rafmacalaba/data-use-mentions-extended huggingface dataset:
restrictiveness of services policies for low‐income countries. Some of the poorest countries, like Ecuador, Ghana, Nigeria, Senegal, and Mongolia are remarkably open, while some of the most restrictive policies today are visible in the fast growing economies of Asia, including China, India, Indonesia, Malaysia, the Philippines and Thailand, as well as in the Middle East, including Egypt, Saudi Arabia and Tunisia.

Figure 2 uses the same data but focuses on average levels of restrictiveness by sector and geographic region. It can be seen that there is a lot of variation across sectors, with some being subject to high discriminatory barriers in all regions (transport and professional services) whereas other sectors are much more restricted in some countries than others. Financial services and retail distribution tend to be much more open to foreign competition in high income countries than in many developing countries.

> 2 The database covers five sectors: financial services (banking and insurance), telecommunications, retail distribution, transportation, and professional services. In each sector, the survey covers the most relevant modes of supplying that service: cross border trade in services (mode 1) in financial, transportation and professional services; commercial presence or FDI (mode 3) in each services sector; and the presence of service supplying individuals (mode 4) in professional services. Results of the survey are summarized in an index of services trade restrictiveness (STRI). For each sector and mode of supply the openness of policy towards foreign suppliers is mapped on a 5‐point scale ranging from 0 (for no restrictions) to 1 (highly restricted), with three intermediate levels of restrictiveness (0.25. 0.50 and 0.75). Sector results are aggregated across modes of supply using weights that reflect judgments of the relative importance of the different modes for a sector. For example, mode 4 (temporary movement of suppliers) is important for professional services, but not for telecommunication, where mode 3 is the dominant mode of contesting
**50%.** Based on administrative tax data, previous research estimated the share of minimum wage earners in Romania in 2020 – 2021 and described their profile (Robayo-Abril, Zamfir, and Wroński, 2024). According to our results, based on the tax data, more than 25% of all employees earn minimum wage. The proportion of minimum wage earners varies across branches of the economy and regions of the country. The share of minimum wage earners is exceptionally high in micro enterprises (less than ten employees), where 67% of all employees are minimum wage earners. In construction, accommodation, and food services, more than 50% of employees earn only the minimum wage; in the transport and storage sector, this share is over 40%. The proportion of minimum wage earners is higher in low-income regions. The share of minimum wage earners is highest among the youngest and oldest employees.

**However, the estimated share of minimum-wage earners in tax data may be subject to measurement error due to tax evasion and unreported income; in particular, it may be biased upwards.** In particular, workers and employers may underreport their actual earnings to avoid paying taxes or to receive government benefits. This behavior could lead to an upward bias in estimating the share of minimum wage earners in the tax data. Moreover, envelope payments, typically made in cash and not reported to tax authorities, are more prevalent among workers in low-wage occupations, further complicating the measurement of the share of minimum-wage earners based on tax data. It is theoretically possible that some employees earn minimum wage formally and more informally, e.g., by receiving envelope payments. To assess the reliability of the estimate of the share of minimum wage earners based on tax data, we compare the share of minimum wage earners in survey data, tax data, and imputed survey data. We define minimum wage earners the same as Robayo-Abril, Zamfir & Wroński (2024); we cover separate minimum wage for the construction
specifically targeted towards retirement planning, which often take the form of seminars, seem to indicate that they can be effective in increasing retirement savings, although there is little experimental evidence for this so far.

For instance, Bernheim and Garrett (2003) use a household survey to analyze the effect of employer-based financial education on the savings behavior of employees, both in general and for retirement. Their cross-sectional analysis finds positive correlation between education programs and savings, with sufficient controls to suggest that the relationship is causal. Lusardi (2003) likewise uses survey data to examine the impact of attending retirement seminars on savings behaviors, specifically among older households. The study finds positive effects on saving behavior, particularly for those with lower education and low initial savings. Clark et al. (2004) evaluate the impact of attending financial education seminars on desired retirement age and expected levels of retirement income, using a pre-test post-test survey. They find changes for both retirement goals and stated savings behavior, particularly for women.

