rafmacalaba/data-use-mentions-extended huggingface dataset:
# Annexes

## Annex 1: Methodological notes

### **Poverty indicators**

Official absolute poverty is estimated based on comparison of disposable income of the household with its budget of subsistence minimum. Disposable income is calculated based on consumption of the households, privileges, and in‐kind income. Budget of subsistence minimum of a household is estimated based on the composition of the household and budgets defined for specific age groups: children age 0‐3, children aged 3‐ 6, children aged 6‐18, working‐age population, retirees.

Moderate poverty is estimated based on the comparison of per capita disposable income with the minimum consumer budget for a family consisting of 4 persons (2 working‐age adults and 2 children).

### **Social groups definition**

We define working‐age individuals who reported income in the form of wages or from self‐employment as employed. We define the fully employed to be those who reported employment related income exceeding BYN 100 for three months in a quarter. Other individuals who reported employment related income are considered partly employed. Non‐employed are those working age individuals who did not report any employment or self‐employment related income. Hence, this group includes the unemployed, inactive population, students, housekeepers, individuals on parental leave.

### **Consumption in real terms**

Consumption of separate goods and services is reported in HBS files in monetary terms, and we convert it into real terms using the consumer price indices of related goods. Subsequently, all goods and services were aggregated into several groups:

- Consumable goods

- Food

- Other consumable goods

- Food related goods and services (alcohol and tobacco, eating out, cultivation of land plot and animal breeding)

- Non‐food goods (clothes, footwear, textile, maintenance of private vehicles)

- Durable goods (household goods and appliances, furniture, purchase of private vehicles)

- Services

- Education and health

- Utilities and public transport

- Other services (
also using data for Bangladesh. Their regressors were measures of accessibility, flooding and the moisture content of the soil. However, these variables had negligible explanatory power. In their regression for Grameen Bank branches, none of the regressors were significant and the adjusted R<sup>2</sup> was 0.04. We suspect that the main reason for this poor explanatory power is the omission of measures for the expected welfare gains from bank activity in different locations, as determined by the relevant socio-economic characteristics of the customers living in each area.

We offer a method for estimating the unexploited gains from non-farm rural development. The method allows us to collapse a potentially large number of determinants of the gains from switching from farming to non-farm activities into a single measure, which can be tested as an explanatory variable for bank branch location. We find that this measure has considerable explanatory power in understanding bank locations in Bangladesh.

Section 2 presents the theoretical model of branch placement which motivates our empirical work. Section 3 discusses our methodology, while section 4 describes our data. The estimated gains from sector switching are presented in section 5, while section 6 looks at the their implications for explaining branch placement. Section 7 concludes.

# **2 Theoretical model of branch placement**

To motivate our empirical work, we begin with a model of the optimal spatial allocation of bank branches. We are clearly dealing with banks which do not necessarily aim to maximize profits. We assume that the bank maximizes a weighted sum of expected utilities, where the weights embody the bank's distributional goals. This characterization of a nonprofit bank's welfare objective would seem to encompass the objectives that one would expect to find in this 4
this would suggest that the collective choice channel accounts for the results. If income and inequality mattered in untreated but not in treated municipalities, this would suggest that the political power channel accounts for the results. If they had significant but distinct effects in treated versus untreated municipalities, this would suggest a role for each channel.

Such a policy experiment is not feasible. However, following Brazil’s return to democracy in 1985, many municipalities put in place an institution that did something similar: Participatory Budgeting (PB). Under PB, citizens vote on how to use a share of municipal revenue designated for their neighborhood, and elect neighborhood representatives to make municipality-wide spending decisions. Citizens deliberate and negotiate over the distribution of public resources. Policymakers are required to publicize budgets and expenditures to promote transparency (Boulding and Wampler 2010). Case studies from Brazil suggest that PB increases participation of marginalized groups, leads to more pro-poor expenditures, and increases government accountability (Souza 2001). By dividing the budget into shares, PB mechanically ensures a quite equal distribution of power (unless the rich somehow live in all neighbors, and dominate policymaking).

According to Melo (2009), “One of the most salient aspects of PB is that most experiences originated in municipalities where an elected mayor lacked the support of the local legislative chamber (municipal council).” The legislature can veto any budget, but finds it politically difficult to do so when the budget comes from the people, through PB. When divided government paralyzes policymaking, mayors implement PB to get the policy process moving again. More often then not, PB has remained in place following its implementation.

I use data on which municipalities had PB at some point during the sample period. The data are available for all municipalities with a population over 50,000—about 14% of the sample. I estimate the baseline model for three sets of municipalities: large municipalities (population over 50,000), large municipalities with PB, and large municipalities without PB.
VI. ADDITIONAL EVIDENCE This analysis aims to shed light on why PYP did not reduce CO by evaluating the effect of PYP on other outcome variables associated with car use: gasoline consumption, vehicle registrations, and vehicle sales.

