rafmacalaba/datause-encoder-data huggingface dataset:
to the US, becomes the weight of the estimated function. In practice, the estimates differed some, but not greatly from those previous.

Figure 4 presents the elasticity of migration to the US with respect to FDI above the axis and below the axis, the impact of an investment of $US100 million on the number of people who migrate. The elasticities are quite low-- on average a doubling of FDI would lead only to a 1.5-2% reduction in migration. Further, for many states the impact of a single $US100 million plant opening decreases migration little. However in some areas, such as Veracruz, where there is little FDI and a substantial amount of migration, the decline in outflows is substantialin the first case approaching 4,000 people. Again, these are intended as back of the envelope calculations that rely heavily on the assumption that the US is considered broadly as the 32<sup>rd</sup> destination for a migrant leaving his state. And again, we are only capturing the impact of redirection of existing migration to other Mexican states, not the overall decline in migration so these must be seen as lower bounds.

# **Conclusions**

This paper evaluated the mechanisms through which NAFTA related variables might work to reduce migration to the US using data from within Mexico. We make three contributions to both the literature on migration more generally, and the impact of trade and FDI flows on migration.

First, we offer the first estimates of determinants of migration flows within Mexico and make some advances in the LDC migration literature. In line with recent innovations in the industrialized country literature, we generate proxies for both the level of amenities, costs of living and networks and generally find significance. Contrary to much of the literature, our specification generates intuitive and significant signs on home state labor market variables: a rise in home earnings or employment levels does deter migration. We argue that part of the traditional poor performance of the origin state variables is that they capture a mix of deterrent effects, and credit constraints.

17
44

||nationality, membership of a social group, or<br>political opinion) (UNHCR)|
|---|---|
|**Asylum**|Asylum‐seekers are individuals who have<br>sought international protection and whose<br>claims for refugee status have not yet been<br>determined, irrespective of when they may<br>have been lodged. (UNHCR)|
|**Forced Return**|Returnees<br>who<br>intended<br>permanent migration but<br>were forced to return.<br>Their<br>preference was to remain abroad but<br>because of external factors they were<br>required to return (Gmelch 1980)|
|**Voluntary Return**|Returnees who return voluntarily fulfill one<br>of the following conditions: 1) He or she faces<br>a clear and open choice either to return to his<br>or her country of origin or to stay<br>permanently and integrate into the host<br>society; or 2) a choice between returning to<br>the country of origin now in a voluntary<br>fashion (perhaps with financial or other<br>incentives) or staying and risking forcible<br>return later<sup>46</sup>.|

|**Resettlement**|Resettlement is the transfer of refugees from<br>an asylum country to another state that has<br>agreed to admit them and ultimately grant<br>them a permanent settlement. (UNHCR)|
|---|---|

Annex 2: The Situation in Countries of Origin of Asylum Seekers and Refugees

# **<u>Afghanistan</u>**

46 Morrison, 2000
-8-

Table 3 Malawi: Imnticit Indirect Tax Rates<sup>a/</sup> (Percentage)

||Import Ta<br>|x rates<br>Imnor<br>|on Private Sector<br>ts<br>|Dome<br>|stic Tax<br>Manufa<br>|Rates on Large<br>cturers<br>|
|---|---|---|---|---|---|---|
|Commodity Class|1975-76|1981-82|Average Compound<br>Annual Growth Rate|1975-76|1981-82|Average Compound<br>Annual Growth Rate|
|Consumption<br>goods<br>Mixed use (Consumption<br>or<br>intermediate)|27.5<br>2.3<br>|40.6<br>71.2<br>|6.6<br>20.8<br>|8.2<br>8.8<br>|10.6<br>3.99<br>|4.32<br>-12.36<br>|
|Intermediate<br>inputs<br>Capital goods|1.9<br>1.9|8.3<br>14.3|28.13<br>40.4|0.9<br>-|2.3<br>-|16.12<br>-|
|Average|11.7|32.1|||||

Source: Table 2.2, Tax Policy for Malawi, World<sup>Bank, November 1985.</sup> a/ Implicit tax rates are defined as the ratio of actual<sup>tax colLections from a</sup> particular base to the size of that base.
et al. (2008), standard errors are clustered at the district level (not the district-year level) and asymptotic inference is acceptable with many more than 50 clusters.

