rafmacalaba/datause-encoder-data huggingface dataset:
_Sources: (a) Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017. (b) U.S. Energy Information Administration._ Rapid urbanization in developing countries presents both challenges and opportunities in addressing the environmental challenges (Kahn 2006). The world’s urban population increased from less than 1.4 billion (or 36%) in 1960 to nearly 4.2 billion (or 55%) in 2018. By 2050, over two-thirds of the world’s population are projected to live in urban areas and the rural to urban migration during this process will mostly occur in developing countries. On the one hand, the high concentration of people and activities in cities could lead to severe traffic congestion and exacerbate air pollution, especially with the rise in vehicle ownership in emerging economies. On the other hand, cities have the potential to organize economic activities spatially to reduce energy consumption and environmental impacts and to better take advantage of the economies of scale in public transit. Understanding the role of transportation in addressing urban environmental challenges has important implications for policy design to foster the emergence of green cities.

The transportation sector, which relies heavily on fossil fuels, is a major source of air pollution and greenhouse gas (GHG) emissions. The WHO estimates that road transport contributes 30 percent of particulate emissions in European cities and up to 50 percent in countries in the Organisation for Economic Co-operation and Development (OECD).<sup>5</sup> According to the US Environmental Protection Agency (EPA), the transportation sector is responsible for about 10 percent of particulate, more than 55 percent of nitrogen oxide (NOx), and about 10 percent of <mark>volatile organic compound</mark> (VOC) emissions in the United States.

Because of the rapid rise in private vehicle ownership and travel demand, as well as the relatively low fuel efficiency in developing countries, the transportation sector plays an increasingly significant role in local air quality. Figure 2 shows the increase in new passenger vehicle registrations in selected countries from 2005 to 2017. Among developed countries, new vehicle sales were stable or declined slightly during this period. By contrast, China and India experienced dramatic increases in vehicle ownership, with total new passenger vehicles increasing fivefold in China and 2.5 times in India. Although per capita vehicle ownership is still

> 5 Charles Carter. “The War on Smog.” Smart Cities World. https://www.smartcitiesworld.net/specialreports/special-reports/the-war-on-smog-.

4
# **I. Introduction**

The World Bank Group has established two goals to guide its strategy in the coming years (World Bank, 2013a). The first goal is to end extreme poverty, as defined by the global extreme poverty measure of $1.25 a day, adjusted for Purchasing Power Parity (PPP). More specifically, the World Bank Group seeks to orient its programs so that the global extreme poverty rate reaches 3 percent by 2030. The second goal is to promote “shared prosperity” by helping every country to foster income growth of the bottom 40 percent of the population. These two goals recognize that the process must be sustained over time and across generations: in other words, this requires promoting environmental, social and economic sustainability.

The second goal has its intellectual origins in the concept of “quintile income” (Basu, 2000). Quintile income was proposed as a simple, easy-to-calculate, and easily understood welfare measure, defined as the income per capita of the bottom quintile (20 percent) of the population. It draws on Rawlsian notions of promoting the welfare of the least fortunate members of society, while also possessing the pragmatic feature of comparability with traditional macroeconomic welfare measures like income per capita. In a developing country context, one may consider the bottom two quintiles – or 40 percent – as more appropriate for a variety of practical considerations. Most notably there is the fact that – even in rapidly growing emerging market economies – the people in the bottom 40 percent have incomes that are well below the poverty line in rich countries.<sup>3</sup> On the other end of the spectrum, in very poor countries, all of the people in the bottom 40 percent are below the extreme poverty line. One would hope that – with economic growth, job creation, and equity-enhancing social programs—these poor people would not only escape this extreme degree of poverty but also progress to more prosperous living standards.

# **II. Shared Prosperity:  Definition and General Properties**

Following Basu (2000), the income profile of a country with _n_ persons can be written as the vector _x_ = ( _x1, x2, ... xn_ ) of nonnegative numbers, where each element of the vector represents the income of person _i_ . Assume that the elements of the vector _x_ are organized from lowest to highest income, or more formally: _x1_ ≤ _x2_ ≤ ... ≤ _xn_ . The number _n_ is the total population size of the country, and one can define _r_ as the largest integer such that _r/n_ ≤ 0.4. The shared prosperity indicator can then be written as:

> 3 See Milanovic (2012) for a discussion of these cross-country differences.

