rafmacalaba/datause-encoder-data huggingface dataset:
# **Table of Contents**

|**1**<br>**Intr**|**oductio**|**n**|**3**|
|---|---|---|---|
|**2**<br>**Eco**|**nomic**|**motivation and outline of the proposed framework**|**6**|
|**3**<br>**Eco**|**nometr**|**ic methodology and data**|**9**|
|3.1|Notio|n of equilibrium and econometric approach. . . . . . . . . . . . . . .|.<br>10|
||3.1.1|Equilibrium as the long-run level . . . . . . . . . . . . . . . . . . . .|.<br>10|
||3.1.2|First-stage ARDL panel regression . . . . . . . . . . . . . . . . . . .|.<br>12|
||3.1.3|Second-stage cross-country regression . . . . . . . . . . . . . . . . .|.<br>14|
|3.2|Data|. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>16|
|**4**<br>**Em**|**pirical r**|**esults**|**18**|
|4.1|Visual|overview of the cross-country long-run coeffcients. . . . . . . . . .|.<br>18|
|4.2|Mean|Group (MG) and Pooled Mean Group (PMG) estimation results. . .|.<br>20|
||4.2.1|Mean Group estimates . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>20|
||4.2.2|Pooled Mean Group Estimates<br>. . . . . . . . . . . . . . . . . . . . .|.<br>21|
||4.2.3|Summary of MG and PMG results . . . . . . . . . . . . . . . . . . .|.<br>23|
|4.3|Linkin|g the cross country variation to country-specifc characteristics . . .|.<br>24|
||4.3.1|Selecting the subset regressors<br>. . . . . . . . . . . . . . . . . . . . .|.<br>25|
||4.3.2|Shrinking the subset regressors . . . . . . . . . . . . . . . . . . . . .|.<br>26|
||4.3.3|<br>Results of the cross-country regression models . . . . . . . . . . . .|.<br>27|
|4.4|Discu|ssion of the cross-country regression results. . . . . . . . . . . . . . .|.<br>29|
||4.4.1|GDP regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>29|
||4.4.2|GDP Defator regression . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>30|
||4.4.3|<br>Speed of adjustment regression . . . . . . . . . . . . . . . . . . . . .|.<br>31|
||4.4.4|Correlation between_β_<sup>_gdp_ </sup>and_β_<sup>_de f_ </sup>estimates<br>. . . . . . . . . . . . .|.<br>32|
|**5**<br>**Con**|**clusio**|**n**|**32**|
|**Refere**|**nces**||**34**|
|**Figures**|**and T**|**ables**|**38**|
|**Appen**|**dix: Ad**|**ditional Estimation Results**|**47**|

2
# Table 7. Financial Self-Sufficiency w/ Interaction term (MFI-level regression)

P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- <u>Panel A: Overall Score Nominal (0 100)</u>

||V|<br>ersion 1(Ful sampl|<br>Depvar: Financial S<br>es)|elf-Sufficiency<br>Version 2|<br>(DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Overall Score Nominal (0-100)|0.0021|0.0022*|0.0014|0.002|0.0021|0.0013|
||[0.380]|[0.078]|[0.489]|[0.394]|[0.102]|[0.557]|
|NBFI dummy * Overall Score Nominal (0-100)|-0.002|-0.0024|-0.0021|-0.0023|-0.0029|-0.0024|
||[0.275]|[0.178]|[0.207]|[0.199]|[0.121]|[0.156]|
|Combined group dummy * Overall Score Nominal (0-100)|-0.0066**|-0.0061***|-0.0059***|-0.0072***|-0.0068***|-0.0060***|
||[0.011]|[0.001]|[0.005]|[0.007]|[0.004]|[0.004]|
|NBFI dummy|0.0958|0.1338|0.1145|0.1039|0.1469|0.1122|
||[0.334]|[0.125]|[0.224]|[0.311]|[0.108]|[0.238]|
|Combined group dummy|0.2849**|0.2808***|0.2665***|0.2775*|0.2747**|0.2325**|
||[0.032]|[0.003]|[0.009]|[0.064]|[0.019]|[0.046]|
|Observations|636|636|611|579|579|556|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.222|0.233|0.26|0.229|0.239|0.261|
|Adjusted R-squared|0.198|0.209|0.236|0.203|0.213|0.235|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|0.0000819|-0.000153|-0.000695|-0.000318|-0.000719|-0.00105|
||[0.972]|[0.924]|[0.742]|[0.893]|[0.672]|[0.632]|
|Combined group dummy|-0.00453**|-0.00389**|-0.00443**|-0.0052*|-0.00462**|-0.00474*|
||[0.047]|[0.028]|[0.034]|[0.066]|[0.039]|[0.093]|

