rafmacalaba/data-use-mentions-extended huggingface dataset:
picture text --> _Source_ : World Bank staff calculations based on utility information and household survey data.

As mentioned above, because extending grid electricity to rural households is costly and off‐grid electricity will be cheaper for low‐density, low‐consumption areas, and because the grid network is nonexistent in many parts of rural areas, these grid‐electricity poverty statistics are arguably more relevant for urban households. Repeating the above exercise for urban households only delivers similar results, with the urban poverty gap at 250 kWh and urban poverty headcount at 100 kWh having the highest correlation coefficients with urban access rates. The highest correlation coefficients between urban access and the measures of grid‐electricity poverty are obtained when the latter are calculated nationally, and different measures of national grid‐ electricity poverty are better correlated with urban access than with national access. There is no obvious explanation for these results.

The poverty gap and headcount at 30 kWh have the smallest correlation coefficients and have the weakest predictive power for access for both national and urban statistics. This may seem counterintuitive at first glance—should the affordability to the poor not determine the rate of access? Several reasons may be offered for this finding. First, the total household expenditures used include freely acquired food and other items, making cash available much smaller for some households, especially in rural areas. Yet excluding freely acquired goods and services is not necessarily a more compelling approach, because two households with the same cash income but one of which does not have to pay for food are clearly not in the same financial situation. Second, a household that can barely pay for 30 kWh a month of electricity may have other pressing demands on its budget. Third, connection costs are high in many countries (see the next paragraph), and a household that can just afford 30 kWh a month may have no financial resources to pay for the initial connection. Lastly, depending on how many higher‐consumption, better‐off consumers are in the same area, it may
affects of the Russian 'flat' tax. _Journal of Policy making, 36_ (2), 226-240.

- Duncan, D., & Sabarianova Peter, K. (2010). Does labour supply respond to a falt tax? Evidence from the Russian tax reform. _Economics of transition, 18_ (2), 365-404.

- Fields, G. (2005). A Guide to multisector labor market models. _Social Protection Discussion Paper Series_ (0505).

Gimpelson, V. &. (2013). Informality and Mobility: Evidence from Russian Panel Data. _IZA DP No. 7703_ .

- Gimpelson, V., & Kapeliushnikov, R. (2011). Labor market adjustment: Is Russia different? _Series WPR labour Markets in Transition_ (WP3/2011/04).

- Gimpelson, V., & Kapeliushnikov, R. (2014). Between Light and Shadow: INformality in the Russian Labour Market. _IZA Discussion Paper_ (8279).

- Gimpelson, V., & Kapeliushnikov, R. (2014). Polarization or Upgrading? Evolution of Employment in Transitionary Russia. _IZA DIscussion Papers_ (8688).

- Gimpelson, V., Kapeliushnikov, R., & Lukiyanova, A. (2009). Employment Protectino legislatino in Russia: reginoal Enforcement and Labour Market Outcomes. _IZA Discussion Papers, 4484_ .

- Gorodnichenko, Y., Martinez-Vazquez, J., & Sabirianova Peter, K. (2009). Myth and Reality of Flat Tax Reform: Micro Estimates of Tax Evasion response and Welfare Effects in Russia. _Journal of Political Economy, 117_ (3), 504-554.

Huppi, M., & Ravallion, M. (1991). Measuring changes in poverty: a methodological case study of

- Indonesa during and adjustmnet period. _World Bank Economic Review, 5_ (1), 57-82.

Informal Employment and Worker’s Wellbeing in Russia / June 2019 28
---|---|---|
|1|19%|23%|1|18%|15%|37%|
|2|19%|23%|2|20%|19%|22%|
|3|20%|18%|3|20%|20%|18%|
|4|21%|18%|4|21%|21%|14%|
|5|21%|18%|5|21%|24%|10%|
|Total|100%|100%|Total|100%|100%|100%|
|Total|9,224|2,052|Total|5,746|3,686|1,663|

> 20 The assessment of farmers that sow rice more intensively can only be carried out in the 2003 survey that includes a variable determining the size of a farmer’s income derived from rice.

**30**
Policy Research Working Paper 10580

# **Abstract**

Using three rounds of the World Bank’s Business Pulse Surveys in South Asia, this paper quantifies the relationship between informality and firms’ investment and employment decisions. Accounting for multidimensionality in definition and the margins of informality, the analysis suggests that _first_ , informal firms remain credit and liquidity constrained before and during the crisis, especially the necessity firms. In the pre-crisis period, access to finance is correlated with the extensive margin of informality, while during the crisis, both margins of informality matter. _Second_ , informal firms perceive uncertainty to be higher because of pessimistic expectations on recovery and lower ability to predict future sales, especially the necessity firms. _Third_ , credit constraints and accentuated uncertainty among informal firms discourage investments. _Finally_ , while employment growth is slow and gradual for formal firms as they begin to recover sales, job growth in informal firms does not correspond to the recovery. The results suggest that countries with a large informal sector may face unusually depressed investments and jobs recovery and may have to deploy additional policy levers to accelerate recovery in the post-crisis period.

