rafmacalaba/data-use-mentions-extended huggingface dataset:
correlation between vote buying and health services. The weaker correlation of health services with these other measures of clientelism, compared to the stronger correlation with the vote buying measures, is attributable to two factors. One, these measures of economic power of the mayor’s family directly captures economic characteristics of a municipality that could be correlated with health investments (both public and private) outside of the channel of clientelism. Two, there is no available data to examine whether mayoral economic power is indeed associated with clientelism; it is therefore a more noisy measure of clientelist practices than the direct measure of vote buying.

The degree to which politicians face competition in accessing office has also been argued to explain the use of vote buying strategies, particularly in interaction with poverty; where both poverty and competitive pressures are high, vote buying may be more likely to be the selected strategy (Weitz-Shapiro, 2012). Measuring variation in political competition across municipalities in the Philippines is tricky. Carizo (2010) provides a detailed qualitative description of competition in the Isabela municipalities studied here, showing: one, that observed variables such as the number of candidates that compete for mayoral office may be endogenous to actual performance of incumbent politicians (well performing incumbents can stand unopposed); two, that local political clans can make deals with each other to alternate in office, such that even when there is an underlying competitive threat, it may not be reflected in observed variables; three, that margins of victory or vote shares may be fraudulently derived, or directly reflecting vote buying outcomes (rather than measuring underlying differences in competitive pressure).

Given these considerations, the most reliable measure of variation in local competitive pressures, in this context, may be the existence and degree of entrenchment of political clans. Based on the information compiled by the Institute for Popular Democracy (IPD) in the Philippines, using raw electoral data from the election commission in the Philippines, about 57 percent (17 out of the sample of 30 municipalities) of municipalities have one or
, 2010.

[14] National Bureau of Statistics. China Energy Statistics Yearbook 2011. Beijing: China Statistics Press, 2011.

[15] National Bureau of Statistics. China Energy Statistics Yearbook 2012. Beijing: China Statistics Press, 2012.

[16] National Bureau of Statistics. China Energy Statistics Yearbook 2013 Beijing: China Statistics Press, 2013.

[17] National Bureau of Statistics. China Energy Statistics Yearbook 2014 Beijing: China Statistics Press, 2014.

[18] China Electricity Council, China Electricity Statistics Collection. 2011. Beijing: 2011 45
relative restrictiveness of individual measures can be inferred from the estimated coefficients of the policy variables, and the overall restrictiveness in the sector-mode can be calculated as a weighted average of these variables using these coefficients as weights. In this method, however, the estimated restrictiveness is conditional on the sample used and framed in terms of the chosen dependent variables.

Using the same subscripts as in section 3.1, let <sup>ˆ</sup> _jk_ denote the estimated coefficient on policy variable _k_ in sector _j_ , and let _xcjk_ denote the corresponding policy variable (typically a dummy variable taking the value of one if country _c_ applies a policy measure _k_ in sector _j_ ). At the sectoral level, the index is then computed as where the last term merely serves to normalize the index on the range from 0 to 100, making it comparable to the STRI of section 3.1.<sup>17</sup> Sectoral scores are aggregated across all sectors _j_  _J_ using the same weights _w j_ as before (see Annex 1, Table A.1): We illustrate this methodology in Section 6, using the results from a regression of global foreign direct investment flows across ten services sectors on policy barriers (and other controls). One difficulty is the paucity of internationally comparable data on outcomes, especially for natural candidates such as the price of the service (ideally controlling for quality differences). Even when we do have data on certain outcomes, such as the level of access to the service, the fact that our policy data is available only for a single point of time obliges us to conduct cross-country analysis and limits our ability to control for non-policy related differences across countries; see e.g. Borchert et al. (2012) who quantify econometrically the effect on market structure and access to services of policy measures in

