rafmacalaba/data-use-mentions-extended huggingface dataset:
no guarantee of future funding, all 77 countries with either IDA (59 countries) or Blend (18 countries) lending status as well as India constitute the focus of the WBG’s commitment to alleviate data deprivation over the period of 2016-2030.</mark><sup>12</sup>

# **III. Approach to survey implementation and technical assistance cost projections**

# **a. Survey implementation unit cost compilation**

The first step in our cost projections was to compile a comprehensive, cross-country database of actual survey implementation unit costs (i.e. costs per sampled household). In compiling this database, we focused on surveys that 1) have a multi-topic focus while collecting information towards the construction of a comprehensive, often consumption-based, welfare aggregate and 2) are implemented by NSOs and integrated into NSSs. This decision was informed by the World Bank Shared Strategy for Household Surveys and its specific recommendations that Bank-

> 10 Since household consumption surveys for countries do not always coincide with the reference year for which global poverty is estimated, the World Bank fills data gaps based on projections, which are far less reliable than poverty and inequality statistics directly estimated from household survey data.

> 11 The broadened definition would necessitate multi-topic survey data production every 3 years.

> 12 <mark>Regarding the non-IDA countries that are considered data deprived under the broadened definition of the concept, we assume that their data deprivation is not a result of a lack of funding but is instead due to other prohibitive factors such as a lack of prioritization of data collection, and limitations to data access.</mark>

6
by group similarity, such as cultural similarity) and peer monitoring (as proxied by proximity of group members) have been argued as good predictors of repayment performance. However, group homogeneity may also create collusion (i.e., the probability of loan default by other members when one member defaults); this, in turn, increases loan default because members in close proximity or with similarity are unable to sanction against default or provide aid when they have similar economic conditions (Besley and Croate 1995). By contrast, a diverse group might have fewer such problems, which helps to reduce the chance for group collusion and, in the process, loan default. Greater diversity might also increase repayment rates because group members’ incomes are less likely to vary together, implying a greater chance to provide mutual aid in times of need (Sadoulet 2003). On the flip side, diversity also means less peer pressure and monitoring, which can reduce, rather than increase, loan repayment rates.

In short, the question of whether group lending leads to higher loan recovery is an empirical issue. Since group attributes are themselves endogenous, empirical verification of whether group liability matters in group lending entails estimating a repayment function that includes not only the probability of individual loan default, but also that of other group members. The central problem in estimating the impact of group features in group-based lending, such as those of the Grameen Bank, is the self-selection of individuals into groups, as well as the joint probability of default by group members. There are unobserved characteristics of individuals that sort them into groups and affect their joint probability of default. Resolving endogeneity in estimation is key to determining whether group liability matters in Grameen Bank–type, group-based lending.

To resolve this issue, this paper proposes a quasi-experimental approach that relies on group panel data, including borrowing and repayment information for all members since 8
|Macroeconomic Mapping in Normal versus Crisis Times . . . . . . . . . . .|29|
||4.4|Linking Macroeconomic Scenarios to Changes in NPLs . . . . . . . . . . . .|30|
||4.5|Accounting for Macroeconomic Risks in Aggregate PDs: The Systemic Com-<br>ponent of Credit Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|32|
||4.6|Incorporating Bank Specifc Characteristics: The Idiosyncratic Component<br>of Credit Risk<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|33|
|**5**|**Exp**|**osures at Default**|**36**|
|**6**|**Con**|**struction of Outcome Indicators**|**38**|
|**7**|**Em**|**pirical Application**|**49**|
||7.1|Data Requirements and Calibration of Parameters . . . . . . . . . . . . . . .|49|
|||7.1.1<br>Macroeconomic Scenarios . . . . . . . . . . . . . . . . . . . . . . . . .|49|
|||7.1.2<br>Bank Level Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|51|
|||7.1.3<br>Estimated
- Hernandez-Canovas, Gines, and Pedro Martinez-Solano (2007). “Effect of the Number of Banking Relationships on Credit Availability: Evidence from Panel Data of Spanish Small Firms,” _Small Business Economics_ 28: 37-53.

