rafmacalaba/datause-encoder-data huggingface dataset:
From the estimation of equation 1 we obtain the residuals _u_ ˆ _ch_ , and by defining _u_ ˆ _c._ as the weighted average of _u_ ˆ _ch_ for a specific cluster we can obtain _e_ ˆ _ch_ :<sup>13</sup> ˆ � where _uc._ is equal to _ηc_ .<sup>14</sup> The unconditional variance of the location effect _ηc_ given by ELL (2002) is: where _u.._ =<sup>�</sup> _c_<sup>_wcuc._(where</sup><sup>_wc_representsthecluster’sweight,i.e.</sup><sup>_wc_= �</sup> _h_<sup>_whc_)and</sup>

# **3.1.1 Estimating the Alpha Model (heteroskedasticity)**<sup>16</sup>

The variance of the idiosyncratic error ( _ech_ ) is specified by ELL (2003) through a parametric form of heteroskedasticity:<sup>17</sup> ˆ In ELL (2003) this is simplified by setting _B_ = 0 and _A_ = 1 _._ 05 max � _e_<sup>2</sup> _ch_ �, and thus the simpler form is estimated via _OLS_ :<sup>18</sup> This approach resembles that of Harvey (1976), nevertheless the prediction is bounded.

_′_ By defining exp� _Zch_<sup>_α_�</sup> = _D_ and using the delta method,<sup>19</sup> the household specific conditional variance estimator for _ech_ is equal to (Elbers, Lanjouw, and Lanjouw, 2002):

> 13 _e_ ˆ _ch_ denotes the household specific error component.

> 14Although, ELL (2003) notes that weigths are to be used, the PovMap software does not utilize weights when obtaining _u_ ˆ _c._

> 15 _nc_ is the number of observations in cluster _c_ .

> 16The heteroskadistic model is relevant for the variance estimation under ELL’s method, as well as the one under Henderson’s method III. In its current form, the methodology is from ELL (2003).

> 17Users have the option to allow for homoskedasticity, in which case _σ_ ˆ _e_ = _σ_ ˆ _u − σ_ ˆ _η_ , where _σ_ ˆ _u_ is the estimated variance of the residuals from the consumption model (equation 1). If the user chooses homoskedasticity, equations 6 through 8 are omitted.

> 18This is the actual model used by PovMap, which we also implement.

> 19The result is the outcome from a second order Taylor expansion for the _E_<sup>�</sup> _σe_<sup>2</sup> _ch_ �.

5
- Pathman, D. E., Konrad, T. R., Freed, G. L., Freeman, V. A., & Koch, G. G. (1996). “The awareness‐to‐ adherence model of the steps to clinical guideline compliance: the case of pediatric vaccine recommendations,” _Medical Care_ , _34_ (9), 873‐889.

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26
26. **It is important to note that the relationship between long-lived capital stock and emissions differs between capital stock associated with energy provision (supply side) and capital stock associated with energy use (demand side)** . **On the energy supply side, emissions are typically a direct function of the installed capital** . Thus, the flow of emissions associated with a particular stock of capital will generally last as long as the underlying investment.<sup>26</sup> 27. **On the energy demand side, on the other hand, the link between physical capital stock and emission flows is generally less rigid.** The relationship depends on the technology used to supply the energy needed to meet the service demand _induced_ by the long-lived capital stock. In other words, demand side lock-ins can undermine the potential for energy conservation, but not the use of any particular type of energy—low carbon or otherwise. If energy were cheap (i.e., abundant relative to demand) and if harmful emissions were free (both locally and globally), then there would not be any need for energy conservation / energy efficiency (i.e., managing both final and intermediate demand). However, if energy is expensive because of scarcity (or because the technology to avoid harmful emissions increases the price), or if new technologies cannot generate energy without some harmful emissions, then reducing the demand for energy becomes a necessary complementary pillar of a low- or no-carbon energy strategy. In fact, according to the IEA (2006c), as much as two thirds of emissions targets to 2030 will have to come from reducing energy demand, rather than fuel switching in energy supply towards low carbon or zero carbon alternatives. Similarly, the IPCC (2007, p.13) notes that “it is often more cost-effective to invest in enduse energy efficiency improvement than in increasing energy supply to satisfy demand for energy services.” 28. The purpose of the next illustration is to highlight how demand-side infrastructure (e.g. a highway network) can lock-in carbon emissions for the long-term (multi-decade), even though partial decoupling can be achieved by changing emissions standards of complementary physical capital (i.e., the fuel efficiency of the vehicle fleet).

