rafmacalaba/data-use-mentions-extended huggingface dataset:
positively vs. 27 percent in comparison areas. Possibly directly related to the efforts of the project, almost all officials in those areas say the LGA has a formal plan for capacity building vs. 62 percent in comparison areas; and conditional on having a plan, less than half of respondents in comparison areas could show the plan vs. three-quarters of respondents in project LGAs. While these differences are statistically significant, we do not observe significant differences in other indicators such as reporting of budget preparation delays, budget execution or share of unplanned settlements. On the topic of fiduciary systems, project areas also perform better: respondents are 16 percentage points more likely to report having the internal auditor position filled, and they are more likely to agree that the internal audit office is independent and effective in monitoring. We do not observe significant differences, however, on most indicators under the topics of infrastructure management and accountability and transparency.

Taken together, these results suggest that between comparison LGAs and those receiving the project, systematic differences exist in the initial survey between respondents. Given the project’s targeting rules, this is an expected result. Our identification strategy, a differencein-differences approach, is well placed to estimate the causal effect of the program under preexisting differences between treatment and comparison groups. We assess the main identification assumption behind a difference-in-differences approach by checking for parallel trends in state capacity levels before the start of the program. Specifically, we examine trends before the first round of data collection on a key measure of local state capacity, LGA’s own source revenues. An important limitation here is that we only have available own source revenue data at the LGA level for the years 2007 to 2011 and data is available for the entire period of 5 years for only 36 out of the 40 LGAs (16 project and 20 comparison).

In Figure C1 we plot the mean per-capita revenues for both project and comparison LGAs. (We normalize for LGA population size in 2011.) The trends follow a very similar path between 2007 and
2 .15<br>.15<br>.1<br>Financial Products<br>Subsidized Healthcare .1<br>.05 .05<br>0 0<br>.25<br>.2<br>.15<br>.1<br>Transfers from Government .05<br>0<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD) optimal bandwidths. Confidence intervals are at the 95% significance level.

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chance (2 out of 15 tests for the joint tests of all coefficients are equal to zero are significant at the 10% level).

Program implementation data were also collected to better understand the quality of services delivered. These included detailed monitoring data from CGA and qualitative surveys with implementing agencies and program participants. 37 semi-structured qualitative interviews were conducted with program participants at different stage of the program, a qualitative surveyor was also regularly sent with the CGA advisors to assess if the study design was respected (29 surveys). In addition, 61 qualitative interviews were conducted with business owners not selected for the program to monitor potential externalities of the program. Finally, focus groups were conducted with the main implementing agencies (CGA, GUFE and both commercial banks).

# **4 Program implementation and take-up**

Data from the implementing partner, in addition to quantitative and qualitative data, suggest that the program was implemented consistently with the study design. Treatment allocation was respected for all businesses, and all components of the program were effectively offered to almost all program beneficiaries. The program was implemented on a rolling basis: CGA advisors started to reach out to informal businesses in April 2014, and completed both visits in February 2015.

Between April 2014 and January 2015, 2,399 “first visits” (100% of total) were attempted by CGA. The take up rate for the first visit was remarkably high and 2,344 visits were completed with success (98% of total). First visits were considered as not completed successfully when CGA advisers were not able to locate the business. Between April 2014 and February 2015, all businesses who received a first visit in treatment groups 2 and 3 were offered a second visit by CGA. Only 932 of these second visits were completed with success (44% of total). According to qualitative surveys and focus groups with the CGA, the main reasons for this relatively low take-up rate were that many businesses were not interested by the second visit, or did not
- Sterck, O., & Delius, A. (2020). Cash Transfers and Micro-Enterprise Performance: Theory and Quasi-Experimental Evidence from Kenya. _CSAE Working Paper Series, 2020-09_ .

- ILO. (2021). _ILO Monitor: COVID-19 and the world of work. Seventh edition, Updated estimates and analysis._ International Labor Organization.