- Hutchinson, John (2006). “Comparing the Impact of Credit Constraints on the Growth of SMEs in a Transition Country with an Established Market Economy,” _Small Business Economics_ 27: 169-179.

- IADB (Inter-American Development Bank) (2004). _Unlocking Credit: The Quest for Deep and Stable Lending_ , Baltimore, MD: Johns Hopkins University Press.

- IFC (International Finance Corporation) (2009). “The SME Banking Knowledge Guide,” The World Bank Group, Washington D.C.

- Keeton, William R. (1995). “Multi-Office Bank Lending to Small Businesses: Some New Evidence,” _Federal Reserve Bank of Kansas City Economic Review_ 80: 45-57.

- Konings, Jozef, Marian Rizov, and Hylke Vandenbussche (2003). “Investment and Credit Constraints in Transition Economies: Micro Evidence from Poland, the Czech Republic, Bulgaria and Romania,” _Economics Letters_ 78: 253–258.

- Murphy, Neil B. (1983). “Loan rates, operating costs and size of loan: The evidence from crosssection data,” In P. Horvitz & R. Pettit (Eds.), _Small Business_ , Connecticut: JAI Press, pp. 51–62.

- OECD (Organisation for Economic Co-operation and Development) (2004). _Promoting Entrepreneurship and Innovative SMEs in a Global Economy: Towards a More Responsible and Inclusive Globalisation_ , Second OECD Conference of Ministers Responsible for Small and Medium-Sized Enterprises (SMEs), Executive Summary of the Background Papers, Paris, France: OECD.

- Peltoniemi, Janne (2007). “The benefits of relationship banking: Evidence from small business financing in Finland,” _Journal of Financial Services Research_ 31: 153–171.

- Rocha, Roberto, Subika Farazi, Rania Khouri, and Douglas Pearce
:** Effects on Village Transfers and Budgets

|||**Adminis**|**trative data**||**Village head survey**|
|---|---|---|---|---|---|
|Village Funds:|Allocated|Utilized|% Spent|FullySpent|Budget|
||(1)|(2)|(3)|(4)|(5)|
|New village head|-0.367|-0.320|0.025|0.038|-0.347|
||(0.625)|(0.704)|(0.062)|(0.140)|(1.205)|
|Observations|423|422|422|422|418|
|Control mean|2.04|1.91|0.87|0.58|2.47|
|Robust p-value|0.517|0.635|0.699|0.854|0.732|
|MSE-opt. bandwidth|28.0|23.7|31.6|28.6|24.7|
|Effective obs.|259|226|275|262|224|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). The sample includes all villages with available data. In column 1, the dependent variable is the 2022 Village Funds Allocation (ADD) in millions of Indonesian Rupiah per capita. The Village Funds Allocation includes funds allocated by district governments to fund village government salaries, benefits, and operations. In column 2, the dependent variable is the 2022 ADD amount utilized by the village for programs and activities in millions of Indonesian Rupiah per capita. In column 3, the dependent variable is the share of ADD utilized. In column
model. I found the two models yield nearly identical results. Furthermore, both Wald and likelihood ratio tests<sup>12</sup> suggest that the unobserved variables influencing the reporting decision are statistically independent of unobserved factors of the electricity demand. Therefore, sample selection can be ignored conditional on observable household characteristics.

# **IV. Data and Descriptive Analysis**

# _Data_

The model is estimated using data from the Turkish HBS in 2008 and 2009. The HBS is conducted annually by the Turkish Statistical Institute to collect information on household socioeconomic status, living standards, income, and consumption expenditures. The survey is a

> 11 A United Nations Development Programme (UNDP) field study on four provinces (Istanbul in both sides of the Bosporus, Cankiri, Kars and Urfa) (Bagdadiogl etc. 2009) suggests that households who did not pay electricity bills during the survey month due to arrears or illegal connections would not report electricity expenditure to the HBS.

