rafmacalaba/datause-encoder-data huggingface dataset:
Consistency of fixed effects estimates, which subtract level differences across groups, depends on a properly linear specification. Column 4 demonstrates that a quadratic term for village-level open defecation is not statistically significant, and indeed changes signs with and without district fixed effects. Potential non-linear relationships between village sanitation coverage and child height will be considered in more detail in 4.1.2.

# **4 Evidence from pooled Indian and African surveys**

Do differences in village-level sanitation coverage explain the difference in height between rural children in India and in sub-Saharan Africa? If so, is this just a spurious reflection of other correlated variables? This section addresses these questions using pooled child-level data from the rural parts of nine DHS surveys: India’s 2005-6 NFHS-3 and eight surveys from Africa in the 2000s.<sup>19</sup> In particular, the DHS surveys nearest 2005 (and balanced before and after) were selected from the five largest African countries available;<sup>20</sup> the included countries account for 46 percent of the 2012 population of sub-Sahara Africa.

The argument of this section proceeds in several stages, building to a statistical decomposition of the India-Africa height difference, in the sense of Oaxaca-Blinder. First, section 4.1 verifies an association between village-level sanitation and height within the two regions. In particular, this section assesses the linearity of the relationship (assumed by some decomposition techniques), and notes that a village-level effect implies the presence of negative externalities. Then, section 4.2 considers a paradox implied by Deaton’s (2007) finding that height is not strongly associated with GDP: the within-region association between open defecation and well-being has a different slope from the across-region association. Next, section

> 19Here I again follow the WHO recommendation of dropping observations with height-for-age _z_ -scores more than 6 standard deviations from the mean.

> 20This excludes South Africa, where height has not been measured in a DHS survey. Beyond this data availability constraint, this exclusion may be appropriate due to South Africa’s unique history and demography; its exceptionally high sanitation coverage (11.6 percent open defecation in 1998) would make it a positive outlier even in the African sample. The eight African DHS surveys used are: DRC 2007, Ethiopia 2000 and 2005, Kenya 2005 and 2008, Nigeria 2003 and 2008, and Tanzania 2004. DHS sampling weights are used throughout.

18
Kung, J.K., 1995. Equal entitlement versus tenure security under a regime of collective property rights:

- peasants’ preference for institutions in post‐reform Chinese agriculture _.” Journal of Comparative Economics_ , 21(1), pp. 82–111.

Kung, J.K. 2000. “Common property rights and land reallocations in rural China: evidence from a village survey.” _World Development_ , 28(4), pp. 701–719.

Kuznets, Simon. 1955. “Economic Growth and Income Inequality,” American Economic Review, 45(1): 1‐ 28.

Lewis, W. Arthur. 1954. “Development with Unlimited Supplies of Labour,” The Manchester School, May 1954, 22: 139‐92.

Lewis, W. Arthur. 1958. “Unlimited Labour: Further Notes,” The Manchester School, January 1958, 26:1‐ 32.

Liu, S., Carter, M.C. and Yao, Y. 1998. “Dimensions and diversity of property rights in rural China:

- dilemmas on the road to further reform.” _World Development_ , 26(10), pp. 1789–1806.

- Liu, Zhiqiang 2005. “Institution and inequality: the hukou system in China.” Journal of Comparative Economics 33: 133‐57.

- Lohmar, Bryan. 2000. “The Effects of Land Tenure and Grain Quota Policies on Farm Household Labor Allocation in China,” PhD Dissertation, Department of Agriculture and Resource Economics, University of California at Davis.

- Martinez‐Bravo, Monica, Gerard Padró i Miquel, Nancy Qian and Yang Yao. 2014. “Political Reform in China: The Effect of Local Elections,” Yale University, mimeo (July 16, 2014).

- Mu, Ren and Zhang, Xiaobo. 2014.”Do Elected Leaders in a Limited Democracy Have Real Power? Evidence from Rural China,” _Journal of Development Economics_ 107: 17‐27.

- Mullan, Katrina, Grosejean, Pauline, and Kontoleon, Andreas. 2011. “Land Tenure Arrangement and Rural‐Urban Migration in China,” World Development 39(1): 123‐33.

- North, Douglass. 1990. Institutions, Institutional Change and Economic Performance. Cambridge: Cambridge University Press

- O’Brien, K., Li, L., 2000. “Accommodating ‘‘democracy’’ in a one‐party state: introducing village elections in China,” China Quarterly 162: 465– 89.

