rafmacalaba/data-use-mentions-extended huggingface dataset:
7 remittances is assumed to be receiving exactly the amount of remittances measured by the survey. This means that households which have migrants who do not remit are not recorded in this study as receiving remittances; rather these households are classified as non-remittance receiving households. This assumption seems sensible because migration surveys in other countries generally find that about half of all migrants do not remit.<sup>3</sup> Because of data limitations, the focus throughout this study is on the receipt of international remittances by the household rather than on migration or the type of person sending remittances. Finally, international remittances include both cash and in-kind remittances. The inclusion of in-kind remittances (food and non-food goods) is important because it leads to a more accurate measure of the actual flow of remittances to households in Indonesia.

Table 1 presents summary data from IFLS 3 (2000) and IFLS 4 (2007). It shows that the number of households receiving international remittances in Indonesia is fairly small: in 2000, 169 households (3.2 percent of all households) receive remittances, and in 2007, 179 households (3.3 percent of all households) receive remittances.<sup>4</sup> According to the table, households receiving international remittances in Indonesia have older household heads, have fewer household members with high school and university education, and are more likely to be located in rural areas. Households receiving international remittances also tend to have lower mean per capita expenditures than households without remittances. For households receiving remittances, remittances represent 26.0 percent of total household expenditures in 2000 and 29.0 percent of expenditures in 2007. However, since households receiving international remittances in Indonesia also have low levels of expenditure, the absolute amount of remittances received in annual per capita terms by households is quite low: not exceeding US $30 in either year.<sup>5</sup>
Table 3

|Compensation for control households|One bottle of palm oil and a 5 kg sack of rice worth around 7 USD|
|---|---|
|Attrition rate|< 1%|
|Compliance rate|87% (18 households declared their Pico‐PV kit to be sold, lost or|
||stolen; One household received kit only during follow‐up)|

_Source:_ Household data set 2011/2012.

# **Results**

# **Balance of Socioeconomic Characteristics of Participating Households**

This section examines the balancing between treatment and control group and, at the same time, portrays the socioeconomic conditions in the study areas. Baseline values of the households’ socioeconomic characteristics show that the randomization process was successful in producing two balanced groups (see The surveyed households are mainly subsistence farmers that live in very modest conditions. The educational level of the head of household is low and households own only a few durable consumption goods. The households in our sample have cash expenditures of on average 0.45 USD (1.12 USD PPP) a day per person with the lower 25%‐stratum having only 0.07 USD (0.18 USD PPP). Even the upper quartile has cash expenditures of 1.14 USD (2.86 USD PPP) only. By any standard, the sampled households qualify as extremely poor.

Also energy consumption patterns illustrate the precarious situation of most households (see Table 3). They consume on average only around three hours of artificial lighting per day which is mainly provided through kerosene wick lamps or battery‐driven small
respectively). The higher use in these services is likely reflecting the relatively higher level of sophistication of chemicals and automotive exports, but also the higher level of sophistication of business services. This is because business services themselves create higher value‐added.

Figure 5: Services share of value added in manufacturing sectors gross exports (2011) <!-- Start of picture text --> Agribusiness 15.4 2.7 3.50.6 5.7<br>Chemicals 10.7 2.2 4.70.9 8.7<br>Automotives 8.1 2.1 5.80.9 8.8<br>Metals 7.7 2.4 4.40.8 4.8<br>0 10 20 30<br>Wholesale & Reta il Transport & Storag e Finance<br>Computer R&D & Business<br><!-- End of picture text --> Source: Authors’ calculations using OECD/WTO TiVA.

Compared to peer countries, Brazil shows stronger forward linkages in services from sectors such as distribution and R&D/business services. Across all sectors and compared to most of its comparator countries, Brazil shows high levels of domestic value added from distribution and business services to GVC sectors (Figure 6). In agribusiness and in chemicals, Brazil has the highest share in domestic value‐added from distribution services across its peer countries. Transport services on the other hand have low contributions to GVC value‐added exports in each of these sectors in Brazil compared to peer countries. Interestingly, the forward linkages of business services in value‐added in these four sectors in Brazil does relatively well. For instance, although the share is still relatively low in agriculture, it is nonetheless highest compared to other countries, including Canada. This is also true for metals, automotives and chemicals in which the services value‐added from business services is highest compared to other countries. By similar token, the
7 on fecundity. The measure of the importance of these other factors is captured in value of phi. This leads Singh et al to conclude more work is needed on these other factors. Despite the complexity of these channels, the effect of education on fertility is trackable at the individual level because of maternal education's more or less exogenous nature and the importance of education's effect through individual behavior.

