rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: Government Officials - Diff-in-Diff regressions

||(1)|(2)|(3)|(4)<br>|(5)|(6)|
|---|---|---|---|---|---|---|
||Master Plan<br>Up to date|Internal Audit<br>independent|Capacity to raise<br>local revenue|Staf capacity<br>Index|Management<br>Index|Performance Culture<br>Index|
|||**P**|**anel A: Simple di**|**ferences-in-dif**|**erences**||
|ULGSP * Round 2 (DiD)|0.056|-0.260|-0.522|0.100|-0.144|-0.024|
||(0.166)|(0.393)|(0.311)|(0.169)|(0.225)|(0.270)|
||[-0.280, 0.390]|[-1.066, 0.563]|[-1.164, 0.0863]|[-0.248, 0.449]|[-0.584, 0.313]|[-0.587, 0.507]|
|ULGSP Indicator|0.190*|0.302|0.547*|-0.149|0.150|0.039|
||(0.104)|(0.291)|(0.284)|(0.138)|(0.146)|(0.175)|
||[-0.0231, 0.403]|[-0.296, 0.903]|[-0.0578, 1.157]|[-0.444, 0.145]|[-0.139, 0.449]|[-0.305, 0.376]|
|Survey Round 2|0.167*|0.730***|0.576**|0.121|0.199|0.436**|
||(0.095)|(0.232)|(0.218)|(0.112)|(0.184)|(0.179)|
||[-0.0315, 0.364]|[0.247, 1.220]|[0.127, 1.065]|[-0.129, 0.346]|[-0.184, 0.579]|[0.0753, 0.811]|
|Observations|240|917|940|709|874|921|
|R-Squared|0.085|0.060|0.058|0.010|0.007|0.045|
|Mean Dep Var|0.454|3.848|4.178|-0.000|-0.000|0.000|
|Number clusters (LGA)|40|40|40|40|40|40|
|||**Panel B**|**: Diferences-in-d**|**iferences inclu**|**ding controls**||
|ULGSP * Round 2 (DiD)|0.065|-0.249|-0.521|0.085|-0.146|-0.015|
||(0.175)|(0.385)|(0.311)|(0.163)|(0.224)|(0.266)|
||[-0.297, 0.431]|[-1.023, 0.528]|[-1.154, 0.122]|[-0.263, 0.434]|[-0.610, 0.320]|[-0.561, 0.505]|
|ULGSP Indicator|0.154|0.287|0.559*|-0.148|0.133|0.020|
||(0.113)|(0.281)|(0.283)|(0.134)|(0.143)|(0.172)|
||[-0.0865, 0.393]|[-0.337, 0.874]|[-0.0272, 1.161]|[-0.423, 0.135]|[-0.152, 0.417]|[-0.339, 0.389]|
|Survey Round 2|0.166|0.715***|0.556**|0.155|0.207|0.418**|
||(0.106)|(0.233)|(0.220)|(0.104)|(0.190)|(0.178)|
||[-0.0596, 0.396]|[0.226, 1.194]|[0.0745, 1.039]|[-0.0602, 0.365]|[-0.195, 0.593]|[0.0327, 0.786]|
|Observations|240|917|940|709|874|921|
|R-Squared|0.131|0.081|0.068|0.237|0.022|0.063|
|Mean Dep Var|0.454|3.848|4.178|-0.000|-0.000|0.000|
|Number clusters (LGA)|40|40|40|40|40|40|

_Note:_ This table reports regressions using each of the described indices as dependent variable. Non-reported comparators in the second panel include welfare index, household size, age, gender, marital status and education levels. Standard errors clustered at the LGA level are reported in parentheses (* p<0.1, ** p<0.05, *** p <0.01), while 95% confidence-intervals constructed using wild-bootstrapping are reported in brackets.

