rafmacalaba/datause-encoder-data huggingface dataset:
distinct local assets in engineering and science. For the reasons outlined above, innovation is increasingly prescribed as the tonic for local economic distress in America.

In a strong‐market metro, by contrast, job growth and population growth both exceed the national average. These markets are economic magnets, retaining locals who can find good jobs locally and attracting migrants from other parts of the country and often from abroad. Since young people are more likely to relocate than older adults, the dynamism of strong‐market labor markets is often reflected in substantial growth in the number of children, increasing fiscal burdens in the short term but setting the stage for further rounds of growth and productivity in the longer term if children are well enough cared for and educated to get good jobs when they enter the labor force. Likewise, the foreign‐born tend to increase local fiscal burden in the near term; their net fiscal contributions are substantial over time, and they accrue primarily at the national level, via income taxes paid (NAS study).

Strong‐market metros have their own challenges with inclusive growth. Comparatively low unemployment, rapid job creation, and substantial investment in real estate may lead decision‐makers to overlook problems of income inequality, long commutes, and unaffordable housing. Low‐paying service sector jobs still dominate the labor market for large sectors of the population, and those low wages are doubly challenging in such high‐cost environments. Finally, there is a substantial risk that strong‐ market metro areas will under‐invest in local talent because employers can attract high‐ skill workers from elsewhere in the nation or abroad. Strong magnets that can import so much talent have less incentive to grow their own, and this lack of commitment to local economic mobility usually has the heaviest consequences for lower income communities of color, whose schools and connections to good jobs are typically the worst in the region.

# Job quality and access

The problems of the persistence of bad jobs and the disparities in access to better ones reflect public policy and employer practices as well as documented skill gaps in the labor force. As described in our opening section, a large proportion of “good” industrial 11
**Figure 8. Foreign equity ownership index in Sub-Saharan Africa**

<!-- Start of picture text --> Zambia 100<br>Uganda 100<br>Rwanda 100<br>Nigeria 100<br>Madagascar 100<br>Chad 100<br>Burundi 100<br>Senegal 100<br>Ghana 100<br>Mauritius 98<br>Mali 97<br>Tanzania 96<br>Cameroon 95<br>Sierra Leone 94<br>Côte d'Ivoire 94<br>Congo, Rep. Dem. 94<br>Regional average 93<br>Kenya 93<br>Mozambique 92<br>Burkina Faso 89<br>Angola 89<br>Liberia (data from 2010) 88<br>South Africa 88<br>Sudan (data from 2010) 67<br>Ethiopia 56<br>Source: FDI Regulations database 2012 0 50 100<br><!-- End of picture text --> In Sub-Saharan Africa, the region’s largest recipient of FDI both in absolute and per capita terms, Nigeria, is also one of the most open to foreign investment (Figure 8). On the other hand, the second most populous economy in the region and one of the economies with the lowest income per capita in the world, Ethiopia, is the most restricted economy in the region and it receives one of the lowest amounts of FDI. All its major services sectors are closed to foreign investment. Of the 24 SubSaharan African economies covered by the project, almost half are completely open or have very few restrictions. The transport and media sectors exhibits fewer restrictions than in other regions and agriculture, forestry, manufacturing, education, accounting and tourism are fully open in all the economies.

# **iii.** _Trends_

In terms of country size, our data show that smaller economies tend to be more open: Guatemala, Honduras, Solomon Islands, Georgia, Kosovo, Montenegro, the Kyrgyz Republic are all fully open to foreign investments in the thirty-two sectors covered by the project. On the other hand, larger economies such as South Africa, Mexico, Turkey, Korea, the Philippines and Indonesia impose more restrictions in strategic sectors such as media, transport, electricity and telecom, presumably because they will remain a principal destination for foreign investments due to their size and growth potential.

However, our analysis and past research shows that there is no correlation between the size of the economy (measured by GDP) and the degree of openness as measured by the _Investing Across Sectors_ indicators. There is a modest positive correlation between GDP per capital and sectoral openness, suggesting that higher-income economies tend to be more open to foreign investment across sectors, although this correlation is not statistically significant (Waglé, 2011).

