rafmacalaba/data-use-mentions-extended huggingface dataset:
_1H Li(fQ )_ _ ln[ _1XLIh(Qk_ l vi*)] _Hh=l_ (24) where _His_ the number of replications of the simulated error. Maximization of (24) summed across households will yield consistent estimators of the parameters in the model above, and will yield a good approximation of the true likelihood even with a moderate _H,_ as long as different values of _u1_ are drawn for each observation (Gourieroux and Monfort, 1993). In the analysis that follows, His set equal to 25.

In the case of the labor intensity decisions, there are only 3 regimes, as described in Appendix 2. The analogues to (23) and (24) for the labor-rental system of equations are straightforward.

# **Section 3 Econometric Results**

To estimate the models developed in the prior section, we turn to panel data derived from two comprehensive surveys administered in eight provinces of China in 1988 and 1993.17 The surveys contained both a household and a village questionnaire. Villages and households were chosen from the Rural Survey Base maintained by the Rural Survey Team of the State Statistical Bureau of China. The household questionnaire asked questions ranging from land rights, land transactions and annual agricultural production to off-farm employment. The village questionnaire asked questions about land tenure arrangements at the village level both in the surveyed years and in the history. In 1997, a supplementary village survey was

> 17 The 1988 and 1993 surveys were designed by the Development Research Center under the State Council of China, with the assistance of the Land Tenure Center at University of Wisconsin-Madison, and implemented by the Rural Survey Team of the State Statistical Bureau. Both surveys covered the same 800 households in Jilin, Henan, Jiangxi, and Zhejiang provinces. Both surveys were financed by the Ford Foundation.

25
the following as ‘data’. Total population of the Greater Dhaka area reaches 24.6 million in 2035, so the average annual population growth rate between 2010 and 2035 is 2.3% percent. Of the population in 2035, 40% are high-skilled (up from 37% in the 2011 data). At a sectoral level, total factor productivity is assumed to grow at 1% per year in each sector. We impose the assumption that the value of land outside the city grows at the same rate as the increase in productivity.

We revise connectivity to include pre-planned transport investments, including 42.3 km of Bus Rapid Transit and 60.1 km of Mass Rapid Transit, both of which are used by low and highskilled workers if it shortens their journey times. Approximately 1,200 km of lanes of road are also built, a combination of the widening of existing routes by adding new lanes, and the construction of entirely new highways. These are shown in maps in the appendix. The increase in high-skilled population, who have access to motorized transport, increases the number of cars on the road, whereas at the same time there is an increase in the number of kilometers of road lanes. We combine these two variables into a number of vehicles per kilometer of road, 10
**Figure 1: Timeline of the EHCVM Survey and Covid-19 response**

<!-- Start of picture text --> January 2020:<br>Household<br>survey begins<br><!-- End of picture text --> <!-- Start of picture text --> 11 March 2020 :<br>Covid-19<br>Declared Global<br>Pandemic<br><!-- End of picture text --> <!-- Start of picture text --> 16 March 2020 :<br>President<br>addresses<br>nation on Covid-<br>19<br><!-- End of picture text --> <!-- Start of picture text --> 23 March 2020 :<br>Interisland<br>travel and<br>international<br>flights<br>suspended<br><!-- End of picture text --> <!-- Start of picture text --> 3rd April 2020 :<br>National plan<br>announced<br><!-- End of picture text --> <!-- Start of picture text --> 1st May 2020 :<br>First Covid-19<br>case reported<br><!-- End of picture text --> <!-- Start of picture text --> September<br>2020 :<br>Household<br>Survey data<br>collection ends<br><!-- End of picture text --> The empirical analysis of the Covid-19 outbreak’s impact on household welfare was undertaken using the 2020 Harmonized Survey on Living Conditions of Households (EHCVM) for the Comoros. The survey was conducted by the National Institute of Statistics and Economic and Demographic Studies and the World Bank and was collected between January and September 2020.<sup>5</sup> Figure 1
9.9||15.8|11.7||
|once a month|12.3|10.5||17.7|21.7||
|once every two weeks|10.0|7.3||13.7|21.5||
|once a week|30.8|24.5||18.6|25.1||
|_Time spent on mathematics homework_||||||0.051**|
|15 minutes or less||||75.8|73.3||
|16 minutes or more||||24.2|26.7||
|_Time spent on science homework_||||||0.045**|
|15 minutes or less||||71.0|77.4||
|16 minutes or more||||29.0|22.6||