41
- **RVOL** - **Maximum** return on acreage eligible for additional voluntary participation (computed from VDPC and RETFGP)

- SPRC - Support price of corn in dollars per bushel _(Feed: Outlook & Situation Report)_

- TPC - Target price of corn in dollars per bushel (support price and additional support payment prior to 1973) _(U.S. Corn Industry)_

- VDFG - Additional voluntary paid diversion for feed grains in percent of base acreage (Feed: Outlook _& Situation Report,_ text of various issues)

- VDPC - Additional voluntary diversion payment for corn in dollars per acre (Feed: _Outlook & Situation Report,_ text of various issues)

- YEAR = Two digit year

- YLDCGS - Yield per planted acre of corn and grain sorghum in bushels per acre (calculated from acreage and production in _Feed:_ Outlook **&** _Situation Report)_

- YLDFGP - Program yield of feed grains (corn **&** grain sorghum) in bushels per acre _(Feed: Outlook & Situation Report,_ text of various issues)

- YLDS - Yield per planted acre of soybeans in bushels per acre (from acreage and production in _Oil Crops: Outlook C Situation Report)_

The definitions of basic variables with quarterly data used in the

# econometric work (in alphabetical order) are as follows:

- BRCH - Broiler-type chicks natched, millions _(Livestock & Poultry: Outlook_ **_&_** _Situation Report)_ Q1 - January-March, Q2 - April-June, Q3 - July-September, Q4 = October-December

- BRHOGKE - Breeding hog inventory for 10-states, 1,000 head, _(Livestock & Poultry: Outlook_ **_&_** _Situation Report,_ Mar, May, Jul, Oct) Q1 = March 1, Q2 - June 1, Q3 = September 1, Q4 - December 1

- BROF - Broilers on feed (computed from BRCH)

- CATPL = Cattle placed on feed in 13-states, 1,000 head _(Livestock_ 6 Poultry: _Outlook_ **_&_** _Situation Report,_ Mar, May, Aug, Dec) Q1 = January-March, Q2 = April-June, Q3 = July-September, Q4 = October-December

- CBRHOGKE = Quarterly change in breeding hog inventory (computed from BRHOGKE) CCOWKE = Quarterly change in cattle breeding herd size (computed from TCOWKE)

- COF = Cattle on feed in 13-states, 1,000 head (Livestock **_&_** _Poultry: Outlook & Situation Report,_ Mar, May, Aug, Dec) Q1 = January 1, Q2 - April 1, Q3 - July 1, Q4 - October 1

- COMPFG - Program participation of corn and grain sorghum in percent of acreage (U.S. Agricultural Stabilization and Conservation Service)

- CPL - Pullet chicks placed in broiler hatchery supply flocks in thousands (Livestock _& Poultry: Outlook & Situation Report)_ Q1 - January-March, Q2 = April-June, Q3 = July-September, 04 October-December

- CRUSH - U.S. crushings of soybeans, million metric tons

- DDFG - U.S. total domestic use of feed grains (corn, sorghum, oats, barley), million metric tons _(Feed: Outlook and Situation Report)_

- DDINDFG - Annual change in U.S. industry use of feed grain (computed from DINDFG)

- DINDFG - U.S. feed grain use by industry (computed from DDFG and DLVKFG)

12
It is important to note that **the list of circumstances does not include all the circumstances that may be relevant in determining a child’s access to opportunities** . This is because the selection of circumstances, which defines the groups between which the equality of opportunities is examined, is necessarily limited by the information that is available from surveys. To consider just one example: conventional wisdom suggests that access to opportunities for a child may be influenced to some extent by the child’s ethnic group or tribe at birth. However, because ethnic and tribal affiliation is not included in the household surveys, this characteristic cannot be incorporated as a circumstance in the analysis. Information on the region where the child is located, wherever available, has been used as an extremely rough proxy to compensate for the absence of information on ethnicity. Similarly, the selection of opportunities is also limited to available information. For example, even when a timely record of vaccinations is critical for the health of children, this information is not provided by respondents in the household surveys.

Given that the list of circumstances cannot be completely comprehensive, the HOI that is computed using just the available circumstances would serve as a theoretical ―upper bound.‖<sup>6</sup> This means that adding the important circumstances currently missing would very likely _add_ to the penalty for inequality and drive HOI downward. In simple terms, the ―true‖ HOI, if one could obtain that, would not show a picture that is ―better‖ than what the HOI, based on limited information, would suggest.