# _The Effect of PYP on Gasoline Consumption_

When analyzing gasoline consumption, it is important to distinguish the PYP effect from the impact of gasoline prices, as this provides insights about the relative effectiveness of market and nonmarket policies. This analysis utilizes data (January 1996–September 2010) on monthly gasoline consumption (regular and premium) obtained from the Finance Agency of Bogotá, gasoline prices published by Ecopetrol,<sup>25</sup> and GDP and total population figures reported by the National Department of Statistics (DANE). The consumption equation is as follows: �� ��������1� ������2� � ���� ����� ����� � D ��� � �� , �2� where _C_ t is the total gasoline consumption per capita in logs at time _t_ , PYP1 is an indicator variable equal to one from August 1998 to January 2009 and zero otherwise, and PYP2 is an indicator variable equal to one after February 2009 and zero otherwise. _P_ t is the gasoline price and _M_ t is the GDP per capita, both in logs at time _t_ . Furthermore, ���� is a polynomial time trend, �� is a group of month-of-year dummies, and �� is the error term. �� and �� measure the effect of PYP on consumption. This specification is closely related to the work by Eskeland and Feyzioglu (1997a) for HNC in Mexico City, although the estimation here includes a polynomial time trend to 25. Monthly prices from 1999 to 2010 were obtained from http://www.ecopetrol.com.co/precios.htm. Information from the digital news archive _El Tiempo_ (www.eltiempo.com)
Sales prices received by non-beneficiary households from our endline survey suggest prices received for the two main warrantage crops were higher in high exposure villages. Figure 5b plots the distribution from which we observe a clear rightward shift of the average standardized price index in high exposure villages. Increased access to warrantage also lowered the variance in prices received by non-beneficiary households from 1.089 in Low Access communities to 0.831 in high exposure communities. These moderate gains do not however translate into discernible positive spillovers with respect to the sales value of warrantage crops in high exposure villages. The coefficient is positive, but noisy (Table 6, Panel B).

In consultations with implementers and local policy makers, we repeatedly heard concerns that warrantage may undermine village relations, particularly with respect to sharing in times of food insecurity. To address this concern, we examine whether non-beneficiary households in high exposure villages changed how much of their warrantage crops were sold, consumed and/or given away. Table 5 Panel B provides suggestive evidence supporting these anecdotal concerns, as nonbeneficiary households in high treatment communities do share more with their neighbors. The estimated effect size is small in magnitude, 3.8 percentage points, but is a meaningful change as it translates into these households sharing 177% more than those in low access villages. Despite the large treatment impact, the overall levels of sharing are very low, suggesting that the overall impact on the affected households is limited. To complement these findings, we also examine the three food security indices (Table 6), which suggests that the negative impacts of sharing more with others by non-treated households is negligible.

We pursued this concern further and examined whether various proxies for village relations are affected. We used several proxies similar to those measuring confidence in warrantageassociated institutions; that is, whether the household had confidence storing crop production within their home village, and whether a village member would return a lost wallet. In addition, we conducted trust experiments to elicit an incentivized trust indicator. Within a sub-sample of
problematic unless separate data exist that can measure off-budget aid such that equation (1) can be estimated rather than equation (4). Off-budget aid is likely to be sizable in many countries and to vary both between and within countries. Thus, the effects of its inappropriate treatment may be important. With regard to aggregate aid, Fagern ̈as and Roberts (2004a) show that OECD DAC figures for Uganda exceed the external financing recorded by the government by substantial margins (in some years, in excess of 10% of GDP). In Zambia, the gap is as wide as 20-40% of GDP in some years (Fagern ̈as and Roberts, 2004b). In both countries, the amount of off-budget aid varies substantially over time. Thus, for aggregate aid, _σOFF_<sup>2in (3) is unlikely to be small relative to</sup><sup>_σ_</sup> _ON_<sup>2.For Senegal,Ouattara (2006) finds that OECD DAC aid</sup> during the 1990s was, on average, twice as high as the aid reported by the local Ministry of Finance (12% vs. 6% of GDP, respectively), although his plots appear to suggest that the variation in aggregate aid over time is predominantly driven by on-budget aid.<sup>5</sup> The correct method of assessing whether earmarked aid is fungible involves separating on- and off-budget sectoral aid and comparing the marginal effect of on-budget aid on recipient sectoral spending to 1 and the marginal effect of off-budget aid to 0. The aim of this paper is to apply this method in the education and health sectors using a newly constructed dataset of disaggregated aid disbursements. Before presenting the empirical 4
as well as the distribution across sectors and by employment type (Panels A-D), has been stable over the years. This is likely linked to a highly degree of informality which absorbs shocks to the labor market and allows for relatively fast adjustment of wages in the short to medium run (Loayza & Rigolini, 2006). Consequently, while important, these dynamics are unlikely to strongly impact short-term projections in a context like Pakistan.