> as of 2003 (source: Ministry of Industry, http://www.industrie.gouv.fr/cgi-

> bin/industrie/frame23e.pl?bandeau=/energie/statisti/be_stats.htm&gauche=/energie/statisti/me_stats.htm&droite=/en ergie/statisti/se_stats6.htm). Entire conversion of domestic boilers from fuel oil to gas would save an estimated maximum of 18 MtCO2e. And in fact this alternative is not available in most rural areas.

> 26 However, there are some cases where retrofit techniques (such as carbon capture and storage in the case of production of energy from coal) can limit the emissions path _ex post_ . In these cases, the life duration of the emissions is not necessarily the same as the life duration of the underlying capital stock, even on the energy supply side. But this is not a common feature of the relationship between emissions and the installed capital stock on the supply side.

12/39
|ZhejiangUrban Environment Project|GuangmingYan|
|---|---|
|ZhejiangUrban Environment Project|GuangmingYan|
|ZhejiangUrban Environment Project|GuangmingYan|
|ZhejiangUrban Environment Project|GuangmingYan|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Local Development Program Support Project|Soulemane Fofana|
|Programmatic Adjustment Loan(PAL)|Albert Martinez|
|EmergencyRecoveryand Rehab. Project|Robert J. Roche|
|EmergencyRecoveryand Rehab. Project|Robert J. Roche|
|EmergencyRecoveryand Rehab. Project|Robert J. Roche|
|EmergencyRecoveryand Rehab. Project|Robert J. Roche|
|EmergencyRecoveryand Rehab. Project|Robert J. Roche|
|EmergencyRecoveryand Rehab. Project|Robert J. Roche|
|EmergencyRecoveryand Rehab. Project|Robert J. Roche|
|EmergencyRecoveryand Rehab. Project|Robert J. Roche|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|ParticipatoryAgricultural Development and Empowerment Project|Zainab Z. Semgalawe|
|Real PropertyRegistration & Cadastre Project|Victoria Stanley|
|Real PropertyRegistration & Cadastre Project|Victoria Stanley|
|Real PropertyRegistration & Cadastre Project|Victoria Stanley|
|Real PropertyRegistration & Cadastre Project|Victoria Stanley|
|Real PropertyRegistration & Cadastre Project|Victoria Stanley|
|Real PropertyRegistration & Cadastre Project|Victoria Stanley|
**Table 3. CPIA and Select Indicators of MIC Economic Performance**

|CPIA<br>Scores,<br>2000|GDP Per<br>Capita,<br>1997<br>(US**$)**|No. of<br>countries|Annual<br>Growth in<br>GDP,<br>1990-97<br>|Annual<br>Gross<br>Growth in Per Capita Private<br>Exports,<br>Capital Inflows,<br>1990-97<br>1997<br>|Insti<br>Invest<br>Septem|tutional<br>or Rating,<br>ber 2000|
|---|---|---|---|---|---|---|
||||**(%)**|**(%)**<br>(US$)|||
||||||Rating|% rated|
||||A. Econo|mic Management|||
|1-3|2163|14|2.2|7.5<br>198.6|24.4|71|
|3-4|1914|20|_1.5_|5.6<br>75.1|36.2|85|
|_4-5_|3502|37|3.7|9.6<br>135.9|50.6|84|
||||B. Str|uctural Policies|||
|1-3|1900|14|-0.2|5.3<br>118.6|24.7|71|
|3-4|1910|30|3.6|8.6<br>96.3|39.7|80|
|4-5|4163|27|3.5|8.7<br>157.5|51.1|89|
|||C.|Policies for|Social Inclusion/Equity|||
|1-3|2483|12|3.4|8.7<br>206.5|29.6|67|
|3-4|2228|40|2.4|6.8<br>87.2|38.1|80|
|4-5|4121|19|3.2|10.8<br>165.2|53.9|95|
|||D. Pu|blic Sector M|anagement and Institutions|||
|1-3|1662|18|0.8|8.5<br>110.6|26.6|78|
|3-4|2670|40|3.4|7.3<br>118.1|42.2|83|
|4-5|4702|13|3.5|10.2<br>160.5|57.5|100|

Source: Staff calculations from Bank data.