The covariate _girl_ is an indicator that the child is a girl; girls are subject to a range of disadvantages and deprivations in rural India. The vector _Xidt_ is a set of controls varying at the household or village level that are available from the Pratham ASER survey data. Some are about the child’s household or parents: housing material, household electrification in general and on the day of the survey, whether the child goes to “tuition” tutoring classes, whether the child’s father and mother have been to school, and whether the child’s mother is literate. Others are about the child’s district, described below, or about the child’s village: whether it has electricity, a road, a health worker (called an ASHA), a school, and a government ration shop.

The district controls are _litdt_ +6 and _lit_<sup>2</sup> _dt_ +6<sup>,ordistrictlevelliteracyintheyearofthe</sup> ASER test as a quadratic polynomial. District literacy data is available from the 2001 and 2011 censuses. We construct annual frequency literacy observations by linearly interpolating between these two values (although the quadratic term is intended to account for any nonlinearity, the result is quite similar if interpolation in logs is used instead). We intend these controls to partially account for unrelated district trends in literacy or education. These two – – sets of controls household/parent and village/district are added sequentially to demonstrate that neither has an important effect on the coefficient on the TSC, suggesting that omitted variable bias is unlikely.

# **4.3 Balance of covariates**

If TSC program intensity were associated with the observable characteristics of children and their environment, then we might doubt that variation in the TSC had an exogenous effect on children’s learning. In this section, we assess whether the program predicts the covariates that are included in the ASER data. This analysis is analogous to verifying that treatment and control groups are balanced on observables in a randomized experiment. Table 2 reports 14
available in our sensitivity analysis. As we document below, here we also find strong results when we are able to utilize the unemployment measure.

Third, and most importantly, the current paper also ultimately directly confronts the changing institutional and policy environment in which emerging economies employ TTBs during 1989-2010. As noted above, when we turn to examine the channels through which the WTO may be impacting TTB import protection, one of our key determinants is defined as the share of the country’s HS-06 tariff lines that are equal to its WTO legal binding, and we look at year-to-year changes in this variable. We expect a positive relationship between this determinant and the count of products subject to new TTBs; i.e., if the share of products with applied MFN tariffs equal to the WTO maximum binding tariff increases, then we expect aggregate-level demand for TTBs to increase, ceteris paribus.<sup>16</sup> Note that while there is inter-temporal variation in this determinant, because both MFN applied rates and WTO tariff commitments are applied equally to all trading partners, there is no cross- trading partner variation within a given policy-imposing economy. Furthermore, the country-specific indicator variable that we employ in the estimation captures any time-invariant differences in the restrictiveness of WTO commitments across countries.<sup>17</sup> In addition, when we compare trade policy formation under the WTO to policy formation during the GATT years, we interact indicator variables for the relevant trade agreement regime with the other determinants of interest.

Finally, we estimate the negative binomial regression model of the contemporaneous (time _t_ =0) count of imported products subject to new import protection, as a function of the value that these explanatory variables take on one year earlier, i.e., at time _t_ =-1. Table 2 presents summary statistics for the data used in the empirical analysis, and the Appendix provides more information on the underlying sources of the data.

16 Indeed, Bown and Crowley (2013a) consider the role of WTO disciplines for high income economies. While the estimated IRRs from that paper are in line with theoretical expectations, they are not precisely estimated. One explanation for the imprecision is the lack of inter-temporal and cross-sectional variation in WTO disciplines across the five high income economies during this sample period.

> 17 To clarify, we might also expect the _level_ of a country’s WTO disciplines to impact TTB determination. I.e., policy-imposing countries that have bound less than 100 percent of their tariffs (see column 1 of Table 1) might be less likely to use TTBs than others because there is no WTO discipline over products with unbound tariffs. However, because there is no inter-temporal variation in the share of a country’s MFN tariffs that are bound during the WTO period, any level differences are captured by the importing country indicator variables.

