rafmacalaba/data-use-mentions-extended huggingface dataset:
(IPIs) are also widespread: One recent study finds that more than 90 percent of children in a study area in the rural highlands of Ecuador were infected with one or more pathogenic IPIs, and more than onehalf the children were infected with helminths (Sackey, Weigel, and Armijos, 2003). We are not aware of any published studies that measure hemoglobin levels or cognitive development among pre-school children in Ecuador.

The sample we use contains urban and rural children, and is drawn from a survey conducted between October 2003 and September 2004. This survey is intended as a baseline for an evaluation of the _Bono de Desarrollo Humano_ , the largest social transfer program in Ecuador.

> 4 The provinces are Azuay, Loja, and Pichincha in the sierra or highlands region of the country, and Esmeraldas, El Oro, and Los Ríos on the coast. Parishes, or “parroquias,” are the smallest administrative units in Ecuador. There are 1,149 parishes in the country.

4
We emphasize that data capacity and transparency matter for a lot more than just the accuracy of GDP growth forecasts. Credible and timely data can serve policy actions. Data that are accessible to the broader research and civil society can generate better analyses which form the basis for discussions and reforms. Data transparency, in general, can build trust, hold governments accountable and improve institutional quality and growth (see Islam and Lederman, 2020).

We conclude by highlighting some practical recommendations that emerged from conversations with World Bank country economists on how to improve the quality of data to generate more accurate forecasts. Fundamentally, increasing the frequency and quality of national accounts data can improve forecasts considerably. It is also important that the underlying data is consistent within the country, and this can be achieved through better communication between ministries and national statistical offices. There is scope for technical assistance from international organizations to help governments improve the quality of national statistics, which in turn will improve the information that is fed into forecast models. Finally, accessing data for countries in conflict is quite challenging. These economies drive a lot of the inaccuracy in regional forecasts. However, there is some hope. For countries in conflict, alternative data sources such as information from satellites (for example, night lights data) are crucial and, in this regard, the international institutions can play an important role in facilitating access to such data.

23
.017|

Table 4: Estimates of _π_ , _λ_ and _P_ Notice that our estimate of _λ_ finds that the percentage of households residing in Cairo and Alexandria with incomes exceeding _τ_ is larger than what the HIECS alone would have us believe. This combined with the earlier observation that _θ_ ˆ _mix < θ_ ˆ _svy_ leads us to believe that relying on survey data alone will arguably under-estimate both the number of households with high incomes as well as the size of their incomes (either because top income earners are missing in the survey or because they under-report their incomes, or both). Table 5 compares estimates of top income shares _S_ obtained using the HIECS to those obtained using both the HIECS and the house price data. The additional columns compare estimates of inequality among top income households (i.e. only including households whose income exceeds _τ_ ) for three different measures of inequality.

|sub-group|_Smix_|_Ssvy_|_Ginimix_|_Ginisvy_|_MLDmix_|_MLDsvy_|_Theilmix_|_Theilsvy_|
|---|---|---|---|---|---|---|---|---|
|Cairo|0.159|0.118|0.414|0.227|0.295|0.087|0.532|0.107|
|Alexandria|0.042|0.036|0.283|0.156|0.141|0.038|0.208|0.041|
|Other|0.063|0.066|0.223|0.223|0.082|0.082|0.097|0.097|

Table 5: Estimates of _S_ , _Gini_ , _MLD_ and _Theil_ (for the top tail) Estimates of total inequality for (urban) Egypt are obtained by adding estimates of bottom- and between inequality to the estimates of top inequality reported in Table 5. Bottom inequality (i.e
development and structure also does not seem farfetched. In the GMM regressions that follow, we will show that the test of over-indentifying restrictions is passed when trust and net oil exports are used as instruments in most cases, and we will offer additional estimation approaches in the few cases when the test is not passed.

Our approach in this section has been to experiment with a wide set of potential instruments suggested by the literature, and to let the data inform us about which ones best explain variation in financial development. At the same time, we bear in mind that legitimate instruments must affect firm labor growth only through their effect on financial development. Our assessment is that trust and net oil exports best meet those criteria, and thus those two variables form the instrument set in the instrumental variables regressions that follow.

