rafmacalaba/data-use-mentions-extended huggingface dataset:
reason to believe that the provision of services by donors and non-state actors could either strengthen or weaken citizens’ deference to government. I will identify the conditions under which these two scenarios are likely to occur. On the one hand, citizens may be less likely to support the government with deference to its laws and regulations when they credit non-state actors or donors for service provision. The provision of services by donors and nonstate actors is likely to prompt citizens to question why they should pay taxes to a government that is not providing them with anything in exchange. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens’ legitimating beliefs and their willingness to defer to governmental laws and regulations if citizens view their government as essential to leveraging and managing these external resources.

I assess these competing hypotheses using multi-level analyses of Afrobarometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows us to analyze associations between donor and nonstate actor service provision and the sense of obligation to comply with the tax authorities. Third, I assess the relationship between the provision of services by donors and non-state actors and citizens’ willingness to defer to two additional authorities, the police and courts. The findings yield support for the hypothesis that the provision of services by donors and non-state actors is strengthening, rather than undermining, the relationship between citizens and the state.

# **2 A Model of Legitimating Beliefs**

I extend the logic of the fiscal contract to consider the implications for citizens’ perceptions of their government’s legitimacy when actors other than the state are involved in providing the basic services that are at the heart of the fiscal contract relationship. My conception of government legitimacy and the reasons citizens adopt legitimating beliefs derives from the work of Weber (1968). I use the concept of legitimacy to denote acceptance of the right of governors to rule; its effect is an increased likelihood of quasi-voluntary compliance with governmental rules and regulations. Quasi-voluntary compliance is compliance motivated by a
population are from China City Statistical Yearbook.<sup>14</sup> All time-varying variables are adjusted to the 1990 constant price with the province-specific GDP deflators from the China Statistical Yearbook.

Local party secretaries, with the greatest _de jure_ and _de facto_ power at all government levels in China (McGregor, 2010), have the ultimate decision power on the privatization of local SOEs. These officials are thus our focus. For the complete list of the 294 prefectural cities in China, we could collect information on 883 party secretaries in 282 cities from 1998 to 2009.

Figures 1a and 1b illustrate the age pattern and the age-of-promotion pattern of the citylevel party secretaries in our sample of 2,631 city-year observations. The age of city-level party secretaries exhibits a hump-shaped distribution from 39 to 61, peaking around the average and the median age of 52. Most local party secretaries, with only a few exceptions, leave their positions in their late 50’s. The incidence of promotion falls dramatically after the age of 55 and drop to zero once a politician reaches the age of 58. Overall, these observations corroborate our earlier discussion of the role of local politicians’ age in their chance of promotion.

Panels B and C of Table 1 describe the summary statistics of all main and control variables.<sup>15</sup> About 88% of the city-level party secretaries have higher education. A small number of local party secretaries have experience working in the central government (4.1%), while most of them have experience working in provincial governments (55.1%). The SOEs in our sample generally displayed poor performance: the average leverage ratio is as high as 72%, whereas the return to asset (ROA) is low, with the averages being -0.3%, making slight losses.

# **4. Effects of Politicians’ Age on Privatization**

We provide evidence that privatization decisions are closely related to the age of city party secretaries. We first
has sales of $12,312, 6 workers,<sup>5</sup> with a labor productivity of $1,950 per worker, and has been operating for 15 years. Figure 1 depicts the trends of our outcome variables, labor productivity, sales, and employment.

critical aspects of the data. The first aspect involves classifying sectors based on their energy intensity following the Climate Policy Relevant Sectors (CPRS) framework introduced by Battiston et al. (2017). The CPRS taxonomy identifies energy-intensive sectors through a multifaceted lens, considering (i) the emissions produced by each sector's economic activities, (ii) the sector's contribution to the Greenhouse Gas (GHG) emissions chain, (iii) the sector's engagement in specific policy processes, including its lobbying capacity; and (iv) the transition risk associated with the sector, which inversely relates to the level of fuel substitutability. A sector is deemed energy-intensive if its activities result in emissions, utilizes fuel (or a mix of fuels) as a primary input, and has a low potential for substituting these fuels. The sector classification by energy usage is systematically outlined in Table A3 in the Annex.