> 17  Lubotsky and Wittenberg (2006) consider the case in which _k_ alternative proxy variables are available to measure the true effect of one latent, unobserved factor
# **Table 4. R-Squared Shapley-Owen Decomposition for Brazil** **_Estimations Using Individual-Level Data_**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|||**Ever**|**Percent**||
|_Outcomes:_|**Graduation**|**employed**<br>**after**<br>**graduation**|<br>**of time**<br>**employed**|**Wages**|
|_Percent of explained variation attributable to:_|||||
|All quality determinants|39.98|19.54|24.62|18.89|
|Infrastructure|4.19|-|0.99|-|
|Costs|7.34|0.45|1.02|0.47|
|Training and curriculum|10.81|7.90|8.30|7.51|
|Faculty|5.49|3.32|2.82|3.48|
|Link with productive sector|8.05|2.37|4.73|2.16|
|Other Practices|4.11|5.50|6.77|5.27|
|Student characteristics (PCA Score)|0.25|1.30|1.63|1.32|
|Program characteristics (PCA Score)|1.03|1.51|1.18|1.51|
|HEI characteristics (PCA Score)|32.42|0.81|0.63|0.73|
|Student administrative variables (PCA Score)|0.41
where _CPI_<sup>_C_</sup> 2017 _, t_<sup>adjustsforthepricedifferencesbetweenthesurveyyear</sup><sup>_t_and2017incountry</sup> and _PPP C,_ 2017 adjusts for the differences in the currency exchange ratios and price levels

> 7For the sake of simplicity, we refer to consumption, instead of income, as a welfare measure in the remainder of this paper.

> 8The international poverty line is derived as the median of the national poverty lines of low-income countries, while the lower-middle-income and higher-middle-income poverty lines, respectively, are the median national poverty lines of lower-middle-income and upper-middle-income countries.

4
unprecedented value (outside observed history) and is specifically aimed at assessing each model’s ability to extrapolate. This more closely resembles the intended use of forecasting the global crisis risks post 2019 based on 2015-2019 data. It follows the previous work of Celiku and Kraay (2017); Andr ́ee et al. (2020) who argue that for prevention purposes, a model must be able to forecast an outbreak (an extreme event) before it occurs, and thus be validated using statistical measures and holdout data that reflect this use case explicitly. This is opposed to using a standard combination of holdout data and statistical measures that are dominated by no-change events. In the extreme value holdout test, a strong drop in prediction performance is visible for both the standard linear and vanilla Cubist models. While the vanilla Cubist model had an R<sup>2</sup> of 0 _._ 94 in the standard 10-fold cross-validation test, performance drops to 0 _._ 82 when the model is trained without each country’s highest observed prevalence rate and then validated against these difficult observations. The RMSE also more than doubles from 3.49 to 7.53, while the MAE increases 2.5 fold. The performance of the Cubist model that has trained on simulated data is not as badly affected. Drawing from richer memory, the model maintains an R<sup>2</sup> of 0.94, while the RMSE and MAE are respectively 34% and 42% below that of the vanilla Cubist model.

In the third experiment, the holdout consists of the 2019 observation. In this test, the models are thus explicitly validated for their ability to forecast future data, training only on history. This similarly zooms in on the intended use of forecasting. Compared to the basic 10-fold validation exercise, there is again a use those in-sample averages to extrapolate onto unobserved countries, and in some cases, there is only a single prevalence rate observed in the country, which would introduce
>17</sup>

> 17 As discussed earlier, the IHDS panel does not provide the perfect benchmark panel data to compare with the NSS given its much less detailed consumption aggregate and considerable attrition rate. Still, it could be useful to qualitatively gauge the mobility patterns if we are to employ the value of rho that is estimated from the IHDS panel, but treating it as repeated cross sections. Table A.2 in Appendix A replicates the estimates for Table 4 above, using the estimated ρ value of 0.39 based on the IHDS. Estimation results remain qualitatively similar. Off-diagonal entries in panels A and B represent 42–49 percent of the population, and more than half of the poor in 2004/05 (about 58 percent) were no longer poor in 2011/12. These estimates provide supportive evidence for the estimation results offered in Table 4.

