FDI(% GDP) GDS (% GDP)<br>1960-70 1971-80 1981-90 1991-00 2001-11 1960-70 1971-80 1981-90 1991-00 2001-11<br>1960-70 1971-80 1981-90 1991-00 2001-11<br><!-- End of picture text --> To carefully examine a county’s financing options in stepping up housing investment, we first examine the relationship between housing investment and gross domestic savings, current account balance, and domestic credit supply and government debt. The correlation between domestic savings, current account balance and investment works as an indicator of capital mobility.<sup>6</sup> Similarly, credit supply in a country provides an indication of the degree to which investment in housing can be leveraged.

# **3.1 Tapping Debt and Equity**

Figures 8 and 9 present the relationship between government debt and domestic credit supply with housing investment. To avoid cross country variations in price levels or inflationary dynamics, we convert each series to (percent) share in GDP. Also, to examine regional patterns, we take regional annual averages as a representative measure for all countries in a region.

While government debt can increase for many reasons, we argue that any sustained effort by the government to increase housing investment will increase government debt overall. Based on this assumption, we find that housing supply or investment has a positive relationship with government debt across regions. The relationship is much more pronounced for MNA and SSA followed by LAC and EAP.

> 6 Current account balance=(Personal saving – investment)+(Tax- government expenditure)

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tests showing that there is no similar interaction with electrification or radio ownership.

**Summary statistics.** Table 4 reports summary statistics for the Bangladesh dataset. Observations are infants and children, so averages do not, in general, correspond to published summary statistics representative of the population of Bangladesh. For example, if young children are disproportionately found in poorer households, our summary statistics will present a worse picture of human development. Indeed, the summary statistics reflect a poor, mainly rural population with high mortality and low maternal nutrition. However, child height, infant mortality, sanitation, maternal nutrition, and electrification all show clear improvements over the three survey rounds.

# **4.2 Empirical strategy**

We identify the association between local sanitation density and child height from crosssectional and over-time variation within districts. The GIS matching described above allows us to use fixed effects that are approximately 10 times finer than the seven regional fixed effects used in the international analysis. We estimate regressions with district and survey round fixed effects for children under 5 years old of the following form: where _i_ indexes individual children, _d_ indexes districts, and _t_ indexes survey rounds. Standard errors are clustered at the district level (that is, pooling all survey rounds within a district). As with prior regressions in which height-for-age is the dependent variable, we include 120 age-in-months by sex indicators _Aidt × sexidt_ . We also add fixed effects for the year in which a child was born, _yearidt_ , to account for overall time trends. As before, we control for an indicator for whether the child’s own household defecates in the open. _δd_ is a district fixed effect and _γt_ is a survey round fixed effect. This strategy allows us to control 20
**Figure 6a: Accounting decomposition of net interest margins by region, 2005-14, continued**

<!-- Start of picture text --> OECD High-income<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2005 2008 2011 2014<br>Overhead - noninterest inc. Tax expenditure<br>Loan loss provision After-tax profit<br>% of total assets<br><!-- End of picture text -->

# **Figure 6b: Accounting decomposition of net interest margins by income level, 2005-14**

<!-- Start of picture text --> High-income economies<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2005 2008 2011 2014<br>Overhead - noninterest inc. Tax expenditure<br>Loan loss provision After-tax profit<br>% of total assets<br><!-- End of picture text --> 22
_Further Training'_<sup>_2_</sup> About 39 percent of CONALEP workers receive

**Figure 4.**

further training at work, a significantly higher level than the 37.2 percent of control group individuals that do (Table 12). It appears that government investment in CONALEP training for an individual leads to additional investment by firms in training for the same individual. Moreover, a significant proportion of CONALEP graduates (89.7 percent) report that their training was <!-- Start of picture text --> Rceived Trairngr<br>35 : 6<br>PactSt dFbtRds<br><!-- End of picture text --> related to their current employment or work activity. Although the 1994-1997 cohort shows a considerable increase in the proportion of CONALEP graduates receiving training related to ! il | !work, a lower proportion of this cohort reported further training than older cohorts did. In response to a question asking the purpose of further training, nearly 60 percent of CONALEP graduates