12
gaps across workers can become smaller over time because different departments in firms start paying more similar wages. The importance of intra-firm wage differences and their contribution to changes in wage inequality are not directly disentangled in the AKM models. To tackle this question, this paper examines the role played by the variability in earnings between and within firms in each country, by considering that in year _t_ , the wage of worker _i_ employed by firm _j_ equals the economywide average wage plus the deviations between the average wage of the worker’s firm and the economy (employer deviation), and the deviation between the worker’s wage and the average wage of the worker’s firm (worker deviation). More formally, as in Song et al. (2018) and Alvarez et al. (2018) for the United States and manufacturing firms in Brazil, respectively, this paper identifies and take variances on both sides of the equation, obtaining The results are summarized in figure 10.<sup>27</sup> The figure plots the evolution of the between and within components of the variance of log earnings for Brazil (panel a), Costa Rica (panel b), and Ecuador (panel c). The between-firm component fell sharply in Brazil and Ecuador and increased in Costa Rica. The variance of wages between firms explains a large share of the changes in the total variance of wages. Specifically, 75 percent of the decrease in wage inequality was associated with a reduction in inter-firm wage differentials in Brazil, and 99 percent in Ecuador. In Costa Rica, 80 percent of the increase in the variance of wages is explained by inter-firm differences in pay.<sup>28</sup>

> 27Table S2.13 in supplementary online appendix S2 presents the detailed results.

> 28These results also hold within sector-occupations in Brazil, the only administrative data set used in the section that contains information on occupations. Decomposing the variance within-sector-occupations into between- and within-firm components (as in Helpman et al. 2017, who carry out a similar analysis for 1986–95), this paper finds that the decline of wage inequality is largely accounted for by a fall in between-firm wage dispersion. The results are available from the authors upon request.

27
# **Box 2:  Particulate Emissions in India**

Particulate matter is by far the most problematic air pollutant on a national scale, with annual average concentrations of Suspended Particulate Matter (SPM) exceeding the National Ambient Air Quality Standards (NAAQS) in most cities (CPCB, 2006; MoEF 2009). India‘s national average of 206.7μm/m3 of Suspended Particulate Matter (SPM) in 2007 was well above the old NAAQS of 140 μg / m3 for residential areas. Most Indian cities exceed, sometimes dramatically, the current NAAQS of 60μm / m3 for Respirable Suspended Particulate Matter (RSPM). Average annual concentration of RSPM in Delhi for example is about 120 μg / m3, as against a residential National Ambient Air Quality Standard of 60 μg / m3 and World Health Organization (WHO) guidelines of 20 μg / m3 (Central Pollution Control Board (CPCB), 2006; Word Health Organization (WHO), 2008). Five of six cities covered in a recent report exceeded the standard in all years 2000-2006 (CPCB, 2011). By contrast, sulfur dioxide (SO2) and nitrogen oxides (NOX) are less of a problem in India. Most cities are below the NAAQS for these pollutants.

The figures refer to both SPM and RSPM. SPM is a broader category referring to all suspended particulate matter of less than 100 micrometers in diameter. Research on the health effects of particulate matter indicates that the smaller particles in RSPM are more dangerous for health because they penetrate more deeply into the lungs (US Environmental Protection Agency (USEPA), 2008). In India, RSPM is defined as fine particles less than 10 μm (PM10). Other countries refer to this pollutant as PM10 and may also measure PM2.5, i.e. smaller particles of less than 2.5 μm in diameter.

Indian standards recognize the danger of air pollution. In November 2009, the Ministry of Environment and Forests (MoEF) announced new NAAQS (CPCB, 2009). Compared to the previous version from 1994, the revised NAAQS brought six new pollutants under regulation (including introducing a standard for PM2.5), tightened the acceptable ambient concentration for other pollutants, and eliminated the distinction between industrial and residential areas. As a result, many urban areas—which may have been out of compliance even with the older norms—must significantly cut emissions to move towards the more stringent, uniform standards now in place. The shift from regulation of ambient SPM to RSPM in the new NAAQS in particular is significant in directing the focus of regulation to those pollutants that matter for human health. India's MOEF has launched a pilot emissions trading scheme in three states to improve air quality and help the states meet the new NAAQS.

