rafmacalaba/datause-encoder-data huggingface dataset:
Figure A7: Distributions of poor populations across urban versus rural areas by country, $3.65 poverty line <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, WorldPop 250m is used.

Figure A8: Comparison of urban and rural poverty rates <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>80 80 80<br>60 60 60 MWI<br>MWI<br>NER<br>40 40 TZA UGA 40 TZA UGA<br>NER<br>20 UGATZA MWI 20 ETH 20 ETHTCD LSO<br>GABMRTSENCIVGIN GNBGHAETHBFATCDAGOLSO NER GABMRTGINCIVSENGHA GNBTCDBFA LSO AGO GABMRTSENCIVGINGNBBFAGHA AGO<br>20 40 60 80 20 40 60 80 20 40 60 80<br>Poverty rate in rural areas (%) Poverty rate in rural areas (%) Poverty rate in rural areas (%)<br>Poverty rate in urban areas (%) Poverty rate in urban areas (%) Poverty rate in urban areas (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: WorldPop 250m is used for the DOU and DB methods. Dashed lines are 45-degree lines. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

32
variation in outreach. Section 4 shows the predictive power of our indicators relating them to household- and firm-survey based indicators of use of financial services. Section 5 examines the correlation of the outreach indicators with other country characteristics, as well as regulatory and policy variables. Section 6 relates the outreach indicators to cross-country survey indicators of firms’ financing obstacles. Section 7 concludes.

# **2.  Data: Indicator Sources and Definitions**

This paper presents a new data set that seeks to measure the access to and use of banking services across 99 countries in 2003-2004. Specifically, the objective of this dataset is to construct indicators of access to physical bank outlets and use of banking services (in particular credit and deposit services). For this purpose, we developed a questionnaire that we circulated among bank regulatory agencies across countries. The main questions from this survey focus on obtaining information on the number of bank branches, number of ATMs, and the aggregate number and value of bank loans and deposits.<sup>6</sup> For countries that did not provide responses to our questionnaire, we gathered data from alternative sources, including government publications and official websites. A detailed list of all the sources used for each country can be found in appendix Table A.1.

Our survey refers exclusively to deposit money banks – all financial institutions that have “liabilities in the form of deposits transferable by check or otherwise usable in making payments” (IMF 1984, p. 29) - for two main reasons. First, in a majority of countries, the banking sector intermediates most of the funds in the economy. Second, the banking sector is regulated and statistical information for this sector is easier to obtain and higher in quality than

> 6 We also included questions on payment transactions (value and number) and on the distribution by size of bank loans and deposits.  However, most countries were unable to provide answers to these questions; hence it is not possible to conduct a systematic analysis of these data.

7
for potential interruptions, and provision of information on support services for GBV survivors. We received research ethics review approval from Health Media Labs IRB.

Cumulatively, the survey took about 1-1.5 hours (per person) to administer. We complemented face-toface data collection with a phone survey in April-May 2020 to correct the data on receipt of the 4P benefits: in our initial round of data collection the question about receipt of the 4P benefits was addressed to the respondent of the tracking questionnaire, which resulted in high number of missing values and inconsistencies. The phone survey was administered to target respondents: young women and men who resided in treatment and control barangays at the time of program rollout in 2008.

# 5. Theory of change and outcomes of interest

A key objective of the CCT programs is breaking the cycle of inter-generational transmission of poverty. Thus, we expect that adolescents, who have benefitted from the 4P program at the age of 12.5-14, will enjoy better socio-economic outcomes as adults. we explore this hypothesis by analyzing impacts on education, labor market outcomes and proxies of welfare. For education, we estimate impact on the likelihood to complete schooling, the number of years of education and probability of attending vocational training. Our labor market outcomes include indicator variables for labor force participation, employment, paid employment and underemployment. We collect a rich set of variables on asset ownership and housing conditions and construct three types of indices: an index based on principal component analysis, average z-scores index based on Katz, Kling and Liebman (2007), and an Inverse Covariance Weights (ICW) index obtained by weighting z-scores of variables with inverse of their covariance matrix (Anderson, 2008). The variables used in the construction of the housing conditions index include roof, walls, floor materials, access to piped water and electricity, number of bedrooms, presence of kitchen and type of toilet, type of cooking fuel used. We create asset index based on ownership of residential plot, non-agricultural buildings, farm machinery and implements, livestock and a long list of durables, such as radio, sewing machine, television set, microwave oven, etc.

