rafmacalaba/data-use-mentions-extended huggingface dataset:
customers. Although growing in popularity worldwide, providing a prepaid option is a relatively new practice in Africa, and its impact is still not well understood. But for those countries for which data are available, roughly 60 percent have introduced the use of prepayment meters (figure 3). Indeed, Lesotho, Namibia, Mozambique, and South Africa already offer this option to a majority of residential customers.

<!-- Start of picture text --> Figure 3. Prepaid services<br>a. Frequency distribution of countries adopting b. Time trend in percentage of customers using<br>prepaid meters prepaid meters<br>2000 2001 2002 2003 2004 2005<br>10<br>9 Tanzania 4 4<br>8 Benin 0 0 6 6 6 7<br>7<br>6 Burkina Faso 10 10 10 10 10 10<br>5 Malawi 2 4 6 8 10 14<br>4<br>Ghana 5 4 4 10 28<br>3<br>2 Rwanda 48 65<br>10 Mozambique 80 73<br>0 1-20 21-50 51-90 91-100 no data Namibia 93 98<br>Lesotho 100 100 100 100<br>percentage of customers with prepaid<br>meters South Africa 100<br>number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Indicators Database

# **b. Nonresidential tariffs**

Nonresidential tariffs can be classified in two groups: commercial and industrial (Table 4). Linear tariffs are the most common regime for nonresidential customers in Africa (figure 4). About 60 percent of commercial customers and over 50 percent of industrial customers are billed based on linear tariffs. A more detailed description on commercial and industrial tariff structures practiced in Africa can be found in Annexes 3 and 4.

Pricing for nonresidential customers is typically more complex than for residential customers. It is usually structured as a three-part tariff including a monthly fixed charge
night light data distributed by the Visible Infrared Imaging Radiometer Suite (VIIRS).

**Conflict intensity exposure** For each FMP, we construct a time-varying measure of conflict intensity exposure as the number of conflict events that occurred in the 5 km radius of the FMP’s location during the corresponding month. Data on conflict events are from the PRIO/Uppsala Armed Conflict and Location Event (ACLED) dataset which covers conflict events worldwide, providing the geo-localization, the date, and the type of event (Raleigh et al., 2010).

**Internet coverage** Data on the geo-localization and the speed connection of cell towers in Libya are collected by OpenCellid ( `https://opencellid.org` ), the world’s largest open database of cell towers, and distributed by GSMA ( `https://www.gsma.com` ), a non-profit industry organization which provides source of data and analysis for the mobile industry worldwide. Data from Libya cover all the network operators present in the country: Almadar Ajadeed, Libyana, LibyaPhone Mobile. Data is available only for a single year: 2021 for the operators Almadar Ajadeed and LibyaPhone Mobile, and 2019 for Libyana.

**Sea patrolling activities and Mediterranean Sea crossings** Data on monthly patrolling activities in the Mediterranean Sea are not available. Yet, data exist on the number of arrivals from Libya to Italy. We retrieve these data from the Operational Data Portal of the United Nations High Commissioner for Refugees (UNHCR).<sup>21</sup> We use these data to construct a dummy variable which takes value one when patrolling activities are intense, i.e., when arrivals during a month are below the median in the period 2018-2020 (1150 individuals), and zero otherwise.<sup>22</sup>

**Economic data for destination countries** Data on country-level employment rate and percapita income are sourced from the International Labour Organization (ILO) and the World Bank (
respectively.

# **6. Are expectations better anchored?**

Kose et al. (2019) find that long-term inflation expectations have declined in the past two decades in both advanced economies and emerging markets and developing economies (EMDEs). Although inflation expectations are less well anchored in EMDEs, their sensitivity to domestic and global shocks has declined. They suggest that an IT regime and greater central bank transparency are associated with better anchoring.<sup>37</sup>

> 37 Lower public debt and greater trade openness are also associated with better anchoring of expectations. Using monthly survey data from Consensus Economics for a sample of 22 EMDEs and 14 advanced economies in a structural vector autoregressive model, Davis (2014) similarly finds that the introduction of inflation targeting is associated with