Source: Greenstone and others (2012) 7
<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Public Sector<br>1<br>.8<br>.6<br>Private Sector .4<br>.2<br>0<br><!-- End of picture text --> Source: ILO Stats Figure 11: Public employment and unemployment rate <!-- Start of picture text --> 0 .1 .2 .3 .4 0 .2 .4 .6<br>Central Gov. General Gov.<br>.4 .6<br>.5<br>.3<br>.4<br>.2 .3<br>Unemployment rate Unemployment rate .2<br>.1<br>.1<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> 0 .1 .2 .3 .4 .5 .6 .7 .8<br>Public Sector<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>Unemployment rate<br>.2<br>.1<br>0<br><!-- End of picture text --> Source: ILO Stats 23
**Table 3. Tenure Structure in Studied Countries by Settlement Category**

|||**Arm**|**enia**|||**Lith**|**uania***||
|---|---|---|---|---|---|---|---|---|
|**% of households**|**Total**|**Capital**|**Urban**|**Rural**|**Total**|**Capital**|**Urban**|**Rural**|
|Owner-occupied|92.7|93.6|90.2|94.1|82.6|81.0|n/a|n/a|
|Other tenure|7.3|6.4|9.8|5.9|17.4|19.0|n/a|n/a|
|Public rental|5.8|3.6|8.3|5.5|3.8|4.1|n/a|n/a|
|Private rental|1.5|2.8|1.6|0.4|12.2|13.6|n/a|n/a|
|Free orother|n/a|n/a|n/a|n/a|1.4|1.3|n/a|n/a|
|||**Pol**|**and**|||**Ro**|**mania**||
|Owner-occupied|66.4|48.7|56.4|87.8|95.7|94.8|94.3|97.4|
|Other tenure|33.6|51.3|43.7|12.3|4.3|5.2|5.7|2.6|
|Public rental|13.8|20.6|20.1|2.2|1.3|1.9|2.0|0.5|
|Private rental|16.8|27.8|21.6|5.7|1.1|1.8|1.8|0.2|
|Free orother|3.0|2.9|2.0|4.4|1.9|1.6|1.9|1.9|
|||**Ru**|**ssia**|||**Serbia and**|**Montenegr**|**o**|
|Owner-occupied|63.8|44.5|62.8|78.7|88.7|88.9|88.7|88.5|
|Other tenure|36.2|55.5|37.2|21.3|11.3|11.1|11.3|11.5|
|Public rental|31.4|53.0|32.6|14.6|2.3|2.5|2.1|2.4|
|Private rental|3.6|1.8|4.0|4.0|4.0|3.6|4.0|4.2|
|Free orother|1.1|0.7|0.6|2.7|5.1|5.0|5.1|5.0|

_Source_ : Table 1.

*Lithuanian Free Market Institute (2002). _Note_ : For Lithuania‚ “capital” refers to the Vilnius region.

Consequently, there are fundamental differences between the two groups of countries regarding the share of rental segments in the housing sectors. There are also essential differences in the structure of private rental housing. Two countries exhibit substantial private rentals: Lithuania (13.6 percent) and Poland (16.8 percent). In Lithuania most private rental is individual privatized apartments rented informally by their owners. In Poland, a large portion of private rentals are apartment buildings managed by licensed property managers for formal “professional” landlords, who report official incomes and use tax deductions for current and capital repairs.

