rafmacalaba/data-use-mentions-extended huggingface dataset:
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World Health Organization. 2014. “Investing In Water and Sanitation: Increasing Access, Reducing Inequalities.” _UN‐Water Global Analysis and Assessment of Sanitation and Drinking‐ Water_ , GLAAS 2014 Report.

53
To test the robustness of our results to different specifications, we analyze the determinants of women’s ownership of property using a multinomial regression model. The dependent variable has three categories - no one in the household owns the asset, the husband owns the asset alone or jointly with someone else (but not the wife) and the wife owns the asset alone or jointly with someone else (including her husband). The results are qualitatively similar (Table A5) to the results from the LPM model. Women in countries with more egalitarian laws are more likely to own property than women in countries with less egalitarian laws. The impact is stronger for housing than for land. Even the magnitude of estimates is similar as in the LPM models. Egalitarian laws reduce the probability of no one in the household owning land, but this is not consistently significant across models. The impact of laws on husbands’ chances of owning property is either insignificant or negative across models, except for equal remuneration laws in rural areas which increase husbands' chances of owning land significantly.

# **5. Conclusions**

This paper investigates the extent of gender gaps in the incidence of property ownership (land and housing) and the factors associated with these gaps in developing countries, focusing on the role of legal systems. Using DHS data from 41 developing countries, we find that there is substantial variation in gender gaps across countries. However, in almost all countries men are more likely to own property than women and gender gaps are most prominent among the rural population and the poorest quintile. There is significant evidence from the existing literature that women’s disadvantage in property ownership limits their bargaining power within the marriage and their fallback options on the dissolution of marriage or on the death of the husband.

The disadvantage in property ownership experienced by women reflects a variety of factors, most importantly discriminatory property laws. Countries with more equal legal regimes for women are associated with higher property ownership by women. The relationship between the legislation and women’s
_Note:_ ***p < .01, **p < .05, *p < .1. Profit has been trimmed at the 99th percentile. All control variables come from the baseline survey. The list of controls include a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned; and dummies for whether the owner is a household head and whether a member in the owner household is a community leader. The control group mean refers to the followup survey. The detailed regression results are provided in Table A.10 in the appendix.

# _6.7 Did the intervention mitigate the adverse effects of the COVID-19 pandemic?_

The period between the baseline and the follow-up survey overlaps with the worldwide COVID-19 pandemic that also affected Burkina Faso. The first case of COVID-19 in Burkina Faso was detected on March 9, 2020. The country went through a prolonged lockdown, a nightly _couvre feu_ , and a closure of the international airport for several months, which affected imports and exports of goods as well as the number of international travelers. Although the health impact was less dramatic as initially feared, the impact on the economy was clearly visible.

Indeed, almost 60 percent of our sampled firms reported in the follow-up survey that they faced
entities were allowed to purchase land in 2024, they accounted for about 20% of total land sales. Figure 3a shows, that sales prices for land for commercial agricultural use decreased to about 80% of the pre-war level after mid-2022. Once they were allowed to enter the market in January 2024, legal entities paid prices for such land that were about 30% higher than those by individuals, reaching or even exceeding the prewar price level. While a similar pattern emerges for personal agricultural land, prices paid for it are above those for commercial land and less affected by the war, consistent with the notion that such land is located closer to villages’ settled area, making it more suitable for non-agricultural uses including housing.

Although, as a result of the land privatization process, land supply is atomistic with individual landowners owning at most a few parcels, demand may be more concentrated. Appendix table 2 makes use of registry data to provide information on the mean and median areas acquired by individual tenants or buyers in each year, separately for land leases (panel A) and sales markets (panel B). For legal entities for which registry information could be accessed (73% of tenants and 67% of buyers post-2024), this is complemented with data on registered area cultivated.

In the lease market, legal entities not only account for the lion’s share of land rented but also pay prices that are significantly higher than those paid by individuals, as noted earlier. With a mean of 158 ha (from 95 ha in 2021 to 46 ha in 2024), the area rented by each legal entity over a period of four years is about eight times that (20 ha) by individuals (panel A). On average, legal entity tenants have registered rights to 1,031 ha of which they cultivated 895 ha.