21
Business Dynamics Laboratory (Laboratorio de Dinámica Laboral y Empresarial [LDLE]), is constructed by INEC using information from IESS, SRI, and other government institutions. It contains worker-level data for over 70,000 firms that meet two criteria: reported sales to SRI and registered employment with IESS (formal employment). Combining these three data sources yields a rich employer-employee database useful for analyzing firm dynamics, aggregate productivity, and labor demand patterns for the 2012–2020 period.<sup>1</sup> The main firm performance variable of interest in this paper is firm revenue based TFP. The control function approach developed by Ackerberg et al. (2015) is used to estimate revenue TFP. The variables necessary for estimating revenue TFP are value-added, capital, labor, and materials (which is used as the proxy variable for the control function approach) at the firm-level. The paper follows the methodology of Avellan and Ferro (2017) to construct these variables using the Ecuadorian firm administrative data, adhering to standard measurements in the literature. Value-added is calculated as firm gross output (sales adjusted for product inventories) minus intermediate consumption of materials and services. Intermediate consumption comprises operational expenditures and production costs. Capital is measured based on the value of fixed assets reported in firms' financial statements, lagged by one year. Labor is measured as the number of workers employed by a firm. Materials are computed as the sum of raw materials, transport costs, gasoline costs, and utilities costs. Monetary variables are transformed into constant prices using sector deflators from the Ecuadorian input-output matrix. Consequently, firm gross production, intermediate consumption, and value-added are deflated using their respective sector deflator indexes. Similarly, materials are deflated using the sector deflators for intermediate consumption, while capital is deflated using the sector deflators for gross fixed capital formation. Lastly, there are two significant firm characteristic variables relevant in the analyses of the paper: sector and age. The firm's sector of activity corresponds to the International Standard Industrial Classification (
culminated in the United Nations Guidelines for Producing Statistics on Asset Ownership from a Gender Perspective (UNSD, 2019).<sup>6</sup> The guidelines, consistent with the previous work by Grown et al. (2005) and Doss et al. (2011), provide empirical evidence in support of (i) reducing the reliance on most knowledgeable household member(s) in collecting individual-disaggregated survey data on ownership of and rights to assets, (ii) expanding the practice of interviewing multiple adults per household (in fact interviewing either all adults or one randomly selected adult for collecting the required data for the SDG 5.a.1), and (iii) probing directly and solely regarding respondents’ personal ownership of and rights to assets, either exclusively or jointly with someone else. The implementation of these recommendations has been demonstrated to provide a more complete picture of ownership of and rights to assets within households, particularly among women; minimize distortionary proxy respondent effects and intra-household discrepancies in reporting; and reveal hidden assets (Kilic and Moylan, 2016).

**Box 1.  Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA)**

MEXA is a randomized household survey experiment that was implemented by the Uganda Bureau of Statistics in 2014, in collaboration with the UN EDGE Initiative and the World Bank Living Standards Measurement Study (LSMS), providing a unique opportunity for more in-depth analysis of gender disparities in asset ownership, with a focus on land ownership.

The experiment targeted 140 enumeration areas (EAs) across Uganda, and randomly allocated four households in each EA to each of five treatments/arms that differed in terms of respondent selection. Regardless of the treatment, the respondent(s) were interviewed alone. The first four treatments/arms included interviewing (1) the self-identified most knowledgeable household member; (2) a randomly selected member of the principal couple; (3) the principal couple together; (4) all adult household members, simultaneously.

In Arms
and on community poverty and inequality levels. A common approach to answering such questions is to use data on migrants and non-migrants, and attempt to control for differences between them in a linear regression framework. For example, researchers may attempt to estimate an equation of the form: Where _Migranti_ is a dummy variable which takes the value 1 if the individual is a migrant, and zero if he or she is not, and **X** _i_ observed characteristics of the individual which are presumed not to have changed with migration (e.g. age, sex and education level,

> 2This section draws heavily on our chapter in the first edition of this handbook (McKenzie and Yang, 2012).

3
data set has around 3,000 observations equally distributed between the manufacturing sector and other sectors excluding agriculture (see Table 2); manly services and within services wholesale and retail. The survey targeted formal firms only and excluded micro firms, although a few firms on the survey are de facto micro when surveyed and have less than 5 employees.

_<u>Table 2 Number of Firms according to their size, sector and country</u>_

|||**Manufacturin**|**g**|**S**|**ervices and ot**|**hers**||
|---|---|---|---|---|---|---|---|
||**small(<20)**|**Medium**<br>**(20-99)**|**Large (100**<br>**and over)**|**small(<20)**|**Medium**<br>**(20-99)**|**Large (100**<br>**and over)**|**_Total_**|
|Ghana|193|66|18|164|84|14|**_539_**|
|DRC|108|48|14|140|39|6|**_355_**|
|Tanzania|163|68|35|170|80|12|**_528_**|
|Uganda|129|50|25|168|55|11|**_438_**|
|Zambia|131|109|21|200|51|21|**_533_**|
|Kenya|104|100|76|153|81|31|**_545_**|
|**_Total_**
prices in the last 12 months. The dichotomous variables are equal to one if the household ranked either of these shocks as being in the top three most important shocks. Further, we include a dichotomous variable that identifies whether any household member was ill during the two weeks preceding the survey interview.

Potential risk coping mechanisms include the number of adult children living in separate households, a dummy for whether any member holds a financial account, and three market network variables. The network variables are constructed using data from the Household Network Roster module, which collected information on the set of people or businesses with whom household members conducted transactions within the course of purchasing agricultural inputs and/or selling agricultural outputs. The three variables are (i) the number of contacts located within the village, (ii) the number of contacts in nearby villages and (iii) the number of contacts located in the district center or further away.