# **IV. Regressions**

# **A. OLS**

We estimate the following base regression: Where is the percentage change in the number of workers employed by firm _i_ in country _j_ over the two years prior to the enterprise survey, as described above. _FIRM_ represents the three firm characteristics that we use as controls: age, size measured in (logarithm of) total workers, and the share of foreign ownership.<sup>10</sup> As described in Section 2, we expect that labor growth would be slower for older, better established firms. We have no strong priors about how firm size or foreign ownership share affect labor growth. _FIN_ represents the indicators of financial development and structure that are the focus of the analysis. These include private credit/GDP, stock market capitalization/GDP, and the shares of sector assets held by foreign and state banks.

We begin with ordinary least square (OLS) regressions describing the associations between indicators of financial development and structure and labor growth. We present results for the full sample of countries, and for high- and low-income sub-samples. We divide countries into high and low-income based on the median per capita income level
resources to exploit opportunities. They are more capable of network formation and information collection, as well as network contacts monitoring (Shane and Nicolaou, 2015), which facilitates the assessment, procurement, and use of resources, all contributing to entrepreneurial success. In summary, personality traits significantly influence labor market outcomes, and the nature of these traits (i.e., task-oriented versus relationship-oriented) may be important in determining the types of work (i.e., paid work, self-employment) that both men and women engage in.

# **3. Data and descriptive statistics**

# **3.1. Data**

The paper uses data from the Ghana Informal Sector Measurement Study conducted from September to November 2022 by the World Bank and the Institute of Statistical Social and Economic Research (ISSER) of the University of Ghana. The survey contains rich information on household and individual demographics, and economic, social and geographic variables. In addition, the survey includes valuable information on individual gender role attitudes and personality traits, allowing for exploring the drivers of labor market participation and employment choice, and understanding the unique roles that gender role attitudes and personality traits play in shaping these decisions.

The survey adopted a multi-stage sampling approach where the first stage involved the purposive selection of districts within the Ashanti and Northern regions of Ghana. In the Ashanti region, Asokore Mampong, Asokwa, Kumasi Metropolitan Area (Bantama, Manhyia North, Manhyia South, Nhyiaeso and Subin municipals), Kwadaso municipal, Oforikrom, Old Tafo municipal and Oforikrom municipal were selected, while the Sagnerigu, Tamale Central, Tamale South, Central Gonja, North East Gonja, Gushiegu, Kumbungu, Mion, Nanton, Savelugu and Tolon were selected in the Northern region for the study. Following the purposive selection of these districts, the second stage involved the random selection of 134 enumeration areas (EAs) from these districts using probability proportional to size (PPS). All the districts and EAs in Kumasi were urban, while the Northern region
**Figure 6:  Contribution of Health to Productivity**

<!-- Start of picture text --> Panel A: Based on Adult Survival Rates<br>Contribution of Health to Relative Productivity (ASR)<br>LBRNERMWICODMDGGNBCOMTGOETHGINMLIGMBHTIBFARWAAFGBENKIRTLSSLBYEMNPLTZASENVUTKENTJKBGDFSMKHMMRTSTPDJIKGZGHAPNGSDNSYRCOGPSEHNDNICPAKMDATONWSMINDVNMMMRLAOBOLCPVGTMGUYPHLMARJAMAGOBTNSFJIBPRYARM L UZBXKXUKRVZGEOVCTDMAJORLCATUNEGYIDNECUBIHLKACUBALBPERMNGGRDCOLDZAIRQLBNBRAMKDDOMCHNSRBVENSURGABBRBTHAMDVCRIAZEMNEBWAMEXIRNBLRBGRMUSARGTURURYPANCHLBHSATGROUTKMHRVRUSKAZMYSLVAGRCPOLSYCHUNPRTESTCYPTLSVKSVN T ISRCZEMLTPRINZLUOESPJPNITAKORABWOMNFRAFINGBRBHRCANAUSTWNBELDNKDEUISLSWEAUTSAUNLDHKGUSACHEKWTIRLBRNNORARESGPMACLUX QAT<br>BDI MOZ SSDZWEUGA CMRMHLZMB NAMZAF GNQ<br>TCD NGA<br>CAF SLE CIV<br>SWZ<br>LSO<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>Panel B: Based on Stunting Rates<br>Contribution of Health to Relative Productivity (Stunting)<br>LCA CHL KORAUSUSA<br>LBR SLEGNBCOMTGOGINMLIGMBHTIBFASSDZWEUGABENSLBTZASENLSOCMRVUTKENTJKKHMMRTSTPDJITUVKGZGHACIVSYRCOGPSEHNDNICMDATONWSMVNMMMRLAOBOLGUYPHLMARJAMBTNSWZFJISLVBLZPRYARMGEOJOREGYTUNIDNECUBIHLKANAMALBPERMNGZAFNRUCOLDZAIRQBRAMKDDOMSRBCHNVENSURGABBRBTHAMDVCRIAZEMNEBWAMEXIRNTURURYPANTKMKAZMYSSYCTTOGNQJPNOMN KWTBRN<br>CAFNERMWIMOZCODETHRWATCDAFGYEMNPL BGDZMBSDNPAKNGAINDGTMAGO<br>MDG TLS PNG<br>BDI<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>1<br>.9<br>.8<br>Productivity Relative to Benchmark<br>.7<br>1<br>.95<br>.9<br>.85<br>Productivity Relative to Benchmark<br>.8<br><!-- End of picture text --> Notes: This graph reports the contribution of cross‐country differences in health outcomes to cross‐country differences in worker productivity. The vertical axis measures the productivity of a worker relative to the benchmark of full health. Differences in health outcomes are converted to productivity differences using estimates of the returns to health detailed in Appendix A3. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)) , with missing countries filled using data from World Bank estimates of GDP at PPP. Graph shows the most recent data for all countries.

