rafmacalaba/data-use-mentions-extended huggingface dataset:
(Liu et al. 2021; Li and Qin 2022).

However, there are concerns around large firms benefiting more as many digital technologies require significant capital investment. Based on data from Belgium, Dhyne et al. (2018) find that large firms experience higher returns to their investment in IT-related capital investments than small firms. Similarly, using data across European countries, Gal et al. (2019) find that small firms benefit most from adopting cloud computing, while large firms benefit most from the adoption of enterprise resource planning (ERP) software. There are also concerns about competition with digital platforms, where a few dominant players can emerge with very low costs of expanding production. This means that their services can be consumed by one person without reducing the amount or quality available to others. These competition concerns are only likely to be heightened during the second wave of digitalization, as AI-related analytics are likely to disproportionately benefit large firms with access to massive amounts of accumulated user data (Acemoglu 2023; Goldfarb and Trefler 2018).

# **_4.3. Does digitalization expand financial inclusion?_**

The digitalization of finance, or fintech, provides opportunities for households and businesses that are excluded by traditional financial intermediaries to access financial services (Ahmad et al. 2020; Bollaert et al. 2021; Jagtiani and Lemieux 2018; Maskara 2021). Mobile money services, in particular, have proven to have significant benefits for the unbanked poor in developing economies by addressing the cost and institutional constraints related to expanding the reach of conventional 16
>6.803***|<br>6.134***|
||(0.043)|(0.136)|(0.049)|(0.146)|(0.040)|(0.118)|(0.138)|(0.332)|
|No. of farms|<br>954|<br>954|<br>959|<br>959|<br>1,913|<br>1,913|<br>1,913|<br>1,913|
|R-squared|0.073|0.170|0.055|0.133|0.221|0.270|0.278|0.289|

Note: Regressions are for monetary yield from main crops (wheat, barley, rapeseed, maize, sunflower and soybean) per hectare using the balanced panel of farms cultivating any crop in the 2021 and 2022 cropping season. Robust standard errors clustered at the farm level in parentheses: * significant at 10%, ** significant at 5% and *** significant at 1%. All regressions include farm fixed effects.

27
<!-- Start of picture text --> A. Quantiles of Permanent Transfers and Growth B. 2008Q2 Stimulus Payments (WL)<br>OK<br>41<br>50<br>WV<br>1 2 3 6789101112 4042434445464748 49Line slope=1.9[t-stat=2.8] CTMA NJCACOMDMNVAILWYWAAKDENHUTNDHINEKSPARIIAORTXWIAZVTMOOHMIINGANCMETNIDFLKYMTNMLA SC ARLine slope=0.28 [t=2.02]AL MS<br>NY SD<br>5<br>4 NV<br>-0.4 -0.2 0.0 0.2 0.4 2 3 4 5 6 7<br>Permanent Social Security Transfers (% of labor income) Transfer stimulus payments, % of labor income (2008Q2)<br>C. 2008Q2 Stimulus Payments (and GDP) D. 2001Q3 EGTRRA Stimulus Payments<br>OK NM<br>DE LAHI WY<br>CT MACANJDENY WA CO MDMNNVILVAUTHIORTXNENHNDIAAZRIKSSD PA WIINOHNCVTMOGAFLLAMIIDNMMETNMTKY SCWVARLine slope=0.9AL [t=2.53] MS NYCTCAMATXCONJGAILAKNVVAWAMNUTNCTNMDMIOHORAZMONEOKPAKSALNHSDFLRISCIDINMSWIARKYIAVTND ME MTWVLine slope=1.26 [t=4.03]<br>1.5 2 2.5 3 3.5 4 4.5 1.5 2 2.5 3 3.5<br>Transfer stimulus payments , % of state GDP (2008Q2) Transfer stimulus payments, % of labor income (2001Q3)<br>0.8 2<br>0.4<br>0<br>0.0<br>-2<br>-0.4<br>-0.8 -4<br>Quarterly Growth in Real Labor Income PC (%) Growth in labor income per capita, 2008Q2 (%)<br>2<br>4<br>1<br>2<br>0<br>0 -1<br>-2<br>-2<br>-3<br>Growth in state GDP
with working in Riga is very big by international standards. In Estonia, Lithuania and Poland the wage premium associated with working in the capital city is just above 20 percent (see Table A3.1 in OECD (2003a); and Newell (2001)).

A substantial part of the effect of job location on wages can be explained by differences in unemployment rates. When local unemployment (measured by the 33 NUTS 4 regions) is controlled for, the wage advantage of Riga, Riga district and Ventspils is strongly reduced (compare columns (4) and (2) of

> 14  Registered unemployment rates (at the end of previous year) were used because they are more reliable for these small regions; LFS-based rates, however, give similar results.