21
back-better” experienced by firms in Germany after a major flood in 2013 was also partially attributed to the availability of government aid, and banks’ willingness to fund re-investments (Noth and Rehbein, 2019). Manufacturing firms, which received the _most financial aid_ post 1959 Ise Bay Typhoon in Nagoya City, Japan, had higher chances of remaining viable as opposed to those in retail and wholesale sectors (Okubo and Strobl, 2021).

# **5 Empirical Benchmarking of Firm Adaptation Progress**

Given the high dimensionality of the adaptation strategy set, how can a researcher using observational data actually detect whether firms are becoming resilient to climate shocks over time? The economic definition of firm level ‘adaptation progress” is that a firm’s willingness to pay to not face a weather shock is declining over time. The ideal measure would be a firm’s willingness to pay to not face a weather shock. The firm’s willingness to pay would reflect the difference between its profits with and without the shock.

Empirical researchers have access to better data. With the rise of nation level geocoded administrative data sets such as the Census of Manufacturers and the Census of Services, there is an increased capacity to track the economic performance of firms over time. By merging in data on the location specific weather events that have taken place, observational econometrics approaches can used to estimate ”climate damage functions”. These are reduced form regressions such as the one presented below.

In this regression, the unit of observation is firm i in sector j located in city l at time t. The dependent variable is a measure of the flow damage (measured in dollars) suffered by a firm. Controlling for firm attributes, the key explanatory variables are measures of the firm’s recent exposure to extreme weather and the firm’s expectations of future weather conditions. Most empirical studies do not include this second term. Assuming that the error term is uncorrelated with the weather realization, this regression yields an estimate of the average effect of
Treasury's net worth if the following conditions are met: the sale value of the public enterprise, plus the present value of future taxes from the latter, minus government expenditures to put the enterprise on the market exceeds the present value of future losses (if not privatized).

Conversely, privatization may have no effect on debt sustainability. If the enterprise was profitable and the net income flowing from the stock to be privatized was included in the fiscal primary balance, there would be no difference in the debt sustainability exercise if that income flow is reduced, but the proceeds from privatization are used to reduce the stock of debt<sup>8</sup> .

For most countries the flow of income from public sector enterprises is not included in Treasury revenue flows. Many of these enterprises either generate losses or their profits accrue only to the enterprise<sup>9</sup> .

# _Debt: gross or net?_

Another issue is the correct measurement of debt. For example, if the central bank issues debt in order to sterilize capital inflows, should there be any change in relevant debt statistics? What if debt is issued to buy a non-financial asset? This is not a minor issue. Country after country in Latin America, in one way or another, has issued a significant amount of debt in order to sterilize capital inflows. Brazil and Chile are prime examples, engaging in zealous sterilization practices.

In the case of foreign exchange purchases, an advisable approach would be to deduct them from gross debt, given that in the primary surplus, interest earned on those reserves is ignored. Non-financial assets are different. They could be useful when assessing government solvency but would complicate matters if net out in the debt sustainability exercise.

Moreover, implicating non-financial assets may muddle calculations. There are instances in which it may be difficult to value the asset counterpart of debt. For example, the central bank grants a rediscount to troubled banks, assumes a portion of the latter's assets as a guarantee
Finally, we have not examined optimal targeting in the context of local politicaleconomy considerations or in the presence of community specific public goods as a result of voluntary contributions at the local level. To the extent that the inequality level in a given locality represents some kind of political economy “equilibrium”, it is not clear how a government transfer to a community will actually get distributed across the population in the community. The inequalities in power and influence that prevail in the community are likely to influence how such transfers are allocated. Such factors are likely to result an overestimation of the impact of the targeting scheme on poverty reduction.<sup>7</sup> In addition, Dasgupta & Kanbur (2003) shows that basic results of the targeting literature can change in the presence of community-specific public goods, and that optimal targeting for poverty alleviation can lead to paradoxical results for certain values of the poverty aversion parameter, for example that targeting transfers to the richer community can result in greater welfare gains for the poor (via the increased provision of public goods by the richer segments).