We expect CCTs to affect empowerment through several channels. Empowerment is a complex construct, with a wide range of measures used to capture empowerment empirically, from socio-economic proxies to more direct measures, such as relative autonomy index (RAI) or self-efficacy index. In this study, we rely on a wide range of measures of empowerment used in previous literature. In the discussion of the theory of change below we link channels to specific measures of empowerment. However, we use these connections as an organizational device, rather than assert dominance of specific channels behind changes in empowerment. The measures we use represent the same latent construct and are 10
<!-- Start of picture text --> KHM THA BGR BGR - wom<br>2So 4 SHON coSs 4 WN<br>2<br>F 3 roy MNGoF . 522 3 ROU ; TM<br>fe}= B _ 5 var<br>Fa 2 Kee TOE ZAF CHL —_ Fa]2 2 “ TUR KGZ RUS<br>GHA 10N CHN KA on ~ Ri ontLa co ION GHACHN<br>4 ETH NPL IND B80 max~~ ane A A NPL IND a PAK BaD<br>0) 10 20 30 40 2 3 4 5 6<br>High-skilled employment (ILO skill levels 3 & 4, % of total), average 2000-18 Hiring and firing regulations, average score 2000-18<br><!-- End of picture text -->
Distributional Impact Analysis of Climate Variability in Rural Indonesia reduces per capita expenditure of the poor by 13 percent but poor people benefits from late onset two years ago approximately by 23 percent. Table 3 below summarizes the key results.

**Table 3. Impact of delayed monsoon onset on rural household’s consumption and farm profit, IFLS.**

|||**Dependent**<br>**variable**|**One year**|**ago**|**Two ye**|**ars ago**|
|---|---|---|---|---|---|---|
|**IFLS**|||Coef|S.E.|Coef|S.E.|
|**All**|All|Log PCE|-0.014|0.088|0.108|0.060*|
||Farm|Log PCE|-0.41|0.080|0.143|0.071*|
|||Profit pc (’00 rp)|3,815|7,121|4,524|5,45|
||Non-farm|Log PCE|0.098|0.065|0.05|0.049|
|**Rich**|All|Log PCE|-0.034|0.114|0.100|0.114|
||Farm|Log PCE<br>Profit pc (’00 rp)|-0.052<br>-48,943|0.180<br>32,211|0.200<br>10,971|0.166<br>13,001|
||Non-farm|Log PCE|-0.243|0.399|-0.018|0.081|
|**Poor**|All|Log PCE|-0.136|0.062**|0.229|0.066***|
||Farm|Log PCE<br>Profit pc (’00 rp)|-0.093<br>2,572|0.058<br>4,251|0.228<br>8,454|0.072***<br>5,715|
||Non-farm|Log PCE|-0.018|.130|0.140|0.110|
|**Susenas**|||||||
|**All**|All|Log PCE|0.001|0.007|-0.003|0.006|
||IFLS<br>equivalent|Log PCE|-0.082|0.038**|-0.011|0.032|

# **2. Results from SUSENAS**

**This section considers the estimated impacts of monsoon onset on consumption using repeated cross-sections from the SUSENAS core survey.** The SUSENAS core data has both advantages and disadvantages as a data source, compared with the IFLS. The main advantage is that annual data are available from 1993 through 2004, rather than only 1993, 1997, and 2000. In addition, since the SUSENAS covers the entire country, 49 weather stations covering 22 provinces can be matched per year.<sup>14</sup> The IFLS, in contrast, only covers 13 provinces and 36 matched weather stations. However, the SUSENAS has two important drawbacks. First, its consumption module in the SUSENAS core surveys covers relatively few goods and may not be sufficiently detailed to accurately detect variation in household consumption. Second, it does not re-interview the same households in successive years. As a result, the effect of rainfall on consumption is estimated with less precision, for a fixed number of households. In addition, it is not possible to separately estimate the effect on onset on different types of households using the SUSENAS, without introducing spurious correlation between household type and the error term.

> 14 Provinces were omitted from the analysis if they did not contain a rain station.