# MORTALITY

Whether mortality decline is necessary or sufficieat for fertility decline and the thresholds for bringing mortality decline about have received a considerable amount of attention. (See Bulatao and Elwan, 1985 for a review.) The consensus is that life expectancies of 50 years are necessary, but not sufficient conditions for fertility decline. The magnitude of the effect of preventing a death on fertility has been studied extensively. The general consensus as suummarized after the 1975 Bangkok Lonference was "...on average, an additional child death in the family, ceteris paribus leads to far less than one additional birth." (Preston, 1978, 11) In an earlier study it had been estimated that the effect varied by level of socio-economic development, but it was less than half in all the countries studied.(Preston, 1975). The World Fertility Survey again provides the opportunity to study infant and child mortality as a determinant of fertility with a broader set of comparable data.

The effect of infant and child mortality is believed to affect fertility through three channels. First, because of the importance of breastfeeding in suppressing fertility in most developing countries as illustrated above, the death of an infant will shorten the period of infecundity and the woman will
from the investment return on the fiscal savings as well as the BoB’s net income (which includes the cost of sterilization).<sup>17</sup> The projection period adopted in this paper is 20 years, with 2010/11 as a starting year. While mediumterm scenarios generally consider a time horizon of 5-10 years, the prospect of a significant change in Botswana’s economic conditions argues for an extended projection period. This will allow policymakers to identify potential future problems and prepare an appropriate policy strategy well in advance, although projections of more distant years are less precise and subject to greater uncertainties.

# **3.2.1 Medium-term Fiscal Projections:  Baseline Scenario**

# **_Macroeconomic Assumptions_**

For Botswana, the backbone of the macro-fiscal framework is the diamond sector’s performance. The diamond sector is assumed to make a decent recovery in 2010/11, after a sharp contraction in 2009/10. Following NDP10, a full recovery of the sector is then assumed to take place over 2011/12—2013/14.<sup>18</sup> Once diamond output returns to the pre-crisis level in 2013/14, thereafter mining sector growth is assumed to plateau because of the base effect. The production profile and real profit per carat of

> problematic for Botswana given the relatively limited availability of macro- and microeconomic data for the identification and estimation of parameters with reasonable precisions.

> 17 The stock of Bank of Botswana Certificates (BoBCs) is not included in the projection of medium-/long-term government domestic debt, due to difficulties in projecting the magnitude of sterilization.

> 18 While the assumption on the diamond sector recovery is in line with NDP10, the growth rates assumed in this paper are somewhat different, taking into account more recent developments since the finalization of the Plan. Specifically, we assume a smaller bounce-back of diamond sector growth, because the actual decline in diamond output in 2009/10 was much smaller than envisaged in NDP10
# **9.  Summary and Conclusions**

The Global Methane Pledge joins 122 countries in a collective effort to reduce global methane emissions at least 30 percent from 2020 levels by 2030. This paper is intended to support Pledge participants in a two-part exercise. First, it introduces a new World Bank database that reports monthly atmospheric methane (CH4) concentrations for the terrestrial globe in a spatial grid with a resolution of 25 km. Monthly means are calculated for “local anomalies” in bias-corrected XCH4 data from the European Space Agency’s Sentinel-5P (TROPOMI) satellite platform. The new database computes monthly mean values from daily time-stamped observations that have been pre-filtered for global, seasonal and latitudinal background effects. We term these “local anomalies” because they register the local emissions component of the data. Recent global research (Sha et al. 2021) has shown that the Sentinel-5P methane measures align closely with measures taken by ground stations in TCCON (the Total Carbon Column Observing Network). The database will be maintained online by the Development Data Hub of the World Bank’s Development Economics Vice Presidency.

The new database is intended to assist Pledge participants in reducing methane emissions from energy, agricultural and waste subsectors whose spatial distributions are highly non-uniform across and within countries. At present, priority areas for emissions reduction are identified with spatially-formatted “bottom-up” estimates provided by national or global emissions inventories such as the Emissions Database for Global Atmospheric Research (EDGAR). These inventories combine sectoral activity data with broadly calibrated emissions factors. Using spatial inventories like EDGAR, policy analysts can identify emissions-intensive areas, trace the emissions to specific subsectors, and perform benefit-cost assessments of alternative technologies for emissions reduction.