In some cases it appears likely that the household survey data underestimate rental sector size. One problem is that the number of households that rent privately is so small that even small sampling errors result in large changes in the observed mean. For this reason, it is important not to focus on whether the observed level is exactly 2 or 3 percent, but rather to keep in the mind the relative significance of the observed levels (that is, that there are very few private renters). Some under-reporting is also possible owing to private renters misstating their status. Reasons could include:

- high levels of rental informality (and sometimes illegality), caused by tax or rent control evasion, which might induce respondents to deny the existence of rent payments or make false claims about ownership;

- confusion about the character of the relationship caused by unclear ownership title, title that conveys usufruct rather than ownership, rental contracts that convey a purchase or cost-free privatization option that has not yet been exercised and is mixed up with ownership;

- unrecorded subletting agreements made by sitting tenants, which may also be unlawful if concerns low-rent municipal apartments;

16
with the absence rates from our survey to calculate both the STR and ESTR by state in 2003 and 2010 (Table 2 - columns 4-9). We see that though all-India STR had been reduced to below 40 in this period, the effective STR after accounting for teacher absence was over 52. The effective STR in 2010 in three of India’s most educationally backward states (Bihar, Jharkhand, and Uttar Pradesh) was as high as 97, 79, and 69. These figures illustrate that teacher absence can sharply increase the effective STR experienced by students relative to the STR calculated using state-level figures on enrollment and number of teachers.

# **3.3 Official Records, Teaching Activity, and Stated Reasons for Absence**

Enumerators recorded whether a teacher had been marked as present in the log-books on the day of the visit and also on the previous day, and we see in Table 3 - Panel A that going by these records would suggest a much lower teacher absence rate of 16 percent using the same day’s records, or as low as 10.2 percent using the previous day’s records (this was not collected in 2003). These data suggest that official records can be easily manipulated, and highlight the importance of measuring teacher absence by direct physical verification as opposed to official records on log books.

Enumerators also recorded the activity that teachers were engaged in at the point of observation, and we see that 53 percent teachers on the payroll were found to be actively teaching, and another 4 percent were coded as passively teaching (defined as minding the class while students do their own work). Just over 19 percent of teachers were in school but were either not in the classroom or not engaged in teaching activity while in the classroom. Thus a total of 42 percent of teachers on the payroll were either absent or not teaching at the time of direct observations (Table 3 - Panel B).<sup>15</sup> In cases where a teacher was not found in the school, enumerators asked the head teacher (or senior-most teacher who was present) for the reason for absence. These stated reasons are summarized in Table 3 (Panel C). Two categories of clearly unauthorized absence (school closure during working hours and no valid reason for absence) account for just under half the cases of teacher absence (48 percent), which provides a lower bound on the extent of unauthorized absences of 11.3 percentage points. The two other categories of stated absence (authorized leave and official duties) that account for 52 percent of the observed absence are plausibly legitimate but cannot be verified.

While head teachers may overstate the extent of official duties to shield absent col-

> 15This is almost surely an underestimate (and hence a lower bound) because in many cases it is easy for a teacher who may not have been teaching to pick up a book and look like he or she is actively teaching when it is known that someone is visiting the school (see Muralidharan and Sundararaman (2010) for evidence documenting this).

10
are often organized by the governments. Indeed, they have the choice between auctioning off markets on the basis of either the highest fee payment or the lowest product/service price (see Estache, Foster and Wodon 2002). Guasch (2003) shows in a survey of 600 concession contracts from around the world that in most cases contracts are tendered for the highest transfer or annual fee. Because fee payments rise with the profitability of the privatized firms, many governments choose policies that increase the firms’ profitability such as exclusivity periods and price liberalization.<sup>5</sup> Prices are sometimes increased ahead of privatization in order to reduce the SOE financing gaps and attract buyers. This has been for instance the case in Zimbabwe, Kenya and Senegal, where governments increased electricity prices by 10 % after reaching an agreement with Vivendi Universal (see AfDB-OECD 2003). An unaccounted part of price increases stems from the termination of illegal electricity connections (Birdsall and Nellis 2002, Estache, Foster and Wodon 2002, AfDB-OECD 2003).