- Hill, R. V., & Narayan, A. (2020). Covid-19 and inequality: a review of the evidence on likely impact and policy options. _Centre for Disaster Protection, Working paper, 3_ .

- Chetty, R., Friedman, J. N., Hendren, N., Stepner, M., & Opportunity Insights Team. (2020). How did COVID-19 and stabilization policies affect spending and employment? A new real-time economic tracker based on private sector data. _NBER Working Paper_ , 1-109.

- Josephson, A., Kilic, T., & Michler, J. D. (2020). Socioeconomic impacts of COVID-19 in four African countries. _World Bank Policy Research Paper, 9466_ .

- Khamis, M., Prinz, D., Newhouse, D., Palacios-Lopez, A., Pape, U., & Weber, M. (2021). The Early Labor Market Impacts of COVID-19 in Developing Countries: Evidence from high-frequency phone surveys. _World Bank Jobs Working Paper, 58_ .

- Zetter, R., & Ruaudel, H. (2016). Refugees’ right to work and access to labor markets – An assessment. _KNOMAD Working Paper_ .

- Eppler, M., Gaetani, S., Köllner, F., Kuhnt, J., Martin-Shields, C., Mebrahtu, N., . . . Preiß, C. (2020). Information and communication technology in the lives of forcibly displaced persons in Kenya. _Discussion Paper, 15/2020_ .

- Hamilton, Z., Casswell, J., & Alonso, A. (2020). _The
2011). These data distinguish, for example for five different types of liquid fuels, between emissions in different sectors and whether pollutants are emitted at ground level or from stacks.

Table 2: Fuel consumption and pollution intensity in Pakistan

|**Fuel**|**Fuel consumption in 2017 (ktoe)**|**Pollution (ug/m3 per ktoe)**|
|---|---|---|
|**Coal**|10,079|5.35E-05|
|**Natural gas**|26,155|2.96E-05|
|**Oil**|7,213|2.82E-04|
|**Gasoline**|8,037|3.31E-04|
|**Diesel**|9,539|4.38E-04|
|**LPG & kerosene**|1,508|3.05E-04|
|**Jet fuel**|115|2.93E-06|

Source: for fuels: : (IEA 2021), for pollution: (World Bank 2020b) Table 2 shows the fuel consumption for 2017 and the pollution intensity for Pakistan according to CPAT. We use the pollution intensity for coal and gas directly from the table. For the liquid fuels we take a weighted average, 3.53E-04 ug/m3 per ktoe. This is assigned to oil, which represents liquid fuels in the model. The concentration of PM 2.5 pollution in Pakistan due to the domestic burning of fossil fuels in the year is be calculated as SS

> 15 <u>https://iiasa.ac.at/web/home/research/researchPrograms/air/GAINS.html</u> 16 Air pollution can be transported over long distances due to wind and cloud chemistry.

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wage income and contribution to agricultural production the household member would have contributed while in New Zealand. Nevertheless, this gain in income is still massive compared to other popular development interventions – it compares to a 8 percent gain in per capita consumption from Progresa/Oportunidades (Fiszbein and Schady, 2009) and to no average increase in per capita consumption from a microfinance intervention (Banerjee et al, 2010).

# **4.2 Impact on subjective well-being**

In addition to measuring household welfare through income and expenditure, our final round survey measured subjective well-being. Households were asked to imagine a 10-step ladder, where on the bottom step were the poorest people and the top step the richest people, and to state which step of the ladder they thought their household was on today, and on which step their household was on two years ago. Ravallion and Lokshin (2001) refer to this as an economic ladder question, and note that it leaves it up to the individual to define what constitutes “poor” or not, and captures subjective economic welfare.

