rafmacalaba/datause-encoder-data huggingface dataset:
We have detailed vehicle attributes at the trim level. A vehicle trim is defined as a unique combination of brand (e.g., Toyota), model (e.g., Camry), fuel-type (e.g., BEV or PHEV), and driving range. After dropping very unpopular models with annual national sales less than 400, there are 27,577 observations with 190 unique trims from 44 firms in our data. Figure 7 presents annual new EV sales ( individual purchases) per million residents by city in 2015 and 2018. There are two salient features. First, sales increased dramatically from an average of 67 per million residents to 497 during 2015-2018. Second, there is large spatial heterogeneity: the highest and lowest sales per million residents were 5,587 and 7 in Shenzhen, Guangdong Province and Hechi, Guangxi Autonomous Region, respectively in 2018. Interestingly, many neighboring cities with similar household income levels have very different level of EV penetration, likely due to differences in local policies.

**Charging Stations** We obtain information on non-private charging stations from China Electric Vehicle Charging Infrastructure Promotion Alliance, a member organization of charging operators. Our data contain information on opening data, location, ownership type (public or dedicated), the number of charging ports/outlets (by AC or DC) on site. Public charging stations are open to all while dedicated are open to a designated group such as residents in a community or company employees. There are about 337,000 (non-private) charging ports/outlets operated by their members, accounting for 69% of all operating charging ports at the end of 2019 in China.<sup>24</sup> Figure 8 shows the number of charging ports per million residents for the 150 sample cities at the end of 2015 and 2018. The average number of charging station per million residents across cities increased from 4.7 to 134 from December 2015 to December 2018. The overall temporal and spatial patterns are very similar to those of EV sales in Figure 7. The correlation coefficient between EV sales and the number of charging ports per million residents is 0.46 and 0.79 for the 150 cities in 2015 and 2018, respectively.

**Policies** We are not aware of a centralized database on various EV policies in China. We collect the central and local policies from a variety of online sources including government websites, industry reports, and newspapers. Central subsidy programs are issued through official government documents that are generally published on the government official websites of the Ministry of Finance (MoF), Ministry of Science and Technology (MoST), Ministry of Industry and Information Technology (MIIT) and National Development and Reform Commission

> 24Among these charging ports, 71% are open to the public and the rest are dedicated to groups.

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Equation A.11 derives from the fact that one can choose to treat individuals equally or to give a particular group of individuals more weight. For example, if more weight is put on poor individuals, it is possible to obtain the social cost of the tax with respect to the poor. Similar exercises can be done with respect to geographic location or ethnic group.

# **Appendix B: Data appendix**

The estimates are based on the series of Mexican National Household Income and Expenditure Surveys (ENIGH) collected by the Insituto Nacional de Estadistica, Geografia e Informatica (INEGI) between 1984 and 2000. Data was collected for the years 1984, 1989, 1992, 1994, 1996, 1998, and 2000. All of the surveys are representative at the national level and were similarly constructed. The design survey is stratified at the state level. The analysis looks specifically at ten categories of food products which are aggregates over varieties. The concordance between the household expenditures and the product categories follows the one developed by Ianchovicina, Nicita and Soloaga (2002).

The application to Mexico relies on seven household surveys that categorize the households into 32 different geographical locations corresponding to the federal states of Mexico. Assuming that price differences within each federal state and survey year are due only to quality issues, measurement error and inflation, the households are classified into clusters according to their geographical location and to the year of survey.<sup>31</sup> Therefore, the households are categorized into 224 clusters with an average of 329 observations per cluster.

Household questionnaires ask respondents to report the physical quantity actually purchased along with the expenditures. The ratio of expenditure and quantity determines the unit value that, with some caution, can be interpreted as a measure of price. Figure A.1 reports the value of the logarithm of unit value for the ten goods from 1984 to 2000. The graphics show a downward trend in the logarithm of the unit values of meat

> 31 Only the 2000 household survey classifies the households in a more disaggregated geographical location.

30
savings of poorer states). However, there may be other factors at play, such as the business environment and level of commercial contract enforcement. Several reports in recent years<sup>55</sup> illustrate the wide divergences in the institutional development and the quality of enforcement across different states in Mexico. As previously mentioned, slow progress has been made to-date in converting public registries to electronic access and linking different states. The time and cost to register property and collateral (taxes, notary charges and registration fees<sup>56</sup> ) is determined at the state or municipal levels and vary substantially<sup>57</sup> . In addition, the procedural complexity and related time and cost (court expenses and attorney fees) of enforcing contracts vary widely by state<sup>58</sup> . The combination of these factors reduces the attractiveness of bank lending and disproportionately hurts SMEs (which are more likely to need to offer collateral) and mortgage borrowers (who depend on the efficiency of the state’s property registry). Although no analysis has been done on the impact of differences in doing business across states, there is anecdotal evidence of banks tightening lending (or being less aggressive in promoting it) in states that are perceived to be riskier and costlier.

