rafmacalaba/datause-encoder-data huggingface dataset:
Panel B of Table 1 shows that the average time to clear customs for imports and its standard deviation decline from 2008 onwards. Interestingly, most of the reduction in average clearance times occurs after 2009, a time period that coincides with the implementation of ASYCUDAWorld, the most advanced of the IT systems that Albania used in our time frame. Summary statistics for the subset of declarations allocated to the red channel indicate that declarations in the red channel always spend more time, on average, in customs, and also have larger standard deviations in their time in customs than is the case for the declarations that are not allocated to the red channel and the differences are statistically significant according to unreported t-tests. Overall, the descriptive evidence so far suggests that the comprehensive customs reforms discussed in Section 2 produced reductions in the share of shipments that are physically inspected, and in the time that a typical shipment spends under the control of customs officials.

**Table 1. Time to clear customs in Albania**

Panel A. Distribution of time for declarations taking more than one day

||2007|2008|2009|2010|2011|2012|
|---|---|---|---|---|---|---|
|Share of import declaratio|ns that clear cus|toms in:|||||
|1 day|87.2%|87.5%|88.9%|91.2%|91.2%|93.2%|
|2 days|8.3%|7.3%|7.9%|5.6%|5.6%|4.4%|
|3 days|1.8%|2.1%|1.7%|1.4%|1.4%|1.0%|
|4 days|0.9%|0.9%|0.6%|0.5%|0.5%|0.4%|
|5‐9 days|1.5%|1.6%|0.7%|0.9%|0.9%|0.8%|
|10‐14 days|0.3%|0.4%|0.1%|0.2%|0.2%|0.1%|
|15‐18 days|0.1%|0.1%|0.1%|0.0%|0.0%|0.0%|
|19 or more days|0.1%|0.2%|0.1%|0.1%|0.1%|0.1%|
|Total number of import|||||||
|declarations|180,984|182,857|187,119|194,370|219,028|227,267|

Panel B. Mean and standard deviation of time in customs

||2007|2008|2009|2010|2011|2012|
|---|---|---|---|---|---|---|
||**_Declaratio_**|**_ns not allocated to_**|**_the red channel f_**|**_or physical inspection_**|||
|Mean number of days|1.22|1.23|1.20|1.16|1.15|1.12|
|Standard deviation of|||||||
|number of days|0.89|0.94|0.79|0.68|0.77|0.72|
||**_Declarati_**|**_ons allocated to t_**|**_he red channel for_**|**_physical inspection_**|||
|Mean number of days|1.33|1.35|1.33|1.30|1.29|1.22|
|Standard deviation of|||||||
|number of days|1.22|1.37|1.13|1.05|1.12|0.93|

Source: Authors’ calculations based on Albanian import transaction level data.

Note: the statistics shown in Panel B are based on all declarations that are not considered to be outliers, i.e., those spending less than 19 days in customs.

15
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective"

# 1. **Introduction**

This paper presents a global perspective on infrastructure coverage and the poor that many people will think they have seen before but in fact have not.<sup>4</sup> It is widely assumed that the poor in developing countries have fewer infrastructure services than middle and upper-income households, but there is surprisingly little information on the actual empirical relationship between household income and infrastructure service coverage in different countries. The available coverage statistics are typically country-wide averages. These are widely used to assess the scope and magnitude of infrastructure problems in developing countries, and they are often the only global, cross-country data available about infrastructure services. When such coverage statistics reveal that many households do not have service (i.e., are "not covered"), it is generally assumed that such households are poor. Global coverage statistics are often compiled by international organizations such as the World Hlealth Organization and the World Bank, and have profoundly shaped the way many people conceptualize infrastructure policy problems.<sup>5</sup> Despite their widespread use and influence, there are in fact numerous problems with tble country-wide infrastructure coverage statistics currently available. The data on household coverage typically come from general-purpose household surveys (such as censuses) that include a few questions designed to determine whether a household has various infrastructure services. For example, a member of a household may be asked whether the house has an in-house piped water connection or electricity. The global statistics from such surveys are usually self-reported by countries and are of varying quality. In many cases the wording of questions in the different surveys

> 4 By "coverage we simply mean whether or not a household has an infrastructure service such as electricity or piped water supply; if a household does have a particular service, it is said to be "covered."