1 For each country, the sample frame was based on the statistical data from the National Statistical Committees of each country at the time of the survey's first implementation. For the later survey waves, the list of companies was updated using lists of business associations and internal lists of entrepreneurs from the survey firm.

2 It is also worth acknowledging that firm weights are unavailable, so the sample is not representative. However, the estimations control for firm characteristics, muting to some extent the composition effects.

3 Sales per worker is a widely used proxy for labor productivity, offering a practical measure of output per unit of labor input, especially when value-added data are unavailable. Bloom et al. (2010) emphasize its effectiveness in capturing labor efficiency and management practices, while Syverson (2011) notes that it
Finally, to the extent that longer trade credit is a way for the supplier to guarantee product quality, we should see small young suppliers, who might suffer the greatest distrust of the quality of their product, most reluctant to offer discounts for early payment. Thus the data on early payment discounts might shed light on the relative merits of the non-financing explanations.

We start by charting the average number of contracts with discounts for suppliers and buyers of different characteristics. We only consider the 34 buyers who receive at least one discount (it may be that the remaining buyers do receive discounts but do not report them, and therefore we do not consider the data reliable for them).<sup>13</sup> Suppliers offer discounts for early payment more frequently to small buyers (Figure 2a and 2c) as well as to non-investment grade buyers (Figure 2b and 2d), consistent with the use of discounts as a way to mitigate the risk of default. Unlike the predictions of the bargaining power theory, however, small suppliers are least likely to offer early discounts to large buyers. Instead, they seem to conserve discounts and offer relatively more to small and non-investment grade buyers (Figures 2a and 2c). A similar pattern can be seen for non-investment grade suppliers – discounts are targeted at riskier buyers.

> 13 Our main results are robust to the inclusion of these buyers in the model that includes supplier fixed effects and adjusts standard errors for clustering at the buyer level. Buyers who never offer discounts are dropped from the model that includes buyer fixed effects.

18
Senegal are also far from the frontier. On a scale from 1 to 5, the sector-specific technology index is 1.59 for the extensive margin, and 1.27 for the intensive margin. Senegalese firms are less likely to adopt advanced sector-specific technologies, compared to general technologies. In addition, the intensive margin being close to 1 implies that even if firms adopt slightly advanced technologies, they do not fully use such technologies and remain using basic technologies in their production processes.

Senegal faces relevant challenges to improve the adoption of technologies by local firms. Our analysis highlights three groups of these challenges, based on perceived obstacles for the firms, as well as the facts associated with these obstacles: i) access to finance; ii) lack of capabilities; and iii) access to markets and uncertainty.

> The paper is structured as follows. Section 2 describes the level of adoption using standard measures of general-purpose technologies, such as access to electricity and Internet,

> 3The coverage of technology questions in standard firm level datasets is almost negligible, even for highincome economies.

> 4The FAT index is described by Cirera et al. (2020)

3
.580)||||
|Dropout/never enroll|||||
|treatment x post|-0.007<br>(0.023)|-0.047**<br>(0.023)|0.041|1,711|
|treatment x post x Goal|0.042<br>(0.036)||||
|Use contraceptive|||||
|treatment x post|-0.174**<br>(0.078)|0.057<br>(0.071)|0.620|1,711|
|treatment x post x Goal|0.078<br>(0.105)||||

_Notes._ In Panel A, the reported coefficients are _β_ 1, _β_ 2, and _γ_ 1 from an adapted version of equation 1 where the outcome variable “With same partner as baseline” is the change in partnership status from baseline to endline. Panel B presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1. Age is the average age in years of all sexual partners listed; Dropout/Never Enrolled is the share of sexual partners listed whose enrollment status is dropped out or never enrolled in school; and Use contraceptive is the share of sexual partners that the female reports use contraceptives. The Quality index is generated by taking the unweighted mean of the indicators after they have each been recoded so that positive coefficients indicate improved outcomes and standardized at baseline and endline separately to the mean and standard deviation among females in ELA only communities who were not assigned to the _Goal_ treatment. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *.