# **8 Appendix**

# **A Selection of the Comparison Group**

Because of the targeted nature of the program (i.e., the program was designed to focus on the 18 LGAs that were among those with faster rates of urbanization at the time the program started), finding a group of comparable set of LGAs was expected to be a difficult task. Additionally, administrative data prior to the program’s inception was incomplete for many potential comparison LGAs; some potential comparison LGAs were newly formed and so no historical administrative data were available at all. Against this backdrop, the research team developed a protocol to select 52
can help facilitate more accurate measurement of business performance in emerging market economies. Given that administrative data are rarely available and expensive to collect, the AAA tool offers a more cost-effective method for obtaining reliable business metrics for small firms. As such, the tool will be valuable for researchers, policy makers, and businesses alike.

The remainder of the paper is organized as follows. Section 2 discusses the concept and design of the new survey tool. Section 3 describes the three field settings, the evaluation protocols, and presents the results. Section 4 concludes.

# **2. CONCEPTUAL FRAMEWORK AND METHODOLOGY**

To motivate the concept and design of the new survey methodology, this section first categorizes four main sources of error in survey-based business data. Subsequently, the discussion focuses on how the AAA survey tool reduces these errors through dynamic correction.

# **2.1. Most Common Survey Errors**

The first and perhaps most important source of noise is _respondent factors_ . Since formal records on business sales and profits are not usually kept by small firms in emerging market settings, survey respondents are typically asked to recall their firm sales for the previous month, quarter or year. De Mel, McKenzie, and Woodruff (2009) find that recall error is important, though they suggest the greatest loss in precision is from one month recall estimates to two months. In addition, resources between the business and household are fungible, making accurate measurement of business costs and income difficult to determine, resulting in more noise.

In addition, understanding the survey questions and reporting on sales and profits (particularly in the way these are asked in a survey) require a certain level of financial and numeric literacy that a typical entrepreneur may struggle with, which could lead to inaccurate responses. Furthermore, as de Mel, McKenzie, and Woodruff (2009) suggest, each survey carries not only a financial and time cost for the researcher and surveyor, but also an opportunity cost for the entrepreneur as time allocated to completing a survey is time away from running the business. This can lead to either interviews being interrupted, or respondent fatigue in which case the entrepreneur may provide rapid (and likely inaccurate) responses to complete the survey as quickly as possible.

7
learning that occur below the minimum proficiency threshold—for example, changes in acquisition of foundational subskills such as mapping sounds to letters, recognizing words, etc., which are critical for developing the foundational reading skills needed to meet the minimum proficiency threshold.

To measure these aspects, the **Learning Poverty Gap** brings together the concepts of Learning Deprivation Gap, which indicates the average effort needed to bring children in school above minimum proficiency, and Schooling Deprivation Gap, which highlights the need to improve access to schooling among those who are out of school. This gap measure provides useful policy-relevant detail that is not captured by the learning poverty measure. For example, as shown in Figure 2 below, there are several countries (such as Morocco and Myanmar) that, under an intermediate scenario, are expected to have similar increases in Learning Poverty but very different expected increases in the Learning Poverty Gap. This finding implies that different levels of effort, resources, and policy focus on children at the bottom or out of school may be required to tackle Learning Poverty.

**Figure 2. Relative Changes in Learning Poverty and Learning Poverty Gap**

<!-- Start of picture text --> 400%<br>350%<br>300%<br>250%<br>HIC<br>200%<br>UMC<br>150% LMC<br>LIC<br>100%<br>MMR<br>50%<br>MAR<br>0%<br>0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%<br>Change in Learning Poverty (%)<br>Change in Learning Poverty Gap (%)<br><!-- End of picture text --> _Note: Figure shows relative percent change from the baseline under the intermediate scenario._ Based on the results of the simulation, under the intermediate scenario, the Learning Poverty Gap will rise by 2.1 percentage points from 9.5% to 11.6% for low- and middle-income countries. The increase in Learning Poverty Gap is particularly high in in Europe and Central Asia and East Asia and the Pacific (Table D2). This means that moving forward, these countries will need to focus on children at the bottom, including those out of school (who are assumed not be learning) to ensure that they are not left behind.