15
years of coverage differ by country, with data from Russia spanning 25 years (1994–2020, with gaps) and data for Moldova covering just 7 years (2003–09). The period 2008–18 has the most extensive country coverage. Our analysis focuses on the working-age population (people 20–65).

The main variable of analysis—the number of years in the current job or job tenure—is available for Albania, Bosnia and Herzegovina, Kosovo, North Macedonia, Russia, <mark>Türkiye,</mark> and Serbia. For Moldova, the tenure variable is available in categories, which we classified as short (less than 1 year), medium (5–10 years), and long (more than 10 years). The data set also contains variables 6
The coverage ratios are for 2000, and are taken from (Demirgüç-Kunt and Sobaci, 2001). We use the inverse of the deposit coverage ratios in our index so as to allow this variable to capture potential _positive_ effects of depositor monitoring, and to be consistent with our treatment of the other sources of monitoring and enforcement, such as rule of law.<sup>7</sup> We also attempt to capture the difference in the _anticipated_ coverage by creating a dummy variable for coinsurance which is found to strengthen the intensity of market monitoring in the literature (e.g., Demirgüç-Kunt and Huizinga, 2004). Coinsurance mechanisms require depositors to bear part of the costs of a banking failure. Our dummy variable equals one if a country has any such arrangement, and zero otherwise.

# <u>E. Other Control Variables</u>

We include the initial level of private credit to GDP (i.e., in 1990) in our regressions to control for the possibility that countries with better developed banking sectors tend to experience slower credit growth. These conditional convergence effects were first made known in the context of cross-country growth regressions, but they have also proved important in financial development regressions.<sup>8</sup> We also include the growth rate of GDP per capita and the inflation rate as macroeconomic controls. Both are averaged over the full sample period. We expect private credit growth to be faster in countries with low inflation and high growth, though inflation and private credit growth might be positively related in an expanding economy. The data on macroeconomic controls are from the World Development Indicators database maintained by the World Bank.

Summary statistics for all variables used in the regressions are in Table 1. Correlation coefficients are reported in Table 2. Neither the correlation between credit growth and related lending nor the correlation between our index of depositor monitoring and related lending is significant. Similarly, the correlations between the related lending index and our control variables are also insignificant, except for the
# **3 Data and Experimental Design**

## **3.1 Data Collection and Sample**

This research is based on a phone survey, conducted between February and April 2023 and targeting a sample of 1,505 adults. The response rate was 35 percent. The survey duration was on average 37 minutes. Respondents did not receive any financial or other benefits for participating in the survey. The survey questions were translated to colloquial Tunisian Arabic and extensively piloted in January 2023. Figure 1 displays a timeline of the data collection process.

**Figure 1:** Timeline

<!-- Start of picture text --> First Second<br>endline endline<br>Dec. 2022 Jan. 2023 Feb. 2023 Mar. 2023 Apr. 2023<br>Data collection<br><!-- End of picture text --> The final sample was obtained by random digital dialing: phone numbers are generated randomly taking into account the country-specific area codes, which allows to reach a fairly representative sample of survey respondents (Stasny, 2001). This naturally generates a stratification by governorate and we furthermore stratified by gender to reach equal shares of female and male respondents in the first survey round.

