rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Rental Housing Supply**

# **_Supply Pattern – Volume and Quality_**

_Rental Housing Supply Volumes._ Most available official statistics for ECA transition countries deal only with ownership, and not with tenure pattern, although in some cases census includes tenure questions.<sup>21</sup> A focus on ownership alone tends to systematically under-estimate rental tenure, because of the significance of individual private rental housing, subletting, and sharing. Such data sources are therefore not useful for analyzing the tenure choice and housing needs of young and socially vulnerable households, information fundamental for housing policy formulation.

Analysis by external experts that could identify tenure from census data indirectly is rendered difficult through high barriers to access to the original census data. Barriers include complex administrative procedures, a general lack of public sector transparency, and sensitivity to the “politics” of the results—for example, in terms of ethnic structure in countries where there has been ethnic conflict.

An alternative way to estimate the size of rental housing stock is to look at reported household tenure status, rather than classification of physical dwellings. The survey data reported in Table 3 below show a non-owner-occupied housing sector of significant scale in the case countries. The share varies from 4.3 percent in Romania to 36.2 percent in Russia. The sector is not confined to public rental housing remaining after the apartment privatizations of the 1990s. In fact, the public sector is now surpassed by the combined private rental and rent-free (often against in-kind payment) sectors in Lithuania, Romania, and Serbia. The private rental sector is particularly significant in Poland (16.8 percent of households) and Lithuania (12.2 percent), followed at a great distance by Serbia (4 percent), Russia (3.6 percent), and Armenia (3 percent).

Moreover, in two of the six studied countries the public rental sector is significant: Poland has 30.6 percent rentals, with the private rental sector (16
`43`<br>`------------`|<br>`43`<br>`--------------`|<br>`38`<br>`-------------------------`|

``` Notes: This table presents four regressions, where each column reports the findings from an OLS regression. The dependent variables, measured in 1999, are: Bank Development equals claims on the private sector by deposit money banks as a share of GDP; Interest Margin equals net interest income divided by total bank assets; Overhead Costs equals total bank overhead costs as a share of total bank assets; and, Nonperforming Loans equals officially reported nonperforming loans as a share of total banks assets. The regulatory indexes are measured in 1999: Capital Regulatory Index measures the extent and stringency of regulatory requirements; Private Monitoring Index measures the degree to which regulatory policies encourage and facilitate private sector monitoring of banks; Official Supervisory Power Index measures the extent to which official supervisory agencies have the authority to take specific actions to prevent and correct problems; Entry Restrictions Index measures the number of specific legal requirements for obtaining a banking license; and Restrictions on Activities Index measures regulatory restriction on the degree to which banks can engage in securities, insurance, and real estate activities plus restrictions on banks owning and controlling nonfinancial firms. Government Owned Banks equals the percentage of the banking system’s assets held by banks that are 50% or more government owned. Though unreported, the regressions also control for the legal origin of each country by including three dummy variables for Common, French civil, and German civil law countries, as well as a constant term. Heteroskedasticity-consistent standard errors are in parentheses under the estimated coefficients. To designate statistically significant benchmarks, *** indicates significance at the 0.01 level
legal structure, but also other city characteristics that can affect firms’ performance, such as human capital or agglomeration economies.<sup>5</sup> The same firm in a different environment will probably experience different challenges that can affect its productivity levels and outcomes.

Air pollution has also been considered an important factor affecting workers and firms through two main channels. On the one hand, pollution levels can affect workers’ health and productivity. Recent studies on the effects of air pollution on worker productivity have quantified the negative effects on different production sectors (Graff-Zivin and Neidell 2012, T. Chang, J. S. Graff-Zivin, et al. 2014, T. Chang, J. Graff-Zivin, et al. 2016, Adhvaryu, Kala and Nyshadham 2014). On the other hand, environmental regulations can impact firms’ costs and influence their location. For example, studies such as Henderson (1974) and Kahn and Mansur, (2013) have shown that manufacturing firms cluster in areas where environmental regulations are not too strict. In developing countries, cities often limit such regulations, offering “pollution havens” that can attract major factories and contribute to development (Birdsall and Wheeler 1993, Cole 2004, Eskeland and Harrison 2003), while exacerbating the negative consequences on the environment.

