rafmacalaba/data-use-mentions-extended huggingface dataset:
would benefit from the collection of additional information within multi-topic household surveys, such as migration aspirations and intentions, and household adaptation and coping strategies in the face of climatic shocks. Strengthening the focus on climate-induced immobility traps can also represent a complementary solution to the ‘rare event’ problem since the ‘trapped’/‘potential’ migrant sample supplements the ‘actual’ migrant sample.<sup>23,24</sup> Exploring concrete possibilities for integrating multi-topic household survey data with mobility data or labor data in destination places, and enhancing the interoperability between these different data sources, will be key for expanding the knowledge base on migration chains; for example, it would allow assessing

> 22 Note that this applies to all kinds of unforeseen shocks. A very recent example is the High-Frequency Phone Surveys on COVID19 (HFPS) supported by the Living Standards Measurement Study (LSMS) team at the World Bank which leveraged existing LSMS (face-to-face) survey systems in seven Sub-Saharan countries for rapid and timely implementation of monthly phone survey rounds to track responses and the socioeconomic impacts of the pandemic in those countries (Gourlay et al., 2021).

> 23 Other ways for addressing or attenuating the ‘rare event’ problem are discussed in section 4.

> 24 By routinely collecting accurate information on migration intentions, actual migration episodes and characteristics of migrants as part of longitudinal household surveys, it will be also possible to assess if and to what extent intentions are able to predict future migratory flows. This can be particularly relevant for policy makers as, if it is shown that migration intentions are good predictors of future moves, this type of data can help in identifying possible emigration hotspots and characterizing the different profiles of the potential migrants, thus anticipating the future challenges the sending and recipient countries may face.

15
# **INTRODUCTION**

Climatic conditions have a direct impact on the hydrology of a river basin. Climatic change will most likely affect the variability of river flows (Jury and Vaux 2005) and have a variety of additional impacts on the hydrologic cycle (Miller and Yates 2006). The change in variability will affect populations who will no longer be able to plan according to current water availability and supply trends (Milly et al. 2008). Changes will not be uniform and each region will experience either increases or decreases in river discharge compared with present observations (Palmer et al. 2008).

Hydrologic variability creates a significant challenge especially for countries sharing international river basins. Unanticipated high flow events or low flow events may lead to flooding damage, severe drought, and/or fatalities. These events give rise to economic shocks and inter-state political tensions. Such tensions have also been found to take place in the intrastate context including even the likelihood of armed violent conflict increasing after natural disasters (Drury and Olson 1998; Nel and Righarts 2008). In the context of inter-state relations, political tensions may ensue even in basins where mitigating institutions (say, in the form of water treaties) have been negotiated. In other words, climate change could increase the probability of flow below treaty specifications and expectations, leading to non-compliance and consequent political tensions between riparians.

Consequently, when designing a water agreement or appending an outdated one, water negotiators from the respective riparian states need to identify what water flow may look like in the future, in order to design the ideal treaty. Having high quality flow data will also determine the appropriate treaty stipulations and institutional mechanisms that can deal with future challenges. Real time data can also provide policy makers with the ability to predict extreme weather events, and address their impact on an existing treaty.

Flow estimation models are already available to policy analysts, but they need improvement to increase the accuracy of the results. This paper contributes to the growing literature of methodologies with a specific application of
:<br> <br> <br> <br> <br> <br> <br>|
|Year<br>2006|2007|2008|<br>2009|2010<br>2011|
|US$billion<br>2.0|2.3|5.4|2.7|5.3<br>11.4|
|% of GDP<br>1.3|1.4|2.6|1.6|2.3<br>4.7|
|_Souces_: IMF for 2006|–2010,|and the|Central|Bank of Nigeria as cited in|
|Nigeria (2012) for 201<br>A market with dereg|1.<br>ulated|diesel|and regu|lated gasoline prices is|

# **Fuel supply conditions**

<mark>US$2/liter at times, several times the official price.</mark> **<mark>Smuggling</mark>** <mark>: Fuel is smuggled to Benin, Cameroon, and other countries. The gasoline price increase in Jan 2012 led to fuel shortages and price hikes in the neighboring countries, which were relying on cheap smuggled fuel from Nigeria.</mark> <mark>A market with deregulated diesel and regulated gasoline prices is unusual, leading to much greater apparent consumption of gasoline than diesel. According to IEA data, the gasoline-to-diesel consumption ratio increased from 2 in 2000 to 7 in 2010, nearly triple the second highest ratio in the world (United States) and possibly suggesting increasing smuggling of subsidized gasoline. Five illegal alternative markets for subsidized kerosene have been reported in the media: (1) much higher prices of kerosene on black markets, (2) adulteration of diesel with kerosene, (3) diversion of subsidized kerosene for household use to the aviation fuel sector, (4) out-smuggling to neighboring countries, and (
revenues, and almost 100 percent of exports.<sup>3</sup> In rural areas, subsistence agriculture constitutes the largest part of production. However, food production was insufficient even before the onset of the conflict. Only the Greenbelt area of Western Equatoria has excessive food production; while all other states are food deficient.<sup>4</sup> The patchy trade data and recent evidence in Calì and Varela (2013) suggest that the food deficit in most parts of the country is filled by imports (mainly from Uganda and Sudan) and to a limited extent by domestic trade.

