rafmacalaba/data-use-mentions-extended huggingface dataset:
05|-0.05|-0.03|-0.04|-0.05|-0.03|-0.03|

_Source_ : Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16.

_Note_ : Demographic controls are included on every model. For the HH level cross-sectional demographic controls include: urban status, macro region and also controls for year fixed effects. For the individual level cross-section and panel models: gender, age, education, , number of years smoked, urban status, macroregion, and year fixed effects.

The total expenditures were deflated using the consumer price index, while the prices for cigarettes were deflated using the average prices provided by the Russian Federal State Statistics Service, Rosstat. To adjust for spatial variation in prices, all monetary variables were spatially deflated using the fixed basket index provided by Rosstat.

The deciles were defined based on per capita total expenditures. The panel model was estimated in both specifications, with fixed and random individual effects. According to the Hausman test, the hypothesis of no correlation between individual random effects and other regressors should be rejected, and the model with random effects provides biased estimates.

Self-reported prices are potentially endogenous because the decision on the brand of cigarettes to buy is taken together with the decision on the quantity of smoking. A few techniques were used to try to address 18
# Normalized difference vegetation index (NDVI) Rohingya Refugee Camps in Cox's Bazar, Bangladesh

<!-- Start of picture text --> Le (ey a Sli rs Slaiel. aa) iPNa<br>aRi ea pyreote ae* VeeWeis eemy PeePeee,a | PEea ch aDeedeh ek|<br>lel ed "laa ey he clea ee a ee ati a a, eT<br>Pe De af Freie ee he baal Mieee FOE einiy Boe<br>r MelA Doe) iS) i aes “ea ag!<br>NDVI (2010) NDVI (2014) * NDVI (2017) ~ NDVI (2020) *<br>0.4Value oa ValueI 0.08 ValueMM 0.0 S ValueWM 0.07G<br>= -0.3 a = -0.1 e Lat “0.1 . _ “0.1 &<br>0 1.25 25 5<br>es Kilometers Scale: 1:100,000 [-_] Refugee Camp Boundary<br><!-- End of picture text -->
- Brubaker, J., T. Kilic, and P. Wollburg (2022). Representativeness of individual-level data in COVID-19 phone surveys: Findings from Sub-Saharan Africa. _PLOS ONE 16_ (11), e0258877.

- Carletto, G., M. Tiberti, and A. Zezza (2022). Measure for measure: Comparing survey based estimates of income and consumption for rural households. _World Bank Research Observer 37_ (1), 1–38.

- Cely-Santos, M. and O. L. Hernández-Manrique (2021). Fighting change: Interactive pressures, gender, and livelihood transformations in a contested region of the Colombian Caribbean. _Geoforum 125_ , 9–24.

- Central Statistical Agency (CSA) of Ethiopia (2019). Ethiopia - Socioeconomic Survey (ESS4) 20182019. Public Use Dataset. Ref: ETH_2018_ESS_v03_M. Dataset downloaded from `https: //doi.org/10.48529/sxbm-w115` .

- Chang, A. C., L. R. Cohen, A. Glazer, and U. Paul (2021). Politicians avoid tax increases around elections. Finance and Economics Discussion Series 2021-004, Board of Governors of the Federal Reserve System.

- Dagunga, G., M. Ayamga, and G. Danso-Abbeam (2020). To what extent should farm households diversify? Implications on multidimensional poverty in Ghana. _World Development Perspectives 20_ , 100264.

- Dietrich, S., V. Giuffrida, B. Martorano, and G. Schmerzeck (2022). COVID-19 policy responses, mobility, and food prices. _American Journal of Agricultural Economics 104_ (2), 569–588.

- Duflo, E., A. Banerjee, A. Finkelstein, L. F. Katz, B. A. Olken, and A. Sautman (2020). In praise of moderation: Suggestions for the scope and use of pre-analysis plans for RCTs in economics. Working Paper 26993, National Bureau of Economic Research.

- Ebhuoma, E. and D
document harassment as a demand mechanism for the reserved space. We start by estimating the impact of riding in the reserved space on the incidence of harassment relative to riding in the public space. We then combine data from our revealed preference experiment with experimental variation in riding location to relate incidence of harassment to riders’ demand for the reserved space. Finally, we explore the role of perceived norms around a woman’s choice of space in driving demand for the reserved space.

# **6.1 Experimental Assignment to the Reserved or the Public Space**

We run an experiment in which we assign riders a paid offer to ride in either the reserved space or the public space at random, and ask them to report on their ride experience through the app.

