rafmacalaba/datause-encoder-data huggingface dataset:
of key policy and economic fundamentals—those that have been found to be critical in influencing investors’ decision-making as well as those that may be particularly important in developing countries, such as energy availability, the level of trade barriers, and institutional capacity. We use cross-country data for 78 countries, both developing and developed, from 2000 to 2010.

**In the broadest sense, all fundamental economic variables that determine growth and the level of development would also likely determine FDI inflows.** Institutions, economic policies, macro stability, and legal and regulatory policies that enhance economic growth and development would also tend to influence FDI flows. Nonetheless, practicality demands being parsimonious in the choice of explanatory variables because many of these factors can be highly correlated, making it observationally difficult to determine the independent impact of all potentially important determinants of FDI growth. For example, measures of institutional development and corruption are likely to be an important determinant of FDI flows, but they are also highly correlated, so distinguishing the independent impacts of both these variables on FDI may be nearly impossible. Moreover, the larger the number of explanatory variables in the analysis, the fewer the degrees of freedom and the number of countries that can be included in the empirical analysis.

**Models of FDI growth are typically based on variants of the neoclassical growth model, with FDI being the variable of interest, rather than growth itself.**<sup>15</sup> Based on this literature, we model the growth rate of FDI as a function of the initial level of FDI, �����FDI0 , along the initial state of other factors that determine growth, Y0, in addition to the factors that specifically are associated with attracting FDI, _X_ : FDİ = f(FDI�����0, Y0, Ẋ). We linearize this equation as:  FDİ = α+ β1�����FDI0 + β2Y0 + β3Ẋ + ε. In this study our period of examination is 2000 to 2010 and we use the following basic cross-country reducedform model:

where FDI represents inward FDI stock relative to GDP, NR represents the stock of natural resources per capita, the Ẋt1−tn denotes the growth rate and X<sup>�</sup> t1−tn denotes the annual average of the variable X over the period t1 −tn, F represents the vector of explanatory variables that includes proxies for human capital, corporate tax rates, energy availability, financial development, infrastructure, macroeconomic stability, trade barriers, investment policy openness, and control of corruption. The variables doil and dregion are dummies for oil exporter status and regional factors. In sum, the basic model explains the growth of inward FDI as a share of GDP between 2000 to 2010, based on the average initial stock of FDI

> 15 For example, Borensztein et al. (1998), Baumol (1986), Barro and Sala-i-Martin (1992, 2003).