We build upon related work by Harrison, Lin and Xu (2014), and Reyes, Robert and Xu (2017). Harrison et al. (2014) use the World Bank Enterprise Survey data to provide evidence that key factors for improving African firm performance are political competition, infrastructure, and access to finance. Reyes et al. (2017) offered a more comprehensive list of the business environment factors and add measures of BE elements such as taxes, labor regulation, entry and exit, along with the agglomeration environment. They conduct a global analysis while controlling for country fixed effects, offering better control of country heterogeneity. They also explore how the effects of BE variables are different depending on firms’ characteristics (such
Policy Research Working Paper 10910

# **Abstract**

Humanitarian models of refugee assistance increasingly promote refugee self-reliance without offering a clear understanding of what constitutes self-reliance, or how to measure it, although measurement is essential to assess whether the promotion of self-reliance has been successful. This paper proposes an approach to measuring self-reliance rooted in global poverty measurement: a refugee is self-reliant if their self-earned income exceeds the locally relevant poverty line. In its empirical application, the paper uses estimates of self-reliance drawn from 11 consumption surveys, which present the universe of data that can be used for such purposes. Refugees in middle-income countries are found to be far more likely to be self-reliant than those in lower income countries, while refugees residing in urban and non-camp settings demonstrate higher levels of self-reliance than those in rural and camp environments, reflecting the presence of greater economic opportunities outside camps and in urban areas. The paper also finds evidence that aid and self-reliance are inversely correlated, with more aid spent in environments where refugee self-reliance is constrained. The paper argues that a different outcome should be envisaged, one in which governments that encourage refugees to be self-reliant receive more aid. Such an approach would be beneficial for refugees, who would gain financial autonomy, and for host country citizens, who often face high levels of poverty and at times are even poorer than refugees.

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 jhoogeveen@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
reduce information frictions for firms, allowing them to find and use better inputs, to find and employ better workers, and to train their workers on the job; at the same time, good roads may remain necessary to take advantage of these inputs, both to allow workers to easily come to workplaces, and for firms to have incentives to improve production through investments to increase labor productivity. Our paper aims to identify whether roads and internet access are substitutes or complements for job outcomes in Africa. Addressing this question has important implications for optimizing the allocation of infrastructure investments in poor countries.

To examine how the expansion of fast internet networks complements or substitutes for the development of roads to encourage job creation, we combine data on characteristics and geographic locations of households and firms, and on the location of optical-fiber nodes and roads in Sub-Saharan African countries. We draw on detailed demographic data from the Demographic and Health Surveys (DHS) Program, the World Bank Enterprises Surveys (WBES), and the Living Standard Measurement Study (LSMS). Novel data on optical-fiber nodes come from Africa Bandwidth Maps, and submarine cable maps from Mahlknecht (2014). Using a combination of difference-in-differences techniques and instrumental-variable approaches with these varied data, we estimate the join effects on local labor markets and workers that stem from changes in the proximity of major roads and accessibility to high-speed internet infrastructure.

We find large, complementary effects of investing in the two types of infrastructure. For example, combined investments of this kind increase the probability of having been employed in the last year by at least 9 percentage points from the probability that is the case when no investments are made. By contrast, road and internet investments undertaken in isolation have lower impacts. Being 1 kilometer closer to a road in a given location increases the likelihood of employment by 0.8 percentage point, and having fast internet access increases the likelihood of employment by 6.7 percentage points. Thus, the additional impact on employment that arises from the complementarities of combining road and internet 3
# **3. Tanzanian Consumption Survey Experiment and Consumption Aggregate**

# _Survey Experiment_

Our survey experiment entailed fielding eight alternative consumption questionnaires randomly assigned to 4,000 households in Tanzania. The eight designs vary by method of data capture, level of respondent, length of reference period, number of items in the recall list, and nature of the cognitive task required of the respondent. They are summarized in Table 1. Modules 1-5 are recall designs and modules 6-8 are diaries. These eight designs were strategically selected to reflect the most common methods utilized. The alternative designs focus on variation in the measurement of food consumption and expenditure on select frequently purchased non-food items, and not on general non-food expenditure where, due to the infrequency of purchase, there is a much greater degree of design harmonization in practice. Food consumption includes the quantity consumed from three sources (purchases, home production, and gifts/payments) and, for purchases only, the corresponding value of the quantity (in Tanzania shillings). Modules 1 and 2 seek the consumption values for a long list of 58 commodities. In module 3, the subset list consists of the 17 most important food items that constitute, on average, 77 percent of food consumption expenditure in Tanzania based on the previous Household Budget Survey. When comparing consumption expenditure in module 3 with other modules, we scale up food expenditures for that module as is commonly done in practice. Module 4 is a collapsed list where the 58 food items are aggregated into 11 comprehensive categories.<sup>10</sup> Among the recall modules, module 5 deviates from a reporting of actual expenditure over a specified time period. Instead it asks for “usual” consumption, following a recommendation in Deaton and Grosh (2000), where households report the number of months in which the food item is usually consumed by the household, the quantity usually consumed, and usual expenditure in those months. These questions aim to measure permanent rather than transitory living standards, without interviewing the same
countries for trade with each other. This is supported by the fact that nine of the eleven possible estimates in panels B and C are negative and more than half of them are statistically significant. The two positive estimates that we obtain are not statistically significant and are for Chemicals and Machines. According to our estimates the border gap is the widest in Food (about 22% tariff equivalent), Minerals (about 15% tariff equivalent), and Wood (about 10% tariff equivalent). Based on the analysis in this section, we conclude that CEFTA members have the potential to face significantly lower barriers to trade upon accession to the European Union, which may lead to significant gains in terms of welfare. We use our structural gravity estimates to quantify the potential for such gains in the next section.