In contrast with the setup in the previous phase, riders are now offered a ride task for a specific space for a fixed payment of $4.70 per ride through the same app.<sup>30</sup> Upon logging into the app, a participant could see only whether she had an offer on that day and, if so, in which space she was assigned to ride (Panel (c), Figure 1). Each participant was offered several iterations of each car type in a random sequence across days and could not predict their sequence of rides. The space assignment on a given day was independent of previous assigned spaces and a rider’s decision to take up those tasks. At the end of each ride, participants were asked to report experiences of harassment on their journey, including whether any stranger had “made comments that made you uncomfortable”, “touched you intentionally in a way that made you feel uncomfortable”, or “stared at you”. Whenever a rider reported any harassment, the app directed her to resources available in the Rio area. Participants were also asked if they felt concerned about physical harassment and to report their emotional state on a scale of 1 to 10, overall and on
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.6. Cost of importing goods by road through alternative gateways**

# **a. Total cost (US$ per tonne)**

<!-- Start of picture text --> 800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Douala-Ndjamena Douala-Bangui Pointe Noire to Brazzaville<br>to Bangui<br>Administrative Border Transport Port<br>b. Composition of cost (% of total cost)<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Douala-Ndjamena Douala-Bangui Pointe Noire to Brazzaville<br>to Bangui<br>Administrative Border Transport Port<br>Costs (US$ per-tonne) of importing goods<br>Share of Costs of importing goods<br><!-- End of picture text --> _Source:_ Data collected from World Bank 2008; AICD ports database; and Teravaninthorn and Raballand 2009.

17
**Table ‎3.2:  Industry Distribution of HE**

|1.7||2006||Change 20|01-2006(Percen|tagepoints)|
|---|---|---|---|---|---|---|
|||All|||||
||Rural|Urban|Total|Rural|All Urban|Total|
||%|%|%|%|%|%|
|Agric., Hunting, Forestry, Fishing|2.6|1.0|1.9|-1.0|0.4|-0.4|
|Mining, Manufacturing, Energy|26.6|17.5|23.0|9.5|8.9|9.5|
|Construction|2.4|2.9|2.6|-3.1|-3.1|-3.1|
|Trade, hotels, restaurants|62.1|71.1|65.6|-6.7|-4.5|-6.0|
|Transport,storage,real estate|2.2|2.8|2.4|1.7|1.5|1.6|
|Public and social/person. Services|4.2|4.7|4.4|-0.4|-3.2|-1.6|
|Total|100.0|100.0|100.0||||

_Source: Calculations based on the ILFS 2000/01 and 2006 data._ 27. Although not based on a representative sample, Table 3.3 enriches the sectoral picture by showing data from the FGD study. HEs are categorized according to three major non-agriculture sectors (manufacturing, trading, and services) on the basis of the participants‟ description of
# **4. IDENTIFICATION**

# **a. Experimental Design**

The evaluation design took advantage of the phased implementation of the program at the district level over a 23-month period. Rwanda manages its health care system at the district level and P4P is no exception. As a result the government mandated that all facilities in a district must be incorporated into the P4P scheme at the same time. Hence, the evaluation employed a stratified cluster randomized designed where districts were first grouped into blocks with common characteristics and then randomly assigned to treatment and comparison groups. Administrative districts with pre-existing P4P schemes were excluded from the experimental design. The remaining districts were grouped into blocks based on similar characteristics for relief, rainfall, and predominant livelihoods as per the 2002 Census.

However, just before implementation, administrative district boundaries were redrawn in the context of a government-wide decentralization effort (MINALOC 2004). As a result, some of the experimental areas were combined into new districts with areas that already had the pilot P4P schemes. Because P4P could not be “removed” from health facilities that were already implementing the schemes, and because P4P is managed at the district level, the MoH required that all facilities within those new districts be in the treatment group. This led the evaluation team to switch the assignment of treatment and comparison for eight districts. In the end, the study included 19 districts, of which 10 belonged to the treatment group and 9 to the comparison group.

The sample included 166 of Rwanda’s 401 primary care facilities, 80 in treatment districts and 86 in comparison districts. The facilities in the treatment group started receiving P4P in 2006, while the facilities in the comparison group continued with traditional input-based financing for an additional 23 months. P4P was introduced in treatment districts over a 5-month period, yielding a minimum program exposure of at least 18 months.

