rafmacalaba/data-use-mentions-extended huggingface dataset:
often included in standard household consumption and well-being measurement surveys. While these bias adjustments did not fully eradicate the bias across all dimensions, they were highly effective at reducing bias. This highlights one advantage to drawing phone survey samples from existing face-to-face, representative surveys rather than from RDD or lists from telecom providers where such adjustment methods can be more limited.

Phone surveys are likely to be widely considered as alternative data collection platforms in developing countries especially in emergency situations. National statistical offices have unique opportunities to implement these techniques using their recently implemented representative F2F surveys that can serve as frames for phone surveys.

48
productivity with respect to local firms when domestic infrastructure or access to finance is worse. Foreign firms are more able to withstand a weaker business environment in general, but need more help in day-to-day interactions with the government.

The business environment effects also differ greatly by firm size. Particularly important elements for relatively small firms include corruption, availability of both basic and modern infrastructure, a better human capital endowment, and access to land. Access to finance is also important for relatively smaller firms for firm growth, but important for large firms for productivity. Like foreign firms, relatively large firms benefit more from physical safety.

The main contributions of this paper are four. First, by using multiple sources of indicators of the business environment from three popular data sets, we are able to ascertain the robustness and the importance of various aspects of the business environment. Second, we offer novel evidence for the importance of contract enforcement in understanding the effects of de jure indicators of the business environment. Third, we offer evidence that the effects of the business environment differ greatly for domestic private and foreign firms, with the latter being better able to withstand a worse business environment in general, but need more help in day-to-day interactions with the government. Fourth, we provide evidence how the effects of the business environment differ by firm size.

The rest of this paper is organized as follows. Section 2 describes our hypotheses. Section 3 discusses the data and measurements. Section 4 presents basic results of the effect of various aspects of the business environment on firm performance, and how these effects interact with government capacity. Ownership-specific and size-varying business environment effects are explored in sections 5 and 6, respectively. Section 7 concludes.

# **2. Hypotheses**

As mentioned earlier, this analysis relies three groups of BE indicators to gauge their effects on firm performance. How should the three groups of BE indicators be associated with firm performance differently? Does it matter whether the indicators are _de jure_ or _de facto_ ?

The DB indicators are de jure
|6|10|
|Ghana|89|47|68|37|91|74|32|41|
|Madagascar|38|6|12|1|44|12|3|4|
|Malawi|38|4|9|1|31|8|0|1|
|Mali|92|57|65|49|80|86|50|52|
|Mozambique|47|2|16|1|51|11|1|2|
|Niger|61|6|15|2|47|20|3|4|
|Nigeria|93|48|64|33|88|88|38|48|
|Rwanda|48|6|12|1|46|6|1|1|
|São Tomé and Príncipe|69|48|59|49|72|62|44|53|
|Senegal|93|32|59|36|84|84|26|44|
|Sierra Leone|42|2|17|3|43|25|1|8|
|South Africa|94|81|89|78|99|87|77|81|
|Swaziland|70|30|40|4|83|45|16|19|
|Tanzania|52|9|20|4|58|7|4|4|
|Togo<sup>a</sup>|80|10|37|6|76|65|6|19|
|Uganda|39|7|15|3|42|6|4|4|
|Zambia|59|16|31|8|78|19|11|12|
|Median|66|13|34|7|65|33|6|11|

_Source_ : World Bank staff analysis of household surveys a. The statistics for Togo are not for those who are connected to the
et al., 2006).<sup>3</sup> In contrast, this paper finds evidence of more persistent employment effects than might be driven by sticky prices. For most economies, investment does not fall in response to a technology shock. Investment, as well as employment, is expected to fall initially in standard New Keynesian models.

In some cases, the SVAR literature has assigned the fall in employment found in some advanced economies following an SVAR-identified technology shock to the displacement of labor for new technologies, rather than the sticky-price mechanism. Canova et al. (2013) and Michelacci and Lopez-Salido (2007) attribute falling employment after a technology shock to Schumpeterian ‘creative destruction’ effects. This paper builds on this tradition, using a significantly larger set of economies under consideration, and using new SVAR identification techniques. However, a range of alternative and complementary approaches have also been implemented to establish the effects of new technologies on employment.