Table 2 presents the coverage rate of opportunities for children of the relevant age groups for which the HOI is computed, and the incidence of circumstances in the population in each year for which a household survey is available. There is wide variation across opportunities as measured in terms of average coverage rates. Access to clean water for children under 15 years is widespread, but declining, during this decade; access to electric light is, in contrast, more limited, but it has increased steadily since 1985. Access to sanitation is somewhere in the middle, with a coverage rate between that of electricity and that of water, but hardly any increase in the last two decades. More worrisome, educational opportunities are more limited across Ivorian children, with relatively unchanged attendance to school; despite the increasing timely primary enrollment, there has been a substantive decline in the percentage of teenagers finishing sixth grade. These changes have taken place in a context of changing circumstances: increasing female household heads, declining presence of children in the household, and declining urbanization, as indicated in table 3.

To compute HOI for each individual’s opportunity, the method outlined in section 2 (and box 3) is followed. Access to a particular opportunity (a dummy variable is assigned the value 1 if the child has the 12
# 5. CAUSAL IDENTIFICATION

48. Identifying the causal impact of e-procurement on access, economy and efficiency is challenging as e-procurement adoption is clearly non-random either at the procuring entity or tender levels (see section 3). In sum, we have to grapple with five main sources of bias: i) omitted variable bias; ii) self-selection of procuring entities into the e-procurement system; iii) strategic sorting of tenders within procuring entities; iv) spillovers from treated to non-treated tenders within procuring entities; and v) learning effects and increasing firm registrations entail heterogeneous effects over time.

49. We employ a range of estimation strategies to address each of the above biases. None of these strategies can address all of these potential sources of bias on their own. Taken together, however, they give a reasonable estimation of causal effects (Table 2). This section first describes each of these challenges in greater detail and then outlines how we address them.

50. First, omitted variable bias stems from our limited ability to observe all relevant characteristics both of individual tenders and of procuring entities, the public bodies administering tenders through manual or e-procurement systems. For example, even if tenders are of similar size and have been procured using the same methods, they might differ on harder-to-observe characteristics, such as the nature of works (such as roads vs. school buildings) that may be associated with outcomes (e.g. the likelihood of time overruns) and treatment status. Similarly, procuring entities have unobserved and hard to precisely measure qualities, like management style, staff motivation, work ethics, or e-government readiness that affect procurement outcomes and may be associated with e-procurement take-up.

51. The second threat to causal identification comes from the potential self-selection of procuring entities into adopting e-procurement. For example, better performing procuring entities or those with a lower propensity for corruption might more rapidly adopt e-procurement—wishing to reap its benefits as early as possible, or in view of signaling their readiness to reform to superiors and the market. 52. Third, procuring entities may sort tenders into e-procurement for strategic reasons (strategic sorting), reflecting their organizational goals and institutional quality. For example, they may initially decide to only use the new e-procurement platform for smaller tenders,23 in view of lowering the risks of making mistakes, due to their lack of experience with the new system. Corrupt procuring entities could try

> 23 In the survey of procuring entity (PE) officials conducted, 108 procurement officials stated that they assigned tenders to e- procurement according to estimated tender value.

18
# **C Deviations from the pre-analysis plan**

This annex compares the outcome and covariate variables as described in the pre-analysis plan (PAP) registered in the AEA Trial Registry and as actually implemented.

As summarized in the tables below, the primary analysis does not deviate from the PAP, but the secondary analysis does differ in a few dimensions from what was described in the PAP. First, we had intended to assess the size of the facility catchment area as a key moderator of responses. However, these data were largely missing, thus leaving us unable to complete this portion of the analysis. We had also intended to include the health worker’s education level as a covariate but were unable to do so due to a lack of variation in this variable. Initially, we had planned to treat midwives as lower-level professionals, at par with community health workers. But the data show that midwives had training, experience, and salaries commensurate with that of a nurse rather than a community health worker. As a result, we include midwives in the medical professional category, although results are robust to the original specification provided in the PAP. Finally, in lieu of the health worker’s education, we explore tenure and experience as additional dimensions along which to assess balance. We added these to the analysis after the PAP was registered. Note that these variables are balanced.

70
country and cross-project experience to inform project assessments and apply common standards. Moreover, since the 1990s most IEG evaluations have been public and the Group pays close attention to comments and criticisms of outside experts, civil society groups, and academia. On the other hand, IEG is primarily staffed by current and future Bank staff and there is some rotation in and out of IEG, although this turnover is considerably lower than in other parts of the Bank. There are also likely various informal channels of communication between IEG and World Bank staff which may affect the ratings process. While the full independence of IEG evaluations cannot be directly verified or contradicted, we can do no more than raise this as a potential question regarding the reliability of outcome measures.