This paper is a product of the International Finance Corporation and the Office of the Chief Economist, South Asia Region. 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 agrover1@ifc.org and mpereiralopez@ifc.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
)|
||0.220|0.012|0.011|0.024|
|p-value (Ef vs Equ)|0.345|0.811|0.529|0.470|
|p-value (Ef vs Env)|0.975|0.284|0.741|0.598|
|p-value (Equ vs Env)|0.545|0.307|0.389|0.350|
|Control Mean|0.443|0.270|0.313||
|Observations|37036|37036|37036||

Note: This table shows the overall impact of the treatments on respondents’ unconditional support for subsidy reform. The regression analysis is based on Equations (1) and (2). Robust standard errors are in parenthesis and p-values of each treatment coefficient are presented below the standard errors. In addition, the table includes p-values comparing differences between the coefficients of the treatments. _Reduce:_ See Q21, which asks about support for a reduction in energy subsidies. The variable takes on the value of 1 if the respondent selects strongly support or support, 0 otherwise. _Eliminate:_ See Q22, which asks about support for an elimination of energy subsidies. The variable takes on the value of 1 if the respondent selects strongly support or support, 0 otherwise. _Cost:_ See Q24, which asks about support for a reduction in energy subsidies if this led to a price increase. The variable takes on the value of 1 if the respondent selects strongly support or support, 0 otherwise. _Index:_ An unweighted average of the z-scores of the unconditional support outcome variables, oriented so that a higher index means more support.
15<br>0.10<br>10<br>0.05<br>0 0.00<br>Power Airlines and airports Roads Railways Power Airlines and airports Roads Railways<br>Operations subsidies Government equity injections Operations subsidies Government equity injections<br>Deferred tax liabilities Government loans Deferred tax liabilities Government loans<br>SOE loans SOE loans<br>Fiscal injections as percent of assets Fiscal injections as percent of GDP<br><!-- End of picture text --> _Source_ : Authors, based with data from the World Bank Infrastructure SOE Database. _Note_ : Data are for 2009–18. Figure was constructed by adding operational subsidies, government equity injections, deferred tax liabilities, government loans, and SOE loans and subtracting changes in assets from the previous year to determine whether there was a fiscal injection. Government loans and SOE loans capture long-term debt or loans. Deferred tax liabilities, government loans, and SOE loans show increases since the previous year.

16
L�,� � β� � Σ����� β��� ED�,�,��� � θ� � e�, � where L�,� is the light level in cell i in month t and ED�,�,� represents the vulnerability curve value in the same cell and at the same time. Lags are allowed from month t to t‐12. β� is the intercept, θ� are the cell fixed effects and e�,� is the error term.

# **III. Earthquakes**

To construct damage indices for earthquakes, this paper uses the same methodology as in Skoufias, Strobl and Tveit (2017), which in turn is based on modeling from contour maps generated by seismological ground stations (International and Regional Development 2001) (Agency 2006) (De Groeve, et al. 2008).

In short, these contour maps are ShakeMaps from USGS, which are automatically generated maps providing several key parameters following an earthquake, such as peak ground acceleration (PGA), peak ground velocity (PGV) and modified Mercalli intensity (MMI), are used as a base for localized impact. More specifically, the ShakeMaps use data from seismic stations that is interpolated using an algorithm which is similar to kriging. To model the intensity in a given coordinate, the model also takes into account ground conditions and the depth of earthquake. The ShakeMaps are interpolated grids with point coordinates spaced approximately 1.5 kilometers apart (0.0167 degrees).

With regards to the output data, PGA is a measure of the maximum horizontal ground acceleration as a percentage of gravity, PGV is the maximum horizontal ground speed in centimeters per second and MMI is the perceived intensity of the earthquake, a subjective measure. It is assumed that damage starts at an MMI level of V and a PGA of 3.9 percent of gravity ( _g_ ). These damage levels are found for California in (Wald, et al.
equal to the 2010 GDP per capita from the World Development Indicators. It should be noted that this is only an approximation to more proper international comparisons (see e.g. Deaton and Dupriez, 2011) and that the HIT database is not the World Bank Group’s official poverty data, which can be found in PovcalNet.<sup>2</sup> Figure 4 pools all the data and shows how the aggregate spending categories identified in our data vary with (the log of) household income per capita (in constant 2010 USD). To start, households spend a large share of their income on food and agricultural products. Across all countries, the average household in our sample spends 44.7% of its income on food items, 17.4% on manufacturing goods, and 15.1% on services. Another 16.9% of expenditure is accounted for by goods households have produced themselves, which highlights the importance of dealing with home consumption in the analysis. This also implies that the total expenditure share in agriculture is 61.6%. While there is huge heterogeneity in spending patterns both across and within countries, the graph shows that, as households get richer, the share of income spent on food decreases, especially for the richest households. Spending on manufacturing goods, services, and other goods first declines with income but then increases sharply. The opposite happens with home consumption. The implication is that the tariff burden on different households will vary as a result of these different consumption patterns.