7 The TIMSS variable describing teachers’ qualifications for grade 4 shows that a majority of teachers in Saudi Arabia have secondary education. This is not consistent with official data from the Ministry of Education or other available data sources, which indicate that most teachers in the country have at least a bachelor’s degree. Due to this inconsistency, the authors of the study decided to omit this variable from the analysis.

11
matched to individuals refers to the amount of profits net of dividends that can be attributed to some shareholders using the shareholder database - this number can be larger than column (4) since profits can be negative. Finally, ”profits matched to individuals with valid ID” refers to the total profits net of dividends that are attributed to a shareholder with valid national ID - that excludes foreign corporations and foreign shareholders, for example, which do not have a national ID.

# **B.2 Creation of shareholder list**

Once we compute net-of-dividends corporate profits for each period, we need to assign them to each shareholder in proportion to their equity in firms. We do so by using a detailed list of corporate shareholders and their shares.

No consolidated and updated shareholder list of corporations exists in Honduras, so we worked with the Tax Authority to build one. We use two main datasources: Annex B of the ”professional relations table”, a document firms file when they are incorporated, listing all relevant parties in the firm and including participation of shareholders; and the withholding form for dividends DMR 113, under the assumption that individuals receiving dividends are shareholders even if not listed in the Annex B. We emphasize that the quality of Annex B information is far from perfect: there is no formal validation of the information imputed, including validating the tax id (RTN) of 49
decomposition, and (ii) it shows the contribution of the physical capital stock (from PWT 9.0) and natural capital (Lange et al. 2018) in the natural resource decomposition. The average annual rate of growth in output per worker for the common sample of 37 countries in Sub-Saharan African during the period 1996-2014 is approximately 2.4 percent.

Figure 5 reports the comparison of the growth decomposition across the different groups within the region when we exclude and include natural capital. The first column of each group shows the contribution of capital, and the second measures a composite value for capital, that includes both physical and natural capital. The second column for each group in figure 5, depicts the growth decomposition that includes accounting for natural capital in the physical capital share, named “composite capital”. Both columns in each group include human capital, and TFP. The first column for Sub-Saharan Africa (traditional decomposition) shows that physical capital explains 36 percent of the observed growth in output per worker (0.88 percent per year) whereas that of TFP accounts for 39 percent (0.95 percent per year). The second column, on the other hand, shows that the composite stock of capital, which includes both physical and natural capital, contributes with 1.6 percent per year (about 65 percent of the observed growth per worker) while TFP grows at an annual rate of 0.25 percent (thus, explaining only 10 percent of growth per worker).

**Figure 5. Contribution of TFP to output per worker growth in SSA, 1996-2014** _(Annual average percentage change, population-weighted)_

<!-- Start of picture text --> 4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5
locations of the electricity generators using two databases, one on dams opening year and another on power plant locations (Platts database). For Cameroon, we use the geolocalized database including all dams in Africa and their year of opening. For Nigeria, we use the global power plant database that includes all power plants per type of energy (hydro, wind, gas, and geothermal) with their capacity and year of commissioning. From the year of dam opening or power plant commissioning onwards, all districts lying along the straight lines connecting the dams or power plants to the main demand centers are considered as having access to electricity. For Nigeria and Cameroon, the main sources of demand vary across time. At the beginning of our panel, all dams in Cameroon have been opened therefore a panel IV is created by varying the sources of demand rather than the supply sources. For Cameroon, we set the threshold of 500,000 inhabitants for a city to be included as a main source of demand for the hydropower supply sources. In 1990, only Douala and Yaounde are included. In 2000, Garoua in the North

> 18While the previous part also includes Internet investments, this subsection only focuses on finding instruments for paved roads and electricity to keep the IV estimation tractable.

