rafmacalaba/data-use-mentions-extended huggingface dataset:
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Zamani, M. 2004. “An Econometrics Forecasting Model of Short-Term Oil Spot Price.” Conference proceedings, 6th International Association of Energy Economics European Conference, Tehran,
# **3 Data and methods**

We set out a methodology to extend the conventional asset risk framework to a welfare risk framework, using an MDP index as a proxy of household welfare. The methodology consists of five components: (1) household MDP analysis, (2) risk analysis and quantification, (3) service accessibility analysis, (4) poverty impact analysis and (5) cost-benefit analysis of adaptation options.

The workflow of the analysis (components 2 to 4) is schematized in Figure 1.

<!-- Start of picture text --> Coastal flood<br><!-- End of picture text --> <!-- Start of picture text --> Wind<br><!-- End of picture text --> <!-- Start of picture text -->
1. We intersect cyclonic wind and flood<br>hazards with the spatial network of<br>households, agriculture and infrastructure.<br>Households & infrastructure<br>3. We quantify the disruption<br>caused by damage to critical<br>infrastructure, such as water and<br><!-- End of picture text -->

<!-- Start of picture text --> Asset damages<br><!-- End of picture text --> <!-- Start of picture text --> Infrastructure disruptions<br><!-- End of picture text --> <!-- Start of picture text --> Poverty effects<br>Poverty<br>effects<br><!-- End of picture text -->

**Figure 1.** Overview of the risk methodology developed in this study, in which hazard and infrastructure data is used to evaluate both the asset damages and household infrastructure service disruptions, and their effect on temporary poverty for affected households.

11
reported in single-country studies (see e.g. Banthin et al. 2008), usually based on data from international databases like the OECD’s Systems of Health Accounts (SHA) and the World Health Organization’s (WHO) Global Health Expenditure Database (GHED). The second set of indicators focus on dispersion: the coefficient of variation (the ratio of standard deviation to the mean) and Q90/Q50 (the ratio of health expenditure of the 90<sup>th</sup> percentile to the median). Gruber and Levy (2009), who first used them to analyze out-of-pocket health expenditures, interpret both as proxies for health care expenditure risk, but acknowledge they are only proxies and do not properly capture ex ante risk. None of the above-mentioned indicators relate out-of-pocket expenditures to a household’s consumption or income. The third indicator – the outof-pocket health expenditure budget share – does this. This does not tell us, however, whether the budget share varies with a household’s income or consumption. The fourth measure – progressivity, measured most commonly using Kakwani’s (1977) index – gets at this by telling us whether the budget share is larger for poorer households (out-of-pocket expenditures are regressive) or smaller
may have both formal and informal employees. We define one formal full-time equivalent worker (FTE) as 12 monthly returns to account for seasonal labor. Wages are annualized and deflated by the Consumer Price Index (CPI) to align real wages to spending power.

> 10 The slight difference in trend between the two measures stems from the convexity of natural log function. 11 Analyses of mining sector performance are based on interviews with Zambia Revenue Authority staff and the annual reports of relevant mining companies in Zambia. Due to the sensitive nature of the firm-level tax data, further details on the specific firms cannot be disclosed.

15
equality in terms of wealth assets or income. This result is robust to different measures.<sup>31</sup> Furthermore, we also find no difference in whether farmers belong to a cooperative in a former FMLN territory relative to areas outside (see Table D.20). This result also aligns with the evidence in Table 1 showing no baseline differences in the 1980 state-implemented land reform across the boundaries that transformed many large plots into cooperatives. Although the FMLN redistributed land to peasants, most of its agricultural programs targeted subsistence crops. They did not teach peasants how to grow and commercialize export crops.

# **VI.B Ruling out differences in land tenure**

We previously posited that enduring distrust of elites and the state in former guerrilla areas prevented the entrance of large landowners and private investment in general, isolating these communities from improvements in productivity. Another possibility is that individuals in former guerrilla areas remained attached to the land since they did not have the right to sell it. This is unlikely since a major aspect of the Peace Agreements was to respect the land tenure that formed during guerrilla territorial control.<sup>32</sup> Indeed, using the Agriculture Census, we do not observe differences between treatment and control areas in land ownership or land property rights.<sup>33</sup>

# **VI.C Ruling out migration**

This section explores potential differences in migration patterns in former FMLN areas compared to those outside the boundary. The guerrillas promoted changes that might have induced different patterns of worker selection. For example, high-ability workers could have migrated from these areas due to fear of expropriation of their income (out-migration). But there could have been adverse selection of workers if guerrilla-held areas attracted less productive peasants or individuals with more egalitarian preferences into the areas (in-migration).