> 15  Interval regression used here is more likely to give unbiased estimates than wage imputation used by OECD.

40
offered a choice between the value of one pack of condoms in cash, and one, two or three packs of condoms. The dependent variable takes value one if the respondent chose the condoms over the monetary amount and zero otherwise. While participants were more likely to choose condoms when the relative price was lower (i.e., when offered a higher number of packs), choice behavior did not differ among those who watched Shuga and those who did not. Results are equally insignificant for women and men (columns 3 to 6). This zero effect of treatment aligns with the results obtained in Table 3 when looking at self-reported condom use.

The absence of an effect on condom use is consistent with different possible explanations. One is that there is strong cultural resistance to condoms in the Nigerian context and Shuga was simply unable to overcome such resistance. Another is an endogenous response to safer sexual behavior by treated individuals. As the incidence of concurrent partnerships is reduced for treated individuals (see Table 3), and possibly the nature of the partners becomes safer, the reduced risk may have induced our treatment group to rely less on condoms.

An additional result consistent with the interpretation that the treatment group adopted a safer behavior comes from the prevalence of sexually transmitted diseases. Panel B of Table 4 shows the effect of treatment on the probability of testing positive for Chlamydia. Results are shown for the full sample, for women and for men. While the estimated effect is negative and comparable in size in all three samples, it is only statistically significant for the female subsample. This is not surprising as Chlamydia is more prevalent among women. Also, the fact that the prevalence is very low in general makes it diffi cult to detect an effect with high precision. On the other hand, the magnitude of the effect is quite sizeable relative to the baseline prevalence rate: exposure to Shuga leads to a 55 percent decrease in the likelihood that women test positive for Chlamydia. As discussed above, even in the absence of
the extent to which saturation weakens the suitability of nightlight as a proxy for local economic activity in the Caribbean.

Table 1 shows the frequency distribution of DNs across pixels for Caribbean islands, along with the information on land areas, population density, and GDP per capita based on purchasing power parity (PPP). In most small states, a large land area (or number of pixels)<sup>10</sup> has a DN value of 0—that is, it has no artificial lights for the entire 12-year sample period. In Haiti, for example, which is relatively more exposed to hurricane strikes, 70 percent of pixels are unlit, while in Cuba, The Bahamas, and the Dominican Republic, more than a quarter of pixels are unlit. Following Bertinellli and Strobl (2013) and Elliott, Strobl and Sun (2015), who also use nightlight data to study the economic impact of tropical storms on local activity, this study assumes there is no economic activity in pixels with a DN value of 0 for the entire sample period and therefore excludes them from the econometric analysis. They make up about 67 percent of the total sample.

Table 1 also shows the number of top-coded pixels (with a DN value of 63) and the mean DN across the sample. The percentages of censored pixels are zero or close to zero, suggesting that nightlight saturation in urban centers might not be a major issue in the Caribbean. By contrast, the average DN value shows some variance across the 29 countries and territories. The island of Sint Maarten/St. Martin, which is half Dutch and half French territory, has the region’s highest average DN (with 53.67 and 46.02, respectively); this is followed by Bermuda (36.24) and Aruba (36.08), countries with the highest income level in the Caribbean. In some way, the correlation between nightlight and income levels could be proved for these countries. However, nightlight might not
Policy Research Working Paper 9336

# **Abstract**

As of 2019, more than 1.2 million Venezuelans passed through Ecuador and more than 400,000 settled (almost 3 percent of Ecuador’s population). This paper analyzes the location choices of Venezuelan migrants in Ecuador and the labor market consequences of these choices, using data from Ecuador’s labor force survey and mobile phone records on the geographic distribution of Venezuelan migrants. Around half of the migrants live in four cantons (of 221). Their location is primarily driven by local economic conditions, rather than point of entry. Overall, the regions with the largest inflows of Venezuelans have not seen any effects on labor market participation or employment, compared with regions with fewer inflows. However, our difference-in-difference estimates clearly indicate that young, low-educated Ecuadoran workers in high-inflow regions have been adversely affected. Specifically, the estimates that these workers have experienced reductions in employment quality, a 5 percentage-point increase in the rate of informality, and a 13 percentage-point reduction in earnings, relative to workers with similar characteristics living in areas with very low or non-existent inflows of Venezuelans.

This paper is a product of the Poverty and Equity Global Practice. 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 solivieri@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and
, and to a lesser extent Gedo were also excluded (see Table 1 for accessibility rates by pre‐war region).