19
|P104901|2013|2016|8/19/2015|23 Closed|India|
|---|---|---|---|---|---|
|P104931|2007|2007||0 Closed|Bhutan|
|P104931|2007|2009|8/26/2008|1 Closed|Bhutan|
|P104937|2009|2009|6/12/2009|1 Closed|Morocco|
|P104937|2009|2010|6/12/2009|1 Closed|Morocco|
|P104937|2009|2019|5/9/2019|2 Closed|Morocco|
|P104943|2008|2008||0 Closed|Uganda|
|P104943|2008|2009||0 Closed|Uganda|
|P104943|2008|2010||0 Closed|Uganda|
|P104943|2008|2011||0 Closed|Uganda|
|P104943|2008|2012||0 Closed|Uganda|
|P104943|2008|2013||0 Closed|Uganda|
|P104943|2008|2019|5/20/2019|1 Closed|Uganda|
|P104945|2007|2008||0 Closed|Mozambique|
|P104945|2007|2009|12/30/2008|1 Closed|Mozambique|
|P104945|2007|2009|5/28/2009|2 Closed|Mozambique|
|P104945|2007|2010|11/30/2009|3 Closed|Mozambique|
|P104945|2007|2010|6/21/2010|4 Closed|Mozambique|
|P104945|2007|2011|12/20/2010|5 Closed|Mozambique|
|P104945|2007|2012|8/16/2011|6 Closed|Mozambique|
|P104945|2007|2012|4/14/2012|7 Closed|Mozambique|
|P104945|2007|2013|11/12/2012|8 Closed|Mozambique|
|P104945|2007|2013|5/10/2013|9 Closed|Mozambique|
|P104945|2007|2014|10/7/2013|10 Closed|Mozambique|
|P104945|2007|2014|5/7/2014|11 Closed|Mozambique|
|P104946|2008|2008||0 Closed|Yemen,Republic of|
|P104946|2008|2009||0 Closed|Yemen,Republic of|
|P104946|2008|2010|5/5/2010|1 Closed|Yemen,Republic of|
|P104946|2008|2011|1/11/2011|2 Closed|Yemen,Republic of|
|P104946|2008|2011|3/12/2011|3 Closed|Yemen,Republic of|
|P104946|2008|2012|7/5/2011|4 Closed|Yemen,Republic of|
|P104946|2008|2012|11/14/2011|5 Closed|Yemen,Republic of|
|P104946|2008|2013|7/10/2012|6 Closed|Yemen,Republic of|
|P104946|2008|2013|12/18/2012|7 Closed|Yemen,Republic of|
|P104946|2008|2014|9/23/2013|8 Closed|Yemen,Republic of|
|P104946|2008|2020|6/11/2020|9 Closed|Yemen,Republic of|
|P104960|2009|2009|12/10/2008|1 Closed|Jordan|
|P104960|2009|2009|6/12/2009|2 Closed|Jordan|
|P104960|2009|2010|12/22/2009|3 Closed|Jordan|
|P104960|2009|2010|6/16/2010|4 Closed|Jordan|
|P104960|2009|2011|2/22/2011|5 Closed|Jordan|
|P104960|2009|2012|7/26/2011|6 Closed|Jordan|
|P104960|2009|2012|4/27/2012|7 Closed|Jordan|
|P104960|2009|2013|12/26/2012|8 Closed|Jordan|
|P104960|2009|2014|7/4/2013|9 Closed|Jordan|
|P104960|2009|2014|12/28/2013|10 Closed|Jordan|
|P104960|2009|2014|5/25/2014|11 Closed|Jordan|
The molecule information for each patent is necessary for matching the patent's molecule with the corresponding commercial drug data from IMS. Interestingly, there are four (out of 39) product patents, for which while the corresponding sales data exists in IMS, the innovator is not present in IMS. For example, Eli Lilly was granted a product patent for its schizophrenia drug Zyprexa (active ingredient Olanzapine) in August 2007. While Olanzapine is manufactured in India by various generic firms (e.g. Cipla, Lupin Labs, Torrent Pharma) as indicated in the IMS data, the innovator Eli Lilly, is not one of the manufacturers in India. One possible explanation could be the existence of an agreement between the innovator and a generic manufacturer such as license deals, or joint ventures. Our interviews with industry executives reveal that comprehensive information on these deals is not publicly available.

The basic model we estimate is as follows: _Yjt = α + β1Patentjt + β2Patentjt*Typejt + γj + θt + Zjt + ejt_ where the outcome variable is the log price of molecule _j_ at quarter _t_ . Since prices are likely to change proportionally rather than by a fixed dollar amount, it is sensible to transform the variable using logs. Furthermore, pharmaceutical prices are highly skewed to the right, and the log transformation produces a distribution that is much less sensitive to outliers (Duggan and Scott Morton, 2006). The _γj’s_ represent a full set of molecule fixed effects that control for unobserved time invariant differences across drugs. The _θt’s_ represent a full set of quarter*year fixed effects to control for any time varying aggregate factors affecting the price or utilization of pharmaceutical products. The standard errors are clustered at the molecule level to account for serial correlation in the error term. _Patentjt_ is an indicator variable for whether molecule _j_ received a product patent in quarter _t_ . _Typejt_ is an indicator variable that takes the value 1 if the product patent is on the active ingredient (the strongest type of product patent) and is zero otherwise. The interaction _Patentjt*Typejt_ allows us to investigate heterogeneous effects of the patent on pharmaceutical prices. _Zjt_ includes a vector of controls such as whether the molecule is under price control or not.

