rafmacalaba/data-use-mentions-extended huggingface dataset:
Ownership of a four‐wheel tractor is included in Models 1 and 2 to assess the importance of agricultural livelihood‐related assets in determining poverty. However, this variable was dropped in Model 3 due to the potential for excluding poor agricultural households that own a tractor. We also drop land phone and water pump variables in Model 3. Land phones are likely to become obsolete over time, and just 2 percent of households own water pumps, limiting the contribution of these variables as predictors of poverty. Other variables (see Table A1) were dropped from Model 3 because of their limited explanatory power. We also tested the inclusion of district and sector categorical variables in the model, but this did not alter the results significantly.<sup>11</sup>

# IV. Results

After several iterations of models testing the inclusion of different variables, we present three models that fit the HIES data well (Table 1). Model 2 differs from Model 1 only in the exclusion of household head variables. Model 3 is a restricted version of Model 2, retaining only highly significant coefficients, with partial sum of squares above particular thresholds (see section II above). Model 1 is the typical PMT model. If the program eligibility continues to be determined at the family level, however, Model 3 would be preferable, because it performs almost as well as Model 2 and requires less intensive data collection and verification.

All models generate high R‐squared statistics, with Model 3 having a slightly lower R‐squared as expected due to the lower number of variables. In Model 1, several of the household head characteristics are not significant contributors to the PMT score. However, widowhood of the head, age, and head working in a government, semi‐government or self‐employed job all affect the PMT score significantly. Across all models, categorical household size variables are the largest contributors to the PMT score, with the score decreasing as household size increases. This is typically the case in a PMT because
distance and historical ties (see, for example, Head and Mayer, 2013).

The first stream of studies presents evidence that is in alignment with horizontal FDI by showing a positive relationship between FDI on the one hand and market size and trade cost on the other. Brainard (1997), one of the first empirical studies examining the proximity-concentration trade-off, finds that the patterns in which country characteristics relate to U.S. FDI are broadly in alignment with the market access motive. Specifically, she uses U.S. trade and affiliate sales data from the 1989 BEA Benchmark Survey of U.S. Direct Investment Abroad and finds that FDI increases with hostcountry income and trade cost including the transportation cost to ship goods between the headquarters country and the host country, consistent with the market access motive in horizontal FDI.

Similar findings are shown in Carr, Markusen, and Maskus (2001) who incorporate both horizontal and vertical FDI into a knowledge-capital model of multinational firms and offer support to both market access and knowledge capital hypotheses. In particular, the elasticity of affiliate sales with respect to distance is estimated to range from -0.8 to -1.8, suggesting strong gravity in FDI patterns. Affiliate sales tend to diminish by around 8 to 18 percent when the distance between parent and 4
Fox and Gaal (2008) proposed another alternative, arguing that knowledge of the biases by module design (e.g. exclusion of screening questions for economic activity) can be used to make an adjustment in the data and bring better (but not complete) comparability. This method involves recognizing that a satisfactory measure of economic participation should be able to capture participation in any form of economic activity, irrespective of whether the respondent deems such an activity primary or secondary. In the absence of screening questions that capture participation in all economic activities over a 12 month period for example, information from various questions in the labor module (primary and secondary employment status) and other modules where economic activities are separately captured (e.g. a household enterprise module, agriculture module and time use or non-labor market activities module), can be used to get a complete picture of LFP. This search of the questionnaire and the data should also include looking at time use data to pick up some domestic duties technically classified as work such as fetching wood and water.<sup>19</sup> This method was applied in Table 10 to estimate the extent of the non-sampling bias in the Uganda data. The steps are as follows.

- a. _Use secondary activity information_ . In the absence of screening, users can identify those who are reportedly inactive, or are missing the status variable in the main employment status question. Then, the user can search for a secondary activity response. If they report a secondary economic activity, the person would be recoded as active, and the secondary activity would be recoded as the primary. For example in 1992/93 a woman might report domestic activity (i.e. economically inactive) in the main employment status question but reports helping without pay in a household enterprise or farm in the secondary employment status question. She should be coded as active. This is the same adjustment shown for Uganda in Table 3 which compared LFPR based on primary activity only to the LFPR including secondary
38 provided goods and services, which are generally not imputed into the survey indicators, and because of possible under-reporting by respondents. Given that the _levels_ of the two series are not expected to match exactly, analysts are usually concerned by deviant trends, which may indicate a problem with the survey instrument. On the other hand, it may plausibly be argued that National Accounts data have errors of their own, and that many of the 'correction' procedures applied to household data rely on reasonably strong assumptions, such as equiproportional underreporting by source.

