rafmacalaba/datause-encoder-data huggingface dataset:
# **2 Data**

Our sample covers 6 low- and middle-income countries in Africa and Eastern Europe: Kenya, Madagascar, Nigeria, Rwanda, Tanzania and Georgia. In order to apply the methodology developed by Atkin and Donaldson (2015), we require two sets of data for each country. First, we need price data on narrowly-defined product varieties, with observations spanning multiple locations within each country at a monthly frequency over several years. Second, we need the locations in which product varieties were produced or from which they were imported. The latter allows for the identification of trading pairs and the corresponding direction of trade from origin to destination locations. In this section, we briefly describe how we construct these data. Appendix A includes additional details about the data preparation process.

## **2.1 Price data**

Our price data come from CPI microdata provided to us by NSOs in each country in our sample. NSOs collect price data from a predefined list of product varieties (including goods and services), designed to capture the consumption basket of a typical household, at specified locations. Apart from the monthly price quote, the information provided to us by NSOs includes a product description, in some cases including the brand and presentation (e.g., “Coca-Cola, canned, 500ml” or “Body lotion, Nivea, 200ml”), the name of the location where the price was collected, and, occasionally, the name of a producer or the country from where the product was imported.

We restrict the choice of products to narrowly defined varieties, i.e., those for which we can observe a detailed product description and a brand. For most countries in our sample, prices are at the town or city level, with the exception of Rwanda, where prices are collected at the (more aggregated) district level.

The analysis in section 4 uses clean samples of price data, obtained by applying a cleaning algorithm to the raw data. Moreover, as in Atkin and Donaldson (2015), we convert all prices to constant U.S. dollars by deflating them using inflation rates at origin locations and bilateral nominal exchange rates between local currencies and the U.S. dollar. The cleaning and deflating procedures are described in Appendix A.

## **2.2 Products’ origin locations**

The estimation strategy proposed by Atkin and Donaldson (2015) depends on precisely identifying locations where a product is produced or imported. To do this, we search over the Internet to determine which manufacturers produce each brand and where their factories are located. For imported goods, we identify the main ports of entry in each 4
# Table 3. Model Without Interaction Between Inequality and Initial Income (Static Panel)

|Dependent Variable is:|||ln(|yit)|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Inequality Variable is:|Market Gini|Net Gini|Market Gini|Net Gini|Market Gini|Net Gini|
|Sample:|All Cou|ntries|Excluding<br>Coun|Transition<br>tries|Excluding<br>Coun|East Asian<br>tries|
|Inequalityit|4.14***<br>(0.38)|4.25***<br>(0.49)|4.39***<br>(0.36)|4.43***<br>(0.52)|3.55***<br>(0.32)|3.55***<br>(0.38)|
||||First Stage fo|r Inequalityit|||
|Residual Inequalityit|0.62***<br>(0.02)|0.72***<br>(0.03)|0.61***<br>(0.03)|0.71***<br>(0.03)|0.65***<br>(0.02)|0.75***<br>(0.02)|
|Kleibergen Paap F-<br>statistic|914|1031|833|942|1112|1372|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|789|789|735|735|759|759|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

28
and the dollarization ratio tend to move in opposite directions when inflation is decreasing.

The results of our partial dollarization exercise are presented in Table 3. As expected, the coefficient of expected appreciation is negative and significant, corroborating the hypothesis of currency substitution being dependent on expected relative returns between currencies. Notice that the foreign interest rates for the period in consideration have been relatively stable which accentuates the correlation between _E_ and _Id._ The coefficient of income is positive and significant: high-income economies tend to have a higher degree of currency substitution. A possible explanation is given by Chang (1994) where postulates that access to dollar-denominated accounts is reserved to wealthy agents. In Latin America, there are usually some fees associated with these type of accounts. Consequently, one would tend to believe that only high-income people can afford to deposit in foreign currency. Finally, although a negative coefficient was expected for the domestic interest rate, this turned out to be positive, though not significantly different from zero.

