rafmacalaba/datause-encoder-data huggingface dataset:
|_Using Both as Independent Variables_<br>**Table 16**<br>**Dynamic Herding Regressions**<br>This table presents the results of two regressions: (i) the typical Sias (2004) herding regression, that is, the<br>regression of the fraction of funds buying a given asset at a moment in time on the lagged fraction of funds<br>buying, and (ii) the same regression adding the lagged rate of return as an additional regressor. The<br>objective is to analyze if herding is driven by momentum. Both regressions are carried out over all asset<br>classes and for each asset class separately. Standard errors are presented in parentheses. T-tests are two-<br>tailed. One asterisk indicates statistical significance at the ten-percent level and two asterisks indicate<br>statistical significance at the five-percent level.|
|---|
|Lagged Fraction of<br> <br>Lagged Fraction of<br> <br>Lagged Return|
|FundsBuying<br>FundsBuying<br>|
|(1)<br>(2)<br>(3)|
|**All Asset Classes**<br>-0.31**<br>-0.34**<br>-0.05<br>(0.00)<br>(0.00)<br>(0.06)|
|**Domestic assets**|
|Former Pension System Bonds<br>1.73**<br>-0.42**<br>1.17**|
|(0.66)<br>(0.01)<br>(0.41)|
|Corporate Bonds<br>-0.28*<br>-0.21**<br>-0.43**|
|(0.17)<br>(0.02)<br>(0.14)|
|Financial Institutions<br>0.01<br>-0.41**<br>-0.58|
|(0.33)<br>(0.03)<br>(0.36)|
|Government Paper<br>0.26**<br>-0.31**<br>-0.12|
|(0.09)<br>(0.01)<br>(0.10)|
|Investment and Mutual Funds<br>0.61<br>-0.34**<br>-0.33|
|(0.49)<br>(0.06)<br>(0.68)|
|Equity<br>0.16**<br>0.23**<br>0.11|
|(0.07)<br>(0.01)<br>(0.07)|
|Mortgage Bonds<br>-1.48**<br>-0.34**<br>-1.29**|
|(0.12)<br>(0.01)<br>(0.09)|
|**Foreign assets**|
|Fixed Income<br>0.98**<br>-0.19**<br>0.77*|
|(0.28)<br>(0.03)<br>(0.42)|
|Investment and Mutual Funds<br>0.49**<br>-0.00<br>0.50**|
|(0.12)<br>(0.02)<br>(0.12)|
|Equity<br>-0.20<br>-0.16*<br>0.36|
|(0.19)<br>(0.09)<br>(0.67)|
balance sheets and income statements for all the financial institutions to control for the performance and health of domestic and foreign banks when comparing lending patterns across these institutions.

# _Argentina_

According to a recent study on small business lending conducted by the central bank of Argentina, loans to this sector go to businesses with total debt between 50,000 and 2.5 million dollars (see Escude _et al._ (2001)). Since we do not have information on borrowers' total debt, we use bank level information on total lending stratified by loan size for the period 1998-2000. According to the aforementioned study, a small business with up to 2.5 million dollars in debt, on average borrows up to I million dollars from any individual bank. Thus, we define as loans to small businesses all loans up to this amount.

# _Chile_

For Chile small business loans are identified based on the total debt of the business rather than the size of individual loans. Total debt is measured by the Chilean Banking Superintendency in 'unidades de fomento' (UF), which are equal to a fixed quantity of Chilean pesos indexed to inflation. Following the guidelines provided by the Superintendency, we define as small business lending all loans to borrowers with less than UF50,000 in total debt, which is roughly equal to 1.5 million dollars.<sup>3</sup>

# _Colombia_

The Superintendency of Banks in Colombia requires banks to keep a separate record of the amount of loans they give to small firms registered with the Superintendency of Businesses. According to Colombian regulations, small businesses are those with assets up to 1.7 million dollars. For each bank in Colombia, we obtained quarterly information on the loans to the 4,067 small businesses registered with the Superintendency of Businesses over the period 1997-1999. Because this might be a partial list of all small businesses in Colombia, we should be careful in trying to draw conclusions across countries.