Agriculture is the most important source of income, accounting for 38.5% of total household income on average. This total comprises a 20.9% share of production for household consumption and 17.6% of sales of agricultural products in the market. Labor income represents 29.6% of household income, on average, and non-farm enterprises account for another 12.7%. Figure 5 shows how these aggregate income shares vary by income (in constant 2010
in migration (Lucas, 2001). The focus on distance as proxy for migration deterrent in this study, is further motivated by the work of Ingelaere et al. (2018), who conduct detailed life history interviews with 75 respondents sampled from the Kagera Health and Development Survey (KHDS), the same survey underlying the analysis in this paper. They show how respondents themselves, when narrating their life histories, talked about distance as an impediment to moving: it raises the cost of moving, it complicates returning home to visit or when things go wrong at destination, it makes it difficult to maintain ties, can erode informal property rights and typically comes with higher cultural barriers to moving.

# **3.2 Econometric specification**

If we are to understand destination choice among migrants then it is as important to know where the migrant moved to as it is to know which potential destinations the migrant did _not_ migrate to. Within the census we can identify 78 locations that were destinations for KHDS migrants, representing either urban or rural locations in 57 districts. Our analysis makes the assumption that these 78 locations are the potential destinations for our sample of migrants. They revealed themselves as practically feasible destinations for at least one of our migrants.

Each of the migrants in our sample has chosen to move to one potential destination; and has therefore also chosen _not_ to move to the 77 other potential destinations. In order to understand that choice better, we create a dyadic data set that contains 78 observations for each migrant _i_ ; one observation for each potential destination _d_ . Our dependent variable _Yid_ is a dummy equal to one if _i_ was found in location _d_ during the last survey round and zero otherwise. Our analysis will consist of studying the correlates of _Yid_ . This kind of dyadic analysis has been used before to understand destination choice by Fafchamps and Shilpi (2013). We follow these authors in analyzing only those who have actually migrated in order to not confound the destination choice with the migration choice. We also
Sub‐Saharan Africa (Hirvonen _et al._ 2017).

We convert the terrain and transport infrastructure to raster travel speeds and conduct appropriate analysis in units of time at a 30m x 30m cell resolution. Doing so requires intricate modeling of various travel modalities and modifiers to standard travel times. The accuracy and reliability of the result was tested and improved through consultations with professionals and organizations well‐acquainted with travel patterns in diverse locations of Nepal. For additional validation we compared model results to reported service travel times from households surveyed in the Nepal Household Risk and Vulnerability Survey (HRVS).

Finally, we consider the accessibility of various service facilities at national, provincial and municipal scales. We compare modeled accessibility levels for each service at each scale to the published standards of the responsible ministry and highlight areas of significant concern.

# Alternative Models of Physical Accessibility

Before describing the methodology underlying our cost time model, we shall justify our belief in its superiority for general usage with a brief discussion of other methods and a comparison of each to cost time models.

The cost time model developed described in this paper is only one of many possible methods of measuring accessibility. Methods must balance thoroughness, data inputs and level of effort, accuracy, generalizability and ease of adaptation / interpretation. In the context of Nepal, we believe our model strikes the strongest balance between these criteria. Other methods emphasize some of these criteria above others, in the process often making them more suitable for specific use cases than general usage (see Table 1 for a summary).
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE wastewater services from the ZINWA to local governments, and the capital city water services to Harare Water, is an important step to restore coverage and service quality to pre-ZINWA levels.

**Figure 10. Distribution losses and labor productivity among southern African utilities**

<!-- Start of picture text --> Latest available year<br>a. Distributional losses (percentage of production) b. Labor productivity (connections per employee)<br>50 1000<br>45 900<br>40 800<br>35 700<br>30 600<br>25 500<br>20 400<br>15 300<br>10 200<br>5 100<br>0 0<br>Percentage of production Connections per employee<br>Mozambique, Av. Zimbabwe, ZINWA Zambia, Av. Swaziland, SWSC Malawi, Av. Madagascar, JIRAMA Lesotho , WASA South Africa , Av. Namibia, Av. Botswana, WUC SSA Madagascar, JIRAMA South Africa , Av. Namibia , Av. Zambia, Av. Lesotho , WASA Zimbabwe, ZINWA Mozambique, Av. Swaziland, SWSC Malawi, Av. SSA<br><!-- End of picture text --> _Source:_ AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Per water connection. Zimbabwe data is for 2008; SSA = Sub-Saharan Africa.