The differential between legal entities and individuals in median (US$ 1,250 vs. US$ 969) or mean prices (US$ 1,650 vs. US$
# **5. Conclusion**

This paper has reviewed evidence from the literature and individual-level surveys in order to develop a better informed perspective on the pros and cons of microfinance for setting up and expanding businesses. The verdict seems to be that while microcredit is primarily useful as a consumption-smoothing and risk-management tool, microsavings potentially has a greater role to play in wealth creation. Further, neither savings nor credit would be able to do much by way of anything in an environment that does not inspire public confidence. The world‟s poor desperately need financial innovations that help them save, borrow and lend and an environment that helps them do so securely.

24
COVID-19 case data provide an interesting illustration of the importance of the interactions between features. Immense value has been derived from the presence of some of the 12 features but constrained by the absence of other features. This timeliness and frequency of the data has allowed countries to compare the possible trajectories they are likely to face and scale their emergency response, social distancing measures, and economic recovery packages accordingly. The case data are often tied to specific geographical locales and to an individual for whom other relevant data, such as age and medical history, are available. This granularity has made it possible to understand how clusters of cases evolved and how mortality rates differ by individual circumstances and characteristics. This has allowed governments to take extra measures for people who are suffering from or are vulnerable to the virus. The accessibility of the data has enabled quick research on the topic, providing the evidence policy makers need when deciding what measures to implement.

While these features have helped shape measures to contain and limit the spread and consequences of the virus, the measures could have improved had the quality of the data been better. In particular, questions about data accuracy and cross-country comparability have led to concerns about their relevance for policy making. As countries have increased their testing capabilities over time, more people have been reported as having contracted the virus. Though increased testing is critical, this has made data on confirmed cases less comparable over time within countries. Data on deaths suffer from cross-country comparability problems, given that the definition of deaths caused by COVID-19 differs across countries, challenging models using cross-country data.

COVID-19 case data also contain cases of positive and negative spillovers between features. On the positive side, the intensive data use during the pandemic will undoubtedly have an impact on the value attached to having timely and accurate data in other domains going forward. On the other hand, as we will argue in more detail later in the paper, the granularity and timeliness of the data have increased the risk of government surveillance for ends beyond just containing the virus
<!-- Start of picture text --> South Asia 6 0<br>Middle East & North Africa 11 0<br>Europe & Central Asia 12 1<br>East Asia & Pacific 10 2<br>Sub-Saharan Africa 25 4<br>Latin America & Caribbean 11 4<br>High income: OECD 8 7<br>KPIs exist No KPIs<br><!-- End of picture text --> _Source: Reform Committee data Note: Note: Figure based on 107 economies with a functioning business reform committee, 54 of which have reported use of KPI._ Similarly, progress towards goals laid down by KPIs can be monitored and measured through different avenues. Progress reports and reform strategy action plans are the most common avenues used to monitor and measure progress towards goals laid down by KPIs, with approximately 60 percent of all initiatives deploying them. Other popular means used by reform committees to monitor and measure their goals include progression towards international benchmarks and closed meetings with supervisors of the reform committee. On the other hand, lack of progress towards these measurable goals usually requires the reform committees to present measures to rectify the situation (Figure 9).

Figure 9: _Monitoring and measuring avenues to measure progress towards KPIs_ <!-- Start of picture text --> Other<br>Progression towards<br>international<br>benchmarks<br>Firm surveys or user<br>feedback<br><!-- End of picture text --> <!-- Start of picture text --> Reform strategy<br>action plan with<br>timeline established<br><!-- End of picture text --> <!-- Start of picture text --> feedback<br>Inspection by other<br>government authorities<br>Closed<br>meetings with<br>the reform<br>committees’<br>supervisors<br><!-- End
seem to be the case. The event study plots in Figure 7 confirm this (top and bottom plots in third column). For both the plots, I cannot reject the null that all effects before the

> 9This estimate is half of the net earnings (net of migration fees) inexperienced potential migrants expect to make from a migration episode lasting 2.21 years (Shrestha, 2017). I take half of the earnings to account for two major effects: misinformation and cost of living abroad that is not factored into the net earnings. In Shrestha (2017), I show that misinformation is at least 26 percent. In The World Bank (2011), returnees from these destinations mentioned that they saved 75 percent of their earnings. Hence, half of the net earnings is a conservative estimate of migrant income.

> 10Author’s calculations from the Nepal Living Standards Survey-III, of 2010.

> 11GDP figures taken from The World Bank.

10
with further increase the cost of service provision,<sup>17</sup> making it more difficult to ascertain absence of competing claims to titled land. Together with foreclosure costs of 48% of property value (World Bank 2008), this increases the cost of using land as collateral for any bank that would be interested in doing so.

