rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Average Lengh of Road Above Different Speed Limits<br>Across Woredas with Different Baseline Nighttime Lights<br>Road Length = 30km/h Road Length 2 50km/h Road Length 2 70km/h<br>= Maximum Value of<br>@ 90- Nighttime Lights<br>w 30- 20- Within Woreda<br>@ 70- m= Dark: 0<br>oO — 4!<br>2 20- 10- a °<br>S 50- === Medium: 6-8<br>< == High: 9+<br>30, ’ ; ’ — OI y y y | o7 Y y | |<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br><!-- End of picture text -->

<!-- Start of picture text --> Length of Roads by Estiamted Speed Limit<br>Across Woredas by Baseline Nightime Lights<br>Dark: Max NTL in Woreda, 0 Low: Max NTL in Woreda, 1 - 5<br>1.00 . Speed<br>0.75 (km/h)<br>x<br>5<br>Fs® 0.50 MisMo<br>2no)& 0.250.00 iWsx<br>a Medium: Max NTL in Woreda, 6 - 8 High: Max NTL in Woreda, 9+ || 30<br>= 1.00<br>S Ms<br>S 0.75 | | 45<br>& 0.50 BW x<br>0.25 [_| 70<br>0.00 1 BB 120<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br><!-- End of picture text -->
# **Tables**

Table 1: Summary statistics

||All Hou|seholds|Househol<br>share of c|ds with land<br>ofee _≥_10%|Househol<br>share of c|ds with land<br>ofee <10%|
|---|---|---|---|---|---|---|
|Variable|Mean|SD|Mean|SD|Mean|SD|
|||P|anel A: H|ousehold outc|omes||
|Number of observations|9967||1147||8820||
|Household size size|5.01|2.37|5.34|2.37|4.96|2.36|
|Total consumption AEq (Birr)|5459.87|5023.29|5206.51|4820.59|5491.74|5047.58|
|Food consumption AEq (Birr)|4413.71|4117.02|4262.43|4523.97|4432.74|4062.84|
|Non-food consumption AEq (Birr)|994.31|2279.72|889.05|1096.29|1007.55|2387.11|
|Received credit|0.25|0.43|0.24|0.43|0.25|0.43|
|Total credit (Birr)|747.72|8644.08|427.02|1457.95|789.03|9168.24|
|Received assistance|0.20|0.40|0.08|0.27|0.21|0.41|
|Total assistance (Birr)|245.65|1072.01|54.75|322.47|270.29|1130.87|
|Land holding (hectare)|0.87|4.84|0.84|2.96|0.88|5.03|
|Rainfall (mm/year)|1132.77|428.64|1486.65|374.86|1087.18|413.65|
|Household on-farm labor (hrs)|1133.93|2072.87|1205.97|1595.08|1124.56|2127.02|
|Male on-farm labor (hrs)|769.63|1730.16|777.66|1146.99|768.59|1792.16|
|Female on-farm labor (hrs)|309.25|578.76|374.72|619.19|300.73|572.78|
|Spouse on-farm labor (hrs)|225.42|430.22|280.09|513.76|218.31|417.64|
|Child on-farm labor (hrs)|55.05|271.10|53.58|166.45|55.24|281.88|
|Number of PSNP days worked|10.05|41.82|6.21|36.09|10.56|42.48|
|PSNP income|231.39|3228.04|83.52|410.75|250.62|3427.88|
|Number of days worked as laborer|12.69|44.34|14.00|47.95|12.52|43.85|
|Labor income|405.96|4347.76|330.66|1465.98|415.75|4591.45|
||||Panel B:|Child outcom|es||
|Number of children|3994||3512||482||
|Weight-for-age z-score|-1.08|1.30|-0.93|1.37|-1.10|1.29|
|Weight-for-height z-score|-0.39|1.48|-0.30|1.47|-0.40|1.48|
|Height-for-age z-score|-1.43|1.89|-1.24|1.93|-1.46|1.88|

Notes: This statistics is calculated from ESS data by restricting to rural samples only. For child outcomes, biologically implausible height and weight records that lead to implausible values of any of WAZ, WHZ or HAZ (i.e above 5 or below -5) are dropped in the calculation of this statistics and in all the regressions below. Consumption spending are in Adult Equivalent (AEq) terms.

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6 crisis, the program was expanded in 2009 to 65 districts to reach 2.5 million households in 2012 (World Bank, 2009). The target areas are concentrated in the Southern Highlands, the Northern Highlands, and the Western Region and focused on agro-ecological zones suitable for the target crops.