- Oi, J., Rozelle, S., 2000. Elections and power: The locus of decision‐making in Chinese villages. The China Quarterly 162, 513–539.

_Land Tenure Insecurity and Migration, Page 33_
- 21 -

average (given the distribution of Table 7) of<sup>2.36 axles per vehicle. The average daily</sup> volume of traffic passing on each km of unpaved<sup>road annually is 136,400. The RDE on</sup> unpaved roads is, by this estimate, cO.23 per<sup>axle-km (i.e., CO.5 per veh.-km for light</sup> vehicles and buses, CO.7 for medium trucks, and<sup>C1.0 for heavy trucks).</sup> An alternative estimate is based on the assumption<sup>that the damage associated with the</sup> passage of an axle of a heavy truck is three times that<sup>of a lighter vehicle. The adaption of</sup> this assumption ge'ierates a different allocation of the<sup>maintenance costs: CO.31 and ¢0.46</sup> per veh.-km of light and medium vehicles, and<sup>a doubling of the RDE attributed to heavy</sup> vehicles to (1.9 per veh.-km.

# B. Marginal Congestion Costs **(MCC)**

A second component of the road user cost is<sup>the congestion cost. Newbery (1988) esti-</sup> mated the cost of interurban congestion<sup>on Tunisian roads at C0.20 per passenger car</sup> equivalent (PCE). Although this estimate was<sup>admittedly crude, requiring many</sup> approximations.

Whe:eas road damage costs are long-lasting,<sup>affecting operating costs of all future users</sup> until the damage is repaired, congestion costs have an<sup>immediate effect on all those who use</sup> the road at the same time. The externality depends<sup>on the number of users and the additional</sup> costs imposed on each one due to the slowdown<sup>of traffic.</sup> To obtain an estimate of these costs one<sup>has to know the relevant speed function, an</sup> estimate of the value of time, the composition of traffic,<sup>and the passenger load factor of the</sup> different types of vehicles. The existing information<sup>on each of these components is quite</sup> sketchy.

A crude estimate of the interurban speed function<sup>for Ghana's trunk roads is provided</sup> by SWKP (1988b): _S_ = 72 - 0.02X where S denotes speed measured in kph and X denotes<sup>the number of vehicles per hour. This</sup> estimate is based on speed measurements undertaken<sup>on a relatively highly traveled road (an</sup> ADT of _4,750_ vehicles). It is based, however,<sup>only on two points: the free flow speed (72</sup> kph) and the existing speed and traffic volume.<sup>The GHA estimated the same linear speed-</sup> flow relationship for a 2-lane 2-way suburban road<sup>in Accra, and the estimated function</sup> (with over 50 observations) was: Both estimates are within the range of speed functions<sup>used by Newbery for different types</sup> of roads.
jobs and a higher share of men are requested in professional and managerial jobs. Lawler & Bae, 1998 studied job announcement data for white-collar and professional jobs from newspapers between 1985 and 1996 in Thailand and analyzed explicit gender discrimination. However, the focus of their paper is on the impact of multinational corporations on overt gender discrimination in the hiring process.

Anand, 2013 investigated gender stereotyping in job recruitment advertising in India using content analysis of 828 job advertisements collected from a widely-read English newspaper. The study found evidence of gender stereotyping in job ads across sectors in India; gender bias is less pronounced in professional jobs such as engineering, medical and other professional categories. On the other hand, a higher level of gender-targeting is found in jobs for secretary, receptionist, call center tele-callers, managerial jobs, teaching, clerical positions inter alia. In the sales sector, men are preferred for field positions and women are preferred for jobs involving tele-marketing.

# **III. Data and Sample**

Our data were acquired from India’s leading job-matching website – Babajob.com, established in 2007, recently bought by and merged with QuikrJobs.com. In 10 years, between 2007 and 2017 more than 1.25 million jobs were posted on the site and over half a million employers and over 5 million jobseekers were registered. The ads posted in the portal include almost equal share of both white- and blue-collar jobs. A variety of access options have been made available for job seekers to utilize the service and ensure better access to the disadvantaged population (Nomura, et al., 2017).

