rafmacalaba/datause-encoder-data huggingface dataset:
The Temporary Trade Barriers Database provides detailed information on US antidumping and safeguard tariffs including the date a petition to restrict imports was filed, the identity of the country accused of dumping, the identity of countries included in the safeguard tariff, tariff-line information on the products involved, the outcome of the investigation and the magnitude of any final antidumping tariff imposed by the US against country _i_ .

All tariff-line level (8- or 10- digit Harmonized System (HS)) trade policy data were concorded to 283 NAICS (2007 version) 5- and 6- digit US industries to merge into the _ikt_ , foreign country-industry-year panel.

Industry-level foreign export supply elasticities facing the US at the 4- digit HS level were concorded to NAICS 5- and 6- digit industries. Because multiple 4-digit HS sectors can sometimes map into each NAICS industry, we record the median 4-digit HS export supply elasticity that maps into each NAICS industry as the elasticity for an industrial sector. Similarly, import demand elasticities at the 3- digit HS level were concorded to NAICS industries with the median elasticity in each industry used as the elasticity in the sector. To address a concern that some observations with extremely high or low import demand or export supply elasticities could be affecting our results, as a robustness check, we experiment with estimating our model on a smaller sample of data for which we drop observations in which either the inverse import demand or inverse export supply elasticity is in the top 5% or bottom 5% of the distribution of our primary estimation sample.

The leading alternative explanation for changes in tariffs over time is that political economy concerns lead governments to protect certain sectors of the economy. Other industry characteristics are frequently used in the literature to control for political economic determinants of an industry’s propensity to obtain import protection. We follow Staiger and Wolak (1994) and Crowley (2011) in the choice of domestic industry characteristics to include in our analysis. Because a free-rider problem must be overcome in filing a request for import protection on behalf of the industry, more concenrael, Italy, Japan, Kenya, Malaysia, Mexico, Netherlands, New Zealand, Norway, Peru, Philippines, Poland, Portugal, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Trinidad, Turkey, United Kingdom, and Venezuela. Data on US manufacturing industries are available at the 5- digit level over the entire sample period. For some larger 5- digit industries, data is also available at the 6- digit level over the entire sample period. When the more disaggregated 6- digit industry data were available for all 6- digit industries within a 5- digit industry, we replaced the more aggregated 5- digit industry data with the less aggregated 6- digit industry data. Because we require two years of lagged data for our explanatory variable, we estimate the model on policy data from 1999-2006.

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alpha of their ratings is 0.81.<sup>3</sup> To create a single index, we employ a double standardization method. First, we standardize both ratings to have mean zero and standard deviation one. Second, we standardize the average of the two standardized ratings to have mean zero and standard deviation one. A higher index score means greater physical attractiveness.

# **2.2.2 Attitudes**

We construct a gender attitudes score by aggregating interviewers’ level of agreement with four statements: “ _Females are not as good as males in school_ ”; “ _Girls can make as good leaders as boys_ ”; “ _If a woman differs with her husband, she must accept his opinion_ ”; and “ _In some circumstances, it is justifiable for a man to beat his wife_ ”. Possible responses were on a scale from 1 (“ _Really agree_ ”) to 4 (“ _Really do not agree_ ”). We recode negative items such that higher values mean more gender equitable views. We standardize the score to have mean zero and standard deviation one.

We measure interviewer attitudes towards the SOS program by asking: “ _How big of a difference do you think a program, like the SOS program, which matches girls to mentors and creates girls’ groups, will make in the lives of the girls who participate in it?_ ”, with possible responses on a scale from 1 (“ _Least_ ”) to 5 (“ _Most_ ”). Among the respondents, 53% selected 5 (“Most”); 27%, 4; and 8%, 3. Given this skewed distribution, we define _mentorship‐enthusiastic_ enumerators as those who selected “Most”.

# **2.2.3 Ability**

To control for interviewer ability, we construct scores for “hard” and “soft” skills. The hard skills score adds two indicators: having prior survey experience (48% have) and a university degree (53% have). The soft skills score combines information on patience and ability to communicate with girls about sex. Patience is measured by asking: “ _On a ladder from 1 to 5, where 1 is ‘not very patient’ and 5 is ‘very patient’, where do you stand?_ ”. We measure communication skills with the question: “ _I feel uncomfortable talking with adolescent girls about their sexual behavior_ ”, with possible responses on a scale from 1 (“ _Really agree_ ”) to 4 (“ _Really disagree_ ”). The score adds two indicators for reporting to be very patient (75% do) and very comfortable talking to girls about sex (65% do).