> 55 See, for example, Moody’s Investors Service (April 2002), Consejo Coordinador Financiero (2004) and World Bank (2006).

> 56 Although most public registries have established a cap on registration fees, there remain some states (e.g. Jalisco, Hidalgo, Tamaulipas and Quintana Roo) that still determine fees on an uncapped basis as a percentage of the value of secured obligations.

> 57 For example, according to the 2006 Doing Business report, the cost of registering property in Mérida (Yucatán) and Tlalnepantla (Estado de Mexico) is 2.4 and 6.1 percent respectively of the property value, while the cost of registering collateral in Aguascalientes and Ciudad Juárez (Chihuahua) is 1 and 3.2 percent respectively of the value of the loan.

> 58 For example, according to the 2006 Doing Business report, the cost of enforcing a contract in Guadalajara (Jalisco) and Mérida (Yucatán) is 5 and 33 percent respectively of the debt owed.

36
Table 4: Effects of refugee presence on household welfare with analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009-2012). <u>Additional test for Exclusion restriction in columns (5) to (8).</u>

|||Log|of welfare (c|onsumption ag|gregate per ad<br>|ult equivalen<br>|t)<br>||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||<br>|<br>Panel A (2SL|S 2<sup>nd</sup>Stage)||||
|Log of refugees (50km)|0.03226|0.03207|0.07808*|0.07384*|0.09030**|0.08324*|0.09800**|0.08859**|
||(0.04466)|(0.04459)|(0.04453)|(0.04302)|(0.04473)|(0.04313)|(0.04532)|(0.04367)|
||||||-0.64678**|-0.48240|-0.61513*|-0.43502|
|Log of Conflict Spillover index|||||(0.29807)|(0.33332)|(0.33319)|(0.38143)|
|Observations|5,453|5,450|4,105|4,102|4,105|4,102|4,105|4,102|
|Kleibergen-Paap rk Wald F|19.34|19.33|14.62|14.15|14.61|14.12|14.48|13.97|
|Root MSE|0.593|0.593|0.533|0.362|0.533|0.362|0.533|0.362|
|||||Panel B (2SL<br>Log of refuge|S 1<sup>st</sup>Stage)<br>es (50km)||||
|Log of Distance IV (mean border distance)|9.24792***|9.24997***|8.71514***|8.68000***|8.93810***|8.92130***|9.21248***|9.21480***|
||(2.10281)|(2.10376)|(2.27960)|(2.30744)|(2.33830)|(2.37396)|(2.42096)|(2.46509)|
|Observations|5,453|5,450|4,105|4,102|4,105|4,102|4,105|4,102|
|Root MSE|0.581|0.582|0.612|0.593|0.610|0.592|0.610|0.591|
|**_Included controls_**|||||||||
|Exogenous controls (age, agesq, sex “male==1”)|_No_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status, education, occupation,<br>other income sources, land ownership)|_No_|_No_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_No_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_Yes_|_No_|_Yes_|
|Fatalities(Conflict Spillover)|_No_|_No_|_No_|_No_|_Yes_|_Yes_|_Yes_|_Yes_|

_Panel A are the results from the OLS, Panel B represents the second stage of the IV regression and Panel C shows the first stage results from the IV regression. Apart from HH size, the more endogenous controls added are as at the base year of the study. In Regression (4) where household fixed effects replace the cluster fixed effects, all the time invariant variables drop off (only age, agesq & male remain). Sampling weights are considered and Standard errors clustered at the cluster level in all regressions. Column (5)-(8) include conflict spill over as control. (5) & (6) considers all neighboring and refugee source countries while (7) & (8) consider only the closest neighboring countries irrespective of being refugee source countries or not. (5) & (7) involve Cluster FE while (6) & (8) involve HH FE. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1._ 23
Given the sampling design discussed in the preceding paragraphs, i is the probability for selecting a host HH household ( ) of stratum , in mauza , and enumeration area . kk PP HH ii jj i kk i = ×<sup>1</sup> ×<sup>1</sup> HH HH mmii × nni i i kk HH HH PP kk kk Where: ∑nn nn ll is the number of mauzas selected in each stratum _i_ . iii is the number of host households according to the 2011 census in mauza of stratum . mm HH i is the number of segments/enumeration areas in mauza j of stratum . nn jj ii i is the number of listed households in stratum mauza , in enumeration area (the target was to nn ii HH select 13 households per segment).kk ll ii, jj kk The three terms on the right-hand side of the equation respectively represent the probability of selecting a mauza within a stratum in the first stage; the probability of selecting an enumeration area in a mauza in the second stage; and the probability of selecting a specific host household in the third stage. jj ii kk jj Where:

