rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 4. The world size distribution of cities**

<!-- Start of picture text --> Histogram regions= 07 data= r cutoff= 2 cells= 20<br><!-- End of picture text --> <!-- Start of picture text --> y2000_2_r_20 y1960_2_r_20<br><!-- End of picture text --> _Source:_ Henderson and Wang 2007.

_Note:_ There are 20 cells on the horizontal axis, each spaced apart by the same percentage increase in size. The vertical axis shows the share of each cell in the number of world cities for the respective year. Sizes are normalized (as is the cutoff point) by the mean size in each year to obtain a relative size distribution. The absolute size distribution is continually shifting right with technological improvements. The intent of normalizing is to compare shapes of the relative size distributions over time. The shapes are consistent with a Pareto distribution. Inspection reveals that the relative size distribution in 1960 is the same as in 2000.

A controversial paper by Gabaix (1999) sparked papers by Eeckhout (2004), Rossi-Hansberg and Wright (2007), and Duranton (2007), among others. Gabaix formulates a stochastic process whereby cities receive repeated draws (uncorrelated with size) on overall productivity or quality of life. Cities that get repeated good draws by luck emerge as big, others are more middling, and the unlucky cities are small. Under certain conditions, a Pareto size distribution theoretically emerges (“Zipf’s Law”), at least in the upper tail of the distribution. Rossi-Hansberg and Wright (2007) show that this formulation can be applied to a specialized hierarchy model, and Duranton (2007) shows it is consistent with ongoing urban innovation and churning. One disconcerting feature is that these models require cities to also churn in the long term, with cities moving up and down the hierarchy of sizes, something we do not observe in the data, at least among bigger cities. One can also formulate a simple, nonstochastic model whereby city productivity depends on a permanent draw of a production amenity (like quality of access to water transport). If those amenities are Pareto distributed, the city-size distribution will approximate a Pareto distribution as well.<sup>27</sup> All these exercises focus on a single dimension, a one-time or repeated draw of a productivity parameter. Recent work by Desmet and Rossi-Hansberg (2012) and Albouy (2008) emphasizes 21
Controlling for characteristics of jobs, including occupational compositions, locations, type of contract and shifts, requirement of qualifications such as years of experience and gender specification, and characteristics of the employing firms, the analysis revealed historical growth trends by occupation and location as well as a unique seasonal patterns of fluctuations in wage offers. Figure 9 presents the stylized trend of real wage offers for all jobs in 20 cities. Taking the index value of 100 for the real wage of January 2011, the wage offers grew steadily over time. However, the wage offers are subject to a consistent pattern of seasonal fluctuations every year. The wage offers tend to grow towards summer months, such as July and August, and tend to face a sudden fall around in October and November. The trend of wage offers is considered to reflect the demand and supply of skills. Considering the fact that Babajob provides many entry-level job opportunities for fresh graduates, this annual cycle shows a correlational pattern with the timing students’ graduation and of job hunting, as well as with changes in skills demand associated with seasonal events.

**Figure 9: Stylized Trend of Real Wage Offer Index (January 2011=100), 2011-2015**

<!-- Start of picture text --> 150<br>139.3 140.6<br>140<br>130<br>117.3<br>120<br>110 100.0 115.0 113.0<br>108.2<br>100<br>101.6<br>90 93.6<br>80<br>1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11<br>2011 2012 2013 2014 2015<br>2011 2012 2013 2014 2015 Linear ()<br><!-- End of picture text --> Source: Authors’ analysis using Babajob database Note: The figure shows the estimated real wage trend after controlling for variables of occupation and type of contracts, qualification requirements, and employers’ types. It includes 20 cities in India.

By extrapolating from current dynamics, analysts and policy makers can use job-portal data to predict future trends in the labor market. The econometric model developed by Areias et al. (forthcoming) can be used to predict the real wage offers by location and occupation. Such predictions, including both nowcasting and forecasting of the near future, would provide valuable information to policy makers to understand the 22
- Fabregas, R., M. Kremer, J. Robinson, and F. Schilbach. 2017. “Netflix for agriculture.”. Work in progress, URL: `https://www.atai-research.org/wp-content/ uploads/2017/06/Netflix-for-Agriculture-Prof.-Michael-Kremer.pdf` (last accessed on October 18, 2019).

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- Feder, G. 2005. “The challenges facing agricultural extension - and a new opportunity.” _New Agriculturalist_ . Available at: `http://new-ag.info/en/view/point.php?a=1253` (last accessed on October 18, 2019).