13/35
**Table 4: Regressions of height-for-age z-scores on interactions between gender and healthcare**

||Treatment c|ost|Travel time||Travel cost||Waiting tim|e|Medicine||
|---|---|---|---|---|---|---|---|---|---|---|
||OLS|FE|OLS|FE|OLS|FE|OLS|FE|OLS|FE|
|Sex (Male=1)|-0.051<br>(0.048)|-0.018<br>(0.044)|-0.115<br>(0.057)**|-0.09<br>(0.054)*|-0.029<br>(0.037)|-0.009<br>(0.034)|-0.035<br>(0.051)|-0.016<br>(0.047)|0.046<br>(0.059)|0.054<br>(0.059)|
|Non-self cluster means of healthc|are variables:||||||||||
|Cost of treating cold (real|||||||||||
|yuan)|0.013<br>(0.006)**|0.005<br>(0.005)|0.015<br>(0.005)***|0.007<br>(0.005)|0.015<br>(0.005)***|0.007<br>(0.005)|0.015<br>(0.005)***|0.007<br>(0.005)|0.015<br>(0.005)***|0.007<br>(0.005)|
|Travel time (minutes)|-0.017|0.003|-0.021|-0.001|-0.017|0.003|-0.017|0.003|-0.017|0.003|
||(0.004)***|(0.003)|(0.004)***|(0.004)|(0.004)***|(0.003)|(0.004)***|(0.003)|(0.004)***|(0.003)|
|Cost of travel (real yuan)|0.121|0.158|0.116|0.154|0.123|0.097|0.122|0.159|0.122|0.158|
||(0.107)|(0.122)|(0.104)|(0.116)|(0.119)|(0.134)|(0.107)|(0.122)|(0.107)|(0.122)|
|Waiting time (minutes)|0.007|0.002|0.007|0.002|0.007|0.002|0.007|0.002|0.007|0.002|
||(0.002)***|(0.002)|(0.002)***|(0.002)|(0.002)***|(0.002)|(0.002)***|(0.002)|(0.002)***|(0.002)|
|Medicine regularly available|-0.031|0.064|-0.032|0.063|-0.032|0.064|-0.032|0.064|0.021|0.102|
||(0.052)|(0.043)|(0.052)|(0.042)|(0.052)|(0.042)|(0.052)|(0.042)|(0.059)|(0.054)*|
|Interaction terms:|||||||||||
|Sex*Cost|0.004<br>(0.006)|0.003<br>(0.005)|||||||||
|Sex*Travel time|||0.008<br>(0.004)**|0.009<br>(0.003)**|||||||
|Sex*Travel cost|||||-0.002|0.129|||||
||||||(0.119)|(0.119)|||||
|Sex*Waiting time|||||||0<br>(0.002)|0.001<br>(0.002)|||
|Sex*Medicine|||||||||-0.098<br>(0.060)|-0.072<br>(0.060)|
|Observations|6199|6199|6199|6199|6199|6199|6199|6199|6199|6199|
|Robust standard errors,clustered|at the commu|nitylevel|,inparenthes|es;|||||||

Source. China Health and Nutrition Survey, 1991-2000. Note. Only select coefficients shown; other coefficients are the same as in the full pooled OLS and FE models (see Table 2). *significant at 10%; **significant at 5%; ***significant at 1%.

14
report statistics for test takers in Grades 6, 9, and 12, respectively. Columns 4 and 5 report statistics for the ENLACE survey sample and the twins sample, respectively, in Grade 6. A simple comparison of means across the first three columns in Table 1 indicates that students who graduate on time from lower- and upper-secondary were more likely to have had higher test scores at Grade 6, be female, and enrolled in a private school. Table A.1 in the Online Appendix reports differences in means between the sample of twins and non-twins. Although statistically significant at conventional levels, the differences are very small and support the idea that multiple (versus single) pregnancies are mostly a random event.

