rafmacalaba/data-use-mentions-extended huggingface dataset:
This database can provide aggregate statistics at the country level and within-country statistics per rural or urban status, and per quintile of total expenditure. It can also provide information on the types of expenditures per transport subcategories. Further analysis is conducted in a companion paper (Lebrand et al., f _orthcoming_ ).

## **4.2. Vehicle Ownership: SSAPOV Harmonized Data Collection**

The World Bank Sub-Saharan Statistical Development team harmonized the existing household surveys in the Sub-Saharan Africa region by extracting about 200 variables and unifying the definitions and variables names. These variables contain the information on household consumption, employment status, education, and health, and there are also some harmonized variables about the ownership of vehicles.<sup>8</sup> The SSAPOV harmonized ownership data can be used to study the large heterogeneity of car and other vehicle ownership among the Sub-Saharan African countries. The country coverage, survey years, and availability of the vehicle ownership variables of the SSAPOV database are listed in Annex 9.

# **5. Suggestions for Designing the Transport Module in LSMS Survey**

LSMS surveys can capture abundant detailed information not only on the households’ ownership of vehicles, transport expenditures, access to facilities, and travel experience regarding human development, but also on the conditions of road and public transportation in the communities, however, the richness of the questions, options and expenditure subcategories varies a lot across countries. To facilitate the data harmonization and cross-country comparison, it is necessary to design and implement a set of structured questions on transport infrastructures, which can be inserted into LSMS surveys as a standalone module, and a series of questions to collect the travel information on education, health, and employment, which can be integrated into the existing modules.

For the sample countries in this review, Mauritius and Uganda designed an organized standalone transport section in their survey instruments, which can add some insights into our guidelines for questionnaire design. Annex 10 contains the transport section in Mauritius’s 2014 Continuous Multi-purpose Household Survey
solely on predefined geospatial features from a range of sources.

These comparisons are important because simpler approaches that rely on publicly available data could be both easier and cheaper to implement at scale, particularly for public organizations interested in their widespread application, and so understanding performance tradeoffs across model architectures and inputs is critical to under- standing how to scale promising new measurement approaches.

Second, a key advantage in our setting is the use of accurate, high-resolution data from national censuses or extracts for model training and evaluation. In contrast to earlier work that relied primarily on publicly available household survey data characterized by spatially imprecise location data and limited household samples, our data cover a much larger set of households in a given location and in some cases are precisely georeferenced. Comparisons against such “gold standard" data allow us to understand whether model prediction errors are a result of inaccurate predictions or noise in the measure of ground truth – an understanding that was often elusive in earlier work in developing countries [6, 29]. In addition, it allows us to consider a wide range of sample sizes to assess the minimum training data requirements for advanced machine learning methods to produce accurate estimates. To quantify the importance of training sample size for performance, we extensively test the extent to which additional training data affects model performance across multiple settings.

Third, our high-resolution census data enables a novel understanding of how satellite imagery and other geospatial data can be used to predict variation in livelihoods within urban areas in Africa – a capability that was again hard to evaluate in previous settings given limited samples and spatial noise in training data. This could be particularly consequential in urban environments that exhibit substantial spatial variability in livelihoods even within small spatial domains. Using comprehensive and precisely georeferenced census data from two cities in Malawi, we are able to train and test models using different resolutions of satellite data, and we find that the models are surprisingly accurate in predicting street- and neighborhood-level variation in wealth within these cities.

Fourth, the censuses and extracts allow us to evaluate
The expression in brackets [ _Wo,G,k/Mo,G,k?_ ] is the ratio of female migrants to male migrants from origin _o_ to all destination countries in the destination subregion _G,_ across all decades _k_ , for which data exist. Of course, complete data are not available for the current decade _t_ since, were that the case, equation (4) would be preferred. Again, once calculated, this share is multiplied by the total number of migrants to determine the number of female migrants.

# _REFERENCES_

- Beine M., Docquier, F., and H. Rapoport. 2007. ―Measuring International Skilled Migration: A New Database Controlling for Age of Entry.‖ _The World Bank Economic Review_ 21 (2): 249–54.

- Bhargava, A., and F. Docquier. 2008. ―HIV Pandemic, Medical Brain Drain, and Economic Development in Sub-Saharan Africa.‖ _The World Bank Economic Review_ 22 (2): 345–66.

- Bilsborrow, R.E., G. Hugo, A.S. Oberai, and H. Zlotnik. 1997. _International Migration Statistics, Guidelines for Improving Data Collection Systems._ Geneva: International Labour Office.