Duflo and Saez (2003), one of the few randomized impact evaluations of any financial education programs in the U.S., demonstrates the importance of peer effects in retirement preparation, and perhaps in the take-up of financial products more generally. They study the effects of providing information to university employees about a benefits information fair on both fair attendance and their subsequent enrollment in a Tax Deferred Account (TDA) retirement plan. The fair attendance and TDA enrollment rates of both the treated individuals (who were also offered a small monetary reward) and their departmental colleagues significantly increased, suggesting not only treatment and motivational reward effects, but also social network effects. Their conclusion is that peer influence likely played an important role in individual’s decision to obtain information about TDAs, and in conjunction with this information, potentially also on their subsequent decision to enroll in one.

Individuals planning for retirement may also be expected to be literate in investment strategies. However, the provision of investment information may or may not have impact on how
We made a number of extensions and changes to adapt the approach in Schwartz et al. (2009) for LAC to MENA. First, wage levels were changed to reflect the fact that they are heterogeneous across and within MENA countries, and different from those in LAC. Following the idea of Schwartz et al. (2009), hourly wage costs for construction work were estimated for the three sub-regions of MENA as given in Table 7 .<sup>7</sup> Computing hourly wages is crucial for all the estimates and is subject to potential underestimation, specifically for the GCC and the developing oil exporting countries. To build our reference values, we take data from Gardiner and Theobald<sup>8</sup> (2007) for Lebanon, Qatar and UAE and data from Tong (2010) for the UAE.<sup>9</sup> Those data correspond to the actual cost of labor, as they encompass the various compensation schemes and not only the direct cash transfers. We need to provide homogenous wage estimates within sub-regions as the investment needs applied later are aggregated values. To do so, we compute regional wage averages, based on the ILO Key Indicators of the Labor Market in 2011 and assume constant difference between the ILO indicators and the total compensation in the selected countries. Our estimates have to be considered carefully as they do not reflect the intensive use of migrant workers in the infrastructure and construction sectors and the high variance of wages in developing oil exporters and GCC economies.

**Table 7 Estimated hourly wages (in 2010 US$) in infrastructure works**

|**_Region_**|**US$ per hour:**<br>**Qualified workers**|**US$ per hour:**<br>**Non-qualified workers**|
|---|---|---|
|**GCC oil exporters**|4.5|3.0|
|*
We see in Figure 1 that the relationship is quite flat at low per capita consumption levels, consistent with the idea of absolute poverty. On the basis of the pattern evident in Figure 1, RCS proposed an international absolute poverty line of $1.25 a day for 2005, which is the mean of the lines found in the poorest 15 countries in terms of consumption per capita.<sup>10</sup> The level of this poverty line is quite robust to the choice of the poorest 15 countries (taking plus or minus five countries ranked by consumption per capita). Focusing on the poorest 15 countries is also consistent with econometric tests reported below in calibrating the weakly relative poverty lines. Of course, there is still a variance in the national poverty lines at any given level of mean consumption, including amongst the poorest countries. The poverty lines found amongst the poorest 15 countries vary from $0.70 to $1.90 per day and RCS estimate the robust standard error of the $1.25 line to be $0.10 per day.

After converting the international poverty line of $1.25 at PPP to local currency in 2005 prices, we convert it to the prices prevailing at each survey date using the best available countryspecific Consumer Price Index (CPI).<sup>11</sup> The weights in this index may or may not accord well with consumer budget shares at the poverty line. In periods of relative price shifts, this will bias our comparisons of the incidence of poverty over time, depending on the extent of utilitycompensated substitution possibilities for people at the poverty line.

Given the steep rise in food prices around 2008, we made extra effort to assure that the price indices we use adequately reflected those increases at country level. This was done in consultation with the Bank’s poverty experts for each country. In some cases, such as India, we are already using CPIs that are anchored reasonably well to consumption behavior of the poor,
secondary) here—is split into two segments. The first segment of lower secondary (LS) has a typical duration of 3 years, followed by another 3-year segment of US. Apart from allowing for consistent comparisons, relating ages with schooling levels is particularly important for normative reasons. While primary education is already compulsory in Central America, there are differences thereafter. The main one is that LS is also compulsory in Costa Rica, Guatemala, El Salvador, and Panama, as well as Honduras as of 2013, but not in Nicaragua (OEI 2010).<sup>5</sup> Another important advantage is that since household surveys incorporate information on a wide set of personal and household characteristics, it is possible to differentiate school dropout patterns for different population subgroups or family situations. Furthermore, the data incorporate other variables such as labor market conditions that are useful for understanding schooling decisions.