In rural Jubbaland, field teams only collected data in areas that were relatively close to main cities (e.g. within a 10‐km radius around Kismayo, Afmadow and Dhobley in Lower Juba). This was due to insecurity and because many rural EAs considered in the sampling frame were found to be empty after reviewing the satellite imagery. The EAs sampled for rural Jubbaland were peri‐urban areas that correspond to large villages or small cities and thus the information was not representative of the rural population there. In addition, data from teams surveying rural Jubbaland showed signs of inconsistency and relatively low quality (highest percentage of invalid submissions compared to other urban and rural areas (Table 4); largest number of flags in the cleaning process of the consumption modules (Table 6), and large differences in the consumption of many food items relative to other rural areas (Table 7)). Interviews with rural households from this region were therefore entirely excluded from the final data set, and poverty estimated from satellite imagery and other geo‐spatial data (section 5).

In urban areas, data collection lasted longer than in any other area covered due to over‐sampling. Insecurity also made it more difficult collecting interviews and thus required more time. Team leaders reported that these issues contributed to fatigue on the part of enumerators, presumably impacting the quality of the data collected in urban Jubbaland.

> 17 Jubbaland region consists of pre‐war regions Gedo, Middle Juba, and Lower Juba (Middle Juba was completely inaccessible). North‐East region consists of pre‐war regions Nugaal, Bari, and Mudug (Table A.12).

22
Marginal buyers are those who consider the higher upfront cost the only obstacle to the adoption of AFVs, or those who view the subsidy amount as sufficient compensation for the utility loss from the other drawbacks of AFVs. Using the EV subsidy receipts data and vehicle transaction prices, Muehlegger & Rapson (2018) show that EV demand by low- and middle-income households is price elastic, and that the pass-through of the subsidy is complete among these consumers. Xing et al. (2019) find that cost effectiveness is greater for policies that that eliminate or reduce subsidies for high-income households, and provide more generous

> 20 Plug-in electric vehicles (PEVs) include both battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs).

> 21 See DeShazo (2016) for a literature review on the effectiveness of US subsidy programs for plug-in electric vehicles.

23
14869.02|51.23|290.27|180.12|2.77|
|Saudi Arabia|12329.56|34.27|359.79|249.65|2.57|
|United Kingdom|15728.91|67.53|232.92|122.77|2.49|
|Australia|10982.38|25.20|435.75|325.61|2.46|
|Viet Nam|3004.90|96.46|31.15|-78.99|-2.29|
|Congo, Dem. Rep.|64.39|86.79|0.74|-109.40|-2.85|
|Philippines|2392.64|108.12|22.13|-88.01|-2.85|
|Brazil|11339.75|211.05|53.73|-56.41|-3.57|
|Ethiopia|198.21|112.08|1.77|-108.38|-3.64|
|Bangladesh|1273.65|163.05|7.81|-102.33|-5.01|
|Indonesia|11061.53|270.63|40.87|-69.27|-5.62|
|Nigeria|2530.88|200.96|12.59|-97.55|-5.88|
|Pakistan|4005.61|216.57|18.50|-91.65|-5.95|
|India|41536.37|1366.42|30.40|-79.75|-32.69|

Source: Author calculations based on ‘Our World in Data‘ (https://ourworldindata.org/).

The key finding of the GCRI based on production-related carbon stocks is that the
informality exist in the literature (e.g., Ulyssea, 2018), the practicalities of data collection oblige a clear and workable definition of the population of interest. The surveys used in this analysis employ a definition of _legal_ informality (World Bank, 2020), i.e., informal businesses are those that lack sufficient registration or licensing requirements to operate. This notably excludes activities that produce goods or render services that are deemed illegal, such as narcotics. The specific details of what registration entails, e.g., which government agencies, were adjusted to cater to each country's regulatory framework.<sup>5</sup>

# **2.1 Data Collection**

The data was collected through face-to-face interviews of owners and managers following an application of the Adaptive Cluster Sampling (ACS) methodology (Thompson, 1990) to study informal businesses (for more details on the adaptation, see the companion paper Aga et al., 2022). Thus far, the study of the informal sector has primarily come from household surveys, notably Labor Force Surveys, where questions on employment and income from the informal sector are added. While this approach provides valuable information, these are not surveys of businesses, and at best, they are a second-best approach to study informal business activity. Other common approaches to collect information about informal businesses rely on building area-based frames, either using the household surveys, or extensive enumeration of economic activities, or a combination of the two. Depending on the details of implementation, these methods either do not necessarily result in representative samples of informal businesses or are prohibitively expensive to implement.

