rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1- Introduction**

Few would deny that water, as a necessity for life, is central to all forms of economic activities. And yet attempts to quantify its contribution to economic growth remain elusive and uncertain. The earliest efforts to establish such links relied upon simple correlations between rainfall and economic growth. Charts showing comovements of rainfall and GDP growth are still presented as prima facia evidence of a link between the availability of water and economic growth by the World Bank and others (World Bank 2006). While perhaps a useful tool for policy discourse, such correlations provide little information about causal patterns.

More recently a number of papers have attempted to investigate the effects of either rainfall or water availability on economic growth. Two canonical papers in the growing climate economy literature empirically examine the joint effects of rainfall and temperature changes on aggregate economic outcomes (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015 – hereafter DJO and BHM). After many robustness checks, both papers conclude that there is little discernible, robust evidence that rainfall has a statistically significant and consistent impact on GDP growth. However, temperature is shown to have a consistent and non-linear effect on GDP growth, with productivity being maximized at 13�C in BHM. Following this pioneering work, recent research in the economics literature has focused upon the consequences of rising temperatures (Deryugina and Hsiang, 2017; Diffenbaugh and Burke, 2019; Henseler and Schumacher, 2019; Burke and Tanatuma, 2019). A typical finding is that temperature has negative economic impacts, but precipitation has no consistent and statistically significant impact.

To our knowledge there are only four recent papers that seek to specifically explore whether rainfall has an impact on aggregate economic activity at a global scale (Barrios et al. 2010, Brown et al. 2013, El Khanji & Hudson 2016, Sadoff et al 2016 – See Table Sup 1).<sup>2</sup> All these studies use national data. Only Brown et al. (2013) find
terms or in time, but they provide a useful comparison of urban and national measures for developing countries and high-income countries.

Relative to countries as a whole, urban areas in both developing and high-income countries obviously have much higher population densities, higher road network densities, somewhat more vehicles per thousand persons, much less road length per person, and more motor vehicles per kilometer of road. Although it is used here as a rough proxy for the volume-to-capacity ratio frequently used in traffic analysis, the number of motor vehicles per kilometer of road is an imperfect measure because it does not contain information on average vehicle utilization and traffic peaking. However, the large difference between National data on population, vehicles, and road length include urban and rural areas. Data on urban road length are from a variety of sources and are not broken down into paved and unpaved categories, as are national data. Typically a greater proportion of urban roads than of national roads are paved, so the analysis of urban roads may be more comparable to the analysis of national paved roads than total roads.
magnitude of the brain drain. However, as recent theories show, it could be that some developing countries have experienced a social gain from this brain drain.

We ...rst summarize in Section 2 the data on the magnitude of the brain drain, and then provide new estimates on the international mobility of the highly skilled; our measures are based on immigration data collected from nearly all OECD countries for 1990 and 2000 by Docquier and Marfouk (2004). These data show that the brain drain has gained in magnitude over the period covered although substantial di¤erences remain across countries and regions. Section 3 presents the theoretical arguments of the ”new” and ”old” brain drain literatures in a fully harmonized framework: we ...rst review the early brain drain literature, and contrast it to more recent models. We also review the various channels whereby skilled migrants may impact on their home country after they have left (remittances, return migration, networks), and provide evidence on these di¤erent channels when available. Section 4 is dedicated to policy implications, with emphasis on migration policy and education policy in a context of international migration. Section 5 concludes.

> 1Grubel and Scott (1969) already mentioned the various possible feedback e¤ects of the brain drain for source countries (remittances, networks, innovation in the host country that may spillover to the origin country, etc), and argued that the short-term loss (due to intragenerational and ...scal externalities) could well be o¤set in the the long run. However, such possible feedbacks were considered too small to make a di¤erence, and the brain drain literature of the 1970s focused on its detrimental (short-term) impact.

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source for accurate and reliable climate migration data and proposes some concrete actions that can be undertaken for achieving this goal in the short- and medium-term.