<mark>13</mark>
_5_ any level below 1000 parts per million, a near-quadrupling of the pre-industrial level.<sup>9</sup> Thus, without intervention for abatement, the carbon emissions driving climate change will continue rising so that the severity of climatic, ecological, and social impacts will surpass those generally discussed in the context of a doubling of CO2 concentrations. This confirms the necessity of enacting a policy of reducing emissions from their predicted trajectories if climate change is to be mitigated.

Although the level of global emissions is likely to rise over time, the inequality in per capita emissions is likely to recede. The model includes two factors that drive emissions inequality downward. First, the decreasing MPE as income increases implied by the negative coefficient of _yjl_<sup>_2_</sup> in (1) tends to reduce emissions of very high income countries. Second the income convergence implied by the GDP projections enables low income countries to grow faster and progressively gain ground on higher income countries. How quickly does emissions convergence proceed? Figure 2 provides the answer. While business-as-usual per capita mean emissions rise steadily, inequality in per capita emissions as measured by the Gini index decreases slowly, albeit faster than income inequality because of the diminishing MPE.

# _2.3. Emissions by groups of countries_

Section 3 uses a group decomposition of the Gini index to analyze the contribution of groups of countries to emissions inequality. In order to understand the forces underlying future inequality trends, it is important to analyze the baseline distribution at the start of the period.

Table 1 summarizes the state of affairs by income group in the first year (1993) and highlights the prevailing disparities in carbon emissions between groups of nations.'" Although more than threequarters of the world's population reside in the two lower income groups, these countries produce less than one-third of gross world product and a slightly smaller share of global carbon emissions. On a per 9Cumulative emissions and corresponding atmospheric concentrations are provided by IPCC (1996).

10Income groups correspond to those of the World Bank (1995), but are adjusted in order to convert from GDP at market exchange rates to GDP at purchasing power parity. Countries are held in one and only one group throughout the period 1993-2100, that of their income level in 1993.
crisis never occurred.<sup>10</sup> The turnaround that began in April 2020 was officially acknowledged on July 19, 2021, when the National Bureau of Economic Research (NBER), the arbiter of recessions, officially declared that the U.S. recession ended in April 2020.<sup>11</sup> The two-month duration make it the shortest recession in U.S. history, just one-third as long as the six-month 1980 recession, the prior shortest on record since 1854.<sup>12</sup> During the recession as economic output fell, personal incomes of many Americans actually rose, and debt loads lightened due to government stimulus programs providing additional income. Research suggests that government policies during the pandemic led poverty rates to fall and low percentiles of income to rise across a range of demographic groups and geographies (e.g., Han, Meyer, and Sullivan, 2020). An Experian report suggests that the average consumer credit scores increased significantly in 2020, mostly driven by a significant reduction in the number of consumers with subprime designation (Lembo Stolba, 2021). Credit card balances reported in the G.19 Consumer Credit statistical release fell by a very large annualized rate of 32 percent by 2020:Q2 (Adams and Bord, 2020). The Wall Street Journal reports that most Americans got richer during the first year of the pandemic owing to fiscal and monetary stimulus, as well as stock and housing market booms.<sup>13</sup> The fates of small businesses – which often are the key income sources for the proprietors and employees that operate them – were not as bleak as first predicted. While many businesses failed during the COVID-19 crisis as discussed above, one study using Bureau of Labor Statistics

> 10 <u>https://www.wsj.com/articles/the-economic-recovery-is-here-rebound-jobs-stock-market-unemployment-bidenaid-package-11622642152</u>

> 11 <u>https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021</u>

> 12 <u><mark>https://earlyretirementnow.com/2020/08/25/the-shortest-recession-ever-my-thoughts-on-the-state-of-the-economy/</mark></u>

> 13 <u>https://www.wsj.com/articles/during-covid-19-most-americans-got-richerespecially-the-rich-11624791602).</u>

