rafmacalaba/data-use-mentions-extended huggingface dataset:
response for a policy maker who cannot perfectly identify the poor) is to use a Proxy Means test and distribute the additional budget to the poorest households (columns 5 and 6). Again, in the case of Albania, and under the baseline shock, it looks like the better response would be to distribute the additional resources to only the pre-shock poor as identified through the PMT, because the shock did not affect much extreme poverty. Note, also, that while using the PMT is an effective, practical response, exclusion errors become substantial – less than 60 percent of the post-shock poor would be covered by the transfer.

The third-best response would be to distribute additional benefits to existing social assistance beneficiaries (column 1a), which is the actual government response. Because the social programs do not cover all the poor, this remains however an inferior response than a more generous coverage which, even if the transfer per household remains lower, would cover all the pre-shock poor.

Next, the fourth- and fifth- best responses would be to cover all the poor classified as such through a PMT, plus people who suffered a loss as identified through an indicator (columns 9 and 10). Again, this remains a worse response than just covering the pre-shock poor because the specific shock we considered did not affect much extreme poverty.

Note also that while a universal transfer would be the only way to guarantee that all the poor would be covered, providing a universal, flat transfer to all households would deliver extremely low poverty impacts (column 4), because to ensure that all the poor are covered the transfer would have to be given to all non-poor households as well, which lowers substantially the amount given to each poor household. There is therefore an important tradeoff between equity considerations (i.e., covering all the poor), and poverty impacts.

19
and other banks are also excluded from coverage. The deposit coverage limit was set at Rs 100,000 in 1987 for private individuals only.

_Source_ : Allrefer.com (2004), Garcia (1999).

**Sweden.** ( _Deposit Guarantee Board_ ) The deposit insurance scheme of Sweden was established in January 1996 based on the EU directive. In 1992, Sweden introduced a temporary guarantee of all bank liabilities for that year. This temporary guarantee mechanism was abolished in July 1996. The new system is officially administered and jointly funded. The government has borne losses in the past. The scheme covers saving and checking accounts as well as foreign currency and inter-bank deposits of up to SEK 250,000 (approximately $34,300 as of 2003).

_Sources_ : Own survey of deposit insurers, Garcia (1999), IADI Survey: Sweden (2003), Institute of International Bankers (1997).

**Switzerland.** ( _Deposit Guarantee Scheme_ ) The deposit guarantee scheme in Switzerland was established in 1984. It is privately administered. The scheme is funded exclusively by the members. There is no permanent fund in place. Banks make ex-post contributions when needed. Membership to the scheme is voluntary. The coverage limit for savings deposits per depositor is currently at CHF 30,000 with no coinsurance.

_Sources_ : Barth, Caprio, and Levine (2004), Garcia (1999), Kyei (1995).

**Taiwan.** ( _Central Deposit Insurance Corporation-CDID, Deposit Insurance Act, Article 46, 1985_ ) The DIS of Taiwan was established in 1985. It is officially administered and jointly funded. Membership to the system is compulsory by all deposit-taking financial institutions and the premium rates based on three different levels of risk are 0.05% 0.055%, and 0.06%. Initially, the maximum coverage of the scheme was NT$ 700,000, which has been set at NT$ 1 million since August 1987. The coverage is calculated per
The studies assessing the relative accuracy of recall vis-à-vis diary- or crop cutting-based approaches to data collection on crop production and yields are few and far between.P4F<sup>5</sup> Deininger et al. (2012) compare recall- versus diary-based estimates of cassava production, captured as part of a national crop diary operation integrated into the Uganda National Household Survey (UNHS) 2005/06. The documented magnitude of discrepancies between the two methods are especially high for cassava. Specifically, the authors show that recall underestimates both the householdlevel incidence of cassava cultivation and the value of cassava production by 28 percent and 24 percent, respectively. And Deininger et al. (2012) acknowledge that these already large discrepancies may constitute a lower bound given the 6-month duration of the diary operation and the fact that cassava can be harvested over a longer period. In the context of Malawi, at the macrolevel, the extent of discrepancies among the international databases, the ministerial data sources, and the national household surveys in terms of reported cassava production and yields further underscore the need for methodological research for improving the statistics on root, tuber and tree crops in general, and cassava in particular.