> 12 See Yamagata (2004) for a discussion on the performance of different tests for sample selection bias.

9
|||||mitigation<br>measure)|mitigation<br>measure)|
|---|---|---|---|---|---|
|Extremepoverty|2.9|7.4|6.9|4.5|4|
|Poverty|13.7|20.9|20.2|7.3|6.5|
|Poverty gapLower|0.5|2.0|0.7|1.5|1.3|
|Poverty gapupper|3.2|6.4|4.2|3.2|1|
|Inequality|37.0|39.5|39.2|2.5|2.2|

Source: Author’s calculations based on EBCNV 2015

# **5. Policy Implications and Conclusions**

Our findings show that the COVID-19 pandemic is likely to affect household welfare depending on the socio-economic characteristics of the population, sector of employment and location. The poor (more likely to be living in overcrowded conditions) and those with chronic diseases are at greater risk to contract the infection, while those without health insurance (largely the poor and those in informal sectors) are faced with greater inability to avail health care. Also given that the dependence on schools for meals and books is the highest among poor, they are most likely to be negatively affected.

The labor market would be impacted by the COVID-19 crisis in various ways. There are six sectors that have been identified as high risk and are expected to be worst hit in Tunisia- Tourism/Hotels and Cafes/Restaurants, Textiles, Mechanical and Electric Industry, Transport, Commerce and, Construction or civil engineering. These sectors engage a larger share of those employed in the Centre and north east regions and 47 percent from the poorest decile. Secondly, the pandemic will negatively affect those employed informally which is widespread and the highest in
Policy Research Working Paper 8880

# **Abstract**

The 2014–15 Ebola epidemic took a devastating human and economic toll on three West African countries, of which Liberia was perhaps the hardest hit. The pathways through which the crisis affected economic activity in these largely agrarian societies remain poorly understood. To study these mechanisms in the context of rural Liberia, this paper links a geographically disaggregated indicator of Ebola disease mortality to nationally representative household survey data on agricultural production and consumption. The paper finds that higher Ebola prevalence (as proxied by local mortality) led to greater disruption of group labor mobilization for planting and harvest, thereby reducing rice area planted as well as rice yields. Household welfare, measured by per capita expenditures spanning two points before and after the crisis, fell by more in Ebola prevalent areas with more intensive rice farming, precisely those areas that were more adversely affected by agricultural labor shortages.

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 adelafuente@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 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 its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
selling assets) when we use additional inverse probability weights to adjust for individuallevel representativeness relative to the GMD vs using only the HFPS survey weights.

With the exception of job changes, the panels of this figure suggest that the results are quite robust to using either of the weighting methods. The correlation is generally very high, over 0.95 for all the outcomes studied. This suggests either that the labor market outcomes of heads were generally consistent with the outcomes of children, or that the limited set of demographic characteristics used to reweight the estimates is unable to adjust effectively for these differences. Distinguishing between these two potential explanations is an important topic for further research.

# **5 Conclusion**

COVID-19 had a severe negative impact on labor markets in all regions. The estimates based on high-frequency phone survey data suggest that in all countries, work was severely reduced. Work stoppage, reduced working hours, and the overall economic impacts of the pandemic led to substantial income loss. Further disruption was apparent through partial or no payment of wage workers and job changes.

To better understand the HFPS measures of work stoppage and income loss, we compare them with changes in GDP growth projections and ILO quarterly employment estimates. Macroeconomic projections do not capture the full impact on households, particularly in Sub-Saharan Africa. HFPS and firm survey data on reduction in business revenues, while only available for a limited set of countries, show a more positive correlation with economic prospects captured by the WEO’s GDP projection. This suggests that the impact of the pandemic on labor markets and households may not be fully captured by the GDP projections, particularly in low-income and lower-middle-income countries and in SSA. This may result from a high level of informality that is not fully reflected in GDP projections.