Another problem is that water tariffs do not cover costs. At an estimated $0.23 and $0.82 per m<sup>3</sup> for residential and nonresidential tariffs, respectively, the ZINWA‘s tariffs are below the tariffs reported in other water-scarce countries in Africa and other developing regions in the world (see table 7).

About 60 percent of total water and sanitation sector spending needs comes from urban areas. This is due to the relatively rapid population growth in urban as opposed
|-0.00052|0.0000156|||||
||(0.000402)||(0.000403)|(0.000663)|(0.000329)*||(0.000335)|(0.000531)|||||
|Share of hhlds with computer||-0.00113|-0.0282|-0.0461||-0.0796|-0.0989|-0.106|||||
|||(0.249)|(0.244)|(0.242)||(0.138)|(0.131)|(0.129)|||||
|IDN exports per worker *||||-0.00142||||-0.00319|||||
|Share of hhlds with computer||||(0.00219)||||(0.00175)*|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.931|0.931|0.931|0.931|0.756|0.756|0.756|0.755|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. High-educated workers: workers with at least complete secondary education. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Port and river transport inefficiencies increase the transit times of imports bound for South Sudan**

<!-- Start of picture text --> 1500<br>1000<br>500<br>0<br>Port Sudan-Juba (intermodal) Port Sudan-Juba Mombasa-Juba<br>Port Road transport River Transport Border Administrative<br>Source: Port data from AICD based on 2006 estimates, road transport times based on AICD calculations, river transport times from KEER-<br>MISC data from 2007, border delays data from Nathan (2010) and Yoshino (2010), and administrative times from Trading across Borders,<br>World Bank.<br>Note: The administrative times are based on aggregates for all parts of Sudan as a whole since data for South Sudan are not currently<br>available.<br>(hours)<br>Total time required to import freight<br><!-- End of picture text -->

**Figure 7. Road and river transport costs compose the lion’s share of prices for importing freight to South Sudan**

<!-- Start of picture text --> 800<br>600<br>400<br>200<br>0<br>Port Sudan-Juba Port Sudan-Juba Mombasa-Juba<br>(intermodal)<br>Port Road transport River Transport Border Administrative<br>(US$)<br>Costs per tonne to import goods<br><!-- End of picture text --> _Source:_ Port data from AICD based on 2006 estimates, road transport times based on AICD calculations, border delays data from Nathan (2010) and Yoshino (2010), and administrative times from Trading across Borders, World Bank. _Note:_ The administrative times are based on aggregates for all parts of Sudan as a whole since data for South Sudan are not currently available.

13
(governance indicator), the earliest available values are taken to indicate the quality of governance for discoveries that occurred prior to those dates. Control variables are the logarithm of the size of the discoveries, a dummy variable for copper deposits, and dummy variables for middle-income and low-income countries. In the absence of mine-specific information on the depth of the deposit over time (as the deposit is depleted), it is not possible to control for the ease of access to gold or copper directly. Country dummies proxy for unobserved characteristics like the landlocked nature of the country.<sup>11</sup> A “good” policy environment conducive for resource investment—as well as non-resource investment—has many dimensions. It includes sound macroeconomic policies that ensure sustainable fiscal positions (as measured by government debt in percent of GDP at the time of discovery) and contain domestic demand pressures (as proxied by inflation at the time of discovery). A more stable macroeconomic environment can be associated with more predictable tax and expenditure decisions. A conducive policy environment includes high quality of institutions, at the time of the discovery, that affect mining operations. This is proxied by the World Bank Governance Indicators for Control of Corruption and by the QOG Institute’s Index of the Quality of Government.<sup>12</sup> These are some of the same conditions that would help avoid the macroeconomic volatility and stunted growth in resource-based economies that has been labelled the “resource curse” (Sachs and Warner 2001; Mehlum, Moene and Torvik 2002; Humphreys, Sachs and Stiglitz 2007).

The regression in Column (1) in Table 1 shows that expected times to production are nearly twice as long for copper deposits, and similarly 30-40 percent higher in MIC and LIC countries. Column (2) shows that high levels of debt lengthen the lead time to production by 16 percent respectively. Higher inflation also lengthens the lead time (by about 8 percent) but the results are only marginally significant. On its own,