Considering the importance of cassava for food security and the absence of best practices in accurate survey data collection on extended-harvest crop production and yields, we present the results of a household survey experiment that randomly assigned sampled households in top cassava-producing districts in Malawi to one of four approaches to cassava production measurement over a 12-month period. These methods were: (1) daily diary-keeping, with semiweekly in-person supervision visits (D1) – the traditional gold standard in survey data collection; (2) daily diary-keeping, with semi-weekly supervisory phone calls (D2); (3) 6-month recall-based data collection in 2 visits that were 6 months apart (R1); and (4) 12-month recall-based data collection in a single visit (R2) – the current approach in Malawi and arguably the most cost effective practice for household and farm surveys collecting information on
Figure 7: Prediction of Mean Number of Mean Growing Degree Days, by Remote Sensing Source and Agro-ecological Zone <!-- Start of picture text --> Tropic-warm/semi-arid (n = 9,095) Tropic-cool/semi-arid (n = 2,840)<br>280<br>200<br>260<br>180<br>240<br>160<br>220<br>140 200<br>120 180<br>2008 2010 2012 2014 2016 2008 2010 2012 2014 2016<br>Tropic-warm/sub-humid (n = 9,009) Tropic-cool/sub-humid (n = 5,886)<br>300<br>184<br>182<br>250<br>180<br>178<br>200<br>176<br>174 150<br>2008 2010 2012 2014 2016 2008 2010 2012 2014 2016<br>Tropic-warm/humid (n = 3,280) Tropic-cool/humid (n = 2,960)<br>190 300<br>188<br>250<br>186<br>200<br>184<br>182 150<br>2008 2010 2012 2014 2016 2008 2010 2012 2014 2016<br>Year Year<br>MERRA-2 ERA5 CPC<br>Growing Degree Days<br>Gorwing Degree Days<br>Growing Degree Days<br><!-- End of picture text --> _Note_ : The figure presents the mean number of growing degree days pooled across all countries and years, disaggregated by agro-ecological zones. Prediction made via Fractional-Polynomial.
Development and Economic Growth in the OECD, 1870–2011.” _European Economic Review_ 101: –

- 605 24.

- Massenot, B., and S. Straub. 2016. “Informal Sector and Economic Development: The Credit Supply Channel _.” Economic Inquiry_ 54(2): 1046-1067.

- Matousek, R., A. Rughoo, N. Sarantis, and A. G. Assaf. 2015. “Bank Performance and Convergence During the Financial Crisis: Evidence from the ‘Old’ European Union and Eurozone.” _Journal of Banking and Finance_ 52: 208-216.

- Medina, L., and F. Schneider. 2018. “Shadow Economies Around the World: What Did We Learn Over the Last 20 Years?” IMF Working Papers 18/17, International Monetary Fund, Washington, DC.

- Medina, L., and F. Schneider. 2019. “Shedding Light on the Shadow Economy: A Global Database and the Interaction with the Official One.” CESifo Working Paper No. 7981.

33
These data are then combined with household surveys to precisely identify levels and determinants of informality.

We find that in 2014/15, i.e., after LTR completion, 5.6% and 1.54% of Kigali’s residential and agricultural land parcels, respectively, were transferred through a registered sale each year (for the other provinces, figures are 0.27% and 0.07% for residential and agricultural land, respectively). A total of US$2.6 billion of mortgage lending is secured by 49,694 mortgages—65%, 30%, and 5% by residential, agricultural, and commercial land/property. Quarterly data on numbers of registered land transactions show that posting of sector land managers (SLMs) had a very positive impact on the level of registered land transfers, showing that such data can be used not only to performance monitoring and change management but also the impact of interventions that are gradually introduced across administrative units.