17
would be to prohibit vertical integration of collection and processing of contributions with the other functions. The gain from prohibiting vertical integration is that it prevents concentration of the trustee function. However, forced disintegration does impose costs. For example, looking in the membership archives held by the account manager allows a cheap solution to some types of errors, while avoiding contact with the employer. Some of these savings can also be obtained by a monopoly collector arranging to be in contact with the processing center of each pension management company. We now consider several options to organize the provision of collections services, given the mandated disintegration of this service.

66. **Policy options in collection and raw processing of contributions.** The first option is a nationalized monopoly. This appears attractive in small countries that have a well-functioning personal income tax collection system, because collections related to pensions can piggyback on that infrastructure. This has been tried in Argentina through DGI, a government-run entity, and could be attempted in small countries such as Costa Rica that have a fairly well-functioning personal income tax collection system. The costs of collections per form charged by DGI is US$ 4.0, and there is just one invoice _(planilla)_ per employer. An important problem with this option is that a government collection agency may not have incentives to adopt the most cost efficient technologies for collections, but rather may wish to offer more employment to relatives and political supporters. For example, DGI uses a lot of manual labor and has yet to adapt the new technologies. A second important problem is that the nationalized monopoly may charge a large monopoly margin not subject to regulation, which acts as a hidden, unlegislated tax on labor. these factors may explain why the price for collections charged by DGI is four times that charged by Chilean banks, and at least 12 times the cost of large vertically integrated AFPs.
>c</sup>|
|_Number of_<br>_households_|_1,891_|_301_|_Number of_<br>_households_|_1,604_|_151_|_398_|

_Notes_ : The sample is restricted to landholding households and respondents. For panels 1, 3, and 4 household tax incidence is calculated by dividing self-reported tax payments by household expenditure. In panel 2, individual tax incidence is calculated by dividing imputed tax payments (apportioned according to individual landholdings) by per capita expenditure. Households and individuals are ranked by the area of landholdings. Tests of equality of means were conducted between (1) male- and female-headed households, between (2) male and female landholders, (3) female-only and dual adult households, and (4) married male-headed and single female-headed households. Significant differences at p<0.05 are reflected in bold. a Male adult only households are excluded in this panel because of small sample size. b Single male-headed households are excluded in this panel because of small sample size.<sup>c</sup> The sample size is below 100 observations.<sup>d</sup> The sample size is below 50 observations. Household sampling weights are used.

# **b) Tax Incidence Using the Amhara, Oromia, and SNNP Tax Schedules**

In this section, we calculate tax incidence using imputed tax liabilities with land area and the tax schedules of Amhara, Oromia, and SNNP regions (Table 10) due to the large difference between self-reported tax payments and imputed tax liabilities found in section 4.3. The assumption is that there is full tax compliance. Our results show that tax incidence using imputed tax liabilities with land size and tax schedules is about a third of the size of tax incidence using self-reported taxes. Some patterns of the tax burden still hold, however. On vertical equity, taxes are regressive. As for horizontal equity, female-headed and female-only households still bear a larger burden than male-oriented households. Tax incidence
At endline, we re-surveyed the 5 households that took the baseline household survey. We also randomly selected 5 of the 15 individuals who took the user-feedback survey at baseline and administered the endline household survey, which was revised to incorporate modules from the user-feedback survey. This generates a sample of 10 households per community at endline (5,080 households). The households in our sample are poor: at baseline, 74 percent live in homes with mud floors and wooden walls; 24 percent have no toilet facility and another 58 percent use a pit latrine; only 20 percent own a mobile phone; and 62 percent have no formal education.

### **3.1.2 Ebola Sub-Sample**

Our data on Ebola cases are aggregated to the section-week. Sections are the smallest administrative units in Sierra Leone: the median section is under 40 square kilometers and had fewer than 2,500 residents in the 10
**The relationship between average return on assets and ownership** (Figure 1.4a) **is inverse.** This result suggests that private sector ownership is more efficient than government ownership. Even mixedownership, SOEs with state participation have on average higher returns on assets. During the crisis, profitability of enterprises decreased, but the inverse relationship between returns and state ownership remained valid.

