rafmacalaba/data-use-mentions-extended huggingface dataset:
5.4|6.5|1.6|
|Collection rate|% of billing|89|34|92|91|
|Cost recovery ratio|%|100|100|89|85|
|Revenue per unit|US cents per KWh|18|3|14|13|
|System losses|% generation|42|40|24|20|
|Total hidden costs|% revenue|451|443|69|0|
|Effective power tariff||Sierra Leone|Countri<br>predomina<br>gener|es with<br>ntly hydro<br>ation|Other developing<br>regions|
|Residential at 100 kWh|US$ cents|0.31–0.43|10.27|||
|Commercial at 900 KWh|US$ cents||11.73||5.0 – 10.0|
|Industrial at 50,000 kWh|US$ cents|0.30|11.39|||

_Source_ : Fragile and non-fragile countries’ figures are as of 2005 and are calculated using the power AICD database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data, which contains household data. For Sierra Leone, national-access data was taken from EIU 2009 and is for 2009. urban-and-rural-access data was taken from Tallapragada and Shkaratan and others (2009) and is for 2003; electricity consumption was taken from the CIA World Fact Book and is for 2007; power-outage data was taken from Rosnes and Vennemo and is for 2005; value-lost-due-to-outages data was taken from Enterprise Surveys 2009; collection-rate data was taken from World Bank 2007 and is for 2007; system-losses data is for 2009 and was taken from World Bank 2010; industrial-tariff data is for 2006 and was based on World Bank 2006; residential-tariff data is based on 2011 estimates provided by World Bank staff.

Power outages are a huge problem in Sierra Leone. The country records 46 days of power
explain the smaller number eventually making it to the city, compared to the towns.

We use household survey and census data from Tanzania to explore our key question, why many more migrants move to seemingly suboptimal towns. As in other settings documented in the literature, the data confirm the existence of rising living standards across the urban spectrum, from rural areas over towns to cities. That urban gradient is visible both in terms of assets and housing wealth as well as amenities. Overlaying the wealth distributions, we find that living standards of the 90th percentile in rural areas still lie below those of the 10th percentile in urban areas.

This is problematic for the basic Todaro (1969) model, which assumes that the rural wage needs to equal the weighted average of the high and low urban wage, with the weights being the likelihood of getting each. Kanbur et al. (2019) consider an extension of the basic model that introduces migration costs.<sup>1</sup> These can bring the Todaro model back in line with the empirical observation in our setting that the rural wage is lower than both the lower and higher urban wage. Their second extension to the model is to consider multiple destinations (town or city), each characterized by different formal and informal destination wages (Kanbur et al., 2019).<sup>2</sup> Guided by this model, we use a novel data set on migrants for whom we have pre-migration and post-migration survey data to explore how migration costs and expected income at destination affect destination choice. At baseline in the early 1990s, the respondents all lived in Kagera, a large, remote and primarily rural region in the northwestern part of Tanzania. By 2010 about half of those who were still alive had moved out to other villages, towns or cities. We use the pre-migration survey data to create a dyadic data set of all possible migrant-destination pairs, among which the migrant will have chosen one and not chosen all others. We then use census
the Caucasus and Central Asia is scant. Since banks are the major source of financing in this region, the level of net interest margins and spreads is an important policy variable. Dabla-Norris and Floerkemeier (2007) examine the determinants of interest rate spreads in Armenia during 2002–2006 and find that spreads are mostly explained by bank-specific factors such as bank size, liquidity, and market power. Teodoru (2020) studies the drivers of bank spreads in the Kyrgyz Republic and finds that low levels of competition, credit risk, interest rate risk and the legal environment are the key explanatory variables. Finally, in a cross-country setting, Almarzoqi and Ben Naceur (2015) analyze the determinants of intermediation spreads in the countries of the Caucasus and Central Asia over the period 1998–2013 and find that operational efficiency is by far the main driver of intermediation spreads. When the analysis is performed on only a small sample of Tajik banks, operational costs remain the key determinant, followed by risk aversion and income diversification.

This paper investigates the factors associated with high interest spreads in Tajikistan’s banking sector during 2011Q1– 2022Q4. It makes several contributions to the literature on interest spreads and margins. First, it contributes to a limited literature on financial development in Tajikistan. Second, it uses a unique panel dataset comprising balance-sheet and income statement quarterly data of Tajikistan’s commercial banks provided by the National Bank of Tajikistan (NBT), the country’s central bank and banking supervisor. Third, as in Ben Nassar, Martinez and Pineda (2014), it uses Beck and Katz’s (1995) OLS-based panel corrected standard errors (PCSE) procedure, which is more appropriate than alternatives (e.g., feasible generalized least squares) given the structure of the panel dataset.