In deciding whether to adjust the PNAD data with reference to the Brazilian National Accounts over this period, we examined the evolution of the ratios of GDP per capita to mean household incomes from the PNAD (for the entire country, and without regional price deflation, for comparability). As Table A5 below shows, these were remarkably stable. In particular, the ratios for the starting and end points of the period covered, which are of particular importance for our analysis, are almost identical. In this light, and since even the disparity with respect to 1981 and 1985 are reasonably small, we judged that the costs of making rough adjustments to the PNAD household incomes on the basis of the National Accounts outweighed the benefits.

|Table A5: Ratios of|GDP per capita to PNAD m|ean household incomes, 197|6-1996|
|---|---|---|---|
|Year|GDP per capita (A)|Mean PNAD income<br>(B)|(A) / (B)|
|1976|336.6|190.2|1.770|
|1981|370.2|187.3|1.976|
|1985|378.3|188.6|2.005|
|1996|412.1|233.0|1.769|

Tables A6 - A9 summarize the main items in the questionnaire concerning these variables, and the
pliance with environmental regulations. Civic activism is proxied here with voter turnout, and income per capita is also used as an explanatory variable. Table 7 shows that voter turnout has a significant and negative effect on compliance. The interpretation of this result is in line with the suggestions of the earlier literature. But it is important to understand the potential reason for the negative sign is that the larger the civic activism, the larger is the pressure for environmental regulations to be enforced. This implies that when there is more social pressure for environmental issues, authorities tackle more effectively these issues and find more environmental irregularities. No significance is found on the effect of GDP per capita when included with voter turnout but it is positive and significant when included without voter turnout. Regarding the self-reported data, one finds that the effect of voter turnout is the opposite, being positive. Following the previous analysis, one could argue that this is in line with the previous result suggesting that more social pressure leads the firms to self-report themselves to be more in compliance. In self-reported data, GDP per capita is only significant and positive when included with voter turnout.

||(1)<br>Self mon.|(2)<br>Self mon.|(3)<br>Self mon.|(4)<br>Inspections|(5)<br>Inspections|(6)<br>Inspections|
|---|---|---|---|---|---|---|
|Voter turnout|0.299<sup>_∗∗∗_</sup>||0.299<sup>_∗∗∗_</sup>|-0.203<sup>_∗∗∗_</sup>||-0.203<sup>_∗∗∗_</sup>|
||(0.055)||(0.
thus reduce the magnitude of those fixed costs and stimulate the exports of smaller firms relatively more than in other sectors.

Firm exports of products with heavier regulatory requirements in terms of SPS and TBT are likely to respond differently than those of products with lighter regulatory requirements. To address this possibility, we consider the specific trade concerns raised by WTO members in the WTO Committees on SPS and on TBT measures, which are viewed as capturing de facto trade barriers (Fontagné et al., 2015).<sup>34</sup> We aggregate our firm-destination-HS 6-digit-year level data to the firm-destination-industry-year level in order to estimate Equation (2) separately for industries subject to at least one specific trade concern versus industries subject to no such concerns. Both panels in Table 6 shows that the effects of SPS and TBT provisions on smaller firms’ exports are entirely driven by industries subject to at least one specific trade concern. This constitutes useful evidence that the mechanism that our results identify is an integration and liberalization of SPS and TBT regulations across partner countries via the inclusion of related provisions in PTAs.

# **5.2 Impacts on Firm Export Market Entry**

Since SPS and TBT requirements may act as a fixed entry cost, regulating SPS and TBT by their inclusion in PTAs would decrease such cost and a boost to firm export market entry could be expected, especially the entry of smaller firms. Due to the fill-in of our database to account for zeros in trade, our baseline results capture a combination of impacts at the intensive and extensive margins. But we explicitly examine firm export market entry defining it as a dummy variable equal to one if a firm-destination-HS 6-digit triplet export a positive trade flow in year _t_ , while exporting a zero trade flow in _t_ -1. We estimate Equation (2) using firm entry as the dependent variable and provide the results in Table 7. The estimates show that including SPS provisions in PTAs significantly increases the entry of smaller firms into new product-markets. PTAs with mutual recognition or harmonization provisions
10 Moody's (1999a, 1999b) methodology for assessing bank credit risk in emerging markets. " l The two panels are unbalanced because not all firms have a rating for each year. The ratings we use are those attributed by Moody's. The basic statistics of the two panels are reported in Table 4. For each year, we attribute to each firm the minimum rating held during that year. In addition, in order to ascertain whether the hypothesized excess sensitivity of emerging economies' private sector ratings to changes in sovereign ratings is asymmetric, we dichotomize firm rating changes into positive (upgradings) and negative (downgradings). Our a priori is that the excess sensitivity might be stronger for negative than for positive changes.