Third, we assume there are no changes in the composition of households. That is, we keep the HIES 2018/19 sampling weights constant across the period, not allowing for population growth, the labor force, or changes in the territorial distribution of households.<sup>14</sup> This is a strong assumption, yet the results appear to be robust.

> 13 <mark>In Pakistan, a passthrough often used 0.86 as the consumption share is around 86 percent in total GDP.</mark>

> 14 We implemented several tests to update the 2018/19 sampling weights to reflect the information on population size and distribution from latest population census. All of our results were robust to these changes.

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through at least three channels:</sup> substitutability of inputs, product demand elasticities,<sup>and the degree of collusion in the</sup>

> 2 Micro- panel data offer more precise estimates<sup>of demand functions, allow factor prices to be taken as</sup> exogenous, permit compensation for unobserved<sup>heterogeneity, and permit distinguishing changes in</sup> parameters arising from firm behavior versus<sup>the entry end exit of firms into the industry. Despite those</sup> benefits, and probably due to the larger efforts<sup>associated with generating micro data sets, only a small</sup> fraction of the nearly two hundred empirical studies<sup>of labor demand functions reviewed by Hamermesh</sup> (1993) are based on establishment-level microeconomic<sup>data.</sup>

2
A further hypothesis relates to potential sample selection bias in the data underlying <u>Figure 1</u> and similar figures in the literature. We will be using panel data on SP spending across countries and over time. However, it is an unbalanced panel, in that not all countries have data in all years. The potential for a non-random pattern in the availability of data suggests another reason we see a rising SPEC. It is a plausible assumption that the latent selection process increases the observed mean share of income devoted to SP spending conditional on mean income; all that is required for such positive selection is that higher SP/GDP values are more <mark>likely to be reported. However, that is not sufficient to explain why we see a rising SPEC. For</mark> that step in the argument, we draw on the literature on estimating models with sample selection bias.<sup>19</sup> Following that literature, let g(p) denote an additive control function for the selective <mark>reporting, which is taken to be some suitably smooth function of the probability of being selected</mark> ′ <mark>into the sample, denoted p.</mark><sup>20</sup> <mark>Then, with positive selection (g (p) > 0), the key issue in the</mark> present context is the sign and size of the correlation between the control function for selection and GDP per capita.

In thinking about that correlation, it is instructive to consider a simple economic model of how selective reporting arises in this context. Consider the costs and benefits of providing public spending data. (Without loss of generality, these can be taken to be the costs and benefits as perceived by public decision makers charged with this task.) It is a plausible assumption that the marginal cost (MC) to a country’s government of collecting, processing and disbursing such data rises with the
EU<br>by Main Sector<br>Agriculture Mining and Quarrying<br>800 JC JC<br>600<br>400<br>200<br>0<br>2000 2005 2010 2015 2020<br>Manufacturing<br>800 JC<br>600<br>400<br>200<br>0<br>2000 2005 2010 2015 2020<br>Year<br>Export Value in M US$<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Integrated Trade Solutions (WITS). The vertical red dotted line splits the periods before and after the implementation of the JC.

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shown by Graetz and Michaels (2018).

In sum, robot adoption has increased significantly over time, although at different speeds in different countries and sectors, which is convenient for identification. Figures A1 and A2 in the Appendix display clearly the extent of variation in robot adoption across sectors, countries and time. In addition, the strong correlations of robot usage with both replaceability and initial GDP per capita suggest the triple interaction between the degree of sectoral replaceability, the initial GDP per capita and the price of robots (proxied by the global robot stock) is a suitable instrument for robotization. Indeed, Figures A3 and A4 in the Appendix show that the change in robot density observed between the initial and final years of our sample period is positively 16
use unit values defined as the ratio of import value to import weight by country-productyear. Moreover, we measure the import share for a specific set of firms, those that are also exporters of commodities, identified based on a database of all firm-level export transactions recorded by customs authorities in the same 53 countries.

Natural resource dependence. We employ two measures capturing natural resource dependence: (i) the share of commodities in total merchandise exports from the World Development Indicators (World Bank, 2024a); and (ii) an index relying only on fluctuations in world commodity prices (Gruss and Kebhaj, 2019).<sup>6</sup> Means and standard deviations of these variables are reported in Table S2. Commodities and natural resources are defined broadly to include food (e.g., animal and vegetable oils and fats, beverages, fruits and vegetables, live animals); fuels (e.g., mineral fuels, lubricants, related materials); and ores and metals (e.g., crude fertilizers, metalliferous ores, non-ferrous metals).