> 3 A higher Cronbach alpha coefficient means greater concordance among raters. The Cronbach alpha for beauty reported in Hamermesh and Biddle [1998] is 0.75.

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**Figure 9: Share of employment by formality status, 2016-2023 (%)**

<!-- Start of picture text --> 100<br>22.8 23.7 24.0 25.0 23.2 23.1 23.3<br>80<br>60<br>40 77.2 76.3 76.0 75.0 76.8 76.9 76.7<br>20<br>0<br>2016 2017 2018 2019 2021 2022 2023<br>Informal Formal<br>Share of employment (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

**Figure 11: Share of employment by formality and sector, 2023 (%)**

<!-- Start of picture text --> 100 3.7<br>28.0 31.7<br>80<br>60<br>96.3<br>40<br>72.0 68.3<br>20<br>0<br>Agriculture Industry Services<br>Informal Formal<br>Share of employment (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

**Figure 10: Share of employment by formality and age group, 2023 (%)**

<!-- Start of picture text --> 100.0 3.1<br>11.4<br>26.7 23.2<br>33.5<br>80.0<br>60.0<br>96.9<br>88.7<br>40.0 73.3 76.8<br>66.5<br>20.0<br>0.0<br>15-24 25-34 35-54 55-64 65+<br>Informal Formal<br>Source: Author’s calculations using Sakernas data<br>Figure 12: Share of employment by<br>formality and education level, 2023 (%)<br>100 3.7<br>25.4<br>80<br>69.5<br>60<br>96.3<br>40<br>74.7<br>20<br>30.5<br>0<br>Primary Secondary Tertiary<br>Informal Formal<br>Share of employment (%)<br>Share of employment (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

# **Workers employed in the services sector and workers with higher levels of education are**

**more likely to be formally employed.** Around 31.7 percent of workers employed in the services sector are formally employed, compared to 28 and 3.7 percent of workers employed in the industrial and agriculture sectors, respectively, shown in Figure 11. At the same time, Figure 12 shows that at 69.5 percent, the majority of tertiary educated workers are formally employed. This is followed by 25.4 percent of secondary educated workers and 3.7 percent of primary educated workers. In other words, workers with higher levels of education have greater access to formal employment.

# **Overall, data presented in this section highlight key differences between older and younger**

**workers.** First, younger workers are more highly educated than older workers. Second, younger workers are more likely to be employed in the services and industrial sectors, while older workers are more likely to be employed in the agriculture sector. Third, younger workers are more likely to be formally employed, which likely stems from the correlation between tertiary education and formal employment. Formal employment is also more prevalent in the services and industrial sectors. The

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The indicators show that market reforms related to international and domestic trade outpaced reforms in other areas that depended on more specific legislation and the building up of institutions. Still, significant progress was made by 2001 in containing inflation, expanding phone service and improving the overall energy efficiency of the economy – steps that are supportive of domestic markets.

## <u>Surveys</u>

During this period of ongoing policy changes, three nationally representative surveys were conducted that asked about food prices. The first survey took place in 1995, prior to the banking crises. The survey was repeated in 1997, just prior to the 1998 reforms and again in April and May of 2001, just prior to an extended period of institutional reform. Of chief interest is the period between the second and third round of surveys, since it is during this interlude that food market reforms were in place but market institutions were judged weak. At the same time, the 1997 survey came on the heels of widespread economic disruption. Consequently, differences in market performance between 1997 and 2001 are potentially attributable to the singular effects of establishing economic stability rather than the related effects of an endogenous development of food markets. For this reason, we make use of the 1995 survey round as well. Together, the three representative surveys allow an evaluation of the combined effects of the set of policy reforms that began in early 1998 against the background of the weak institutions that motivated further lending by the World Bank and the European Union in 2001, and against a 1995 pre-crises benchmark of food market performance.<sup>14</sup>

# **3 The model**

Descriptively, the model starts with the notion that, at any point in time, commodities are stored and offered for sale at spatially diverse locations. The locations are connected by information and distribution systems that collectively comprise a market network. Associated with the network are sets of pair-wise transfer costs relating geographically distinct markets. Some components of overall transaction costs relate to distance or other fixed characteristics of the trading pair. Other components relate to specific characteristics of the traded commodity, such as storability or the ease by which quality can be determined. Other aspects, such as changes in policy, relate to time. Consequently, spatial, temporal and commodity characteristics are expected to play a role in determining observed price spreads. The estimation strategy then is to separate out the components of markets that remain fixed and unaffected by policy from the components that policy can change.

