rafmacalaba/data-use-mentions-extended huggingface dataset:
_Table 2: KPI derived from tracking data in the LPI 2023_

|**Data provider**|**KPI name**|**Definition and significance**|**Period**|**Unit**|**Why it matters**|
|---|---|---|---|---|---|
|Cargo iQ|Number of part-<br>ners|Count of distinct origin country partners per destina-<br>tion country|Q1-2 2022|Number of<br>countries|Air cargo connectivity metric|
|Cargo iQ|Aviation dwell<br>time|Time difference between event NFD (Notification of<br>Readiness for Delivery of Cargo) and DLV (Cargo Deliv-<br>ered to Consignee) at destination country<br>Median and quartiles are provided|<br>Q1-2 2022|days|Efficiency of handling and clearance<br>and notification to consignee|
|UPU|Number of part-<br>ners|Count of distinct number of country partners|2019|Number of<br>countries|Postal connectivity|
|UPU|Postal delivery<br>time|Median time difference between event EMD (Arrival<br>at inwards office of exchange) and events EMH (Un-<br>successful delivery) or EMI (Final delivery) at destina-<br>tion country<br>Median andquartiles areprovided|2019|Days|Efficiency of clearance and postal lo-<br>gistics at destination|
|MDST|Number of ser-<br>vices|Total number of maritime services (operated through<br>liner shipping companies on a predefined rotation) be<br>tween the two countries|-<br>Q1-2 2022|Number of ser-<br>vices|Availability of services and fre-<br>quency of connections|
|MDST|Number of alli-<
Surprisingly little attention has been paid to whether differences in survey methods may affect the estimated returns to education. This is especially relevant for low income countries, where analysis more often relies on data generated by different surveys. This study focuses on two dimensions of survey design (i) the specific questions used in the questionnaire, considering two common approaches ‐ and (ii) whether the questions are answered by the respondent himself or herself, or by a another person.

The detail and wording of questions can have important effects on survey findings. In particular, labor questions have been shown to affect the labor statistics they generate, including those reflecting labor force participation and occupational distribution. This may be especially relevant in settings where there is substantial variation in the proportion of individuals who are employed in wage work versus household‐owned enterprises or home production (who are not directly remunerated in the form of a salary or wage). Likewise, challenges may arise in settings where employment is highly seasonal or where an important proportion of workers are casual laborers. These differences in the categorization of subjects (in labor force participation and wage work) may also affect the estimated returns to education, as they result in a different subsample of wage workers for whom the returns are estimated.

Several studies have investigated different aspects of questionnaire design, including question style and wording (open vs. closed questions; positive vs. negative statements; etc.) or the specific place of questions within the survey questionnaire (see Kalton and Schuman (1982) for a review). While the general conclusion is that question‐wording can have important effects, the direction of these effects is frequently unpredictable. Sustained research efforts to revise employment questions in the US provide interesting insights. Concerned that irregular, unpaid, and marginal activities may be underreported in the Current Population Survey (CPS), among others because people may not think of their activity as work, respondents were asked in a debriefing study to categorize different hypothetical situations as “work”, “job
population, so the impact on their income is affected more by the relatively lower increase in the returns to capital.<sup>14</sup> Skilled labor is more evenly distributed across income deciles, reflecting the fact that government employees such as researchers and teachers often receive very low wages, and that retirees living on a pension were often skilled workers.<sup>15</sup> Rural households typically gain less than urban households. This is due to the fact that rural households have less education and are therefore classified as less skilled than urban workers in the same income group, and unskilled wages do not increase as much as skilled worker wages.