The remainder of the paper is organized as follows. Section 2 provides essential information on data sources and characteristics of climate-related migration, summarizes the most recent findings of the empirical literature, and assesses the main remaining data gaps hindering further understanding of the climate-migration nexus. Section 3 proposes an empirically-oriented conceptual framework on climate migration and outlines a methodological agenda that serves as a guide to maximize and boost the potential of household surveys for the study of climate related migration. Section 4 puts forward empirical guidelines for the use of existing surveys. Section 5 concludes.

# **2. Assessing the climate migration data landscape: Gaps, limitations, and the quest for doing better**

# **2.1 Definitions, metrics, and data sources: Navigating an imperfect (but promising) world**

Measuring migration is a long-standing thorny issue. In general, data on any type of migration is difficult to collect precisely. The intrinsic complexity associated with the concept of migration, its dynamic nature (across space and time), and the lack of universally-accepted definitions represent a challenge in the development of appropriate data collection systems on migration. This applies especially where national statistical systems are weak, which is more common in low- and lower-middle-income countries. In such cases, regular, systematic 5
# **<u>Table A2. Impact of Land Tenure on Productivity (Fixed Effect Estimation)</u>**

||Gross|revenue<br>**Subsample with 5**|Net re<br>**0/50 sharing rule**|venue|
|---|---|---|---|---|
|Barga land|-0.168***|-0.162***|-0.173***|-0.170***|
||(3.67)|(3.59)|(2.99)|(2.98)|
|Land size (acre)|-0.042|-0.039|0.038|0.045|
||(1.40)|(1.48)|(0.80)|(0.97)|
|Distance to homestead (km)|-0.109***|-0.099***|-0.098***|-0.094***|
||(3.80)|(3.62)|(2.80)|(2.87)|
|Observations (plots)|2521|2507|2441|2427|
|R-squared|0.65|0.66|0.60|0.61|
|||**Subsample with 7**|**5/25 sharing rule**||
|Barga land|-0.173***|-0.178***|-0.173***|-0.178***|
||(4.05)|(4.15)|(3.63)|(3.66)|
|Land size (acre)|-
Gains from targeting also vary with the choice of poverty line. The higher the poverty line, the less need for targeting, as leakage to the non-poor diminishes to zero. In this study, we select as benchmark the a poverty line that yields a poverty rate of exactly 20% in both 1999 and 2009, respectively.

# **_7.3. Simulating the impact of uniform transfers_**

Our policy simulation in the benchmark case of uniform transfers is calculated in a very straightforward manner. Budget _S_ is divided by total population _N_ . The resulting transfer _a_ is ( _r_ ) added to each predicted expenditure in our database, to yield _ych +a_ . For each replication _r_ we estimate post-transfer national poverty. The average across the _r_ replications of the estimated post-transfer poverty rates yields our expected poverty rate associated with the benchmark, untargeted lump-sum transfer scheme. This new estimated poverty rate can be compared to the original national-level poverty estimate from the poverty map to gauge the impact of the transfer.

# **_7.4. Simulating the impact of “optimal” geographic targeting_**

Simulating the impact of the “optimal” targeting scheme is a bit more complicated. Following Kanbur (1987) we want to equalize the following expression across the poorest locations of a country: which is _z_ times the poverty gap in location _c_ , after every person in the location has received a transfer _ac. Fc(y)_ is the average of the R simulated expenditure distributions of _c_ . The function ( _x_ )<sup>_+_</sup> gives the ‘positive part’ of its argument, i.e. ( _x)_<sup>_+_</sup> _=x,_ if _x_ is positive, otherwise 0. Transfers _ac_ (which must be nonnegative) add up to a given budget _S_ : where _Nc_ is the population size of location _c_ . After transfers there is a group of locations all sharing the same (maximum) poverty gap rate
there is no improvement in enrollment and graduation rates from those observed at time _t_ . In other words, the average educational attainment of these young cohorts in the future is equal to the average educational levels of the 20 to 24 cohort of time _t_ . This is a conservative assumption given that the 20 to 24 cohort observed at time t may not have the maximum educational level attainable.<sup>21</sup> The second step is to adjust factor returns by skill and sector in accordance with the results of the CGE model. The GIDD imposes an entirely new vector of earnings on each worker, conditional on that worker being in sector s and having and educational attainment e. The third step adjusts the average income/consumption per capita to guarantee that it changes exactly in line with the CGE results. Lastly, GIDD constructs a household-specific deflator to adjust for changes in relative prices. The price deflator is constructed using initial and final prices indexes of food versus non-food from the macro model and household-specific budget consumption shares for food and non-food observed in micro data.