The second part of the paper explores the potential contribution of Sentinel-5P data to the selection of priority areas. The value added of satellite-based methane observations can only be judged after a round of comparative empirical work. In this paper, we contribute with a pilot study of CH4 emissions from irrigated rice production, which accounts for about 10% of agricultural
originated abroad and the part that was originally produced by _s_ itself and then re-imported.

Indeed, by exploiting Inter-Country Input-Output tables, gross exports can be broken down according to the country that initially produced each component. Koopman et al. (2010) use the global Leontief inverse matrix to trace back the total gross output produced by each country _j_ to deliver one unit of country _s_ exports ( **B** _js_ ), and the related value-added shares ( **V** _j_ ). The part that originated in country _s_ is referred to as the ‘domestic content of exports’ ( **DC** _sr_ ), whereas the remaining

> 3Hummels et al. (2001) measures can be computed without resorting to an Inter-Country InputOutput database since they were developed for national Input-Output tables that distinguish only between imported and domestic inputs.

> 4In particular, later in the paper we show that the **VS** indicator is a good measure of the participation of a country in the downstream phases of international production chains. Moreover, the complementary part of the **VS** share of exports is one of the possible measures of domestic value-added embedded in exports (see also Johnson, 2018, on the same point).

6
from monopolistic practices move away from a ‘quiet life’ of limited innovation (Hicks, 1935, pg. 8).

Interestingly we do find that industries targeted by closed cases appear to be having an increase in the variance of revenue productivity. This is consistent with the evidence that closed cases have increasing informality. Since informal firms are numerous and less productive, greater informality increases productivity dispersion (Levy Algazi et al., 2018). At the same time, these industries also experience an increase in the correlation of market share and revenue productivity, which can reflect a greater mass of informal low-productivity firms with little scale or market share.

One concern with these results is that measures of TFPR may conflate profitability and productivity. Foster, Haltiwanger and Syverson (2008) distinguish TFPR, which is measured using revenues, from total factor quantity productivity (TFPQ), which is measured using physical output. If high markup firms are also the most productive, consumers will substitute away from high markup firms, and aggregate productivity will be less than if all firms had the same markup. Hsieh and Klenow (2009) show that it is possible to infer price vs. quantity, and therefore TFPR vs. TFPQ using an assumed demand function. Though only sales (revenues) are observed in the Economic Census, establishments with high physical output must have a lower price to explain why buyers would demand more 31
**

Typically, technology-identifying SVARs have been applied to quarterly datasets. Data shortcomings for EMDEs (with typically less than 10 years of quarterly data on employment or productivity) pose severe constraints. Hence, annual data are used to estimate the VARs. This choice significantly lengthens the period over which the VAR is estimated for many EMDEs. The time span of the data is critical for identifying technology shocks as those that drive long-term developments in productivity. Separately, the literature typically uses total hours worked. However, this type of data is largely unavailable for EMDEs for sufficiently long time spans. Instead, the estimations here rely on employment to ensure the comparability of results.

The VARs are estimated over the maximum length of data available for each country. Data on macroeconomic aggregates such as GDP and employment are taken from the World Bank’s World Development Indicators (WDI) database and The Conference Board’s Total Economy Database 8
MONGOLIA'S INFORMAL SECTOR Reasons for Growth _22_

# **Access to Premises? Not a Problem for Informals**

54. Informal operations, at least in Mongolia, rarely require a permanent place of doing business. Vendors at the various markets stake out their territory one day at a time, and informal transport carriers go where the market demands. Only a kiosk requires a permanent site. But even in the case of kiosks, - the amount of land needed is no more than a few square meters they are granted a place to establish operations as part of the licensing process. By contrast, formnal operations generally require a permanent place of doing business, and the supply of such premises is limited in Mongolia. There is still no private ownership of land,<sup>65</sup> and urban building permits are difficult to obtain.

# **Small Scale Means Independence from Formal Banking**

55. A significant barrier to entry faced by formal sector entrants is that credit is expensive and hard to find. Due to the scale of operations, however, informals can usually finance their activities through family savings or by borrowing from relatives. A sample survey of 30 informals<sup>66</sup> found that 20 had started their operations with personal or family savings, or by borrowing from family or friends with no interest; 8 had borrowed from family or friends with positive interest; 1 had borrowed from the organization where her husband worked; and the other, a street vendor of soft drinks, began her operations by borrowing from a pawn shop. This last case was the closest connection to formal finance - found during the survey none of the informals surveyed used commercial banks in any way.

56. Despite the relative ease of entry afforded informals by the limited need for finance, we must - remember that the informal sector defies stereotyping the average experience is not the universal experience. Although only about a third of the informals surveyed began their operations by borrowing money, those that did
decisions and careers in order to prevent their access to the sources of rents (e.g. natural resources) when political checks and balances are low and electoral competition is absent.