# **b. The Incentive Effect**

Since a primary objective of the evaluation was to isolate the impact of the P4P incentives separately from the
The extended data samples S2 and S3 allow us to run separate regressions for NMS group and old European countries. Although the estimated coefficients suggest different behavior of the two groups of countries, the results of applied Chow tests do not allow for rejection of the hypothesis of stability of regression coefficients between these two groups of countries in either of the sub-models.

**_Table 7. Results of Model 5– dependent variable CONTRACT_**

||S1 – Mo<br>|del 5.1<br>||S2 – Model 5.2<br>|||S3 – Model 5.3<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|
||Total|Old Europe|Total|Old Europe|NMS|Total|Old Europe|NMS||
|EPL|4.1585 ***|0.0980|562.1071 ***|653.9701 ***|499.0793 ***|2.7880 **|6.5737 ***|6.9879|***|
|MW|-0.5562 *|-0.2438|-0.1224|-0.0793|4.6800 ***|-0.0925|-0.4403|6.2950|**|
|TU|-0.1036 *|-0.0752||||-0.1006 *|-0.0865 **|-0.5563|***|
|TAX|_0.1701_*|0.0614|-0.3391 **|-0.4405|-0.8808 ***|-0.1898 **|-0.2152|-0.2119||
|LMPA
consume as much, saving more and turning savings into investment. On the other hand, lowincome people have a high marginal propensity to consume, but do not have enough funds. This represents an important reason for high household savings.

<!-- Start of picture text --> Figure 47: Income Inequality in China<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br>Rural Urban National<br>Source: Ravallion and Chen (2007).<br>GINI Index<br><!-- End of picture text --> 100. What is the origin of the increase in income disparity and concentration of wealth in the large corporate sector in China? Income disparity and high corporate savings in China can be explained by three components of its current economic structure: the financial sector, the resource sector, and monopolies in the telecommunications and financial sectors. Figure 48 shows the evolution of saving in China from 1995 to 2006. Total savings as a share of GDP were below 40 percent until 2003. Household savings were high, but still were not very different from other countries such as India. But since 2004, savings increased significantly. This discrete jump was mainly driven by the hike in corporate savings.<sup>77</sup> Four reasons explain this development.

> 77 See also Prasad (2011).

64
and “subjects” in research is vastly unexplored. If our purpose as researchers is to assist in the process by which people become better-off, then more can be done to design and share research with people, and even co-create tools so that they can conduct research on themselves without the mediation of experts. This has implications for many things, including the measurement of well-being and poverty.

# <u>References</u>

<mark>Ada Lovelace Institute, 2021,</mark> _<mark>Participatory Data Stewardship: A Framework for Involving People in the Use of Data,</mark>_ <mark>September, Creative Commons License</mark> <mark>Alexander JT, R Andersen R, PW Cookson, K Edin, J. Fisher, D.B Grusky, M. Mattingly, C. Varner, 2017, A Qualitative Census of Rural and Urban Poverty.</mark> _The ANNALS of the American Academy of Political and Social Science_ <mark>.;672(1):143-161.</mark> Anderson, Elijah, 1990, _Streetwise: Race, Class and Change in an Urban Community_ , University of Chicago Press, Chicago and London Ang, Yuen Yuen, 2019, _Integrating Big Data and Thick Data to Transform Public Services Delivery_ , IBM Center for the Business of Government, Washington DC Appadurai, Arjun, 1989, “Small-Scale Techniques and Large-Scale Objectives,” Chapter 11 in P. Bardhan (editor), _Conversations Between Economists and Anthropologists_ , Oxford University Press, Oxford and Delhi 19
New Broad-based Index of Financial Development.” IMF Working Paper 16/5, International Monetary Fund, Washington, DC.

Ulyssea, G. 2020. “Informality: Causes and Consequences for Development.” _Annual Review of Economics_ 12: 527-546.

Voghouei, H., M. Azali, and M. Jamali. 2011. “A Survey of the Determinants of Financial Development.” _Asian-Pacific Economic Literature_ 25 (2): 1-20.