2
The values are substantially higher than those observed by Ghani, Kerr and Tewari (2013) for the manufacturing sector only. For 2005, the earlier study found a mean value of 6.65 and a median value of 4.49. This study also discussed the comparability of the 6.65 value to what is observed with U.S. manufacturing data. The higher values in this study for specialization indices are not surprising. As discussed in the methodology section above, the specialization index measures the extreme value observed over a set of industries. By almost doubling the underlying set of industries, we greatly increase the potential for the metric to capture larger values.

Figure 1a compares the mean and median series for manufacturing only compared to manufacturing and services. While the levels differences exist, the trends in the post 2000 period are broadly comparable, especially for the median. An important subject of Ghani, Kerr and Tewari (2013) was the dramatic decline in specialization levels from manufacturing in 1989. Around the time of liberalization, India’s manufacturing distribution displayed more specialization and greater spatial variation in specialization rates than the U.S. data. India has converged towards the U.S. baseline in the two decades since then. As the services data only begin in 2000, we cannot observe this longer trend in our current work.

Panel B of Table 1a considers the relative diversity index. The average diversity in 2005 is 1.38, which is very close to the 1.41 derived with just manufacturing data in the earlier study. Unlike the construction of the specialization index, it is quite possible for the diversity index to decline in average value following the addition of the services industries. Similar to the specialization index, we observe a re-widening of the distributions in 2010 (greater standard deviation) after a decline from 2000 to 2005.

Figure 1b shows the remarkably tight connection between trends in overall district diversity levels and those present in just manufacturing alone. This stability is both striking and encouraging for our empirical work and its broad robustness. Table 1b shows a very similar set of patterns when considering only the urban areas of districts, with the additional observation that the specialization of urban areas tends to exceed that for the district as a whole, while diversity levels are very similar.

Figure 2 graphs the specialization and diversity index values against each other using 2000 Indian data. The bubble size in each graph represents the size of the local manufacturing 12
# How Costly Are Labor Gender Gaps? Estimates for the Balkans and Turkey<sup>_∗_</sup>

David Cuberes Marc Teignier Clark University Universitat de Barcelona May 15, 2015 Abstract In this paper, survey data are used to document the presence of gender gaps in selfemployment, employership, and labor force participation in seven Balkan countries and Turkey. The paper examines the quantitative eects of the gender gaps on aggregate productivity and income per capita in these countries. In the model used to carry out this calculation, agents choose between being workers, self-employed, or employers, and women face several restrictions in the labor market. The data display very large gaps in labor force participation and in the percentage of employers and self-employed in the labor force. In almost all cases, these gaps reveal a clear underrepresentation of women. The calculations show that, on average, the loss associated with these gaps is about 17 percent of income per capita. One-third of this loss is due to distortions in the choice of occupations between men and women. The remaining two-thirds corresponds to the costs associated with gaps in labor force participation. The dimensions of these gender gaps and their associated costs vary considerably across ages groups, with the age bracket 3650 years being responsible for most of the losses.

JEL classication numbers: E2, J21, J24, O40.

Keywords: gender inequality, entrepreneurship talent, factor allocation, aggregate productivity, span of control, Balkans, Turkey.

> _∗_ We thank Maria Eugenia Dávalos and Ana Maria Muñoz for useful comments and Jose Montes for helping with the data. Note: The views expressed here are those of the authors and do not necessarily represent or reect the views of the World Bank.
|Combined passenger<br>transport|Transport of individuals and groups of persons and luggage by two or<br>more modes of transport when the expenditure cannot be<br>apportioned between them; includes transport of private vehicles;<br>excludes package holidays.|
|---|---|
|Other purchase transport<br>services|Funicular, cable-car, and chairlift transport; removal and storage<br>services; services of porters and left-luggage and luggage-forwarding<br>offices; travel agents' commissions, if separately priced; excludes<br>cable-car and chairlift transport at ski resorts and holidaycenters.|

