rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix 3: Matching program data to administrative data on formalization**

This appendix describes the protocol to match businesses in the administrative database on formalization provided by the GUFE (around 550 businesses every month) with the program data (3,596 informal businesses prior to the program start).

# **Information available:**

We had the following information in both databases:

- Surname of the business owner. It can be written with different spellings in each database.

- Between 1 and 5 first names. In GUFE data we usually have more than one first name. In the program data we only have one first name in most of the cases.

- The business activity as described by the owner (no codes). The business activity is missing for 30% of businesses in the GUFE data.

- Business addresses. In the GUFE data addresses were given by the business owner whereas in the program data, we are using “official” addresses used by the tax administration (there are 144 neighborhoods in Cotonou). In practice only neighborhoods can be matched. In GUFE data, there are few missing variables and some cases for which the neighborhood does not belong to the official list of neighborhoods.

- Gender of the business owner.

- Phone number of the business owner.

# **Definition of a match:**

We consider it to be a match if: (i) the surname, at least one first name, the activity, and the neighborhood match or (ii) if surname and at least one first name match and either the activity, the neighborhood or the phone number also match, and the others are missing (or does not exist for the neighborhood or the phone number).

# **Method of matching:**

We used first the STATA command “reclinck” designed for fuzzy name matching. This command uses record linkage methods to generate matching scores. For this first step, we used tree variables: surname, first name and gender
<br>year.<br>Source: Worldwide Governance Indicators,<br>World Bank.|
|Freedom From Regulation (lagged)|Freedom from Regulation indicator from<br>Fraser Institute’s Economic Freedom of the<br>World database. Higher values of the variable<br>imply less regulation (of formal businesses).<br>The variable is lagged by 2 years from the<br>fiscal year covered by the Enterprise Survey in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years), where the weights used are the<br>proportion of firms covered in the calendar<br>year.<br>Source: Economic Freedom of the World,<br>Fraser Institute.|

41
- “Leave-one-out” COVID-19 shock variable: the leave-one-out share of respondents at the same ITI as the respondent who reported experiencing either the COVID-19 household shock or the COVID-19 education shock, as defined above.

- District-level aggregate COVID shock: the cumulative number of COVID-19 deaths that occurred between January 30, 2020 and October 31, 2021, per 100,000 district population from 2011 Census of India. As no more recent population data at the district level was available and the district Charkhi Dadri was only separated from Bhiwani and established as a new district in 2016, these two districts were analyzed as one district in our analyses.

- Reservation wage: the absolute minimum take-home salary (in INR per month) that a respondent would accept for any permanent, full-time job when she graduates from her ITI.

- Expected starting salary: the salary (in INR per month) that a respondent expects to earn when she starts working.

- Aspiration gap: the difference between winsorized reservation wage and winsorized expected starting salary (in INR per month).

- Unrealistic high aspiration: an indicator variable that equals one if the aspiration gap takes a positive value, and zero otherwise.

- Realistic aspiration: an indicator variable that equals one if the aspiration gap equals zero, and zero otherwise.

- Unrealistic low aspiration: an indicator variable that equals one if the aspiration gap takes a negative value, and zero otherwise.

- Willingness to migrate for work: an indicator variable that equals one if the respondent is willing to move to a different city for a salary increase of INR 5,000, and zero otherwise.

- District-level population density: population per-square kilometer from 2011 Census of India.

- District-level share of urban population: share of population living in urban areas out of the total population in the district from 2011 Census of India.

- District-level share of population living in slums: share of population living in slums out of the total population in the district from 2011 Census of India.

