rafmacalaba/data-use-mentions-extended huggingface dataset:
some of the coping strategies HEs adopt to overcome these constraints or mitigate their effects. Some of these findings are corroborated by the results of the recent FinScope survey (Table A1.18).

68. Most HEs survive from a variety of risks and constraints with resilience, hard work, and good use of a network of family and community support. Some draw on their limited household savings and switch between working on farm and running their HEs to augment low income. Others sell on credit, which is made possible by previous business relationships. Urban HEs cope with the risk of theft and burglary by making their own security arrangements. For example, HE operators sharing a business premise with other operators make a monthly contribution to pay for hired security guards. In the rural areas, it is common to find a group of HEs guarding their property on a rotation basis.

69. Although most HEs adopt coping strategies that are useful in mitigating risks, others resort to deceptive, sometimes illegal practices. For example, some operators admitted during interviews to charging wealthier-looking customers higher than the regular price for services rendered (e.g., TSh 500 instead of TSh 200 for shoe-shining), or tampering with their weighing machines such that customers pay more for lesser amount of the goods they buy. Hawkers reported to have paid bribes to be able to operate in restricted areas. In some extreme cases, HE owners resorted to operating at night to avoid harassment by local officials.

70. Support from relatives and friends is very useful for HEs, especially as a source of capital. Since access to credit from MFIs is found to be biased in favor of urban areas, the only option for HEs in the rural areas is to rely on the generosity of relatives and friends, or to draw 34
Human Capital Index seeks to measure. However, the HCI is expressed relative to a theoretical maximum of complete education and health (which are assigned a score of one), rather than relative to the productivity of raw labor, as is needed here. Hence, we need to calculate h= H M where H M = 0.2 is the theoretical minimum HCI (for someone with no education and poor health).<sup>20</sup> I also express the employment variables in in MM MM II/H II II terms of BERs and OERs. So, the weight becomes (equivalent to Equation 6): ωω ωω h×(B +B <u>)</u> (9) where h= H M h×(B +B RRMM RRFF ωω= RRMM RRFF)+OOBBRRMM+OOBBRRFF II/H II MM

# **3.3 Data – Full GEGI**

The primary source of data on employers, non-agricultural employees and total employment for the numerator of the SEBJ is ILO series “Employment by sex, status in employment and economic activity (Thousands)”, using the most recent year available.<sup>22</sup> The “Status in Employment” used is defined by the International Classification by Status in Employment, 1993 (ICSE-93), which breaks total employment into Employees, Employers, Own-account workers, Members of producers’ cooperatives, Contributing family workers, and Workers not classifiable by status, where I use data on the first two categories as parts of better employment.<sup>23</sup> In terms of “economic activity”, I use the broad sectors “Total” (for employers and total employment) and “non-agriculture” (for employees). The SEBJ calculated using ILO data is multiplied by the employment-to-population ratio from the ILO used in the basic GEGI.<sup>24</sup> The secondary source is the JOIN database microdata. At the time of data collection
0.05, * p _<_ 0.1 _Notes_ : Equation 1 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. The model additionally controls for illegality in the return migration equation (3). For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination 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 of the logarithm on 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.

58
between different job attributes. Third, we tested the choice experiment through a pilot survey, and modified the attribute levels based on feedback regarding the appropriateness and realism of the scenarios. Finally, in the actual survey, we used practice questions to ensure that respondents understood the choice exercise.

More broadly, there is encouraging validating evidence that when choice experiments are well-designed, they can produce findings that are in line with observed market transactions. Mas and Pallais (2017) used a choice experiment, embedded in a real hiring process, to estimate job applicants’ valuations for scheduling flexibility and the option of working from home. In addition, they used a similar choice experiment to elicit preferences through a survey of a different population (unrelated to the hiring process). They found that the survey-based valuations were close to the valuations from the experiment that was embedded in the real hiring process. Further evidence in support of the consistency of choice experiments is that recent studies suggest that individuals who have a given amenity in their current job tend to value that amenity more highly than workers who do not have that amenity (Maestas et al. 2018). As discussed below, we also find this pattern; most notably, government employees in our sample, who have the highest job stability (Gutierrez et al. 2019) and are much more likely to have long-term contracts, exhibit the highest valuation for these contracts.

Existing estimates of WTP for non-wage job attributes vary substantially. Traditional methods using hedonic wage regressions have generally found small valuations (Bonhomme and Jolivet 2009); as noted above, there results are likely biased downwards due to unobserved heterogeneity among workers and firms as well as search frictions. Alternative approaches to estimate WTP from market transactions include the analysis of 6
In the discussion below we refer mostly to the challenges of measuring the top tail of the income distribution. But most of the challenges we discuss apply to measuring the wealth distribution as well. We provide a specific section on how these challenges differ for wealth below.