# **2.2 Sectoral evidence**

A large body of evidence has shown that jobs have become increasingly polarized into low- and high-skill occupations in the U.S. and Europe in recent decades, as a combination of automation and offshoring has reduced demand for middle and low-skilled workers performing routine and codifiable jobs (Acemoglu, 1999; Autor, 2015; Goos et al., 2014). Many of these lost occupations were in the industrial sector, even as value-added produced by the sector remained resilient; in the United States, employment of machine operators, assemblers, and other production employees fell by over one-third every 10 years between 1980 and 2005 (Autor and Dorn, 2013). In a study of 16 European economies during 1993-2010, the share of employment accounted for by middle- and lowskilled industrial sector occupations fell by nearly 10 percentage points (Goos et al., 2014). In the United States and France, the increased use of robotics in industry is found to be inversely related to industrial employment levels since 1990 and 2010,
(transfers). In addition, data for VAT expenditures is not available and is not part of the analysis. INE’s ENGIH 2006 is the most recent source for data on expenditures.<sup>17</sup> The 2016 round of the ENGIH has not been released yet. Finally, evaluating the quality of the policies studies or their effectiveness with respect to other fiscal alternatives with an income-redistributive objective is outside the scope of this study. The effects described here aim to inform about the poverty and inequality impacts of a set of fiscal policies in place in Uruguay in 2017.

> 17 This was used in Bucheli et al (2013).

13
#  Senegal River Valley in Senegal

Table 2 summarizes the major characteristics of each scheme and the features of the survey data. In addition to the differences in water delivery systems, size, climatic conditions, management body, and proximity to city, one notable difference can be found in supporting institutions. Neither Uganda nor Mozambique provides support for fertilizer purchase, agricultural credit, and output procurement to rice farmers. Meanwhile, all four Sahelian countries received an informal (Burkina Faso and Mail) or formal (Niger and Senegal) subsidy on fertilizer. In addition, in Mali and Senegal, there exist savings and loan programs or farmer organizations that facilitate group purchase of fertilizer. Niger‟s government provides price support (Kore, 2006). Generally speaking, institutions are more supportive in the four Sahelian countries than in the two East African countries.

In all surveys except the one in Niger, data were collected by random or stratified random sampling methods to obtain representative farmers in each irrigation scheme.<sup>2</sup> The sampling unit is farming households. We use only rice farmers in the schemes for our analyses. The Doho‟s survey was conducted by one of the authors in 2007.<sup>3</sup> The International Rice Research Institute conducted the survey in Chokwe in 2007. The survey of four Sahelian countries was conducted by the Africa Rice Center and its country partners in 2005-06 or in 2006-07. Since the surveys were conducted independently with their own research focus, the available variables are not completely comparable. Moreover, as of 2010, the data sets for the four Sahelian countries are not yet cleaned for the estimation of the determinants of yield and input use. Hence, the

> 2 The sampling in the Doho rice scheme is stratified by irrigation blocks. The other studies use simple random sampling.

> 3 See Nakano (2009) for more details.

5
(minor gaps imputed using a structural time series model) so that the model has an accurate representation of observed temporal trends. Finally, the institutional exchange rate is added to inform the model about currency depreciation effects.<sup>2</sup>

> 1Recall that the data consists of individual price series for different market locations, and each price series may have different data support. This makes any single-step imputation for all missing entries difficult. First, the individual market-level price time series are imputed using a structural time series model. The individual price series are then aggregated into a single commodity specific country trend, using an exponentially weighted average based on the amount of actual observations (giving exponentially more weight to the less imputed price series). The market-level price series that have sufficient data to carry out univariate modeling, are then imputed using a Generalized Additive Model that uses the commodity specific country trend as predictor of the observed data. Remaining market-commodity pairs without enough data support, are finally spatially interpolated using inverse distance weighting. Individual market-level price time series that do not have enough data support for modeling, or no observations at all, can be initialized by calculating the _K_ -nearest market locations using a Guassian kernel around market coordinates and observed price data. The market-level price time series of these _similar_ markets can then serve as proxies. This patter-matching idea is detailed by Andree (2022). Using these interpolations, the application then adds a homoskedastic disturbance drawn from a Gaussian distribution with a standard deviation of 5% in price levels for each initialization.