# 5.1 The case of non-bank corporations

The non-bank corporation panel contains observations referring to 980 rated firms-rated in one or more years over 1990-99-from 40 countries.2 Tables 4 and 5 describe the composition of the sample in 1999, when we observe a rating for 895 of the 980 firms. Country-wise, 84.5 per cent of the firmns are based in high-income countries. Sector-wise, approximately one third of the firms are utilities (oil; electric; telecom; water). The median rating is substantially higher in high-income countries (65 against 40); in low-income countries median firn ratings are closer to their respective sovereign ratings (40 to 52.5 against 65 to 100 for high-income countries). Median firm ratings are higher for most utilities and automotive firms; they are lower for firms in the metal industry and in the service sector.

Table 6 reports the results of five different specifications of the panel regression.<sup>13</sup> The first specification is the base one. Results show that non-bank ratings for firm i at time t are negatively related to rating changes for firm i at time _t-1_ (RATING(T-1)) and are strongly positively related to sovereign rating changes at time t (ASOVEREIGN).1 4 i The relationship between sovereign
45 levels are marked as follows: * 10%, ** 5%, *** 1%.

**<u>Table 8: Cost-Benefit Analysis of ITN Delivery in Kenya between 2003 and 2008</u>**

|**Estimation of infant deaths averted by ITNs**<br>Live births p.a. per 1000 households<br>Infant deaths averted by ITNs per 1000 live births|136.23<br>12.07|
|---|---|
|Infant deaths averted by ITNs p.a. per 1000 households|1.64|
|**Estimation of costs of ITNs delivery**||
|Number of ITNs distributed 2003-2008|15,639,783|
|Number of households|8,767,954|
|Number of ITNs distributed p.a. per 1000 households|356.75|
|Cost per ITN in US$|5.00|
|Costs of ITNs distributed p.a. per 1000 households in US$|1,784|
|**Cost-benefit analysis of ITNs delivery**||
|Costper infant death averted byITNs in US$|1,085|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata and World Malaria Reports data. Notes: Estimate of live births is based on the actual live births that took place 1 to 12 months before the Kenya DHS 2008 survey. Infant deaths averted by ITNs are based on the Oaxaca-Blinder decomposition results, see Table 5. Estimate of distributed ITNs is based on the number of ITN and LLIN bednets distributed over a five year period mid-2003 – mid-2008 (World Malaria Report 2008, World Malaria Report 2010). Number of households stems from Kenya Census 2009. The estimated cost of US$ 5 represents average costs per ITN delivered over a five year period 2003-2008. For an overview of literature on costs of various ITN delivery initiatives in Sub-Saharan Africa see Annex.
using different methodologies such as setting negative values to 0 or using absolute values with different thresholds for counting a value as 0.

To provide some further details into the noise and distribution of the VIIRS data, Figures 2 and 3 show the distribution of VIIRS values below 2<sup>5</sup> for all countries and their populated areas and the number of cloud free days per month. Looking at Figure 2 first, one sees a clustering on both sides of 0, with the bin containing 0 and very small values constituting more than 15 percent. Furthermore, more than 13 percent of the total points have a negative value and 46 percent more have a value between 0 and 0.3, meaning that almost 60 percent of all points have negative or very small values. It is also worth noting again that these are only points that have been identified as being populated,<sup>6</sup> meaning that they should be relatively free of disturbance from non‐human sources. Despite this, there are still very small deviations from 0.

Even when limiting the light to cells with population over 50 (approximately 30 percent of populated cells), the distribution stays similar with 85 percent of light values being below 2, 75 percent below 1, 46 percent below 0.3 and 5 percent being negative. This distribution pattern follows through to high population numbers. When looking at population numbers above 1,000 (less than 12 percent of the total) 17 percent of the points still have nightlight values below 0.3 albeit only 0.3 of points have values below 0. The question will then be if VIIRS is only useful for very urbanized areas, if at all.

> 5 Values below 2 constitute approximately 95 percent of the total. This was done due to the maximum VIIRS value being 17,699 and hence a density graph would be meaningless going from ‐1.5 to 17,699. Also, any bin above 2 is very small and would not
For this reason, in the case of export reporting, Senegal’s own reports may be preferred. BACI data also seems to prefer these reports over the mirror, mainly because Senegal’s export destinations are much poorer reporters than Senegal’s import partners. This may be counterintuitive to the conventional approach, where import data is always preferred because of reliability assumptions. A more detailed look at Senegal’s trade reporting is given in section A6 of the Appendix.

# 6. Conclusion

This paper offers a review of bilateral trade reporting discrepancies seen in the UN Comtrade database, the largest and most widely used database for empirical investigations of international trade. As such, this paper is intended to serve as a guide for users on when the use of mirror trade data is appropriate. It outlines that (1) discrepancies in international trade reporting can occur because of both logistic and non-logistic factors. Logistic factors relating to reporting guidelines or valuation methods are guaranteed to induce certain types of discrepancies, such as an import valuation that is higher than its corresponding export valuation. Therefore, there is a clear need to define measures of bilateral asymmetry that penalize discrepancies that are least likely to be explained by some logistic constraint.

