rafmacalaba/data-use-mentions-extended huggingface dataset:
because the value of its currency had been kept artificially high for quite some time; since the devaluation in May 2012, the currency has depreciated by more than two-thirds. Turkey, Uruguay, and Senegal are other developing countries with high retail prices. The United States had one of the lowest gasoline prices among the sampled countries, reflecting the economies of scale in the market, its high efficiency, and its low fuel taxation: at the national level, the excise tax on gasoline has been frozen at a mere 4.9 U.S. cents per liter since 1993, and total federal and state taxes and fees amounted to 12.9 U.S. cents per liter in January 2012 (API 2012). Further, January 2012 also saw one of the lowest margins (sum of distribution and marketing costs and margins) in a decade.<sup>3</sup>

> 3 Based on the difference between retail prices and spot prices for conventional regular gasoline using price data from the U.S. EIA available at www.eia.gov/petroleum/data.cfm#prices.
of age, a plurality of firms are mature<sup>17</sup> — having been in operation for at least 10 years — however, a third of firms are also young start-ups, being under 5 years old. On average, most firms (43 percent) are “mature,” having been in business for more than 11 years, while 25 percent of firms have been operating between 6 and 10 years on average (“mid-age”). Young firms operating between 3 and 5 years represent 19 percent of firms, while start-ups (2 years) and new entrants (in operation for one year or less) represent 6 percent and 7 percent, respectively (Figure 3). The share of entrants and start-ups is higher in the service sector than in industry (14 percent and 9 percent, respectively), indicating lower entry costs in the service sector. This may be an indication that firm entry may be relatively robust, which

> 15 ES data are collected on an establishment-level basis. Only an estimated 1.6% of establishments in Ghana are estimated to be part of a larger firm, and so establishment and firm are used interchangeably.

> 16 The size categorization refers to the average number of permanent and temporary employees, based on reported and recall data.

> 17 As noted above, firms are classified by their age as of 2010. The reasons for this are twofold. First, as growth measures are considered the age of firms as of their first period is more appropriate; secondly, as the ES data rely on sampling from source lists that are often lagged by a few years, there can be an upward bias in terms of firm age as very young firms are frequently omitted. Using age as of 2010 ameliorates this concern. For firms that were founded after 2010, their age is considered <5. For employment measures, 2010 employment is set to zero.

11
# AGE-EARNINGS PROFILES

The private rate of return to schooling is also estimated using the complete or discounted earnings profile method. The age-earnings profiles estimated from the VLSS are not very well behaved, a problem attributable to the small numbers in the VLSS data set. But after removing outliers and smoothing out the data, estimation of the private rate of return to schooling results in high estimates at the primary level and low estimates at the secondary and university levels (Table 10). In terms of pricing policy, were primary education free at the primary level, at least in terms of direct monetary costs, then the returns to primary schooling would be very high. However, parents do face direct monetary charges for their children's enrollment in primary school. The VLSS collects this information and it is used here to estimate the private rate of return to schooling.

When the full private direct cost of schooling is added to the calculations, the private rate of return to primary schooling decreases by 27 percent. Addition of the private costs reduces the returns by 18 and 20 percent for secondary and university graduates. This reflects the fact that higher levels of education are relatively more highly subsidized than primary education-an aspect of pricing policy in Vietnam that benefits the better off at the expense of the poor, and therefore raises serious issues of equity. Nevertheless, primary education remains a profitable investment on average. But since the poor are likely to have more children, then the costs of schooling become a significant burden. Secondary education does not appear to be a very good investment, but the analysis masks one of the key benefits that individuals derive from secondary schooling21
**Figure 2: Average bound and applied import tariffs on agricultural goods**

<!-- Start of picture text --> 120<br>Bound tariff<br>Applied tariff in1998<br>100<br>80<br>60<br>40<br>20<br>0<br>Argentina Brazil Colombia Costa Rica Ecuador El Salvador Guatemala India Indonesia Mexico Morocco Nicaragua Pakistan Panama Paraguay Philippines Republic of Korea Tunisia Uruguay Vanezuela<br><!-- End of picture text --> _Note_ : Bound tariffs are MFN rates based on final URAA implementation. Applied tariffs represent annual average. Data on average applied import tariffs refer to 1998, with the exception of Costa Rica (1995), Republic of Korea (1996), Indonesia (1996), India (1997) and Morocco (1997). Source: Economic Research Service, USDA.
from Covid-19: Global Evidence from Google Search. World Bank, mimeo.

- Acemoglu, D., V. Chernozhukov, I. Werning, and M. D. Whinston (2020). A Multi-Risk SIR Model with Optimally Targeted Lockdown. NBER, Working Paper 27102 .