To the extent that product patent enforcement results in the withdrawal of generics from the market, we should expect to find a negative effect on the number of products sold that contain the patented molecule. Similarly, the withdrawal of generics from the market is likely to result in an increase in the fraction of total quantity sold to the innovator. We are also interested in the effect on utilization. On the one hand as prices rise we may expect to find lower utilization. On the other hand, the innovator may spend more on advertising, which could serve to increase utilization. In addition, the 10
**-** **<u>% Junior Men(/Women)</u>** Proportion of production workers who were men (women) for whom junior high school – either technical or vocational - was their highest level of schooling.

**-** **<u>% High + Men(/Women)</u>** Proportion of production workers who were men (women)  who, at a

minimum, completed high school: workers with a Bachelors, Masters or PhD degree are also included in this category.

# **A.2 Data Cleaning Procedures**

The problem of non-persistent extreme values for many variables is widely discussed in published work using the SI (see for example, Blalock et al., 2008, Rijkers and Hallward-Driemeier, 2013). In some cases, these values may be the result of key punch errors, where, for example, ownership share is recorded as 340 percent rather than 34 percent. Where shares (exports and ownership) were reported, these were easily corrected, but for balance sheet variables, more extensive cleaning was required.

These likely key punch errors and other highly volatile trends were corrected in the data by identifying firms with implausibly large non-persistent changes over one or two years and replacing values with those of preceding and succeeding years (or just one or the other of the observation was at beginning or end of the series). Based on close observation of normal variation of these variables, the thresholds for identifying large non-persistent shifts were a 100% change in labor, a 200% in real value added and real output, a 150% change in real inputs, and 100% change in capital and average wages. Next, firms were dropped from the data when they had such a significant number of non-persistent jumps that it was impossible to interpolate (this constituted less than 1% of the sample). Finally remaining outliers were identified by eyeballing plots of key relationships (for example inputs per worker) and spot interpolating obvious remaining outliers.

42
Subsequently, we perform further tests in Table A2 (Appendix) to ensure the robustness of our results against different temperature measures. We conduct these tests by employing (i) log of temperature (Column 1); (ii) temperature data at 0.5<sup>◦</sup> resolution from the Climate Research Unit of the University of East Anglia (CRU) (Column 2); (iii) the number of days when temperature exceeds 28<sup>o</sup> C (Column 3); (iv) excluding regions where temperature exceeds this level (Column 4); and (v) employing temperature shock, defined as the difference between actual temperature and long-term temperature being greater (less) than 2 (-2) standard deviations (Column 5). The outcomes of these tests show little deviation from our baseline specification. We also conduct alternative measurements of temperature bins for Figure 2 by dividing the temperature distribution within each region into deciles, with the 60<sup>th</sup> percentile serving as the baseline group. The results presented in Figure A1 (Appendix A) indicate that the effects of extreme weather remain consistent with those observed in Figure 2.

Finally, we examine alternative chronic poverty measures. Specifically, we define chronic poverty using the poverty line of $2.15/day (both number of incidences and proportion of incidence), and we also measure the average poverty rate using various window lengths (4year and 5-year windows). For this test, we employ the fixed-effects model as outlined in Equation (2). The results shown in Table A3 (Appendix A) reaffirm our previous findings.

# **5. Conclusion**

We offer an updated and comprehensive review of recent studies on the effects of climate change on poverty and inequality, paying special attention to studies’ data sources as well as empirical methods. Our findings suggest that while studies generally find negative impacts of climate change on poverty, especially for poorer countries, there is less agreement on its impacts on inequality. We further analyze a recent global database at the subnational unit level that is constructed from official national household income and consumption surveys for more 33
(13) LogPt - allogw*it + a2 (W/W*)it + a3INVLt + a4DUTYjt +f, + y, A number of implications arise from the reduced form (13). First, the coefficient on w* should be equal to 1. Second, the coefficient on w/w*, INV andl DUTY should be positive. Third, the coefficient on DUTY should be greater than or equal to the coefficient on INV.