Beginning with a distribution of earning from labor by sector and skill [y�,�] in the macro data, define a set of wage gaps as follows: and a similar set of wage gaps for the macroeconomic counterfactual scenario: where y1,1 is the average earnings from labor of unskilled workers in agriculture and y��,� and y��,� are their predicted values from the CGE model in the counterfactual scenario. All right hand side values in equation 1 are known data in the CGE model benchmark data set, and all right hand side values in equation 2 are known values in the CGE model simulations.

The micro data will have also a set of wage premia which, in general, will differ from the CGE data. Analogous to equations 1 and 2, define: � where g�,� are the wage premia based on averages by skill group and sector in the household data; y′�,
**Graph 4: Outcome ratings of public sector operations and change in overall government effectiveness 2000 to 2010**

<!-- Start of picture text --> Change in government effectiveness 2000 to 2010 and<br>public sector operation outcome ratings<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>HS<br>S 1<br>0<br>MS<br>-35 -25 -15 -5 5 15 25 35<br>-1<br>MUS<br>-2<br>US<br>-3<br>HUS<br>Project outcome ratings<br><!-- End of picture text -->

**Change in government effectiveness (WGI data) 2000 to 2010**

Note: project ratings are as follows: HUS = highly unsatisfactory, US = unsatisfactory, MUS = moderately unsatisfactory, MS = moderately satisfactory, S = satisfactory, HS = highly satisfactory. Changes in overall government effectiveness were calculated based on WGI data for 2010 and for 2000; and are provided as percentile changes.

The following section explores how teams capture and mitigate political economy challenges and assesses the impact the level of attention paid to political economy factors has on project performance.

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suffering from psychological distress (Goldberg and Williams, 1988). We supplement these two indicators with five secondary outcomes: (i) The PHQ-8 score (range: 0-24); (ii) the GHQ-12 score (0-12); (iii) the score on the Rosenberg self-esteem scale (0-30) (Rosenberg, 1965); (iv) the score on the Child and Youth Resilience Measure-Revised (0-34) (Jefferies et al., 2019); and (v) the locus of control score (1-10). The discrete PHQ-8 and GHQ-12 scores allow the assessment of impact on the severity of distress in the sample, while the remaining outcomes capture several distinct dimensions of mental health (Shah et al., 2024).

The full set of human capital outcomes is shown in Table 2. The outcomes measured differ slightly by round. At the rapid resurvey, primary and secondary outcomes focused on intentions and expectations as useful predictors of subsequent investments in human capital, as it was too early to see broader human capital changes. These outcomes include life expectancy; desired fertility; educational aspirations (aspires to tertiary education), desired age of first marriage, desired age of first pregnancy, expectations of paid work at age 25; timepreferences; and the score on a set of competencies (skills), using a module developed by the investigators (Baird et al., 2019). The competencies involved making correct change during a set of hypothetical market transactions that aimed to test the respondent’s numeracy, reading and listening comprehension, and ability to follow instructions.

At midline and endline, we added a number of primary and secondary outcomes that are further down the causal pathway, i.e., that provide more concrete evidence of whether or not there are knock-on effects of improved mental health on human capital accumulation. These outcomes include an indicator for being enrolled in school; the incidences of marriage and childbearing since baseline; risky sexual behavior (condom use during last sexual encounter); and self-efficacy (Schwarzer
etc.

It is for such a country that one might expect that implementing a best quality electricity regulator would increase per capita generation capacity in the long run by 15-25%.

The policy implication of this is, firstly, that the quality of overall country governance matters considerably for the impact of regulation on outcomes (eg as in the rule of law); and secondly, that countries cannot expect to achieve the gains we have estimated by enacting low quality regulatory laws or introducing autonomous regulatory agencies with very low staffing levels. But, the corollary is that the potential gains from introducing an electricity regulator could be significantly higher than the average level for countries with good overall governance who deliberately try to introduce best practice regulatory agencies and practices.