# <u>Listing methodology</u>

The main listing exercise was an important part of the preparation of the survey as existing population counts for the host population dated 2011. Analysis of the IPA count for the host population indicated that this exercise did not provide reliable updated population counts of the host population due to the partial coverage of areas, as well as the large estimation error coming from key informants reporting figures centrally. Rohingya counts in camps were updated more frequently, however, movements across areas or campsites could have happened between the IOM count and the data collection. Therefore, a listing exercise was conducted for both host and Rohingya displace in the selected enumeration areas. In this section we explain how the listing exercise was conducted and highlight some challenges that arose when implementing the listing and their solutions.

The resources available for refugee camps and host communities were significantly different, but the proposed listing methodologies for both samples were based on the respective maps available for the enumeration areas. Field teams were instructed to identify the enumeration areas with assistance from local authorities and using landmarks from the maps. Once at the enumeration area, the listing instructions were divided into three categories:

- For EAs with less than 400 households in both camps and hosts, the entire EA was to be listed.

- For larger EAs with more than 400 reported households in camps (based on NPM population counts), the block was divided into sub-blocks. The segmentation methodology yielded subblocks of 100-150 households each based on the number of buildings and geographical

17
on employment. However, our paper improves on the previous works by extending the baseline period to 2016 to capture more years of seasonal pre-pandemic trends, while also providing a larger sample size and hence more precise effect estimates. Multiple baseline periods are useful in estimating the appropriate counterfactuals, and these are often missing from LMIC studies that leveraged the use of HFPS (for example, the work of Khamis et al., 2021; Kugler et al., 2021; Delaporte and Pena, 2020; Jain et al., 2020; Seck et al., 2021; Bundervoet, Davalos and Garcia, 2021; Genoni et al., 2020; James et al., 2021).

To account for the potential issue of simultaneous inference and multiple hypothesis testing, we follow Benjamini and Hochberg’s (1995) concept of a false discovery rate (FDR). Whereas a typical unadjusted p-value of 0.05 suggests that 5 percent of all tests are incorrectly discovered as statistically significant, an FDR-adjusted q-value of 0.05 suggests that 5 percent of all statistically significant tests are false positives. The FDR adjustment for multiple hypothesis testing imposes a higher bar for a coefficient to be considered as having a statistically significant effect than without the adjustment. However, this approach is less stringent than the extremely conservative Bonferroni (1935) correction, where the p-values would simply be inflated by the number of tests performed. We calculate FDR-adjusted q-values within each table.

Figure 1 illustrates our identification strategy and the visual plausibility of the parallel trends assumption required for this strategy, and also indicates the presence of seasonal variations in employment-to-population ratios, both for the male and female populations. Generally, the employment-to-population ratio is lower in August than in February, which is attributable to the cyclicality of harvest seasons in Indonesia. However, the graph points to the strong presence of an anomaly in 2020 (indicated by the red line), where the downward slope of the employment-topopulation ratio is much steeper than in preceding years. This indicates that there are components 22
These data raise a final policy concern that the potential forces of “cascading contingent protection” impose particularly complex _coordination_ issues thus impacting the incentives and ability for Turkey’s policymakers to _remove_ TTBs. TTBs currently covering many downstream and upstream segments of the value chain for a particular industry (e.g., from petrochemicals to synthetic fibers and yarns to textiles and apparel) may require a coordinated removal of the trade barriers so as to best neutralize the overall impact to firms throughout the industry. For example, a Turkish firm may be more willing to have policymakers remove a tariff on a competing foreign firm’s _output_ if it would be offset by the contemporaneous removal of a separate Turkish import tariff on that Turkish firm’s _inputs_ . However, Turkey’s current institutional system assesses removal of each product’s TTB as an independent policy decision without consideration of spillover effects through input-output linkages.

# **3.2.4 The duration problem of Turkey’s TTBs**

Another concern for a number of economies that are active users of TTBs is the sluggishness with which policymakers are able to remove such _temporary_ trade barriers. Indeed, the stock of Turkey’s import products covered by TTBs has been increasing during the 2008-11 period mainly because of policymakers’ failure to _remove_ these policies in a timely manner (see again Figure 2).