We explore these migration patterns empirically in Table 8 using data from the 2007 census. Columns 1–5 examine impacts on international migration. In particular, we estimate equation
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an important obstacle to their operations.<sup>4</sup>

# **3.1.3 Living Standards Measurement Study (LSMS)**

The LSMS is a household-survey program led by the World Bank. The LSMS project works in partnership with the national statistics offices of its eight affiliated countries in Sub-Saharan Africa using representative panel, household surveys. The sampling procedure applied is a stratified two-stage (two-stage probability sample). In the first stage, an enumeration area (EA) is selected, according to a probability relative to the total number of households in that area.<sup>5</sup> We analyzed the different survey waves for each country as cross-sectional data. This paper considers seven of the eight countries that are part of the LSMS; Cˆote d’Ivoire is excluded because of a lack of georeferenced data for this country. This sample contains 1,577,278 respondents, with ages ranging from 20 to 65 years old. The average population age is 35.8 years old. About 86.2 percent of the sample lives in rural areas. About 52 percent of the population is female.

# **3.1.4 The Roads Dataset**

We use the road network from Jedwab and Storeygard (2021), who relied on a previous 2004 network by Nelson and Deichmann (2004) and digitized 64 Michelin road maps constructed between 1961 and 2014 to characterize the then-current road outlook.<sup>6</sup> To address the lack of Michelin maps for certain states and periods, we use grid fixed effects. We use georeferenced survey data to estimate the distance between the centroid of each enumeration area and the nearest main road (highway, paved and/or improved road). Throughout the paper, we consider the distance between an individual or a household and the nearest road as the distance between the centroid of the cluster in which that individual or household is located and the nearest road. Similarly, in the WBES, the distance
when firms signed PCs which contained all the "good" features--managerial bonds, profit orientation, higher wage elasticity, and lower markup ratios-one estimate suggests that their productivity growth rate would increase by 10 per cent.

Our findings cast strong doubts on perforimance contracts as a way of reforming or regulating SOEs. The Chinese government tried to implement PCs quite seriously, as demonstrated by the use of firm-level wage elasticity, large managerial bonds, bidding procedures, measures considerably more radical than those observed in other countries we have studied (Shirley and Xu, 1997). In fact, the central government once hailed the contract system as the official mode for SOE reforms on a national scale. Yet we still observed that the overwhelming majority of the PCs have little or negative growth rate effects, and that the observed frequency for contracts with "good" provisions was exceedingly low. These results, coupled with our earlier findings, suggest that the political econoimy of incentive contracts in government settings merits further study. Political determinants may preclude the design of incentive contracts for government actors that procluce the sort of productivity gains they have achieved in private firms.

# **Appendix A. the Data Set**

The data set we use is _A Survey of Chinese State Enterprises: 1980-1989._ It covers 769 SOBs in 21 cities of four provinces of China (Shanxi, Jilin, Jiangsu, and Sichuan). Chosen on the basis of a stratified random sampling, the 769 firms were all in manufacturing. The size of the SOEs shows large variations: while the median firm had 930 employees, the 10th percentile firm had 304, and the 90th percentile had 3175.

The data set has two parts. Part one is a quantitative table filled by the accountants of an enterprise. It includes 321 variables covering details about products, costs, wages and labor utilization, investment, financing, fixed assets, profit distribution, taxes, prices, and material inputs. Part two is a questionnaire answered by the manager of the enterprise. The manager
We follow the approach proposed by Skoufias et al. (2023) to estimate a measure of “vulnerability to poverty” over different countries of LAC using cross-sectional household data and aggregate data on the propensity of climate hazards in the region. The method is based on Gunther & Hartgen (2009) following Hox (2010) and Chaudhuri’s (2002, 2003) model. The approach includes covariate (aggregate) and idiosyncratic (specific) shocks, modeling them as a function of observable households and community (district or state) characteristics. Moreover, the model distinguishes between povertyinduced (PI) and risk-induced (RI) household vulnerability.