World Bank. 2021. _Global Economic Prospects_ . June. Washington, DC: World Bank. World Bank. 2022. _The Global Findex Database 2021_ . Washington, DC: World Bank. Yermack, D. 2018. “FinTech in Sub-Saharan Africa: What Has Worked Well, and What 35
Figure 17. Credit Card Ownership <!-- Start of picture text --> Equity Adjust Coverage, (Individual level, FINDEX)<br>2011 2014<br>60 60 54.7 54.9<br>50 45.1 45.1 46.0 50 46.4<br>41.6 42.4 42.9<br>38.9<br>40 35.3 40 36.6<br>32.5 33.2 32.7 33.7 32.8 32.7<br>28.9 29.7<br>30 26.3 30 23.7 26.724.1<br>21.7 21.8<br>20 16.6 13.6 20 15.3<br>10.6<br>10 10<br>0 0<br>Netherlands Spain Turkey Denmark Taiwan, Netherlands Spain Turkey Denmark Taiwan,<br>China China<br>Coverage Dissimilarity EACR<br>Equity Adjust Coverage, (Household-level, LITS)<br>80<br>69.4<br>70 64.4<br>60 52.2 55.4 55.9 57.2<br>50 45.7 42.3 47.4 48.7<br>40<br>30 23.7<br>20 12.4 15.1 14.8<br>7.1<br>10<br>0<br>France Hungary Great Britain Turkey Sweden<br>Coverage Dissimilarity EACR<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base and LITS is household level. In FINDEX, 145 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown
# **<mark>Excess Poverty: Is the effect on poverty driven by more than “the size of the pie?”</mark>**

<mark>As has been shown in the literature (e.g. Borenzstein and Panizza, 2009) and confirmed in this paper, sovereign defaults coincide with significant output collapses. The question is then: Does a sovereign default worsen the impact of an output decline on a society’s poorest?</mark> The change of poverty headcounts around default appears as a key metric to turn to when trying to answer this question. However, much of the dynamics of poverty headcounts that we presented in the previous section are driven by changes in aggregate income because of two factors. First, mechanically, for a given shift of the income distribution, an output collapse will increase the poverty headcount. Second, while the World Bank’s Poverty and Inequality Platform, where we take our data from, often presents time series data on poverty, most of the observations available are imputations derived from income growth dynamics and an assumed log normal income distribution. In other words, it assumes the impact of an output collapse to be distribution-neutral, which ties both poverty and growth outcomes together for most crosscountry analyses. As is well established in the literature (Clementi and Gallegati, 2005, Souma 2001, McDonald 2008), income size distributions are typically lognormal, with the density

> <sup>σ2</sup> mean e(μ + 2 ~~)~~ and median eμ. With knowledge of the parameters μ and σ, one can arrive at the poverty headcount, _x_ , using the cumulative distribution function:

where Φ is the cumulative distribution function of the standard normal distribution. Using two moments of the distribution prior to default, e.g. the median and the mean, we derive the parameters μ and σ before the default. Because distribution neutral growth implies that each member of the society is affected the same, relative
<mark>885 January_27,_2016]. Center of analysis of social exclusion. Report 69. ISSN 1465-3001.</mark> Hills, J. (2012). Getting the measure of fuel poverty: Final Report of the Fuel Poverty Review. Link: <u>https://sticerd.lse.ac.uk/dps/case/cr/casereport72.pdf</u> IBroad (2018). Factsheet: Bulgaria. Current use of EPCs and potential links to iBRoad. Link: - - <u>http://ibroad project.eu/wp content/uploads/2018/01/iBROAD_CountryFactsheet_BULGARIA.pdf</u>

- King Baudouin Foundation, 2018. The energy poverty barometer. Link: <u>https://media.kbs frb.be/en/media/7604/20180315NT.pdf</u>

Laderchi et al. (2013). Balancing Act: Cutting Energy Subsidies While Protecting Affordability. <u>https://books.google.de/books?hl=de&lr=&id=D9hOnwAFBVQC&oi=fnd&pg=PP2&dq=balancing+act +2013+energy+poverty&ots=NYVSy6Y91U&sig=P7hQH_rJSBxeC1KEamqaG0W0ENc#v=onepage&q&f</u> =false <mark>Lan, J., Khan, S. U., Sadiq, M., Chien, F., & Baloch, Z. A. (2022). Evaluating energy poverty and its effects using multidimensional based DEA-like mathematical composite indicator approach: Findings from Asia. Energy Policy, 165, 112933.</mark> Li, Xinyi, et al. (2018) "Developing urban residential reference buildings using clustering analysis of satellite images." Energy and Buildings 169 (2018): 417-429.