The final sample we used for the analysis includes 830,929 unique job advertisements posted between May 2011 and April 2017 in the top 20 cities. Our final sample constitutes 65 percent of all ads that span from May 2007 to May 2017. From the complete list of Babajob ads, we disregarded the 3 percent that were posted without any offered salary and another 0.6 percent of ads with salary outliers either under Rs. 800 or over Rs. 68,000. We deflated salaries using the monthly state-level urban consumer price index (CPI). Since CPIs were not available for years before 2011, we further restricted our sample to ads posted either in 2011 or later. Once an ad is posted it can stay in the portal for 90 days before it expires. We removed the duplicate ads if those were posted within 90 days of the first listing. There were 50,819 duplicate ads which got removed in the process. Each job ad comes with a location that mentions state, city and pincode. We ranked the cities based on the number of ads posted under each city. The final sample of 830,929 ads are only from the top 20 cities. The cities included in the sample are Ahmedabad, Bangalore, Chandigarh, Chennai, Coimbatore, Delhi, Gurgaon, Hyderabad, Indore, Jaipur, Kolkata, Luchknow, Ludhiana, Mumbai, Nagpur, Noida, Patna, Pune, Ranchi, and Thane. Half of the jobs advertised in our sample were based in three major cities – Bangalore, Delhi, and Mumbai.

4
# **Table 3.1.1 : Additional Argentine Data Included in AD-ARG-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-AR**|**G-Master:**||
|**U.**|**INIT_GP**|The government publication initiating the investigation|
|**V.**|**P_GP**|The government publication containing the preliminary finding|
|**W.**|**P_MEASURES_GP**|The government publication containing the preliminary measures|
|**X.**|**P_AD_DUTY**|The preliminary antidumping duty imposed with details on the<br>measure(s).|
|**Y.**|**F_GP**|The government publication containing the final finding|
|**Z.**|**F_MEASURES_GP**|The government publication containing the final measures|
|**AA.**|**F_AD_DUTY**|The final antidumping duty imposed with details on the measure(s).|
|**AB.**|**F_AD_MARGIN(min)**|The final minimum dumping margin found|
|**AC.**|**F_AD_MARGIN(max)**|The final maximum dumping margin found|
|**AD.**|**DUTY_RULE**|Either FD- Full duty or LD- Lesser Duty, where full duty implies the full<br>dumping margin was imposed as an antidumping duty while lesser duty<br>implies that the antidumping duty imposed was less than the full margin.<br>_Source:_Nogués and Baracat (2005)|
|**AE.**|**METHOD**|Methodology used to determine the dumping margin (DM- Domestic<br>market, TC- Third country, CB- Cost based)<br>_Source:_Nogués and Baracat (2005)|
|**AF.**|**TYPE**|Either D-Dumping Case or RD -Review of Dumping Case<br>_Source:_Nogués and Baracat (2005)|

In terms of incomplete or missing data, the Argentine data set does not have complete information on the revocation or continuation of measures, as this information is only available for cases that have undergone a review. Furthermore, the database contains only partial information on foreign exporters as only some were named in the official documents.

# **3.2 Australia (AUS)**

The Australian Customs Service (or Customs) is solely responsible for anti-dumping investigations and determines if dumping and/or injury has occurred for each case. The decisions of the Australian Customs Service’s Dumping Unit are sometimes reviewed by a Trade Measures Review Officer from the Trade Measures Review Secretariat. The initiation and outcomes of each investigation are published in the Australian Commonwealth Gazette and in Australian Customs Dumping Notices (ACDNs), which are available online at the Customs website. The sources for data included in the database is online 16
level. Existing research either focuses on detailed evidence from individual countries (Tiepolo & Galligari, 2021) or select world regions (Henderson _et al._ , 2017) or it links climate change to urbanization statistics aggregated at the country level (Castells-Quintana _et al._ , 2021; Maurel & Tuccio, 2016). Invaluable as the insights from existing studies are, they struggle to simultaneously account for subnational variation in both climate and urbanization and establish larger regional and global patterns that are likely at play.

In this paper, we assess the nexus between climate change and growing cities at a global level, exploiting a novel panel dataset that covers every country in the world between 1985 and 2014. We operationalize our variables utilizing the PRIO-GRID v2.0 cell structure that divides the entire globe into 0.5° _×_ 0.5°cells (Tollefsen _et al._ , 2012). Our independent variable is the proportion of months out of 12 months that are part of the longest streak of consecutive months ending in a given year that experienced drought. Our main dependent variable is the annual increment in built-up pixels within each PRIO-GRID cell as captured by the World Settlement Footprint database (Marconcini _et al._ , 2020). This remotely sensed and fine-grained data allows us to understand exactly where cities are growing rather than defaulting to aggregate urbanization statistics. We examine the link between drought and built-up area using multiple spatially and temporally lagged versions of our independent variable, more accurately modeling the processes by which unpropitious climatic conditions impact growing cities. Our models control for numerous potential confounders of the relationship between climatic conditions and migration, such as distance to international borders and the area covered by land in each grid cell.

