rafmacalaba/data-use-mentions-extended huggingface dataset:
Rural Total<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>% of households<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data.

Among the countries with one‐fifth or more of grid‐connected households reporting zero expenditures, only South Africa and Zambia enable further analysis. Of the two, only Zambia isolates electricity for inclusion in rent, whereas South Africa includes water in addition to electricity, making disaggregation of electricity from other items not possible. If every appearance of combined electricity and water bills paid is assumed always to include electricity, and if everyone receiving free electricity is considered to rely only on free electricity, which is unlikely—the percentage of households receiving free electricity increases from 2 percent in the bottom two quintiles to 3 percent in the top three quintiles—the share of households not paying for grid electricity without justification declines from 29, 11, and 24 percent for urban, rural, and national to 2 percent each in South Africa. The reduction is primarily from inclusion of electricity in rent or electricity and water bills being bundled.

In Zambia, the number of grid‐connected households who had zero expenditures on electricity but whose rent included electricity was significant in urban areas. The share of non‐paying households decreases from
# **1. Introduction**

Worldwide, billions of people are unable to access work and educational opportunities because transport services remain either unavailable or unaffordable. The Sustainable Development Goals (SDGs) include a goal to increase rural accessibility, but they do not set a target. Specifically, SDG indicator 9.1.1 refers to the proportion of the rural population who live within 2 kilometers of an all-season road. An " _all season road"_ refers to _"a road that is motorable all year round by the prevailing means of rural transport_ ." In countries with a significant monsoon season, this implies paved roads; but in drier countries, all-season roads can also include gravel roads.

Access to an all-season road is believed to significantly increase households’ welfare if it can help rural populations access new job markets and social services. For example, in Bangladesh, a road paving project implemented from 1997–2001 increased household expenditure by 9-10 percent on average (Khandker and Koolwal, 2011). Similarly, in Ethiopia, access to an all-season road reduced poverty by 7 percent, and increased household consumption by 16 percent (Dercon et al., 2008). However, a road may bring little economic benefit in areas with no market to sustain non-agricultural jobs.

One measure of rural accessibility is the Rural Access Index (RAI), defined by (Roberts et al., 2006) as "the number of rural people who live within two kilometers (typically equivalent to a walk of 20-25 minutes) of an all-season road as a proportion of the total rural population". (Roberts et al., 2006) assessed the RAI in a small number of countries using household survey data. However, this approach was costly, the surveys lacked spatial representativeness, and the resulting RAIs were not necessarily comparable between countries. Ten years later, (Iimi et al., 2016) proposed a new method based on GIS data and applied it in eight countries. They assessed the RAI using WorldPop data for
nutrition. But in Household A, 2/5 eligible persons are deprived, whereas in Household B, it is 4/5. Furthermore, in terms of gender, in Household A both deprived persons are female, and no males are deprived. But in Household B, the only non-deprived person is female. Household-level information would code both situations as identical, but we can use the underlying microdata to go further. the matrix below illustrates this example.

||HH-A|||||HH-B|||||
|---|---|---|---|---|---|---|---|---|---|---|
|Deprived|**D**|**D**|ND|ND|ND|**ND**|**D**|**D**|**D**|**D**|
|Gender|**F**|**F**|M|M|M|F|**F**|**M**|**M**|**M**|

When both individual and household metrics are analyzed in tandem, this rather simple methodology creates very powerful information for policy design. The MPI analysis can show what proportion of eligible individuals who are deprived in that indicator are MPI poor, meaning this deprivation is compounded by other simultaneous predicaments. It also shows _which_ deprived individuals live in _non-poor_ households, thus unmasking individual deprivations within the household. More broadly, the MPI also provides information on the composition of deprivations that individually deprived persons experience, and whether there are different regularized patterns or clusters among deprivations, and how these vary across household types. This can inform policy responses. By way of comparison, if one only knows the gendered patterns of nutrition but does not know the multidimensional profile of deprivations
in potentially alleviating the impact of adverse weather droughts.

The rest of this report is structured as follows. Section 2 reviews the recent placement of rural roads in Ethiopia. Section 3 describes the data: the Ethiopia road database and the ESS. Section 4 explains the empirical strategy to identify and estimate impacts of rural road developments on welfare and other outcomes. Section 5 reports the estimation results of DID matching models. Finally, Section 6 concludes with a brief summary of the findings and discussion.