In order to check the robustness of our specification when studying currency substitution, suggested by equation (6), we run several auxiliary regressions. We first estimated the fixedeffects and the random-effects models and compared their estimates. Usually with small samples such as ours, the magnitude, sign and significance of the estimates vary widely from one specification to the next. However, our model has similar estimates and significance levels, suggesting a robust specification (see Table 3).8 Theoretically speaking, we are only interested in the fixed-effects panel since we are not randomly drawing countries from our sample. In order to statistically test that the fixed effects specification is the preferred one, we performed the Haussman specification test. Table 3 reports the Haussman statistic used to test the validity of the fixed-effects versus the random-effects model. The null hypotheses is accepted at 95% of significance.

We also considered the possibility of endogeneity for some of the regressors. Since the model comes from a partial equilibrium model we assume income to be exogenous. The possible endogenous variables in the model are the real interest rate and expected appreciation. Using one-period lagged values as instruments for each of these two variables, the Haussman endogeneity test was performed. The critical statistical values for the interest rate and expected appreciation are 2.02 and 0, respectively.<sup>9</sup> Consequently, we reject the endogeneity of 8In order to check that our results do not depend on superfluous (non-significant) explanatory variables, we excluded them from the regression and found that the sign and significance level corresponding to the rest of the variables describing the dollarization phenomena did not substantially change (see Table 4). 9 The actual statistic was -0.20, as is in the case whenever the variance-covariance elements are very close 9
# **C. Asset and Liability Structure**

In several countries insurance assets exceed 50 percent of GDP, while contractual savings, combining the assets of both insurance companies and pension funds, exceed 150 percent of GDP in such countries as the Netherlands, South Africa, Switzerland, the UK and the US. In Jordan, contractual savings amount to 30 percent cent of GDP, but the lion’s share of this is accounted for by the assets of the Social Security Corporation, a rare example of public sector dominance in the Jordanian financial landscape.

The total assets of insurance companies amounted to 260.5 million JD in 2002, equivalent to 3.9 percent of GDP. Assets grew slightly faster than GDP, at a real growth rate of 6.3 percent per year between 1997 and 2002. The low level of assets reflects the preponderance of general insurance, which involves a limited creation of assets compared to life (and pension) business that gives rise to a large accumulation of long-term financial resources. Life companies accumulate assets that are 5 to 7 times their annual premiums, whereas in general insurance companies total assets rarely exceed twice the level of annual premiums.

The 3 largest insurance companies had 36 percent of total assets. Liquid assets represented 30 percent of total assets, securities (mostly company shares) 27 percent, real estate 11 percent, and other assets 32 percent (Table 3). Other assets include accounts receivable, claims on reinsurers, and fixed assets. Among securities, company shares accounted for 16 percent of total assets, corporate bonds for 8 percent, and government bonds for 3 percent.

**Table 3: Composition of Assets and Liabilities, 1997-2002**

||1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Total (mn JD)|177.6|199.1|211.0|221.1|236.6|260.5|
|Total (% GDP)|3.5|3.6|3.7|3.7|3.7|3.9|
|Liquid Assets|30.9|32.3|33.9|33.2|35.1|29.9|
|Securities|25.9|26.3|26.9|23.6|23.0|27.0|
|(Gov Bonds)|(3.2)|(3.5)|(3.2)|(3.5)|(3.4)|(2.5)|
|(Other Bonds)|(1.5)|(3.0)|(3.5)|(3.6)|(4.0)|(7.9)|
|(Company Shares)|(21.2)|(19.8)|(20.2)|(16.5)|(15.6)|(16.6)|
|Real Estate|11.4|12.0|14.1|13.5|11.5|11.5|
|Other Assets|31.8|29.4|25.1|29.8|35.3|31.6|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|
|Equity|39.8|40.8|40.4|38.5|37.5|34.9|
|Technical Reserves|42.7|42.5|42.6|44.3|46.0|46.9|
|Other Liabilities|17.5|16.7|17.0|17.2|16.5|18.2|
||D<br>So|ata for 200<br>urce: Insur|2 are preli<br>ance Com|minary<br>mission|||

The equity position of the insurance sector is quite strong with an equity ratio of 35 percent of total assets, although this declined from 41 percent a few years back. The equity position also appears very solid with regard to gross premiums (62 percent) and 8
duties and labor supply among their members. For instance, a household may shift caregiving to workers with relatively worse earning opportunities to minimize the negative shock in earnings, and consumption. However, we find no evidence of intra-household reallocation of hours worked in response to air pollution shocks. There are no significant differences in the effect of pollution associated with age, gender, education, or position within the household. Consistent with the net reduction in hours worked within households, we find a negative effect of air pollution on earnings.