The supply relation (13) is identified using two key assumptions. First, we assume that the technology is characterized by constant marginal costs. The marginal cost curve, which is flat, only shifts if input prices or the exchange rate move, which is capture- by w*. Second, we assume that changes in the number of firms or in the elasticity of demand may be captured by industry and time dummies. These assumptions together allow us to exclude quantity and income variables on the right hand side of (]3). Quantity measures at the 4-digit SITC level were not available for imports to test the accuracy of the specification. One modification which could be desirable in future research would be to make either marginal costs or the demand elasticity explicitly a function of output indices. It is possible that omitting changes in output, which cannot be captured by industry or time effects, bias the results. For example, if increased output is associated with falling prices and higher likelihood of an investigation, then the coefficient on investigations will be biased downwards.

# Data

Equation (13) is estimated using quarterly data for 1981-1986 for 41 industries. The price variable logP is taken from the BLS import price index series which calculates import price indices for industries at the 4-digit SITC 11
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# **b. Labor market outcomes**

<!-- Start of picture text --> 90<br>80 75 77 76 1999<br>70<br>60 53<br>47<br>50 44<br>37<br>40 32 31<br>27 29<br>30<br>19<br>20 16<br>12 11<br>10<br>0<br>Household Household Household Household HH receives wage<br>employment rate employment rate employment rate employment rate income<br>in agriculture in manufacturing in services<br>stayed poor escaped poverty non-poor<br>80<br>72 71 2006<br>70 66<br>60<br>52 53<br>50<br>50 44<br>39<br>40<br>30 31<br>30 26 27<br>17 18<br>20<br>12<br>10 4 6 7<br>0<br>Household Household Household Household HH receives wage Household receives<br>employment rate employment rate in employment rate in employment rate in income remittances<br>agriculture manufacturing services<br>stayed poor escaped poverty non-poor<br><!-- End of picture text -->

## Source: Calculations based on HBS data.

Note: _Poor_ refers to those with consumption levels lower than $2.5 per day. Results presented are for the lower-bound mobility estimates using the synthetic panel methodology by Dang et al (2011). Results for falling into poverty are not presented as samples sizes are small.

**Labor market outcomes are also associated with transitions out of poverty** . Differences in households’ employment rate are not large across groups, however, the sector of employment does seem to matter. Households who stayed poor had a relatively higher share of members employed in agriculture (44 percent and 50 percent in 1999 and 2006, respectively), and relatively fewer members employed in manufacturing or services, compared to other groups. Further, households escaping poverty more often received wage income. A higher share of households receiving remittances in 2006 stayed out of poverty

23
concerns of pre-existing trends, we include lagged export growth.

The resulting specification is run separately to each destination, _n_ = _US_ , _CH_ , _RW_ : where _λ_<sup>_n_</sup> _iω_<sup>istheshareofvariety</sup><sup>_iω_tocountry</sup><sup>_n_intotalexportsofcountry</sup><sup>_i_.The</sup><sup>_λn_</sup> _iω_<sup>sharesare</sup> defined as the (pre-war) export values for continuing products divided by total country exports.

# **5.1 Heterogeneous Export Growth in Targeted Products**

The analysis reveals two key takeaways: i) substantial cross-country heterogeneity in export growth in targeted products compared to non-targeted products; and ii) a central role for the country-specific component of the tariff elasticities, rather than any other component, in explaining of this heterogeneity.