> 5 Coverage data can aid in the description of an existing infrastructure situation, but they cannot be used to determine why such a situation exists, even if one were able to go back to the original data sets. This is because most surveys on which the coverage summaries are based do not ask respondents what services they could have chosen (but did not) and the attributes of such service options (e.g., price, quality, reliability). What we see in the coverage statistics is the _outcome_ of both supply and demand factors that bear on a household's infrastructure choices, but policy analysts cannot generally disentangle such factors from the coverage statistics.

I
As a second instrument, we note that Mexico has for a long time played a significant role in the flow of illegal drugs to the United States. In the past two decades, Mexico’s role in drug trafficking has increased, especially following the increased intervention efforts along the Florida coast in the 1980s (Smith 1997). Arguably, involvement in drug trafficking is driven by geography rather than institutions. For example, the states along the Pacific coast of Mexico are all heavily involved in the drug trade. We expect that drug trafficking would undermine the quality of government and the level of generalized trust in those states in which trafficking is prevalent. Astorga (undated) lists the states with the largest cocaine seizures, marijuana production, poppy production as well as the states involved in heroin trafficking. We create a variable which takes a value of one if a state is one of the 16 states which shows up on Astorga’s list, and zero otherwise. This variable is our second instrument for judicial quality.<sup>21</sup> The two instruments explain 32 percent, 20 percent and 15 percent of the crossstate variation in judicial effectiveness, costs of regulation, and generalized trust, but only 6 percent and 11 percent of the cross-state variation in the financial development residual and foreign ownership variable.

Panel B of Table 7 presents the instrumental variables regressions, instrumenting for judicial effectiveness, regulatory costs, generalized trust, financial development, foreign ownership, and market size. In each of the regressions, we also instrument for the size of the market, using the log of the population in the state. The results are consistent with those reported in Panel A. Market size has a somewhat smaller but still substantial impact on firm size. Judicial effectiveness also has a slightly smaller measured effect,

> 21 The states are Baja California, Baja California Sur, Chiapas, Chihuahua, Colima, the Federal District, Durango, Guerrero, Jalisco, Michoacán, Nayarit, Oaxaca, Quintana Roo, Sonora, Tamaulipas, and Vera Cruz.

32
9

||Rice|Sugar|Milk|Beef|Cotton|
|---|---|---|---|---|---|
|WRIglobal average|**140.9**|**86.7**|**72.8**|**68.1**|**44.7**|
|Ukraine||0.3|0.4|0.9||
|US|4.5|8.5|7.2|0.2|43.8|
|Vietnam|12.5|2.0||||
|Zambia|0.0||||0.3|
|Zimbabwe|||||2.6|
|Sum|100.0|100.0|100.0|100.0|100.0|

Source: Anderson and Valenzuela (2008), based on CTE estimates reported in national studies covering 75 focus countries.

Note: the decomposition over the 5-year period can be greater than or less than 100, even though the decomposition sums to 100 in any one year. We have scaled the 5-year averages, so that the decompositions sum to 100.
Paravasini, 2011; Behr and Sonnekalb, 2012). These studies typically examine the implementation or expansion of credit information systems that showed some variation in terms of which lenders used the credit information or which firms were covered in the information sharing system. This generates a counterfactual or comparison group for the treatment group of lenders or firms with credit information sharing. The results are causal under the assumption that the counterfactual is valid (i.e., the treatment and comparison groups are indeed comparable in terms of their observable and unobservable characteristics). While the causal impact is well identified in these studies, because the focus is on very specific experiments, the external validity of these results is more questionable.