- Foster, A. D., and M. R. Rosenzweig. 1995. “Learning by doing and learning from others: Human capital and technical change in agriculture.” _Journal of Political Economy_ 103 (6): 1176–1209.

- Gabaix, X. 2014. “A sparsity-based model of bounded rationality.” _Quarterly Journal of Economics_ 129 (4): 1661–1710.

- Gabaix, X., D. Laibson, G. Moloche, and S. Weinberg. 2006. “Costly information acquisition: Experimental analysis of a boundedly rational model.” _American Economic Review_ 96 (4): 1043–1068.

- Gido, E. O., K. W. Sibiko, O. I. Ayuya, and J. K. Mwangi. 2015. “Demand for agricultural extension services among small-scale maize farmers: Micro-level evidence from Kenya.” _Journal of Agricultural Education and Extension_ 21 (2): 177–192.

- Goecke, H., W. J. Luhan, and M. W. Roos. 2013. “Rational inattentiveness in a forecasting experiment.” _Journal of Economic Behavior and Organization_ 94 (2013): 80–89.

- Gollin, D., and C. Udry. 2017. “Heterogeneity, measurement error, and misallocation: Evidence from African agriculture.”. Unpublished working paper.

- Greenwood, P., and R. Parasuraman. 1991. “Effects of aging on the speed and attentional cost of cognitive operations.” _Developmental Neuropsychology_ 7 (4): 421–434.

- Handel, B., and J. Schwartzstein. 2018. “Frictions or mental gaps: What’s behind the information we (don’t) use and when do we care?.” _Journal of Economic Perspectives_ 32 (1): 155–78.

- Hanna, R., S. Mullainathan, and J. Schwartzstein. 2014. “Learning through noticing: Theory and evidence from a field experiment.” _Quarterly Journal of Economics_ 129 (3): 1311–1353.

- Haushofer, J., and E. Fehr. 2014. “On the psychology of poverty.” _Science_ 344 (6186): 862–867.

- IFAD. 2013. _Smallholders, food security, and the environment_ . International Fund for Agricultural Development, Rome.

- Jama, B., and G. Pizarro. 2008. “Agriculture in Africa: Strategies to improve and sustain smallholder production systems.” _Annals of the New York Academy of Sciences_ 1136 (1): 218–232.

- Jayachandran, S. 2006. “Selling labor low: Wage responses to productivity shocks in developing countries.” _Journal of Political Economy_ 114 (3): 538–575.

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<!-- Start of picture text --> Density ‐ based on Administrative boundaries<br>250<br>200<br>150<br>100<br>50<br>0<br>Kigali Nairobi Addis Dakar Dar es Paris Barcelona Kuala<br>Ababa Salaam Lumpur<br>Source: Population statistics and land area numbers from http://citypopulation.de/, calculations<br>made by author.<br>people per hectare<br><!-- End of picture text --> While this measurement provides a helpful indication of overall density, it relies on administrative boundaries rather than the ‘true’ physical boundary of the city, and does not provide insight into variation within the city’s landscape. To overcome this limitation, alternative more in depth approaches have been developed which ignore political lines and examine the varying levels of density throughout a city. Using data sets derived from satellite imagery and disaggregated population estimates, modeled population grids such as Landscan, can be used to measure how a city’s population density changes across space. Referred to as an urban density profile, this graph can shed light on how a city’s population changes as one moves away from the center to the periphery. Previous studies by Bertaud (2001, 2003),<sup>1516</sup> Muth (1967)<sup>17</sup> and many others have demonstrated the value of density profiles when studying urban form.

A variation in a city’s population density is arguably more influential and indicative of its performance than its average density. Following a monocentric model that assumes most jobs are located in the city center and that residents will tend to cluster around those jobs, a population gradient will show a higher concentration of people living near the city center than the periphery. In this simplified model, residents will choose to live near the center because they are likely to benefit from shorter commutes to work, school and other destinations.<sup>18</sup> If the gradient reveals something different than a gradual decline, such as a spike or dip in population density, it might be indicative of unusual land use. For example, if a city has a population density

> 15 Bertaud, Alain. (2001) Metropolis: A Measure of the Spatial Organization of 7 Large Cities. 29 April 2001.

> 16 Bertaud, Alain and Stephen Malpezzi. (2003) The Spatial Distribution of Population in 48 World Cities: Implications for Economies in Transition.

> 17 Muth, Richard. (1967). Determinants of Investment Behavior. Chapter title: ‘The Distribution of Population Within Urban Areas’ NBER (p. 271 ‐ 299).

> 18 Bertaud, Alain. Metropolis: A Measure of the Spatial Organization of 7 Large Cities. 29 April 2001.