A further qualification regarding these project outcomes ratings is that they are explicitly measured relative to the stated “development objective” of each project, rather than relative to some common standard across them. It could be the case that task managers set modest “development objectives” for projects undertaken in difficult countries, and set more ambitious ones in countries with good institutions, policies, and a good track record of implementing World Bank projects. This would understate the importance of country factors in determining the success of projects. At the same time, it could be the case that more experienced task managers set more modest development objectives, which are then more likely to be attained. This would in turn result in an overstatement of the importance of task manager effects in driving project success.<sup>5</sup> Our overall impression is that while the evaluation outcome data described here are far from perfect, arguably they meaningfully capture the experience and insights over the years of many World Bank staff on how well projects have fared. Of course, even well-measured individual project outcomes will not be perfectly informative about the overall aggregate development impact of aid, as there are there may be complementarities between projects, as well as the potential scope for aid-financed spending crowding out other sorts of public spending. Nevertheless, while surely there is considerable remaining measurement error in the outcome measures, it is still useful to investigate a range of

> 5 However, the Bank’s review process has some safeguards to prevent this.  First, during the concept review stage of project preparation the teams are routinely questioned about the “realism” of project objectives.  In addition, for much of the sample period, World Bank staff from the Quality Assurance Group (QAG) reviewed a sample of projects in "real time" and advised teams and management about various aspects of proposed projects, including their development objectives.  While there is no formal common standard for development objectives, there are formal mechanisms in place that attempt to ensure that project objectives are both feasible and ambitious enough to attain the broader development objectives of a the recipient country.

9
Policy ReseaRch WoRking PaPeR 4621

# **Abstract**

In 2005, China participated for the first time in the International Comparison Program (ICP), which collects primary data across countries on the prices for an internationally comparable list of goods and services. This paper examines the implications of the new Purchasing Power Parity (PPP) rate (derived by the ICP) for China’s poverty rate (by international standards) and how it has changed over time. We provide estimates with and without adjustment for a likely sampling bias in the ICP data. Using an international poverty line of $1.25 at 2005 PPP, we find a substantially higher poverty rate for China than past estimates, with about 15% of the population living in consumption poverty, implying about 130 million more poor by this standard. The income poverty rate in 2005 is 10%, implying about 65 million more people living in poverty. However, the new ICP data suggest an even larger reduction in the number of poor since 1981.

This paper—a product of the Director's office, Development Research Group—is part of a larger effort in the department to monitor the developing world's progress against poverty. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mravallion@worldbank.org, schen@worldbank.org and psangraula@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
<mark>3.7 Opportunities for NSOs to improve microdata accessibility</mark> Collecting microdata is costly, which may be one reason why relatively few microdata sets are collected in the MENA region. While the frequency with which microdata are collected may not change overnight, our search for microdata revealed opportunities for NSOs to improve their data accessibility at almost no additional cost. No MENA NSO makes price surveys (or at least item level price indices) publicly available even though such information would be relevant to a host of users. Almost all MENA NSOs possess recent population census data, but few make them publicly available. The exceptions are Morocco and the Islamic Republic of Iran where a sample of anonymized individual and household level data is available for download. <mark>Additional suggested practices that can improve accessibility of microdata on NSO websites are outlined below.</mark> Suggested practice 1: Provide an English version of the website <mark>While the primary audience for NSO statistics are nationals, many potential data users live abroad. Since English is understood by majority of people in almost every region of the world, it is best practice for NSOs to make available an adequate English version/translation of their website. At present, not all MENA NSOs have an adequate English version of their website. For instance, an English version of the website of the Islamic Republic of Iran NSO exists, but several data sets available on the Persian version of the website are not available on the English version. This includes the consumption (welfare), labor force survey as well as the population census data reported to have satisfactory openness in table 4. Consequently, non-Persian speakers would have difficulty identifying the wealth of data that is available, particularly as the Islamic Republic of Iran is exemplary in providing data access. All recent, available microdata sets are downloadable from the website, some like the household budget surveys at an annual frequency.</mark> Suggested practice 2: Provide a microdata catalog, data tab and a search button on website landing page Given the multiplicity of information that is typically available on an NSO’s website, ensuring a good routing through the website is critical. For primary microdata users, a data tab and/or microdata catalog that present all data available on the website is a useful tool. This will make microdata on the website easy to find and download. Egypt for instance has a tab “MetaData” on its landing page that leads visitors to a central data catalog. This is very helpful for website visitors interested in the country’s data. Some countries go even further. UAE’s open data portal allows users to search for data by the organization within UAE that owns the data. More generally, a search button is important to facilitate finding relevant information on the website and ultimately ensuring a favorable user experience. To date, not all MENA NSOs include a data tab, search button and/or microdata catalog on the landing page of their website. <mark>There is a</mark> 14