<!-- Start of picture text --> Figure 5.<br>Trahing related tD wk<br>.. E.<br>! .<br>Oortd W 3 I Ireceiving<br>84 85 85 8 8 89 9)<br>Powt d Pespmderts<br><!-- End of picture text --> said that they received training in order to update their technical knowledge (Table 14). Compared to the ENECE99 control group rate (near 32 percent), the CONALEP rate is quite high. This could indicate that employers invest more in training CONALEP graduates than they do in the control group because investment in the

> 12 Data for this section are presented as follows. Table 12 shows the proportion of CONALEP graduates compared to the ENECE99 control group that receive further training at work. Table 14 indicates the reasons for further training among CONALEP graduates as well as individuals from the control groups.

34
27

**Figure 2. Incidence of Military Interventions by Regime Type of Intervener, by Year, 1960–96**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995<br>Democratic Intervention Autocratic Intervention<br><!-- End of picture text --> The figure shows the percentage of all countries with an on-going democratic or autocratic intervention in that year.

Thus, there is indeed a great deal of democratic intervention, but there is no rising trend. Democratic interventions are now much more frequent than autocratic interventions, but this is mainly because of the sharp decline of autocratic interventions after the end of the Cold War. We have also compiled the onset of democratic and autocratic interventions (Figure 3). Here we do discern a long-term rising trend in the onset of democratic interventions that largely coincides with the third wave of democratization and a parallel decline in new autocratic interventions. However, the curve for the onset of democratic interventions declines again after the end of the Cold War.
7 Table 3: Poulation bY Sex and **Ace** Grou. 1969 and 1980 (tercentaaes)

|Age Group|M<br>1969|ale<br> <br>1980|Fe<br>1969|male<br>1980|To<br>1969|tal<br> <br>1980|
|---|---|---|---|---|---|---|
|Under 15 years|47.5|50.7|45.6|48.7|46.4|49.8|
|15-44 years|37.9|<br>36.3|42.9|40.0|40.5|38.3|
|45-64 years|12.1|<br>9.6|9.5|8.7|10.8|<br>9.1|
|65+ years|2.5|<br>3.3|2.0|2.6|2.3|2.8|

Source: GRZ 1980a, Table 2.3
and NGOs for social protection purposes (cf. Gentilini et al., 2020, 2021; Aiken et al., 2021). CDR have also been shared with the public sector for public health and humanitarian aid applications (Milusheva et al., 2021). Access issues aside, CDR contain private and sensitive data, including phone numbers and location traces. While much has been written about enabling responsible use of CDR for humanitarian response (e.g. de Montjoye et al., 2018; Oliver et al., 2020), to date no consistent privacy standards exist. Informed consent can increase participant agency, but also complicates the implementation logistics. Data minimization may provide a complementary pathway to privacy: as reflected in the feature importances in Table S2, our models rely primarily on only a fraction of the features we — derive from mobile phone data it may therefore be possible to restrict models to features that minimize privacy risk (such as statistics that do not involve contact networks or mobility patterns) without compromising model accuracy. Finally, there may be ways to incorporate differential privacy or other privacy enhancing technologies into a CDR-based targeting system, but such privatization would likely decrease targeting accuracy (Hu et al., 2015).

**Algorithmic transparency and strategic behavior** Using CDR to determine program eligibility may introduce incentives for people to manipulate if and how they use their phone. For instance, while a program that targeted households without phones first might make sense in the context of one-off emergency response, it could not be deployed in equilibrium, as it would introduce undesirable incentives for people to not use their phones. In less extreme settings, we might still expect strategic manipulation of how people use their phones, if they know such behavior is being monitored. These considerations are not unique to CDR, as degrees of manipulation have been documented in social programs that use proxy means tests and other traditional targeting mechanisms (Camacho & Conover, 2011; Banerjee et al., 2018). While complex machine learning algorithms like the one presented in this paper may obfuscate the logic behind targeting decisions and thus reduce the scope for manipulation, this is not a ‘solution.’ Society often demands transparency in algorithmic decision-making, as black-box decisions are difficult to audit or hold to account. There is therefore a tension between the goals of increasing transparency and reducing manipulation, though recent advances in machine learning explore mechanisms for pursuing both objectives at once (Bj ̈orkegren et al., 2020).