# _Unit non-response_

One key challenge in measuring the top tail of the income distribution is unit non-response. This means that a household was included in the sample, but no information was collected on that household. The main reasons for unit non-response are refusals and non-contact, with refusals likely to be more common (Ravallion, 2022). Unit non-response is usually classified as being missing completely at random (MCAR), missing at random (MAR) or not missing at random (NMAR) (Lohr, 2009). These mean, respectively, that non-response is ignorable, that non-response is ignorable conditional on some covariates or that it is not ignorable, i.e., that it is correlated with the outcome of interest, here income or wealth.

We did not find a comprehensive overview of unit non-response levels and trends for the World Bank’s Living Standards Measurement Surveys (LSMS). Scott et al. (2005) did review response rates from 8 LSMSs in the late 1990s and early 2000s, finding a non-response rate of 11%. Vaessen et al. (2005) reported an average non-response rate of 2.5% to the Demographic and Health Surveys conducted in 44 developing countries between 1990 and 2000. This, plus the fact that questions about incomes are probably less than or similarly sensitive to questions about health, suggests that unit non-response rates to surveys that include questions on incomes are likely to be much lower in low-income countries than in rich countries. Hlasny (2020) uses surveys collated and harmonized by the Luxembourg Income Study for 38 middle- and high-income countries to show that non-response rates are correlated with GDP per capita. Countries with PPP GDP per capita of around $10,000 had an average non-response rate of around 10%
# **-** **<u>Table 7: Development of a multi dimensional poverty proxy</u>**

The creation of the poverty proxy is based on the search for objective and readily verifiable indicators that show a strong statistical correlation with household income. In order to minimize administrative costs, the search was limited to variables that are already collected is the Argentine SISFAM (Unique Identification and Registration System for Beneficiaries of Social Programs), many of which also appear in the commonly used index of Unsatisfied Basic Needs.

The multi-dimensional poverty proxy aims to capture per capita family income, based on a weighted index of the following five variables, selected on the basis of being simple, objective and easily verifiable.

Overcrowding ( _O_ ) – More than three persons per room in the family dwelling. Inappropriate housing ( _H_ ) – Housing in workplace, guesthouse, or shantytown. Unsanitary conditions ( _S_ ) – Housing without bathroom, latrine, or sewerage. Presence of infants ( _I_ ) – Presence of children under the age of five in the household. Head of household education ( _E_ ) – Head did not complete primary education.

## _Proxy_ =β0+β1 _O_ +β2 _H_ +β3 _S_ +β4 _I_ +β5 _E_

The weighting factors used to calculate the proxy are determined by a statistical estimation that maximizes the correlation between per capita household income and the five variables indicated. Different models are estimated for each geographic region, with results reported in Table A5 of the Annex. In order to determine whether a household qualifies for subsidy based on this indicator, the household’s score on the multidimensional poverty proxy is compared to the official poverty (or extreme poverty) line. Those below the line are eligible, while those above the line are ineligible.

5.8 However, a recent international study on the issue of targeting social programs concludes that programs that make use of targeting devices are able to transfer, on average, 25% more resources to low income households than those that
20|0.18|

_Source:_ World Bank estimates using ENIGH 2014. a. According to current income (MX pesos of August of 2014). b. The average impact on well-being (moving from concentrated to competitive market) c. The average absolute impact over the average income times 100.

Concerning the impact on poverty and inequality, Table 5 suggests that introducing reforms to enhance competition in the mobile telecommunication market could help reduce different measures of poverty such as the poverty headcount. The results show the upper-bound expected effects, equivalent to 0.39 percentage points of poverty reduction of simulating a movement from a concentrated market with four firms to a perfectively competitive market. The scenario of moving towards 12 firms yields a poverty reduction of 0.11 percentage point. However, the expected impact of entrance of new firms in the mobile communication market on inequality is negligible, even under the upper-bound scenario. This occurs partially due to a small share of expenditure on mobile telecommunication services and since the share of expenditure in mobile communication across the income distribution is relatively stable. In contrast, when the relative shares of expenditure in a specific good differ across the distribution, the relative impacts on inequality tend to be larger.

**Table 5. Market concentration in mobile telecommunication, poverty, and inequality**

||<br>Headcount|Poverty indicat<br>Poverty gap|ors<br>Squared<br>poverty gap|Gini|Inequality indic<br>Atkinson<br>(epsilon=0.5)|ators<br>Entropy<br>(theta=0.0)|
|---|---|---|---|---|---|---|
|Baseline|53.35|21.09|11.01|0.497|0.2096|0.4328|
|Moving into competition|53.24|21.03|10.97|0
control trial in Mexico.” _Journal of Development Economics_ 135: 318–348.