However relying on markets for consumption is both costly and perilous in South Sudan. The very poor transport infrastructure in large parts of the country – especially in northern states – creates significant transportation costs leading to considerable price variations across the country. In addition the violence, within South Sudan and sporadically with Sudan, has greatly undermined the security of trade and thus increased price volatility.

We estimate the degree of integration in seven food markets, and two energy markets across eight state capitals.<sup>5</sup> The former group includes sorghum, maize, millet, wheat flour, sesame, broad beans and groundnuts; the latter group includes diesel and gasoline. The choice of products reflects the combination of its importance in the consumption basket of South Sudanese and of data availability. The period analyzed spans 2009-2013, with exceptions again due to data availability.<sup>6</sup> The analysis reveals four main messages.

First, markets in South Sudan are highly segmented. Price differences for narrowly defined products, across cities exceed in certain instances 100 percent. For sorghum, for example, a key staple in South Sudan, inter-city prices differences are on average, 43 percent. These differences within South Sudan exceed those reported in the literature for inter-countries price gaps in Central and Western Africa (Brenton et al., 2015).

Second, price volatility has increased substantially since the imposition of
to observe in administrative data and may require surveys, audit studies, and other non-traditional data to be accurately measured.

In this paper, we study consumer learning in the context of payroll accounts, a simple financial technology that is currently being rolled out to millions of workers worldwide in response to demands for increased supply chain transparency and as a result of the Covid-19 pandemic.<sup>2</sup><sup>_,_3</sup> We conduct a field experiment with a population of salaried factory workers in Bangladesh who, prior to our study, received their wages entirely in cash. In the experiment, workers were randomly assigned to either continue receiving wages in cash, or begin receiving wage payments into a bank or mobile money payroll account. In a set of comparison treatments, workers were given an account but

> 1See, Mullainathan et al. (2009), and Beshears et al. (2018) for perspectives on behaviorally informed regulation, and Agarwal et al. (2015) on the effectiveness of current consumer financial protection regulation.

> 2More than 50% of employees globally and 85% of employees in developing countries receive wages in cash. Approximately 300 million individuals earn regular verifiable wage income but do not have access to a formal financial account (Demirg ̈u ̧c-Kunt et al., 2017). In response to allegations of widespread labor rights and minimum wage violations, many international brands have started to require suppliers to pay employees into digital payroll accounts. This, in turn, has led entire industries to begin digitizing wage payments to their global workforce. See, for example, _“Fashion Brands Team Up To Spearhead Campaign Around Wage Transparency”_ , Forbes November 5, 2019.

> 3Several countries, including Bangladesh where our study is set, have made government bailouts during the pandemic conditional on firms phasing out in-person cash payments and introducing payroll accounts for workers.

1
literature relies on disparate data sources – either national accounts or household surveys – and looks at issues of territorial income differences and dynamics separately. Therefore, it provides an incomplete or inconsistent regional picture on the sources and evolution of territorial income differences in Latin America. By differentiating between households and localities and by combining analyses of territorial labor and local productivity differences and dynamics, this paper builds a region-wide assessment of the spatial patterns in local productivity levels and growth. It also identifies the main factors behind the territorial income differences with leading metropolitan areas and therefore opportunities to lift welfare through migration to these areas.

# **3. Analytical approach**

We first present the methodology employed to estimate labor and place productivity by location, the approach for assessing productivity dynamics, and the estimation technique for decomposing the income gap with leading areas into an endowment component and a rate-of-return component. The latter allows us to determine the extent to which households can increase their incomes by relocating to the leading metropolitan area.

> 7 In countries with federal systems (e.g. Mexico and Brazil), state and local governments have the authority to change laws, including taxes, and de jure and de facto institutions, e.g., the degree of enforcement of national laws, the functioning of the judiciary, and the degree of de facto control by local elites.

> 8 Dell (2010) links the poor outcomes for some communities to the long-run effect of the mita – an extensive forced mining labor system in effect in Peru and Bolivia during the colonial era – which lowered incomes and access to education and infrastructure. Acemoglu et al. (2007) emphasize the link between political inequality in 19<sup>th</sup> century Cundinamarca, Colombia and its current economic outcomes. Similar conclusions are drawn by Naritomi, Soares, and Assuncao (2007) for Brazil.