> 2In many countries, the exchange rates from official or public sources and available from for instance the European Central Bank (ECB) or Yahoo, are nearly identical and reasonable to use in conversions. In some low-income countries, the institutional rates are not reflective at all of actual retail exchange rates. In such countries, some effort is needed to construct reasonable exchange rate data from country-specific sources. In this paper, daily rates from the ECB finance were used and aggregated
McCabe, 1995). Since then, there has been a growing body of work using experimental methods to measure trust, beliefs, and norms in di↵erent settings (see, e.g., Dufwenbergand Gneezy 2000; Burks, Carpenter, and Verhoogen 2003; and Johnson and Mislin 2011). Acemoglu and co-authors (2020) showed in an experimental setting that (truthful) information about reduced delays in state courts in rural Pakistan increases trust. Some researchers are now using a mixed method by applying the traditional trust game to large samples of respondents online (Aruguete, Calvo et al., 2021; Bejarano, Busso and Scartascini, 2021). A study by Murtin et al. (2018), combining an experimental design and a large-scale survey, bridged the two approaches and showed that self-reported measures of trust in institutions were validated experimentally.

This paper provides empirical evidence to support the hypothesis that processes were more important factors of trust than outcomes in the case of Morocco during the period studied. Results show that positive perceptions of the quality of policy processes correlate with institutional trust with more statistical significance and larger coefficients, and that the relation is more robust than the relation between satisfaction with policy outcomes and institutional trust. This is not to say that outcomes do not matter as we also find that policy outcomes – including positive perceptions of the delivery of public goods and services – are positively associated with Moroccan citizens’ trust in institutions. For example, citizens who are satisfied with the Government of Morrocco’s (GoM) economic policies or with service delivery are more likely to report higher levels of trust in institutions. Yet, institutional trust is even more strongly associated with the quality of public policy and service delivery processes, and in particular with positive perceptions of public integrity and policy credibility. For example, citizens who perceive that the GoM manages public resources in a transparent manner or that policy announcements are e↵ectively followed by actions are substantially more likely to trust institutions.

Given the nature of our dataset
Annualize|d change|-19.3%|-10.4%|4.7%|6.6%|23.6%|-5.0%|
|Uganda|2013|7.8|27.9|34.7|6.7|22.9|70.4|
||2016|16.0|30.0|33.5|4.5|16.0|79.5|
|Annualize|d change|26.9%|2.5%|-1.2%|-12.7%|-11.1%|4.1%|
|Zambia|2010|4.5|27.0|27.6|13.6|27.4|59.1|
||2015|4.2|28.3|29.2|13.1|25.2|61.7|
|Annualize|d change|-1.5%|1.0%|1.1%|-0.6%|-1.6%|0.9%|

Source: Own elaboration based on GMD data.

The mixed pattern of informality fluctuations across countries is not related to differential rates of economic growth in the short-run. If we correlate the annualized rates of informality increase with the annualized rate of GDP growth over the same period, we find a positive association (Figure 6, Panel A). This is in direct contrast to Kiaga and Leung (2020) and Mehrotra (2020), who found that formality increased in periods of positive economic growth in Africa and Asia, respectively. But our results are consistent with Merotto, Weber and Aterido (2018) who found that informal employment persists even in relatively richer countries, and even when labor is no longer mostly in agriculture or self-employment, and with Fiess et al (2010) and Bosch and Maloney (2008)
Second, we use the World Bank’s Informal Enterprise Survey (IES) to profile firms. Firms included in this survey are, by definition, informal, i.e., the businesses are not registered with their governments.<sup>16</sup> The IES is conducted only with manufacturing or services firms in main urban areas. We use the most recent Mozambique, Zambia, and Zimbabwe surveys for our analysis,<sup>17</sup> which follow a sampling strategy that allows for the generation of sample weights.<sup>18</sup> Third, we use geospatial big data and a national Census extract to analyze how the urban environment correlates with employment types. The Mozambique Census was collected in 2017 and provides detailed information on employment type and geo-location at the neighborhood level. A 10 percent extract was shared by the statistical department. Four geospatial big data sets were utilized, each providing data for 1 km by 1 km tessellation (grid) of the country. First, the NASA’s Earth Observatory raster dataset<sup>19</sup> provides a measure of annual nighttime light brightness composites.<sup>20</sup> Second, the University of Michigan's High Resolution Electricity Access (HREA) data uses the aforementioned nighttime lights data and machine learning techniques to estimate electrification access between 2012 and present. Third, the WorldPop raster provides information on population density, building characteristics, and an urbanization composite at 1 km by 1 km tessellations. Fourth, the Open CellID database provides locations of cell phone towers.