# **4.2 On the GE of CEFTA-EU Harmonization**

We employ the standard structural gravity general equilibrium framework following Dekle, Eaton and Kortum (2007, 2008) to perform two counterfactual experiments that quantify the total GE impact on the exports of the countries in our sample.<sup>21</sup> The first experiment simulates harmonization of MFN tariffs by the CEFTA countries to the EU tariff rates. To perform these analyses, we use data on actual tariffs, as described in the Data Section 3.2, in order to change the vector of trade costs for the CEFTA members. As the tariff data were available at the HS17 8-digit classification, we aggregated them up using trade shares as weights to match the level of aggregation for the GE analysis. For the counterfactual analysis we set MFN tariffs to zero for trade between EU member countries and CEFTA member countries and set the MFN import tariffs of the CEFTA members for all their other trading partners besides the EU member countries to the MFN import tariff level of the EU.

The second experiment simulates accession of the CEFTA countries to the EU in terms of the trade costs that these countries face for their trade with the EU. To perform
provides information on employment and livelihoods, education, displacement characteristics, coping with food insecurity and consumption. The maps presented in **Figures A2b** and **A2c** show the spatial spread of surveyed host communities and camps.

Of the surveyed households, 46% are located in the urban area of Maiduguri (the capital of Borno state). Another 31% are located in the adjacent, rural Jere district, while the remaining 23% are spread across the six states in North-east Nigeria. Of the IDP households that reside in host communities, 51% are located in Maiduguri and 30% in Jere. Among IDPs in camps, 35% of the households are located in Maiduguri and 39% in Jere.

The total survey sample includes 17,951 individuals from 2,947 households:

- 563 IDP households (3,290 individuals) that are located in IDP camps;

- 874 IDP households (5,234 individuals) that are located in host communities;

- 1,510 non-IDP households (9,427 individuals) that are located in host communities;

Thus, 47% of the surveyed individuals are IDPs, and 18% (of the full sample) are camp-based IDPs. The survey includes both household information and a household roster module containing information on socio-demographic indicators, health, education (for members ages 6 or older) and employment (for members ages 15 or older).

**Conflict event data.** We match the survey data spatio-temporally with conflict event data from the Armed Conflict Location & Event Data Project (ACLED), using detailed geo- and time-tagged information on events of violence (Raleigh et al., 2010). Events reported by ACLED include various forms of violence, and allow us to calculate a local violence measure for IDP and non-IDP households, which counts the total number of violent events in the 12 months prior to the survey.

**Study sample.** The surveyed population is fairly young, with a mean age of 18.
E Plan is also experimenting with a program so that regular savers can earn access to a small line of credit up to R 500 (US$112) in a 'way to pay' program One of the advantages of the automated accounts is that it can lower costs of processing loan applications. Information on past account balances and activity from E Plan can be used in a credit check/screening process to evaluate loan applications and South African banks use credit scoring models heavily for evaluation of consumer loans. But to date, the lending product is still not breaking-even<sup>5</sup> because it continues to require a great deal of staff time and intervention. But E Plan management is looking for ways to automate more parts of the delivery process and lower costs, e.g., so that the loan amount can be withdrawn from the ATM rather than from the staff. This seems to be a very promising approach to providing small loans to low income clients.

Looking ahead Mr. Bob Tucker outlined the strategy for E Plan to continue to push basic banking to more sophisticated cost effective delivery systems. To date, E Plan has moved from passbook to card, from teller to ATM, from ATM to POS in stores with cash back. In the future, it is expected that there will be additional movement from magnetic stripe card to off-line stored value smart card, and from bank ownership of deposit distribution centers to franchise in-store ownership.