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equal to the corresponding sub-national AgGDP values. The last equation (15) is a natural constraint for the percentage of AgGDP, which is also the probability in the cross-entropy model. The modeling framework is flexible in that more constraints can be added if more data are available and/or more reasonable assumptions on how AgGDP should be spatially disaggregated are discovered.<sup>10</sup> Last but not least, we multiply the total regional agricultural GDP by the probability in the cross-entropy model to derive the final pixel level agricultural GDP:

# **2.4 Natural hazards**

We use measures of two natural hazards to gain insight into the spatial distribution of agricultural activity with regards to drought and water scarcity. We calculate the Standardized Precipitation-Evapotranspiration Index (SPEI) (Sergio M Vicente-Serrano, Beguerıa, and L ́opez-Moreno, 2010), which measures the difference between observed precipitation and estimated potential evapotranspiration with a 3 month interval using the base climatology of 1980 to 2019, which is implemented in R (Beguer ́ıa and Sergio M. Vicente-Serrano, 2017) using climate data from Harris et al. (2020). Extra dry years are defined as the number of years that are less than or equal to -2.0 during the period from 2000 to 2009. Figure 5 shows the results of the SPEI.

The Water Crowding Index (WCI) is a measure of water scarcity considering the local population as the annual water availability per capita (Falkenmark, 1986; Falkenmark, 2013). Veldkamp et al. (2015) model global water crowding index with return periods. We take the mean of any pixels of the ensemble WCI with a 10 year return period within an agricultural GDP pixel. Following the literature (e.g. Alcamo, Fl ̈orke, and M ̈arker, 2007; Arnell, 2003; Kummu, Ward, et al., 2010; Veldkamp et al., 2015), we categorize the WCI into four categories: Absolute is less than 500 m
**Figure 6. The share of adults making digital payments ranges from 25 percent to 83 percent in Europe and Central Asia**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Made or received digital payments Did not make or receive digital payments<br><!-- End of picture text --> _Source_ : Global Findex database.

_Note_ : The height of the bars represents the share of adults (aged 15 and above) who have an account.

# **Table 3. The share of adults who made or received a digital payment the previous year rose in most countries in Europe and Central Asia**

|_Subregion/country_|_2014_|_2017_|
|---|---|---|
|_Central Asia_|33|41|
|Kazakhstan|40|54|
|Kyrgyz Republic|14|36|
|Tajikistan|8|44|
|Turkmenistan|..|34|
|Uzbekistan|38|34|
|_Central Europe and the_|||
|_Baltics_|61|73|
|Bulgaria|48|65|
|Croatia|72|83|
|Czech Republic|78|80|
|Estonia|95|97|
|Hungary|64|71|
|Latvia|84|91|

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(rayon) level (Section 3). This provides a unique opportunity to compare the welfare rankings obtained at the district and administrative level under the two approaches (Section 4). Furthermore, census enumeration areas have been matched with a digital settlement map of Azerbaijan, producing a digital census map which it is linked to the 1999 census enumeration areas (see Section 5 for details).

# **2. Creating a poverty map using the ‘imputed welfare’ method**

Individual and household welfare can be measured in many ways. Conventionally economists argue that individual’s material welfare is best measured by their consumption. The main source of information on consumption in Azerbaijan is the Household Budget Survey (HBS). However, the HBS only provides statistically reliable spatial estimates of welfare at the regional (oblast) level, separately for urban and rural areas. The census, which can provide more disaggregated data, does not contain any information on consumption. The first method applied to derived a poverty map for Azerbaijan is therefore based on a statistical procedure developed by Elbers et al. (2002) that combines household survey data with population census data. This technique uses the strength of both the detailed information about living standards available in the household budget survey and the more extensive coverage of a census to derived spatially disaggregated poverty estimates based on a consumption indicator of welfare.

For the implementation of this technique we combine the AHBS 2002 survey, which covers 8,157 households and 33,000 individuals, and the 1999 Census which covers around 1.7 million households containing around 8 million individuals. Administratively Azerbaijan has 9 economic regions (oblast), 73 districts (rayons) and around 1,000 administrative units. For the census data collection, the State Statistical Committee considered around 4,500 enumeration areas. Each administrative unit comprises 3-5 enumeration areas. The AHBS 2002 has been 3
the impact on duration spells of differentiated versus homogeneous products (Besedes and Prusa 2006b); the effect of the GDP in origin or destination country (Hess and Persson 2011b); the importance of initial export values and fixed costs to export (Fugazza and Molina 2009); and contract institutions (Araujo, Mion and Ornelas 2012).