The ACS is a data collection methodology that provides representative estimates of the population of informal businesses, with the efficiency gain obtained through taking advantage of the feature that informal business activity is clustered in certain areas. This method is primarily applied to study populations whose individuals tend to cluster and are usually hard to reach (Turk and Borkowski 2005, Heckathorn 1997). For such
# I. Introduction

In many economies, informality predominates: it provides an undeniably large source of employment as well as economic activity. While a formal definition of informality can be hard to come by, it is useful to distinguish between informal employment and those businesses that operate informally. Informal employment can be defined as simply as work that is not covered by labor taxes and public pension contributions. These jobs are almost certainly off-the-books but can exist in either formal or informal firms. Somewhat apart, informal sector businesses operate outside of the official channels by which formalized firms are registered, engage the legal system, pay taxes, and deal with government officials. These businesses largely consist of self-employed operators, who occasionally employ others, who are almost invariably informally employed. On its face, then, operating informally can expose these businesses to frequent (but unstructured) encounters with police and government officials. In some cases, these interactions leave informal businesses exposed to harassment and demands for bribes. Yet while there are substantial literatures on both informality and corruption, there are few attempts to join the two, particularly using micro-level data.<sup>2</sup> One reason for this gap in the literature is a lack of adequate data sources. Those data that are commonly available rely on household-level measurement, and in turn, focus on labor dynamics between formal and informal employment. However, households do not necessarily encounter bribery the same way that businesses do, even if those businesses are operated out of the home. This paper leverages unique, survey data on informal sector businesses in Harare, Zimbabwe, collected in 2017. These data were collected using a geographically based, adaptive sampling approach and can be considered representative. Observations are, by construction, centered on informal businesses, a fact which makes the data well-suited to assess under what conditions those activities are exposed to extractions of bribes and harassment at the hands of public officials. This is likely important when assessing the informal sector’s exposure to corruption: operators of informal businesses may
Second, it is easy to see how both type 1 and type 2 errors could introduce noise into our measure. We may over-predict the share of family firms in countries with a small group of very common surnames. Reporting requirements for the number of management staff may also differ across regulatory bodies. Our regional regressions, which rely on within-country variation in regional trust, difference out any such factors that vary at the country-level. Moreover, since this affects all firms equally and is unlikely to be correlated with measures of contracting institutions, we do not believe this influences our results beyond reducing the precision of our estimates.

Figure A1 and Table A1 in the Online Appendix correlate our measures of family-management with those in the World Management Surveys, that explicitly ask about family ownership and family management, for 1,349 firms which we were able to match to our dataset. The correlation between our measures reaches as high as 0.653 at the country-level and 0.433 at the firm-level. We find this high correlation at the country-level encouraging, given that our definition is likely to be noisy at the firm-level but should be well-positioned to capture changes in the relative share of family-managed firms across countries.

To further motivate our empirical analysis, Figure II provides an overview of the relationship between the share of family firms, and GDP per capita as well as our three proxies of contractual institutions across countries. These correlations confirm that poorer countries, and countries with weaker contracting institutions, are characterized by a higher prevalence of family firms.

# V. RESULTS

## **_V.A. Cross-Country_**

_Management Mode Choice._ In order to test Prediction 1, that improvements in contractual institutions decrease the probability of being FOFM, we run where _FOFMick_ is a dummy for whether firm _i_ in country _c_ and industry _k_ is family-owned-familymanaged, _Lick_ is firm employment as reported in BvD Amadeus and Rule of Law _c_ is the WGI rule of law index (measured in standard deviations). The control vector _Xic_ includes the logarithm of GDP per
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, there is some evidence that poorly designed projects are difficult to turn around and that supervision cannot wholly compensate for poor quality at entry (Corral and McCarthy, 2020; Limodio, 2011).

Others have investigated the association of supervisory staff characteristics with project success, including attempts to proxy for supervision inputs on the quality margin as well as the quantity margin. Denizer et al. (2013) proxy for good supervisory leadership with a measure of supervisory quality defined as the weighted average success rate of all projects that the TTL has ever worked on (with weights that are proportional to how much time the TTL spent on each project). They estimate that a one standard deviation increase in the TTL track record variable is linked with an increase in the chance of project success by 6%. Moll et al. (2015) extend the work on supervisor quality to include a quantity measure of experience, as well as the average success of past projects. They find a positive and significant relation of project success with the TTL quality variable, as found before, but they also find that the number of projects taken to the Board by the TTL is not a reliable indicator of the likelihood of project success.

The role of staff in other key phases of the project cycle, such as project design, or staff who serve as overall management at the country or sector level, have been little studied. Two notable exceptions, using World Bank project and personnel data, are Honig (2020) and Limodio (2021). Since these studies rely on publicly available and web-scrapped data, there are differences in data set size and quality: while Limodio (2021) analyzed a selected sample of 715 TTLs who post their CVs on public websites, we rely on Human

> 5 IEG project evaluations include a ‘quality at entry’ rating, which captures the ‘extent to which the World Bank identified, facilitated preparation of, and appraised the project so that it was most likely to achieve planned development outcomes and was consistent with the