We find a robust positive relationship between drought and urban expansion. Our results indicate that if an entire year were spent under drought conditions, nearby cities would experience a 27 percent increase in _newly_ built-up area. In other words, drought in regions that surround urban centers accelerates the rate at which cities expand. Although climatic conditions that can be described as drought lasting for the entirety of the calendar year are relatively rare, the relationship between drought and urban growth remains substantial. Our global findings are accompanied by several notable sources of heterogeneity. Unsurprisingly, the effect is decidedly more pronounced among middle2
areas are more likely than average to escape poverty, while people living in urban areas are more likely to fall into poverty. Such dynamics suggest that the poverty trend in Bhutan during the years considered is somehow linked to productivity or price shocks in the agriculture sector, which have benefited rural families compared to urban ones, independently from other household characteristics. This hypothesis is also supported by the analysis of available macroeconomic indicators and other extra-survey sources, which also show a sectoral shift away from agriculture: the share of employment in the primary sector decreased steadily, with marginal producers shifting to the (mainly urban) tertiary sector. Rural migration outflows, combined with a favorable increase in food prices, might explain why living in rural areas can increase the likelihood of escaping poverty (Amendola et al., 2023).

The rest of the paper is organized as follows. Section 2 presents the data used throughout the paper, which are drawn from the Bhutan Living Standards Survey (BLSS). Section 3 discusses methodological aspects in detail: it describes a solution to the problem of estimating utility-consistent poverty lines in the presence of partially reliable CPI (section 3.1) and illustrates the estimation methodology based on synthetic panels (section 3.2). Section 4 contains: estimates of transition probabilities, both conditional and unconditional (section 4.1); a profile of households exhibiting characteristics of chronic poverty, as well as those that have managed to escape poverty (section 4.2); and a general analysis of the statistical robustness of the results (Section 4.3). Section 5 presents a summary of the main findings.

# **2 Data**

The Bhutan Living Standards Survey (BLSS) is a nation-wide cross-sectional household survey conducted by the National Statistical Bureau, which has been carried out every five years since 2003. It is the main source of information on household living conditions in Bhutan, and focuses on household expenditures, within the framework of the World Bank’s Living Standard Measurement Surveys (LSMS).

Many key features of the survey have been kept the same over time for comparability purposes. The two most recent BLSS waves, 2017 and 2022, are no exception: the questionnaires are close in structure, content, and level of detail (Amendola et al. 2023). Both surveys were administered through computer-assisted personal interviewing (CAPI) 3
# **Table A3: Panel B – Multinomial Regressions, 2005**

Log Odds ratio between Outright Owners and Mortgage holders (Marginal Effects)