**Centralized vs. local knowledge** CDR-based methods enable a top-down, centralized and standardized approach to program targeting, rather than a bottom-up approach that

17
Table 3A: Summary statistics for graduating countries, 1988-2016

||(1)<br>eyA <br>graduation|(2)<br> >=‘sin <br>_graduation|(3)<br> Difference<br>2)-()|(4)<br>Percentage<br>Coange|(5)<br>nee,a" <br>graduation|(6)<br> ae<br>_graduation|(7)<br>Difference<br>)-@)|(8)<br>Percentage<br>anaes|
|---|---|---|---|---|---|---|---|---|
|All lending (2010 dollars)|||||||||
|Total (millions)|784.36|310.55|-473.80*|-60.41|656.87|264.55|-392.32**|-59.73|
|Totalpercapita|25.05|17.31|-7.73|-30.87|19.42|21.53|2.12|10.90|
|<br>HDlending(2010dollars)|||||||||
|<br>Total(millions)|87.91|17.91|-69.99**|-79.62|84.72|12.45|-712.27***|-85.30|
|<br>Sectors (millions)|90.74|22.60|-68.14*|-75.09|83.84|15.60|-68.24***|-81.39|
|Totalpercapita|3.07|2.16|-0.91|-29.65|2.95|1.92|-1.03|-35.01|
|<br>Sectors per capita|2.12|2.38|0.26|12.22|1.91|3.12|122|63.93|
|HDPracticeAreas(2010dollars)|||||||||
|<br>Education(millions)|40.10|8.78|-31.32|-78.11|40.95|5.32|-35.62**|-87.00|
|<br>Health, Nutrition andPopulation (millions)|34.51|6.35|-28.17*|-81.62|34.06|4.69|-29.36**|-86.22|
|SocialProtectionand Labor(millions)|13.29|2.79|-10.50|-79.01|9.72|2.44|-7.28*|-74.90|
|<br>Numberofprojects|||||||||
|<br>All|4.64|2.60|-2.04**|-43.96|4.35|2.33|-2.02***«|-46.45|
|HD|0.87|0.38|-0.49**|-56.26|0.86|0.28|-0.58***|-67.52|
|Education|0.25|0.12|-0.13|-51.20|0.25|0.08|-0.17***|-68.96|
|Health,NutritionandPopulation|0.33|0.12|-0.21**|-63.40|0.34|0.11|-0.23***|-68.55|
|<br>SocialProtectionandLabor|0.30|0.14|-0.16|-52.56|0.27|0.10|-0.18***|-64.95|

Notes: Altogether 33 countries graduated IDA between 1988 and 2016. The data excludes the exact years in which countries graduate IDA. In columns (3) and (6), significance stars reports results from testing for statistical significance for difference in the two means. * Significant at 10%, ** Significant at 5%, ** Significant at 1%.
- 10 -

and if that road was rehabilitated at the point of time _t_ . _DitURRAP_ 2 _km_ is defined in the same way. To allow this identification strategy, our surveys covered both households who live close to and far from each project road.

This identification strategy has the advantage of controlling for unobserved local conditions since we have both beneficiaries and nonbeneficiaries within the same local area along a given road. Though, it is a naïve approach because household placement may be selfselected. It is plausible that those who live closer to the road network may be systematically more productive than remote households. The individual fixed-effects are expected to remove such time-invariant factors.