# **2. Recent rural road development in Ethiopia**

Among several programs that have contributed to recent rural road developments in Ethiopia, the URRAP has provided all-weather connections to rural communities (or kebeles) at the largest scale since 2010.<sup>6</sup> Through the upgrading of existing fair-weather access roads or unimproved tracks, URRAP brings those unclassified roads into the classified network. With 100% funding from the central government and technical support from regional and zonal governments, woredas identify project areas by prioritizing based on population, traffic, and facilities served. However, actual practice of prioritization is unclear and varies regionally. URRAP road construction work is partly the responsibility of the beneficiary communities. Communities in principle carry out site clearance and preliminary earth work as a contribution in kind, yet actual practice differs by regions. On

> 6 A ‘kebele’ is a subdivision of woreda and the smallest administrative unit in Ethiopia. It is similar to a ward, a neighborhood, or parish in other countries. We refer to it as community in the remaining part of this paper. Woreda is an administrative unit of one level higher than kebele.

4
common for the majority of micro-credit. Though part of the lower loan-to-new-toilet conversion rate could be explained by other constraints (e.g. absence of complementary investments or additional funds), this evidence also suggests that some households might have been sufficiently sophisticated to be less affected by the loan label. In line with this, we note that 28 percent of clients that took a sanitation loan already had a toilet before the roll-out of the intervention, and 9 percent of clients who took a sanitation loan and reported using it for sanitation did not have a toilet at the time of our endline survey. Thus, this evidence suggests that the label effect is likely to be heterogeneous across households.

Our study contributes to a number of literatures. First, it relates to the literature on credit constraints and human capital investments. A wide range of methodologies have been used to assess the importance of credit constraints in explaining education outcomes in developed and developing countries (Lochner and Monge-Naranjo, 2012; Solis, 2017 among others); and health investments (Dupas and Robinson, 2013; Guiteras et al., 2016). Within this, a small, but growing, literature uses randomised experiments to assess the scope for (bundled) microcredit loans to boost investments in preventive health products such as water connections (Devoto et al., 2012), bed-nets (Tarozzi et al., 2014), water filters (Guiteras et al., 2016) and sanitation construction materials (BenYishay et al., 2017). In contrast to many of these studies, we analyse a particularly lumpy investment that constitutes a much larger share of households’ wealth, and that was not bundled with the loan. .

It also contributes to a growing literature studying the role of labelling, and of the fungibility of money. Labels can be thought of as a ‘nudge’, defined by Thaler and Sunstein [2008] as “any aspect of the choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly
<br>_0.929_|
|Number of Observations||734|400|334||384|350||
|P-value ofjoint F-test that all coefficients are 0<br>Note: In Panel A, we use village-level data from Village Survey to compare Arusha<br>Arusha villages using data from the VVC survey, which interviewed up to 2 VVC m<br>source (baseline or follow-up) can be found in Column 1. In Column 2, we report th<br>p-value of the equality t-test. In Columns 6-8, we compare meeting and non-meeting<br>test that all coefficients of a regression of lottery status (Column 5) or meeting dum<br>described in Cameron et al. (2008). Clustering was done at the village level. Range|villages in different treatment ar<br>embers per village. In Panel C<br>e average in all villages. In Colu<br>villages, also reporting average<br>my (Column 8) against charac<br>of values for categorical variab|ms (except th<br>, we compare<br>mns 3-5, we<br>s and p-value<br>teristics are z<br>les are given|e number of a<br>Arusha villag<br>compare lotter<br>s. At the botto<br>ero. All p-valu<br>in parenthesis|grodeales in the v<br>es using househol<br>y and non-lottery<br>m of each panel w<br>es in Panel C ar<br>. (0/1) indicates|_0.881_<br>illage, computed<br>d-level data from<br>villages by repo<br>e report the num<br>e computed usin<br>dummy variables|sing data from<br>the Baseline V<br>rting averages f<br>ber of observat
An alternative method of checking for aggregation bias is to use simulation methods that apply country-level growth projections to baseline distributions at country level and then aggregate up to the global level. This calculation is more complicated. (Note that the base-year distribution is projected forward for each country separately. A constant elasticity is not assumed, as this is unlikely to hold.) However, reassuringly, the calculation is in close accord with the prior calculation based on aggregate trends. Trajectories for poverty in 2015 by this method are reported in the 2012 Global Monitoring Report, drawing on the Bank’s countryspecific economic growth projections. These projections give a poverty rate of 16.3 percent for 2015 (World Bank 2012a),<sup>14</sup> which is very close to that implied by the first method, namely, 15.9 percent. Table 1 includes the forecasts by region for 2015 from the Global Monitoring Report. As is clear from table 1, success in reducing poverty in South Asia and sub-Saharan Africa will be crucial to overall progress in poverty reduction. Consistent with the more optimistic scenario above, 80 percent of the projected extreme poverty count for 2015 is in these two regions (about equally).