**Annex 9. Countries and Data Availability of the Ownership Variables in SSAPOV Collection**

|**Country**|**Survey year**|**Car**|**Motorcycle**|**Bicycle**|**Oxcart**|**Boat**|
|---|---|---|---|---|---|---|
|Angola|2018|X|X|X|X|X|
|Benin|2015|X|X|X|X|X|
|Burkina Faso|2014|X|X|X|||
|Burundi|2013|X|X|X|X|X|
|Cabo Verde|2015|X|X|||X|
|Cameroon|2014|X|X|X|X||
|Comoros|2013|X|X|X|||
|Congo,Dem. Rep.|2012|X|X|X|X|X|
|Congo,Rep.|2011|X|X|X|||
|Eswatini|2016|X|X|X|||
|Ethiopia|2015|X|X|X|X||
|Gabon|2017|X|X|X|||
|Gambia|2015|X|X|X|X|X|
|Ghana|2016|X|X|X|X|X|
|Guinea-Bissau|2010|X|X|X|||
|Lesotho|2017|X|X|X|X||
|Liberia|2016|X|X|X|||
|Madagascar|2012|X||X|X||
|Malawi|2016|X|X|X|X||
|Mali|2009|X|X|X|X|X|
|Mauritania|2014|X|||X|X|
|Mauritius|2017|X|X||||
|Mozambique|2014|X|X|X|X||
|Namibia|2015|X|X|X|X|X|
|Niger|2014|X|X|X|X||
|Nigeria|2015|X|X|X|||
|Rwanda|2016|X|X|X|||
|Senegal|2011|X|X|X|X|X|
|Sierra Leone|2011|X|X|X|||

56
|________________<br>Country<br>________________|____________<br>Model<br>____________|_____<br> <br>_____|____________<br>All<br>firms<br>____________|___________________<br>20 – 49<br>employees<br>___________________|______________________<br>50 – 249<br>employees<br>______________________|_____________________<br>250 – 499<br>employees<br>_____________________|_____________________<br>500 and more<br>employees<br>_____________________|
|---|---|---|---|---|---|---|---|
|Spain|Pooled|ß|27.5|31.0|29.3|22.0|6.5|
|1990 - 1999||p|[0.00]|[0.00]|[0.00]|[0.00]|[0.07]|
||Fixed|ß|8.1|7.1|6.0|14.2|7.8|
||effects|p|[0.00]|[0.00]|[0.00]|[0.00]|[0.02]|
||N||2,123|809|652|385|277|
||NxT||12,806|4,256|3,930|2,644|1,976|
|Sweden|Pooled|ß|6.7|6.8|9.00|-6.8|3.9|
|1997 - 2004||p|[0.00]|[0.00]|[0.00]|[0.93]|[0.68]|
||Fixed|ß|-0.1|-0.5|-0.6|4.1|7.3|
||effects|p|[0.85]|[0.61]|[0.70]|[0.44]|[0.50]|
||N (medi|an)|4,035|2,193|1,471|190|178|
||NxT||31,838|17,381|11,530|1,510|1,417|
|UK|Pooled|ß|9,9|9,2|10,6|8,7|10,3|
|1995 - 2004||p|[0.00]|[0.00]|[0.00]|[0.00]|[0.00]|
||Fixed|ß|3,9|7,5|4,0|6,9|-3,4|
||effects|p|[0.00]|[0.00]|[0.00]|[0.00]|[0.08]|
||N||9,450|2,060|5,211|1,073|1,106|
||NxT||52,593|9,200|29,367|7,114|6,912|

________________________________________________________________________________________________________________________________ Note: Results are for firms from ISIC industries 15 – 36 with at least 20 employees at the median over the years covered in the panel. The firms with the bottom / top one percent of labor productivity (defined as total sales per employee) in a year are excluded from all computations. Firms are classified into size classes according to the median of the number of employees over the years covered. ß is the estimated regression coefficient from an OLS-regression of log (labor productivity) on a dummy variable for exporting firms, controlling for the log of the number of employees and its squared value, the log of wages and salaries per employee, and a full set of interaction terms of 4digit industry-dummies and year dummies; the fixed effects model adds firm fixed effects. To facilitate interpretation the estimated coefficients for the exporter dummy variable have been transformed by 100(exp(ß)-1). p is the prob-value. N ist the number of firms, NxT is the number of observations.

37
of existing knowledge. The fi rm-level dataset we use allows us to construct a broad measure of technological innovation, which includes the creation of new production processes but also the adoption and adaptation of existing technologies to local conditions. In particular, the survey collects information on whether fi rms introduced recently new technology that substantially changed their main process of production. This measure of technological innovation is more likely to re fl ect advances of the fi rm towards the country’s knowledge frontier, rather than movements of the technological frontier itself. This is arguably a better measure to study technological innovations and their international diffusion, especially in the context of developing countries. Also important to our analysis, the survey collects very detailed information on fi rm characteristics, including their export and import activities and foreign ownership.