- Alon, T., M. Kim, D. Lagakos, and M. VanVuren (2020). How should policy responses to the Covid-19 pandemic dier in the developing world? CEPR, Covid Economics, Issue 15 .

- Baland, J., G. Cassan, and B. Decerf (2020). too young to die. deprivation measures combining poverty and premature mortality. Discussion paper .

- Banerjee, A., L. Pasea, S. Harris, G.-I. A., T. A., L. Shallcross, and C. Pagel (2020). Estimating excess 1-year mortality associated with the covid-19 pandemic according to underlying conditions and age: a population-based cohort study. The Lancet .

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shocks for passenger EVs. While it is possible that some heavy-duty EVs might not share the same charging stations with passenger EVs, the construction and operation cost of charging infrastructure for heavy-duty EVs will spillover to those for passenger EVs, which suggests correlation between heavy-duty EV stock and passenger EV charging infrastructure. Similarly, the construct labor cost reflect supply side cost shocks which directly affects the construction of charging stations but unlikely to be correlated with demand side factors. The interaction with the fast charging model dummy allows for the effect to be potentially different for fast charging models and slow charging models.

It is possible that financial and non-financial incentives that we are interested in are correlated with unobserved demand shocks or other policies. We include time fixed effects to control for common demand shocks around the globe and country fixed effects to control for nation-specific shocks that do not vary over time. However, we acknowledge that these EV incentive policies could be endogenous if they are designed and implemented in response to time-varying and country-specific shocks.

# **Regression Results**

In this section we report the effects of key market drives for global EV diffusion based on the empirical framework described in the previous section.

**Parameter estimates** Table 2 reports the estimation results of the EV demand model from five specifications. We include in our regressions the cost of purchasing a new vehicle measured by price minus financial incentive, the number of available charging ports, driving range, vehicle size and an indicator for non-financial incentives. The first three columns report estimates from OLS, in which we add different sets of fixed effects to control for potential confounding factors. The last two columns are estimates using different instrumental variable strategies to address the price and charging station endogeneity aforementioned.

The regression in column (1) suffers from different sources of confounding factors which bias the price coefficient towards zero, although all the coefficients have intuitive signs. Column (2) adds brand (e.g., Tesla) fixed effects in order to control for brand-specific time-invariant confounders such as brand
ratio – the share of income or expenditure allocated to a specific good or service. Figure 2 illustrates the affordability ratio of electricity for all income quintiles from 2003 to 2008.

As indicated in Figure 2, electricity is a normal good in the sense that its budget share declines as household income rises. This is consistent with observations on energy consumption patterns in other developing countries (World Bank, 2004 and 2009). Additionally, expenditures on electricity have been a moderate component of the total budget of the Turkish households even after the significant price increase in 2008. For an average income household, they represented 2.9 percent of the household’s disposable income in 2007 and increased to 3.5 percent in 2008. For the lowest quintile households, they rose to 6.3 percent in 2008 from 5.4 percent in 2007, which is still below the 10-percent benchmark affordability level (Lee, 2007).

# **V. Results**

# _Demand Parameters_

Table 3 presents two sets of regression results. The first set presents electricity demand regressions based on 2008 HBS data (Columns (1) – (5)). The second set of regressions was performed based on the combined 2008 and 2009 datasets. Comparison of the results suggests that all parameter estimates enjoy a high degree of stability across the two datasets.

In addition, parameter estimates are largely insensitive to the choice of model. Columns (1) and (6) report results from estimating determinants of the log of household monthly electricity consumption identified in Equation (1) via OLS model. Columns (2) and (7) report estimation from the Heckman selection model (maximum likelihood). Both Wald and Likelihood Ratio tests strongly support the contention that the unobserved variables influencing the reporting or nonreporting decision are statistically independent of unobserved factors of the electricity demand. Table 4 reports the first stage regression of the Heckman selection model. The estimated correlation coefficient ( is statistically indistinguishable from zero. Therefore, the selection process can be ignored conditional on
modeling can be used to develop crop insurance contracts. Ratemaking procedures for existing insurance schemes and new schemes are detailed in Section 5. Section 6 examines how to extend coverage to address price risk through crop revenue insurance. Finally, the key issues are summarized in the conclusions.

# **2.  Practical Considerations in Devising Crop Insurance**

The theory underlying the construction of insurance contracts is generally straightforward. However, there are a number of issues that are relevant to any practical application of these principles. Indeed, it is attention to such issues that makes up much of actuarial sciences. In this section, we discuss these issues and various approaches used in the practical development of crop insurance tools.