> 14 More information about the surveys can be found on the internet at: <u>http://www.worldbank.org/LSMS/</u> . Annex table 1 provides a chronology of major political events in Bulgaria during the study period.

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Panel A of Figure 2 plots all of our 623 estimates of national poverty lines in 2011 PPP dollars and highlights the poverty lines used in years when the country was classified as a LowIncome Country (LIC) by the World Bank. Panel B focuses in on the 155 observations for LICs and highlights the latest observation for existing LICs. Because of the limitations of relying on CPI over long time periods, we restrict the sample to only allow one observation per country and select the most recent observation if there are multiple estimates of national lines. This gives a set of 32 national poverty thresholds for LICs, with a median value of $1.92. These 32 countries more than double the coverage of the poor relative to the 15-country approach. In 2005, the sample of 32 countries represent about 25 percent of the population of poor people.

As another attribute, the 32 thresholds are on average from 2008 and thus much less sensitive to errors in national CPI, than the ones underpinning the $1.25 line which on average are from 1997. We take median values of the lines, rather than means, to reduce the potential issue that over time we expect there to be more bunching at the upper portion of the low-income cutoff.<sup>27</sup> Repeating this exercise to estimate a LIC line using 2005 PPP data, the median value of the poverty lines is $1.25.<sup>28</sup> The fact that this procedure results in an estimated poverty line that exactly coincides with the widely accepted $1.25 global poverty line is a desirable attribute in terms of avoiding revisions to global poverty headcounts denominated in 2005 PPPs.

> 27 Of the 623 poverty lines, 155 come from countries that were classified as low-income countries and as such form our reference sample for the global poverty line. A country can be observed multiple times in this reference sample if they have multiple poverty headcount estimates across years. And, since the real value of national poverty lines frequently change, we argue that multiple entries for a country should be included in our estimates. To prevent a country from having greater influence on the average simply because it has poverty lines for several years, we weight each line by the reciprocal of the number of times it appears in the sample. For example, if a country has three poverty lines in the reference sample, then each line receives a weight of one third. The resulting weighted median value of these 155 poverty lines is $2.05 (based on inflating each line to the reference year of 2011, and using the 2011 PPP exchange rates). We also are exploring alternative taking the midpoint value from a fitted line, where the midpoint is defined over the GNI range of low-income countries.

28 This estimate is $1.26 when we expand our sample to consider all observations of poverty line for each Low Income Country (n=155).

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# **6. Individual Investors and Demand for Liquid Securities**

## **6.1 Common Demand for Liquid Securities**

In this section, I extend the analysis to consider how households as a group make liquidity decisions over time. As described in Section 2, commonality in liquidity can arise from investors demanding liquidity at the same time. Increase in uncertainty about changes in future income or wealth, for instance, can cause investors to tilt their portfolios towards more liquid assets at the same time. To test whether there is systematic variation in the trades of liquid assets, I employ a similar methodology used in Kumar and Lee (2006) and Barber, Odean and Zhu (2003), who investigate correlation in the trades of individual investors. Since I make comparisons over time under different regimes of aggregate liquidity, I consider stock liquidity rankings instead of stock liquidity levels. Each month, I rank stocks based on the adjusted Amihud illiquidity measure and assign them to percentile ranks. A stock ranked in the 100<sup>th</sup> percentile would be the most illiquid stock in a given month. Similarly, a stock ranked in the 1<sup>st</sup> percentile would be the most liquid.

For groups of non-overlapping investors, _G_ , I compute a time series of normalized differences in the liquidity ranks of stocks purchased and sold:

> where _BVti_ and _SVti_ are the total value of purchases and sales, respectively, for investor _i_ in month _t_ . _BAdjIlliqRank ti_ 1and _SAdjIlliqRankti_ 1 are the weighted average adjusted illiquidity rank of stock holdings of investor _i_ belonging to group _G_ in month using one month lagged adjusted illiquidity ranks. _IlliqBSItG_ is similar to a buy-sell imbalance index and indicates whether investors belonging to group _G_ are net buyers or sellers of liquid securities in a given month.<sup>24</sup> If the trades of liquid securities are independent

> 24 There are number of different ways to formulate this analysis.  Another approach would be to examine how investors over time tilt their portfolios towards stocks in the top/bottom illiquidity deciles.  Kumar

25
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Without taking a stand on relative merits of national accounts versus household surveys as a measure of average living standards, we perform some simple robustness checks to see how our findings change if we rely on national accounts growth rates instead of household survey mean growth rates.