While the Learning Poverty Gap measure shows how far students are behind the minimum proficiency level on average, it is not distribution-sensitive, and cannot distinguish between changes in the learning gap driven 11
<!-- Start of picture text --> Share of irths that were not attendedby skilledheatthstaff (4)<br>100,0%<br>20,0%<br>60,0% 52% 52%<br>40,0% 69% 35% 4%9 40% 39%<br>18% 16% 9 oe "6<br>20,0%Ms 10% 8% 5%<br>0.0%<br>Urban Rural Urban, LowerEgypt | UpperEgypt | Frontier TOTAL<br>Govemorates Govemorates<br>Urban Rural Location Region<br>5000 m>008<br><!-- End of picture text -->

<!-- Start of picture text --> 100,0%<br>56 of the births in<br>20,0% quintile using facility<br>60.0% and attendant<br>40,0% Na<br>20.0%<br>0.0%<br>Public Health Private Health) Home Public Health|Private He alt Home<br>Facility Facility Facility Facility<br>Quintile 1 Quintiles<br>@doctor mMnurse/midwife mtraditional birth attendant mnoone mother<br><!-- End of picture text -->
**Table 1: Summary of IPRs Provisions in Legally Enforceable IP-Related PTAs**

|**Panel A: all IPR provisions as of 2015**|**US L**<br>|**E IPAs**<br>|**(13)**<br>|**EU/EFT**<br> <br>|**A LE I**<br>|**PAs (45)**<br>|**Other**<br>|**LE IPA**<br>|**s (42)**<br>|
|---|---|---|---|---|---|---|---|---|---|
||Avg|Min|Max|<br>Avg|Min|Max|Avg|Min|Max|
|Accession/Ratification (n = 15)|11.5|2|14|3.3|0|13|2.0|0|13|
|National Treatment (n = 2)|2.0|2|2|0.9|0|2|0.6|0|2|
|Trademarks (n = 15)|9.4|4|15|1.6|0|7|1.5|0|11|
|Geographical Indications (n = 7)|2.6|0|4|2.0|0|7|0.7|0|3|
|Patents (n = 14)|4.8|1|13|1.0|0|3|0.7|0|10|
|Data Protection (n = 5)|2.8|0|5|0.9|0|2|0.1|0|5|
|Copyrights (n = 14)|10.5|4|14|2.0|0|12|1.9|0|12|
|Enforcement (n = 23)|17.2|4|20|7.6|0|17|4.8|0|17|
|**Pl B BTRIPS ii f 2015**|**US L**|**E IPAs**|**(13)**|**EU/EFT**|**A LE I**|**PAs (45)**|**Other**|**LE IPA**|**s (42)**|
|**ane : provsons as o**|Avg|Min|Max|Avg|Min|Max|Avg|Min|Max|
|Trademarks (n = 4)|2.3|2|4|0.2|0|2|0.4|0|4|
|Geographical Indications (n = 3)|0.9|0|1|0.8|0|3|0.3|0|2|
|Patents (n = 5)|1.2|0|4|0.4|0|1|0.2|0|3|
|Data Protection (n = 5)|2.8|0|5|0.9|0|2|0.1|0|5|
|Copyrights (n = 6)|5.6|1|6|1.0|0|6|0.8|0|6|
|Enforcement (n=10)|7.1|1|9|3.1|0|7|2.0|0|7|

10
even in cases where data constraints are so severe that only very few control variables are available. Our paper thus aims at providing a systematic and comprehensive treatment of surveyto-survey poverty imputation methods that appear to be implemented on a somewhat _ad hoc_ basis in most of the existing economics literature.