_Global Free Trade and the Doha Agenda Scenarios._ We see from the results in **c** olumn (4) that the impact of global free trade, removal of export subsidies and reduction in domestic support yields very little difference in the results across deciles. Household welfare in all deciles increases by an amount between 0.1 percent and 0.3 percent of consumption. That is, the results for all deciles falls within 0.1 percent of the weighted average for the entire population (shown previously in table 5) of 0.2 percent welfare increase as a percent of consumption. Similar results apply to the Doha scenario shown in column 5—the weighted average estimated welfare gains for virtually all deciles is within 0.1 percent of the estimated weighted average for the entire population in the respective scenario. The standard deviations for households within the deciles is 0.2 percent of consumption for all but one group.

> 14  Household income in Russia exceeds household consumption for almost all households. The reason is that Russian has a large current account surplus. Consistency between the macro balances and the household data in construction of the Social Accounting Matrix implies that household factor income must be larger than household consumption for most households to allow for the transfer of capital to foreigners as well as to pay for investment. It follows that the change
-0.060|-7.18|8,826|
||Yes|0.819|0.748|0.806|0.791|0.743|-0.076|-9.31|476|

_Note:_ As explained in the text, _NDVIww_ is the NDVI for all fields categorized as winter wheat in the crop cover analysis for the year in question.

_Source:_ Own computation from crop maps for winter crop area and sentinel imagery 2019-22.

17
basis.<sup>14</sup> This variable is clearly also not ideal for the purposes of our analysis, nor are the other shadow employment proxies and we are thus dealing with second- and third-best variables to identify informal workers. Yet, no other official data on shadow employment with a comparable methodology are available. Given the above-mentioned deficiencies of the whole second set of indicators on shadow employment, we will mostly use the first dataset on shadow production estimated by Schneider et al. (2010) further in this section.

Generally, Europe ranks rather low on the informality scale. According to Schneider et al. (2010), the average size of the shadow economy was 34.0% of GDP in eighty-four developing countries in 2007, 32.6% in twenty Eastern European and Central Asian transition countries and 16.6% in twenty-five OECD countries. The respective average for twenty-eight selected European countries examined in this paper was 21.1% in 2007: 25.9% in the NMS group and 17.4% in old European countries. Yet, there still persist large differences among the particular countries.

The heterogeneity in the old European countries group is stable: in period 1999-2007, the variation coefficient hovered around 30% without any clear trend in the old Europe. The heterogeneity of shadow economies in the NMS group was substantially lower throughout the whole examined period with a moderate decreasing trend between 1999 and 2004―variation coefficient fell from 20.4% down to 18.8% and subsequently hovered around 19%. Alongside mild reduction of the heterogeneity within its own respective group, differences between new and old member states have been diminishing only moderately as Table 1 and Figure 1 show: the gap between the average values of these two groups shrank from 9.9 to 8.4 percentage points between 1999 and 2007 with a local peak in 2000 (10.3 percentage points). The share of shadow economy seems to be decreasing in recent years in the entire sample with
.<br>Time between start-up and registering for a municipal license Time between start-up and registering for a RUC<br>Likelihood of obtaining finance from formal sources Likelihood of obtaining finance from formal sources<br><!-- End of picture text --> _Source_ : Authors’ calculations with EMES data.

# **6. Conclusions**

In order to understand the links between formality and firm profitability, this paper relies on a new survey of micro and small firms in the urban areas of Ecuador collected specifically for the purposes of this study. The paper finds that most entrepreneurs face substantial difficulties in performing daily businessrelated activities, access to credit is limited, and firm revenues and profits relatively low. Defining formality as compliance with a broad spectrum of legal and regulatory requirements, the paper finds that the vast majority of firms operate somewhere along the informality continuum, with compliance varying substantially by firm size and type of regulation.

> 20 Public banks include Banco Nacional de Fomento and Corporación Financiera Nacional. The micro-finance category also includes financing received through Credito de Desarrollo Humano (CDH).

> 21 Knowing whether a firm received a loan from a formal source in the last year does not resolve the issue because, for example, more formal firms could have a lower demand for loans due to their higher profitability which could allow them to use retained earnings instead of credit.