**Figure 3. Distribution of distance traveled from households to market**

<!-- Start of picture text --> 0 10 20 30<br>Distance to market (km)<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text --> Under the same identification strategies, two additional models are examined. First, the farmers’ technology adoption decision is analyzed to understand the reasons for possible agricultural income growth. As in earlier studies (e.g., Taffesse et al., 2012; Rashid et al., 2013), access to advanced inputs is a critical constraint in Ethiopia. To this end, the following specification is considered:
<!-- Start of picture text --> Ln M2/GDP and Ln Inst. Inv./ GDP versus PPP<br>@ inm2/coP GDP/ Capita<br>t In institutional investments/GDP 93 OFCD Countries<br>5.50 a eee ¢<br>5.00 2<br>asoa<br>esee ee. qo<br>on na<br>2.50<br>Sd<br>2.00<br>1.50<br>1.00<br>10000 15000 20000 25000 30000 35000 40000 45000 50000<br><!-- End of picture text -->
to resolve business disputes undermine economic activity and investment. In such an environment, asset holders are forced to look for other options enabling them to secure their wealth. They benefit from tax optimization and seek regulatory arbitrage, yet it is not their sole concern.

Policy makers from the emerging economies also have to confront the implications of offshores for domestic corruption. Secrecy jurisdictions allow laundering the illicit funds, thereby becoming ‘enablers’ of corrupt deeds potentially even amplifying such trends (Judah and Sibley 2018, Davidson 2017). Offshore centers, therefore, provide opportunities to both secure assets threatened by ineffective institutions and to combine the benefits from laundering and securing assets that were often obtained with the use of these ineffective institutions.

The domestic institutional effects of global financial secrecy are also present in the world affected by the health pandemic. At the early stages of the pandemic, the focus on saving lives in a state of high uncertainty and lack of reliable information and expertise strained policy makers. Anti-crisis policies and programs have been rolled out in rapid succession targeting specific social groups and vulnerable economic areas. At the recovery stage, even more programs will be needed. The urgency and scale of these interventions make it difficult to monitor and to audit the procurement practices and legitimacy of massive spending. The scarcity of resources and disrupted supplies expose organizations to legal and compliance challenges. Corruption, fraud, and money laundering could thrive in this environment. These issues could be especially acute in emerging economies where we already see cases of corruption linked to pandemic-induced spending. The Organized Crime and Corruption Reporting Project (OCCRP) has recently reported cases of dodgy spending in Romania, Slovenia, and North Macedonia as well as Colombia and the Philippines.<sup>6</sup> Covid-related graft probes are underway in Saudi Arabia and Brazil. The law enforcement agencies in the Russian Federation, where regional governments were made responsible for Covid-related policy making, are also investigating cases of potential Covidrelated corruption and fraud. The proceeds from these newly created graft opportunities are likely to end up in offshore accounts, once again reminding the citizens of these countries about the ‘dark side of globalization.’ Such elements of the global financial system that are taken advantage of by corrupt

> 6 https://www.occrp.org/en/coronavirus/in-europes-scramble-to-buy-COVID-19-supplies-anti-corruption-measuresfall-away https://www.occrp.org/en/daily/12207-colombian-officials-accused-of-misusing-covid-19-funds https://www.occrp.org/en/daily/12292-philippines-offers-600-for-information-on-corrupt-officials <u>https://www.occrp.org/en/daily/12248-after-clashing-with-gang-russian-police-seize-counterfeit-ventilators</u>

7
<!-- Start of picture text --> Duty Station Staff in FCS countries<br>=<br>S<br>o<br>re)<br>5 ~<br>é<br>re)<br>28<br>zs"<br>2<br>oOo<br>re)<br>o<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>EN RS) 6sMR LS<br>Source: HR PeopleSoft database covering all WBG EC2+ and GF+ staff<br><!-- End of picture text --> <!-- Start of picture text --> Duty Station Staff in non-FCS countries<br>3<br>=<br>5 oO<br>3$8<br>3%<br>F<br>°<br>o<br>oOo<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>EN RS) MR. LS<br>Source: HR PeopleSoft database covering all WBG EC2+ and GF+ staff<br><!-- End of picture text --> <!-- Start of picture text --> Duty Station Staff per FCS project<br>°=<br>o<br>3<br>a<br>=a<br>a<br>&<br>=<br>gt<br>=<br>“<br>o<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>EN RS) 6s MR LS<br>Source: HR PeopleSoft database covering all WBG EC2+ and GF+ staff<br><!-- End of picture text --> <!-- Start of picture text --> Duty Station Staff per non-FCS project<br>g<br>»<br>37=<br>3<br>See<br>g<br>=<br>wo<br>oOo<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>EN RS) MR. LS<br>Source: HR PeopleSoft database covering all WBG EC2+ and GF+ staff<br><!-- End of picture text -->
CONSUMPTION SUB-AGGREGATES 4 goods _cj_ and _ck_ that are both in _Xi_ the consumer’s demand for _cj_ and _ck_ can be written as a function just of prices for goods in _Xi_ and of expenditures on the aggregate _x_<sup>_i_</sup> ; demands for goods in aggregate _Xi_ written in this form then will not depend on the prices of commodities in _other_ aggregates, except to the extent that these prices affect _xi_ .