- Docquier, F., and A. Marfouk. 2006. ―International Migration by Educational Attainment (1990-2000): Release 1.1.‖ In _International Migration, Remittances and Development,_ ed. C. Ozden and M. Schiff, [151-201]. New York: Palgrave Macmillan.

- Docquier, F., B.L. Lowell, and A. Marfouk. 2009. ―A Gendered Assessment of Highly Skilled Emigration.‖ _Population and Development Review_ 35 (2): 297–321.

- Harrison, A., T. Britton, and A. Swanson. (2003. _Working Abroad: the Benefits from Nationals Working in Other Economies._ Paris: Organisation for Economic Cooperation and Development.

- Integrated Public Use Microdata Series
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- <mark>In Pakistan, an open and transparent bidding process has resulted in savings of more than Rs 187 million (US $3.1m) for the Karachi Water and Sewerage Board.</mark>

- After introducing transparent procurement procedures in the energy sector, <mark>Bangladesh was able to reduce electricity prices to less than US $0.03 a kilowatthour, roughly half the price of directly negotiated deals in Indonesia.</mark>

- Source: OECD (2003).

On close inspection, the sources for the Bangladeshi, Columbian, Guatemalan, and Pakistani cases do not provide sufficient data to demonstrate the claims made.<sup>15</sup> The point here is not to criticize the authors of the OECD study; rather it is to make the point that if procurement reform is to be given a higher profile in national policymaking better studies of actual reform experiences are needed.

Other information is more quantitative in nature and offers some _circumstantial_ evidence that is suggestive of sizeable gains from procurement reforms. Using the OECD (2002a) data on the size of national procurement markets in 1998, one can calculate the dollar value of the savings that would result if procurement reforms yielded a 10 percent reduction in the prices paid by the government on its existing purchases. This gain can then be compared to the amount of aid a given country received in 1998, providing an “equivalent” percentage increase in aid that would free up the same amount of resources as the procurement reform specified above. This calculation provides a sense of how large self-help (that is, domestic procurement reform) compares to current levels of assistance from abroad.

For 39 of the 106 developing countries in the OECD (2002a) study data on aid receipts is reported in _World Development Indicators Online_ . Figure 2 plots the cumulative distribution of the aid “equivalent” of a 10 percent reduction in state expenditures brought about by a potential procurement reform. The findings are quite striking: in all but two developing countries the
different definitions of informal employment may lead to substantially different results and ―may imply very different conceptual understandings of informality‖, exact definitions applied in particular studies are often dictated by data availability. To give an example, Bernabè (2008) suggests a classification of informal employment, but operational definitions based on available household surveys for seven CIS countries appear to be far from identical. For cross-country studies, ability to apply a comparable operational definition is crucial. This study fills an apparent gap in the literature in this respect for Europe.

Details of our suggested classification of the employed population by ̳formality‘ of employment are given in Table 1. The ESS questionnaire does not ask details on work activities other than main job, hence informal employment outside main job is beyond the scope of this study. Hence, our estimates of informality rates are quite conservative. _Formal employment_ includes anybody holding an employment contract (including family workers with a contract).

Employers with more than five employees, as well as the self-employed without workers who work as professionals (i.e. those belonging to ISCO main group 2) are considered _formally self-employed_ . The ̳five workers threshold‘ is a natural extension of the approach used in the literature when classifying the ―formality‖ of employees based on data without information on contract type. On the other hand, professionals are more often operating legally with some kind of license and pay taxes from at least some part of their income; unreported part of their income, if any, if not relevant for our classification – as are ̳envelope wage‘ payments received by legally employed workers. Other self-employed persons (i.e. all non-professional self-employed operating solely, as well as employers with 5 or fewer workers) are considered _informally self-employed_ . Thus, all employers, including those working as professionals, are treated according to the firm-size criterion<sup>9</sup> .

Persons working without a contract for own family‘s business ( _family workers_ ) form a separate (small) category.
Implementing data access, however, poses a few practical issues. While individual identifiers are generally stripped out of data that are publically posted, community identifiers often are not. For example, global positioning system [GPS] coordinates are frequently used in data analysis and are valuable for steps such as determining the distance from the household to a school or clinic. Since, in principle, GPS data can be used to locate a household, it is common practice to either use a shifter to disguise the original site or to convert GPS information to commonly used distances and then to remove the raw indicator. Still, a determined researcher can likely use location data to extract information as to the likely households that participated in the survey and undermine the guarantee of anonymity. Fortunately, however, such steps increase the required effort and thus plausibly make the exertion not worth undertaking.