Figure 1.4a: A relationship between average returns on assets and ownership: Industry, Trade, Construction and Transport Sectors <!-- Start of picture text --> 8.0%<br>7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>Private SOE (Mixed) SOE (pure)<br>Before Crisis (2004-2008) After Crisis (2009-2010)<br><!-- End of picture text --> Figure 1.4b: Relative shares of loss-making enterprises (all sectors of economy) <!-- Start of picture text --> 25.0%<br>2006 2007<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>Private SOE (Mixed) SOE (Pure)<br><!-- End of picture text --> Source: Authors estimates based on Belstat dataset.

**Second, the share of loss-making enterprises in the private sector is significantly lower than that among mixed-ownership or state-owned firms** (Figure 1.4b). This fact suggests that if resources allocated to the private sector do not generate income sufficient to recuperate their cost firms do not survive.  In contrast, the percentage of loss-making firms among mixed-property or state
between domestic and imported goods in the short run. Similarly, the allocation of domestic output between domestic sales and exports (equation (A4)) was also assumed to be subject to a partial adjustment process.

We calibrated the model for 2002, the most recent year for which we were able to construct a complete set of macro accounts. Data on national accounts, fiscal accounts, balance of payments (based on IMF estimates), and OECD data were combined to produce a consistent set of estimates (see Appendix C). Significant discrepancies appeared in the aid data between national sources, the OECD’s DAC database, and the fiscal and balance-of-payments accounts; we chose to use the OECD data, which are the most comprehensive, and adjusted the other information accordingly while keeping intact major equilibrium relationships (namely, the fiscal 31
# **A2. Consumption deflator**

The expenditure data were deflated to account for price variations over time and across regions, by expenditure type and month of data collection. For deflation across time, the consumption categories above were matched with the corresponding consumption categories in Turkish Statistical Institute’s Consumer Price Index (CPI), using the Classification of Individual Consumption by Purpose (COICOP) codes. For consumption categories that did not have a direct match, the general CPI was used instead. Using month- and region- specific (at NUTS2 level) values of these categories, expenditure types were deflated to be all expressed in 2017 as base year. The deflated consumption value Y of item-category _c_ , in region _r_ , during year _y_ and month _m_ , was therefore obtained using the following formula: To take into account spatial differences in cost of living, the result of the previous formula was deflated using Turkish Statistical Institute’s 2017 Purchasing Power Indices (PPI) for the 26 NUTS-2 regions of the country, by consumption category. Therefore, the following formula was used to obtain the final temporally and regionally deflated expenditure values, using the values of Y deflated to 2017 as in the previous step, and the PPI for item-category _c_ , in region _r_ , and year 2017, with the index for Turkey being equal to 100: 60
current value and rate of depreciation) is added to the consumption aggregate (NISR 2015).

#  Cost of Living Index

As the cost of basic food and non-food needs varies across regions within a country, the aggregate nominal expenditure needs to be adjusted for spatial price differences. Moreover, temporal price adjustments need to be done to account for seasonality of consumption and price variation within survey months. A common way to do this is to construct a cost of living index (COLI). COLI is a numeric factor which describes price deviations over the survey period across regions from national average prices of a ‘selected’ basket of goods as recorded in the ‘base month’. NISR uses January as the base month for welfare estimation, thus EICV3 adjusts all expenditures to January 2001 prices, and EICV4 uses January 2014 prices as reference.

The Rwandan COLI can best be described as a poor’s price index. This means that the index is based on a selected ‘basket’ of food and non-food items according to their relative weight in the budget share of the poorest households.<sup>3</sup> To adjust the nominal consumption aggregate in EICV3, NISR uses the consumption patterns of the bottom 60 percent of the households. In EICV4, the choice is restricted to the bottom 40 percent of households. This is because the poverty headcount was estimated at 45 percent in EICV3, and being a poor price index, COLI was restricted to households strictly below the poverty threshold. The final

> 3 The availability of price data throughout the survey period is also a factor in the determination of the goods that make it into the COLI basket.