Complementing household-level explanatory variables, we control for a number of community characteristics that are expected to impact household welfare and ability to cope with shocks. We include a dummy for whether or not households in the community have access to a MASAF program or to school feeding programs. We include two community leadership characteristics -- the proportion of communitylevel key informants who are women and who have no education. Additional variables include a measure of landholding inequality in the community, the number of groups operating in the community, an index of infrastructure and services in the community, the number of radio stations operating in the district, and the number of government marketing warehouses in the district.

Finally, the regressions control for (i) interactions of survey year and livelihood zone fixed effects to control for time-varying livelihood zone-specific unobserved heterogeneity, and (ii) dichotomous variables identifying the survey interview months to control for potential seasonality effects on household aid receipt and welfare outcomes.

17
# **Determinants of Property Tax Revenue: Lessons from Empirical Analysis**

## **I. Introduction**

Property tax revenue plays an important role in tax collection in developed countries. It becomes revealing, theoretically and empirically, that property tax is the most efficient tax, encompassing a number of other virtues of a tax instrument: transparency, equity, and direct linkage to benefits.<sup>1</sup> The tax has become even more relevant in the evolving trends of urbanization and fiscal decentralization.<sup>2</sup> In some of the high-performing countries, the immovable property tax brings in as much as 3 percent of GDP in revenues. At the same time, many developing countries have struggled with realizing sufficient revenues from property tax. However, as developing countries experience economic growth they are also seeing property values rising, providing a bigger tax base from which to realize revenues. Technology has made tax administration easier and more effective and developing country governments have been improving their quality of governance and have been considering introducing or enhancing property tax revenue collection to diversify their tax and fiscal revenues. Taxation of immovable properties not only improves the tax revenue collections but also works as an instrument for income redistribution, given that it is progressive by nature.

Exploring the determinants of property tax revenue using data from developed countries gives us important insights into understanding the extent to which these factors impact property tax collection. This research provides useful experiences for developing countries that are considering or planning to enforce the property tax collection.

In this research, we review literature that has focused on the determinants of property tax revenue throughout the world. We then collect state/provincial-level data for the United States, Canada, Australia, and Chile, as well as country-level data for all the OECD countries. Our data allow us to build up a panel data set for sophisticated econometrics regressions and estimations. We use OLS, Fixed Effects Model, and Random Effects Models to estimate the impact of various determinants on the
3 Africa; (iv) EBRD data on privatization in Europe and Central Asia (the tens of thousands of enterprises that were privatized through mass privatization or voucher schemes are not covered in this database); (iv) Privatization Barometer for selected European countries; (v) various other sources such as Latin Finance and Privatization International; and (vi) various government and non-government websites. All data are in current U.S. dollars.

4. While proceeds are commonly used to measure privatization trends, several caveats should be highlighted upfront. First, the heterogeneity of data sources covering a long time period may inevitably lead to data discrepancies, though every effort has been made to ensure consistency. Second, proceeds may be underreported in some countries because of lack of data or poor quality data. Third, data on privatization methods, the percent of shares divested, and the extent of foreign vs. domestic participation are only available in a few cases or for the earlier time period, and these factors are therefore not covered in any great detail in the analysis. Fourth, and related, proceeds from minority share sales may raise revenues but do not necessarily imply major changes in the control of state-owned enterprises or their improved efficiency. Fifth, proceeds are subject to swings from a few large transactions in a few countries and are thus not necessarily indicative of widespread activity or radical restructuring of the state sector in a particular country or region. Sixth, there are varying definitions of proceeds but for the most part data are based on gross proceeds rather than net proceeds to the government, which are likely to be lower after all privatization costs have been covered. Finally, the analysis includes only central level enterprises, with a few exceptions of state or provincial level utilities in a few major countries for which such data are available. In light of these caveats, the set of numbers presented here are meant to be seen as an overall indication of approximate broad trends rather than an exhaustive listing of each and every transaction in developing countries.
Morgan (2018). “Extreme Inequality: Evidence from Brazil, India, the Middle East, and South Africa.” _American Economic Review, Papers and Proceedings_ 108:119123.

Atkinson, Anthony B., Thomas Piketty and Emmanuel Saez (2011). “Top Incomes in the Long Run of history.” _Journal of Economic Literature_ 49(1): 3-71.

Bagchi S., Svejnar J., Bischoff K. (2016). “Does Wealth Distribution and the Source of Wealth Matter for Economic Growth? Inherited v. Uninherited Billionaire Wealth and Billionaires’ Political Connections.” In: Basu K., Stiglitz J.E. (eds) Inequality and Growth: Patterns and Policy. International Economics Association. Palgrave Macmillan, London.