There are also times when the research actually may want to retain the identity of the household in the data set. Not only is a baseline-resurvey approach a standard research tool, panels are increasing used to identify long term impacts of programs (see, for example, Maluccio et al. 2009) or the dynamics of investments in children (see, Helmers and Patnam, 2011). To reduce attrition in such panel data sets, researchers commonly ask for names of individuals who will be in a position to help locate the initial respondents (and, not infrequently, their children) in the future. As the team conducting follow up research may be quite distinct from the original research team – and, indeed, the initial survey team may not have even conceived of the topic of the follow up study and thus would not have discussed the subject of the new research with the respondent – the validity of the initial confidentiality agreement ultimately rests on the chain of trust between the respondent, the initial researchers, the repository of the data, and the subsequent users of the archived data.

12
# 2 Estimating AVEs of NTBs

To obtain AVEs of NTBs we fi rst estimate the quantity-impact of NTBs on imports and then we turn into the transformation of quantity effects into price effects.

## 2.1 Estimating the impact of NTBs on imports

The theoretical foundation for this kind of studies is the n-good n-factor general equilibrium model with log-linear utilities and log-linear constant returns to scale technologies (see Leamer, 1988 and Leamer, 1990). One of the speci fi cations commonly used (e.g., Leamer, 1990, Harrigan, 1993, Tre fl er, 1993, Lee and Swagel, 1997 and Wang, 2001) and adopted in this study is the following: where mn,c is import value of good n in country c;<sup>5</sup> αn are product dummies that capture any good speci fi c effect; Cc<sup>kare k variables that provide country characteristics; more precisely as in Leamer’s</sup> (1990) comparative advantage approach we used relative factor endowments (agricultural land over GDP, capital over GDP and labor over GDP), as well as GDP to capture economic size. We also introduced two gravity type variables: a dummy for islands and a measure of the average distance to the world of each countries (i.e., the import-weighted distance to each trading partner); α<sup>k</sup> c<sup>are</sup> parameters in front of the variables that capture country characteristics; Coren,cs is a dummy variable indicating the presence of a core NTB; logDSn,c is the log of agricultural domestic support, which is continuous and measured in dollars (for a description of NTB data, see subsection 3.1.1); β<sup>Core</sup> n,c is the parameter that captures the impact that a core NTB imposed on good n in country c has on imports of good n in country c; similarly β
result is not robust. The evidence concerning the link statistically insignificant when the change in government wages is considered as an explanatory variable.<sup>4</sup> The prominent role played by average government wages may be due to the presence of several state-owned enterprises in the BDF samples. Wage decisions in these firms could indeed be responsive to govemment pay policies. To assess the extent to which the labor costs of private sector firms are affected by government wages it would be necessary to have information on state ownership by sectors. Unfortunately, information of this sort is not available at this stage. The conclusion that changes in the SMIG do not have a significant impact on labor costs, on the other hand, should not be affected by the inclusion of data on state ownership by sectors.<sup>5</sup> The results in Tables 4 and 5 also suggest that labor costs in large firms are only weakly responsive to real shocks, as captured by the economy-wide fluctuations in output and the terms of trade. The coefficients associated to these two variables are not statistically significant in the case of CMte d'Ivoire. And only the changes in the terms-of-trade level appear to be relevant in the case of Senegal (the coefficient multiplying the change in economic activity is not robust to changes in the set of control variables). Real shocks could still affect average labor costs through the SMIG, because the latter is endogenous, as will be shown below. But the SMIG was found to have little or no influence on pay decisions by large firms. Real rigidity thus appears to be an important feature of average labor costs in the formal sector of the economy.

- 4 Microeconomic evidence on how labor costs reacted to the 1994 devaluation of the CFA Franc is ambiguous in this respect too. Using plant-level data for Cameroon, Barba Navaretti _et al._ (1996) show that the median wage fell by 11 percent compared to output prices between 1992-93 and 1994-95. But on the other hand the average wage increased
firm is old enough that the owner cannot remember when, exactly, it started).

All industry groups except “various professional activities” have lower odds of formality compared to wholesale and retail trade, usually significantly so in the pooled model. The industry that is least likely to have formal firms, on average, is construction, followed by transport and storage. However, these results appear to differ across data set. Construction is least likely to be formal in the ELMPS 2012 and highly likely to be formal in EcC, but that effect is measured with a great deal of imprecision. Similarly, transport and storage is less likely to be formal in the two ELMPS waves than in the EcC. It should be kept in mind that few construction and transport firms are actually within establishments, so the group showing up here may be highly and differently selected.