# **3. Data and descriptive statistics**

To gain a better understanding of the size of Zambia’s medium and large farm sector and its temporal and spatial evolution, we combine a listing and mapping of large farms with digitized administrative records and a detailed large farm performance survey for the country’s Eastern Province. We find that at most 26% of owned and 12% of cultivated land is accounted for by large farms; that formalized holdings are clustered in blocks with better soil quality and infrastructure access though neither yields nor profits are higher inside as compared to outside blocks. While titled land does not seem to enjoy a productivity advantage, there is considerable unsatisfied demand for title.

# **3.1 Emergent farmer census**

To obtain a complete enumeration of all large farms in Eastern Province, a farm module was added to the mapping exercise for the 2020 population census conducted between January and March 2020. Computer assisted personal interviewing was used to list all structures, their purpose, and for those that were inhabited, administer a short questionnaire to residents. At the end of this questionnaire, a question was added asking respondents to indicate if they owned, operated, or guarded an agricultural holding greater than 5 ha.

For those answering in the affirmative, basic questions on the nature of the property were asked and boundaries of farms in the same district mapped using high resolution satellite imagery pre-loaded on enumerators’ tablets as a base map.<sup>18</sup> The listing identified 8,301 farms in 1,729 enumeration areas (EAs) of which 6,782 were mapped (and 6,703 had a mapped
that they could be understood and accurately completed by the women, and to make sure that the questions were culturally appropriate. Second, to maintain data quality, the enumerators visited women on a weekly basis to inspect the diaries and note any problems with them. During these visits, they answered questions about the diaries and continued to train women who were having difficulty.<sup>19</sup> There are several important differences between the two versions of the diaries which will affect our analysis. First, the Round 2 diaries included information on client characteristics, for each transaction. These include the client’s occupation, perceived wealth level, attractiveness, and cleanliness. In addition, they include measures of whether the sex worker thought the client was at high risk of HIV, whether she thought that the client saw a higher than average number of sex workers, and whether the client was circumcised. Unfortunately, these measures are somewhat problematic in that they were collected from the sex worker rather than the clients themselves, so measurement error is a concern. In addition, recording this level of detail for each client was burdensome for women. Consequently, even in the Round 2 data, many observations are missing client information.<sup>20</sup> Finally, to supplement the diary information, we administered a one-time background survey which included questions on demographics, asset ownership, knowledge of HIV/AIDS, attitudes towards sex work, and related issues. In total, the final dataset includes 192 women, 19,041 transactions, and 12,526 sex worker days.

> 19. Since the diaries were self-filled, it was necessary for women to be literate to complete them. Although the majority of the sample is literate, we also made an effort to keep illiterate women in the sample. Every illiterate woman sampled for the project was visited by her peer educator once a day to help her fill out the diary. 20. An important complication this causes is that while we know whether a particular
for African firms. This huge effect translates in an increase of 16.03% in employment growth rate. At the mean level, the annual employment growth rate in Africa will go from 4.1% to 20.1%. For labor productivity, the same exercise yields an increase in labor productivity growth of 38.63 percentage points, which at the mean translates in a positive growth rate of 32.8%.

# 6. Conclusions

In this paper, we shed some light on how air pollution and business environment affect city competitiveness through firm performance, particularly for African firms. We measure firm performance with two variables, employment growth and labor productivity growth. We use 23
on certain topics.<sup>33</sup> The survey was conducted with all adolescents living in sampled households who fell into the targeted age range. In total, information from 3,237 adolescents across 2,393 households was collected at baseline.

# **5.2 Lab-in-the-Field Experimental Data**

Lab-in-the-field experiments supplement the survey data at endline. The experiments allow us to study in which way gender attitudes differ if expressed privately or publicly. To do this, we first randomly grouped participants into teams of five with different gender compositions and asked them to choose a leader and deputy for the team. Next, each adolescent in the experiment sample was privately interviewed and asked to choose if they agree or disagree with three gender attitudes statements and play two line-match games in private. During the line-match game, participants were shown a set of lines and they were asked to indicate which of these lines had the same length as the reference line which was also shown to them. Next, we repeated the two exercises in their team setting. That is, all participants were asked to express their gender attitudes and the line choice in their teams one after the other in public where they could deviate from their private answers. We use the private and public statements to measure social conformity which is similar to the conformity line game conducted by the psychologist Solomon Asch (Asch, 1951, 1952). Further details of the lab-in-the-field experiments are included in the Appendix.