Third, we also show that, given some standard assumptions, our framework can be generally applied to imputation either from one survey to another survey with the same design, or to another survey of a different design. The former is relevant to situations where consumption data in a more recent survey round are not consistent with those in an earlier round (say, owing to measurement errors or poorly constructed consumption aggregates), or where no reliable consumer price index (CPI) data exist to update the poverty line over time. On the other hand, imputation from one survey to another of a different design is pertinent to situations where one survey is implemented less frequently but collects consumption data (e.g., household expenditure or budget surveys), while the other survey is conducted more frequently but does not collect consumption data (e.g., labor force surveys). Using surveys of different designs can remarkably expand the application range of imputation methods, but the inevitable tradeoff is that the sample statistics estimated from surveys of different designs would likely be different due to various reasons, which would in turn render imputation-based estimates incomparable. We propose rather straightforward standardization procedures to harmonize the different surveys and show that employing these procedures can produce estimates that are statistically indistinguishable from the actual poverty rates, in sharp contrast to the severely biased estimates obtained from non-standardized data.

Finally, in constructing our framework, we offer a critical review of the economics literature and of the related studies on data imputation in statistics. Our paper thus also represents an early 5
# **1. Introduction**

We use a unique data set for Peru that captures the elimination of subnational regulatory barriers to firm entry and competition across 1,800 municipalities. These were caused by legal reforms in 2013 and 2014 that strengthened the mandate of Peru’s competition authority over the implementation of business regulation by subnational government bodies. We match these data with establishment census panel data from 2008-15 to estimate the impact on changes in establishments’ productivity and markups. We find that the reforms have raised firm productivity. To the best of our knowledge, it is the first time that a positive impact of strengthening a country’s institutions to enforce the elimination of barriers to market entry and competition on firm productivity in that country has been established.

According to conventional wisdom, product market competition improves the allocative efficiency among firms and makes market structures more conducive to innovation and growth. Specifically, declining market entry costs are expected to level the playing field and encourage firms to increase their cost effectiveness to outperform their competitors.

But the empirical evidence linking competition to productivity growth has been relatively scant, except for reforms related to international trade liberalization, due to the lack of adequate data. Cross-country data do not seem adequate given the difficulty to identify exogenous regulatory changes across countries with unobservable institutional characteristics and given the limited low-frequency sample sizes relative to the seemingly open-ended list of growth correlates. Microeconomic data, on the other hand, often lack sufficient disaggregated variation needed to identify an appropriate control group (of firms) not affected by regulatory changes.

We argue that recent reforms expanding the legal mandate of Peru’s competition authority to enforce the elimination of subnational regulatory barriers to market entry and competition provide a unique quasi-experimental setting to identify the impact of strengthening the enforcement of competition, that is the elimination of regulatory barriers to market entry, within the controlled institutional environment of a single country. Specifically, Peru provides a unique setting for at least two reasons.

First, Peru has one of the most liberal international trade regimes in the world and has a best-practice national legal framework for competition. But its large number of municipalities have far-reaching authority to regulate critical aspects of local market access—each municipality 2
traditional foreign exchange market transactions are conducted in just 6 centers: UK, US, Switzerland, Japan, Singapore and Hong Kong), proponents of the Tobin tax as a revenue source have seen it chiefly as being international in its revenue goals, and not suitable as a source of national revenue (Spahn, 2002). Of course, another problem with getting national revenue from the tax is the fact that unilateral tax increases on foreign exchange dealings are likely to result in considerable base migration.

Despite earlier proposals for a CTT tax of as high as 1 percent, a consensus had emerged in the literature by the mid-1990s that 0.1 percent should be regarded as a ceiling on CTT rates beyond which they would reduce liquidity too much, thereby deterring international trade (Nissanke, 2004).<sup>8</sup> Nissanke examines the revenue potential of rates in the region 0.01% to 0.02%, which she believes would reduce transaction volumes only modestly and generate worldwide annual revenue in the range USD 17-30 billion (on the basis of 2001 transactions).<sup>9</sup> Interestingly, Mende and Menkhoff (2003) claim that sorting the Tobin tax proposals by their date of issue reveals that the suggested rates have become lower and lower over time. Spratt’s (2006) version of this tax has a rather comprehensive base said to be over €100 trillion covering all spot and derivative foreign exchange transactions, but he proposes a very low tax rate of just 0.005% designed to raise about €5 billion for development assistance. At this rate the tax should evidently have no very little effect on speculative flows; it does not have a corrective objective.