Another type of empirical investigation into survival determinants uses firm-level data, including panels of firms and customs records, but these are generally much harder to obtain. They have tended to be country or sector specific, looking, among others, at the impact of diversification and receiving support from the export promotion agency in Peru (Volpe and Carballo 2008), sequencing of destinations for firms in Argentina (Albornoz, et al. 2011) and the importance of networks of exporting firms in regions of Colombia (Tovar and Martinez 2011). More recently, with increasing availability of customs data, cross-country comparisons of firm-level data have allowed a more refined understanding of local specificities (Cadot, et al. 2010). This culminated 2
SubSaharan Africa, it will be crucial for policy makers to have a better understanding of the size, characteristics and nature of urban labor markets and, especially, urban informality. The existing literature explores urban workers, formal-informal workers, and household enterprises but has yet to pull them together into a single picture of urban labor markets in SSA. The sheer magnitude of the urban informal sector calls for a deeper understanding of the heterogeneity within to enable effective policy making.

This paper provides a detailed description of the magnitude of and heterogeneity within urban SSA labor markets, with an emphasis on informality, to encourage policy makers to design more nuanced interventions to serve the different segments within. It uses data from 26 harmonized household surveys, representing 61 percent of the SSA population,<sup>9</sup> and three Informal Enterprises Surveys (IES) to sketch a profile of the urban

> 6 ILO (2018) also finds that informal sector workers are not prime-aged adults, namely they are youth and older workers.

> 7Danquah et al (2019) find that in a four-country, national sample, women are over-represented among informal wage workers but under-represented among the self-employed. 8 Similar patterns are found in Perry et al (2008) for Latin America, and in Kanbur et al (2017) and ILO (2018) globally.

> 9 While there are 1.1 billion Sub-Saharan Africans, only 31 percent are working. The urban workforce is 36 percent of the national workforce. Thus, there are roughly 125 million urban workers in the region. We have data for 85 million urban workers in Sub-Saharan Africa.

3
# _I) Data and Methods_

# _A) The Demographic and Health Surveys_

The Demographic and Health Surveys (DHS) are large nationally representative household surveys.<sup>3</sup> The surveys have been carried out using a nearly identical survey instrument in over fifty developing countries.<sup>4</sup> While the main purpose of the surveys is to inquire about family planning and child and maternal health, the surveys also contain an educational history of all household members from a chosen respondent.

The education variables we analyze are based on four questions:

* Has [name] ever been to school?

- If attended school:

what is the highest level of school [name] attended?

what is the highest grade/years [name] completed at that level?

- If attended school:

Is [name] still in school?

These questions are used to construct an " attainment" history for a recent cohort, those aged 15 to 19 inclusive. This attainment profile is the proportion of the cohort who have completed any given grade or higher.

The analysis so far has covered 35 countries. Countries have been grouped into six regions. The groups, ranked from lowest to highest median attainment of the bottom 40

> 'Table 1 shows that the samples of individuals in the 15-19 age range are usually above 2,000, but vary from 1,355 in Kazakhstan to over 50,000 in India.

> 4 There are three main designs of the survey instrument. DHS I surveys were carried out between 1985 and 1989, DHS II between 1990 and 1993, and DHS III are those that have been carried out since 1994.

_5_
LAC that are officially classified as urban are actually embedded in an agriculturally based countryside. See also the World Bank’s 2005 LAC Flagship report “Beyond the City: The Rural Contribution to Development” (de Ferranti _et al_ ., 2005), for which the paper by Chomitz _et al_ . was originally prepared as input. This report was one of the first, if not the first, official World Bank publication to raise the issue of the lack of comparability of official urban population shares across LAC countries.

> 11 Dijkstra and Poelman’s algorithm for identifying urban clusters is closely related to that which Rozenfield _et al._ (2011) apply to identify cities in the United States.