The rest of the paper is organized as follows. Section 2 provides background information on particulate matter and pollution in Lima city. Section 3 describes the data and discusses the empirical strategy. Section 4 presents the main results, while Section 5 concludes.

# **2 Background**

## **2.1 Fine particulate matter**

In this paper, we focus on the effect of fine particulate matter (PM2 _._ 5) on labor supply. This is motivated by the evidence linking it to respiratory and cardiovascular diseases.<sup>4</sup> PM2 _._ 5 is an air pollutant that consists of tiny particles less than 2.5 micrometers in diameter.<sup>5</sup> It can be produced by natural sources, like wildfires, but it largely comes from the combustion of fossil fuels and chemical reactions of air emissions. The concentration of PM2 _._ 5 in a given location is affected by proximity to its main sources but also by other local environmental factors, such as wind speed and direction, air temperature, humidity, precipitation and vegetation (Beckett et al., 2000; Hien et al., 2002; Janh ̈all, 2015). These factors create potential for seasonal and intra-urban variations in PM2 _._ 5 levels (Vecchi et al., 2004; Wilson et al., 2005).

Given their small size, PM2 _._ 5 can penetrate deep into the lungs and into the bloodstream (Bell et al., 2004). Moreover, it is harder to avoid than other pollutants since it can easily penetrate indoors (Thatcher and Layton, 1995; Vette et al., 2001). These features make it a particularly harmful pollutant (Bell et al., 2004; Pope III and Dockery, 2006).<sup>6</sup> A large medical literature finds evidence of a causal effect of short term exposure to PM2 _._ 5

> 4 However, we also explore other air pollutants such as PM10, nitrogen dioxide (NO2) and sulfur dioxide (SO2), see Section C.

> 5 Other types of particulate matter include PM10 and coarse particulate matter (PM2 _._ 5 _−_ 10).

> 6See U.S. EPA (2009) for a comprehensive review on health effects of particulate matter.

4
29 Composition of upstream tariff by largest contributing sectors 30, <!-- Start of picture text --> 29 Composition of upstream tariff by largest contributing sectors<br>30<br>Baseline Share in input<br>contribution to cost structure<br>31 # Sector in NAICS upstream tariff Baseline Tariff (US)<br>32 1 CRP - Chemical, rubber, plastic products 46.6% 8.3% 23.4%<br>3334 | 23 TEXNMM- -TextilesMineral products n.e.c. 43.1%8.3% 10.8%13.5% 2.6%16.6%<br>35 4 FMP - Metal products 1.6% 9.5% 0.7%<br>36 5 WAP - Wearing apparel 0.3% 19.0% 0.1%<br>37 6 LEA - Leather products 0.1% 11.9% 0.0%<br>38 rd COA - Coal 0.1% 5.0% 0.0%<br>39 | 8 P_C - Petroleum, coal products 0.1% 8.9% 0.0%<br>4041): 910 OMEOMF -- MachineryManufactures andn.e.c. equipment n.e.c. 0.0%0.0% 79%7.8% 0.0%0.0%<br><!-- End of picture text -->

|29|Comp|osition of upstream tariff by largest contri|buting sectors|||
|---|---|---|---|---|---|
|30||||||
||||Baseline<br>contribution to||Share in input<br>cost structure|
|31|#|Sector in NAICS|upstream tariff|Baseline Tariff|(US)|
|32|1|CRP - Chemical, rubber, plastic products|46.6%|8.3%|23.4%|
|33<br>||2|TEX - Textiles|43.1%|10.8%|16.6%|
|34|3|NMM- Mineral products n.e.c.|8.3%|13.5%|2.6%|
|35|4|FMP - Metal products|1.6%|9.5%|0.7%|
|36|5|WAP - Wearing apparel|0.3%|19.0%|0.1%|
|37|6|LEA - Leather products|0.1%|11.9%|0.0%|
|38<br><br>|rd<br>|COA-Coal<br>|0.1%<br>|5.0%<br>|0.0%<br>|
|39<br>||8|<br>P_C - Petroleum, coal products|0.1%|8.9%|0.0%|
|40<br>:|9|OME-Machineryandequipmentn.e.c.|0.0%|79%|0.0%|
|41)|10|<br>OMF-Manufacturesn.e.c.|0.0%|7.8%|0.0%|