A further concern is that a constant annual-percentage-point decline cannot continue indefinitely. The trajectory can be expected to slow, becoming nonlinear in time. What is much less clear is when the trajectory is likely to slow. The next section will use a version of the simulation method (as described above for addressing aggregation bias) to determine whether the linear trajectory for the overall poverty-reduction rate is consistent with the expected economic growth rates associated with the optimistic path under explicit assumptions about income distribution.

# **An alternative approach using simulations**

We have observed that the linear projection of the time series of poverty measures for the developing world suggests that one billion people could be lifted out of poverty by 2027. However, the above discussion has also pointed to some concerns about whether the linearity will hold that long.
Figure 1: Administrative Data – Impact on NVRB Balance by Month and Overdrafter Status <!-- Start of picture text --> A. Non-Overdrafters<br>2014m1 2014m7 2015m1 2015m7 2016m1 2016m7<br>Month<br>B. Overdrafters<br>2014m1 2014m7 2015m1 2015m7 2016m1 2016m7<br>Month<br>1000<br>500<br>0<br>-500<br>1000<br>500<br>0<br>-500<br><!-- End of picture text --> _Notes:_ Whiskers give 90 and 95 percent confidence intervals on estimates. Average monthly balance is top-coded at the 99 percent level by month.

21
– on average, about 12% of the grid cells included in our sample experienced a drought, while approximately 6% recorded abundant rainfall conditions.

# 3 Empirical specification

3.1 Modelling the impact of extreme weather on water supply interruptions To analyze the relationship between water shortages and weather variability, we look at the impact of rainfall and temperature within a location defined by a 0.5-degree grid cell. However, average temperatures and precipitation levels may be endogenous given that firms may choose to locate in areas of moderate precipitation and temperature. Thus, rather than focusing on average rainfall, our main variables of interest capture the frequency of rainfall shocks, specifically, unanticipated deviations in annual amounts of rainfall from long-run local averages. While the relationship between firm outcomes and average weather patterns may be endogenous, shocks to such longrun averages should be exogenous. Before calculating rainfall shocks, the raw rainfall data are converted to the Standardized Precipitation Index (SPI), to account for the non-normal distribution of rainfall. Positive rainfall shocks are then defined as periods when the SPI has a value of 1.5 or higher; i.e., situations when the normalized rainfall data are 1.5 standard deviations above the longrun mean for that grid cell. Negative rainfall shocks are situations in which the SPI has a value of -1.5 or less. The +/-1.5 SPI thresholds were deliberately chosen to analyze how drought conditions impact water outages for firms; according to the climatology literature, an SPI value of -1.5 or lower is reflective of drought conditions (McKee et al., 1993). Two dummy variables are used to capture the impact of drought and of large positive rainfall shocks. The first model we estimate is outlined in equation (1) below: WaterOutage��� The dependent variable, WaterOutage��� is the number of water outages per day experienced by firm _i_ in location _r_ and sector _j,_ in a typical month. Large_Pos��� is a
year period, follow a group of households over a quarter and interview a new group of households at the beginning of each quarter. Each quarter around 2,000 households are selected; resulting in a total sample of 8,157 households in the 2002 AHBS.

Following the redesign of the HBS, the total number of permanent staff employed as interviewers has risen from 64 to 84. The new design allows the selection of 84 territorial units (or Primary Sampling units), and a single interviewer is responsible for the conduct of all household interviews within a particular area. Unfortunately, the dependence upon a single interviewer has the potential to introduce an ‘interviewer effect’ that is impossible to isolate from other ‘location effects’. This may be particularly significant for the production of spatially disaggregated statistics of the kind employed in this study.

# **<u>Implementation</u>**

In the ‘zero’ stage we first carefully compare the questions of household budget and census to identify a set of variables that are common to both sources of data. These variables are then compared on a statistical basis by considering the mean values in the two dataset. This second type of checks is important as even when the survey and census questions are identically worded, subtle differences in the way the questions are asked, or different ordering of questions may cause the information content to differ between the survey and the census. Tables 1 and 2 in Appendix C show the tabulation of the mean values in the HBS and the Census. Those variables for which the census mean was within the 95 confidence interval of the mean in the HBS were then selected for inclusion in the model.