Our paper contributes to the existing literature on the impact of credit information sharing schemes in a number of substantial ways. Our analysis uses the most comprehensive coverage of firms and countries to date. We also take a wide-ranging approach to measuring firm financing instead of relying on self-assessed perceived financial constraint ratings considered by previous firm-level analyses. In particular, we use five varied objective measures of use and cost of financing. We also have the most complete data to date on credit information schemes, including their introduction, coverage, and detailed information on changes in scope and quality, some of which consists of internal raw data not made publicly available in the annual Doing Business reports. Most importantly, our estimation strategy is able to identify the causal impact of credit information on firm financing. Following a difference-in-difference type of approach, we compare access to bank finance pre and post the reform of credit information sharing system in 27 countries (i.e., the reform or treatment sample) against firms in all countries that did not implement these reforms during our sample frame (36 countries). This difference-in-difference approach controlling for fixed country and time effects allows us to isolate the causal impact 6
SWP. All villages were in counties covered under the government’s poor-area program, to assure that we will identify the impact of the SWP, on top of the government’s program. There are 112 SWP villages and 86 non-SWP villages.<sup>10</sup> The SWP villages were a random sample from all project villages, while the non-SWP villages were a random sample from all other villages in the designated poor counties. Ten randomly sampled households were interviewed in each village.

The 1996-2000 and 2004/05 surveys included community, household and individual questionnaires. The community schedule collected data on natural conditions, infrastructure and access to services. The household survey collected data on ( _inter alia_ ) incomes, consumptions and assets. The individual questionnaires covered gender, age, education and occupation. A data set was collected from 1997 to 2001 on development project activities (both SWP and under other existing government programs). There are 34 project activities identified in these data, in seven categories (farming, animal husbandry, forestry, infrastructure, education, health and labor migration).<sup>11</sup> We follow Ravallion and Chen (2005) in using 1996 as the baseline. There are serious comparability problems between the 1995 survey and later surveys.<sup>12</sup> As a baseline, the 1996 data are not free of contamination; 17% of the program’s total disbursement on household projects had been made by the end of 1996. We check robustness to using 1995 as the baseline.

Relative to other household surveys, unusual effort went into obtaining accurate estimates of consumption and income from the 1996-2000 and 2004/05 surveys. While the community, individual and project activity surveys used conventional one-time interviews, the household survey was quite different. The household surveys from 1996 onwards were closely modeled on NBS’s Rural Household Survey (RHS) (which is

> 10 In the 2004/05 survey, two villages (one SWP and one not) were inadvertently replaced by two different villages in the same township.

> 11 A project activity survey in 1998 also gathered information about the scale and the starting year of each SWP sub-project at village and household level, as well as data on other funding these villages and households received from the government and other sources.

> 12 Because of delays in NBS being told the locations of SWP villages, the first survey in December 1995 had to use a one-time interview method, asking recall over the full year. The use of this long recall period is likely to lead to underestimation of income and consumption (though this is of less concern for village-level characteristics). The subsequent surveys used the dailydiary method over the full year, allowing more accurate income and consumption data.

9
26

||they are<br>located.|data of EU<br>citizens.|from personal<br>information<br>protection<br>violations occurring<br>within itsjurisdiction.”|information from<br>APEC economies.|<br>personal data,<br>without regard<br>to location of<br>data.|
|---|---|---|---|---|---|
|**Enforcement**|National<br>adequacy<br>finding – data<br>destination<br>country<br>enforces.<br>BCR & SCC –<br>data source EU<br>country<br>enforces<br>against local<br>entity.|US (data<br>destination)<br>country enforces;<br>i.e. EU recognizes<br>US enforcement<br>procedures.|Unspecified. Depends<br>on national privacy<br>law.|Data source<br>country by APEC<br>Accountability<br>Agent<sup>ii</sup>and<br>Privacy<br>Enforcement<br>Authority (PEA)<sup>iii</sup>,<br>with cross‐<br>border<br>enforcement<br>cooperation<br>facilitated by<br>APEC Cross<br>Border Privacy<br>Arrangement.<sup>iv</sup>|Data source<br>country<br>enforcement<br>against data<br>controller.|

i An entity is CBPR compliant when its self‐assessment of compliance with its own data privacy policies against the APEC Privacy Framework has been reviewed by an APEC‐recognized Accountability Agent.