> 9 These countries are Bolivia, Brazil, Colombia, Ecuador, Guatemala, Mexico, Nicaragua, and Peru.

6
-|---|---|---|---|
|Total population ('000)|16,774|21,769|27,058|32,382|
|Working age population (15-64 years) ('000)|9,795|13,267|17,839|22,570|
|Share of working age population (%)|58.4|60.9|65.9|69.7|
|Employment as a share of working age population (%)|61.1|64.6|59.1|65.5|

Source: Authors’ calculations based on data from DOSM

**Figure 4: Share of working age population among overall population, 1987-2018**

**Figure 5: Annual growth in working age population (%), 1987-2018**

<!-- Start of picture text --> 70 4.0<br>3.5<br>68<br>3.0<br>66<br>2.5<br>64 2.0<br>1.5<br>62<br>1.0<br>60<br>0.5<br>58 0.0<br>-0.5<br>Source: Authors’ calculations based on data from DOSM Source: Authors’ calculations based on data from DOSM<br>Share of working age population (%)<br>Growth in working age population (%)<br>988<br>1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 1 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> The share of the working age population that is in employment – subsequently referred to as the employment rate – also increased between 1987 and 2018.
6

**Figure 3:  Indicators of the Balance Sheet Strength of Chilean Firms, Average over 2001-2003**

<!-- Start of picture text --> Leverage Total liabilities / shareholders funds Interest cover EBITDA / Interest charges<br>LT liabilities / shareholders funds EBITDA / (interest charges + LT maturing within a year)<br>LT financial liabilities / shareholders funds<br>1.5 1.3 250 227<br>200<br>145<br>1.0 0.7 150<br>0.4 100<br>0.5<br>50<br>- -<br>Average 2001-3 Average 2001-3<br>Current ratio Profitability RoE<br>Cost of capital<br>80 12% 10%<br>59.2 10%<br>60<br>8%<br>6%<br>40 6%<br>4%<br>20<br>2%<br>- 0%<br>Av e r age 2 00 1- 3 Av e r age 2 00 1- 3<br>Source: Author’s calculations based on FECUs<br><!-- End of picture text --> Compared to other countries and to rating agency benchmarks, the financial situation of Chilean firms remains sound, although profitability is low. A comparison with US firms by sector (see Figure 4) highlights that leverages are smaller in Chile than in the US in all sectors, while the profitability of Chilean firms is much lower than that of their US peers in all sectors, except in mining (and in the services sector where the difference in profitability is small). Compared to rating agencies’ benchmarks, this conclusion remains valid (see Figure 5): on average Chilean firms have a low leverage (equivalent to that of AA / A rated firms), an excellent interest cover (well above AAA firms) but a
DHS data have provided some valuable new information.

> 12 There is evidence of lower usage rates for condoms among the poor; the aforementioned analyses of the DHS data indicate a condom usage rate of 18% for the poorest quintile, versus 27% on average (http://devdata.worldbank.org/hnpstats/pvd.asp.).  Against this effect, access to multiple sex partners probably rises with income (at least among males). 13 See at http://www1.worldbank.org/prem/poverty/health/data/index.htm.

13
(2013). Misallocation and productivity. Review of Economic Dynamics, 16(1), 1-10.</mark>

- <mark>Restuccia, D., & Rogerson, R. (2017). The causes and costs of misallocation. Journal of Economic Perspectives, 31(3), 151-74.</mark>

- <mark>Ruesga, S., Pérez, L. y Delgado, J. (2020). Estimación del tamaño de la economía sumergida en Ecuador a través del Modelo MIMIC. Revista Espacios, 41(12), 16-29.</mark>

- <mark>Van Biesebroeck, Johannes. How tight is the link between wages and productivity?: a survey of the literature. ILO, 2014.</mark>

- <mark>Vijil, M (2021). Determinantes del Crecimiento de la Demanda Laboral y de la Productividad del Sector Privado. World Bank.</mark>

- Wooldridge, J. M. (2009). On estimating firm-level production functions using proxy variables to control for unobservables. Economics letters, 104(3), 112-114.

38
48.2%|40.0%|47.7%|41.7%|46.0%|40.9%|48.0%|39.0%|46.3%|42.5%|46.8%|
|Spain|31.5%|38.0%|32.6%|37.9%|30.0%|35.0%|34.8%|41.2%|34.8%|40.2%|35.0%|41.5%|25.3%|30.2%|

Note: Blanks correspond to country-year pairs for which the Eurostat’s SBS Database does not report information. Montenegro and Ukraine are excluded from this table as the SBS Database does not include information for any of the years in our sample.
and fourth, laborers and servants. The formerly untouchable _jatis_ occupy a place outside the _varna_ scheme (they are “outcastes”), whereas all other _jatis_ are within it. The usual name for the formerly untouchable castes is Dalits, which means oppressed in Sanskrit. The official name is Scheduled Castes. This name comes from the schedule made by the British colonial government in 1932.