15
Table 1: Simple difference in differences

|||Births p<br>|er 100<br>||Schools<br>|per 100<br>|
|---|---|---|---|---|---|---|
||2000|2009|Difference|2000|2009|Difference|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A. Cohort births age 15–19|||||||
|Control municipality|43.93|36.48|-7.45|1.13|1.15|0.02|
||||(0.54)|||(0.01)|
|Treated municipality|44.56|36.47|-8.10<br>(0.56)|1.06|1.88|0.81<br>(0.03)|
|Difference|0.64|-0.01|-0.65|-0.06|0.73|0.79|
||(0.68)|(0.65)|(0.66)|(0.03)|(0.03)|(0.02)|
|B. Cohort births age 25–29|||||||
|Control municipality|46.10|41.66|-4.44<br>(0.59)||||
|Treated municipality|47.33|42.89|-4.45<br>(0.43)||||
|Difference|1.24|1.23|-0.01||||
||(0.65)|(0.45)|(0.72)||||

Notes: This table shows a the difference in cohort births between ages 15 and 19 per 100 (Panel A), and between ages 25 and 29 per 100 (Panel B). Sample includes municipalities with populations >10,000 and <500,000. Estimates weighted by population size. _Treated municipalities_ received at least one new secondary school between 2000 and 2009, _control municipalities_ did not receive a new school between 2000 and 2009.

Source: Authors’ analysis based on school data from the 2002 wave of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age from Brazilian Vital Statistics.

31
Hence, we add a lower bound and an upper bound to our parameters. Those bounds will be equal to ( _−Inf ,_ 0) for those variables that result in a negative value from the regression and to (0 _,_ + _Inf_ ) for all others. We perform a different regression for each area by checking where the signs agree between them. If there are differences–in some regressions some parameters could be either positive or negative–we opt for signs consistent with economic theory. The COBYLA<sup>7</sup> also requires an initial value from which to start. We use a constant, k, such that the value of k multiplied by the sum of mean values of covariates will be equal to the mean of the dependent variable. The regression parameters are not used since, if we start with this initial value, there is a difference in terms of scale in that some are denominated in tenths and others in thousandths, making the conversion particularly complex.

To estimate the coefficients’ variability, we use a bootstrap resampling method. First, we resample the data with replacement and the size of the resample equal to the size of the original data set. Then the parameters are computed from the resample from the first step. We repeat this routine _n_ = 100 times. Once the parameters and their variance are estimated and the results are verified, we rebuild estimates for the years in between the two surveys.

Estimation of temporally linked values assumes that _"everything is related to everything else, but near things are more related than distant things.”_<sup>8</sup> We estimate parameters for the initial year (2000) and the final year (2007) rather than for the years in between and use a weighted mean of _β_ 2000 and _β_ 2007. If _ωty_ represents the weights and _βty_ the parameters, where _ty_ indicates a generic year between _βmin_ and _βMAX_ and min and MAX are, respectively, the first and last year for which we have estimates, estimates are obtained by: Where: This type of weighting allows us to remove the hypothesis that regressors are stable and completely invariant over time. Finally, we obtain: From these values, we decided not to use an expansion estimator because even a small difference in the sampling scheme, and thus in the way the weights are obtained, could produce a bias. As shown by Newhouse et al. (2014), a change in the sampling scheme could be the reason that SSITs sometimes underperform. We estimate

> 7 _−_ 8 The stopping criteria used for this algorithm are a tolerance of 1 _e_ or a max evaluation of 100000 loop.

> 8First law of geography, Tobler (1970)

10
|Ozone at ground level: a<br>pollutant<br>generated<br>from<br>photochemical reaction with<br>other chemical compounds,<br>which are themselves are<br>pollutants<br>(e.g.,<br>nitrogen<br>oxides<br>from<br>vehicle<br>and<br>industry<br>emissions<br>and<br>volatile organic compounds<br>emitted by vehicles, solvents<br>and industry); sunny weather<br>can generate highest levels of<br>ozone pollution.|It<br>can<br>cause<br>breathing<br>problems,<br>trigger<br>asthma,<br>reduce lung function and<br>cause lung diseases|100 μg/m3 8-hour mean|
|---|---|---|
|Nitrogen dioxide (NO2)|Long-term<br>exposure<br>can<br>increase<br>bronchitis<br>in<br>asthmatic children and reduce<br>lungfunction|40 μg/m<sup>3</sup>(annual mean) and<br>200 μg/m<sup>3</sup>(1-hour mean)|
|Sulfur dioxide (SO2)|It can affect the respiratory<br>system and the functions of<br>the lungs (coughing, mucus<br>secretion,<br>aggravation<br>of<br>asthma<br>and<br>chronic<br>bronchitis)<br>and<br>causes<br>irritationofthe eyes|20 μg/m<sup>3</sup>(24-hour mean) and<br>500 μg/m<sup>3</sup>(10-minute mean)|