Specifically, the following two-level hierarchical model of income is estimated: ln I I i i 2 i i (1) where denotes the households ( ) at level one, and j denotes the communities where households resided at level two (such as districts or states, depending on the country’s data). We exploit five rounds of surveys per country, so we control for a vector of dummies ii ii= 1, ... , NN i where each dummy takes value 1 if data correspond to each period survey.<sup>2</sup> We further assumed that the tt coefficients and vary across communities. In hierarchical models, this is a varying-intercept and varying-slope model. The varying coefficients are modeled using community characteristics 0ii 1ii ββ ββ related to socioeconomic conditions and climate change as ii ii ZZ II 00 01 02 , (2) 0ii ii ii ooii ββ = γγ10 + γγ11 ZZ + γγ12 II + uu . (3) 1ii ii ii 1ii The error terms and ββ are residuals of level two, representing the unexplained differences in = γγ + γγ ZZ + γγ II + uu income across communities. Replacing equations (2) and (3) into equation (1), we obtain the full ooii 1ii uu uu model re-written as ln I I i 00 01 02 10 11 12 i 2 i i i , (4)
exploitation of natural resources, and other areas; (b) privileged access to inputs, including finance and land; (c) tax breaks and subsidies; (d) privileged treatment in public procurement; (e) designation as mandatory partners in foreign joint ventures; and (f) price regulation that resulted in supra-normal profits (McLeod, 2000).

Many of these favors were extended to specific firms, rather than entire industries. Using the Indonesian manufacturing survey, the same data set we use in this study, Mobarak and Purbasari (2006a) show that politically connected firms were more likely to receive an import license than their competitors, 5
Table 2: Estimated average treatment effect on number of fire occurrences excluding the forest fringe

|Month|Year|(1)<br>No thresh.|(2)<br>Fire occurrences<br>1 km thresh.|(3)<br>2 km thresh.|
|---|---|---|---|---|
||2014|–0.073|–0.053|–0.010|
|||[0.284]|[0.285]|[0.971]|
||2015|–0.093***|–0.174***|–0.081|
|||[0.002]|[0.001]|[0.116]|
||2016|–0.126***|–0.158**|–0.106|
|Nov||[0.000]|[0.023]|[0.139]|
|.|2017|–0.026***|–0.025|–0.039|
|||[0.003]|[0.424]|[0.474]|
||2018|–0.054|–0.118|–0.060|
|||[0.459]|[0.318]|[0.698]|
||2019|0.023|–0.011|0.048|
|||[0.212]|[0.276]|[0.955]|

_Note_ : Estimates are based on the synthetic control method using forest-month level data. We use fires with 30% or better confidence to construct the outcomes. Treatment start from October 2014. P-values from the placebo analyses, in which treatment effects are standardized by pre-treatment RMSPE,
# **5 The Fiscal Cost of Weak Governance**

# **5.1 The Fiscal Cost of Teacher Absence**

High levels of teacher absence translate into considerable waste of public funds since teacher salaries are the largest component of education spending in most countries, including India.<sup>26</sup> Calculating these fiscal costs requires us to estimate and exclude the extent of legitimate absence from our calculations. As part of the institutional background work for this project, we obtained teacher policy documents from several states across India. Analysis of these documents indicates that the annual allowance for personal and sick leave is 5 percent on average across states. This is close to the survey estimate of 5.9 percent (Table 3), but we use the official data since the stated reasons may be over-reported. Estimating the extent of legitimate absence due to ‘official duty’ (outside the school) is more difficult because there are no standard figures for the ‘expected’ level of teacher absence for official duties. Policy norms prescribe minimal disruption to teachers during the school day and stipulate that meetings and trainings be carried out on non-school days or outside school hours. Since we are not able to verify the claim that teachers were on official duty, and there is evidence that head teachers try to cover up for teacher absences by claiming that these are due to ‘official duties’, our default estimate treats half of these cases as legitimate. This gives us a base case of legitimate absence of 8 percent (5 percent authorized leave, and 3 percent official duty). We also consider a more conservative case where the legitimate rate of absence is 10 percent. This 8-10 percent range of legitimate absence also makes sense because the fraction of teacher observations that are classified as either ‘authorized leave’ or ‘official duty’ is in this range for the five states with the lowest overall absence rates - even treating the stated reasons for absence as being fully true (tables available on request).

To estimate
appropriate and evidence‐based policies to promote innovation and support economic growth in developing countries.

One of the main reasons for this lack of evidence is a lack of adequate data. Despite significant efforts in recent years by national and international agencies to implement innovation surveys in developing countries (Unesco, 2015; Nepad, 2014), innovation data are still scattered and suffer from several limitations. First, available innovation data are hardly comparable (Bogliacino et. al, 2012). Innovation surveys in developing countries are mainly conducted on a country‐by‐country basis without following a standardized methodology. Differences in survey instruments, sampling methodology and population of inference make comparison and benchmarking of innovation indicators across countries extremely challenging.