Figure 2 plots the export growth defined in (10) across countries.<sup>17</sup> By export growth, we specifically mean the growth of products taxed by US or China relative to other products within each exporter, importer, and sector. Hence, the figure indicates the countries that better exploited global export opportunities in products targeted by the trade-war tariffs.

On average, countries’ exports in targeted products increase by 6.7%, with a standard deviation just as large at 6.3%. For example, the increases in exports of targeted relative to untargeted products in Thailand and Mexico are 14.5% (se 4.9%) and 10.9% (se 6.4%), respectively, while Ukraine’s exports fall 12.1% (se 8.7%) and Canada’s export growth is just 2.1% (se 5.4%).<sup>18</sup> Through the lens of our model, the heterogeneity is expected. By construction of (10), it may be due to the country-specific demand or supply parameters, or due to differences in specialization across products with different tariff changes or with different supply elasticities elasticities. We find that the bulk of the cross-country variation in Figure 2 comes from the country-specific component of the tariff elasticities (the _βzi_<sup>_n_in (8)), and not from specialization (</sup><sup>_λn_</sup> _iω_<sup>), sectoral tariff</sup> elasticities ( _βzj_<sup>_n_</sup> ( _ω_ )<sup>), or size-specific tariff elasticities (Γ</sup> _z_<sup>_n_).</sup>

> 17We report bootstrapped confidence intervals for ∆ ln� _Xi_<sup>_W D_</sup> . We construct bootstrapped standard errors to each aggregate response by cluster bootstrapping specifications (9). We sample with replacement within products, estimate the specifications in (18We correlate ∆ ln�9 _X_ ), construct the aggregate predicted exports to the each estimation using ( _i_<sup>_W D_</sup> with _i_ ’s characteristics: size, distance to the US and China; the share of exports covered by10), and repeat 50 times. “deep” trade agreements (Mattoo et al., 2020); and 2017 FDI stock (Financial Times FDI Markets Database and Refinitiv). These descriptive relationships suggest that greater predicted exports for countries that are larger, further from the US, and with more exports covered by trade agreements. This is consistent with Alfaro et al. (2023), who find that trade agreements result in cross-country export complementarities for firms.

9
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30
Table 2: Impact on Fishery Income

||Income<br>(main job)|Total<br>income|Income<br>(main job)|Total<br>income|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**[Panel A] Ge**|**ographic co**|**ntrol group**|||
|treat _×_ post|-0.297***|-0.298***|-0.433***|-0.441***|
||(0.070)|(0.070)|(0.063)|(0.062)|
|Observations|2,477|2,477|872|872|
|R-squared|0.253|0.249|0.122|0.125|
|**[Panel B] Ind**|**ustry contr**|**ol group**|||
|treat _×_ post|-0.301***|-0.299***|-0.461***|-0.465***|
||(0.058)|(0.056)|(0.057)|(0.056)|
|Observations|7,991|7,991|2,090|2,090|
|R-squared|0.310|0.294|0.066|0.068|
|Month FE|Yes|Yes|Yes|Yes|
|Individual FE|No|No|Yes|Yes|

Standard errors in parentheses, *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1 Note: this table shows the impact of Formosa on fishery income (monthly;’000 VND; log-transformed) in 2016. “Treat” indicates saltwater fishers living in Ha Tinh, Quang Binh, Quang Tri, and Hue. “Post” indicates May-2016 or after. Geographic control group (Panel A) consists of all saltwater fishers living in provinces south of Phu Yen (i.e. distant from Formosa region). Industry control group (Panel B) consists of workers in unaffected industries (i.e. manufacturing, construction, and retail) living in the four Formosa-affected provinces. Columns 1-2 report results using a sample consisting of all individuals identified as saltwater fishery workers (for Panel A) and workers in the control industries (for Panel B) before May-2016. Columns 3-4 report results using a sub-sample restricting to only individuals who were surveyed twice in the Labor Force Survey 2016 – before and after May-2016. Standard errors are clustered at the district level.

32