_Source._ Panel A: Female data, outcome from endline, controls from baseline, balanced panel. Panel B:
. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table D.10. Effects of Guerrilla Territorial Control on Main Outcomes for Individuals Who Have Always Lived in the Same Place

||Wealth Index|Years of Education|Literacy Rate|
|---|---|---|---|
||(2007)|(2007)|(2007)|
||(1)|(2)|(3)|
|Guerrilla control|-0.132***|-0.402***|-0.0261***|
||(0.0356)|(0.112)|(0.00563)|
|Observations|3,621|3,633|3,633|
|Bandwidth (Km)|2.266|2.266|2.266|
|Dependent mean|-0.0280|6.785|0.817|

_Note:_ The table presents main results for the sample of people who have always lived in the same place. The unit of observation in all Columns is the census tract. The information was obtained from the Latin American Public Opinion Project survey (LAPOP). Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _
period (see Figure 4). Thus while agriculture remains a source of income for 77 percent of households in Uganda and 87 percent of rural households, by 2005/6 the overwhelming majority of households were complementing it with income from other activities which are usually more productive – meaning that they added activities over the period, rather than switching sectors entirely. In urban areas, the expansion of wage employment was an important driver of income diversification, but in rural areas it clearly was NFE.

The shift appears more dramatic in the household level analysis because most households still rate their enterprises as a secondary activity, (especially in rural areas). So the high growth in non-farm enterprises is not reflected as dramatically in the employment data above, which shows only primary employment. This leads to the perception of the stagnation in the structure of the labor force when structural change is measured in the traditional way, e.g. using primary employment data. Note that as we have four types of activities, we actually have combinations, even though we only show the top nine in the figure. The expansion in the ̳other category refers to these omitted combination, including the households with four types of income.

Uganda‘s third structural transformation was within the agricultural sector.<sup>11</sup> Since 1992, Ugandan farmers have gained productivity, diversified their crops, and become more commercialized. In 2005/6, household survey data suggests that most Ugandan framers produce four or more crops during the year, including several for the market. Among the top 25% farmers, the average sale to production ratio was 50%. By 2005/6 many farmers depended on either crop sales or non-farm income for their staple foods instead of their own production – roughly 40 percent of all farming

> 11 See World Bank, 2011b for further discussion of the evolution of Uganda‘s agricultural sector.

19
lost to economic outcomes

Pollution impacts the economy on the one hand by reducing the labor force due to pollution-related deaths, and reducing total labor supply (measured in hours worked) by the hours lost by those workers who suffer from pollution related disease but do not die.

> 17 The disability-adjusted life year, , is a summary measure which combines time lost through premature death and time lived in states of less than optimal health or “disability”. DALY, for a specific cause , sex , age and year is defined as: DDAAWWYY cc ss SS ,SS EE,ff,bb,tt EE,ff,bb,tt EE,ff,bb,tt where YLL are years of life lost and YLD are years lived with disease. The values for DDAAWWYY = YYW + YYWWDD , and are obtained from the GBD results tool (IHME 2018). DDAAWWYYss YYW YYWWDD

> 18 The population attributable fraction ( ), is the reduction in incidence that would be observed if the population were entirely unexposed, compared with its current exposure pattern. for each mortality type and age range is calculated as PPAAFF PPAAFF = ∑ ∑CCii=1 ii,bb∗�R∗cc <u>�R</u> EE,bb<sup>−1</sup> + 1<sup><u>�</u></sup> ~~,~~ SS EE,bb CC PP PPAAFF ii=1 ii,bb EE,bb<sup>−1�</sup> where R PP EE,bb<sup>is the relative risk for causeccand age groupSSandPP</sup> ii<sup>is the percentage of the population exposed to level of pollutionii.</sup>