5
economy. Until recently, deterrent measures, such as detecting, criminalizing, and penalizing undeclared work, have been prevalent, but EU countries have increasingly adopted incentive measures for inducing voluntary compliance (Williams 2009). According to a survey of policy measures adopted in the EU countries, almost all countries impose administrative or penal sanctions on either purchasers or suppliers of goods and services involved in undeclared work (Dekker et al. 2010). However, despite buyers’ gains from tax evasion, fewer countries impose penalties on purchasers (Jensen and Wőhlbier 2012, 28). The concept of a penalty for the purchase of undeclared work is based on the buyer’s illegal benefit from the low price of undeclared work resulting from tax evasion.

In Italy, for example, under the Decreto Bersani, a law passed in 2006, a retailer that fails to issue a sales receipt three times in a five‐year period can be closed, and construction sites can be shut down if employment irregularities are found. It is also illegal to use cash to pay more than €100 for professional services (A. T. Kearney and Schneider 2009). Buyers of undeclared work must keep receipts to present to tax authorities, if asked, to avoid sanctions. Norway requires purchasers to pay using electronic means or to keep invoices for a specified time to avoid criminal liability for purchases of undeclared services (Jensen and Wőhlbier 2012, 28). Penalizing both providers and buyers of undeclared work can be effective in that it more effectively breaks the collusive relationship between them. However, the process of monitoring a large number of small and micro businesses to determine if they are issuing receipts—and then ensuring that the general public keeps those receipts—is almost impossible, given the tax authorities’ limited resources. Those measures have potential deterrent effects, but they may not be implementable in practice. With this in mind, many scholars have questioned the effectiveness of deterrent measures, in general, including detecting and punishing (Williams and Schneider 2016, 147
To calibrate _hz_ and _bz_ , I use firm-specific R&D data as a proxy for the innovation effort. As documented in Aw, Roberts, and Xu (2011) **,** firms that invest in R&D on average increase their productivity over time. Boothby, Lau, and Songsakul (2008) and Ranasinghe (2014) documented the patterns of R&D expenditure for U.S. firms based on a data set provided by the National Science Foundation, and they found that R&D (when measured as employment) is increasing. Following Ranasinghe’s (2014) strategy, I calibrate the curvature parameter _bz_ of the innovation cost to target average R&D spending by firms that have between 500 and 1,000 employees relative to average R&D spending by firms that have between 1,000 and 5,000 employees. The scale parameter _hz_ of the cost of R&D is closely related to the total spending on innovation. Following the literature, I target the ratio of R&D to GDP in the United States, which for 2016 was equal to 2.7 percent.

**Bribing parameters** . Based on the data provided by the WBES, total bribes increase with firm size (measured as employment), with large differences across firms. For example, the average total bribe paid by firms with 50 to 100 employees is 40 percent of the average total bribe paid by those with 100 to 500 employees.<sup>20</sup> In contrast, firms with 100 to 500 employees spend on average 80 percent as much on bribes as do firms with 500 to 1,000 employees. Based on these features of the data, I calibrate the curvature parameter _bz_ of the cost of rent-seeking to target average bribes for firms that have between 500 and 1,000 employees relative to average bribes for firms that have between 1,000 and 5,000 employees. I calibrate the probability of keeping a firm’s distortion
basic services they are in charge of, such as education. Likewise, local administrations can contribute their funds or royalty resources to finance the service. The Ministry of Education (MEN), in turn, can provide resources for programs to improve education, such as the School Meals Program, which is considered as a separate transfer. The MEN formulates educational policy and monitors and regulates the delivery of the educational service; while it is responsible for the distribution of the resources of the SGP, among other functions. Meanwhile, ETCs are primarily responsible for directing, planning, and providing the education service.

Higher education in Colombia is regulated by Law 30 of 1993. People can gain access to it once they have completed secondary education and submit the results of the standardized state tests Saber 11. Tertiary education has two levels of training, undergraduate and graduate, while the undergraduate level includes technical, technological, and professional levels, and the graduate includes education at the level of specialization, masters, and doctorate. In 2017, undergraduate enrollments reached 2.28 million, which represents 52.4 percent coverage for this level.