3
IRB approvals, study registration, and protocols.

# **4 Results**

## **4.1 Estimation strategy**

We begin by presenting the impact of offering discounts for LARCs using: where _Yi_ denotes the outcome for client _i_ . _Li_ denotes binary LARC price indicators for whether clients were offered LARCs at _Low_ (CFA 150 or 0) or _Mid_ prices (CFA 2,000 or 1,000), respectively. _Xi_ denotes covariate adjustments, which, by design, only includes an indicator variable for SARCs

> 12We supplement these data with follow-up surveys of HGOPY clients, who were enrolled into the adaptive experiment that followed the pilot, within one to two weeks of their initial FP counseling session. We use these data to assess if service quality and client satisfaction differ by counseling approach.

> 13Survey evidence from the sample of clients enrolled in the ongoing adaptive experiment indicates that every client who adopted a method later, adopted it at HGOPY.

> 14Approximately 90% of clients, who have returned to HGOPY to adopt (or remove or switch) their chosen method, have done so within 100 days of their initial visit. Therefore, our data include clients whose initial visit was between 9 June 2020 and the 9 March 2021, who may have returned to HGOPY for a follow-up visit until 17 June 2021.

9
-1.2|1379|1357|-21.0|33|
|Sub-Saharan<br>Africa|86.2|85.5|-0.8|901|893|-8.1|21|
|**World**|**49.9**|**49.9**|**0.0**|**2924**|**2924**|**0.0**|**132**|
|Other High<br>Income|1.3|1.7|0.4|13|18|4.5|5|
|Europe and<br>Central Asia|12.6|14.2|1.5|62|70|7.5|16|
|Latin America &<br>Caribbean|23.0|25.0|2.0|134|146|11.7|14|
|East Asia &<br>Pacific<br>Source:Authors’ calcul|44.4<br>ations from th|46.4<br>e Global Mon|2.0<br>itoring Datab|284<br>ase (GMD) an|297<br>d Luxembour|13.1<br>g Income Stu|25<br>dy (LIS)|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: This table reports poverty estimates for the 2017 reference year. For countries with no surveys conducted</u> exactly in 2017, the poverty estimates are extrapolated from the latest survey if conducted before 2017, otherwise extrapolated or interpolated from the closest surveys before and after 2017. The extrapolation and interpolation rules applied are the same ones the World Bank applies when “lining-up” global poverty estimates for every year (see the - Poverty
The association of a slum’s pradhan with a particular political party—as in the Congress Party in the case of Radha—and the conditionality of services based on political affiliation suggests that pradhans play a central role in the quid pro quo between slum dwellers and elected politicians, where blocks of votes and manpower at rallies are exchanged for services and protection.

Of the other external authorities, bureaucrats and police featured prominently in the qualitative module. For obtaining the delivery of services such as electricity and water, pradhans such as Ganesan seem to act as crucial intermediaries between slum dwellers and bureaucrats, their role perhaps magnified by the lack of incentives bureaucrats face in dealing with the poor. In contrast, police officers appear to play differing roles, enforcing property rights for the unorganized Bengalis of the Rajiv colony, but being themselves responsible for expropriation in Madanpuri. The role of the pradhan thus lies in shielding slum dwellers from unwanted police attention in less organized slums and providing a substitute for formal police enforcement in their more established counterparts.

To test whether in fact the relationships uncovered in the qualitative module held for Delhi as a whole, we used the representative sample from the quantitative module to evaluate the extent to which slum dwellers knew and had interacted with external authority figures was influenced by their own characteristics and social networks, and the role played by their pradhan in providing access to different formal authorities. The dependent variables we examine are whether a respondent “knows and has interacted with” bureaucrats, elected politicians, policeman and pradhans themselves. Thus, we analyzed linear probability regressions of the following form: P( _governance network_ i) = α + X1i Β i + X2c Γ c + _contactsi_ Κ i + _pradhani_ Π i + ε i (1) 18