First, antidumping is a policy that, under the sunset review provisions of the WTO, is supposed to be removed five years after the date of application. As of the end of 2011, 70 percent (89 of the 127) of the antidumping measures that Turkey had in effect had been in place for longer than 5 years.<sup>20</sup> However, it is important to note that Turkey has not been universally unable to remove applied antidumping measures. On a trade-weighted basis, the sharp decline in the imports covered by TTBs in 2009 illustrated in Figure 2b is due to Turkey’s removal of antidumping barriers on steel billets from Russia, Ukraine and Moldova described earlier that covered a large amount of imports and which had been in effect since 1995. An open question is what made removal of this particularly economically sizeable set of import restrictions possible – even at the height of uncertainty with the global economic crisis – and yet other temporary barriers have not been removed.

Second, global safeguards are typically applied for 3 or 4 years, and that is inclusive of a phaseout period in the run-up to their removal. In especially difficult circumstances, WTO rules permit the

> 20 Moore (2006) provides an early assessment of the United States’ difficulties in removing previously-imposed antidumping import restrictions despite the Sunset Review provisions introduced in the WTO as a result of the Uruguay Round negotiations.

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**Table 6: Triple difference estimates of NREGS** **<u>participation impacts for households with and without casual laborers</u>**

|||**DDD**||**DD**|**D and PSM **||
|---|---|---|---|---|---|---|
||**PHASE**|**1 DISTRIC**|**TS**||||
|**Households with a casual laborer**|||||||
|Consumption (Rs/year)|1,748|(456)|***|1,293|(479)|***|
|Energy intake (Kcal/day)|343|(95)|***|223|(95)|**|
|Protein intake (g/day)|5.31|(1.78)|***|3.54|(1.76)|**|
|Nonfinancial assets (Rs/year)|1,014|(461)|**|787|(528)||
|Consumption (log)|0.157|(0.044)|***|0.094|(0.045)|**|
|<br>Energy intake (log)|0.138|(0.045)|***|0.091|(0.048)|*|
|Protein intake (log)|0.099|(0.041)|**|0.062|(0.046)||
|Nonfinancial assets (log)|0.459|(0.137)|***|0.360|(0.135)|***|
|<br>Number of observations|779+ 768|= 1,547||767 + 742|= 1,509||
|**Households with no casual laborer**|||||||
|Consumption (Rs/year)|429|(824)||333|(788)||
|<br>Energy intake (Kcal/day)|-69|(140)||-120|(129)||
|Protein intake (g/day)|-2.92|(2.66)||-3.41|(2.79)||
|Nonfinancial assets (Rs/year)|1,674|(860)|*|1,871|(848)|**|
|Consumption (log)|0.012|(0.067)||0.002|(0.066)||
|Energy intake (log)|-0.057|(0.059)||-0.069|(0.059)||
|Protein intake (log)|-0.060|(0.058)||-0.065|(0.059)||
|Nonfinancial assets (log)|0.521|(0.190)|***|0.409|(0.205)|**|
|Number of observations|238 + 63|6 = 874||231 + 582|= 813||
||**PHASE 2**|**and 3 DISTR**|**ICTS**||||
|**Households with a casual laborer**|||||||
|Consumption (Rs/year)|-58|(642)||293|(669)||
|<br>Energy intake (Kcal/day)|51|(126)||209|(127)||
|Protein intake (g/day)|2.43|(2.28)||5.78|(2.29)|**|
|Nonfinancial assets (Rs/year)|-503|(500)||-359|(535)||
|<br>Consumption (log)|-0.026|<br>(0.066)||0.037|<br>(0.069)||
|Energy intake (log)|0.014|(0.058)||0.086|(0.062)||
|Protein intake (log)|0.047|(0.054)||0.122|(0.055)|<br>**|
|Nonfinancial assets (log)|-0.263|(0.286)||-0.253|(0.305)||
|<br>Number of observations|315 + 74|<br>2 = 1,057||308 +|<br>704 = 1,012||
|**Households with no casual laborer**|||||||
|Consumption (Rs/year)|461|(1192)||1,531|(1300)||
|<br>Energy intake (Kcal/day)|170|<br>(211)||<br>242|<br>(221)||
|Protein intake (g/day)|2.81|(4.15)||3.65|(4.35)||
|<br>Nonfinancial assets (Rs/year)|216|(1015)||1,445|(1014)||
|<br>Consumption (log)|0.009|(0.116)||0.129|(0.145)||
|<br>Energy intake (log)|0.087|(0.094)||0.115|(0.099)||
|<br>Protein intake (log)|0.090|(0.091)||0.099|(0.100)||
|Nonfinancial assets (log)|0.129|(0.334)||-0.069|(0.371)||
|Number of observations|124 + 5|99= 723||106 +|371 = 477||

_Source:_ Authors’ computation from household survey and administrative data. _Notes:_ All figures in per capita terms. DDD = triple difference estimation; PSM = propensity score matching. Robust standard errors are in parentheses. Significance level: *: 10%, **: 5%, ***: 1%.

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