## **3.2 Experimental Design**

## **3.2.1 Baseline Survey**

Figure 2 provides an illustration of the experimental design. A baseline survey collected relevant socio-economic characteristics (gender, age, governorate of residence, number of siblings, marital status, number of children if any, educational attainment, employment status). The baseline survey also contained questions on respondents’ level of religious 13
you done any of these things or any other work?’._

# 4.2.2. An activity list

The second approach uses an activity list, which consists of a complete list of jobs/activities that are common in the region. Some surveys, such as the Nepal LSMS survey, ask respondents to list all productive activities of the last 12 months. Other LSMS(‐ISA) surveys include a pre‐ specified list of activities. Such a list is not necessarily limited to strictly defined ‘productive’ activities but can also include activities such as domestic and care work or collecting water and firewood. Given the pre‐specified activity list, respondents report whether they engaged in any of these activities and, in some surveys, the time dedicated to these activities in a reference period (ILO, 2018e). Several authors have argued that a carefully prepared activity list is easily understood and allows capturing multiple job holdings as well as atypical employment (Langsten & Salen, 2008; Oya, 2013).

In practice, the use of a detailed activity list in the labor module is exceptional. Of all surveys reviewed, only a few (i.e. the 2014 Guatemala LSMS, the 2010 Nepal LSMS, the 2007 Nepal LFS and the 2013 Mali LFS) include a pre‐specified activity list. Moreover, this list is typically limited to unpaid domestic and care work, while work for pay and profit is measured with stylized questions. For instance, the 2014 Guatemala LSMS includes a list of questions such as _‘Yesterday, did you take care of animals?; Yesterday, did you make repairs to your dwelling of any type: electrical, plumbing, bricklaying, etc.?; Yesterday, did you clean the house?;_

> 12 For instance, the 2008 Malawi population and housing census included 7 questions about employment. The keyword question reads as ‘ _Aside from his/her own housework, did [name] work during the last 7 days?’._
|_2 to 5_|2.80|10.03|2.87|1.94|17.65|
|---|---|---|---|---|---|
|_6 to 10_|1.02|7.98|6.15|7.01|22.16|
|_11 and above_|0.37|7.00|9.53|41.14|58.04|
|**_Total_**|5.35|25.86|18.67|50.11|100.00|

Source: Authors’ calculations based on data from Comtrade.

**Figure 170 Product vs. geographical portfolios**

<!-- Start of picture text --> Average Number of Products per Firm Average Number of Destinations per Firm<br>18 4<br>16 3.5<br>14<br>3<br>12<br>2.5<br>10<br>2<br>8<br>1.5<br>6<br>4 1<br>2 0.5<br>0 0<br>2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009<br>PAK BGR COL ECU PER ZAF PAK BGR COL ECU PER ZAF<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

Statistics about high-performing exporters, the so called “gazelles”, may be seen as consistent with the above assessment. Indeed, when exploring the characteristics of high-performing exporters, i.e. firms with an annual growth rate of at least 75% for four consecutive years (between 2001 and 2010), interesting patterns are established. It is clear that firms that perform consistently well over this period share a few common characteristics,
*</sup>|-0.020<sup>***</sup>|-0.011<sup>***</sup>|-0.015<sup>***</sup>|-0.015<sup>***</sup>|-0.010<sup>***</sup>|
|_(Herfindahl index)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|
|_Natural Resource Exports_|||||||
|Natural resource exports|..|..|..|0.005<sup>***</sup>|0.004<sup>**</sup>|-0.00005|
|_(% of GDP)_||||_(0.000)_|(-0.003)|(-0.956)|
|<br>Natural resource exports|..|..|..|-0.015<sup>***</sup>|<br>-0.012<sup>***</sup>|<br>-0.005<sup>**</sup>|
|<br>_(% of total exports)_||||_(0.000)_|_(0.000)_|(-0.002)|
|_Additional Growth Drivers_|||||||
|Secondary enrollment rate|0.004<sup>***</sup>|-0.002|0.002<sup>**</sup>|0.
12 from five African countries form the primary information base for this study.<sup>2</sup> They have been stratified to be representative for rural and urban areas. The surveys gather a wide range of socioeconomic information on households and the communities of which they are a part, with detailed attention to their sources of income and geo-referenced, plot-level information on their agricultural activities and crops grown. Most importantly for this study, they also include for all uncultivated plots detailed information about the type of crop (including tree crops), the harvest, and expenses incurred. For fallow or uncultivated plots, farmers were explicitly asked whether they contained trees. In countries where two seasons of agricultural data were collected (Malawi, Tanzania and Uganda), the average presence of trees across both seasons was taken. Across these countries a total of 12,500 rural households (and 30,000 plots) were surveyed and through application of survey sampling weights a representative portrait of on-farm tree prevalence and their economic contribution to rural household incomes was obtained.