# **2.1 Defining Employment Types to Classify Workers**

Inspired by Danquah et al (2019), we create a classification of six urban employment types using the GMD. We use information on workers’ employment category in their main job to classify workers as employees, self-employed or employers. To assign formality status to employees, we use variables that indicate receipt of labor benefits (social security payments, paid
3 of 21 resolution, which included a cropland class. Xiong et al. (2017) produced a nominal 30-meter cropland extent map of continental Africa by using Sentinel-2 and Landsat 8 data (GFSAD30AFCE). From an African context, this product represents a major improvement for food and water security assessments and is a first step for exploring not only cropland extent but also crop type, intensity, and change. The European Space Agency released a prototype land-cover 20-meter map of Africa for 2016 with cropland as one of its classes.<sup>1</sup> However, these products still present some limitations that hamper the extraction of information on land-use dynamics for specific regions of Africa. Cropland systems across the continent are highly diverse and are often adapted to very specific environmental conditions. The phenological signatures of the different land-use types in a region can be very similar (Xiong et al. 2017). As a consequence, mapping croplands at the continental level requires large, up-to-date training and validating data sets (Xiong et al. 2017). Although high-resolution imagery and crowdsourcing (Fritz et al. 2015) are gaining ground as sources of training and data validation, these remain scarce in remote regions, constraining the precision of supervised learning algorithms. Consequently, continental and global medium-resolution products that implement a single mapping approach are not always flexible enough to accurately characterize land-use processes in specific regions of the continent.

Northern Nigeria presents a concrete example of a data-poor region where land-use information is crucial for human development. Poverty indicators over the last decade show a growing north-south divide (World Bank 2016). Although poverty rates are only 16 percent in the southern part of the country, an estimated 50.2 percent of the population lives below the poverty line in northern Nigeria, where up to 70 percent of households rely primarily on agriculture (World Bank 2016). Ongoing land-cover change and land-use processes compromise households’ main livelihood strategies, reduce their adaptation alternatives, and thus increase their vulnerability. Yet the lack of consistent and reliable land-use
Policy Research Working Paper 10773

# **Abstract**

Can alternative survey methods address the underreporting of women’s and youths’ labor market outcomes, and thus improve the measurement of the underlying gender- and age-based gaps? This paper addresses this question using a survey experiment in El Salvador that compares two alternative survey methods—a list of activities survey module and enforced self-responses—against a traditional household survey, which consists of proxy responses without a list of activities module. The findings show that including the list of activities module yields higher work and employment rates for the average respondent compared to the standard household survey. Notably, when using the list of activities module, the reported work gap between men and women falls by 8.1 percentage points. Moreover, when using enforced self-responses, the male age gaps in employment and work rates fall by 13.9 and 12.3 percentage points, respectively. The paper provides evidence that the prevalence of peers’ informal employment or social norms for domestic obligations drive these results.

This paper is a product of the Development Data and the Development Research Groups, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at icontreras@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>
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across countries. Section III reports estimates of the impact of regulation on economic growth and the size of the informal sector. Section IV offers some concluding remarks.

# **II. Measuring Business Regulation**

In this section, we briefly describe our measures of regulation and their sources. We also discuss differences in regulation intensity across countries for different levels of economic development.

We construct indices to measure business regulation in the following seven areas: firm entry, labor markets, fiscal burden **,** trade barriers, financial markets, contract enforcement, and bankruptcy regulation. Each index is obtained as an average of related components, normalized to vary within a unit interval with higher values representing heavier regulatory burden.<sup>6</sup> The components used to construct the seven synthetic indices are obtained from the following data sources: Doing Business (The World Bank Group), Index of Economic Freedom (The Heritage Foundation), Economic Freedom of the World (The Fraser Institute), Labor Market Indicators Database (M. Rama and R. Artecona, 2000), The Corporate Tax Rates Survey (KPMG), and International Country Risk Guide (The PRS Group). These sources cover the largest number of countries and areas under regulation, and their measures use a clear methodology and are straightforward. Except for the Labor Market Indicators Database, all sources are public. Our sample covers 75 countries.

In most cases, data are based on surveys conducted in a single year (in the late 1990s) in a large group of countries; for components with observations for more than one year, we use average values over the period. Therefore, our indices should be interpreted as average regulation levels in the late 1990s. We should note, however, that regulation tends to stay constant over long periods of time.

Table 1 shows simple correlations between the seven regulation indices. The strongly positive correlations among all but the fiscal burden and labor indices suggest that regulation policy comes in “packages.” Judging