A sufficient condition that the aggregation _A_ be separable is that the consumers’ utility function can be written as a set of _m_ sub-utility functions _{uj}_<sup>_m_</sup> _j_ =1<sup>andanaggregatingutilityfunction</sup><sup>_u_0:R</sup><sup>_m→_R</sup> such that where _c_<sup>_j_</sup> = ( _ci_ ) _i∈Xj_ is a set of consumption aggregates (Gorman 1961).

2.3. **Consumption aggregation and FGT poverty measures.** Lanjouw and Lanjouw (2001) consider the case in which total nondurable expenditures _x_ can be divided into an aggregation ( _X_ 1 _, X_ 2) with corresponding expenditures ( _x_<sup>1</sup> _, x_<sup>2</sup> ); each of these aggregates is then assumed to depend only on total non-durable expenditures _x_ , so that _x ≡ x_<sup>1</sup> ( _x_ ) + _x_<sup>2</sup> ( _x_ ).<sup>4</sup> Call the function relating expenditures on a particular aggregate to total expenditures the _Engel curve_ for the aggregate; then the Lanjouws assume that the Engel curve _x_<sup>1</sup> ( _x_ ) is a continuous and strictly increasing function of _x_ , so that, observing some level of expenditures _x_ ˆ<sup>1</sup> one can invert _x_<sup>1</sup> ( _x_ ) to infer what the ˆ overall level of expenditures is, obtaining, say, _x_ = _g_ 1(ˆ _x_<sup>1</sup> ).

We extend the ideas of Lanjouw and Lanjouw (2001) in two important ways. First, they give conditions under which head-count poverty statistics for a population will not depend on whether one uses total expenditures or a expenditures on a smaller aggregate as a proxy for total expenditures. We provide conditions under which all FGT poverty measures will be similarly invariant to the use of an aggregate. Second, by assuming that expenditures on different aggregates depend only on total expenditures, the Lanjouws implicitly assume that relative prices are unchanging. In a simple application of demand theory we show how to relax this assumption as well, allowing one to apply these methods to data on expenditures over time, where we may expect changes in economic conditions to lead to changes in relative prices.

We are interested in identifying a particular aggregate that can be used to measure poverty and changes in poverty over time. Our two

> 4. In what follows it’s useful to follow some common notational conventions in functional analysis, that blur the lines between variables and functions. For example, _x_<sup>1</sup> will always take a value, but may implicitly or explicitly be regarded as a function of other quantities, as in _x_<sup>1</sup> ( _x_ ).
**Figure 4: Product-space for Penang, 1995**

<!-- Start of picture text --> 0 10 20 30 40 50<br>invdensity<br>Electronics and Electricals Other, High-Technology<br>Automotive Process<br>Engineering Textile, garment & footwear<br>Other, Low-Technology Agro-Based<br>Other, Resource-Based Primary Products<br>20000<br>10000<br>0<br>-10000<br>-20000<br><!-- End of picture text --> Source: authors’ calculation

**Figure 5: Product-space for Penang, 2000**

<!-- Start of picture text --> 5 10 15 20<br>invdensity<br>Electronics and Electricals Other, High-Technology<br>Automotive Process<br>Engineering Textile, garment & footwear<br>Other, Low-Technology Agro-Based<br>Other, Resource-Based Primary Products<br>20000<br>10000<br>0<br>-10000<br>-20000<br><!-- End of picture text --> Source: authors’ calculation 13