18
the information system of _Familias en Acción_ . The first research design employs matching techniques to compare the school completion rates and test scores of different cohorts of children from treatment and control areas that could have finished high school during the program implementation period of 2003-2009 and were interviewed prior to the initiation of the program. The second design exploits variation in assignment to treatment arising from the sharp discontinuity that emerges at the eligibility threshold defined to participate in the program. Household are assigned a poverty index score from a census of poor people, which determines their eligibility into different social programs (including FA) with different thresholds.

We show that the program helps participant children to increase their school attainment by making them more likely to complete high school. Results from the first empirical approach (matching analysis) indicate that program effects vary between 4 and 8 percentage points. The RDD (Regression Discontinuity Design) <spell out RD> approach yields estimates of program effects on school completion that are similar in direction and magnitude. Overall, focusing on the preferred specifications, we estimate that beneficiary children that belong to households near the threshold of eligibility are between 3 and 6.5 percentage points more likely to graduate from high school. There is also evidence of heterogeneity in program impacts, with effects on school completion being larger for girls and beneficiaries in rural areas. Regarding long-term impacts on tests scores, the analysis shows that program recipients who graduate from high school perform at the same level as equally poor non-recipient graduates in Mathematics, Spanish, or the overall test. This result still holds after correcting for possible selection bias when low-performing students enter school in the treatment group. Finally, there is no indication of indirect program effects on the school completion of ineligible older children residing in the same households as participant children. The results are robust to a variety of controls for observable differences between participants and non-participants, the possibility of sorting around the threshold of eligibility or manipulation of the proxymeans test used to allocate the program, misspecification bias, and differences in the accuracy
<!-- Start of picture text --> Figure 1e: Bangladesh<br>Firm Count & Employment by Firm Size<br>Number of Firms Share of Firms<br>1995 22,830 1995 0.79<br>10-49 Workers 2005 20,444 10-49 Workers 2005 0.74<br>2012 25,974 2012 0.62<br>1995 3,532 1995 0.12<br>50-199 Workers 2005 3,270 50-199 Workers 2005 0.12<br>2012 10,251 2012 0.25<br>1995 1,640 1995 0.06<br>200-499 Workers 2005 1,416 200-499 Workers 2005 0.05<br>2012 3,791 2012 0.09<br>1995 767 1995 0.03<br>> 500 Workers 2005 2,333 > 500 Workers 2005 0.08<br>2012 1,700 2012 0.04<br>Source: Census of Manufacturing Industries, Source: Census of Manufacturing Industries, 1995, 2005, & 2012. Shares may not<br>1995, 2005, & 2012. sum to one due to rounding errors.<br>Total Employment Employment Share<br>1995 384,460 1995 0.19<br>10-49 Workers 2005 419,730 10-49 Workers 2005 0.18<br>2012 573,713 2012 0.12<br>1995 361,069 1995 0.18<br>50-199 Workers 2005 297,136 50-199 Workers 2005 0.13<br>2012 1,043,740 2012 0.21<br>1995 520,058 1995 0.26<br>200-499 Workers 2005 462,905 200-499 Workers 2005 0.20<br>2012 1,100,902 2012 0.22<br>1995 751,051 1995 0.37<br>> 500 Workers 2005 1,168,044 > 500 Workers 2005 0.50<br>2012 2
means from Eurostat’s 6 From World Development Indicators (WDI), we use the following series for national accounts data: Household final consumption expenditure (current LCU) [NE.CON.PRVT.CN]; Household final consumption expenditure (constant LCU) [NE.CON.PRVT.KN]; Household final consumption expenditure (constant 2010 US$) [NE.CON.PRVT.KD]; GDP (current LCU) [NY.GDP.MKTP.CN] ; GDP (constant LCU) [NY.GDP.MKTP.KN]; GDP (constant 2010 US$) [NY.GDP.MKTP.KD]; and we use Population, total [SP.POP.TOTL] to construct per capita measures where needed. In the levels analysis we make use of the current LCU data, while in analysis of growth rates we use the constant series.