# _Peru_

Officials from the Peruvian Banking Superintendency estimate that small business loans range between $20,000 and $500,000. Thus, using a breakdown of bank lending by size for loans to businesses 7
Policy Research Working Paper 6490

# **Abstract**

Land area is a fundamental component of agricultural statistics, and of analyses undertaken by agricultural economists. While household surveys in developing countries have traditionally relied on farmers’ own, potentially error-prone, land area assessments, the availability of affordable and reliable Global Positioning System (GPS) units has made GPS-based area measurement a practical alternative. Nonetheless, in an attempt to reduce costs, keep interview durations within reasonable limits, and avoid the difficulty of asking respondents to accompany interviewers to distant plots, survey implementing agencies typically require interviewers to record GPS-based area measurements _only_ for plots within a given radius of dwelling locations. It is, therefore, common for as much as a third of the sample plots not to be measured, and research has not shed light on the possible selection bias in analyses relying on partial data due to gaps in GPS-based area measures. This paper explores the patterns of missingness in GPS-based plot areas, and investigates their implications for land productivity estimates and the inverse scale-land productivity relationship. Using Multiple Imputation (MI) to predict missing GPSbased plot areas in nationally-representative survey data from Uganda and Tanzania, the paper highlights the potential of MI in reliably simulating the missing data, and confirms the existence of an inverse scale-land productivity relationship, which is strengthened by using the complete, multiply-imputed dataset. The study demonstrates the usefulness of judiciously reconstructed GPS-based areas in alleviating concerns over potential measurement error in farmer–reported areas, and with regards to systematic bias in plot selection for GPS-based area measurement.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Figure 7 Foreign Exchange Reserves and M1**

**Foreign Exchange Reserves to GDP**

<!-- Start of picture text --> 40%<br>30%<br>20%<br>10%<br>0%<br>China India East Asia Eastern Europe Latin America G7<br>M1 to GDP<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>China India East Asia Eastern Latin America G7<br>Europe<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br><!-- End of picture text --> East Asia is the average of Indonesia, Korea, Malaysia, and Thailand. G7 is the average of Canada, France, Germany, Italy, Japan, United Kingdom, and United States. Latin America is the average of Argentina, Brazil, Chile, and Mexico. Eastern Europe is the average of Czech Republic, Hungary, and Poland. M1/GDP average for G7 does not include UK. Source: World Bank World Development Indicators and International Monetary Fund International Financial Statistics.
11 Standard poverty measures are immediately decomposable by population subgroups and, therefore, easy to aggregate up from sub-groups. The numbers of poor can simply be added across countries, while poverty incidences and poverty gaps are first weighted by the country’s population share and then summed. This simple procedure underlies the global poverty incidence and the global absolute numbers of the poor that are reported in the previous section.

The analogous procedure for inequality indices is more involved for two reasons. First, it has to contend with the fact that global inequality is not merely an aggregation of within-country inequalities. It also contains a component that corresponds to inequality _between_ countries. Second, once the world is treated as a single entity, with a well-defined distribution of living standards, then the scale in which each individual national distribution is expressed matters again. While PPP exchange rate calculations are not needed if one simply wants to compare national levels of inequality, they are crucial for the construction of a global inequality index.

By “global inequality” we shall mean inequality amongst all people of the world, ignoring where they live. This is calculated by combining the surveys from all the different countries (at the appropriate PPP exchange rates) into a single world distribution of income, and then computing inequality indices for this distribution. As long as the inequality index is additively decomposable (such as MLD), it will be possible to separate this overall measure into a component corresponding to inequality between countries, and one that aggregates the inequality within all the different countries. Only recently have household surveys been available for a sufficient number of countries for this approach to be feasible. Since then, this approach has become dominant among researchers interested in global interpersonal inequality—for the simple reason that it does not ignore inequality within countries.

The earlier literature contains two (simpler) approaches to measuring overall inequality in the world. The first takes each country as the relevant unit of observation, and computes inequality between these “country means”. This is what Milanovic (2005) calls Concept 1 inequality, and what World Bank (2005) calls inter-country inequality. Second, it is possible to take account of different population sizes by weighting each country mean by its share of world population—giving Milanovic’s Concept 2 inequality, or what World Bank (2005) calls international inequality. Both of these approaches are unsatisfactory since they ignore inequality within countries, and capture only the between-country differences.
foreign currency debt. The share of local currency debt is over 70% now, as of end-2011. Eighty-eight percent of total liabilities are in the form of securities or bonds issued in local and international markets, in contrast with 77% at the end of 2002. The remainder includes external loans.