- Bandura, A., C. Barbaranelli, G. V. Caprara, and C. Pastorelli. 2001. “Self-efficacy beliefs as shapers of children's aspirations and career trajectories." _Child Development_ 72(1): 187–206.

- Beaman, L., E. Duflo, R. Pande, and P. Topalova. 2012. “Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India.” _Science_ 335(6068): 582–586.

- Bernard, T., and A. S. Taffesse. 2014. “Aspirations: An Approach to Measurement with Validation Using Ethiopian Data.” _Journal of African Economies_ 23(2): 189–224.

- Bernard, T., S. Dercon, K. Orkin, G. Schinaia, and A. S. Taffesse. 2022. “The Future in Mind: Aspirations and Forward-Looking Behaviour in Rural Ethiopia.” _Manuscript_ . Oxford: University of Oxford. August 9 version, available at: https://bit.ly/3UIadkE.

- Blackwell, L. S., K. H. Trzesniewski, and C. S. Dweck. 2007. “Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention.” _Child Development_ 78(1): 246–263.

- Bloem, J. R. 2021. “Aspirations and Real Estate Investments in Rural Myanmar.” _Journal of Economic Inequality_ 19: 727–752.

- Breda, T., J. Grenet, M. Monnet, and C. Van Effenterre. 2020. “Can female role models reduce the gender gap in science? Evidence from classroom interventions in French high schools.” Revise and resubmit at the Economic Journal. April 2020 version (IZA Discussion Paper 13163) available at: https://bit.ly/3QtkPl2.

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of conflict due to commodity price movement.

The rest of the paper is organized as follows. Section 2 briefly describes the datasets and some key summary statistics. Section 3 gives an overview of coffee production and marketing in Ethiopia. Section 4 describes the empirical strategy, while Section 5 presents the results. Section 6 concludes the paper.

# **2 Data**

## **2.1 Data sources**

The main dataset used in this study is the Ethiopian Socioeconomic Survey (ESS) data collected by the Central Statistical Agency (CSA) in collaboration with the World Bank (see Central Statistical Agency and World Bank (2012) for details about this data). This is an exceptionally

> 3According to UNICEF (2015), almost half of under-5 mortality is attributable to under-nutrition in poor countries.

5
exporters<br>2<br>1.8 Exports to all economies subject to TTBs in effect<br>1.6<br>1.41.2 Exports to G20 High Income economies subject to TTBs in effect<br>1<br>0.8 Exports to G20 Emerging economies subject to TTBs in effect<br>0.6<br>0.4<br>0.2 Exports to all other economies subject to TTBs in effect<br>0<br>1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br><!-- End of picture text --> Notes: Shares of nonoil exports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

24
Table 1. Descriptive statistics for selected variables used in the analysis of the rural China sample, May 2007 to April 2009.

||Sample|Mean<br>(Proportion)|Standard Deviation|
|---|---|---|---|
|Off-farm employment|2,803|0.63|0.49|
|By sex||||
|Male|1,555|0.75|0.45|
|Female|1,248|0.48|0.50|
|By age group||||
|<=30 years|733|0.83|0.38|
|30~50 years|1,173|0.67|0.48|
|>50 years|897|0.38|0.48|
|By education level||||
|Elementary school or less|1,170|0.47|0.50|
|Middle school|1,302|0.72|0.46|
|High school or higher|331|0.81|0.41|

Data source: Authors’ own data (CNRS dataset).

28
even more drastic in states that had experienced a large housing boom, or in the rust belt where the relative size of manufacturing is important. Unemployment increased more for workers without qualifications, as they were dominating sectors hit by the crisis (construction, leisure and transportation) **.** Average unemployment duration is still increasing, and the share of long-term unemployed has reached unprecedented levels (Figure 14).

# **Figure 14: U.S. Unemployment Trends**

<!-- Start of picture text --> a) Unemployment by Sector b) Unemployment by Educational Level<br>30% 18%<br>16%<br>25%<br>14%<br>20% 12%<br>10%<br>15%<br>8%<br>10% 6%<br>4%<br>5%<br>2%<br>0% 0%<br>Construction Manufacturing Less than High School High School<br>Durable Goods Wholesale & Retail Less than College College<br>Hospitality National<br>Source: Bureau of Labor Statistics. Source: Bureau of Labor Statistics.<br>Note: Unemployment data not seasonally adjusted. Note: Unemployment data seasonally adjusted.<br>Unemployment rate Unemployment rate<br><!-- End of picture text -->

## **c) Long Term Unemployment**

<!-- Start of picture text --> 12% 50%<br>10% 40%<br>8% 30%<br>6% 20%<br>4% 10%<br>2% 0%<br>Jan 1950 Nov 1956 Sep 1963 Jul 1970 May 1977 Mar 1984 Jan 1991 Nov 1997 Sep 2004 Jul 2011<br>National (left axis) Share Unemployed more than 27 weeks<br>Source: Bureau of Labor Statistics.<br>Note:
As the data for medical costs only consists of public expenditure, medical costs in the analysis cannot be understood as a direct household expenditure change. Instead, it offers an estimated reflection of the overall change in medical costs attributable to taxation. This encompasses the impact on patients in both the public and private sectors, with the public sector's cost data serving as the foundational reference point.