# (ii) Revenue from STT

Securities transactions taxes (STT) are now as likely to be advocated for their revenue potential as for any dampening effect on speculation. That of Schulmeister et al. (2008) is quite comprehensive for wholesale transactions, applying to spot transactions for stocks and bonds, and derivative transactions (both exchange-traded and over-the-counter -- OTC). On the other hand, they consider low tax rates, ranging from 0.01% to 0.1% of the transaction value. This results in projected revenue yields of up to about 1% of GDP

> 8 This reflects the fact that spread in the wholesale interbank foreign exchange market are well below 0.1 percent.

> 9 Spahn (2002) proposed a rate of 0.01% for a projected annual revenue of €17 billion (based on 2001 data).

10
62

# **Table D2**

_Content – Descriptive Statistics_

||All E|valuated Progra<br>|ms||At-scale Progra<br>|ms|
|---|---|---|---|---|---|---|
|||tanar|||tanar||
|Content|Mean|<br>Deviation|Obs|Mean|<br>Deviation|Obs|
|Focus is subject content|0.636|0.489|33|0.273|0.447|139|
|Focus is pedagogy|0.576|0.502|33|0.374|0.486|139|
|Focus is technology|0.212|0.415|33|0.137|0.345|139|
|Focus is counseling|0.091|0.292|33|0.036|0.187|139|
|Focus is classroom management|0.121|0.331|33|0.079|0.271|139|
|Focus is a specific tool|0.091|0.292|33|0.065|0.247|139|
|No subject focus|0.061|0.242|33|0.083|0.279|48|
|Subject focus is literacy/language|0.515|0.508|33|0.521|0.505|48|
|Subject focus is math|0.152|0.364|33|0.542|0.504|48|
|Subject focus is science|0.091|0.292|33|0.292|0.459|48|
|Subject focus is information technology|0.030|0.174|33|0.229|0.425|48|
|Subject focus is language & math|0.061|0.242|33|0.000|0.000|48|
|Subject focus is other|0.030|0.174|33|0.229|0.425|48|
|Training involves lectures|0.950|0.224|20|0.604|0.491|139|
|Training involves discussion|0.750|0.444|20|0.842|0.366|139|
|Training involves lesson enactment|0.600|0.503|20|0.727|0.447|139|
|Training involves materials development|0.200|0.410|20|0.729|0.449|48|
|Training involves how to conduct diagnostics|0.238|0.436|21|0.354|0.483|48|
|Training involves lesson planning|0.480|0.510|25|0.625|0.489|48|
|Traininginvolves use of scripted lessons|0.333|0.482|24|0.438|0.501|48|

Obs refers to the number of PD programs in each sample (top performing, evaluated programs, and at-scale programs) that report whether or not they have a given characteristic.
# **_References_**

- Ahlin, Christian, Jocelyn Lin, and Michael Maio, 2011. “Where does Microfinance Flourish? Microfinance Institution Performance in Macroeconomic Context.” _Journal of Development Economics_ 95(2): 105-120.

- Baquero, Guillermo, Malika Hamadi, and Andréas Heinen, 2012. “Competition, Loan Rates and Information Dispersion in Microcredit Markets.” European School of Management and Technology Working Paper No. 12-02. Berlin.

- Center for the Study of Financial Innovation, 2012. “Staying Relevant: Microfinance Banana Skins 2012,” The CSFI Survey of Microfinance.

- Christen, Robert Peck, Timothy Lyman, and Richard Rosenberg, 2003. Guiding Principles for Regulation and Supervision of Microfinance, Washington, DC: Consultative Group to Assist the Poor.