Using this fi rm-level dataset covering 43 developing countries, we will analyze the following questions. First, which fi rms have a larger incentive to engage in technological innovations? Second, is the fi rm’s integration into global markets an important channel for the acquisition of new technologies? Which speci fi c channels for the transfer of new technology are explored by those fi rms that engage in trade or have some foreign ownership?

Our paper relates to two strands of literature. First, it relates to the large micro literature that studies the determinants of innovation, which originated in the work by Schumpeter (1942) (e.g., Cohen and Levin, 1989; Cohen, 1995; Aghion et al., 2005). Particularly related to our work is Criscuolo et al. (2005). Like us they explore fi rm-level data to compute a broad measure of innovation and relate the fi rm’s propensity to innovate with its integration into global markets. Their fi ndings show that those UK. fi rms that are more integrated into global markets are more likely to innovate, but that most of the difference is explained by the number of scientists and researchers used. Vishwasrao and Bosshardt (2001) also fi nd that foreign-owned fi rms in India are more likely to adopt new technology than domestic-owned rms.<sup>3</sup> Second, our paper relates to the vast literature on the effects of integration into global

> 3The cross-country evidence also points to a positive correlation between trade openness and the speed at which countries adopt new technologies (Comin and Hobijn, 2004) or invest in R&D (Lederman and Maloney, 2003).

4
_Connecting Lagging and Leading Regions: The role of labor mobility_ The 1991 and 2000 Brazil censuses provide information on current residence and birth state for most individuals. Therefore, we define migration by an individual’s current location relative to his birth state. That is, we use a long-run measure of migration. One could also employ a short-run definition of migration – i.e., relative to where the individual was living 1, 2, or 5 years before, if necessary data are available. We use 3659 AMC’s (i.e. minimally comparable areas) as destination locations and 27 states as origin locations. AMCs are similar to counties but are aggregated in some cases to make them comparable over time.

For each census year, we focus our attention on household heads who were between the ages of 25 and 35 years. In this way, it is less likely that the same household head will show up in both the 1991 and 2000 samples. Moreover, by using individuals from this cohort, we focus our attention on first migration decisions – i.e., those made after an individual initially finishes school and/or leaves his parent’s home. This move may be accompanied by marriage, the birth of a child, etc. Our goal is to avoid mixing these individuals with older individuals who may be making location decisions based on retirement considerations, or who may have made location decisions many years in the past. Finally, we also control for individual attributes, since amenities and employment opportunities are likely to have different effects on migration behavior for different types of individuals. Given that age has already been restricted to be between 25 and 35 years, we further divide those household heads according to their education level. Household heads with post-secondary education are excluded from the analysis.

The Brazilian censuses also contain information on employment and income. Recall that our current model ignores the possibility that the individual would be unable to find work. We therefore keep only those household heads who were employed. In the 2000 census, over 90% of all Brazilians between the ages of 25-35 (with less than college education) reported that they were working. Thus, dropping unemployed household heads is not a major problem in this context. For each household head in our sample, we can observe his wage in the destination location where he actually resides. However, in order to 12
Newson, R. B. (2010). Frequentist q-values for multiple-test procedures. _Stata Journal_ , _10_ (4), 568-584.

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Phillips, J., & Simon-Davies, J. (2016). Migration to Australia: a quick guide to the statistics. Canberra: Parliamentary Library.

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Roodman, D. (2011). Fitting fully observed recursive mixed-process models with cmp. _Stata Journal_ , _11_ (2), 159-206.

Ruiz, I., & Vargas-Silva, C. (2018). Differences in labour market outcomes between natives, refugees and other migrants in the UK. _Journal of Economic Geography_ , _18_ (4), 855-885.

- Shergold, P., Benson, K., & Piper, M. (2019). Investing in refugees, investing in Australia. Department of Home Affairs (Australia).

Singhal, S. (2019). Early life shocks and mental health: The long-term effect of war in Vietnam. _Journal of Development Economics_ , _141_ , 102244.