|**Country**|**parttime**|**unemp**|**retired**|**urban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|**Hausman F**<br>**Statistic**|**-**<br>**Pseudo r**<sup>**2**</sup>|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|0.0535|0.0250|-0.0415|-0.1097 ***||-0.0881||-0.1202 **|3.96 ***|0.24|
|Estonia|-0.0378|0.0436|0.0133|0.0300 **||0.0837 *||-0.0190|0.65|0.21|
|Hungary|-0.0587|-0.0267|0.0006|-0.0400 ***|0.1118|0.2545 **|0.1424|0.1944 *|2.48 ***|0.12|
|Latvia|0.0929 **|0.0721|0.0771 *|0.0186|-0.0003|-0.0427|0.0362|0.0570 *|0.46|0.09|
|Lithuania||0.0029||0.0126||-0.0061||-0.0037|1.13|0.22|
|Poland|0.0212|-0.0898 ***|-0.0103|-0.1821 ***|-0.0697|-0.0755|-0.0164|0.0065|2.49 ***|0.34|
|Slovak Republic|0.0556|0.0360|0.0156|-0.1187 ***|-0.1148||-0.0059|-0.0233|10.30 ***|0.17|
|Slovenia|-0.2574 **|-0.2096 **|-0.1215 **||0.0762 *|0.1375 **|0.0563 ***|0.0445|1.88 **|0.15|
|_EU-15 and Cyprus_|||||||||||
|Austria|-0.0495|-0.0862|0.0084|-0.0758 ***||0.1495 **||0.0316|2.13 ***|0.25|
|Belgium|-0.0412|0.0239|0.1427 ***|-0.0488 **|0.2080|0.0611|0.1580 **|0.1253 *|2.31 ***|0.35|
|Cyprus|-0.0110|-0.1098|-0.0310|-0.1112 ***|0.1001|0.1239|0.0460|0.0674|2.05 ***|0.16|
|Finland|0.0268|0.0034|0.0298|-0.0241|0.0871|0.3399 ***|0.0134|0.0461|3.13 ***|0.31|
|France|0.0484|-0.0696 *|-0.0006|0.0068|0.0121|0.1141|0.0164|0.0154|2.80 ***|0.32|
|Greece|-0.0647|-0.0934|-0.0241|-0.0821 ***|-0.0121|-0.0249|0.0873 ***|0.0834 **|1.76 **|0.13|
|Ireland|0.0078|-0.0364|0.2068 ***|-0.1677 ***|-0.2133 **|-0.0410|0.0501|0.0540|2.52 ***|0.28|
|Italy|-0.1057 ***|-0.0573|0.0249|-0.0518 ***|-0.1359 ***|-0.0876|0.0768 ***|0.1091 ***|5.04 ***|0.12|
|Luxembourg|-0.0458|-0.0701|-0.0146|0.0040|0.2475|0.2383|0.0596|0.0274|1.03|0.35|
|Portugal|-0.0378|-0.2982 ***|-0.1984 ***|-0.1664 ***|-0.1718 **|0.0015|0.0269|0.0624|2.96 ***|0.19|
|Spain|-0.1229 ***|-0.0851 *|0.0382|-0.0240 *|-0.0663|-0.1295 **|0.0180|0.0735 ***|3.01 ***|0.15|
|Sweden|-0.0108|0.0383|0.0177|-0.0130 *|-0.0443|-0.0732|-0.0290 *|-0.0317 **|1.59 **|0.31|
|United Kingdom|0.2245 ***|0.0700|0.2677 ***|-0.0252 *|-0.2665 ***|||0.1144 ***|9.79 ***|0.32|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2005 30
More specifically, wind data is obtained at a six-hour frequency since 2000 for all 52 locations in the US, thus yielding 1.55 million data points. To obtain a measure of storm frequency for a given country, we count all observations that exceed the 99.5<sup>th</sup> percentile from all wind speed observations across all states and years. In the US, this threshold corresponds to about 40 km/h, which corresponds to the lower bound of a level 6 storm on the Beaufort scale. This approach implies that storms are defined at the national level, and certain states with consistently high wind speeds are strongly represented in the analysis. As a robustness check **,** Appendix 4 offers results for a state-specific storm specification that uses a state level wind speed distribution, instead of the national level wind speed distribution. It should be noted that reanalysis datasets like ERA5 tend to underrepresent high-intensity tail events, such as tropical cyclones and severe local convective storms (Hodges et al., 2017).

Similarly, temperature data is obtained at midday with a 24-hour frequency since 2000 for all subnational locations. In line with the approach taken for wind speeds, a heat hazard is defined by counting days on which the midday temperature simultaneously exceeds 20 degrees Celsius and the 99.5<sup>th</sup> percentile of the state-specific temperature distribution since 2000.

# 2.2.2. Climate data for Bangladesh

Climatic hazards in Bangladesh are governed by the monsoon season from July to September. Tropical cyclones occur at the tail ends of the monsoon season. In addition, Severe Local Convective Storms (SLCS) occur in the pre-monsoon season in April and May, and are characterized by localized short and intense thunderstorms – known in Bangladesh as Nor’wester or Kalbaisakhi. We combine two sources of data to capture these distinct events: (i) ERA5 climate data, and (ii) lightning data based on NASA’s Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS).

ERA5 climate data for Bangladesh During the monsoon season, which starts in June and peaks in August, the country experiences high temperatures and precipitation, as well as increased average wind speeds (Figure 2.1). In order to measure these phenomena, climate data is retrieved from the 5<sup>th</sup> global reanalysis (ERA5) conducted by the ECMWF<sup>12</sup> . We retrieve precipitation, temperature and wind speed data for the centroids of all seven divisions of Bangladesh from the ERA5 database. Daily climatic data correspond to the maximum daily value recorded at these locations.

The data, presented in Figure 2.1, show that average temperatures start to rise at the beginning of April and reach a maximum of about 30°C in May. The climate data captures high average wind speeds and high precipitation during the period July to September, which coincide with the monsoon season. Tropical cyclones are also captured in the data, though their intensity is underrepresented (Hodges _et al._ , 2017).

> 12 See analysis conducted in the US for further details on the model and structure of the data (section 2.2.1.).

6