- Ciccone, Antonio, and Elias Papaioannou, 2007. "Red Tape And Delayed Entry." _Journal of the European Economic Association_ 5(2-3): 444-458.

- Cull, Robert, Asli Demirgüç-Kunt, and Jonathan Morduch, Forthcoming. “Banks and Microbanks.” _Journal of Financial Services Research_ .

- Cull, Robert, Asli Demirgüç-Kunt, and Jonathan Morduch, 2011. “Does Regulatory Supervision Curtail Microfinance Profitability and Outreach?”  (joint with Asli Demirgüç-Kunt and Jonathan Morduch), _World Development_ , 39(6): 949-965.

- Cull, Robert, Asli Demirgüç-Kunt, and Jonathan Morduch, 2009. “Microfinance Meets the Market.” _Journal of Economic Perspectives_ , 23(1): 167-192.

- Cull, Robert, Asli Demirgüç-Kunt, and Jonathan Morduch, 2007. “Financial Performance and Outreach: A Global Analysis of Leading Microbanks.” _Economic Journal_ , 117(517): F107-F133.

- Djankov, Simeon, 2009. "The Regulation Of Entry: A Survey." _World Bank Research Observer_ , 24(2): 183-203.

- Djankov, Simeon, Rafael La Porta, Florencio Lopez-De-Silanes, and Andrei Shleifer, 2002. "The Regulation Of Entry." _Quarterly Journal of Economics_ , 117(1): 1-37.

- Djankov, Simeon, Caralee McLiesh, and Rita Ramalho, 2006. “Regulation and Growth.” _Economics Letters_ , 92(3): 395–401.

- Doing Business, 2012. "Doing Business 2013 - Smarter Regulations for Small and Medium-Size Enterprises " _Doing Business - Measuring Business Regulations - World Bank Group._

16
While more data are being collected on differences<sup>in bankruptcyregimesacross</sup> countries,' to date little is known on the effects of<sup>specificcreditor rightfeatures and their</sup> interaction with the judicial system and other country characteristics.<sup>Thecross-country</sup> empirical evidence has largely been limited to the<sup>generaleffects of creditorrights.Even</sup> here the evidence has been mixed, with some finding<sup>only limitedornosignificanceof</sup> the aggregate strength of creditor rights on financial<sup>development.</sup> Furthermore, the precise channels through which a country's institutional<sup>inheritanceaffects its financial</sup> development and what aspects of legal systems are<sup>mostimportantfor firm financing are</sup> still being investigated. Much research is being conducted,<sup>for example,onwhat aspects</sup> of a Common (Civil) Law heritage help explain<sup>thatsuchcountrieshavemore (less)</sup> developed equity and financial markets. More robust tests and indirect measures are being used to explore the channels through which<sup>countries'legalandinstitutional</sup> "structure" matter.<sup>2</sup> One indirect measure that may help shed light<sup>onthesechannelsmightbethe</sup> actual use of bankruptcy as a means to resolve<sup>financialdistress andtherelationship</sup> between actual use and a country's institutional<sup>features,includingitscreditorrights,</sup> legal heritage and judicial system. To date, however, it is not known how often bankruptcy is actually being used in countries<sup>around the world.Neither is it known why</sup> its usage varies by country characteristics like differences<sup>inlegalsystems,accounting</sup> l The World Bank, Asian Development Bank, and<sup>Inter-AmericanDevelopmentBank</sup> have started to document the detailed features of<sup>bankruptcysystemsin manycountries.</sup> The World Bank has also undertaken a review of<sup>desirableprinciplesand guidelinesfor</sup> bankruptcy systems.

2 See Acemoglu, Johnson and Robinson, 2001, Beck,<sup>Levineand Loayza,2000,Berglof</sup> and Von Thadden, 1999, Coffee, 2000, La Porta<sup>etal.,2002,Rajan andZingales,1999</sup> and 2002, and Stulz and Williamson, 2001.

4