45
|**Conakry**|196|1,608,238|1,714,000|
|---|---|---|---|
|**Lusaka**|837|1,489,053|2,187,000|
|**Bamako**|478|2,967,235|2,219,000|
|**Kigali**|454|929,723|951,000|
|**Kampala**|834|3,101,666|2,577,000|
|**Cape Town**|2467|2,699,422|4,100,000|
|**Dar es Salaam**|1000|3,673,173|6,048,000|
|**Dakar**|2325|3,065,622|2,978,000|
|**Douala**|288|2,802,368|3,259,000|
|**Harare**|1454|2,273,485|1,500,000|

TABLE 1: FRAMES OF ANALYSIS The data sources used are summarized in the table below.

|**City**|**Population**<br>**Data**|**Opportunity Data**|**Transport Data**|
|---|---|---|---|
|Nairobi|WorldPop|EOA Analysis|Digital Matatus|
|Lusaka|WorldPop|EOA Analysis|WheresMyTransport|
|Kampala|WorldPop|EOA Analysis|WheresMyTransport|
|Cape Town|WorldPop|EOA Analysis|WheresMyTransport|
|Kigali|WorldPop|Rwanda Census|IGC|
|Bamako|WorldPop|Bamako Business<br>Registry|Bamako Mayor’s Office|
|Conakry|WorldPop|EOA Analysis|Plan de Déplacement<br>Urbain (EU, Min
# **4.3 Empirical results**

This section presents a second set of main empirical results. As the structural shocks from the PVAR in the last section is uncorrelated with output, they are used as a measure of austerity. In section 3, the Dabla-Norris and Lima(2018) narrative dataset only covers changes in taxation. This measure of austerity covers both changes to taxation and government spending. The following set of panel regression is estimated: where ∆ _Yit_ is output or consumption growth. The first regression looks at the general relationship between output/consumption and the structural fiscal shocks, and the second regression interacts the shocks with regime dummies, _Dit_ , to explore the presence of anti-Keynesian effects of austerity programs. _Dit_ is a debt regime dummy that equals 1 in a state of high-debt and 0 otherwise.

20
6 particularly Ecuador and Peru, were negatively affected by the warming of the Pacific Ocean observed in 1998, caused by "El Nifio" (Fig. 4)6.

Figure 2 <!-- Start of picture text --> Latin America Big 8*: Terms of Trade<br>115.00<br>110.00<br>105.00<br>0 4 100.00 III\<br>0<br>095.00<br>90.00<br>86.00<br>8s.00 ~ ~ . Md. P WV..1. . .....<br>' AeMia, &aziL Chle, Colo,ba 8aiador. Me,doo. Pev5e,s Venzuela.<br><!-- End of picture text --> Fig. 3*

**SOVEREIGN SPREADS**

<!-- Start of picture text --> Non-Latin Emerging Markets Brady Bond SpreadS<br>i800<br>1600<br>1400 M.0<br>1200<br>1000<br>0.0 .0- U'C tc~f Cr (( ) CO C ^O CO CO (00( (0 c=O<br><!-- End of picture text --> <!-- Start of picture text -->
*Source  JPMorgan<br><!-- End of picture text -->

6 The intensity of El Niflo is captured by the Southern Oscillation Index (SQI), obtained from the Australian Commonwealth Bureau of Meteorology. The SOI is based on the monthly seasonal fluctuations in the air pressure difference between Tahiti and Darwin Sustained negative values of the Southern Oscillation Index (SQL) often indicate El Niflo episodes.
<!-- Start of picture text --> a<br>Germany US Brazil Indonesia<br>UK Japan India<br>France China Nigeria<br>s<br>= o-<br>oO<br>Qo<br>a<br>a<br>a ||<br>a<br>o|<br>& -0.1- ,<br>ct]<br>nn<br>ia<br>=<br>=<br>|<br>Global distribution of temperature observations (lh<br>Global distribution of population iT il<br>Global distribution of GDP Mh,<br>r r rr s<br>a] 5 10 15 20 es25 |30<br>Annual average temperature (°C)<br><!-- End of picture text -->
**_<u>________________________________________________________</u>_**