The argument above follows not just from the logic of our fixed effect modelling but is supported by the suggestive implications of a potential interactive and/or independent impact of overall country governance (eg as measured by the Kaufmann index) on our regulatory governance measure. (See Section 5.1.2.2 above.) These arguments do not, of course, apply just to this paper. They also apply to the similar models of Gutierrez, Edwards and Waverman, etc.

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PT DE HR EE CZ RO HU PL LV BG SE LU FI DK NL<br>Market (Scenario A vs. D) Disposable (Scenario C vs. D) Disposable (Scenario A vs. D)<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>-14<br>-16<br><!-- End of picture text --> Note: See Figure A1 in the Appendix for income quintile specific results. Source: Own calculations based on EUROMOD I4.0+.

Figure A1 in the Appendix presents the same analysis but looking at changes in mean market and disposable income by income quintiles based on equivalized household disposable income in the pre-COVID-19 scenario (scenario D). In most countries, the largest drop in market income is observed in the first two quintiles and the smallest at the top confirming the findings by Christl et al. (2022b) on the regressiveness of the impact on market incomes. The only exceptions are Greece where the largest drop is in quintile three and the smallest in quintile four, Croatia where mean market income remains broadly unchanged in the first quintile and the largest drops (of similar 9
. Gill, M. Dellepiane, E. Skoufias, K. Vinha, and Y. Xu (2015) ‘Estimating poverty rates in target populations: An assessment of the simple poverty scorecard and alternative approaches.’ _mimeo_

- Douidich, M., A. Ezzrari, R. van der Weide, and P. Verme (2015) ‘Estimating quarterly poverty rates using labor force surveys: A primer.’ _World Bank Economic Review._ Advance Access pulished 2015

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, and cm = 0.5∑x - 7μ + 5εm for males, where μ , εf and εm, are independent normal (0,1) random variables. The outcome is y = 1.0*cf + 1.0*cm + 0.5∑x + 7μ + 5ν, where ν is an independent normal (0,1) random variable. Half of the generated sample is female and half male. Treatment was made available to half of each gender-defined sub-sample. One hundred data sets with 5000 observations each were independently generated and the models estimated with maximum likelihood using Roodman’s _cmp_ estimation package to trace the converged likelihood over a range of the credit effect parameters δ = δf = δm starting at -0.3 and incrementing by 0.1 until δf =1.5 . The gender breakdown is not really relevant in this exercise, since the point is to examine the shape of the log-likelihood surface with respect to δ, the parameter on credit.

In Table 4, as noted, the log-likelihood with respect to δ is bimodal in every single simulated dataset. The global maximum was tightly clustered around the true value of δ = 1, and the local maximum (inferior mode) was clustered around δ = -0.1.<sup>23</sup> Figure 1 presents the log-likelihood surface for the 1<sup>st</sup> , 25<sup>th</sup> , 75<sup>th</sup> , and 100<sup>th</sup> simulated data set. The resemblance to the PK log-likelihood surface presented by RM (see also our Figure 2) is striking. One attribute of a bimodal log-likelihood is that selectively dropping observations can “cut off” the mode corresponding to the global maximum, resulting in a discrete (and negative, in this case) jump to the local mode. Indeed, it was not difficult to find data-generating

> 23 When iterated to convergence,
distribution.<sup>4</sup> We denote by E the number of calories provided by the reference food basket . The ratio between E and the minimum energy requirement _ER_ , identified (qq) based on a normative criterion, provides the coefficient ⁄E needed to rescale qq (qq) the basket to return a basket yielding exactly the normative energy requirement _ER._ λλ= E (qq) 00 qq qq =<sup>qq</sup> 00 qq Finally, to value , a price vector is needed. Consistently with the choice underlying λλ quantities, we take the median values of the unit values for households belonging to first 00 qq decile of per capita expenditure as a reference group. Denoting by the vector of prices of the food items included in the bundle for period _t_ , the food poverty line for period _t_ pp<sup>tt</sup> is given by: 00 qq F 00 Similarly, the food poverty line at _t+_ 1 prices can be computed as: FF<sup>tt</sup> = pp<sup>tt</sup> ∙qq Similarly, the food poverty line at _t+_ F 00 FF<sup>tt+1</sup> = pp<sup>tt+1</sup> ∙qq The ratio between the food poverty lines provides an estimate of the true-cost-of-living index between _t+_ 1 and _t_ , as a Laspeyres-type price index based on the period _t_ subsistence/reference basket: The identification of the food basket can be based on period _t+_ 1 data instead of period _t_ . That would imply repeating the steps above designing the basket with reference to the consumption pattern of period _t+_ 1, creating a Paasche-type price index.