34. The needs for policy reform vary considerably across the MICs. Many lowerincome MICs have still to complete "first-generation" reforms-fiscal adjustment and rationalization of public expenditures and basic market-opening reforms such as trade liberalization, removal of arbitrary government intervention in domestic product markets, financial deregulation, liberalization of foreign investment, and privatization of state enterprises. Removal of market impediments and distortions remains a major agenda items for these countries. At the same time, they need to embark on a more complex set of reforms to strengthen the institutional underpinnings of markets, such as the legal and regulatory framework and the capacity and efficiency of market institutions. One important lesson of recent development experience is that these "second-generation" reforms should not lag seriously behind the market-opening reforms, as they are essential to the efficient and stable functioning of the markets liberalized under the "firstgeneration" reforms.

35. In the upper-middle income countries there has been greater progress on the firstgeneration reforms, but there is a substantial unfinished agenda of second-generation reforms to strengthen the institutional framework for markets. These include the development of sound and competitive financial systems, capital market development, 12
**Table 7: The Impact of Migration on the Education of Females Aged 16 to 18**

||(1)<br>OLS|(2)<br>2SLS|(3)<br>Ordered <br>Probit|(4)<br> IV-Ordered <br>Probit|(5)<br>Censored<br> Ordered<br>Probit|(6)<br>IV-Censored<br>Ordered<br>Probit|
|---|---|---|---|---|---|---|
|Child is in a Migrant Household|0.338|-1.443|0.098|-0.663|0.037|-0.824|
||(2.75)*|(2.21)*|(1.92)|(2.83)**|(0.48)|(3.26)**|
|Age of Child|0.695|1.354|0.291|0.581|-0.236|0.128|
||(0.19)|(0.33)|(0.18)|(0.35)|(1.62)|(0.02)|
|Age of Child Squared|-0.015|-0.034|-0.006|-0.014|0.005|-0.005|
||(0.14)|(0.28)|(0.12)|(0.29)|(1.08)|(0.02)|
|Mother's Years of Schooling|0.418|0.414|0.176|0.176|0.215|0.213|
||(16.33)**|(15.07)**|(18.32)**|(17.76)**|(14.72)**|(14.77)**|
|Proportion of rural households owning land in 1910|-0.140|-0.069|-0.052|-0.021|-0.058|-0.023|
||(2.07)*|(0.83)|(1.95)|(0.89)|(2.27)**|(0.91)|
|Female School Attendance in 1930 (6 to 10 year olds)|-0.027|-0.006|-0.012|-0.003|-0.008|0.002|
||(1.75)|(0.39)|(1.93)|(0.56)|(1.72)|(0.41)|
|Gini of Income in 1960|-3.478|-2.477|-1.477|-1.054|-1.30|-0.751|
||(1.94)|(1.21)|(2.10)*|(1.51)|(2.38)**|(1.38)|
|Number of Schools per 1000 population in 1930|0.396|-0.270|0.184|-0.101|0.153|-0.176|
||(1.23)|(0.59)|(1.45)|(0.70)|(1.54)|(1.34)|
|Gini of Female Years of Schooling for 15-20 year olds in 1960|-7.445|-6.971|-2.987|-2.801|-1.846|-1.638|
||(2.79)**|(2.78)**|(2.85)**|(3.09)**|(2.06)**|(1.85)|
|Average Female Years of Schooling in 1960 for 15-20 year olds|-0.534|-0.537|-0.218|-0.221|-0.153|-0.152|
||(1.76)|(1.56)|(1.86)|(1.76)|(1.37)|(1.33)|
|Observations|3431|3431|3431|3431|1716|1716|

# Robust t-statistics in parentheses clustered at the state level. Instrument is the 1924 state-level migration rate.

Censored ordered probit regressions are carried out on a 50% random sample, and use school attendance as censoring variable. * significant at 5%; ** significant at 1% 39
Financial Regulation and Development