5
**Table** 5. **Products with the Largest Change in Regional Orientation** _Toward_ **Mercosur Markets Over 1988 to 1994.**

||Exports (|$000)|Regional Ori|ent Index|RO Index<br>Change|1994 RCA<br>Index|
|---|---|---|---|---|---|---|
|Commodity (SITC)|1988|1994|1988|1994|||
|Non-Alcoholic Beverages (111)|349|26,238|2.35|48.47|46.12|0.05|
|Lead (685)|642|219|3.03|25.42|22.39|0.00|
|Prepared Dairy (022-024)|23,495|204,019|4.31|22.49|18.17|0.13|
|<br>Non-Wheat Meal or Flour (047)|4|954|0.05|17.26|17.21|0.04|
|Perfumes and Cosmetics (553)|4,766|86,282|5.22|13.37|8.16|0.14|
|Wheat Meal or Flour (046)|65|35,051|0.22|5.67|5.44|1.08|
|Cork Manufactures (633)|18|721|1.18|6.30|5.13|0.05|
|<br>Preserved Vegetables (055)|23,404|48,745|17.66|22.61|4.95|0.13|
|Articles of Paper (642)|15,763|72,249|2.16|7.10|4.93|0.20|
|Road Vehicles Non-Motor (733)|3,118|35,854|2.23|6.88|4.65|0.13|
|Alcoholic Beverages (122)|4,137|81,671|1.87|6.48|4.61|0.19|
|Agricultural Machinery (712)|39,608|121,294|2.08|5.88|3.81|0.45|
|Domestic Electrical Equipment (725)|12,568|97,322|2.19|5.94|3.76|0.23|
|Road Motor Vehicles (732)|206,996|2,112,750|1.25|4.42|3.17|0.45|
|Materials of Rubber (621)|3,636|30,780|3.13|6.26|3.13|0.32|
|Glassware (665)|5,381|45,017|2.21|5.09|2.88|0.38|
|Synthetic Fibers (266)|13,381|21,170|6.28|9.14|2.87|0.11|
|Rice Glazed or Polished (042.2)|22,583|148,079|9.28|11.65|2.37|1.03|
|Lace and Ribbons (654)|1,386|13,157|3.56|5.86|2.29|0.22|
|Food Preparations nes (099)|7,727|45,412|2.10|4.35|2.25|0.28|
|Structures and Parts (691)|1,783|19,834|0.72|1.77|1.05|0.39|
|Tobacco Manufactures (122)|2,032|112,681|0.95|1.86|0.91|0.39|
|Textile Yarn and Thread (651)|26,523|118,120|0.85|1.73|0.88|0.90|
|Metal Manufactures nes (698)|5,984|51,430|0.90|1.77|0.87|0.33|
|Nails, Nuts and Bolts (694)|3,021|24,782|2.86|3.66|0.81|0.28|
|<br>Non-electric Power Machinery (711)|25,140|290,687|0.40|1.20|0.79|0.45|
|Nonfur Clothing (841)|19,342|138,805|0.63|1.34|0.71|0.27|
|Plumbing and Lighting Equipment (812)|3,819|14,363|2.23|2.93|0.70|0.13|
|Electrical Distributing Machinery (723)|6,821|35,775|1.55|2.22|0.67|0.26|
|Glass (664)|4,851|25,079|1.27|1.93|0.65|0.36|
|ALL ABOVE ITEMS|488,345|4,058,540|2.83|5.97|3.14|0.42|
**Figure 5: Analysis of Dimension Scores in Guangdong Pilot Counties**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>Elementary<br>3 School<br>2<br>Middle<br>School<br>1<br>0<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

Figure 6 shows the dimension-level breakdown, as well as the U.S. comparator, which is an average of the results from the three United States–based studies. The dimensions weakest for the Guangdong classrooms are also the areas where U.S. classrooms are the weakest: Regard for Student Perspectives, and Analysis and Inquiry; a lack of Negative Climate is a strength for all classrooms.

When comparing these results, one should consider if raters’ relative beliefs influenced their ratings at all. That is to say, whether a 4 in Content Understanding in Guangdong is equivalent in quantity and quality to a 4 in a U.S. classroom. If the 4 is relative to a pre-existing cultural baseline, it is possible that the intercultural comparisons may be harder. That does not, however, limit the utility of the tool for improving teacher training, as even a cursory glance at the data shows that the observed classrooms rank significantly lower in key areas. Further studies may benefit from a side-by-side comparison of videotaped classrooms from the United States and China to measure whether there are any issues on different baselines.