Linking such data to household survey evidence at the parcel level produces reliable estimates of informality. Doing so shows that, Rwanda’s success in first-time registration notwithstanding, in rural areas a substantial share of land transfers remain informal. Low awareness of relevant regulations, subdivision restrictions, high registration fees, and travel cost to reach relevant offices are some underlying factors. Given the high level of land market activity in the country, dealing with this issue will be important.

The paper is structured as follows. Section two reviews conceptual arguments and empirical evidence on costs and benefits of land registration and draws out implications for sustainability of land administration. It also describes Rwanda’s legal and institutional structure, key elements of the country’s program of land tenure regularization program, including efforts to integrate it into the country’s decentralized system of governance, as well as data sources. Section three provides evidence on the level and incidence of informal transactions. Section four provides data on and the cost of registering transfers, compares them to households
where is a standard error term with mean zero and variance . We predict the expected U earnings of Mexican migrants (mm M ) if they migrate to the U.S. using the estimated coefficients εε σσ<sup>2</sup> ( ) and characteristics of Mexican workers ( M ) from the ENOE data. When predicting ww� migrant wages in the US, we assume that Mexican migrants in the U.S. are employed in the mm ββ̂<sup>U</sup> XX private sector (not working for the federal, state, or local government, and not in the armed forces). We also assume that all migrants are Hispanic, single (for the purpose of work migration), and have no U.S. Citizenship or permanent residency status. The expected earnings in the U.S. of Mexican migrants ( MU ) is then: �MU Mww� and MU �MU ⁄2] (8) MM mm MM where is the unbiased estimator of ln ww = ββ̂<sup>U</sup> XX from (7) ww� = exp [ln ww(Wooldridge 2012+ σσ�<sup>2</sup> <u>).</u> We postulate that a migrant makes a migration decision assuming that his earnings in the σσ�<sup>2</sup> σσ<sup>2</sup> We postulate that a migrant makes a migration decision assuming that his earnings in the U.S. are functions of his specific human capital characteristics and his occupation in Mexico. Here, the migration decision is also a function of migrant’s professional experience in his home country. A Mexican electrician plans to work in that occupation in the U.S., forming his wage expectations ( MU ) based on information about wages of electricians in the U.S. The other explanatory variables U � include information about age, gender, marital status, race
complete financial indicator data in the analysis. Comparisons between the two groups form the basis of most of the statistical analysis that follows.<sup>7</sup>

# **3. Methodology**

For our base results, we rely on simple fixed-effects panel regressions of the following form: where _i_ represents country, and _t_ is time measured in years since 1991. The time variable takes values from one to twelve, and the estimated coefficient _βwb_ thus represents the average growth rate for indicator of financial development _Y_ for countries that received adjustment loans to supports financial reform. We test whether _βwb =βnon-wb_ to assess whether adjustment loans had a beneficial impact on financial sector development. To the extent that the growth rates for the control countries were the same as (or greater than) those for those that received World Bank assistance, the value of that assistance would be questioned. All regressions also include αi, a country-specific fixed effect. Our results, therefore, should be interpreted as changes relative to the country-specific mean for the indicator in question. Later we offer additional, more direct methods for addressing potential selection problems.

_X_ is a vector of macroeconomic and institutional controls that includes inflation, real growth, government budget surplus(-deficit)/GDP and the CPIA index, a broad measure of institutional development. CPIA is the Country Policy and Institutional Assessment done annually by the World Bank to assess the quality of a country’s present policy and institutional framework. The index is based on twenty criteria, which are grouped in four clusters: economic management; structural policies; policies for social inclusion and equity; and public sector management and institutions. All of the macroeconomic and institutional controls are lagged one year in the panel regressions that follow to help mitigate problems arising from the controls and the dependent variable being simultaneously determined.