# **_2.1.  Data Availability_**

The feasibility of any particular crop insurance plan is often influenced by the availability of yield data. Detailed time series of individual farm data of the sort needed in measuring yield risks are usually rare, even in cases where farm records are maintained. In the case of the U.S. crop insurance program, only 4-10 years of yield data are used in determining the yield guarantees. Seldom, if ever, do substantially rich yield records exist at the individual farm level that would permit the detailed rating of crop insurance contracts at the individual level. This suggests that the development of rates and levels of protection for individual farmers may be difficult and that developers will likely need to give consideration to the use of aggregate data. The use of such aggregate data in devising crop insurance contracts may follow two distinct avenues: individual insurance coverage, and index-based insurance coverage.<sup>3</sup>

# _Individual insurance coverage_

One may wish to use aggregate data to make inferences about the risks facing individual farmers. This is very common in the development of individual plans. For example, under the U.S. crop insurance program, twenty years of the loss cost experience at the county level is used to obtain a measure of the typical level of risk for
Here _Pft_ denotes the probability of a firm _f_ exiting the network in year _t_ , _log_ ( _wgt.degreeft−_ 1) represents the logarithm of the firm’s weighted degree centrality from the previous year ( _t −_ 1), and _log_ ( _salesft−_ 1) is the logarithm of the firm’s annual revenue, adjusted to constant 2020 USD, from the previous year. In some specifications, the model also includes year-fixed effects ( _θt_ ) to account for time-specific variations and a dummy variable to capture different types of linkages.

We define exiters as firms that are no longer observed in our dataset in subsequent years, indicating their departure from the sample. This model helps in understanding how a firm’s connectivity and economic performance influence its stability and longevity within the agribusiness network.

# **4 Results**

This section presents the results of the analysis proposed in Section 3. Our first step involves a descriptive assessment focused on the role of U.S. Fortune 500 relative to nonFortune 500 firms within the agri-food value chains. These lead firms are found to be not only more interconnected but also increasingly central to the agro-food global value chains (GVCs). Intriguingly, post-2018, there is a noticeable trend of these corporations consolidating activities within their corporate boundaries, contrasting with the expansive strategies of non-U.S. firms. Building on these findings, we extend our analysis to a systematic examination of firm-to-firm connectivity across all firms in the FactSet database related to the agri-food sectors. Here, we uncover that highly connected firms not only increase their customer and supplier base over time but also maintain central roles within the network without diversifying their core relationships within their primary industries. Moreover, we explore the broader implications of these connectivity patterns on firm longevity and success through an empirical investigation into the factors influencing a likelihood of the This multifaceted some im- firm’s exiting industry. analysis highlights 14
]. We believe the no anticipation assumption is likely to be satisfied because negotiations between AP and country governments prior to transference are not publicly disclosed.

Unless otherwise noted, the data in all regressions are at the level of protected area-year, and the control variables are protected area fixed effects, calendar year fixed effects, and functions of temperature and precipitation. Controlling for weather may improve the precision of estimated effects on outcomes that depend on weather, as well as avoid omitted variables bias if AP management incidence or transference timing depends on contemporaneous weather. We cluster standard errors at the protected area level because that is the level at which treatment is assigned [99].

The temperature and precipitation control variables originate from the ERA5-Land dataset [100]. The temporal resolution of the dataset is hourly, and the spatial resolution is approximately 9 by 9 km grid cells. We calculate non-linear transformations of temperature and precipitation at the original resolution of the data before aggregating to the level of protected area-year. Specifically, we calculate squared and cubed precipitation in meters, and degree hours in 3 C bins. For example, an observation with a temperature of 13 C would have a value of 2 in the 11-to-14 C bin (because 13 minus 11 is 2) and a value of 0 in all other bins. We convert from degree hours to degree days, and consolidate some of the sparse degree day bins. The weather control variables we include in our regressions are a third-order polynomial in precipitation in m, and the following 12 degree day bins: -19 to 5 C, 5 to 8 C, 8 to 11 C, 11 to 14 C, 14 to 17 C, 17 to 20 C, 20 to 23 C, 23 to 26 C, 26 to 29 C, 29 to 32 C, 32 to 35 16
caloric intake estimates beginning around 2000 is likely the result of substantial measurement error.

# **4 Conclusion**

In this paper, population level Total Energy Expenditures (TEE) for India over the 1983-2012 period are quantified using a combination of time-use data, anthropometric data, and the NSS Schedule 10 employment survey. The main finding is that population TEE was fairly flat over this period, rising slightly until 2005 and then declining slightly only in the 2005-2012 period. This finding is driven by two main features of the data. First, household characteristics that tend to predict lower activity levels, such as education, expenditures, and industry/occupation, also tend to be associated with greater height and weight that imply offsetting increases in metabolic requirements. Second, even though the TEE estimates for adults in India fell 17
26 industries. For comparability, I also show the results for estimating equations 1 and 2 without controlling for US imports in the sample of 26 industries for which I have US import data.