In turn, there are three types of higher education institutions: universities, technical schools and colleges. Public or private institutions can provide higher education. State institutions are funded from the National General Budget, the local administrations, and the resources they obtain through registration, tuition, and others. During 2017, the Ministry of Education transferred 3,029,180 million pesos to public universities,<sup>24</sup> which represents 0.3 percent of GDP.

> 23 A municipality can be certified in education before a department as long as it meets the technical, administrative and financial requirements to administer the service, in accordance with Law 715 of 2001.

> 24 The figure was taken from the Budget Execution Report of the public universities of the 2017 fiscal year, excluding items transferred by pension concurrences. The information was reviewed on March 1, 2019.

12
Benítez-Silva et al. (2015) exploit the longitudinal panel structures of the Health and Retirement Study and the American Housing Survey and are able to compare the transaction prices at a given time and self-reported housing values before the sales of the same housing. They conclude that homeowners, on average, overestimate the current values of their properties by around 8 percent. Arévalo and Ruiz-Castillo (2006) compare self-assessed values with the results of a hedonic model on the same dwellings in Spain, calibrating the model on the reported rents paid by tenants in the same survey. They find that, on average, self-assessed values are close to the results of the hedonic model.

This paper proposes the use of matching estimators to assess the accuracy of homeowner self-assessed rents based only on the information available in household budget surveys, thus departing from most of the existing literature. This represents an important advantage because researchers and practitioners working on rent imputation in developing countries are left with few sources of information, and there are often no alternative surveys, reliable administrative data on market transactions or tax assessments, or data collected through professional appraisals. The paper argues that ensuring homeowner and tenant dwellings exhibit similar characteristics is key for an accurate comparison, and attention should therefore be paid to the choice of the comparison groups to avoid comparing the incomparable.

# **3. The Housing Market in Peru**

This section briefly describes trends in the housing market in Peru in the recent past. The housing market has been dynamic. The dynamism has been driven by a rapid process of urbanization, changes in policies, and robust growth in the construction sector. The current process of urbanization started in the mid-1950s, when the rural population began moving from the Andes toward Lima Province, in which the capital is located. The urban population grew from about 47 percent of the country’s population in 1960 to almost 78 percent in 5
Policy Research Working Paper 9937

# **Abstract**

This paper studies the distribution of credit during crisis times and its impact on firm indebtedness and macroeconomic risk. Whereas policies can help firms in need of financing, they can lead to adverse selection from riskier firms and higher default risk. The paper analyzes a largescale program of public credit guarantees in Chile during the COVID-19 pandemic using unique transaction-level data on the demand and supply of credit, matched with administrative tax data, for the universe of banks and firms.

Credit demand channels loans toward riskier firms, distributing 4.6 percent of gross domestic product and increasing firm leverage. Despite increased lending to riskier firms, macroeconomic risks remain small. Several factors mitigate aggregate risk: the small weight of riskier firms, the exclusion of the riskiest firms, bank screening, contained expected defaults, and the government absorption of tail risk. The empirical findings are confirmed with a model of heterogeneous firms and endogenous default.

This paper is a product of the Development Research Group, Development Economics. 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 fhuneeus@bcentral.cl, jkaboski@nd.edu, mauricio.larrain@uc.cl, sschmukler@worldbank.org, and mavera@cmfchile.cl.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
# **1 Introduction**

The European economic landscape has undergone major transformations since the early 1990s through international migration. During this recent period, the volume of migration in the European Union (EU) has more than doubled, with the number of foreign-born residents in EU countries reaching 60 million in 2019. This represents 12% of the EU resident population and over 23% of the stock of global migrants, while the EU population accounts for only less than 7% of the total world population. Among the current stock of the foreign born in the EU, more than 21 million individuals (or 35% of the total) are migrants from other EU member countries.<sup>1</sup> What motivates migration decisions? Individual rational actors decide to migrate because a cost-benefit calculation leads them to expect a positive net return – both monetary and non-monetary – from movement (Sjaastad 1962, Schwartz 1973, Todaro and Maruszko 1987). In this microeconomic model of individual choice, where both push and pull constructs shape migration patterns, potential migrants move to where the expected discounted net returns are greatest (Lee 1966, Borjas 1987).<sup>2</sup> Then, because of its large economic and social costs, corruption could be an important determinant affecting migration decisions. In particular, does a high level of corruption in a country promote emigration and deter immigration? In order to make progress on this important question, in this paper we use a gravity model specification to investigate whether corruption could be identified as a push factor and/or as a pull factor shaping migration patterns.