# **Annex G.   Trade Logistics Indicators: Selected Examples**

||**Core Measure**|**Unit**|**Definition of Term**|**Sources of data**|
|---|---|---|---|---|
|1.1|Clearance Time|Days|For imports, average time taken<br>from when goods arrive at the<br>port of entry until the time they<br>are claimed from customs|World Business<br>Environment Survey<br>(WBES)|
|1.2|Longest day to clear<br>customs for imports|Days|For imports, longest time taken<br>from when goods arrive at the<br>port of entry until the time they<br>are claimed from customs|Investment Climate<br>Assessments (ICAs)|
|1.3|Clearance Time:<br>TemporaryEntry||||
|1.3.1|Clearance Time for<br>sea cargo: Temporary<br>Entry|<br>Days|Clearance time for temporary<br>entry sea cargo (LCL and FCL)|International Exhibition<br>Logistics Associates<br>(IELA)|
|1.3.2|Clearance Time for<br>air cargo: Temporary<br>Entry|Days|Clearance time for temporary<br>entry air cargo|International Exhibition<br>Logistics Associates<br>(IELA)|
|1.4|Release Time of<br>goods|Days|Normally from the time of<br>arrival of goods at the<br>port/airport/land border until<br>their release to the importer or a<br>third party on his behalf|World Customs<br>Organization :
that wildfire smoke plumes affect crop yields by changing the amount and type of sunlight available for photosynthesis rather than changing meteorological determinants of yield, we examine the extent to which smoke plumes impact weather inputs directly and how smoke plumes change aerosol optical depth (AOD).

We use satellite-derived measures of the AOD to verify that plumes of both types increase AOD relative to no plumes and that the effect of a high-density plume on daily AOD is roughly 3 _×_ greater than that of a low-density plume (Table SI2). Turning to impacts on meteorological variables, we do not find evidence that smoke plumes of either density meaningfully impact the number of growing degree days or extreme degree days during a growing season (Figure SI1).

We do find some evidence that there is less precipitation on days with plumes of either density. We also see that cloud cover is lower on days with smoke plumes, which suggests that the impact on daily precipitation may be an artifact of the construction of the smoke plume data—it is more difficult for analysts to draw smoke plumes on days with dense cloud cover, which are the same days when precipitation is most likely. At the same time, there is evidence from the Amazon that smoke plumes can directly impact cloud formation, with the specific mechanics depending 5
Nine million children are currently benefiting from the program, 1.9 million of which are in high school.

Currently, the targeting for eligibility for the benefits of the 4Ps program is aimed primarily towards alleviating chronic poverty among eligible poor households and not towards identifying those that are vulnerable to poverty. Beneficiaries are objectively selected through the National Household Targeting System, also known as _Listathanan,_ which is based from a survey of the physical structure of their houses, the number of rooms and occupants, their access to running water, and other factors affecting their living conditions (Velarde, 2018). The program has one of the most comprehensive poverty targeting databases in the world today, covering 75% of the country’s population. It has been used extensively to identify poor and near-poor beneficiaries for national and local government programs.

The rationale of scalable social protection programs is to expand coverage in the event of a disaster to include not only the poor but also some of those believed to be vulnerable to falling into poverty when exposed to a shock. The objective is to mitigate the direct impact on household asset and welfare losses in the direct aftermath of a disaster, but also to prevent some of the intermediate term impacts – including forced displacement, and negative coping mechanisms used by poor households to handle the shock – such as selling off productive assets.

This paper builds on the existing literature assessing _ex-post_ the quantitative effects of natural disasters on different dimensions of household welfare (e.g. Anttila-Hughes and Hsiang, 2013; Strobl, 2019; Ishizawa and Miranda, 2016) to make progress towards the _ex-ante_ identification of households vulnerable to poverty due to natural disasters. A wind model for the Philippines is employed to estimate local wind speeds at any particular locality where a tropical typhoon directly passes over or nearby. The estimated wind speeds are merged to the household FIES survey at the barangay level and consumption expenditures are regressed against windspeed (or a related damage index) and socioeconomic household characteristics. The paper shows that different specifications of typhoon