rafmacalaba/data-use-mentions-extended huggingface dataset:
informal loans made by the household to other individuals.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 10 in the pre-analysis plan.

Table (G11) Impacts of the Program on Physical and Mental Health

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Life Satisfaction (SD)|-0.32**|(0.15)|[0.06]|0.01|(0.98)|1,097|
|Subjective Health (SD)|-0.30*|(0.16)|[0.06]|0.02|(0.99)|1,102|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: Life satisfaction corresponds to a standardized measure from “All things considered, how satisfied are you with your life as a whole these days on a scale of 1 to 10?” Subjective health is
<!-- Start of picture text --> Figure 2. Histogram of built-up proportions<br>omitting unbuilt areas<br>7%<br>6%<br>5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>1-10% 11-20% 21-30% 31-40% 41-50% 51-60% 61-70% 71-80% 81-90% 91-100%<br>1975 (actual) 2014 (actual) 2053 (forecast) 2092 (forecast)<br><!-- End of picture text --> Notes: Figure 2 shows a stacked histogram of the frequency distribution of built-up proportion divided into deciles of buildup proportion in 1975. Fully built cells are included in the top decile. Since an overwhelming proportion of cells are unbuilt in all years, they are omitted from the Figure for the sake of clarity. The 1975 and 2014 values are taken directly from the data. The 2053 and 2092 forecasts are obtained as follows. We first form the decile transition matrix using data from 1975 and 2014 (a 39 year interval). We then project the built-up frequency distribution from 2014 to 2053 and 2092 as two iterations of the transition matrix. The resulting forecasts should be understood as predictions of what the distribution of buildup will look like if the transition matrix remains unchanged. Similar forecasts are obtained if we use transition matrices constructed using different data years. The forecast do not specify which cells will be built.
makers can better target their efforts to maximize the impact of resource allocation and intervention strategies, ensuring that critical areas receive the necessary support to reduce poverty effectively.

Altogether, this analysis, based on Ethiopia’s data, demonstrates the potential of integrating the Poverty Space with PPI for understanding the dynamics of multidimensional poverty and informing policy interventions. By identifying the optimal spillover rate and analyzing how changes in the Poverty Space could affect the dynamics the censored headcount ratio, this analysis could provide a nuanced understanding of how interconnected factors might influence poverty reduction. It is important to note that our approach, while demonstrated with Ethiopia, is adaptable and can be applied to other countries with relevant Poverty Space data to tailor effective policy strategies.

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## **4.5. Job search and finding employment**

One additional question we investigate is whether the lockdown and economic slowdown may have reduced job search activity. To answer this question, we use individual-level data from the quarterly LFS to estimate the probability that those not working in a specific quarter were actively searching for a job during that quarter. The estimation sample consists of jobless individuals observed in the first quarters of 20172020 who lost their jobs over the previous two years. We estimate the following: Prob(Ui=1) = a + Y<sup>2020</sup> ×[b<sup>q</sup> Q<sup>q</sup> i + γΧi]+ εi (3) where U indicates the i-th non-employed jobseeker, Y<sup>2020</sup> is a dummy indicator which takes the value of 1 for the first quarter of 2020 and 0 for first quarters of earlier years, Q is a vector of quarter dummies (q=1,...,8) and X is a vector of observable individual characteristics, i.e. gender, age, country of birth, education, region and sector of economic activity in the individual’s last job, as well as indicators for the reason they stopped working (i.e. laid-off, contract termination, and other reasons).

In addition, we estimate the probability of finding employment during the first quarter in each of 2017, 2018, 2019, and 2020 for those were out of employment prior to that quarter.

Prob(Ei=1) = α + Y<sup>2020</sup> ×[β<sup>t</sup> T<sup>t</sup> i + δZi]+ ηi (4) where E indicates the i-th individual entered into employment, Y<sup>2020</sup> is a dummy indicator which takes the value of 1 for the first quarter
and income growth through the development of HEs requires several perspectives – an understanding of HEs as business, and an understanding of HE ownership as an employment choice. This section presents our analysis on HEs from both perspectives using the survey data.

## **HE enterprises**

In the employment data above, the distinction between HEs and NFEs was usually not justified. But mostly, this does not matter. Over 90 percent of NFEs found through household surveys are HEs, with 7 out of 10 of these reporting no family help at all - just the owner operating the HE (Figure 6). Despite so few people engaged in each enterprise, HEs still provide employment to more than 80 percent of people employed in NFEs including the employees of MEs. HEs are the dominant group in the sector - a point which is often missed in policy discussions and rarely touched upon in the SME literature. As documented below, HEs and MEs are different enterprises.

12
., 2022) and extend directly to consequential outcomes such as policy preferences and voting.