In comparison of mean household size during this ‘zero’ stage it became clear that Azerbaijan had experienced significant fluctuations in fertility during the three years between 1999 Census and 2002 HBS. The mean household size showed a decrease of almost one member per household and this trend was found across all regions. Following further investigation and 35
**Figure 9. Incidence of main components of tax benefit system by types of location, share of final income, percent.**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>big cities small towns rural north center south Chisinau<br>SSC Direct Taxes Pensions Direct Transfers<br>Indirect Taxes In-kind Health In-kind Education Total<br><!-- End of picture text --> Source: authors’ calculations using HBS-2017 data.

# **4.2. Distributional impact and marginal contributions of fiscal interventions**

The tax benefit system in Moldova significantly reduces income inequality. The Gini index falls from 48 percent for market income to 30 percent for final income (Table 12) or by 18 percentage points, equivalent to a 38 percent reduction. The biggest decline of inequality comes on account of pensions – the Gini at the level of market income plus pensions is 14 percentage points or 29 percent lower than at the original level. After direct taxes and transfers the Gini index falls further by 4 percentage points or another 10 percent. However, the next layer – indirect taxes is dis-equalizing in the sense that these components increase the Gini index marginally by 2 percentage points. After in-kind transfers, the Gini index falls further by another 2 percentage points.

Poverty declines rapidly after tax-benefit interventions in Moldova. Depending on the poverty line, the poverty headcount falls by 20-60 percent from market income to consumable income<sup>9</sup> (Table 12). The effect is stronger for the lower poverty lines – for the international line of 3.2 USD/day in 2011 PPP the poverty rate goes down by 64 percent. For the international line of 5.5 USD/day in 2011 PPP and national
# **1 Introduction**

This paper focuses on the interpretation of the subjective assessment of relative welfare using the Life in Transition Surveys (LiTS). These surveys have been carried out in three waves (2006, 2010, 2016) in about 30 countries in Central and Eastern Europe, Central Asia, as well as Western Europe. Looking at the data from these surveys, a puzzle emerges. In responding to the question of where they place their household on a 10-step ladder (also known as “Cantril’s ladder”) where each step should represent an equal share of people from the poorest to the richest, most surveyed individuals answer that they belong to the middle deciles of the distribution. This ‘bunching in the middle’ implies that there are many rich people who believe they are less (relatively) rich than they actually are when their position is determined by their monetary consumption or assets. Similarly, individuals who are poor according to their objectively measured consumption or assets levels, upgrade their relative positions and place themselves in the steps above those at the bottom. How can this “bunching in the middle” puzzle be resolved? Why is – it that individuals do not distribute uniformly along the ten steps i.e. 10 percent for – each step according to their actual (objectively measured) incomes? Shall one dismiss these subjective assessments as simply mistakes or misperceptions, or do they contain useful information? Can this information be used to guide policy design and assessment, especially when inequality and other distributional issues are emerging as key concerns behind the recent polarization of voting and the emergence of populism? These questions motivate this paper.

The discrepancy between objective and subjective indicators of economic standing has been noted before (Ravallion and Lokshin, 2002). In fact, such discrepancy is one of the most contentious issues dividing those who support adopting indicators of social and economic progress that go beyond the narrow measure of economic production (GDP per capita), and those who dismiss these as “rather silly analyses
Table E6: Reallocation of Workers across Firms and Sectors due to GDP Decline in Firms’ Destinations - Ecuador

||Baseline<br>efect<br>(1)|Initial<br>frm<br>(2)|Other frm in<br>same sector<br>(3)|Other frm in<br>tradable<br>sector<br>(4)|Other frm in<br>non-tradable<br>sector<br>(5)|
|---|---|---|---|---|---|
|_Panel A: Average months worked_||||||
|GFC frm shock|-0.0276***|-0.0646*|0.0214|0.0076|0.0080|
||(0.0085)|(0.0339)|(0.0211)|(0.0076)|(0.0116)|
|Observations|661,060|661,060|661,060|661,060|661,060|
|R-squared|0.1365|0.1778|0.0789|0.0528|0.0727|
|2-digit industry X Year FE|Yes|Yes|Yes|Yes|Yes|
|State X Year FE|Yes|Yes|Yes|Yes|Yes|
|_Panel B: Average real wages_||||||
|GFC frm shock|-0.0138|-0.0164|0.0027|0.0010|-0.0011|
||(0.0149)|(0.0118)|(0.0030)|(0.0025)|(0.0041)|
|Observations|659,870|659,870|659,870|659,870|659,870|
|R-squared|0.1004|0.0478|
23

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