6
Necessity<br>De Soto Parasite<br>Informal (all types)<br>Kernel density<br><!-- End of picture text --> Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

8Arguably, our classification hinges on the arbitrary quartile definition, but, in general, firms classified under these three categories are at least similar to the informality view they intend to represent. We also tested a stricter threshold with necessity firms below the minimum of the conditional productivity, and the results (available upon request) do not change much, but standard errors increase significantly due to the smaller number of observations in the necessity category of informality.

9By construction, we observe more overlap between the distribution of De Soto type of firms and the distribution of formal firms, while the density for necessity firms is shifted to the left. The classification is constructed at the country-sector level, and this figure pools all countries and sectors together.

9
and to request the results of various medical and laboratory tests. The first enumerator offers pre-specified responses to those questions and the second enumerator records how well the physician adheres to clinical guidelines in the questions asked and clinical actions undertaken. By the end of the vignette, the health care provider must provide a diagnosis and proposed treatment for the clinical case.

In the SDI surveys considered in this paper, health care providers are tested on five core vignettes: childhood diarrhea with dehydration, childhood pneumonia, adult tuberculosis (TB), adult diabetes mellitus, and childhood malaria with anemia. Additionally, countries may choose to add specific vignettes and occasionally remove vignettes (such as in Kenya where the malaria with anemia vignette was not administered). These vignettes represent common clinical cases that a health care provider would face in the low- and middle-income country (LMIC) context. These diseases also reflect some of the highest burden conditions, making up 30% of all-age disability-adjusted life years (DALYs) in Sub-Saharan Africa.<sup>20</sup> Each provider is scored on the percent of vignettes for which they provide the correct diagnosis and treatment. Correct treatment is not made conditional on correct diagnosis, so providers may occasionally recommend the correct treatment without a correct diagnosis.

To shed further light on clinical capacity to address maternal and child health issues, the SDI includes two additional vignettes designed to assess providers’ responses to more urgent medical conditions: neonatal asphyxia and post-partum hemorrhage. Both of these conditions have a clear set of guidelines and recommended actions that the providers should undertake. For the purposes of scoring, providers are assessed on seven actions for neonatal asphyxia (calling for help, placing the baby in a neutral position, checking the baby’s heartrate, checking

> 15 Rao & Sheffel (2018). 16 Escamilla et al. (2018). 17 Das & Leonard (2006). 18 Peabody et al. (2000). 19 Das et al. (2008).

20 Diarrhea at 7.6
can be quite regressive. Furthermore, from the point of view of energy efficiency policy, energy savings from removing subsidies to poor households will be less effective than imposing a tax on high-income households because the latter is more sensitive to price changes. Finally, given the relatively high affordability ratios for the poorest households, there is a strong case for carefully crafting social protection policies to ensure that stringent energy pricing policies do not impose undue hardship on poorest households.

Last, the work presented in this paper illustrates the advantages of using a rich micro dataset to analyze the energy demand of household sector. There are other developing countries experiencing similar energy price reform. From a methodological point of view, the availability of household survey data in other developing countries suggests that similar analyses could be carried out in settings where differentiated price elasticity estimates are needed to evaluate the distributional impacts of price increase on households.

15
# **Appendix Table 4: Parameter values and data sources for calculating benefit-to-cost ratio for preprimary interventions in lowand middle-income countries**