The rest of the paper is organized as follows. The next section describes our data. Section III details our empirical strategy and Section IV presents our results. Some brief concluding remarks are offered in Section V.

# **II Data**

Data on pro-climate outcomes – including beliefs, behaviors, policy preferences, and voting outcomes – come from the European Social Survey (ESS).<sup>5</sup> The ESS is conducted biannually across dozens of European countries using stratified random sampling with a total sample size ranging from 20,000 to 40,000 individuals per round. The ESS is a large microdata set capturing information on a host of social issues and is harmonized over time and across countries. In 2016, the ESS introduced novel questions on climate outcomes, such as “how often do you do things to reduce energy use?” and “how likely are you to buy energy efficient appliances?” Moreover, the ESS collected data on policy preferences such as “to what extent are you in favour or against using public money to subsidise renewable energy such as wind and solar power?” Finally, we include

> 3Green political parties’ environmental focus includes climate change, pollution, and industrial agriculture.

> 4A recent analysis showed that only 24% of countries mention youth education in the context of the Paris Agreement (Kwauk, 2021) – a historic international treaty on climate change.

> 5European Social Survey European Research Infrastructure (ESS ERIC). (2020). ESS8 - integrated file, edition 2.2 [Data set]. Sikt - Norwegian Agency for Shared Services in Education and Research. https://doi.org/10.21338/ESS8E02 ~~2~~

3
with the PTAs (Roy, 2011 and Marchetti et al, 2013, USITC, 2016), a comparison of PTAs with applied policies is relatively rare (Gootiiz and Mattoo, 2015). 4 Available from the website of the WB Services Trade Restrictions Database: http://iresearch.worldbank.org/servicetrade/home.htm

> 5 At the end of 2015, we updated the previous applied policy information for most of the developing country TPP members, including Vietnam, Malaysia, Mexico and Chile.

2
Policy Research Working Paper 9311

# **Abstract**

This study uses a contingent valuation approach to value the willingness-to-pay (WTP) for improved service experienced by households in Nepal following the end of the country’s load-shedding crisis of 2008-2016. Using a detailed survey of grid-connected Nepali households, the authors calculate the WTP per outage-day avoided and the residential value of lost (VoLL) and analyze their key drivers. Households are willing to pay, on average, 123.32 NR ($1.11) per month, or 65 percent of the actual average monthly bill for improved quality of power supply. The preferred estimates of the VoLL are in the range of 5 to 15 NR/kWh (¢4.7–¢14/kWh). These estimates are below the marginal cost of avoided load shedding, and virtually the same as valuations at the beginning of the load-shedding crisis.

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 jsteinbuks@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
& water supply, (ii) construction, (iii) trade, (iv) hotels & restaurants, (v) railways, (vi) transport by other means, (vii) storage, (viii) communication, (ix) banking & insurance, (x) real estate, (xi) public administration & defense, and (xii) other services. For deflating purposes, we use the three-digit NIC codes and description to allocate each services establishment into these broad disaggregate categories.

When comparing manufacturing with the services data, we use the following years from organized and unorganized manufacturing: 2000-01 and 2005-06 for both sectors, and additionally 2009-10 from the ASI and 2010-11 from the NSSO. Otherwise, based on the policy experiment we are conducting, we take advantage of the appropriate sample years for organized and unorganized manufacturing available to us. For the sake of simplicity, we refer to the sample year by the start year in the case of services survey. For manufacturing, we refer to the surveys by their end years

> 33 Irrespective of the size of establishments, the 57th and the 67th round surveys include all establishment enterprises in the main geographical sampling frame (“area frame”), while the 63rd round introduced a separate “list frame” for the largest enterprises in the corporate sector. Although 998 of such large service sector firms were initially identified in the list frame, for multiple reasons only 438 of them were finally surveyed. While this sampling difference could raise concerns, they are alleviated by the fact that the former two surveys also paid attention to large enterprises in a manner very similar to the list frame of the 63rd round. For instance, the 57th round surveyed all enterprises with 200 or more workers, thus making the 57th survey round comparable with the 63rd round.