As a result of these caveats, it is important to stress that the gains from fine geographic targeting illustrated in this paper should be viewed as illustrative only. These _potential_ gains should be juxtaposed against the potential costs of such targeting and political-economy considerations. Policymakers need to assess such programs on a case-by-case basis to determine whether fine-geographic targeting is the appropriate strategy.

Our paper is structured as follows. In the next section we briefly summarize the methodology and data underpinning the poverty map estimates in Ecuador, Madagascar and Cambodia. We emphasize that the spatially disaggregated poverty data available to us are estimates, with confidence bounds, rather than actual measures of poverty. We indicate how

> 7 On the other hand, it is also possible that the infusion of transfers into a poor community would increase risk-sharing in that community and thereby contribute to further reductions in poverty.

9
hypothesis.<sup>4</sup> The reminder of the paper is organized as follows. Section 2 provides the background on Nepal's civil conict, major events that helped shape the war, and the physical and economic costs of the war. Conict intensity from the INSEC's database and individual data from the 2016 NDHS are discussed in section 3. Section 4 presents empirical strategies for evaluating both the rst and second-generation impacts. Empirical results along with identication validation and potential mechanisms for intergenerational transmission are presented in section 5 and concluding remarks are presented in section 6.

# 2 Background

Nepal is a landlocked country between India and China. Because of its highly mountainous and rugged terrain and lack of adequate infrastructure and economic development, most parts of the country remain remote, and access to basic services remains unattainable to many. With twothirds of its 30 million inhabitants (estimated as of July 2017) relying on agriculture and a quarter living under the poverty line, Nepal is one of the least developed nations in the world (CIA World Factbook, 2019) and is among the lowest in health, sanitation, primary education and electricity in South Asia.

Figure A.1 shows the administrative divisions of Nepal before the implementation of a new constitution in 2015. The country was divided into ve geographically homogeneous development regions, which were further divided to form 75 districts. Districts were further divided into rural (village development committee, VDC) and urban (municipalities) areas, which were the lowest level of administrative units. At the time of the 2011 population census, Nepal consisted of 3,914 VDCs and 58 municipalities (Central Bureau of Statistics, 2012). I calculate conict intensity at the level of these 3,972 local administrative areas.<sup>5</sup>

> 4Along these lines, Nepal et al. (2018) nd that Nepal's civil conict increased women's desired and actual fertility during the conict by 22 percent.

> 5For
Thus, with respect to (15) the hypothesis of interest is: In addition, it should be noted that the parameter � measuring the elasticity of sunkenness is identi...ed but not the parameter �: The role of contestability in tightening the distribution will be supported if the estimated coe¢ cients b1 and b2 are signi...cantly positive and b3 is signi...cantly negative.

The potential role of contestability in tightening the size distribution of ...rms can be assessed more accurately by utilizing the measure of dispersion Vi(s) which represents the variance of market shares in industry i. Thus, setting �i equal to Vi(s) in (14) points to the estimating equation where the error components �i are assumed to be distributed according to N (0; ��<sup>2).</sup> The contestability hypothesis will be supported if c1 and c2 are signi...cantly positive while c3 is signi...cantly negative. A comparison of (14) and (18) indicates that with respect to (18) the hypothesis of interest is: To evaluate the potential impact of intangible sunk costs and taking into account (11), (15), and (18), we adopt the parsimonious speci...cations and where the error components �i and �i are assumed to be distributed according to N (0; ��<sup>2)andN(0; �</sup> �<sup>2)respectively.</sup>

# 6 Data and Measurement Issues

Equations (15) and (18) are ...t to 1982, 1987, and 1992 Census and Annual Survey of Manufactures (ASM) data on four-digit US manufacturing industries. After 1992, 11
<!-- Start of picture text --> Figure 2<br>WFt<br>WA ́(t)<br>WFt,A(∞)<br>WA(t)<br>WDt(∞)<br>t =0 t<br><!-- End of picture text -->

# **3. Imperfect Factor Mobility**

The quantification of trade adjustment costs is difficult and various techniques have been used since the 1970s. I will review those shortly. But before doing that, it is useful to document briefly the prevalence of the underlying cause of TAC, factor immobility, in modern economies.