In our application _t_ = 2017 and _t+_ 1 = 2022. Table 1 compares the food poverty lines based on the 2017 and 2022 food baskets, respectively. If we assume that the energy requirement correctly identifies a
_Site Selection_ .<sup>3</sup> This journal, in a series of articles called the _The Million Dollar Plant_ , reports the final location of a new plant creation, along with the identity of the potential alternate or the _runner-up_ location(s). We refer to each such set of winning and losing counties as a _deal_ . Our final number of deals varies depending on the outcomes we are looking at (e.g. labor market or retail inflation) which determine our sample sizes.<sup>4</sup>

# **3.2 Retail Price Data: IRI Data**

Our primary retail price data set covers various grocery stores and drugstores from 2001 to 2012 and is provided by IRI Worldwide. The data set includes store-week-UPC sales and quantity information for products in 31 categories, representing roughly 15 percent of household spending in the Consumer Expenditure Survey. In total, the data cover around 7,200 stores in about 47 IRI markets, corresponding to 968 US counties. IRI markets are composed of U.S. counties, which can represent a single metropolitan area (e.g. Los Angeles) or various metropolitan statistical areas aggregated into one region (e.g. West Texas/New Mexico covers all FIPS counties of New Mexico but only some of Western Texas). There are a large number of papers in the literature that study regional inflation using the definition of market constructed by the IRI (Pandya and Venkatesan, 2016; Bronnenberg et al., 2008).<sup>5</sup> While the raw data are sampled weekly, we construct quarterly prices both at category and barcode level, similar to Stroebel and Vavra (2019), since this reduces high-frequency noise. We construct the prices at quarterly-IRI market-store-barcode level by summing the total value of products sold in a particular quarter in a particular IRI market in that store and then dividing that by the total quantity sold of that product for that barcode/UPC in that store. Table
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# **Country E R Pricing** **<u>policy and strategy</u>**

# **Fuel supply conditions**

<mark>kg since Nov 2003 (with higher prices earlier). Ex-refinery prices of available on the market; instead hydrous gasoline and diesel were raised twice in 2012, and in Jan and Feb ethanol and a gasoline-anhydrous ethanol 2013. Government adjusts a tax on gasoline and diesel contributing to blend are sold for spark-ignition vehicles. intervention in the economic domain, called CIDE (Contribuição e The amount of ethanol in the gasolineIntervenção no Domínio Econômico), to further stabilize retail prices. ethanol blend is regulated and varies A negative CIDE tax was levied on LPG until Apr 2002. Monthly between 18% and 25%. The blending average retail prices of diesel (averaged across the country) and FOB percentage is determined largely by benchmark prices relevant to Brazil since 2007 are shown below. The sugarcane production and world sugar plot shows significant price smoothing. prices. Decreasing the percentage of</mark> 2.5 <mark>ethanol increases demand for gasoline, requiring gasoline imports in recent years and forcing Petrobras to import at world</mark> 2.0 <mark>prices and sell at a loss on the domestic market.</mark> 1.5 1.0 0.5 U.S. Gulf Coast spot price Retail in Brazil 0.0 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 **<mark>Consequences of pricing policy</mark>** <mark>: Petrobras’ refining, transportation, and marketing division lost US$8.5 billion in 2011 and US$17.5 billion in 2012. The recent decline in the company’s financial performance, including its first quarterly loss in 13 years posted in August 2012, has been blamed in large part on low domestic fuel prices. Cuts in CIDE in recent years have cost hundreds of millions of dollars.</mark>

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