ii An APEC Accountability Agent has met the APEC recognition criteria to the satisfaction of APEC economies. iii A Privacy Enforcement Authority is any public body responsible for enforcing privacy law that can conduct investigations or pursue enforcement proceeding.

iv Endorsed by APEC Ministers in 2009, CPEA is a voluntary framework that aims to facilitate cooperation amongst PEAs in enforcing CBPR, such as parallel or joint investigations or enforcement actions. Information sharing and cooperation is also encouraged with privacy enforcement authorities outside of APEC.

This range of approaches to developing privacy principles, their scope and enforcement mean that a spectrum of options exist to access the economic opportunities from cross‐border data flows while securing strong privacy protection. Action can range from unilateral action by source countries to mutually binding agreements between the source and destination countries. The location on this spectrum will depend on the degree of convergence between national privacy standards and extent of trust between national regulatory authorities.

- i. _Unilateral action by data source countries_ :  At one end of the spectrum is the EU regime.  Here, the data source country unilaterally sets its standards and determines whether destination countries comply with the standard.  Furthermore, the source country retains complete discretion at each point of time in setting its standards.

- ii. _Additional commitments by individual data source countries a la GATS Article XVIII_ :  As a first step in cooperation, the data source country may still specify conditions unilaterally and determine conformity unilaterally, but it may lend additional transparency and predictability to its requirements by listing them, e.g. as Additional Commitments under GATS Art. XVII. This provision is meant to house commitments of a regulatory nature that do not constitute limitations on either market access or national treatment.  The Telecommunications Reference Paper – pertaining to
**Figure 2.** Sectoral Carbon Intensities in the Base Year (2007)

# **Base Year Carbon Intensity By Sector**

<!-- Start of picture text --> Agricultural sector<br>Sugar cane industry<br>Soy beans industry<br>Forest sector<br>Livestock sector<br>Food and beverage industry<br>Crude oil and natural gas production<br>Mining of metals and minerals<br>Coal mining<br>Textile and leather industry<br>Wood production<br>Pulp paper and furniture industry<br>Petroleum refinery<br>Biofuel sector<br>Chemical industry<br>Non metalic industry<br>Metal industry<br>Machinery equipment<br>Other manufacturing industry<br>Electricity: Hydro<br>Electricity: Nuclear<br>Electricity: Coal<br>Electricity: Gas<br>Electricity: Oil<br>Electricity: Wind<br>Electricity: Biomass<br>Processed gas<br>Construction sector<br>Commercial sector<br>Transport sector<br>Service sector<br>0 1000 2000 3000<br>t-CO2/million R$<br><!-- End of picture text --> Sources: Pereira et al. (2010) and Ministry of Science and Technology (2010). Note that CO2 emissions from deforestation are not included in the calculation of forest sector carbon intensity.

# **IV  CARBON MITIGATION IMPACT**

This section explores the potential impacts of achieving the national reduction target on the Brazilian economy under various scenarios. In the simulations this study follows the country‟s voluntary target for GHG reduction and represents it by a 37.5% reduction of national CO2 emissions relative to the projected BAU level in 2020. The model is solved every five years from 2010 to 2040. PNMC, which aims at reducing 70% deforestation relative to BAU by 2017, is assumed to be effective from 2010 onward. A linear interpolation is used to calculate the 16
Bullies and plunderers—who could never flourish if the rules of the game were crystal clear and reliably enforced—cannot be expected to promote or enforce a system that will radically devalue the rude skills of acquisition and domination they have perfected in the state of nature.

Efficiency in stripping may also increase as it becomes more institutionalized (and similarly, the ability to engage in growth-enhancing investments may increase with use, or atrophy without it).

An offsetting factor would be that as the stock of assets goes down, the returns from further stripping decline. When assets to strip run out, everyone would support the rule of law. However, in natural resource-rich economies, such as Russia’s, this would not happen quickly.