The second robustness check excludes the three industries in the sample (382, 388 and 389) that are directly related to auto manufacturing, along with the three corresponding caliper matched industries (3310, 3510, and 390). The reason for excluding these industries is that both Mexico and the US implemented programs to increase consumer’s demand for cars in 2009, which could potentially influence the results.<sup>9</sup> Similarly, the US also supported General Motors and Chrysler with bailout funding in December 2008 and February 2009. Both of these companies have operations in Mexico and received funds through the wage subsidy program. I thus verify that the findings are not driven by these car‐ industry specific programs instead, by checking whether the results hold when car industries are excluded from the sample.

# **4.2 Effects of the wage subsidy program on employment**

Table 3 shows the results for estimating the effects of the wage subsidy program during its duration and after the program ended. Between January and August 2009, the difference in employment levels across eligible and ineligible industries was not greater than in the pre‐program period. The estimated effect of the program is positive, ranging from 5.7 percent to 13.2 percent in magnitude, depending on the specification, but it is not statistically significant in most specifications. However, after the program ended, eligible industries had about 24 percent higher employment than ineligible industries, compared to the pre‐program difference in employment. The size of this effect is relatively stable across the different specifications in Table 3.

Table 4 examines the time path of the program’s effect in more detail, estimating a separate effect for each year after the program (2009 to 2013). The results show that the effect of the program increases over time –
work year of 40 weeks.

- _13_ We are assuming unemployment is _not_ a major factor in the analysis. Several surveys of labor markets in Sub-Saharan Africa have indicated that unemployment is largely an urban phenomenon. In fact, as Vandemoortele (1991) indicates, unemployment in the region _increases_ with educational and skill level. The rural labor sector (on which this study exclusively focuses) primarily comprises unskilled workers (with relatively low levels of education) as well as subsistence farmers. Therefore, increases in productive labor time _are_ expected to result in the augmentation of agricultural output.

- i This methodology follows that in Kim and Benton (1995) and Kim _et al._ (1997). The Cobb-Douglas function relates output Y to inputs E (effective labor input in efficiency units) and K (land/capital) in the following manner: Y = _AErKb,_ where A is a technology parameter. The parameters a and b represent the elasticity of output with respect to inputs: a, for example, is the percentage increase in output caused by a unit percentage increase in labor input. Also, a represents the labor share of output. Similarly for b. Constant returns to scale can be represented by the assumption _a+b = 1,_ which means that if both land and labor inputs increased by a certain proportion, then output would also increase by that same proportion: for example, a doubling of both labor and land inputs would yield double the output.

- _15_ In a subsequent section, we assess the sensitivity of our analysis to increases as well as decreases in the projected annual incidence of dracunculiasis over time in the absence of GDEC.

6
Zealand admitted 1482 Tongans, of which 58 entered through a business/skilled category, 549 through family sponsored categories and 749 through the Pacific Access Category.<sup>7</sup> Australia admitted 284 Tongans during the same financial year.<sup>8</sup> The United States admitted 324 Tongans in the 2004 calendar year, comprising only 5 under employmentbased preferences and 290 under immediate relative or family-sponsored categories.<sup>9</sup> Thus, the PAC accounted for 42% of all migration to these three countries, and over 90% of non-family category migration.

The data used here are from the Tongan component of the Pacific Island-New Zealand Migration Survey (PINZMS), a comprehensive household survey designed to measure multiple aspects of the migration process. Questions on household demographics, education, labor supply, income, asset ownership and food consumption, were based where possible on the most widely used surveys in New Zealand and the Pacific Islands to enhance comparability. The survey design and enumeration, which was overseen by the authors in the first half of 2005, covered random samples of four groups: (i) Tongan migrants to New Zealand, who were successful participants in the 2002/03 and 2003/04 PAC lotteries, (ii) successful participants from the same lotteries who were still in Tonga, either because their application for New Zealand residence was not approved (typically because of lack of a suitable job offer) or was still being processed, (iii) unsuccessful

> 7 Source: Residence Decisions by Financial Year datasheet provided by New Zealand Department of Labour. Note that the high number of PAC approvals in the 2004/05 financial year reflects backlog from prior PAC ballots which were not approved until this time.

> 8 Source: Settler Arrivals 2004-2005, Australian Government Department of Immigration and Multicultural Affairs.

> 9 Source: 2004 Yearbook of Immigration Statistics, U.S. Department of Homeland Security Office of Immigration Statistics.

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