Finally, Student Engagement jumps out as an area in which the Guangdong classrooms are significantly more successful than U.S. classrooms (although this dimension is not included in any of the domain scores as it is its own category).

14
i.e. aggregation bias in the tariff variable should make it easier to find a significant NTB effect for TA notified under the Enabling Clause.

All the challenges discussed above work in favor of finding a positive NTB-effect. Hence, our NTB coefficients should be interpreted as upper bounds.

# **3 Data**

To revisit the trade-creating effects of NTBs, we combine trade data with information on ad valorem tariffs and on the depth of trade agreements. For tariffs, we use the new Global Tariff Database (GTD) put together by Teti (2020) that deals with misreporting of tariffs (details can be found in the corresponding paper). We aggregate the data that are available at the HS6digit level to two broad sectors, i.e. agriculture and manufacturing, and for the disaggregated analysis to 18 sectors, respectively; in the area of services, tariffs are set to zero. For the baseline results, we use simple averages, results using trade-weighted averages are available upon request.

We want to relate our new estimates to the ones the literature has reported in the past and get a better understanding of how severe the bias due to mismeasured tariff data is. To do so, we download the effectively applied tariff from WITS.<sup>5</sup> Using Trains as a primary source, we download the effectively applied tariff for ISIC Rev. 3 at the four-digit level and aggregate it to the sector level. We use the ISIC Rev. 3 classification instead of the HS classification because it involves fewer steps for concording across different classifications and nomenclatures to get to the sectors that are used in the trade data, i.e. ITPDE and WIOD.

We use the International Trade and Production Database for Estimation (ITPDE, Release 2) put together by Borchert et al. (2020) for our baseline results for three reasons: first, to estimate Equation 4 theory-consistent and to be able to include trends for globalization, we need data on international and domestic trade flows (Yotov 2021). In contrast to other standard sources for trade data such as UN Comtrade or CEPII’s Baci (Gaulier and Zignago 2010), the ITPDE covers both, inter- and intranational trade. Second, deep trade agreements typically cover trade in both goods and services (Mattoo, Rocha, et al. 2020) while tariffs are only applicable for trade in goods. It could be possible that trade agreements do not reduce NTBs for goods trade but do so for services. To get a full picture of the trade-creating effect of NTBs, we would therefore like to include also services. Lastly, the ITPDE covers the whole world while the comparable WIOD data only report trade for 44 countries (Timmer et al. 2015). The ITPDE

> 5 WITS is available at https://wits.worldbank.org/.

11
# _D. Hypotheses: The links between financial development and undernourishment_

Since there exists a strong relationship between income poverty and undernourishment and since financial development reduces income poverty, financial development can be expected to reduce the prevalence of undernourishment largely via income poverty reduction. There is ample country-specific and other evidence that income poverty is the main cause of undernourishment. For example, in Indonesia during 1984-1987 rising income standards reduced malnutrition in the country and the fraction of people living at less than 1,400 calories declined with 26 percent (World Bank 1993).

Given these results, one would expect an impact of financial development on the prevalence of undernourishment, similar to that on poverty. Important though are the specific channels through which financial sector development affects undernourishment. In theory, some specific channels can be identified where financial sector development is especially important for undernourishment. First, access to financial services like savings and credit products may reduce undernourishment because it allows for consumption smoothing by poor households in the face of income and other shocks. Access to financial services makes agricultural workers less vulnerable to the impact of (economic) shocks, decreasing the need to inefficiently sell of their productive assets (e.g., cows, equipment), which would otherwise force them into a vicious spiral. Second, access to financial services (directly or indirectly) eases the financing of productivity improving agricultural equipment, thereby increasing agricultural yields and improving the income of those active in agriculture, thus reducing undernourishment. Third, there can be a link between financial development and undernourishment even when undernourished households do not gain (directly or indirectly) access to finance. One main reason is that higher agricultural productivity will translate into higher food output and lower food prices which is beneficial for all the poor, regardless whether they are active in agriculture or not. Similarly, to the extent financial sector development increases overall incomes, undernourishment will decline.

8
# **Section 4. An illustration of Yunnan Province**

Different regions in China exhibit different patterns and trends in upper secondary education expansion in recent years—largely associated with the varied socioeconomic development paths and characteristics across the regions. Located at the southwest side of China, bordering the Lao People’s Democratic Republic, Myanmar, and Vietnam, Yunnan is a medium-size Chinese province with a population of 46 million, of which 30 million reside in rural areas and 16 million in urban areas. Yunnan’s progress and challenges in upper secondary education typifies that of the less developed provinces in China and is presented in this report to allow for a more in-depth understanding of the upper secondary education subsector.