Second, although innovation in developing economies is likely to be different from innovation in developed countries ‐ more incremental and less radical ‐ most of the questionnaires used in innovation surveys are based on the Oslo or Bogota manuals (OECD/Eurostat, 2005 and Jaramillo et al. 2000) and seem to have some difficulties in capturing the relevance of innovations, to the extent that innovation 2
lowest mobility reduction potential (8.8 percent). In Buenos Aires, trips for shopping purposes in 2018 had a relatively homogeneous share across all income groups although with a positive correlation. The potential for trip reduction in 2018 ranged from 10.9 percent among the lowest income quintile to 12.4 percent among the highest quintile.

The potential for reduction in inter-peak hours is considerable but at peak hours is very small in either Bogota or Buenos Aires, especially in the morning peak hour (in Bogota, 3.7 percent, compared to 9.1 percent for the day as a whole, and in Buenos Aires, 0.7 percent, compared to 11.7 in the day overall). Trips for shopping purposes reach the highest weight in overall mobility in the morning at 10:00 (after the morning peak hours), at 28 percent in Bogota and 42 percent in Buenos Aires (Figure 2). At this time, up to 26.1 percent of trips in Bogota and up to 41.3 percent in Buenos Aires could be avoided by e-commerce according to the analysis of the household mobility surveys of 2019 and 2018, respectively. In the late afternoon, a new peak is recorded in both cities, but it is about half the intensity as in the morning. The share of linked trips (part of trip chains) in Bogota is much higher in the evening hours than in the morning, but this is not the case in Buenos Aires.

7
commerce service system reaching 1,000 counties and 100,000 villages to encourage e-commerce activities in rural areas.<sup>5</sup> In this paper, we aim to fill the gap in the literature to study the association of e-commerce participation and household income growth in Taobao Villages, where e-commerce development is more advanced in rural China and economies of scale can potentially play a role. The paper also provides policy suggestions to strengthen the role of e-commerce in inclusive growth. This study will draw on the data from the Taobao Village Survey, with information at the village, household, and individual levels, to examine the role of e-commerce participation. To our knowledge, this study will be the first to draw on systematic data in rural China, where engagement in e-commerce is strong. It has three objectives: 1) examine the factors that determine the participation in e- commerce; 2) compare with the counterfactual (without e-commerce participation) to estimate the direct effect of e-commerce on household income, and 3) investigate the distribution of the benefits of e-commerce participation among households of different characteristics and its inclusiveness.

The paper is structured as follows: section 2 provides a broad picture of the Taobao Villages, drawing on the Taobao Village Survey; section 3 presents the research methodology of propensity score matching; section 4 discusses the empirical results related to the role of e- commerce participation on household income growth in Taobao Villages and for different groups of the population; and section 5 concludes.

# **2. Overview of Taobao Villages**

In recent years, villages with a large share of online sellers and a large share of GDP coming from e-commerce, or Taobao Villages, emerged and developed rapidly in rural China, particularly in the coastal region. This rapid growth demonstrates that the transition from physical to digital commerce, often perceived as a phenomenon of high-income countries in urban areas, can happen at a lower level of development and in rural areas as well.

# **_2.1. Taobao Village
, increased by conflict. It has been estimated that some $200 billion in 2018 prices would be needed to restore “hard” infrastructure to pre-ISIS levels in Iraq, almost equal to the country’s 2018 GDP (Gunter 2018). In the Republic of Yemen, recovery and reconstruction costs are estimated at $20-25 billion cumulatively over a five-year period, equivalent to 1.1-1.3 times the country’s 2020 GDP (World Bank 2020a).

Education and health investment. Beyond investment in infrastructure and physical capital, the COVID-19 pandemic has underscored the need to invest in health and education. This is especially urgent in SSA, as it remains well behind other regions in human capital development, but also in ECA, LAC, and MNA to ensure that education systems provide the skills needed for productive employment.

LAC spends more as a proportion of GDP on education and healthcare than any other EMDE region, but outcomes suggest that better value could be derived from these investments. Educational attainment is highly unequal across income levels, and the region on average attains only mediocre Programme for International Student Assessment (PISA) scores.

In ECA, despite above-average levels of education, learning outcomes, as measured by PISA scores, have deteriorated over the past decade in some economies. There have also been substantial learning losses from the pandemic. With regard to healthcare, since 2000