We estimate equation (3) without including baseline subjective wellbeing as a control since it is only measured ex post. The results are shown in the first row of Table 4.<sup>14</sup> In Tonga, participating in the RSE is estimated to increase subjective welfare by 0.43 steps on the ladder, about 45 percent of a standard deviation. This effect is strongly significant. Adding the household’s recalled subjective well-being from two years earlier only slightly reduces this coefficient, to 0.36 for the PS-2 screened group, and is still strongly significant (p<0.001). Participating in the RSE is estimated to increase subjective welfare by 0.71-0.83 steps on the ladder in Vanuatu, which is 43-50 percent of a standard deviation and strongly significant. Adding the household’s recalled subjective well-being from two years earlier does not change these results, yielding coefficients in the 0.74-0.85
11 for rice in high-income countries, the average deviation is well above the mean NRA for each product (which is reported in the right-hand half of Table 3).

# **How different are NRAs in periods of international price spikes?**

We move now to a closer examination of periods of extreme spikes in international food prices. The only such periods prior to 2008 in the World Bank’s distortions database are those around 1974 (an upward price spike) and in 1986 (a downward price spike). In Table 4 we focus on the annual average nominal assistance coefficient<sup>5</sup> (NAC = 1 + NRA/100) in the spike year plus the two years each side of it, relative to the longer period either side of each spike period. The expectation is that the NAC would be lower in the upward spike periods than in the average of the two adjoining longer non-spike periods, and conversely for the downward spike period around 1986. That is indeed what is evident in Table 4, where the spike periods are shown in bold italics and the percentage change in their average NACs from the prior non-spike period are shown in the lower half of the table.

Looking more closely at rice and wheat, for which new NAC estimates are available for the period since 2004 when their international prices were gradually rising before spiking in mid-2008, they too are lower than in the preceding 1988-2004 non-spike period. The proportional extent to which the rice and wheat NACs were lowered is greater in the recent period than in the 1970s’ spike period. That difference may be even greater once estimates are available for the two years following the 2008 spike instead of having to use, as a substitute, estimates for 2004 and 2005 when international rice and wheat prices were lower (see Figure 1).

Also provided in Table 4 is a breakdown of rice and wheat countries into importcompeting and exporting country sub-sets. The same down-up-down pattern is present for
Gunasekera et al., 2015; Rentschler and Salhab, 2020; Ward et al., 2020). Gross Domestic Product (GDP) is a critical economic indicator in the measurement and monitoring of an economy in a country that is typically only available at national and occasionally subnational levels. Regional indicators play a key role in the necessary variation to forecast regional GDP (Lehmann and Wohlrabe, 2015) and food security (Andree et al., 2020). Previous efforts to estimate local GDP use high resolution spatial auxiliary information such as luminosity or population data to provide local variation. Methods by Kummu, Taka, and Guillaume (2018), Murakami and Yamagata (2019), Nordhaus (2006), and World Bank and UNEP (2011) took advantage of gridded population data, which is the result of a model disaggregating the most detailed level population data into grids (e.g. see review in Leyk et al., 2019). However, wealth is not evenly distributed among people nor infrastructure (Berg, Blankespoor, and Selod, 2018). In fact, the divide between the rich and poor is even widening in our time (Dabla-Norris et al., 2015). The method used in World Bank and UNEP (2011) stratify the population by rural and urban, yet definition of these geographic areas can vary based on the selection of the population model (Leyk et al., 2019). These measurements matter in application to stylized facts such as the strong negative correlation of the level of urbanization with the size of its agricultural sector (Roberts et al., 2017). Also, the uniform distribution of labor in agriculture is another key concern (Gollin, Lagakos, and Waugh, 2014). Other methods used land cover such as vegetation and built-up indices, however did not incorporate types of agriculture like cropland and livestock (Goldblatt, Heilmann, and Vaizman, 2019; Rashmin Gunasekera et al., 2015).