Yunnan is one of the least developed provinces in China and its income gap with the rest of the country is closing at a slow pace. In 2016, Yunnan’s GDP per capita was CNY 31,093 (approximately US$4,935) or 58 percent of the national average that year (CNY 53,935 or US$8,561). While Yunnan is slowly bridging the gap with the rest of the country, it is not doing so quickly enough. Its GDP per capita grew from 52 percent of national average in 2007 to 58 percent of national average in 2016. Even at this rate, Yunnan will not converge with the national average until 2080. Overall, Yunnan appears to have a slower pace than the rest of the country in structural transformation. The share of value added of the primary sector is declining at a slower pace, and the tertiary sector is also expanding at a slower pace than the national average.

Figure 4.1 GDP per capita, Yunnan versus national average (CNY) <!-- Start of picture text --> Yunan National<br>70000<br>59660<br>60000 53980<br>50251<br>46531<br>50000 43214<br>40000<br>30000<br>34546<br>31265<br>28806<br>20000 27185<br>25009<br>10000<br>0<br>2 0 1 3 2014 2015 2016 2017<br>IN RMB<br><!-- End of picture text --> _Source_ : National Bureau of Statistics of China 2017.

The lack of human capital is a major constraint for Yunnan’s economic development, as in many less developed regions in China. Yunnan people, on average, have fewer years of schooling. As mentioned in Section 1 of this report, in 2015, the employed labor force in Yunnan has on average 8.4 years of schooling, lower than the national average of 10 years. This is consistent with the statistics from the population survey carried out by the National Bureau of Statistics in 2016 (see Figure 4.2). It shows that for the population of age 6 and above, Yunnan has smaller shares of its population having attained education levels of secondary and above while much larger shares of education levels of primary and below. Perhaps not surprisingly, Yunnan has one of the lowest 46
definition, about 30 percent of South Asia are in urban areas. The travel distance is defined as the travel time from a given location to the nearest settlement and is calculated using a least-cost path algorithm on a friction surface where each pixel has a cost and travel time associated with it (Nelson et al. 2019). The friction surface incorporates a variety of factors such as connectivities, elevation, road network, land cover and slope, etc. The travel time obtained is validated against actual travel times from Google Maps.

# **2.2 Estimation**

We estimate the relationship between temperature or flooding and relative wealth, as measured by the RWI, or between temperature or flooding and firm size using Ordinary Least Squares (OLS) regressions.

We use the following specifications for households: R s = T T 28s + T T 29s + T T 30s + T T 31s + T T 33s + T T 34s + T T 35s + T T 36s s (1) II ss R s = A f s + vv + uus (2) ss Where R s is the average RWI at grid cell II AA f dd + vv in state or province + uu . The basic specification includes country fixed effects to take into account the country specific information used in the II gg ss construction of the RWI, but the preferred specification includes state fixed effects, , to take into account state specific characteristics within a country. All standard errors are clustered at the ss νν district level (third administrative unit). For temperature (eq. 1), in the main specification, the variable T e s includes indicators for the average maximum temperature between 29 degrees Celcius or lower and 36 degrees Celsius, relative to 32 degrees Celsius (the omitted ttT ttuueeTT dummy). This specification takes into account potential non-linearities in the relationship between RWI and temperature. For flooding (eq. 2), the first specification uses the variable A f s , an indicator that takes the value one if a location is ever flooded between 2000 and 2018. To AA f dd analyze the relationship with the number of flooding, the same specification is estimated using the number of floods as the dependent variable.

7
32

# Labor-intensive Net Exports Per Worker

<!-- Start of picture text --> Log Scale<br><!-- End of picture text --> <!-- Start of picture text --> 8 Tai<br>Malt Kor Ita<br>Ma&nfg DnPart<br>6 Grc De<br>Mal IrI Dom Yugo Belg<br>Jam 'nTurJa<br>4 961;x Bra Chi<br>Peru Indo India<br>02<br>0<br>X Bur<br>Ul<br>0} O EIS Arg<br>z Qkj<br>w -2 iSiFfg Ecu Fra<br>Ven Chl<br>.4 Para Egy<br>Liby NCy Fin Aust<br>-6 Can%A<br>Ice Swi<br>-a<br>-6 -4 -2 0 2 4<br>1965 Net Exports Per Worker<br><!-- End of picture text --> <!-- Start of picture text --> 6<br><!-- End of picture text --> Figure E.7.b