Yet another advantage, which will be illustrated in the following sections, is that household surveys include representative samples of all age groups because they are normally structured around the demographics of each country. This allows us to follow the trajectory of different cohorts over time. The case of El Salvador, where 19 observations are available over a 24-year period, illustrates this. If one identifies all individuals ages 1-5 in the first survey of 1989, it is possible to locate the same group six years later in the 1995 survey, when they are ages 7-11,

> 4 An alternative followed by other studies has been to rely on official administrative records from the schooling system itself. However, these types of data bases do not usually include information of those _not_ attending education services, which are a key component for our analysis.

> 5 Some countries in Latin America with slightly different arrangements are Bolivia, Chile and the Dominican Republic where LS has a 2-year duration, while in Colombia and Peru secondary comprises 5 years and is not divided into two segments.

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our labor market measure from the HFPS with official ILO employment data and also find that differences between the HFPS and ILO data for the countries for which both data were available exist when measuring some features of the labor market.

The findings highlight the value of the substantial effort to collect, harmonize, and compare phone survey data across countries, both to better understand the nature of the COVID19 shock, as well as its effects on different sectors and countries. Our findings that the phone surveys contribute additional information are consistent with Heath et al. (2021), who find that in urban Ghana interviews conducted on the phone and in-person led to differences in measures of employment, hours and days worked for the self-employed. The phone survey data, while far from perfect, contribute valuable new information on how households in a broad cross-section of developing countries were affected by this severe shock.<sup>3</sup> The remainder of this paper proceeds as follows. Section 2 provides a review of the related literature and the background on the COVID-19 pandemic and the pre-pandemic labor market situation of the countries studied. Section 3 introduces the high-frequency phone survey data, the process of selecting the sample, and the weighting method. Section 4 reports results on the labor market impact of the pandemic, compares our estimates to other projections of economic and labor market activity and also provides a robustness check for the weighting method employed. Section 5 concludes.

# **2 Literature and Background**

## **2.1 Related Literature**

Our work contributes to the recent and growing literature examining the labor market impacts of the COVID-19 pandemic around the world. Most analysis of early labor market

> 3Even in developed countries, the Economist (2020) highlighted that data from other sources and official data sources do not always map perfectly with each other. However, these data provide valuable information to find turning points and one could pick up different patterns in the economy earlier than is possible with data sources.

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>household<br><!-- End of picture text --> _Notes:_ Household sampling weights are used. Based on respondents’ self-reporting, 80 percent of male landholders in femaleheaded households are adult sons of household heads.

# **a) Gender Difference in Area of Agricultural Landholding**

There is a gender difference in land size by households. Male-headed households’ landholding size is about 35 percent larger than that held by female-headed households (Table 5).<sup>16</sup> These differences in land area persist across the expenditure distribution (see Appendix 8). Female-headed and female-only household landholdings 16 A recent study reached the same result using 2016 land registry data in the Tigray region (Holden and Tiluhan 2020). They also found that women own half of all landholdings, which also corroborates our findings.

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Mexico to be processed by conventional penal courts. The administrative records of alleged criminals provide part of the legal statistics on crime. This information is captured during all three stages of the criminal justice procedures: preliminary investigation, pre-instruction, and sentencing. In particular, data from alleged offenders is captured after the prosecutor has substantiated the accusation on the existence of a crime and the presumed responsibility of the subject. At this time, the offender is brought before a judge, who will determine his legal status by evaluating the elements linking him to the commission of the offense.

> 18 In any case, if we take into account anecdotic evidence on DTO’s wages, opportunity costs between the legal and the illegal labour market involves significantly higher wages in the criminal spectrum. While an average monthly salary in the maquila sector is around $4,000 pesos, DTO’s starting salaries begin around at 8,800 pesos:

> i. A 31 page Payroll found in documents confiscated from Los Caballeros Templarios established that those working in the administrative “area” of the organization earned between $12,000 and $16,000 pesos per month; those in the Communications office earned between $16,000 and $20,000 pesos per month; those in the area of Operations were divided in different groups: Sicarios earned between $12,000 and $16,000, falcons or vigilantes between $8,800 and $15,200 pesos per month, depending on weather they had vehicles or were established in a single place, and if they were following someone in particular or taking care of an area (Aristegui Noticias, Jan 21, 2014, http://goo.gl/z7KOgK, with information from La Razón).