To measure these discrepancies, we (2) introduce DI as a measure of bilateral asymmetry and derive indicators that summarize some properties of the distribution of this index. We show that compared to similar measures, the DI is neatly bounded between -1 and 1, penalizes export overreporting more and can directly indicate the direction of discrepancies. Finally, (3) we also show how our different reliability indicators could be used to infer several types of reporting problems at the country, commodity, or trade partnership level. These tools are intended to support any analysis that examines bilateral trade asymmetries on a case-by-case basis and analysis aiming to track reporting discrepancies to specific causes, whether logistic or otherwise.

However, it should be noted that many important limitations remain. The problem of tracking reexports and the non-uniform standard by which they
|6.66|0.222|0.185|
|Behavior scores|7.39|7.28|7.43|0.085|0.185|
|Absenteeism Q1 2016|1.69|2.21|1.52|0.010|0.110|
|**PANEL C: CLUBS’ CHARACTERISTICS**||||||
|Average club size|-|-|13.4|-|-|
|Average club take-up|-|-|0.57|-|-|
|Community tutors|-|-|0.31|-|-|
|Club category||||||
|Leadership|-|-|0.29|-|-|
|Art and Culture|-|-|0.16|-|-|
|Sports|-|-|0.26|-|-|
|Science|-|-|0.29|-|-|
|Share of treated by course|-|-|0.42|-|-|
|Observations|1056|258|798|||

Table 1 shows descriptive statistics of the available variables at baseline for the sample of enrolled children. Panel A summarizes information obtained from the enrollment form that was used as determinants in the IVV estimation. Panel B presents administrative data provided by schools. This data is from the first quarter of academic year 2016, before the clubs were implemented. We imputed average course grade for missing observations. For the absenteeism data, we have no records for some courses; thus we imputed the average school absenteeism. The scale of grades in El Salvador is 0–10 points. Panel C presents club characteristics. Take-up is estimated as number of hours attended by student _i_ /total hours in each club. _p_ -values in column (5) have been adjusted for multiple hypothesis testing of means and FWER using a command developed
progress in this area—firms in 2010 recorded only a sevenday delay, on average, in obtaining a power connection (World Bank 2010b).

## **Challenges**

But increased investments in power infrastructure have not necessarily translated into widespread electrification. As of 2008 only a little more than 30 percent of Angola’s population benefited from access to power, lower than the 46 percent average for the nation’s resource-rich African peers. No disaggregated data are available on the levels of rural versus urban access in Angola (World Bank 2010a), but it is known that Luanda consumes around two-thirds of the nation’s electricity, suggesting relatively high access in the urban and peri-urban areas of the capital. Further, at least 85 percent of Luanda’s municipalities indicate that they use electricity for lighting, corroborating that the availability of electricity in urban areas is high (World Bank 2005). It is estimated that about half of the connected residential consumers in Luanda are served by informal providers who pay a bulk tariff of around US$0.04 cents per kilowatt-hour to the utility and resell to consumers at approximately three times this price. Given the limited attention given to rural electrification to date, rural access can be expected to be quite low.

10
address other regulatory issues.<sup>13</sup> But both of these areas go beyond the scope of this paper and are hence not reviewed here.

# _3.1.1. Energy_

The literature on this sector is growing fast but it has many common features. We identified almost 30 papers, reported in Table 2, covering full systems, generation only or distribution only. Most of the papers are on the electricity sector. Only two deal with the gas sector in developing or transition economies and both are on Argentina, the only country in which a tariff revision including a discussion of the revision of the cap actually took place.<sup>14</sup> Most papers focus on a single output. In most cases, they focus on the production side of the business but cost data are much more difficult to obtain. DEA is the most common approach. There is an unusually large number of papers relying on both nonparametric and parametric approaches to check the robustness of the results or of the policy conclusions.

Most of them (over 50% of the papers) rely on country specific data. This is because in many countries, the reforms have created many business units which can be compared with each other. Among developing countries, Latin America is the most studied region, reflecting the payoff of the efforts made by the Latin American Association of Energy Regulators to generate comparable data.

In general, when authors focus on the cost side of the business, the main difference across papers comes from the differences of the expenditure type they focus on: operational, capital expenditures or total expenditures. The papers focusing on the production side deal with either the number of clients served or total energy sold. Since in many countries the operators tend to control more the energy supplied than the number of customers--in general operators are subject to connection obligations, not necessarily to service obligations at any tariff-- the most common proxy for output is energy sold.

As in all other sectors,