Other methods to estimate GDP at a local level take
# **2.1 Health data**

We choose infant mortality as our primary health outcome. This choice conveys at least two significant statistical advantages. First, as noted by Chay and Greenstone (2003), infant survival is a shortterm measure; other measures of health stock, captured later in life, necessarily reflect the effect of many more health shocks experienced in an individual’s lifetime. Second, complete birth histories are available in certain Indian demographic surveys, so we can construct long pseudo-panels of infant survival to maximize statistical power. In contrast, morbidity variables such as diarrhea incidence and low birth weight are only available cross-sectionally from the time of survey; too few of such surveys have been completed for us to construct a meaningful panel. Panel variation in infant mortality allows us to include detailed temporal and cross-sectional fixed effects in regression analyses, which account for time-varying shocks to national infant health and time-invariant characteristics of specific areas, respectively.

We focus primarily on deaths within the first month of life, i.e., neonatal mortality, although we also test for a policy impact on one- _year_ mortality. The first month is the most vulnerable period of an infant’s life (United Nations Children’s Fund 2015). Globally, neonatal mortality accounts for over half of all infant deaths (World Development Indicators 2017); in our own data from India, its share of under-one mortality is 70 percent. Furthermore, several existing studies of pollution – in both water and air – and child health show that the largest impacts occur within one month of birth (Chay and Greenstone 2003; Gluckman et al. 2008; Currie and Almond 2011; and Brainerd and Menon 2014). While neonatal mortality is not a complete measure of the health costs imposed by water pollution, it nonetheless represents a very large loss of life, especially in the Indian context.

Our infant health data come from the Reproductive and Child Health II (RCH-2) module of the District-Level Household Survey II (DLHS-2),
helps to define their value by clarifying whether the occupation is subject to uncertainty and whether it is short or long term.

# **6. Conclusions**

The paper addresses the theme of the inclusion of use rights held in the form of usufruct in household wealth sample estimates. After recalling the theoretical appropriateness of correcting traditional estimates for this component, the paper presents an application to the Italian case. Using SHIW data shows that, although the phenomenon of usufruct is relatively uncommon (around 3 percent of households), the distribution of wealth is significantly affected by such an adjustment. Younger households appear to be relatively slightly wealthier than in unadjusted estimates. Overall, adjusted estimates exhibit levels of the Gini concentration index that are lower than unadjusted ones by approximately 1 percentage point.

The estimates do not take account of a different form of use right, namely the right to inhabit, which typically arises when one spouse passes away and leaves behind a residential home that was jointly owned or owned solely by the deceased spouse. On the one hand, SHIW data do not provide information about this condition; on the other, the right to inhabit is personal and cannot be transferred or assigned to another individual; in the absence of a market, the monetary evaluation of this right can be more complex. In any case, this additional element should also be considered in the measurement of household wealth.

We also conducted a brief analysis of residential homes occupied free of charge by households in European countries whose data are included in the HFCS. In several countries, residential homes occupied for free are relatively widespread, with shares similar to that found in Italy. However, the lack of data does not allow us to identify those cases in which the occupation takes place on the basis of a right of use which justifies its inclusion in households' wealth. An initial tentative evaluation shows that the effects of such an adjustment could lead to a significant reduction in wealth inequality.

These results also suggest that the replication of the analysis in countries where
<!-- Start of picture text --> Figure 1: Bus Service Take-up according to Prices 12<br>g p<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>MK 0 MK 10 MK 20 MK 50 MK 100 MK 300 MK 500<br>Source: Observational data collected by bus operators<br>Sample Size = 936<br>Proportion of adults that used the bus<br><!-- End of picture text -->

> 12 Results are based on the individuals that participated in Phase 2.

8
regulations, helps reduce the cost of trading (both in terms of exports and imports). In addition, their results suggest that aid to economic infrastructure increases exports, whereas aid to productive capacity appears to have no significant impact on exports. The authors correctly point out that aid for trade is possibly endogenous to exports; particularly aid to productive capacity. For instance, if better performing sectors tend to receive more aid for trade than other sectors, it would generate an upward bias in the aid coefficient. To address this endogeneity, the authors instrument aid for trade with the degree of respect for civil and political liberties measured by the Freedom House Index and with an index proposed by Gartzke (2009) that measures the ―affinity of nations‖<sup>6</sup> . The authors argue that many donors choose recipients based upon development and democratic measures like these which make these indicators correlated with aid but not with exports. However, the authors acknowledge that their instruments are not appropriate for sectoral analysis, as they only vary across country-year and not across country-sector-year.