> 7 A number of the countries in the control group received non-lending services in the sense that the World Bank maintained a policy dialogue with them throughout the period.  A number also had
support. USC IRB approval UP-20-00522 and Universidad del Rosario IRB Approval CS239. This study is registered in the AEA RCT Registry with unique identifying number AEARCTR-0006190. The contents of this study are the responsibility of the authors and do not necessarily reflect the views of their employers, funding agencies, or governments. Marisol Rodr ́ıguez Chatruc acknowledges funding from the Inter-American Development Bank.

> †Inter-American Development Bank, email: marisolro@iadb.org.

> ‡Development Research Group, World Bank, email: sandrarozo@worldbank.org.
bridge funding for subsidy eligible households.

We are not the first to show that micro-credit can be effective in increasing sanitation coverage. BenYishay et al. [2017] established in the context of Cambodia that sanitation demand can be boosted significantly when offering micro-credit for the product. Studies of micro-credit programs that are bundled with specific health investments do indeed more generally report on the presence of the investment as measured by survey enumerators (Benhassine et al. [2015], Tarozzi et al. [2014]). Interestingly though, despite providing construction materials to the door-step of the clients, rather than providing cash, BenYishay et al. [2017] find that only 30-40% of loan-takers had installed the toilet 1.5-2 years after the sales. This conversion rate from sanitation loan to used toilet is significantly lower than in our study context. Not only do we show that conversion rates are higher, but we demonstrate that this can be achieved by providing a loan rather than construction materials. Our study shows that labeling the loan is an effective mean to induce toilet construction, while allowing households to make their own choices about what type of toilet they would like to construct.

We note that context might play an important role in explaining the differences in findings between the two studies. Not only do BenYishay et al. [2017] study a different setting to us – Cambodia – but product design differences might play a role. For one, the credit BenYishay et al. [2017] provide is given as an individual liability loan, whereas in our context loans are joint liability. Attanasio et al. [2015] show that group liability attracts different types of borrowers than individual liability contracts do. Second, our implementing partner introduced a rule that only clients who had been with them for at least one year could avail a sanitation loan, whereas in the context of BenYishay et al. [2017] loans were given to those who wanted to purchase a specific toilet model, regardless of their borrowing 39
stock estimates and the Gross Domestic Product (GDP) statistics are drawn from the Penn World Tables (Feenstra, Inklaar, & Timmer, 2015).

Baseline data on labor force participation rates (LFPR) by sex and five-years age groups are obtained from the ILOSTAT repository (ILO, 2020). The earnings by sex which are taken for the parametrization of wages are taken by the Household Income and Expenditure Survey (HIES) 2014 data (Liberia Institute for Statistics and Geo-Information Services, 2015). Baseline data on human capital is composed of the average educational attainment (in years) by sex and five-years age groups, obtained from the Barro-Lee dataset (Barro & Lee, 2013), and the average height (in meters) by sex and five-years age groups, obtained from the Demographic and Health Survey (DHS) by the Liberia Institute of Statistics and Geo-Information Services, The Ministry of Health and Social Welfare/Liberia, National AIDS Control Program/Liberia, and ICF International (2014). Baseline estimates of age-specific savings rates are gathered from Bloom, Canning, Mansfield, & Moore (2007).

The non-tradable production module is composed of the average time (in hours) allocated to housework and domestic chores by sex, obtained at baseline from the United Nations Global SDG Database of the United Nations Statistics Division (2020) and UN Women (2019). To these measures, the time allocated to fetching water or firewood are added and obtained at baselined from the HIES 2014 from LISGIS (2015). Appendix 1 describes each source of data.