Given the change in the portfolio structure, the debt database primarily accounts for securities. Each security is represented with an MS Access Database record with identifying attributes such as instrument type, currency, coupon rate etc. The database is updated at the end of each month by using designated queries—delete overdue bonds, add new issuances, and update coupon payment dates—in order to calculate key debt structure metrics.

At the start of the simulation procedure, the debt stock database is checked to ensure that it reflects recent issuances and redemptions, and adjusted if needed. This data is fed into the cash-flow engine module to calculate the debt redemptions arising from outstanding bond issues.

The cash-flow characteristics of the non-securitized loans are not standard and differ significantly from market instruments such as bond and bills. There are also considerable differences among the many loans with regard to their amortization and interest payment schemes. From a modeling perspective, and in terms of a cost-benefit analysis, the time and other resources required to emulate the cash-flows of the loan portfolio in detail would not be justified by their diminishing share in the government’s debt stock. Therefore, a different approach is adopted and the cash flows of loans are computed outside the Debt Database and aggregated into clusters of two main foreign currencies: US Dollar (USD) and Euro. The loans denominated in other currencies are represented in the USD cluster using prevailing cross currency rates. The computed payments for each currency group are then fed to the ―Cash-Flow Engine‖ as underlying assumptions.

# **4.2 Financial Scenario Generation**

The cost and risk distributions of alternative strategies depend entirely on the scenario paths created for financial variables. As discussed in Section 2, the general approach in creating these scenarios is to use statistical or structural models. In either case, the scenario generators are calibrated by time series analysis, and therefore the process of creating financial scenarios, is heavily influenced by the quality of the historical data. In countries where the value of past data is questionable from a modeling perspective, one can consider adopting flexible approaches that allow using several models interchangeably, as model performance can vary 19
**Table 1** : Life Expectancy at Birth and Schooling

||I|II|III|IV|
|---|---|---|---|---|
|Life expectancy at birth|_0.120***_<br>_[0.015]_|_0.097***_<br>_[0.014]_|_0.094***_<br>_[0.008]_|_0.117***_<br>_[0.026]_|
|% Urban||6.009***<br>[1.687]|6.043***<br>[1.748]|6.737***<br>[1.057]|
|% Male||-1.212|-1.342|-0.858*|
|||[1.047]|[0.922]|[0.516]|
|Parents Life Expectancy|||0.004<br>[0.019]|0.026*<br>[0.014]|
|Birth Year Fixed Effects||||YES|
|Survey Fixed Effects|YES|YES|YES|YES|
|Constant|3.476***|1.574|1.625|-1.748|
||[0.916]|[2.298]|[2.455]|[2.256]|
|N|4670|4185|3861|3861|
|Adjusted R-square|0.987|0.985|0.985|0.987|

Note: Standard errors in brackets. Significance levels: * p<0.05, ** p<0.01, *** p<0.001.

39
14

# **Appendix. Data Description and Full Results of the Regressions, Including Alternative Specifications**

_Data description_ All data come from the World Bank WDI database, unless stated otherwise.

GNI per capita: Gross national income divided by midyear population, Atlas method (current US$) IDA operational cutoff: Threshold which GNI per capita should be above to be considered a candidate for graduation. _Source:_ IDA Creditworthiness: Institutional Investors Creditworthiness Index, ranging from 0 (least creditworthy) to 100.

Poverty headcount: The proportion of a population that lives below the international poverty line (US$1.90 a day in 2011 PPP). _Source:_ PovcalNet database Institutional development: The ICRG Quality of Government index produced by the PRS Group, with scores ranging from 0 to 1 (highest quality of government) Urban population share: Percentage of population living in urban areas as defined by the country Resource rents/GDP: Total natural resources rents are defined as the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents Worldwide Governance Indicators: ‘Government effectiveness’, ‘regulatory quality,’ and ‘rule of law’ used to proxy ICRG, where data are not available.