The present paper studies the privatization decision as the result of the government’s cost-benefit analysis where the social benefit obtained from the termination of subsidies to unprofitable public firms and the fiscal benefit from the cash-flows generated by the public firms’ divesture are balanced against the loss in consumer surplus induced by the higher prices in privatized industries and to the social cost of the foregone taxable revenues that are available in profitable public firms. To get clear cut results privatization is treated as the move from public ownership with entry and price regulation to private ownership with price liberalization.<sup>6</sup> Privatization is close but not equivalent to _laissez-faire_ because entry remains regulated (i.e. through license and entry fees). If, as we show, privatization with

> 5Wallsten (2000) studies the impact of the exclusivity period on the privatization price of twenty telecommunication firms in fifteen developing countries. In his sample, two thirds of the countries chose to allocate exclusivity periods for an average of 7.42 years. They apparently had a very good reason for doing so. According to the author’s computations, granting a monopoly in fixed local services would more than double the price private investors pay for the firm. This comes at the cost of high prices and lower network growth relative to privatization without exclusivity periods.

6Under the complete contract approach adopted in the paper there exists no difference between public and private ownerships under entry and price regulation. We thus model the difference between the two ownership structures as the choice between regulated public firms and unregulated private ones. This assumption is consistent with empirical evidence.

3
N CCC 2100, Small Farms +> IMP_farm.D100_ccc_s %Change of Ag Income g DH 0.0071 IY) 0.0072 -0.0500 HN 0.0501 -0.1000 HN 0.1001 -0.2000 HE 0.2001 -0.3000 BEE 0.3001 -0.9900 TLSLSLSI —__s—*iT Decimal Degrees NIGGOL SEO, YALE UNIV. 0 4 8 16 24 32
We also provide a test of our methodology to calculate AVEs of NTBs.<sup>18</sup> Using the observed tariff information we created a dummy which takes the value 1 if the tariff is positive and zero otherwise. We then use this new dummy variable and calculated its impact on imports and proceeded to transform the quantity impact into a price-equivalent as we did with the NTB information.<sup>19</sup> We then calculated the correlation between the ad-valorem equivalents for tariffs obtained using this methodology and the actual tariffs. The correlation is 0.31 and signi fi cant at the 1 percent level The average actual tariff in the sample is 10.7 percent with a standard deviation of 23.0 percent, whereas the ad-valorem equivalent of the tariff dummy has an average of 11.3 with a standard deviation of 23.7 percent. Overall this suggests that the methodology we used to estimate ad-valorem equivalent of border barriers is doing a relatively good job.

# 5.2 Trade restrictiveness indices

Table 3 provides our estimates of TRIs, OTRIs and MA-OTRIs for 91 countries (counting European Union members as 1 country).<sup>20</sup> The fi rst three columns provide estimates of trade restrictiveness using tariff data only. The following three columns show estimates that include both tariff and NTBs. The following two columns provide estimates of OTRI and MA-OTRI for agriculture products (HS 01 to 24) that include both tariffs and NTBs. The last two columns provide similar indices but for manufacturing products (HS 25 to 97).

One can make several important observations. First, NTBs have a signi fi cant contribution to the level of trade restrictiveness. Indeed, NTBs add on average an additional 70 percent to the level of trade restrictiveness imposed by tariffs. In 21 countries (out of 91) the contribution of NTBs to the overall level of restrictiveness is higher than the contribution of tariffs.<sup>21</sup> Thus neglecting the restrictiveness of NTBs can be very misleading.

Second, and as discussed earlier, the TRI which uses welfare as a reference is always higher than the OTRI. On average the TRI is around 70 percent higher than the OTRI (regardless of whether we include NTBs or not). The largest differences between OTRIs and TRIs are to be found in

> 18 We are grateful to Alan Deardorff for suggesting this.

> 19 Obviously, on the left hand side of (2) we only had log of imports, and the tariff dummy was now on the right-hand-side. To correct for its endogeneity it was instrumented using the same method used on NTBs.

> 20 Estimates for the European Union are for extra-EU trade.

> 21 It is also worth noting that on average the contribution of NTBs to the overall level of protection is 30 higher in agriculture than manufacturing.

20