15
# 1. Introduction

As in other countries in Sub-Saharan Africa (SSA), both the frequency and intensity of climate and weather shocks are increasing in Zambia, pushing or locking rural households—especially smallholder farmers—into poverty (Thurlow, et al., 2012; Al Mamun, et al., 2018; Braimoh, et al., 2018; Ngoma, et al., 2019). It is estimated that climate variability reduced growth in Zambia’s agricultural Gross Domestic Product (GDP) and national GDP by 4 percent and 10 percent, respectively, between 2006 and 2016 (Thurlow, et al., 2012), with an associated increase in the national poverty rate of 2 percent.<sup>1</sup> In severe drought years, such as the 1991/1992 season, Thurlow et al. (2012) estimated that GDP reduced by 6.6 percent and poverty increased by 7.5 percentage points.<sup>2</sup> Climate change and variability are therefore important contributors to the high poverty incidence of <mark>76.6 percent in rural Zambia.</mark><sup>3</sup> <mark>Reliance o</mark> n rainfed agriculture <mark>drives the high prevalence of poverty in rural Zambia and among smallholder farmers.</mark> This dependence means that rural households are particularly strongly exposed to climate shocks. <mark>According to the 2015 Living Conditions Monitoring Survey (LCMS), poverty among small-scale farming households was 78.9 percent compared to 64. 5 percent among medium-scale farmers (CSO, 2015). Poverty is highest in the Northern (79.7 percent), Eastern (70 percent), Southern (57.6 percent), and Central (56.2 percent) Provinces. Except for the Central and Northern Provinces, all have a higher population density than the national average, indicating a combination of high poverty rates and high absolute numbers of poor people
have access to the Port of Douala. National connectivity is provided by a transport network that links each country’s provincial capitals and cities with a population of at least 25,000. High capital expenditure on transport is justified by substantial upgrades of the road sector ($81.2 million per year). O&M needs are calculated assuming that all transport infrastructure is maintained in fair condition.

**Table 16. Transport needs**

<u>$ millions per year</u>

||Improve<br>condition|Upgrade<br>category|Expand capacity|Capital|Operations and<br>maintenance|Total|
|---|---|---|---|---|---|---|
|Roads|8.2|81.2|0.2|89.6|68.6|158.2|
|Regional|1.4|42.5|0|43.9|17.9|61.8|
|National|0.3|35.4|0.2|35.9|14.3|50.2|
|RAI|0.3|3.3|0|3.6|32|35.6|
|UAI|6.2|0|0|6.2|4.4|10.6|
|Airports|0|1|0|1|0.4|1.5|
|Total|8.2|82.2|0.2|90.6|69.1|159.7|

_Source:_ Carruthers, Krishnamani, and Murray 2009. _Note:_ RAI= Rural accessibility index; UAI= Urban accessibility index 37
**_Housing Surveys_ .** Some countries implement surveys specific to the housing sector on a periodic basis or as part of a one-off policy or program formulation effort. These are typically sample surveys that focus on the number and characteristics of housing units as well as the households that occupy those units. In the case of the American Housing Survey, which is administered every two years, the survey is longitudinal to allow for analysis of trends over time.  Depending on the questions included, a national or local housing survey could collect the data necessary to evaluate the affordability of housing using one or more of the approaches described earlier.

# 4. Towards Refining the Measurement of Housing Affordability

To evaluate the feasibility of using a more refined measure of housing affordability in the developing country context, especially in World Bank-supported projects and analytics,<sup>15</sup> the application of the following approaches could be tested: (i) a modified EIR, progressively calibrated by income segment; (ii) a modified RIM that uses household expenditure instead of income and a simplified budget standard for non-housing expenses; (iii) a proxy non-housing budget standard based available consumption data; and (iv) simplified equivalence scales for different household types that could be used in both approaches. These are explained in more detail in this section, followed by Annex 1 which presents the findings of the application of the modified RIM in urban Pakistan.

## 4.1 Define a Progressive Expenditure-to-Income Ratio

The EIR approach is simple and widely used to measure affordability at both the aggregate and household levels. However, as mentioned earlier, the application of a single percentage threshold across households in all income segments in a country does not adequately account for differences in the composition of (i) their household budgets and expenditure patterns, or (ii) their household size and per capita consumption patterns. The most straightforward way to address this conceptual deficit is to calibrate the EIR affordability threshold by income segment.

As such, the use of a _progressive_ EIR is
emergence of voluntary information sharing in a laboratory experiment. To our knowledge, our paper is the first to empirically test the relationship between banking competition and the voluntary emergence of a credit bureau with cross-country data. We use two recent, underexplored datasets on credit reporting institutions and bank entry barriers that allow us to study this relationship in a sample of close to 195 countries. In line with the existing literature, we find that countries with higher bank entry barriers (i.e. a lower threat of competition) are significantly more likely to have a credit bureau. However, we also show that where a credit bureau exists, the absence of competitive pressures is associated with less depth and transparency of the credit information that is made available by lenders.