Measure (i) captures both price and quantity variation in commodity exports, and is our preferred measure of natural resource dependence. In some specifications we separate this into exports of different types of commodities: food, fuels, and ores and metals. Measure (ii) captures only price variation. The advantage of this measure is that it is plausibly exogenous, since it excludes quantities determined by local production costs and world prices should not be affected by local exports as economies in our sample are small relative to the commodity markets they export to. A disadvantage of this measure is that price variation alone does not capture the dependence of an economy on natural resources for export revenue. In sum, there is a trade-off between ”quantity” (export share) and ”quality” (price index) of variation in natural resource dependence.

6For country c in year t this index is given by<sup>�K</sup>
_Figure 9: At-risk of poverty rate (measured via the equivalized income reported in the HBS) and energy poverty rate (low-income-high-cost measure)_ _Figure 10: Overlap of at-risk-of-poverty rate (measured via the equivalized income reported in the HBS) and energy poverty rate (low-income-high-cost measure)_ <!-- Start of picture text --> 30.0 100%<br>25.0 80%<br>25.0<br>71.5<br>60%<br>20.0<br>40%<br>3.5<br>20%<br>15.0 21.4<br>0% 3.6<br>10.0<br>7.1 Neither nor<br>Only energy poor (LIHC)<br>5.0<br>Only monetary poor<br>0.0 Energy (LIHC) and monetary poor<br>At-risk-of poverty Energy poor (LIHC)<br><!-- End of picture text --> _Notes: The at-risk-of-poverty rate is calculated using household income from the 2021 household budget survey and, therefore, does not correspond to the official poverty estimate. The resulting estimate is slightly above the official poverty estimate from the EU-SILC (22.9% in the 2022 survey year and 2021 income reference year). A household is considered energy-poor if two conditions are met: 1) if they have an energy expenditure above the national median, and 2) if the remaining income after they spend that amount (in equivalized terms) is below the official at-risk-of-poverty threshold. By the definition of the LIHC, all monetary income poor are also energy-poor if they have energy spending shares above the national median._ _Source: Own estimates based on HBS (2021)._

**The overlap between energy and monetary poverty varies considerably between the different**

**measures.** By construction, all monetary poor are energy poor in the
%<br>36.1|**42.0**|36.7|
|Revenue collection|% sales<br>97.2|**70.0**|100.2|
|Distribution losses|% production<br>33.8|**40.8**|32.8|
|Cost recovery|% operating expenses<br>covered by revenues<br>107.3|**64.4**|82.3|
|Total hidden costs|% of revenue<br>112.9||202.3|
|U.S. cents per m<sup>3</sup>at 10 m<sup>3</sup>|**DRC**|Fragile states|Other developing regions|
|Residential tariff|4.6|42.0||
|Nonresidential tariff|0.6|150.0|3.0–60.0|

_Source:_ Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.** 20
we note that the algorithm could easily be updated and re-run when new data become available. With the increasing prevalence of open-source data in development contexts (e.g. AidData.org, GRID3), these data gaps are likely to be filled before the next census.

Manual digitization of EAs may be still a preferable option in some cases, where no data on roads or natural boundaries are available, or where local context weighs significantly in terms of choosing EAs. For example, areas where security or gerrymandering may be important considerations will require more hands-on control of EAs to include or exclude specific houses or neighborhoods. In addition, manual digitization does not rely on ancillary data sets as long as high-resolution satellite imagery is available, the EAs can be manually digitized. However, manual digitization is prone to many errors including leaving gaps, unclosed EAs and greater irregularity in actual population within EAs. Importantly, intensive multi-resource involvement in manual EA delineation must be considered.

Although the actual household size per urban EA was one of the inputs of the population modeling, deviation still exists between actual population (based on HH size) and estimated population based on the modeling in manual urban EAs. This result may be explained by the fact that the modeling considered additional recent data sets besides the urban HH information to distribute the population within the grid cells (see appendixes 2 and 3). In addition, there were discrepancies between PESS 2014 HH number within urban EAs and regional population estimates. This means that, if HH size per EAs were considered to generate the total population per region, similar total population on the region could not be achieved as it was reported in the PESS 2014 particularly in Gedo. In addition, the very high population size based on the gridded population data set in the manual urban EAs (Figure 6) could be a result of their large spatial coverage. For instance, there is a case that an urban EA contains 1,500 HH (The HH were listed in the field based on PESS 2014)