The results are shown in Table 5, again focusing on the sample of spells at least five years long. Panel A repeats the results for all eight measures using the survey data. Panel B alternatively uses the growth rate of real private consumption from the national accounts. The slopes all continue to be very close to one. The main difference is that the share of welfare growth attributed to income growth declines modestly when we move to national accounts data. For example, for A(1) that share is 92% using the survey data; it declines to 85% using the alternative measure from the national accounts.

# **4.3  Why Is the Importance of Growth Different Across Social Welfare Functions?**

One striking feature of our empirical results is that the role of growth in accounting for changes in social welfare appears to be smaller for more bottom-sensitive social welfare functions. For example, in the variance decompositions reported in Table 2, we find that growth in average incomes accounts for just 41 percent of the variance of growth in incomes of the bottom 10 percent of the income distribution, but 77 percent for the social welfare function corresponding to growth in average incomes of the bottom 40 percent. Similarly, for the Atkinson Index with an inequality aversion parameter of θ= 1 , growth accounts for 92 percent of the variation in growth in social welfare, but when the inequality aversion parameter increases to θ= 3 , we find that growth accounts for just 52 percent of the variation.

Mechanically, this finding reflects the fact that the growth rate of the income shares of the poorest deciles are the most volatile across spells in our dataset. This can be seen clearly in the top panel of Figure 5, which reports the standard deviation of the average annual growth rate of each decile share in the sample of spells at least five years long. The variance (across spells) of the growth rate of the income share of the bottom decile is 5.2 percent, while it just 1.4 percent for the fifth decile, and only 0.8 percent for the ninth decile share. To see why this matters for our variance decomposition, recall Equation (6) which decomposes growth in social welfare into growth in average incomes, and a weighted average of growth in the income shares of each decile. As we move to more bottom-sensitive social welfare functions, the weight on the lowest deciles increases. Since the growth rates of these lowest decile shares are the most volatile, this increases the variance of the second term in Equation (6), which in turn increases the share of the variance in growth in social welfare that is due to changes in 16
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# **3.2 Measuring infiltration**

The random distribution of indoor air pollution monitors in our experiment offers a setting where the ownership of an indoor monitor is not endogenous to household characteristics.<sup>10</sup> To do so we examine the relationship between the indoor pollution measured in the houses of households in Treatment 3 _Outdoor and Indoor_ and the level of outdoor pollution measured by the outdoor monitors closest to the household.

To calculate infiltration rates, we estimate a lagged model with the level of indoor pollution in a given household as a function of the previous six hours of outdoor pollution measured by the nearest monitors (Burke et al., 2021). For each household for which we have indoor pollution data, we run the following specification: To absorb idiosyncratic variation in indoor pollution that may be due to factors unrelated to infiltration (e.g. cooking), we include hour ( _ηh_ ), day ( _γd_ ), and week (∆ _w_ ) fixed effects. These fixed effects are specific to each individual household in the sample and capture household-specific details like the typical times at which they return from work or cook meals.

Our measure of infiltration is the sum over all lags of _βl_ . This provides an estimate of the change in indoor air pollution for a 1 _μg/m_<sup>3</sup> cumulative increase in outdoor pollution over the previous six hours. We include lags because it may take time for outdoor pollution to filter into homes. To assess the sensitivity of our results to our choice of lag structure we estimate several alternative lagged models as in Burke et al. (2021): Our preferred model with six lags of outdoor pollution, one including six lags of indoor pollution and no outdoor lags, one with no lags of indoor or outdoor pollution, and one with ten lags of outdoor pollution. Our estimates of average infiltration are consistent across each of these different specifications (Figure A4).

To understand the drivers of variation in infiltration rates, we estimate the following equation:

> 10For example, Burke et al. (2021) observe that households in high-income areas in their sample are much more likely to own an indoor monitor than those in low-income areas.

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