**Table 4. Countries with GNI Per Capita over 80 Percent of IDA Operational Cutoff: Summary Statistics**

||**N**|**Mean**|**Min**|**Max**|**Standard deviation**|
|---|---|---|---|---|---|
|GNI per capita|764|1,893|510|8,690|1,354|
|GNI per capita/operational cutoff|764|1.75|0.80|7.59|1.09|
|Years over cutoff|764|5.86|0|26|5.73|
|Creditworthiness|742|29.0|3.6|80.2|14.3|
|Poverty headcount|880|0.21|0.00|0.96|0.22|
|Log(population)|1001|16.3|13.8|21.0|1.5|
|Life expectancy|999|64.2|41.4|78.5|8.7|
|Urban population share|970|0.42|0.11|0.69|0.14|
|ICRG rating|690|0.40|0.04|0.69|0.13|
|Resource rents/GDP|830|0.09|0.00|0.60|0.11|
|OECD real GNI per capita|1,010|33,412|24,991|39,416|4,526|
# **Table 1: Interviewer Characteristics and Adolescent Girls’ Responses to Questions Related to Sexual and Romantic Behavior**

## **Logit marginal estimates, standard errors clustered by interviewer reported in parentheses**

||**Lifetime B**|**ehavior**<br>|**Current B**<br>|**ehavior**<br>|
|---|---|---|---|---|
||**(1) Ever had sex**|**(2) Ever had**<br>**boyfriend**|**(3) Had sex in past**<br>**30 days**|**(4) Has boyfriend**<br>**currently**|
|**_Mean of dependent variable:_**|.170|.204|.102|.150|
|**_Panel A: Interviewer Controls_**|||||
|**Physical Attractiveness [z‐score]**|.034**|.045**|.028**|.044***|
||(.015)|(.019)|(.014)|(.015)|
|**Male [yes=1]**|.019|.035|‐.001|.023|
||(.024)|(.031)|(.029)|(.026)|
|**Gender Attitudes [z‐score]**|.027**|.035**|.003|.033***|
||(.014)|(.014)|(.013)|(.013)|
|**Program Enthusiastic [yes=1]**|‐.079***|‐.077**|‐.059***|‐.066**|
||(.027)|(.032)|(.020)|(.029)|
|**Soft Skills [0‐2 score]**|.072***|.084***|.067***|.080***|
||(.025)|(.027)|(.020)|(.022)|
|**Hard Skills [0‐2 score]**|.056**|.043|.038*|.061**|
||(.028)|(.029)|(.023)|(.026)|
|**Age**|.001|.004*|‐.001|.003*|
||(.002)|(.002)|(.002)|(.002)|
|**Respondents [interviewers]**|1,210 [40]|1,210 [40]|1,210 [40]|1,210 [40]|
|**_Panel B: Interviewer and Respondent Controls_**|||||
|**Physical Attractiveness [z‐score]**|.032**|.045***|.024**|.042***|
||(.013)|(.016)|(.012)|(.013)|
|**Male [yes=1]**|.018|.036|‐.002|.020|
||(.022)|(.028)|(.023)|(.021)|
|**Gender Attitudes [z‐score]**|.024**|.033***|.001|.029***|
||(.012)|(.012)|(.010)|(.010)|
|**Program Enthusiastic [yes=1]**|‐.054**|‐.055*|‐.041**|‐.048*|
||(.025)|(.029)|(.017)|(.026)|
|**Soft Skills [0‐2 score]**|.070***|.085***|.059***|.075***|
||(.022)|(.024)|(.016)|(.019)|
|**Hard Skills [0‐2 score]**|.063**|.054**|.039**|.064***|
||(.025)|(.027)|(.018)|(.022)|
|**Age**|.000|.003|‐.001|.002|
||(.002)|(.002)|(.002)|(.002)|
|**Respondents [interviewers]**|1,210 [40]|1,210 [40]|1,210 [40]|1,210 [40]|

**Notes:** *** denotes significance at 1%, ** at 5%, and * at 10% level. All columns report logit estimates, where marginal effects are reported in each case. Standard errors are in parentheses, and are clustered by interviewer. Panel A only controls for interviewer characteristics. Panel B also controls for respondent controls, which include girl’s age, years of schooling, household size, number of assets owned by the household, and dummies for household head’s educational attainment.

7
according to some economic and **geographical** variables, which consequently form the list as a **geographical serpentine.**

- c) <u>Poverty map: this is the design proposed in the present paper; PSUs are</u> firstly stratified in the 24 governorates, and then they are further stratified (implicitly) with systematic sampling by ordering the lists of PSUs by average per capita consumption expenditure estimated with the poverty mapping method presented in Section 3.