In the absence of a standard classification of crops as trees, potential trees were first identified from the LSMS-ISA agricultural crop production data, following the biological convention that to qualify as a tree a plant must be a woody perennial with a trunk or elongated stem that supports branches and leaves. With the help of several experts this list was subsequently divided into five subcategories: (1) fruit trees (e.g. mango, orange, etc.); (2) tree cash crops (e.g. coffee, tea, etc.); (3) timber and fuelwood trees (e.g. Mahogany, bamboo, etc.); (4) plant/herb/grass/roots (e.g. maize, banana, etc.); and (5) unidentified (e.g. voandzou, wechino, etc.) (Table A.1 includes a detailed list of all the crops considered as trees and their further
this anecdotal explanation - since the baseline matters - e.g. if pollution is persistent.

Figure 4 presents the results of extending the Google Mobility Index (which always lags by one week) with air quality-related information: temperature and fine particulate matter (PM2.5), for countries for which data are available. The information is gathered from the Air Quality Open Data Platform from January 1, 2019 to April 30, 2020 for Argentina, Brazil, Chile, Colombia, Mexico, Peru and El Salvador. The shaded areas represent the information estimated backward and forward using Equation 6. The extended information suggests that the period prior to the COVID-19 crisis was signaling a recovery in Argentina, and a significant decline in Chile and Mexico, although stronger in the former. Meanwhile, the index points to stability in Brazil, Peru and El Salvador. Appending air quality data to the mobility index is warranted on both statistical and economic grounds, with the latter being the main motivation for this analysis. The predicted value of the Google Mobility Index using more up-to-date information confirms that economies may have bottomed-out in April, with Mexico being the exception.

The final set of results, which nowcasts industrial production using the appended Google Mobility Index, is summarized in Table 1 for Brazil, Chile, Colombia and Mexico.<sup>1</sup> The monthly growth rates are gathered from the Economic Indicators database of the OECD. The _R − sq._ achieves a maximum of 25 percent in Brazil and a minimum of 18 percent in Colombia. In most cases, the results point to a deterioration of March growth rates compared to February, and an even stronger decline in April. Specifically, Mexico is expected to decline by 5 and 6 percent for March and April, respectively, from a 0.7 percent decline in February. Similarly, Brazil is expected to decline by nearly 7 and 3 percent, while Chile is expected to decline by 1 and 2 percent; and Colombia 0.4 and 2 percent, respectively
collected for both males and females, and was processed accordingly. We would not expect the sex-disaggregated coverage rate to exceed the population coverage, and it never does. Moreover, if the country has a sex-disaggregated data point they also have a population estimate. But the reverse is not the case. For six of these 32 indicators (19%), while there is some reported data for the population indicator, there is no sex-disaggregated data reported. As an example, 56% of countries report a population rate for SDG indicator 10.2.1 (the proportion of people living below 50 percent of median income), yet no country reports this statistic by sex. These missing data are not the result of missing sex in underlying data source (in this case, household surveys). The measure itself (living below an income threshold) is defined at the household level and so one can produce a sex-disaggregated estimate based on the households in which individuals reside. For example, Munoz Boudet et al. (2021) report poverty rates by sex.<sup>6</sup> In the remaining 17 cases (out of 32), where there is some reported data for both the population and by sex-disaggregation, in a handful of cases there are large gaps between the percentage of countries with a recent value by sex and those reporting a population estimate (i.e. comparing the last two columns in Table 1 when both columns are non-zero). For SDG indicator 1.3.1, on the proportion of population covered by social protection floors/systems, 79% of countries have a recent value for the population, but only 8% of countries have a sex disaggregated data point. A less drastic example is SDG indicator 4.1.1, related to early childhood education: 64 percent of countries have a population estimate for this indicator but only 53 percent have an estimate by sex.

> 6 This is not related to the issue of measuring income or poverty at the individual