Source: WHO (2006)

# **3. Accidental Risks in Energy Supply Systems**

Almost every activity under energy supply chains poses adverse impacts to human health through accidents. For example, every stage of coal mining or production – both surface mining and underground mining,<sup>1</sup> coal washing or processing,<sup>2</sup> and coal transportation (Lockwood et al., 2009) are prone to accidents. Some studies (e.g., Burgherr and Hirschberg, 2014) have analyzed accidental impacts from the energy sector, from coal mining to LPG use in kitchen. Based on

> 1 Surface mining is used when coal reserves is open or buried less than 200 feet underground. It includes removing top soils and layers of rock to expose large beds of coal and its extraction using heavy machineries. Underground mining involves when coal reserve is buried several hundred feet below the surface.  Deep wells with elevators and deep mine shafts are used in this process.

> 2 When coal is extracted from mine, it is sent to, normally using conveyor belts, to preparation plants located at the mining site to clean or wash it removing dirt, rock, ash, sulfur, and other unwanted materials. Cleaning or washing increases heating value of coal per unit of weight.

8
sources generating pollution (e.g. less wood-smoke from heating) to be trapped by inversions that occur during the summer. Regardless, we show that omitting the summer inversions does not appear to impact the predictive ability of winter inversions (Table 2, Columns 2-3).

One might be concerned that the predictive power of our inversions instrument is due to some artefact of the construction of our measures of pollution. These are based on satellite measurements but also include inputs from dispersion modeling. To alleviate this concern we conduct the following robustness check. For 21 cities around India we download data on daily pollution levels from the Indian Central Pollution Control Board from 2015 to 2020. This gives us an unbalanced panel of pollution (due to missing data) at the daily level across these cities. We then use our weather data to construct daily measures of inversions and ground level weather controls for each of these cities. We test whether our measure of inversions successfully predicts higher levels of pollution in these cities using this pollution dataset that is unrelated to our primary pollution data. We find that a day with an inversion leads to PM2 _._ 5 levels that are roughly 5% ( _t-stat:_ 1.91) higher relative to the mean. This test, using daily data distinct from our primary pollution data suggests that the inversion instrument is both a good predictor of higher pollution levels and that this predictive power comes from true impacts in the real world rather than because of any feature of the pollution data construction.

# **5.2 Second stage**

Our results indicate that higher levels of pollution result in smaller year-to-year changes in GDP. We find, instrumenting for pollution levels with the number of inversions during the fiscal year, a 1 _μg/m_<sup>3</sup> year-to-year increase in PM2 _._ 5 pollution reduces the year-to-year change in GDP by 0.7 percentage points (Table 3). In our sample the median year-to-year change in pollution is 0.8 _μg/m_<sup>3</sup> and the median year-to-year change in GDP is 6.4 percent.

Our results imply that in a year where pollution levels fell by 1 _μg/m_<sup>3</sup> relative to the – – prior year a change opposite signed than the median change we observe in our data year over year GDP change would be 7.1 percent rather than 6.4 percent. In standard deviation terms, a decrease in pollution levels of 1 standard deviation of the typical change would result in an increase in the year over year GDP change of 0.4 standard deviations.

A 1 _μg/m_<sup>3</sup> change in annual average PM2 _._ 5 relative to the prior year may not seem large, especially relative to the size of our estimated GDP effects. That is a mis-perception. A 1 _μg/m_<sup>3</sup> change in the average annual level of pollution implies large changes in daily pollution levels. To see this consider the following example. Assume that a given Indian district experienced daily PM2 _._ 5 levels exactly equal to the annual national average for each 12