A further source of history dependence are labor adjustment costs if hired labor is specialized to either stripping assets or building value, _e.g._ mobsters _vs_ . engineers. A formal model is Krugman (1991).

# 2.2. _History-dependent Beliefs_

The experience of the transition may reinforce one or another view of man; one can learn to trust, or not to. A history of corruption may influence a social group’s norms in ways that would make it harder to achieve a rule of law state (Fisman and Miguel 2006). The following response of a Russian minister to allegations of corruption illustrates that the abuse of power can come to be publicly perceived as legitimate: Vladimir Rushaylo has flatly denied the allegations that 70 per cent of all Russian officials are corrupted ... “Only those who have links with the organized criminal gangs can be regarded as corrupted officials. Do not mistake bribe-taking for corruption,” the Russian Interior Minister stressed.

(RIA news agency, Moscow, March 13, 2001/BBC Monitoring © BBC)

# 2.3 _Cumulative Exit Costs_

We assumed that only those assets stripped in the transition period were subject to recapture, whereas assets stripped in earlier periods were “grandfathered”—time had gained them 17
**Table** **<u>6: Impact of formal preschool, Seemingly Unrelated Regression estimation by age at endline</u>**

||N|IMPACT<br>**Age < 66 months**|SE|P-val|
|---|---|---|---|---|
|All scores|484|-0.018|(0.054)|0.741|
|ASQ|479|0.049|(0.069)|0.48|
|MOTOR|482|0.068|(0.057)|0.234|
|COGNITIVE|481|-0.064|(0.067)|0.346|
|NON COGNITIVE|484|-0.03|(0.073)|0.679|
|COG+NON COG|484|-0.047|(0.053)|0.377|
|||**Age > 66 and < 78 months**|||
|All scores|537|-0.091**|(0.040)|0.023|
|ASQ|531|-0.155**|(0.068)|0.023|
|MOTOR|537|-0.03|(0.053)|0.564|
|COGNITIVE|534|-0.227***|(0.078)|0.004|
|NON COGNITIVE|533|0.034|(0.057)|0.55|
|COG+NON COG|535|-0.096**|(0.044)|0.028|
|||**Age > 78 months**|||
|All scores|522|0.015|(0.035)|0.679|
|ASQ|518|-0.004|(0.041)|0.93|
|MOTOR|522|0.025|(0.035)|0.479|
|COGNITIVE|521|0.073|(0.064)|0.257|
|NON COGNITIVE|522|-0.06|(0.057)|0.291|
|COG+NON COG|522|0.007|(0.051)|0.898|

Note: “All scores” represents all test scores; “ASQ,” all subsections of ASQ; and “MOTOR,” all estimates for gross motor, fine motor, weight for age, and height for age. “COGNITIVE” is an index of all cognitive tests (TVIP, WJ, and “problemsolving” and “communication” subsections from ASQ), and “NON COGNITIVE” is an index of the strength and difficulties questionnaire. "IMPACT" column presents coefficients from the SUR model. Standard errors (SE) are robust to heteroskedasticiy and intravillage correlation and estimated using Seemingly Unrelated Regression (SUR). ** 5% significance; *** 1% significance.

# _Interpretation_

The robust negative impact of preschool exposure on the middle cohort is striking, and clearly it is important to understand the underlying mechanisms at work. A number of potential explanations are worth highlighting.

First, the cohort between 66 and 78 months is the one in which children in the treatment group are more likely to enter primary school at the right age, whereas children in the control group tend to enter primary school earlier, as discussed in Section 5.1. The ITT estimates show that treatment reduces enrollment in primary school by 20 percentage points in this specific age group. In contrast, for children older than 78 months (and up to 90 months)—an age group for which no differential primary school enrollment can be measured—no negative impacts on cognition are found.

Figure 2 shows that there is a significant difference in primary school enrollment between the control and treatment groups from the age of 65 months at endline (that is, 35 months at baseline) until the age of 75 months (45 months at baseline). One potential explanation for the negative effect of preschool on cognitive development could be that, for this specific age16