## **4.2 Calibration and Convergence**

We use parameters generated in CKW 2017 and impute additional parameters for our extension to the model. The CKW 2017 model parametrizes the reduction in labor market participation due to an additional child, the endogenous response of fertility to changes in education, the impact of fertility on 11
the PM2.5 data from the regulatory-grade monitors (Panel 4A). This reflects the fact that in 2019-2020, the regulatory data comes primarily from a new monitor installed in 2018 in Guediawaye, which measures much higher levels of PM2.5 compared to the other regulatory monitors in the city, and thus aligns more closely with the higher estimates of pollution generated with the satellite data.<sup>19</sup> When aggregated to the weekly level, the two data sources still track each other closely for 2019-2020 but there is greater disparity in the levels than when aggregated to the monthly level. There are some discrepancies between the two measures at the daily level, with the satellite data recording greater levels of PM2.5 than the regulatory-grade data at some times and lower levels at other times (Figures 4C and 4D).

Next, we look at alignment between the two data sources at higher frequency. One of the advantages of the data from MERRA-2 over other sources of satellite data is that it can generate estimates of PM2.5 at the hourly level. The estimates from MERRA-2 and the data from the regulatory-grade monitor are not as closely aligned at this higher temporal frequency. Figure 5 uses data from March 2020 to illustrate that the measurements of PM2.5 at the hourly level from these two data sources frequently diverge by

> 50 _μ_ g _/m_<sup>3</sup> or more and are often moving in the opposite direction (i.e. one data series is not a level shift

> 18Because of the inconsistent availability of data from the regulatory-grade monitors across years, we combined data from different monitors, using data from Bel Air for 2017, data from Republique in 2018, from Guediewaye in 2019 and data for Republique and Guediewaye in 2020.

> 19The higher levels of pollution measured by the monitor at Guediawaye may reflect higher levels of pollution in this part of the city or may be due to the calibration of the instrument.

14
adoption.

The reminder of the paper is structured as follows. The next section describes the data sources and variables. Section 3 outlines the empirical strategy. Section 4 discusses the results and their implications and presents the robustness checks. And Section 5 concludes.

# **2. Data**

This paper relies on a number of data sets collected by the World Bank. Our primary variables of interest, e-filing and e-procurement adoption dates and system functionality, are taken from the _Global e- Government Systems Database (GeGSD)_ , which was prepared for the _World Development Report 2016: Digital Dividends._ Data were compiled by World Bank experts by visiting government websites and consulting World Bank project documents, national legislation and implementation reports, and validated by country economists and government officials.<sup>5</sup> These data cover 198 countries, comprising

> 4 Quantifying the effects of e-filing on tax revenue, however, is beyond the scope of this paper.

> 5 The _Global e-Government Systems Database_ was prepared by a World Bank team comprising Cem Dener, Sophiko Skhirtladze, and Irene H. Zhan for the _World Development Report 2016: Digital Dividends_ . It is updated - - annually in August. Data and summary statistics can be accessed at http://data.worldbank.org/data <u>catalog/pfm systems-eservices-dataset. A related dataset focused on digital identification can be found at http://data.worldbank.org/data-catalog/id4d-dataset.</u>

4
% 0%<br>None Less than Primary Primary Lower Secondary None Less than Primary Primary Lower Secondary<br>Upper Secondary Vocational University Upper Secondary Vocational University<br>E. Education Attainment of Monthly Wage Workers F. Educational Attainment of the Labor Force in<br>(%), 1991 to 2004 1991 to 2004 991 to 2004 1 to 2004 to 2004 2004 004 4 East Asia, End 1980s and End 1990s<br>F91A91F92A92F93A93F94A94F95A95F96A96F97A97F98A98F99A99F00A00F01A01F02A02F03A03F04A04<br>4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>None Less than Primary Primary Lower Secondary<br>Upper Secondary Vocational University<br>E. Education Attainment of Monthly Wage Workers<br>(%), 1991 to 2004 1991 to 2004 991 to 2004 1 to 2004 to 2004 2004 004 4<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>None Less than Primary Primary Lower Secondary<br>Upper Secondary Vocational University<br>F91A91F92A92F93A93F94A94F95A95F96A96F97A97F98A98F99A99F00A00F01A01F02A02F03A03F04A04<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04