12 Other global gridded population data sets include CIESIN’s GRUMP (Global Rural-Urban Mapping Project) Population Count Grid <u>(http://sedac.ciesin.columbia.edu/data/set/grump-v1-population-count) and its Gridded</u> Population of the World version 4 (GPW v 4) (http://beta.sedac.ciesin.columbia.edu/data/collection/gpw-v4) data sets. Various institutions and authors have also created gridded population data sets for individual countries or regions. These include a European population grid for 2006 produced by the Austrian Institute of Technology (http://www.efgs.info/data/) which covers EU28 and EFTA countries; and population grids for Haiti, Pakistan and 4
Summing up, while not dismissing the effects of other determining factors, this paper acknowledges that “media” has a very significant role to play in enhancing environmental performance.

The study has potential limitations that need to be discussed or addressed in the future. First, the fact that only firms which have been traded in the stock market were retained could induce some bias. If bias there is, it is, however, very small. Indeed, the results were not too different from the ones here when we included in the sample six firms which were not traded in the market. In fact, the rationale for dealing only with firms which were traded was too see whether those firms which registered market reaction also had environmental performance reaction. Unfortunately, there were too few of them to conduct a valid generalization. Second, the issue of endogeneity of some explanatory variables is a valid one. However, if we assume that our explanatory variables most reflecting the period 1990-2000 are lagged variables captured in the 2005 survey, then the issue fades away. In any case, in the future some test statistics need to be implemented to confirm or refute our hypothesis. Third, as the role of media is established here it is worth moving the debate forward by attempting to establish the link between media, environmental performance and financial performance or between media, financial performance and environmental performance.

16
) to ensure comparability with the 2008-2011 World Bank exercise; and 4) to adapt to – and be consistent with – the coverage of measures in the OECD database, which served as a fundamental source of information for economies covered by both the STPD and the OECD STRI. The list of measures selected for the construction of the STRI is provided in Annex 5 of Borchert et al. (2019).

# **2.2  Determination of level of restrictiveness**

Trade policies are assessed by looking at specific types of measures for each subsector and mode. For example, for commercial banking, we obtain separate measures for the cross-border supply, commercial presence and the presence of natural persons. We assign a score depicting the level of restrictiveness to each measure. For each individual measure, we generally consider 6 distinct levels of restrictiveness, each of which attracts a different (monotonically increasing) score:

1. not restrictive;

2. minimal procedural/transparency issues; 3. minor restriction; 4. “intermediate” category to reflect measures that are neither minor nor major restrictions; 5. major restriction; 6. service provision not possible.

Table 1 provides examples of concrete policy measures and associates a score to measures for each stage.

7
perhaps due to the need for at least one woman in the household to be available to assume responsibilities such as cooking and cleaning and looking after children. Relatedly, a woman is more likely to work outside the home for pay if there is presence of adolescent girl(s) in her household, indicating that young girls could act as substitutes for working women’s domestic responsibilities.<sup>32</sup>

**Table 7. Explanatory power of “rational” factors on women’s work outcomes vs. that of social norms**

||**PAID WORK**|
|---|---|
|PNB Index - HH Roles|-0.08**<br>(0.03)|
|SNE Index - HH Roles|-0.05<br>(0.04)|
|PNB Index - Public Space|-0.01<br>(0.03)|
|SNE Index- Public Space|-0.01<br>(0.03)|
|Years of education|0.00**<br>(0.00)|
|Log of household's total income minus working females'|-0.03***|
|earned income (NPR)|(0.01)|
|Presence of other working-age women in HH in labor force|-0.12***<br>(0.03)|
|Indicator for adolescent girl(s) in respondent's HH|0.07*<br>(0.04)|
|Respondent is aware of non-network methods of job search|0.01<br>(0.03)|
|Constant|0.64***<br>(0.07)|
|Observations|2,000|
|Adjusted R-squared|0.05|
|Regional FE|YES|

_Notes_ : (1) Apart from
The recent sharp falls in commodity prices have highlighted that South Africa’s export basket can no longer afford to rely so heavily on commodities. However, non‐minerals manufactured exports – driven by chemicals, metal manufactures and automotive products – may not be well‐positioned to take up the slack. Despite some recovery from the crisis, non‐minerals formed a small part in overall export growth: fuel, minerals and basic metal products still accounted for almost 90 percent of South Africa’s export growth since 2007 (World Bank, 2014).