# **Discrimination**

In standard economics, individuals are rational actors and economic forces undermine institutions that impose large inefficiencies. Standard economics predicts that discrimination in competitive markets will not persist, since discriminators have higher costs than non-discriminators. Behavioral economics, with the first studies appearing in the 1970s (most of them written by psychologists), has different models of the individual: he or she is a “quasi-rational actor” influenced by seemingly irrelevant features of context (emphasized in Strand I of behavioral economics) and an “enculturated actor” whose cognition and preferences are influenced by social constructions (emphasized in Strand II of behavioral economics). The distinction between the two strands is made in Hoff and Stiglitz. Behavioral sciences helps explain the persistence of the caste system. Social constructions that are shared within a culture shape how people perceive and understand the world and themselves.

In the rural sector of India, the “pollution barrier” between former untouchable castes and all others remains strong. A study of 565 villages in 11 Indian states found that despite legal bans, untouchability continued to be practiced in almost 80 percent of the villages. The following percentages indicate the proportion of villages in which each kind 3
employment earnings penalties or premiums in low income countries outside of Latin America. Our paper contributes to the literature on informal and self-employment wage penalties or premiums by estimating and comparing these earnings differentials for a wider range of developing and high income countries than currently exists in the literature.

# **III. Data**

The data source for this paper is a database of micro-level household surveys harmonized by the Development Economics Research Group of the World Bank, the International Income Distribution Database (I2D2).<sup>4</sup> This database consists of nationally representative labor force surveys, budget surveys or living standards measurement surveys. In many cases, the surveys provide information on the earnings of the self-employed as well as of wage and salary employees, in addition to other relevant information on individual socioeconomic characteristics. The data include three sets of consistently defined and coded variables: (i) demographic variables, (ii) education variables, and (iii) labor force variables.

Not all variables are available in all countries and years. We limit our analysis, to surveys where we can identify whether the worker is self-employed or a wage and salary employee, also where data is collected on the earnings of both the self-employed and wage and salaried workers.<sup>5</sup> In most countries, data are available for multiple years. Our full sample consists of 347 surveys (country/year combinations), representing 73 countries, from 1980 to 2013. Within each country, we limit our samples to the working age population, 15-65 years old. The full country–year combinations available for our analysis, as well as the estimated earnings premiums(+)/penalties(-) for each country/year observation, are listed in the appendix in table A1.

We begin by examining the earnings differentials between self-employed workers and all wage and salaried employees. Self-employed workers include both own-account workers and employers. We are able to examine differences between self-employed workers and employees using 347 household surveys from 73 countries
sample is available. Even in the ESPS sample (see _Appendix S1_ ), which includes 13559 households, the imputed rent for owner households in rural areas is based on the observation of rents paid by 171 households (on a total of 4987 rural households).

9 In addition, data cleaning systematically tracked potential double counting to correct it.

10 To maintain comparability with existing works on poverty in Senegal, we use the same equivalence scale as that of Ndoye et al. (2009): one consumption unit for each adult, and one half for each child less than 15 years old. We test for the robustness of our results to this choice in section 5.

11 Clearly, collecting data at a more disaggregated level also induces a risk of additional measurement error. We will therefore assess the sensitivity of the results of our analysis to measurement errors.

12 The fact that the underestimation of consumption is not distribution neutral pleads against anchoring poverty measure to National Accounts estimates of consumption, as it is likely to result in an overestimation of poor household consumption levels, following an argument made by Ravallion (2000).

13 It is slightly lower when using per adult equivalent measure of consumption. See Table S3.1 in the Appendix.

> 14 Country ranking by GINI index (Most recent World Bank estimate).

15 The measure of inequality of comparison countries (Venezuela and Chile) is based on data from standard household consumption surveys. Changing the survey methodology to collect consumption data at the level of subgroups in the households might affect the inequality assessment also for those countries. Given that households in Venezuela and Chile are more likely to be nuclear, they are potentially less affected by such change in methodology. The comparison remains illustrative of the fact that Senegal is as unequal as we currently believe Venezuela and Chile to be.

29
is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. We use an alternative definition of undocumented migration. We define undocumented migrants as migrants who entered the destination country without having a visa or official document, for countries that required an entry visa, and as those who did not have a work contract for their employment abroad, for countries that had a visa waiver for Egyptians. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

55