Banerjee, Abhijit and Thomas Piketty (2005). “Top Indian Incomes, 1922-2000.” _World Bank Economic Review_ 19(1):1-20.

Bartels, Larry M. (2008). Unequal Democracy: The Political Economy of the New Gilded Age. Princeton University Press.

Bivens, Josh and Lawrence Mishel (2013). “The Pay of Corporative Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes.” _Journal of Economic Perspectives_ 27(3): 57-78.

Borge, Lars-Erik, Pernille Parmer, and Ragnar Torvik (2015). “Local Natural Resource Curse?” _Journal of Public Economics_ 131:101-114.

11
(WGSS), an enhanced short set of 12 questions (WGSS-enhanced), an extended list of more than 30 questions (WGES), and finally a functioning module for children developed in collaboration with UNICEF (Altman 2016, Adans et al 2018). The recommended short set includes six questions on limitations in the domains of seeing, hearing, walking or climbing steps, concentrating, communicating, and self-care (specifically, showering or dressing). The United Nations encourages governments to include (at least) the short set of questions recommended by the Washington Group in censuses and surveys. This option of using the short set of questions to measure disability does not capture all areas of functioning and would not satisfy all data needs but does provide a way to use population censuses and mainstream household surveys to monitor the SDGs and the CRPD.<sup>1</sup> In light of disability data needs to monitor the 2030 Agenda and the CRPD and recent progress in developing and testing internationally-comparable disability questions, the goal of this paper is to review the extent to which household surveys and population censuses include disability questions in low- and middle-income countries from 2009 to 2018. Section 2 provides background on measuring disability through household surveys and censuses. Section 3 reviews different types of questions on disability, and section 4 describes the data sets under review. Section 5 presents the results and section 6 provides the discussion and conclusion.

# **2. Background**

Measuring disability through household surveys and population censuses is a very complicated task and there is not a gold standard approach. How disability is measured depends on the objective of the measurement exercise. The goal might be to document disability prevalence and incidence, assess inequalities associated with disabilities, or evaluate service needs as well as policies and laws.

More fundamentally, measurement also reflects how disability is understood and defined, whether implicitly or explicitly. Because disability can be defined in a variety of ways, it can also be measured in many ways. Different conceptual models have been developed
sampling, ordering PSUs by the average household size and prevalent economic status of households, as described in a document from the previous survey (INS, 2010). Moreover, it has been verified _ex-post_ that further implicit stratification was also introduced by ordering PSUs, at the minimum, by delegation, thus forming a sort of geographic serpentine. These three factors contributed to better implement the design and the resulting errors became smaller than those expected when planning the design. Taking into account such implicit stratification, Betti and Pavelesku (2019) have estimated the real design effect of the EBCNV 2015 and the relative standard errors for estimates of mean per capita consumption expenditures at the governorate level. These results are reported in Table 4.

Since the aim of the EBCNV 2015 was to reach significative estimates at the governorate level, sub-sample sizes were planned to be as proportional as possible to sub-population sizes. This fact has resulted in homogenous post-stratification or calibration weights and small Kish effects, mostly well below 1.10. Moreover, the maximum errors estimated _ex-post_ have been very small; even for the Governorates of Manouba and Mahdia have not been higher than 7%, well below the planned maximum percentage error of 10%.

**Table 4. Per capita consumption expenditure, standard error and its** **<u>components</u>**

|**Gov**|**Domain**|**Expenditure**<br>**per capita**|**Standard**<br>**error**|**Cv %**|**Kish**|**Deft**|
|---|---|---|---|---|---|---|
|1|Tunis|5,810.12|296.78|5.11%|1.03|1.73|
|2|Ariana|5,461.10
# **Tables and Figures**

<mark>Table 1: Poverty Headcount - Synthetic vs. Actual Panel Data</mark>

|<br>Status in<br>|<br>Lo<br>|<br>wer Bou<br>|<br>nd Estim<br>|<br> <br> <br>ates<br>Truth<br>Upp<br><br><br>|<br>er Bou<br>|<br>nd Esti<br>|<br>mates<br>|
|---|---|---|---|---|---|---|---|
|Year[1]|[1]|[2]|[3]|[4]<br>[5]<br>[6]|[7]|[8]|[9]|
|_Panel A: Chil_|_e 1996_|||||||
|PovertyRate<br>|15.16|17.02|16.44|.<br>24.22<br>28.34<br>(20.07,28.37)|23.20|24.24|.|
|_Panel B: Nica_|_ragua 20_|_01_||||||
|PovertyRate<br>|40.63|39.44|40.65|.<br>39.03<br>43.56<br>(34.08,43.97)|41.01|41.25|.|
|_Panel C: Per_|_u 2008_|||||||
|PovertyRate<br>|35.16|34.22|33.57|33.19<br>33.35<br>36.52<