|31|#|Sector in NAICS|Baseline<br>contribution to<br>upstream tariff|Baseline Tariff|Share in input<br>cost structure<br>(US)|
|---|---|---|---|---|---|
|32<br>||1|TEX-Textiles|37.2%|10.8%|16.4%|
|33<br>||2|<br>NMA-Mineralproductsn.e.c.|18.8%|13.5%|6.7%|
|34|=|<br>FMP - Metal products|17.0%|9.5%|8.5%|
|35|4|CRP = Chemical, rubber, plastic products|12.6%|8.3%|7.2%|
|36|5|PFB - Plant-based fibers|10.5%|4.3%|11.7%|
|37<br><br>|6<br>|MVH-Motorvehiculesandparts<br>|27%<br>|24.8%<br>|0.5%<br>|
|38<br>|<br>|7<br>|<br>IS - Ferrous metals<br>|0.9%<br>|10.7%<br>|0.4%<br>|
|39|8|COA-Coal|0.2%|5.0%|0.2%|
|40)|9|<br>P_C-Petroleum,coalproducts|0.1%|8.9%|0.0%|
|41<br>||10|<br>OME-Machineryandequipmentn.e.c.|0.0%|7.9%|0.0%|
- 4 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

Developing countries have been much less studied. Most of them treat specific cases such as Lagos, Nigeria (Barredo et al., 2004); Morelia city, Mexico (Lopez et al., 2001); or Cairo, Arab Republic of Egypt (Sutton and Fahmi, 2001). From the best of our knowledge there is evidence of three crosscountry studies that include LA cities in their sample: Huang et al. (2007) study 17 Latin American cities that are part of a total sample of 77 metropolitan areas in Asia, the United States, Europe, and Australia. Using a semi-automatic process for delineating the urban extents from Landsat ETM imagery, the authors calculate seven spatial metrics that capture five dimensions of urban form: compactness, centrality, complexity, porosity and density. Angel et al. (2010a) include 17 Latin American cities in a total sample of 120 cities from all around the world. Using also satellite images the authors calculate four urban fragmentation metrics: edge index, openness index, core open space ratio, and city footprint ratio. Finally, Inostroza et al. (2013) use Landsat imagery to study urban sprawl and fragmentation in 10 Latin American cities. The metrics for measuring the sprawl are: built-up area, density, spatial configuration, and speed. The common finding in these three studies is that Latin American cities are more compact and denser than their counterparts in either Europe or North America.

In our paper we contribute to the literature on urban form in three ways: First, we combine the use of NTL imagery, for urban extent delineation, with recently developed software on computational geometry and network topology (Angel et al., 2010b; Boeing, 2017), to generate a highly automated procedure that makes possible to analyze 919 urban extents in Latin America and the Caribbean. This is the most comprehensive study of urban form for this region. Second, the indicators used to describe urban form are tightly connected to the definition of urban form from the urban planning perspective, which describes urban form as a combination of external shape and internal structure (Whyte, 1968). In this paper we implement a definition of urban form that includes three dimensions: Shape of the urban extent, internal urban structure and land use pattern (Prosperi et al., 2009; Batty, 2008). This definition allows us to go beyond the debate sprawl vs. compact and cover other important aspects of urban form. Finally, we explore the relationship between urban form and factors such as topography, size of the city, economic strength (measured with NTL), and heritage of colonial urban planning.

**3 Data**
**Figure 1. Population within 100 kilometers of coast, 1975-2007**

<!-- Start of picture text --> 3,000<br>2,500<br>2,000<br>World<br>1,500<br>Developing Countries<br>Developed Countries<br>1,000<br>500<br>0<br>1975 1980 1985 1990 1995 2000 2005<br>Population (millions) within 100 km of coast<br><!-- End of picture text --> Source: United Nations Environment Programme (UNEP). 2014b. The UNEP Environmental Data Explorer, as compiled from UNEP/DEWA/GRID‐Geneva. UNEP, Geneva. http://geodata.grid.unep.ch.