# **2.3 ENILEMS-ENLACE**

Every quarter, the Mexican statistics office, INEGI, collects labor market information through the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), a nationally- and staterepresentative rotating household survey.<sup>6</sup> In most quarters ENOE’s core survey is complemented by a thematic module usually capturing information sought by different secretariats and government agencies. During the third quarter of 2010 (July to September), ENOE’s special module was the _Encuesta Nacional de Inserción Laboral de los Egresados de Educación Media Superior_ (ENILEMS), a survey targeting upper secondary school graduates aged 18, 19, and 20. The objective of ENILEMS was to provide information on the transition between the end of mandatory schooling (Grade 12) and higher education or the labor market.<sup>7</sup> The ENILEMS-ENLACE panel merges information from the respondents of the ENILEMS 2010 survey with their results in the ENLACE Grade 12 taken in May of 2008, 2009, or 2010. Although ENILEMS 2010 did not capture the CURP, it included all the necessary information to create a pseudo-CURP formed by name, sex, age, and state where the student was born. The difference between the pseudo-CURP and the CURP is that the former does not have the last three digits of the student identification code that the Mexican government generates. We created the pseudo-CURP for 7,105 observations included

> 6For more information on ENOE, see http://www.beta.inegi.org.mx/proyectos/enchogares/regulares/enoe/.

> 7For more information on ENILEMS, see http://www.beta.inegi.org.mx/proyectos/enchogares/modulos/enilems/.

10
12 Scholar. Modifying the harvesting tool to aggregate across these variants would be very hard, because one needs to know which records are indeed variants of a single publication and which are simply similarly named publications.

Finally, we should note that our citation data are discipline-blind. We do not—and could not—impose the requirement that the citing author be an economist. Doing so would necessitate working manually through each document citing each of the 33,000 records in or database and establishing the discipline of the citing author. In any case, it is not clear whether such an exercise make sense; we are, after all, interested in the overall impact of each item in our database. We should also point out that we do not eliminate self-citations. Doing so would necessitate another manual check all documents citing each of our 33,000 publications. This would have the advantage of eliminating gratuitous self-citation. But insofar as an author is citing his or her previous work in taking the idea forward in a new publication, a self-citation is no different from a citation by anyone else. So even if it were feasible to eliminate self-citations in our citation data, it is not clear we would want to.

# **Topic classification**

Beyond influence measured through citations we are also interested in the topics covered by the more influential of our 33,000 publications, and how the topic mix has varied over time and varies across the more influential institutions.

The JEL system used by EconLit does have subcodes, but as the article by Rubin and Chang (2003) makes clear, it is not a very informative one. Alternative classifications exist that we could have used to apply a topic code to the EconLit records. The first was developed by Cooper and Culyer (1973). At that time, the subject was relatively undeveloped and the literature sparse. They used four categories: ―economic efficiency‖, ―improving decisions‖, ―hospitals‖, and ―the value of human lives‖, revealing a British set of interests (though the literature selected for inclusion was not merely British). Much later Williams (1987) developed a ―plumbing‖ diagram for categorizing and linking topics within health economics. This still betrayed a British focus (for example ̳medical insurance‘ found little space in it) and Culyer enlarged and modified it in a number of ways in his four volume collection of reprinted papers (Culyer, 2006). Jones‘ (2006) classification scheme for ―The Elgar Companion to Health
Figure 7: Employment and Wage Changes in Private Sector Companies: Alternative Firm Quality Measures (a) Employment Changes <!-- Start of picture text --> Above median TFP<br>Below median TFP<br>Foreign<br>Domestic<br>Above median firm-level wage<br>Below median firm-level wage<br>Above median AKM FE<br>Below median AKM FE<br>-.002 0 .002 .004 .006<br>Private sector employment rate<br>High quality Low quality<br><!-- End of picture text --> (b) Wage Changes <!-- Start of picture text --> Above median TFP<br>Below median TFP<br>Foreign<br>Domestic<br>Above median firm-level wage<br>Below median firm-level wage<br>Above median AKM FE<br>Below median AKM FE<br>-.5 0 .5 1 1.5<br>Pass-through rate<br>High quality Low quality<br><!-- End of picture text --> _Note_ : Panel (a) of figure shows difference-in-differences estimates of the impact of the payroll tax cut on private sector employment based on estimating equation (9). Panel (b) shows wage estimates (pass-through rate) separately for above-median (in red) and below-median (in blue) quality firms. The pass-through rate at below-median firms is _β_ 1, the pass-through rate at above-median firms is the sum of _β_ 1 and _β_ 3 in equation (13). The red vertical line corresponds to a pass-through rate of one—all the tax cut passed through to workers by raising their wages. The difference-in-differences estimate compares the change in outcome (employment or wages) among the 55 to 57 age group that was affected by the payroll tax cut with the change in employment and wages among the 52 to 54 age group that was not affected by the tax cut. In both panels, the first two rows replicate our baseline results with high- and low-productivity firms. The third and the fourth rows uses foreign ownership (more than 50%) as a quality measure. In Hungary, foreign-owned firms are the most productive firms offering the highest paying, highest quality jobs. The fifth and sixth rows measure quality based on average wages, while the last two rows measure quality using the firm-level wage premium estimated using an Abowd, Kramarz, Margolis (AKM) style decomposition. 95% confidence intervals are reported with standard errors clustered at the age _×_ period level.