We propose an alternative identification strategy that allows us to analyze the impact of sectoral aid. To address reverse causality, we exploit the links between the service sector and the manufacturing sector relying on input-output tables. Although input-output data has not been used in the analysis of aid effectiveness so far, a number of studies, particularly in the FDI literature, make use of it. Given the difficulty of finding consistent input-output matrices across countries most studies rely on input-output data from the United States to describe the technological possibilities of firms in a given economy. Acemoglu, Johnson, and Mitton (2009) investigate how contracting costs and financial development determine the extent of vertical integration across countries. Alfaro and Charlton (2009) use new firm level data at the 4-digit sector level and U.S. input-output tables to classify firms between horizontal and vertical

> 6 In preliminary research we also attempted to use other instrumental variables for aid for trade such as immunization rates,
that the household will be best represented by a man; a woman or even a child can be a head of a case, depending on standard operating procedures.

> 7 Even when traditional household survey data are gathered at the individual level, the information is often collected from a single respondent. The respondent is usually the self‐identified ‘most knowledgeable’ household member, which overwhelmingly corresponds to the ‘head’ of the household. In the case of a household survey that solicits information on ‘headship’, this information is gathered often through the question: “Who is the head of this household?”

3
three years after enrolling in EMS. On-time graduation rates vary across types of degree programs, with general schools showing the highest rate (69.2 percent), followed by technological schools with rates very close to the national average (67 percent) and technical schools showing the lowest (54.1 percent).

Table 1 compares the characteristics of 12th grade students from the four different providers of upper secondary using information from the context questionnaire of the national standardized test PLANEA. Students attending schools run by the federal government, the universe from which the sample of PODER was drawn, display levels of math achievement that are in between the levels observed by those attending schools run by states and autonomous universities. This is consistent with socio-economic indicators (parents’ education, books and computer at home), students in federal schools come from better-off households compared to students in state-run schools but are relatively poorer than students in EMS linked to autonomous universities.

5
## 1.3 Outline of Data and Methodology

A gendered global database was first constructed by incorporating female employment and wages across sectors and countries in select South Asian countries. The study followed an approach similar to the one developed by Narayanan and Walmsley (2011) and Narayanan et al (2016) in the context of child labor, to generate gender differentiated employment and wages data in the current GTAP database version 9.

To generate gender differentiated employment and wages data in the current GTAP database, gendered employment data and industry average wages, across industry and occupations, for each country, were compiled and reconciled using the Yearbook of Labor Statistics from the International Labor Organization (ILO). To fill in the data gaps, data were obtained from various published government sources for the select countries. For instance, in case of India, latest data from summary results from national sample survey (NSS) 68th round on employment and unemployment 2011-2012 were used.

Female labor force participation and the gender wage gap were calculated using data disaggregated by economic activity according to the International Standard Industrial Classification of All Economic Activities (ISIC) at the 2-digit level of the classification and for the latest data available for that country. The gender pay gap is defined as a measure of the difference between men’s and women’s average earnings. It is measured as “female average wage/salary as a percentage of male average wage/salary” for different industrial sectors where data are available.

In the context of green trade, the approach proposed in Narayanan and Khorana (2011) was used to split the green trade sectors from others. The trade data were obtained from UN COMTRADE and the classification used follows the one used by the United States Bureau of Labor Statistics (BLS) to split green sectors. BLS has identified 333 detailed industries where green goods and services are produced.

# **2. Review of the Literature**

In the existing literature, there are several studies that have quantitatively examined the impact of trade liberalization on