- Slewa-Younan, S., Rioseco, P., Guajardo, M. G. U., & Mond, J. (2019). Predictors of professional help-seeking for emotional problems in Afghan and Iraqi refugees in Australia: Findings from the Building a New Life in Australia Database. _BMC Public Health_ , _19_ (1), 1-11.

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- Staubli, S., & Zweimüller, J. (2013). Does raising the early retirement age increase employment of older workers? _Journal of Public Economics_ , _108_ , 17-32.

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- Steel, Z., Momartin, S., Silove, D., Coello, M., Aroche, J., & Tay, K. W. (2011). Two year psychosocial and mental health outcomes for refugees subjected to restrictive or supportive immigration policies. _Social Science & Medicine_ , _72_ (7), 1149-1156.

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25
# **<u>Table 6: IV regressions for impact of title on land-related investment</u>**

||<br>**Constr. SWC**<br>**structures**|**Trees planted**|**Number of**<br>**trees per ha**|**Practiced**<br>**fallow**|**Use of**<br>**manure**<sup>a</sup>|
|---|---|---|---|---|---|
|**Panel A: All formal documents**||||||
|Parcel with formal doc|0.0466|-0.00957|-0.794|0.0226|0.0477|
||(0.0419)|(0.0507)|(2.589)|(0.0246)|(0.0424)|
|Parcel with chief certificate|-0.0450*|-0.0133|-0.822|-0.00723|0.00240|
||(0.0253)|(0.0309)|(1.580)|(0.0149)|(0.0272)|
|State land with no title|-0.158**|-0.114|-1.561|-0.0510|0.0587|
||(0.0616)|(0.0747)|(3.815)|(0.0362)|(0.0659)|
|Log parcel area in ha|0.0108*|0.00984|-0.324|0.000853|0.0112|
||(0.00629)|(0.00769)|(0.393)|(0.00370)|(0.00798)|
|Sargan overidentification statistic|3.163|2.257|0.982|16.65|5.277|
|Chi-sq(1) p-value|0.0753|0.133|0.322|0.0000449|0.0216|
|**Panel B: Formal documents by pr**|**ovenance**|||||
|State land with title (_β1_)|0.0640|-0.0259|-1.506|0.0273|0.0608|
||(0.0423)|(0.0510)|(2.611)|(0.0249)|(0.0434)|
|Prev. cust. land with title (_β2_)|-0.0735|0.104|4.119|-0.0144|-0.0724|
||(0.106)|(0.129)|(6.576)|(0.0621)|(0.136)|
|Parcel with chief certificate|-0.0435*|-0.0147|-0.885|-0.00668|0.00459|
||(0.0254)|(0.0310)|(1.585)|(0.0149)|(0.0275)|
|State land with no title|-0.176***|-0.0976|-0.841|-0.0558|0.0457|
||(0.0621)|(0.0751)|(3.842)|(0.0365)|(0.0671)|
|Log parcel area in ha|0.0110*|0.00956|-0.335|0.000967|0.0122|
||(0.00631)|(0.00769)|(0.394)|(0.00371)|(0.00811)|
|Sargan overidentification statistic|2.911|2.098|0.889|16.38|4.929|
|Chi-sq(1) p-value|0.0880|0.147|0.346|0.0000519|0.0264|
|χ<sup>2</sup>-test:_β1_=_β2_|1.710|1.032|0.740|0.455|0.930|
|p-value|0.191|0.310|0.390|0.500|0.335|
|**Panel C: State vs customary land (**|**OLS regressions)**|||||
|State land|-0.00356|-0.0356**|-0.747|-0.00184|0.0625***|
||(0.0140)|(0.0172)|(0.878)|(0.00827)|(0.0150)|
|Log parcel area in ha|0.0138**|0.0117|-0.326|0.00233|0.0110|
||(0.00581)|(0.00714)|(0.365)|(0.00342)|(0.00800)|
|Mean of dep. var.|0.17|0.40|2.89|0.06|0.13|
|Numberofobservations|2,644|2,638|2,638|2,644|2,664|

_Note:_ Field characteristics (subjective soil quality and slope measures) and owner characteristics (head’s gender, age, literacy status, membership in farms group and having a leadership position in local organization as well value of household and farm assets) are included throughout but coefficients not reported. Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.010 a Sample includes cultivated parcels including rented-in land.

25