Previous studies have emphasized that the increasing concentration of populations in the LECZ are vulnerable to sea-level rise and other coastal hazards such as storm surges (Brecht et al. 2012; Dasgupta et al. 2011; IPCC Working Group II 2014; McGranahan et al. 2007; Moser et al. 2012; Nicholls and Cazenave 2010; Nicholls et al. 2007 and 2010). Although the LECZ covers only 2% of the world’s land area, in developing countries it contains 14% of the population and 21% of urban dwellers (McGranahan et al. 2007). As a consequence, about 40 million urban residents globally are exposed to a 1-in-100-year coastal flooding event, and by 2070, it will be 150 million people (Nicholls et al. 2007). A 1 meter (m) sea-level rise accompanied by a10% intensification of storm surges would threaten an additional 31 million people in developing countries with inundation – approximately doubling the exposed population – and would increase the extent of coastal urban area exposed from 7% to 12.6% (Dasgupta et al. 2011). It is estimated that 10 cities in developing countries will account for 67% of the future coastal population exposure to the risks from sea-level rise and storm surge (Brecht et al. 2012).

There is little doubt that, as the LECZ in developing countries becomes more densely settled, these growing urban areas will become more vulnerable to the future risks resulting from 3
# **Table 2.2 Labor income** **<u>adjusted for labor income share of mixed income, 2003–2011</u>**

|Year|Labor income<br>adjusted for<br>mixed income<br>(LS4 method)|Adjusted labor<br>income (LS4)<br>over NDP at<br>factor costs|Labor income<br>adjusted for<br>mixed income<br>(LS5 method)|Adjusted labor<br>income (LS5)<br>over NDP at<br>factor costs|Average labor<br>income adjusted<br>for mixed<br>income|Average<br>adjusted labor<br>income over<br>NDP at factor<br>costs|
|---|---|---|---|---|---|---|
|2003|3,000,166|46.3%|3,751,545|57.9%|3,375,856|52.1%|
|2004|3,183,578|43.0%|4,014,771|54.2%|3,599,175|48.6%|
|2005|3,562,248|44.4%|4,244,344|52.9%|3,903,296|48.7%|
|2006|3,819,165|42.2%|4,519,693|49.9%|4,169,429|46.0%|
|2007|4,113,612|41.6%|4,843,730|49.0%|4,478,671|45.3%|
|2008|4,446,331|41.4%|5,173,867|48.2%|4,810,099|44.8%|
|2009|4,543,887|44.9%|5,274,945|52.1%|4,909,416|48.5%|
|2010|4,835,612|43.3%|5,299,665|47.4%|5,067,639|45.3%|
|2011|5,179,720|41.6%|5,992,660|48.1%|5,586,190|44.8%|

_Source:_ SHCP; for mixed income: UN and SHCP.

# **_3.2.2 Capital stocks_**

**The perpetual inventory method is used to estimate the stocks of capital for the public and private sectors.** We also use disaggregated series of investments into private and public stocks of capital divided into construction and equipment. Using standard procedures, we adopted medium levels of depreciation, 2.5 percent per year for construction and 6 percent for equipment. As is the case in many studies, the sensitivity of the ratios of stocks of capital to GDP or the reported rates of return to private capital to different plausible depreciation rates did not have a significant impact, since the depreciation rates enter both the numerator and the denominator of the ratios. The total capital to GDP (or K/Y), which averaged 2.9 over the 2003–2011 period, is in line with many other studies in Mexico (Cervini, 2004). The ratio of private capital over GDP (Kpri/Y) averaged 2.23 over the same period.

# **_3.2.3 Land and inventory stocks_**

**The estimates of capital income and returns are further adjusted for the value of land in capital stocks.** The value of land calculations are drawn from studies that estimate the composition of assets in a range of countries, including Mexico (see Caselli and Feyrer, 2007; World Bank, 2006). Based on these studies, an estimate of 18 percent is used here as the share of the value of land out of the value of reproducible capital plus land. The 18 percent represents the share of urban land over the total capital stocks. This is low compared to the estimate for the U.S. land share of total capital value, which comes to 20–26 percent over the 1960–2003 period (OMB, 2005). According to Caselli and Feyrer (2007), for world average parameters, the estimates of the share of reproducible capital on the total stock is about 75 percent—in other words, the proportion of land and land-related resources to reproducible capital stocks is close to 25 percent. In this study we adopt a conservative estimate.

16