The aim was for each community cluster in the study sample to host at least two experimental sessions. Each of these sessions consisted of 20 adolescents (10 girls and 10 boys) divided into four teams, each with one of four gender compositions (all boys, all girls, mixed majority boys, mixed majority girls). Each team included both control group and treatment group participants. Games were completed for a subset of the study sample randomly selected.<sup>34</sup> The lab-in-the-field experimental games were completed between November 2020 and June
European Social Survey (2004-2009). Data files: Round 2 (edition 3.2), Round 3 (edition 3.3), Round 4 (edition 4.0). _Norwegian Social Science Data Services._ Feld, L. P. and F. Schneider (2010). Survey on the Shadow Economy and Undeclared Earnings in OECD Countries. _German Economic Review_ 11(2): 109–149 Fialova, K. and O. Schneider (2011). Labor Institutions and their Impact on Shadow Economies in Europe. Background paper for ― _In from the Shadow: Integrating Europe’s Informal Labor_ ‖ World Bank, Washington DC, mimeo Flaquer, L. and A. Escobedo (2009). The metamorphosis of informal work in Spain: family solidarity,

- female immigration and development of social rights‘. In B. Pfau-Effinger, L. Flaquer and P. H.

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Frey, B. S. and H. Weck-Hanneman (1984). The Hidden Economy as an ―Unobserved‖ Variable.

- _European Economic Review_ 26(1): 33-53.

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Greene, W. H. (2008). Econometric Analysis. 6th ed. Upper Saddle River, NJ: Prentice–Hall.

Hanousek, J. and F. Palda (2003). Why People Evade Taxes in the Czech and Slovak Republics:

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Harris, J. and M. Todaro (1970). Migration, Unemployment, and Development
-2.58|1.49|-0.42|-0.80|-0.04|
||South Kivu|399|-2.49|1.48|395|-2.50|1.50|0.02|-0.16|0.19|
|WASH institutions index|Kongo Central|18|0.27|0.66|20|0.59|0.57|0.31|-0.06|0.68|
||Kasai|43|-0.48|1.10|48|0.21|0.75|0.70|0.21|1.19|
||Kasai Central|65|-0.18|0.85|16|0.28|0.61|0.35|0.03|0.68|
||South Kivu|59|0.46|0.96|60|0.67|0.78|0.23|-0.25|0.71|

ITT = intention-to-treat effect estimate. Effects are estimated with models that include controls for randomization blocks based on province and number of villages per cluster. There were 121 clusters in total. The WASH institutions index was calculated by rescaling each variable in the index (e.g., presence of WASH committee) so that higher values imply better outcomes, then standardizing relative to the control group, following Kling _et al_ . Effects are in standard deviation units.
and poverty gap estimates when using a FH model. Molina and Rao (2010) though, using the same data sources as Molina and Morales (2009) but applying unit level models and EB prediction, obtain considerably larger gains. However, when the available census and survey are not from the same year, small area estimates based on unit level models may result in biased estimates. In such scenarios, FH models offer an alternative because these do not require census microdata. Alternatively, twofold models such as that of Torabi and Rao (2014) could also be considered to achieve larger gains as noted by Molina (2019).

Another potential solution is to use only aggregated covariates in the model for the household level welfare. This alternative is presented by Nguyen (2012) in an application for Vietnam. The author proposes a model where the dependent variable is household level logarithm of per capita expenditure from a recent survey, in this case the Vietnam Household Living Standard Survey from 2006, whereas all covariates are commune level means. These means are obtained from a dated (1999) census, although the author notes geographic information system data (GIS) could also be included into the set of covariates. Nguyen (2012) obtains ELL estimates for small areas under that model and compares the performance with that of typical ELL estimates obtained using unit level covariates from the Vietnam Household Living Standard Survey from 2006 and the 2006 Rural Agriculture and Fishery Census. The author finds provinces and districts hovering around the middle of the distribution suffer from considerable re-rankings across methods, but those at the top and the bottom are relatively stable.

Lange et al. (2018) present an approach similar to Nguyen’s (2012) which the authors suggest as an alternative in cases when census and survey data are not from similar periods, though the same issues noted above for the ELL method would likely persist in a model using only arealevel covariates. Masaki et al. (2020) use a similar modeling approach to Nguyen’s (2012)