We also test an additional set of related hypotheses. Pagano and Jappelli (1993) argue that banks may not join a credit bureau, or share only limited or incomplete client data, since they can capture monopoly rents by not sharing proprietary credit information. We point out that, due to increasing returns to scale, the disincentive for information sharing is particularly relevant for very large banks. This implies that barriers to credit information sharing may be particularly pronounced in markets characterized by a high degree of bank concentration. Our data support this hypothesis: countries with higher bank concentration are significantly less likely to have a credit bureau. We also find some evidence that bank concentration is associated with lower coverage of the information reported to existing credit bureaus, potentially because banks try to hold on to monopoly rents by sharing only partial information.

Finally, bank competition or concentration should not be correlated with the probability of having a credit registry since participation in a registry is mandatory and banks’ incentives are only relevant for voluntary information sharing. Confirming this hypothesis, we find no correlation between bank competition or concentration and the existence of a credit registry.

This paper contributes to the broader empirical literature on the relevance of bank competition and concentration for the efficiency of financial intermediation. One strand of this literature argues
of service industries. Due to past policy biases, the service sector in China is very underdeveloped for a country of its GDP per capita level (figure 5). In 2001, China’s share of services in GDP (33.6) is even lower than that of Bangladesh (51.7), Kenya (62.9), Ethiopia (36.6), and Tanzania (39.4), not to mention the developed countries, and made almost no progress from the 1991 level of 33.4 percent. Although China has an impressive FDI (foreign direct investment) stock, most of it (66.6%) is concentrated in manufacturing and related industries, with only 31.6% in services in 2000. Furthermore, the bulk of this investment (23.6% of total FDI) goes to the real estate business, leaving only 8% in other service industries.43 This unbalanced structure is mainly due to the policy constraints imposed on foreign participation in services.

**Figure 5  Underdeveloped Service Sector in China**

<!-- Start of picture text --> Service as % of GDP (2001) *<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>China<br>20<br>10<br>0<br>0 1 2 3 4 5<br> Per capita GDP (Constant 1995 US$, log)<br> * For some countries, data are from 2000.<br>%<br><!-- End of picture text --> Source: World Bank SIMA database.

In the short and medium terms, the underdeveloped service sector will be the major source for economic development and job creation. It is generally believed among Chinese scholars that a 1% increase in GNP would create either 800,000 jobs in secondary industry or 1.3 million
absolute terms As percentage<br>Fall in Poverty Rate (in percentage points)<br>Fall in Poverty Rate (as percentage of total fall in poverty)<br><!-- End of picture text --> Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). See Appendix B for details. Sources: Authors’ estimations based on SEDLAC (CEDLAS and World Bank) and WEO (October 2018).

25
Euro Area banks, and Havránek et al. (2016) test the pass-through based on data from 52 Czech banks. Typically, the studies use data for a single country (967 estimates). Only a few studies estimate the passthrough using cross-country panel data (Bernhofer and van Treeck, 2013; Leroy and Lucotte, 2015). One reason may be that the economic, social, and political environment is specific to each country and researchers try to avoid this source of heterogeneity. To increase power of the estimates and help

> 3 The ECB Statistics Bulletin: <u>http://sdw.ecb.europa.eu/servlet/desis?node=1000004045.</u>

10
**MONGOLIA'S INFORMAL** SECTOR -

**Notes**

**59**

these two informal sector activities has been estimated to be sizable relative to official GDP, even though they represent only a minute fiaction of Ulaanbaatar's informal activity. Ulaanbaatar's kiosks are estimated to generate value added equivalent to about **3/4%** of GDP for the entire country, or roughly 2.3% of the Ulaanbaatar economy. Ulaanbaatar's taxicabs, most of whom are part-time drivers and moonlighters, are estimated to generate value added equivalent to about 3% of GDP for the entire country, or roughly 9% of the Ulaanbaatar economy. See Appendix A for details about these and other informal occupations, and Appendix B for an explanation of the methodology used to estimate value added. Many case studies of micro entrepreneurship, including kiosks, can also be found in Centre for Social Development, 1996.

47.For a discussion of how empirical studies of the informal sector reach wildly different estimates of the size of the informal sector, depending on assumptions, see Poapongsakorn (1991) for Thailand; Alonzo (1991) for the Philippines; and Porter and Bayer (1984) for a discussion of divergent estimates of the size of the "underground" economy in the United States, even within a single family of analyses (those using monetary aggregates).

48.The project, "Research on the Consequences of Large-Scale Privatization in Mongolia," entailed a survey of 250 enterprises which had passed through Mongolia's large privatization program by mid-1996. The estimates quoted in the text are true for both Ulaanbaatar and for Mongolia as a whole. See Anderson, Korsun, Murrell, forthcoming.

49.As discussed earlier, those laid off from formal sector jobs may be entitled to unemployment benefits, but the benefits expire rapidly.

50.The reference to "no skills" in this paragraph should not be confused with the reference to difficulty finding a "job to match their skills