HFPS data are a valuable source of information to monitor the impacts of COVID-19 25
significant over-triggering tendency, potentially leading to alarm fatigue. Nevertheless, its loss values, specificity. Our further analysis shows that the indicator performs much better when taking a high false negative rate (FNR) of 0.94 for alerts and 0.96 for alarms. This extreme conservatism, reflected more balanced error mixture), reaching similar loss values as the conflict and displacement indicators. Conflict and displacement indicators, employing RSI of the EMA of local and neighboring conflict fatalities, highlight a different challenge. With notably high FNRs at the alert level (0.49 for conflict and a propensity to under-predict emergencies. Their loss values reflect this, with a significant reduction in predictive error from alerts to alarms, for conflict (from 0.42 to 0.34) and displacements (from 0.42 to <!-- Start of picture text --> Food price index (Average of top 5 food items) Displacements index (Local displacements)<br>8 0.07<br>0.06<br>® 0.05<br>, 3 004<br>& & 0.03<br>2 0.02<br>° LAA . /\ PR<br>os 0.9 1.0 11 1.2 13 00 oO 20 40 60 80 100<br>Percentage of EMA RSI of EMA<br>Conflict index (Regional fatalities) Fuel price index (Average of diesel and petrol)<br>0.025<br>2.5<br>0.020<br>2.0<br>2 0.015 4 15<br>0.005 05<br>8 0.0100.000 I Pv) 4 100.0 eee<br>oO 20 40 60 80 100 0.5 1.0 LS 2.0 2.5 3.0<br>RSI of EMA Percentage of MA<br>Exchange rate index (YER to USD)
). Second, refugees with worse mental health are less likely to have a permanent job, although the effect is statistically insignificant (Column 2). They are also 3.2 and 5.6

> 11 Our findings remain consistent when using the inverse hyperbolic sine transformation of the weekly labor income outcome (see Table A18, Appendix A).

> 12 Our results differ from Connor (2010), who employs an non-instrumented approach and finds little evidence for the correlation between self-reported sadness/depression and earning among refugees in the United States. But the bias of the non-instrumented FE estimates that we find may partially contribute to this difference. 13 The foreign-born population, however, is underrepresented in the HILDA survey (Watson and Wooden, 2010). Furthermore, it is not possible to identify refugees from HILDA as information on visa type is unavailable. 14 We use the same IV approach as in Table 1 to examine other labor market outcomes. This requires an assumption that the exclusion restriction remains valid, or our instrument affects labor market outcomes only through changes in mental health.

14
Despite the small share of firms with more than 50 employees, formal employment is concentrated in large firms. Firms with more than 50 employees represent fewer than 3 percent of formal firms, but employ more than 50 percent of formal workers. Medium firms (10–49 workers) account for close to one-fourth of total employment; small firms employ 21 percent of formal workers. The distribution of formal workers across firms by size, and the greater exposure of small firms to the minimum wage have implications for the potential employment effects of the minimum wage.

Table 1 also shows that the minimum wage is strongly binding for formal firms in Turkey. In the quarter before the 2016 minimum-wage increase, more than half of the formal workers in formal firms received a wage roughly equal to the formal minimum wage. There is a clear relationship between the size of firms and the wages of workers. Microenterprises and small firms pay the lowest wages in the formal sector, and they employ a large share of workers at the minimum wage. Shortly before the 2016 increase, 93 percent of formal workers in microenterprises earned the minimum wage. By comparison, about 70 percent of workers in medium firms earned the minimum wage, and 40 percent of workers in large firms earned the minimum wage. Thus, any increase in the minimum wage thus results in sizable labor cost expansions among formal firms, but particularly within small firms.