50
through a third country. The lack of integrated transport networks in the region clearly raise cargo shipping costs. This is a critical problem particularly for landlocked countries, including for example Afghanistan, Bhutan, and Nepal. A significant factor driving costs and barriers is that at most every border in the region goods must be transshipped. In addition, labor strikes in the region can cause delays in transit and congestion in land transport networks. These constraints keep shippers from taking the routes that offer the most efficient shipping route in terms of time and cost.

Figure 4: Port Efficiency Indicators (maritime and air) <!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>Maritime<br>0.5<br>Air<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>South Asia East Asia Global Average<br><!-- End of picture text --> Source: Wilson, Mann, Otsuki (WMO) database 2004 Inland roads are a major means of moving goods across South Asia and India has “an extensive 3.3 million km road network making it one of the largest in the world” (RIS, 2004). A number of road corridors in the region, however, are not maintained and of limited capacity.<sup>16</sup> This makes it expensive to move commodities across long distances with countries imposing load limits. For example, in India the percentage of paved roads at 56 percent is lower that than in countries of East Asia which averages 88 percent (World Bank).

The costs of road transport can be high. For example, the average transport costs on the Kolkata-Petrapole route between Bangladesh and India is Rs2543 which is about 40 percent higher than other highways (Das and Pohit, 2004). Costs are also a function of vehicle maintenance. A carpet manufacture in Kathnabdu reported that because of the poor quality of the local roads, it must “repair one of its vehicles every week and spends NRs 100,000 a year on maintenance" (Biggs et al., 2000). Road transport is also affected by aging bridges and lack of capacity. This in turn limits truck and cargo weight and therefore efficiency in freight movement. Other restrictions are based in licensing restrictions, for example foreign trucks are not permitted to enter Bangladesh. All of these conditions prevent shippers from taking the most efficient routes – extending time and cost which impede opportunities for international and intra-regional trade.

> 16 For a more detailed discussion see Subramanian (2001).

12
available for international construction firms seemed to make these firms particularly eager to promote, invest in and develop their own projects. At the current time, while the th?-d world debt crisis continues to restrict sovereign bcrrowings, and while government budgets are still constrained, international construction firms are busy and may no longer be as interested in promoting BOT projects as they were earlier.

In spite of great interest on the part of host governments, potential sponsors, lenders and foreign aid agencies, and although numerous BOT projects have been proposed, relatively few large infrastructure projects have actually proceeded to financial close, let alone been fully implemented in developing countries.

° Extensive negotiations in Turkey, where the BOT label was invented, have apparently still not resulted in the implementation of a single major project. The negotiations for one cr more large coal fired power plants, which several times have been announced as virtually completed, as well as for a metro system and an airport expansion, are apparently continuing.

- 3 The governments of Malaysia, the Philippines, Pakistan, Thailand and Indonesia continue to show strong interest in the BOT approach.

- -3

ROT projects are highly complex from both a legal and financial point of view. They require potential sponsors to spenld years and millions of dollars on development and negotiation. They present novel "sues for many host governments as to the proper all.. tion of risks and rewards among the parties.

e Proponents believe that a BOT project, if properly structured and priced, can provide significant "additionality" in financial resources for developing countries while achieving overall cost savings from efficiencies in design, construction and operation.

- ° The World Bank Group has supported the development of BOT projects in a number of countries. In addition to sector studies, feasibility studies and advice for host governments generally,

- Go The World Bank has provided or has proposed loans to the governments of Pakistan and the Philippines which those governments will in

- vi -