South Africa’s exporting firms are highly concentrated. The top 5 percent of South Africa’s exporters account for 90 percent of all exports, with the top 1 percent accounting for close to 80 percent (World Bank, 2014c). These firms are particularly concentrated within the minerals and metals sectors. Recent trends suggest that the most dynamic markets for South African exports lie in the Southern African Customs Union (SACU) and wider regional markets, which is precisely where smaller exporters and exporters in non‐mining sectors are focused.

# _2.2 Addressing transport is critical to South Africa’s export success_

South Africa produces less than 0.5 percent of the world’s GDP, but requires 2 percent of the world’s surface freight tonne‐kilometers to do so (Havenga, 2012). One third of South Africa’s gross value‐add is concentrated in Gauteng, which is 570km from the Port of Durban –which has the largest container terminal in the country. Furthermore, much of South Africa’s non‐containerized export activity (mineral commodities and base metals) also occurs inland. As a result, comparative advantages in resources and other areas can be quickly eroded by high transport costs.

Despite a well‐developed road, rail, port and airport network South Africa ranks poorly in the Doing Business index for “Trading across Borders” (100<sup>th</sup> out of 189 countries), reflecting
‐district panel created from the firm censuses. The first is administrative data on imports and exports in each of the 13 industries. This is obtained from the Atlas for Economic Complexity.<sup>5</sup> The Atlas data were collected 4 Note that for both NIC (2003) and IBES (2014) it was necessary to use population census data from 2010 are there is no data from closer years available.

5 The data are available for download on the Atlas website: <u>https://intl‐atlas‐downloads.s3.amazonaws.com/index.html</u> 8
It is important to mention that given that the timing of the introduction of telecommunications reforms is not necessarily exogenous, our findings should be interpreted with caution. While they do not prove that telecommunications reforms had a causal effect on labor market outcomes, they do support the prediction that if the Internet affects the labor market, it would disproportionately do so among ICT‐ intensive sectors. Another caveat is that the labor force surveys used in this paper may not be able to capture new forms of work such as online freelancing or multiple job holding. Thereby, exploring the causal effects of new technologies on the rise of alternative work arrangements ‐ in particular using more detailed surveys aimed to capture these new forms of work ‐ is an important avenue for future research.

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|**Maritime**|25 km/h|25 km/h|
|---|---|---|
|**Upgraded rail**|50 km/h|75 km/h|
|**New rail**|–|75 km/h|

The choice of transportation mode is difficult to model and follows different logics and trade‐offs which are specific to the sector under consideration. It depends on the different elements that affect shipping costs such as the bulkiness of the good, subsidies, oil price, market structure and many others that cannot be modeled in a sensible way in our analysis. To account for the very high share of maritime shipping in international trade, we use a network preference to produce a maritime alternative where land connectivity exists. Within the optimal path algorithm, this preference is implemented in the following way: for any city pair that has access to both a maritime and a rail link, the algorithm selects the maritime option whenever the shipping time is lower than four times the shipping time incurred using the rail link. While such a criterion can be seen as a strong simplification of the actual trade‐off faced by many logistic companies, the imposition of such a preference for maritime links allows us to have a representation of the world that is very much in line with other data sources in terms of both modal shares and total shipping times at the city‐pair level. For example, in our pre‐BRI scenario with the global database, maritime transportation represents a total of 82.3% of all trade routes. In the following sub‐section, we explain in detail how we deal with the possibility of mode switching once all BRI projects are implemented.

On top of the time cost computed along the network elements, we account for processing times when a shipment is reaching / leaving ports, based on the estimates in Slack, Comtois, Wiegmans and Witte (2018). In their paper, Slack