As in Keefer and Knack (2007) we adopt the measure checks and balances cum elections from the Database of Political Institutions (DPI).<sup>16</sup> This measure is a function of the number of parties in the government coalition (for parliamentary systems), whether the president's party has a majority in the legislature (presidential systems), whether elections are governed by closed-list or open-list rules (with the former granting more authority to the heads of parties), and of the DPI's legislative index of electoral competitiveness.

The use of formal checks and balances presents, however, some limitations. Since it is only one of many possible political determinants of low institutional quality, the results are unlikely to be as strong as for quality of governance. Moreover, this measure draws on a fixed view of organization of government and some checks and balances may be hard to observe.

We would expect both quality of governance and checks and balances to show little variation over time and great variation across countries. However, the relatively wide time span allows the within standard deviation to be almost half of the across standard deviation (2.6 against 5.6) for quality of governance<sup>17</sup> and nearly the same for checks and balances (1.3 against 1.0), justifying the panel analysis.

A set of other factors is likely to impact the quantity and the volatility of public investment. As observed in Knack and Keefer (2007), left-leaning governments might prove more prone to intervene in the economy and favoring more redistribution as opposed to right governments, therefore increasing the volatility of public investment. Thus, a dummy variable that takes the value one if the largest party in the legislature is coded in DPI as left-leaning, and zero otherwise, is used
- 27 -

Equation (2) can be estimated by a generalized count regression model, i.e., zero-truncated negative binomial model, because the observed number of bidders _num_ is a positive integer and typical of count data (e.g., Li and Perrigne, 2003; Li and Zheng, 2006; Ohashi, 2009). The bidder participation is assumed to depend on security conditions, auction design, contract size, and other project-specific technical characteristics in _X_ . As discussed, the impact of security issues is of particular concern in Nepal. Aggravating security might be discouraging firms from applying for public tenders. _Z_ is a vector of instrument variables that particularly affect the firms’ entry decisions. The equation can also be estimated as a first stage regression, when the bid function is estimated with the number of bidders instrumented directly by _Z_ .

Another approach to estimate the bidders’ entry strategy is a binary probability model where each bidder’s decision about whether to apply for a public tender is regressed on explanatory variables. Letting a binary variable, _entryit_ , be 1 if bidder _i_ participates in auction _t_ and zero otherwise, the probability of bidder _i_ ’s participating in auction _t_ is: In this case, bidder-specific characteristics, such as _dist_ and _ethnicity_ , can be incorporated in the model.

Following the endogenous auction literature, three instruments are considered for _Z_ : (i) the number of prospective contractors who can potentially apply for each tender, (ii) costs of bidding documents, and (iii) the length of bid preparation periods.

The first instrument is the number of firms who bought the bidding documents (denoted by _bdnum_ ). This is considered as the maximum number of contenders that could participate in each auction. The underlying idea is the same as the use of the number of plan holders or
7,778,000 records, corresponding to one year of monthly bills for 649,000 residential customers;<sup>15</sup> data from AAWSA included 4,368,000 records, corresponding to one year of monthly bills for 364,000 residential customers.<sup>16</sup> The billing records of both EEU and AAWSA included customers with connections for the exclusive use of family members and for customers that share the service with other households, and they also included customer identification numbers. During the household survey, we collected utility customer ID numbers from respondents whose households had EEU and AAWSA connections; thus we were able to match the data in the utilities’ billing records with the socioeconomic information collected in the household survey.

# **_Collection of information on the costs of electricity and water supply services_**

To calculate the total average cost of electricity and water services, we collected data on each utility’s capital and operation and maintenance (O&M) costs, production levels, and losses during production and distribution. From the EEU we collected information on various departments (e.g., Projects Office, Budgetary Control Office, Finance Department) related to generation, transmission, and distribution. From the AAWSA we collected data on the Planning Department and the Project Finance Department; these data enabled us to estimate the costs of water and sewerage services separately.

# **_Sampling_**

Figure 1 summarizes the different electricity and water service household situations encountered during our fieldwork in Addis Ababa, and it shows the households that were included in (or excluded from) our sample of 366 households with private electricity and private water connections. The first-

> 15 A small number of residential customers in Addis have prepaid electricity meters. Such households are excluded from our sample.

> 16 <mark>Eight AAWSA billing offices serve the various geographical locations in Addis Ababa, and</mark> each office provided us with their customers’ billing records.

8