Using data on bilateral migration flows across EU28 and EFTA countries between 2008 and 2018, we show that four different measures of corruption – both subjective and objective indicators – are strongly linked with migration flows. Across all the specifications considered in this paper, corruption is found to act both as push factor and as a pull factor for migration decisions. A 1-unit increase in the corruption level in the origin country is
these two effects. In contrast, demographics contributed to only 35 percent of Namibia’s poverty reduction.

**Figure 6: South Africa will maintain the highest share of people 65+ in SACU till 2050**

Population share of people 65+ (percent) <!-- Start of picture text --> 11<br>Botswana<br>Lesotho<br>10 Namibia<br>South Africa<br>9 Swaziland<br>Sub-Saharan Africa<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1950 60 70 80 90 2000 10 20 30 40 50<br><!-- End of picture text --> Source: Authors’ estimates Note: Data from UN (2015). The value for Sub-Saharan Africa is a weighted average.

**Figure 7: Demographic change and realizing the demographic dividend can account for 35 to 75 percent of poverty reduction in SACU economies by 2050**

Poverty headcount rate, 2012 (percent) Change in poverty headcount rate, 2012-50 (percentage points) <!-- Start of picture text --> South<br> 60<br>Botswana Lesotho Namibia Africa Swaziland<br>0<br> 50<br>-10<br> 40 (11.6) (13.4)<br>-20 (16.9)<br> 30<br>-30 (26.6)<br> 20<br>-40 (37.9)<br> 10 -50 Realizing demographic dividends<br>Botswana Lesotho Namibia South Swaziland Shifts in household distribution<br>Africa<br>No demographic effects<br><!-- End of picture text --> Source: Panel A is from PovcalNet (2016). Panel B is from authors’ simulation results. Note: The poverty headcount rate is based on $1
children (the control group). Standard errors are clustered and in parentheses. Source: EU-LSF (2010-2019).

Across each set of controls, the DD estimate is stable between 2.6 and 4.1 percentage points (or 4.3 percent and 6.6 percent from an average of 61.2 percent). Results are significant at the one-percent significance level. These findings imply that the reform led to a considerable increase in the maternal labor supply of mothers of six-year-old children and that substituting maternal childcare with childcare provided by the government is an effective way to increase the maternal labor supply.

The size of the effect is similar to the impact of the EITC on maternal employment in the US (Bastian 2020) or the impact of the universal childcare reforms in Germany (Geyer, Haan, and Wrohlich 2015). The impact is lower than the one found for universal, full-time public childcare for 3-year-olds in Spain (Nollenberger and Rodr ́ıguez-Planas 2015) or for greater grandparent availability (Bratti, Frattini, and Scervini 2018), which could be due to preparatory classes only taking place in the morning.

Results are robust to restricting the age range of mothers (Online Appendix Table B1 and B2), abstracting from survey weights (Online Appendix Table B3), using women without 12
3423|0.3424|
|Observations|1314120|1313807|1313807|1314123|1313810|1313810|1314119|1313806|1313806|
||||||Urban|||||
|CatchmentArea X Post|0.1524|0.1509|0.1525|0.1562|0.1357|0.1339|0.1458|0.1584|0.1615|
||(0.1363)|(0.1245)|(0.1295)|(0.1335)|(0.1331)|(0.1367)|(0.1328)|(0.1174)|(0.1225)|
||[ 0.275 ]|[ 0.242 ]|[ 0.241 ]|[ 0.256 ]|[ 0.309 ]|[ 0.309 ]|[ 0.28 ]|[ 0.196 ]|[ 0.194 ]|
|R-squared|0.5513|0.5717|0.5718|0.5355|0.5554|0.5556|0.5254|0.5474|0.5475|
|Observations|172407|172371|172371|172407|172371|172371|172407|172371|172371|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No
- **Bhagwati, Jagdish N., Richard A. Brecher, Elias Dinopoulos, and T.N. Srinivasan.** 1987. “Quid Pro Quo Foreign Investment and Welfare: A Political-EconomyTheoretic Approach.” _Journal of Development Economics_ , 27(1): 127–138.

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