Two pieces of evidence, direct and indirect, support the presence of labor immobility.<sup>3</sup> Direct evidence on employment reallocation suggests that trade liberalization both destructs and creates of jobs _within_ industries but that it induces only limited flows of labor into expanding sectors with comparative advantage. An example is Wacziarg and Wallack (2004) who use cross-country panel data to document little inter-industry flows after liberalization. In turn, Muendler (2010) and Menezes-Filho and Muendler (2007) use the linked employer-employee Brazilian data to show that trade liberalization displaced workers from the de-protected industries but that these workers were only absorbed by the comparative advantage sectors after several years.

Labor immobility is also indirectly suggested by the presence of wage differentials. Various papers have identified large inter-industry wage differentials in both developed and developing countries. The seminal work of Krueger and Summers (1989), for instance, shows that industry effects explain a large fraction of wage heterogeneity across workers. For the U.S., Gaston and Trefler (2004) find a negative correlation between these wage premiums and tariff protection. Attanasio, Goldberg and Pavcnik (2004) and Goldberg and Pavcnik (2005) also find industry-wage premium in Colombia that are, in addition, partly explained by inter-industry tariff differences. For Argentina, Galiani and Porto (2010) document a skill premium
a regular Farm Structure Survey employing a common methodology devised by Eurostat (Istat, 2008). The EU FIELDFACT Project is one of many schemes that aim to raise farmer awareness on the use of GPS in land measurement for the purposes of precision agriculture and more accurate and transparent subsidy claims through the EU’s Common Agricultural Policy.<sup>1</sup> Frequent land measurement is encouraged by several developed country governments (e.g. USA, UK, Germany, Australia) at the individual farmer level in promotion of precision agriculture, whereby the production process is tailored to farmland size estimates obtained via GPS/GNSS and remote sensing. By knowing exact area measurements, farmers are able to adjust input use accordingly and, thus, optimize yields while cutting down on costs. Effective land area measurement, therefore, also contributes to a more focused application of fertilizers and pesticides that could, in turn, alleviate environmental degradation and pollution.

> 1 <u>http://www.gsa.europa.eu/introduction-and-promotion-gnss-agriculture</u>

3
to Persian Gulf FOB, and cash transfers were paid to virtually everyone to reduce opposition. Nine years after this major overhaul, the price of energy is still very low compared to international levels mainly due to the instability of the rial. The gasoline price, for example, was 10,000 IRR (around US$0.10) per liter before the new gasoline price reform of 2019.

On November 15, 2019, the National Iranian Oil Products Distribution Company (NIOPDC) implemented a 50 percent increase in the of price of petrol at re-introduced rationed amounts (e.g. 60 liters/month for private cars) and a 200 percent increase for consumption beyond the ration amount. The official announcement came only one day before the start of the price reform, in which it was specified that revenues generated from the price increase were supposed to be exclusively redistributed back to 18 million means tested households. This drastic change was introduced and went into effect overnight and led to street protests which quickly became violent. To the best of our knowledge, until now there has been no comprehensive empirical evidence on the welfare and fiscal implications of the 2019 reform in the Islamic Republic of Iran.

This paper uses the most recently available household expenditure and income survey (HEIS) from 2018/2019 to estimate direct and indirect impacts of the proposed price increase on poverty, inequality and government revenues. The direct impact measures the impact of a price change on household wellbeing via the consumption of the subsidized products. Indirect effects measure the impact via the consumption of products that are affected indirectly by the change in price of subsidized products. For example, the prices of goods and services which use gasoline in their production or delivery will also be affected by the increase in gasoline prices. In addition, we compare poverty and fiscal implications from the proposed government cash transfer scheme with a scenario of universal or perfectly targeted cash transfers needed to bring poverty back to the pre-reform level. All estimations are based on a particular 2