Li, X., & Zhou, Y. (2017). A stepwise calibration of global DMSP/OLS stable nighttime light data (1992
versus consumption measures, as suggested by Deaton (2005) and Ravallion (2003). Rather, the average gaps are closely aligned with the level of economic development, with gaps being largest for middle-income countries, both in terms of levels and growth rates. The gaps, and corresponding implications for poverty and inequality measures, are largest for middle-income countries which have experienced periods of rapid growth. With the large majority of the extreme global poor currently living in middle-income countries, the implications of these gaps for measuring and understanding the evolution of global poverty and inequality aggregates are large.

It is increasingly documented that the HHS-NAS gap at least partly originates from the inability of surveys to capture the full level of consumption and income for all households. This paper illustrates the potential implications for common poverty and inequality measures from adjusting survey data based on differing assumptions of the source of the gap. Under a distribution-neutral gap scenario, which would justify substituting survey means with national accounts means, global poverty would be much lower using the $1.90 line, and the Sustainable Development Goal of ‘ending’ extreme poverty would already be close to being met, or at least be easily within reach. This is in line with findings of Sala-i-Martin and Pinkovskiy (2016) that uses this method to measure global poverty. However, we argue that when using national accounts for measuring poverty, the $1.90 line should be adjusted. Using an international poverty line adjusted for systematic differences between surveys and national accounts would result in global poverty measures that are more in line with survey-only measures, but still cause relatively large changes to country level measures. Regardless of the poverty line used with national accounts, the rate of poverty reduction is greater than that measured in surveys because national accounts growth rates are typically higher. This last observation illustrates the perils of using growth rates from national accounts to extrapolate global poverty, which is the current practice. The current approach likely exaggerates the decline in poverty estimates when no survey data is available.

The scenario which
education; occupation; location; industry; unionization-along with firm size dummies, and a dummy to control for the change between the two years. Teal summarizes the results of our interest as follows: _"Standardizing for skill, workers in small firms, which are those employing between 5 and 29 workers, earn 21 per cent more than workers in microfirms ie., those employing 5 or less. Medium sizedfirms, i.e. those employing 30 and 100 workers, pay an additional 14 per cent, while the category of large firms, those employing more than 100 workers pay afurther 13 per cent. The cumulative effect of these earning differences across firms is that workers in_ **_firms_** _employing more than 100 workers earn 55 per cent more than those working in micro firms.. " (Teal 1994, p. 13, using results from Table 1)._ Referring back to the topic of the last section, it should be emphasized that sate owned enterprise are generally large firms. Thus the earnings level in public enterprises, which were seen to enjoy a premium of 35 per cent or so, could be as much as 100 per cent more than in the micro enterprises ( after controlling for all the other significant factors affecting wages).

# **4.2 Analysis by Mazumdar**

Mazumdar's work (1994, 1995) on the RPED data for a number of different countries follows a different procedure. The purpose of the empirical work on the earnings data is to classify size groups of enterprises in each country which tend to show different levels of earnings. This way we seek to avoid the imposition of an arbitrary 20
Another approach using the dynamic GMM model in equations (2) and (3) is to see how project effects vary by household quantile/initial position  ( _y_ 0) in the outcome distribution (e.g., whether the outcome is expenditure or assets): _Q_  ( _y_ 0 ) (  _yit_    _yit_  1   1  _xit_   2  _xit_  1  1  _pit_  ( _vi_ 0 * _t_ )   _uit_ ),   ()1,0 (5) In the analysis, we examine distributional impacts through both equations (4) and (5).

# **5. Data and context**

The panel data used in this study, collected by BIDS, are based on household and community surveys conducted prior to and following implementation of RRMIMP. Households in control and treatment villages were followed over the period. The project was funded by the World Bank as part of its efforts to promote rural infrastructure development and, in turn, rural income growth and poverty reduction.

RRMIMP included improvement of 574 km of feeder roads to bitumen-surfaced standard, construction of 1,900 meters (m) of culverts, 1,750 m of bridges, and 2,200 m of small drainage structures on rural roads. In total, 10 roads across various districts were selected for the project. Also, two control roads were selected from two separate districts in the same region.

Data collection was financed as part of the road-paving projects and conducted by BIDS as part of the Bangladesh government’s efforts to assess household- and village-level impacts of rural road improvements over time. The data in our study cover 1,284 households across 28 villages over the three rounds.<sup>5</sup> The first phase of the RRMIMP survey collected pre-project benchmark information on project and control households during May–September 1997. The second phase

> 5 This represents an increase in the sample of 415