As suggested by the descriptive statistics, firms in the Alexandria and Suez Canal region have higher odds of being formal than those in the Greater Cairo Region. Surprisingly, according to two of the four data sets (EcC and EsC), firms in Lower Egypt and Upper Egypt, two provincial regions, also have higher odds of being formal than those in Greater Cairo. This strongly suggests that being in a metropolitan region does not necessarily raise the odds of formality.

The pooled data results confirm the pattern observed in the descriptives that the two ELMPS waves produce higher measures of formality than the EcC and the EsC. After correcting for size, industry and region, the EcC produces odds of formality that are three-fifths lower and the EsC produces odds that are about half of what they are in ELMPS 2018.

21
.002|
|( parentng tranng)||(0.063)|(0.060)|(0.074)|(0.071)|
|Ld Ddt Vibl Bli|||0.244***||0.107***|
|agge epenen arae (asene)|||(0.025)||(0.023)|
|Missing Lagged Dependent Variable (Baseline)|||0.112<br>(0.089)||-0.209*<br>(0.107)|
||T2=T3|0.511|0.985|0.386|0.525|
|F-test for Equality of Parameters (p-value)|T2=T4|0.003***|0.006***|0.214|0.383|
||T3=T4|0.057*|0.025**|0.720|0.833|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|
|Lagged Dependent Variable and Age Dummies?||No|Yes|No|Yes|
|Number of observations||2,022|2,022|2,022|2,022|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at children level using baseline and 36-month data on the Strengths and Difficulties Questionnaire. Standard errors in parentheses are clustered at the CBCC level. Strengths and Difficulties Questionnaire: Total Difficulties Score had its score inverted so that a higher score is better. We weight observations using sampling weights. For each round, indices are stardardized using the (weighted) mean and standard deviation for the control group in that round.

52
|**The estimated share of shadow emplo**<br>% of all newly employed in shadow<br>employment|**yment in total e**<br>8.7%|**mployment amon**<br>8.7%|**g the newly empl**<br>9.1%|**oyed.**<br>9%|8.5%|

Source: Author‘s calculations based on CASE_2007 and LFS datasets for Poland. Notes: Base category for contract variable = full time, open term contract The last step of the analysis is to examine the relationship between shadow employment and relative probability of transitions between various types of labor contracts or out of employment. However before engaging in an econometric estimation, it is interesting to observe the actual labor market transitions for groups with various labor market positions.

It is obvious that for any type of labor arrangement the highest is the probability that in the next year – ―t+1‖ (see Table 4) the labor market status will be the same as in current year – ―t‖. The continuation of the same labor market arrangement is the most natural behavior on the labor market. On the other hand, the strength of this stability depends on the nature of the labor market arrangements as such and also on the general situation of the market.

At first those with full time and open term contracts and the self employed are the two most stable groups. Over the entire period, more than 90% of those with either of these working arrangements in time ―t‖ did not change it within a year. The same applies to those who are
with a discussion of its main aspects and of the database. Finally, results and conclusions are presented.

# **1. Poverty and income distribution evolution in Brazil: An overview**

Although Brazil is a country with a large number of poor people, its population is not among the poorest in the world. Drawing on the 1999 Report on Human Development, Barros et al (2001) show that around 64 percent of the countries in the world have per capita income less than in Brazil, a figure that mounts to 77 percent if we consider the number of persons in the same condition. The same authors show that, while in Brazil 30 percent of the total population is poor, on average only 10 percent are poor in other countries with similar per capita income. Indeed, based on the same report the authors define an international norm that, based on per capita income, would impute only 8 percent of poor for Brazil. That is, if the inequality of income in Brazil were to correspond to the world average inequality for countries in the same per capita income range just 8 percent (rather than 30 percent) of the Brazilian population would be expected to be poor.

Taking the concept of poverty in its particular dimension of income insufficiency, the same authors show that in 1999 about 14 percent of the Brazilian population lived in households with income below the line of extreme poverty (indigence line, about 22 million people), and 34 percent of the population lived in households with income below the poverty line (about 53 million people). Even though the percentage of poor in the population has declined from 40 percent in 1977 to 34 percent in 1999, this level is still very high and, it seems, stable. The size of poverty in Brazil, measured either as a percentage of the population or in terms of a poverty gap, stabilizes in the second half of the 1980s, although at a lower level than was observed in the previous period.

2