# Concluding Comments

We chose to document the E Plan program because it demonstrates how a commercial bank used market information to bundle services for low income clients. Further study will be needed to check on customer satisfaction or to investigate the impact on the clients. We did not have household survey data that would allow us to investigate whether the clients of E Plan evidenced different liquidity management or savings behavior from the rest of the population in a similar income band. Standard Bank was extremely open and provided all information requested, but we did not dwell on the structure of
(1978 and Bass 1985) in which a leader works in cooperation with their subordinates to identify the changes needed, to create a vision to guide the change through inspiration, and to execute the change in cooperation with the team. T</mark> he meta-analysis brought forward by the authors indicates especially that students from schools where principals ensure orderly learning conditions tend to perform better. 2 As teachers may be subject to higher occurrences of intimidation, threat and assault by students in schools much affected by low safety.

> 3 This includes regions covered by the TIMSS and PIRLS assessments for the years 2007 to 2015.

3
# **5 Experimental Design**

All informal settlement property owners in the utility database were first called to confirm contact details and receive the base intervention. Eligibility criteria into the study included: (i) property owners were able to be contacted and their contact details could be updated; (ii) their payment accounts were in arrears and (iii) property owners did not hold multiple accounts (multiple-property owners). All eligible property owners then received the basic information intervention and contact details were updated. Figure 3 describes the experimental design and sample selection. Starting from the group of 5,091 property owners that completed the phone survey in August 2018, just over 50% (2,584) indicated that they had tenants residing in the property. These 2,584 accounts were randomly assigned into a group of 1,292 who received the engagement treatment and an equally sized control group. The engagement intervention was successfully implemented in 885 (69%) of the 1,292 accounts assigned to the treatment group. Reasons for non-compliance included not being able to find the property, tenants being unavailable at the time of visit, and incorrect recording of the compound as having tenants when this was not the case.

For the enforcement intervention, we started with the same 5,091 accounts used for the engagement intervention and removed two informal settlements because these settlements are characterized by multi-story apartment blocks where individual disconnections pose a technical challenge. The remaining sample of 3,253 accounts from 4 settlements (which included compounds with and without tenants) were then clustered by street using GPS and address data. This generated 147 distinct street clusters. We then randomized 73 clusters consisting of 1,584 accounts into the treatment group, while the remaining 74 clusters (1,669 accounts) were left as pure controls. Within the treatment clusters there were 649 compounds eligible for disconnection, and 327 of these were randomly assigned to receive the disconnection notifications, following the protocols described in Section 4. The remaining 322 control compounds in the
a raw indicator and replace skewed indicators with their log. We then take the z-score of these transformed variables in a similar way to the above measure.

Finally, we move away from distributional concerns by taking the ordinal rank of absolute errors within an indicator and divide this by the maximum rank, thereby creating a ‘rank-proportion’ score. This can be interpreted as the percentile of error made by an official relative to other officials in our data set. The Data Appendix provides further details on how the error variables we use in the analysis were created.

# **2.5 Measuring the Incentive Environment**

To understand the incentive environments in which public officials in the organizations we are studying make decisions on information absorption, we also collected data on management practices. Following recent efforts to collect data on the management practices of public administrations (Rasul and Rogger, 2018; Rasul et al., 2018) we used a public sector version of the World Management Survey (WMS; Bloom and Van Reenen, 2007; Bloom et al., 2012) to elicit measures of the management practices under which public officials operate.

of the underlying benchmark data, and then created the various measures outlined below. Taking this approach does not qualitatively change our core results.

12
.026)_|_(.000)_|_(.632)_|_(.050)_|_(.367)_|
|Treatment|All|All|All|All|All|All|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Time fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Continental trends<br>|Yes|Yes|Yes|Yes|Yes|Yes|
|_R_<sup>_2_</sup>(within)|0.239|0.146|0.228|0.279|0.391|0.424|
|Country-sectors|519|440|269|238|80|112|
|Observations|13,278|9,558|8,932|6,920|2,510|3,869|

_Source:_ Own calculations based on the data described in the text.

_Notes_ : _p_ values based on robust standard errors clustered at country-sectors in parentheses. Additional controls include the _log of per capita GDP_ , the _log of population_ , _agricultural employment share_ , _land per capita_ , and _product value shares_ . All regressions include time and country fixed effects and interactions between years and continental dummies (for African, Asian, and Latin American countries).

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