||Labor force<br>participation<br>rate|Average<br>nominal<br>monthly<br>wage<br>(country<br>currency)|Annual<br>real wage<br>growth|Lowest<br>treatment<br>effect on<br>cognitive<br>skills (SD)|Lowest<br>treatment<br>effect on<br>social-<br>emotional<br>skills (SD)|Data sources|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(5)|(6)|(7)|
|India (Dillon et al, 2017)|46.3|13143|0|0.09|0.165|Periodic Labour Force Survey, 2019;<br>India Ministry of Statistics and<br>Programme Implementation;<br>BloombergNewswire|
|Malawi (Ozler et al, 2018)|37.2|125000|3.4|0.185|0.252|Integrated Household Survey, 2017 ;<br>National Statistical Office of Malawi|
|Ghana (Wolf et al, 2019a)|57|618|2.16|0.107|0.18|Living Standards Survey, 2017;<br>Ghana Statistical Service; Bloomberg<br>Newswire|
|Peru (Gallego et al, 2019)|77.4|1570|1.7|0.19|---|Encuesta Nacional de Hogares, 2019;<br>ILO SIALC; BloombergNewswire|
|Indonesia (Brinkman et al, 2017)|70.1|2913897|-0.2|
0.725|
|Sending FE|Yes|Yes|Yes|No|
|Receiving FE|Yes|Yes|Yes|No|
|Year FE|Yes|Yes|Yes|No|
|Sending FE*Time FE|No|No|No|Yes|
|Receiving FE*Time FE|No|No|No|Yes|
|Controls|Yes|Yes|Yes|Yes|
|LogLik|-14236.62|-15541.05|-11836.79|-10463.32|

Source: Authors’ analysis based on data described in the text. Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

# _Further Robustness Analysis_

To further check the robustness of the results, table 7 runs the baseline specification using different estimation methods and dependent variables—for the case of coinventorship only. In particular, it includes OLS and Negative Binomial models. For both cases the estimated coefficients are slightly larger compared to table 2, but they are fairly comparable. Column (3) takes on board the potential heterogeneity of international co-patents in terms of quality and weights them according to the number of forward citations received within five years after their priority year. As can be seen, results are robust for this different approach to computing the dependent variable—
In estimating equations [10] and [11], we use urban household disposable income as our measure of urban wages and rural household per capita income as our measure of rural wages. This is analogous to NEG-wage equation studies which use GDP per capita or worker as a proxy for wages in aggregate data (e.g. Redding and Venables, 2004; Au and Henderson, 2006; Hering and Poncet, 2010a). In contrast to both Au and Henderson (2006) and Hering and Poncet (2010a), the definition of urban implied by our proxy for urban wages includes all urban households in a prefectural region, even if they live outside the "city proper" (i.e. outside the "districts" of Chinese prefectures). Further discussion of the dataset used in the estimation of equations [10] and [11] can be found in Appendix A.

# _4.1. Econometric Issues_

Broadly speaking, the empirical NEG literature provides two strategies for the estimation of equations similar to [10] and [11]. The first is a two-stage strategy in which a gravity trade equation is estimated (Redding and Venables, 2004). This allows for the construction of a measure of RMA which is then used in the estimation of the wage equation. The second is a direct estimation strategy which aims to measure RMA much more directly, thereby cutting out the first stage of the two-stage strategy (Hanson, 1997). Generally, use of the gravity approach is not possible with within-country data because of the lack of domestic trade data, although with some heroic assumptions one could impute such data. Two-stage estimation has been applied to China by Hering and Poncet (2010a, 2010b). Internal bilateral trade flows have been constructed using Chinese regional input-output tables, but this can only be done at the provincial level, meaning that crucially, somewhat arbitrary allocation rules then need to be adopted to calculate RMA at the prefectural level. In our context, these rules would involve assuming, for
the error variances.

> 31This approach was applied, inter alia, by Adrianzen (2014) to data clustered in 26 villages and by Akosa Antwi, Moriya, and Simon (2013) to 28 quarter-year groups.

> 32The wild cluster-bootstrap calculates t-statistics for each bootstrap sample and estimates rejection rates based on the resulting distribution of t-statistics. Because this method does not calculate standard errors, I report clustered standard errors throughout the text. Implementation of the bootstrap in Stata is done based on the do-file written by Douglas Miller, which can be accessed online: http://www.econ.ucdavis.edu/faculty/dlmiller/statafiles/.

19