_Table 4_ - Medical expenditure related to dietary risks

|Diseases related to dietary risks|2018|2019|2020|2021|2022|Average<br>2018-2022|
|---|---|---|---|---|---|---|
|Other heart diseases|173.06|169.51|129.86|122.58|149.26|148.85|
|Intracranial hemorrhage|121.89|124.07|121.75|116.90|123.61|121.64|
|Other peripheral vascular diseases|25.63|27.00|27.11|28.20|32.89|28.17|
|Malignant neoplasm of trachea, bronchus, and lung|37.74|40.03|36.77|34.09|38.27|37.38|
|Other hypertensive diseases|16.10|14.12|12.97|11.92|15.85|14.19|
|Other ischemic heart diseases|710.05|711.00|559.94|500.76|553.90|607.13|
|Conduction disorders and cardiac arrhythmias|279.89|291.49|249.04|238.26|225.23|256.78|
|Diabetes mellitus|100.56|104.30|99.55|95.91|104.32|100.93|
|Malignant neoplasm of the breast|148.
al., 2010).<sup>7</sup> LASSO models were fitted separately to four dependent variables. These are the transformed versions of the four labor market outcomes, AGEB male or female labor force participation and unemployment rates, using the arcsine transformation. The candidate predictors included in the LASSO model include all of the AGEB and municipal level geospatial auxiliary data described in the last section, as well as dummy variables for states. The state dummies were included with no penalty, and were included to both improve the model and to prioritize the selection of variables that predict variation in the labor market outcomes within states.

# **d. Model estimation**

We begin by constructing two separate estimates of LFP and unemployment in Mexico:

1. Direct estimates using only the pseudo survey of sampled AGEBs, which can only be constructed for in-sample municipalities.

2. Small area estimates using the pseudo survey of sampled AGEBs and the auxiliary geospatial data, which can be constructed for all municipalities, both in and out of sample.

These estimates are compared with the full census results along several different dimensions to assess their accuracy, as discussed below.

To generate the small area estimates for municipalities, we utilize a standard nested error model at the AGEB level (Battese, Harter, and Fuller, 1988, Jiang and Lahiri, 2006, Molina and Rao, 2010). The transformed version of the labor market outcome at the AGEB level is modeled as a linear function of geospatial auxiliary variables. The dependent variable was transformed using an arcsine transformation, a common transformation for proportions bounded between zero and one. The model used to generate municipal estimates is specified as follows: (1) s s 1<sup>+XX</sup> s 2<sup>+ XX</sup> 3<sup>+ υυ</sup> s s ss<sup>) = XX</sup> ss<sup>ββββ</sup> ss<sup>ββ</
We recruit 363 Brazilian women commuters to crowdsource data on _≈_ 22,000 of their rides and proceed in three steps.<sup>3</sup> First, we show that women commuters are willing to pay to ride in the reserved space, but this demand is concentrated among a minority of riders. Second, we show that avoiding harassment partially explains women’s demand for the reserved space. Riders who use the reserved space perceive a higher risk of harassment and are most likely to be harassed in the public space. Riding in the reserved space allows women to avoid harassment; other factors such as crowding and non-sexual crimes do not explain demand for the reserved space. Third, we document that commuters are likely to associate women in the public space with higher openness to sexual advances. We show that perception of this norm predicts higher demand for the reserved space among women commuters.

We start by eliciting women commuters’ willingness to pay to ride in the women-reserved space. This demand is concentrated: twenty percent of riders place a positive value on accessing a women-reserved space as high as twenty percent of the transit fare. Moreover, demand is highest at times when the reservation rule is well followed, with few men in the reserved space. Demand for the reserved space does not go to zero when presence of men equalizes across the reserved and the public space, suggesting women attach a fixed-amenity value to riding in the reserved space. In addition, a third of riders make up eighty percent of the demand for the reserved space and are ten times more willing to pay for the reserved car than the rest of the sample. We show that these high-level users also perceive a higher risk of being harassed, and we show that this risk perception is associated with a higher demand for the reserved space. We use rich data on ride conditions to rule out alternative demand mechanisms such as crowding and fear of property crime.

Second, we ask: How much of women riders’ valuation of the reserved space