> ii. In an interview with Vice Magazine, a women working for Los Zetas (another DTO) explained how she recruited 50 falcons (the lowest position in DTO’s conformed by people in charge of alerting about activities of security forces or other gangs)
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE percent in 2000 to 40 percent in 2007 in rural parts of the country (figure 10). Similarly, urban access to safe water within 30 minutes from home decreased from 81 percent in 2000 to 75 percent in 2007.

**Figure 10. Access to safe water is declining, 2000-2007**

<!-- Start of picture text --> 100<br>2000<br>2007 2000<br>80 2007<br>60<br>2000<br>2007<br>40<br>2007<br>20<br>2000<br>0<br>urban rural urban rural<br>access to clean and safe water<br>access to clean and safe water<br>within 30 min (rural 2000 data NA)<br>percentage of households<br><!-- End of picture text --> Source: Monitoring Progress in Water and Sanitation, WaterAid, Tanzania, March 2009 (HBS 2007).

In addition, Tanzania’s utilities are highly inefficient (table 6a). Distribution losses are typically around 45 percent, compared with 33 percent among peers. At around $0.40 per cubic meter, Tanzania’s water tariffs are substantially lower than those found in other African countries, covering only two thirds of the cost of service provision. The hidden costs of these inefficiencies are very substantial amounting to 145 percent of sector revenues.

There is significant performance variation across Tanzania’s utilities (table 6b). System losses range from 33 to 52 percent. Collection ratios range from 85 to over 100 percent. Cost recovery ratios range from 32 to 60 percent. Only the better performers (Dodoma, Mwanza) have hidden costs around the level of the low income country benchmark for Africa, while the worst performers (Dawasco, Mbeya, Moshi) have hidden costs in excess of 200 percent of revenues. In absolute terms, the hidden costs associated with
is whether machine learning methods can be effectively used to select weather metrics and remote sensing sources. Our approach has relied on traditional regression techniques to estimate a large contingent of regressions and then determine which metrics matter over a large set of results. However, these research questions also seem well suited to a number of machine learning techniques, in particular feature selection. We are interested to see how the findings of a machine learning algorithm comport with our findings using traditional regression analysis.

# **6 Conclusion**

In this paper, we investigate the significance and magnitude of the effect of measurement error from various sources on modeling agricultural productivity. Using geospatial weather data from a set of weather products with geo-referenced household survey data from six Sub-Saharan African countries, we are able to provide systematic evidence of measurement error, as related to spatial measurement of weather metrics over a large set of crops and countries. We find that while the extraction method does not introduce substantial measurement error into the analysis, results vary depending on the choice of weather metric and the choice of remote sensing data source. Based 48
), we include year effects to control for global factors. The autocorrelation test and the robust estimates of the coefficient standard error assume no correlation across individuals in the idiosyncratic disturbances and the introduction of time dummies makes such assumption more likely to hold.

> 19 Fisher panel data unit root test could not reject the null hypothesis that all panels contain unit roots for GDP per capita in PPP terms. On the contrary, the null hypothesis is rejected when the growth rate of the same variable is considered.

> 20 The estimation procedure exploits lagged instruments that are weakly exogenous if they are not correlated with future error terms. The lagged dependent variable is “predetermined” as it is correlated with past error terms, but uncorrelated with the current and future error terms. However, the other variables are potentially endogenous given that they are correlated with the current and past error terms, but are assumed to be uncorrelated with future errors. In other words, predetermined and endogenous variables are uncorrelated to unanticipated shocks (future error terms), albeit expected future dynamics may affect them. Therefore, a set of instruments is given by the lagged levels.

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in the absence of an increase in demand for corn, prices would have been 15% lower in 2007, prices would have been 10% lower without the increase in biofuel production (Table 6). If we ignore the fact that the co-product of corn ethanol production, namely, distillers grains, is a substitute for corn, then biofuels appropriate a larger quantity of corn that is traditionally consumed as feed and, as a result, become responsible for about 12% of the price increase in 2007. The single largest use of corn is feed grain for animals, which is used for meat and dairy. Furthermore, meat consumption tends to increase with income, resulting in higher demand for corn in emerging economies. As per FAO statistics, in China, which witnessed average growth rate of 8.5% between 1990 and 2003, per capita meat consumption increased 150% from approximately 20 kg per person per year in the year 1985, to approximately 50 kg per person per year by the year 2000. Furthermore, increase in per 45