On the basis of the new information from the current population in Tunisia, and the design effect of 2.19 estimated on the basis of the EBCNV 2015, Table 5 reports the expected sample sizes to obtain maximum percentage errors reported in the left column. The expected design effects corresponding to scenarios a) and c) have been estimated using equation (3) in Section 2, where Y is the mean per capita consumption expenditure from the poverty mapping in equation (6) in Section 3.

First of all, we can observe that in order to obtain a percentage error of about 10% a very high sample size is not needed when good designs with implicit stratification are taken into account (such as the classical “Serpentine”). However, this can be verified when very complex methods of variance estimation, usually based on replication techniques, are implemented. The recent JRR version, proposed by Verma and Betti (2011), and implemented to estimate the design effect 2.19 of the “Serpentine” design, is much more easily implemented by National Statistical Offices.

**<u>Table 5. Sample sizes in function of expected errors and type of design</u>**

|||Explicit<br>stratification|Serpentine|Poverty map|
|---|---|---|---|---|
||**deft**|**3.71**|**2.19**|**1.87**|
||4.5%|141315|49306|35856|
||5.0%|114073|39801|28944|
||5.5%|95345|33267|24192|
|error|5.7%|85129|29702|21600|
||5.9%|76616|26732|19440|
||6.0%|71509|24950|18144|
||7.0%|59591|20792|15120|
||8.0%|49375|17227|12528|
||9.0%|40862|14257|10368|
||10.0%|28944|10099|7344|

18
Table 6 npoyme nt Out_ome fa tr nmen versu Comped,oo Group (ENEUI - MUl

||Tran..oaftd<br>.E .|a""|3.10|
|---|---|---|---|
|.<br>-/WkExp<br>.*~~f~p ..<br>.:<br>**_.._**<br>|*.<br>: .:.|wiWkE|no W:<br>**_p_**|
|%EMPFOYED<br>3 monrtaettr|650<br>322|||
|6 m<br>ront<br>after|.<br>.<br>700<br>367|60.0<br>|34.6<br>|
|9 month after|.<br>.<br>712<br>_37A_|69.8<br>|33.1<br>|
|12 rronth atetr|.<br> <br>764<br>409|73.2<br>|_40A_<br>|
|<br> <br>|.<br>.|74.6|49.3|
|DIFFERENCE<br>BETWEEN<br>CHANGES FOR<br>TRAINEES<br>AND FOR COMPAMISON<br>GROUP||||
|<br><br>after 3 month|60<br>*2A|||
|eftar Snonti.|.<br><br>08<br>26|||
|ater 9 month|.<br>.<br>-20<br>-30|||
|after 12 mon|.<br>.<br>1.9<br>-8.A|||

Nete:* Unemploye **d** efired - those who re not workb,not not t not retired (wider 5661 dbl to work (not sik or disabled). regades of whether activey ser n for job **_ar_** not.

Employnt Outomes for tralses Compeison Group _(ELED)_ - FwnseI

|_i,_<br>_' i.., :.....:_<br>_'i_<br>_..._N<br>_.2i..._<br>_i .. ,;,_<br>_. ,m_<br>_'i_<br>_'_<br>|_.:._<br>_' '"'_|p<br>.|||
|---|---|---|---|---|
|_%_EMPLOYED<br>3 monts eter|433|161|324||
|* montho after|.<br>500|.<br>174|.<br>2|23.9<br>|
|9 month fter|.<br>600|.<br>190|36.<br>333|28.0<br>|
|12 mnontii after|.<br>602|.<br>216|.<br>|34.6<br>|
|<br> <br>|.|.|34.3|38.9|
|DIFFERENCE<br>BETWEEN<br>CHANGES FOR<br>TRAINEES<br>AND FOR COMPARISON<br>GROUP|||||
|aftr 3 month|109|87|||
|afterOmonte|.<br>148|-.<br>-112|||
|_dte_9 month|.<br>167|.<br>-156|||
|_aftw_122month|.<br>16.9|.<br>-16.3|||

Note ' Unemploye **d** efined o thse who re not working, not studing, not retired _Wer_ 661. not t*lin cre of the hoe,. _and_ able to work (not sick or dsbld). reads of whether acively achV for job oa not 5e. it cudes discoeraed worwkwe who resnd they ar not crchr becae they don't expet to find a pbl