# **Appendix D: Testing the Relation between Bank Size and Unit Costs**

Table D1: Bank Size and Efficiency: Annual Reports

|Variables|(1)|Administrative Costs|(2)|
|---|---|---|---|
||on Asset||on Employee|
|Big Bank|_−_0.00446***||_−_0.0227***|
||(0.00127)||(0.00563)|
|Year FE|Yes||Yes|
|Obs.|40||49|
|_R_<sup>2</sup>|0.335||0.506|
|Adjusted _R_<sup>2</sup>|0.258||0.435|
|Mean Dep. Var.|0.0113||0.0504|
|SD Dep. Var.|0.00449||0.0228|

_Note:_ This table reports OLS estimates. The unit of observation is bank level, and year fixed effects are included. Davidson and McKinnon (1993) robust standard errors are reported in parentheses, which for this case are more conservative than bootstrapped bank-level clustered. Administrative costs are defined from banks’ annual reports as expenses related to the organization of the whole bank as opposed to an individual branch. These are normalized by bank assets in column (1) and by number of employees in coumn (2). Their means and standard deviations are reported in the final two rows. Big Bank is a dummy that takes unit value for the six largest Ethiopian Banks, and * zero otherwise. The _R_<sup>2</sup> and adjusted _R_<sup>2</sup> of these regressions are also reported. ***, **, and indicate significance at the 1%, 5%, and 10% levels, respectively.

# **Appendix E: Testing Compliance of the NBE Bill Policy**

This section verifies that 0.27 NBE bills were purchased for each birr of private lending and that this was no different between big and small banks. We present a simple fixed effect regression, introducing a dummy variable for the months in which the policy is in place. The following empirical model is tested: Here, _vity_ is the amount of NBE bills purchased by bank _i_ in month _t_ of year _y_ , and is run over a dummy variable taking unit value after April 2011, _Policyty_ , interacted with the volume of lending, _Lendingity_ , and then another term where we also explore whether there were systematic differences across big and small banks, _Policyty × Lendingity × Big Banki_ . The null hypothesis of these tests is _b_ = 0 _._ 27 and _c_ = 0. Table E.1 does not reject both of these. Indeed, column (1) shows that the point estimate of _b_ is indeed very close to 0.27 (0.265) and even the standard errors are very small, despite using a clustered bootstrapped method (alternative procedures, such as robust, hc3 or unclustered bootstrap, provide less conservative standard errors). In column (2), we introduce an 48
consider drivers of the direct COVID-19 age pattern it is useful to note that the age-specific mortality rate is the product of the age-specific cumulative infection rate ( _IR_ ) and the agespecific cumulative infection fatality rate ( _IFR_ ). Using the notation employed earlier and where I(x, i) designates the cumulative number of infections of age group _x_ in country _i_ , this can be expressed as follows: The greater flatness of direct COVID-19 age-specific mortality rates in lower income countries could result from differing patterns across countries by age of _IR_ , _IFR_ , or both. A number of studies have documented possible contributing factors to variation across countries in overall COVID-19 mortality and those same factors could generate different patterns in mortality by age.<sup>24–28</sup> The relative IR of younger vs. older people may vary across countries for many reasons. First, in lower income countries, a large share of the population cannot work remotely.<sup>29</sup> Second, in developing countries, a lower share of older people are long-term care facility residents, who are at high risk for infection and death. One-quarter of recognized COVID-19 deaths during the first few months of the pandemic in the United Kingdom were residents of care facilities, and 31 percent of recognized COVID-19 deaths in the United States as of June 1, 2021 were care facility residents or staff.<sup>30,31</sup> Both of these characteristics of developing countries would tend to increase the relative risk of infection for younger people. On the other hand, in low- and middleincome countries, intergenerational households are more common. This means older people are more exposed to infection risk, particularly because in those countries people are more likely to live in crowded conditions. Roughly 20 percent of households in LMICs have at least one household member under age 20 and one over age 60, as compared to just 5 percent in highincome countries.<sup>32</sup> In 54 LMICs with recent Demographic and Health Surveys, just half of the households have no more than two people per sleeping room.<sup>27</sup> Among those infected, age-specific IFRs may vary by age due to many different factors. One systematic review found that overall IFRs in developing countries are 1.3-2.5 times higher than in high-income countries.<sup>33</sup> Country-level factors like the profile of comorbidities, differences in 15
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33