Figure 3 reports the total number of formal firms in the EIS administrative dataset by quarter in 2012–16. The number of formal firms in Turkey rose steadily from the first quarter of 2012 to the last quarter of 2015. The number of formal firms then dropped during the first quarter of 2016, right after the new minimum wage, and this number then continued to decline in the following quarters. Similarly, the growth rate in formal employment fell during the quarter immediately after the minimum wage rose; formal employment then continued to decline in absolute terms in the following quarters (Figure 4). This paper investigates whether the relationship
way is to use an instrumental variable (IV) correlated with imports but unrelated to exports. Given that firm-level IVs are often difficult to identify, here we adopt the control function approach from Olley and Pakes (1996) (OP) to control for the unobserved productivity. Intuitively, firms make decisions on inputs and investments based on their productivity. Part of the investment is imported capital goods, which can be observed from customs transaction data. Thus, firm-level imported capital goods could serve as a for the unobserved similar to OP used investment in proxy productivity, firm production function estimation to proxy for productivity.

10
The first method is based on analogy and estimates benefits based on changes in property prices in comparable areas where similar projects were implemented. As an alternative, they can also use the double appraisal method where as discussed in the case of Colombia, before and after prices of the properties in the area of influence are compared. Lima has faced several challenges in implementing betterment levies. First, the municipality lost key professionals, including engineers, architects, economists, etc., and the remaining staff was not compelled to participate in training courses on implementing betterment levies. Second, the political will from municipal authorities is non-existent while middle and upper class residents are dissatisfied with levies. These classes feel they are overburdened with taxes; meanwhile projects are not well marketed so citizens do not understand their full benefits. The outdated land registry has also posed challenges as it makes the administration of the levy particularly difficult (Huayapa 2001: 5).

While the methodology for Lima’s betterment levies is nearly identical to Colombia’s, the lack of training and technical knowledge on implementation translates to potential missteps at every turn. Like Colombia, public participation is a part of the betterment levy process in Lima. A citizen representative is selected by democratic process to act as a liaison between the community and the municipality (Huayapa 2001).

Successful implementation of betterment levies requires several prerequisites to be in place. First, strong institutions must be in place to support the process and build capacity in terms of implementation and collection of taxes. A clear understanding of the methods to calculate benefits and define the distribution of the levy among tax payers is essential. Just as important is the attitude of tax payers. Encouraging citizen participation and working towards a strong tax payment culture will also support the success of betterment levies.

# <mark>4.3 DEVELOPMENT/IMPACT FEES AND EXACTIONS</mark>

Development/impact fees and exactions are one-time fees collected by a municipality from a developer, to dedicate land or build or pay for costs of capital
the location being studied (Fuente et al. 2016). Furthermore, no previous studies have obtained such information from both utility providers (water and electricity) for the same location. Finally, we found no studies in the literature that matched household socioeconomic data to billing records obtained from both water and electricity utility companies.

In short, hardly any studies have simultaneously analyzed the incidence of residential water and electricity subsidies. Of the few that do, none have used customer billing records from utilities to determine household water and electricity use. In this study we match households’ customer identification (ID) numbers—obtained from our household survey of socioeconomic conditions— with the billing records of the water and electricity utility companies in Addis Ababa, Ethiopia.<sup>5</sup> Also, we estimate the total average cost of both services using financial data collected from the electricity and water utility companies serving the Addis Ababa population.

# **_<mark>Comparison of water versus electricity subsidies in the residential sector</mark>_**

<mark>Most studies on subsidy incidence and the cost recovery of water and electricity public services report that utilities use IBTs to calculate household bills. Foster and Yepes (2006) look at both the water and electricity sectors and present an analysis of cost recovery by these utilities for several large cities in Latin America. Utilities in most of those cities use IBT structures. Of the 17 water utilities, 15 use IBTs, and 8 of 14 electricity utilities use IBTs. Komives et al. (2006) review previous studies on water and electricity subsidy incidence in the residential sector in developing countries. Their case studies come from 21 different countries. For 12 cases in the water sector, these authors find that seven utilities used IBTs, one used geographically defined tariffs with IBTs, one used a uniform volumetric tariff, and three used means-tested discounts. For the electricity sector, four of 12 cases</mark>

> 5 We were able to nearly match all our