The input package distributed consisted originally of three vouchers: 1) one for two bags of urea, 2) one for one bag of Di-ammonium Phosphates (DAP) or two bags of Minjingu Rock Phosphate (MRP) with nitrogen supplement, and 3) one for 10 kilograms of hybrid or open-pollinated maize seeds or 16 kilograms of rice seeds, sufficient for half a hectare of maize or rice. Vouchers for each input had a face value equivalent to 50 percent of the market price of the respective input. The remaining 50 percent was to be paid by the farmers.<sup>5</sup> The central government allocates the vouchers to the target regions,<sup>6</sup> taking into consideration the total area of maize and rice under cultivation, the regional maize and rice output as well as the soil and rainfall patterns. To distribute the vouchers further voucher committees were established in the seat of the different local government councils.<sup>7</sup> The Regional Voucher Committees estimate the demand for inputs using historical production data and allocate the vouchers to the districts. The District Voucher Committees (DVC) are responsible for the distribution of the vouchers to the villages. They collect and review information about their maize and rice production, their input use, and other related information. Vouchers are then allocated to the selected villages based on the number of farmers who grow maize and rice and the average land size per farmer. The village council, in consultation with the village assembly, organizes the election of the Village Voucher Committee (VVC), which draws up a list of beneficiary farmers for approval by the village assembly.<sup>8</sup> After approval, the VVC

> 5 Co-financing posed a problem at times and since 2009/2010, the voucher for urea was reduced to one bag. 6 In addition to the central government, there are four levels of local government: regions, districts, wards and villages.

> 7 See Mniwasa and Shauri (2001) for more details on the government structure in Tanzania.

> 8 The village assembly consists of all persons aged 18 and above, while the village council comprises of 15 to 25 members elected by the village assembly.  The council consists of a chairperson, all chairpersons of the sub-
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# **Figure 10. Base: Headcount poverty rate by year (%)**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text -->

**Figure 11. Base: Gini coefficient by year (%)**

**Figure 12. Base: Consumption and inequality by household**

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21
In addition to the standard questions on household characteristics and expenditures (including imputed values of self-produced food) common to multi-topic household questionnaires, subjective welfare data were collected at two different points in the questionnaire. In the Subjective Poverty and Food Security Module, respondents were asked: _“Imagine a 6-step ladder where on the bottom, the first step, stand the poorest people, and the highest step, the sixth, stand the rich. On which step are you today?”_ In a later section of the questionnaire, respondents were asked to place four vignettes of hypothetical households on a six-step ladder and then to place themselves on the same scale. The latter is the same question asked in the earlier portion of the questionnaire. The actual vignettes from the questionnaire (translated into English) are given in Appendix 2.

We developed the vignettes for this experiment in consultation with local counterparts. The vignettes were designed to capture representative snapshots of various levels of welfare in Tajikistan. Characteristics incorporated in the vignettes include land holdings, education, diet, clothing, and the ability to heat the home during the winter. The vignettes were developed in a clear, expected hierarchy with respect to welfare, with all aspects of socio-economic status increasing monotonically. This structure was used to minimize the effects of multidimensionality. Multi-dimensionality can lead to the perverse sequencing of the vignettes with respect to the intended ordering if respondents place different values on various characteristics contained in the vignettes with respect to overall welfare (King et al., 2004). This does not seem to be a major concern for our vignettes: of the 4,860 households in the sample, only 89 have any instances of an “incorrect” ordering. The most common characteristic of respondents who perversely order the vignettes is a low level of education of the household head; see Appendix 3 for results from a probit estimation of the correlates of perverse ordering. These 89 households are excluded from our analysis.

# _Pre and post vignettes_

If responses are influenced by heterogeneous scales, it might be expected that subjective welfare responses will be affected by familiarity with the vignettes. By asking the vignette questions, the survey may focus the respondent to think about, and possibly revise, their own scale used to report their self-assessment welfare.

In Table 2 we compare the pre- and post-vignette responses to the household’s selfassessment welfare. On average, respondents place themselves between steps 2 and 3 in both the 7
mortality statistics draws on a second solid source, reports of the numbers of death certificates issued by the health system. The present study uses the homicide rate per 100,000 inhabitants as the main measurement of crime and violence in Mexico. This indicator is collected monthly by the Mexican Statistical Office (INEGI).

As a robustness test, the incidence of car theft is used as a complementary measure of crime. According to the most recent victimization survey, about 83 percent of cars stolen are reported, making it a generally reliable measurement of crime (ENVIPE 2014).<sup>10</sup> However, while this is a good proxy for crime in general, it is a limited one for violence because not all car thefts involve violence. The information on car robbery is gathered by the National Security System (SNSP) based on preliminary inquiries ( _averiguaciones previas_ ) by the judiciary system.

The estimates for the number of ninis come from the _Encuesta Nacional de Empleo_ (ENE), an annual employment survey conducted between 1995 and 2004, and from the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), a quarterly survey conducted in Mexico since 2005. Both ENE and ENOE are statistically representative at the state level. Since ENE is collected during the second quarter of each year, we include information from ENOE only for the second quarter of each year; this has the double advantage of making both employment series compatible while avoiding variations due to seasonality. We define as ninis those individuals in the 15-24 age range who are neither enrolled in formal schooling (whether public or private) nor working at the time of being surveyed. Working youth are defined—following the conventional definition—as individuals who have worked at least one hour in the past week.

All but one of the controls were constructed from the employment surveys (ENE and ENOE). The income measure used is the log of the hourly labor remunerations; inequality is captured by the Gini coefficient of the per capita labor remunerations; the identification of informal

> 10 Even though the rate of car stolen that are reported is relatively high, a biased explained by unreported events is still possible. However, since our empirical strategy relies on the correlation between changes in crime and changes in the rate of ninis and the rate of unreported stolen cars have changed little over time, the expected bias is small.

14
The context also requires several caveats in interpreting these results. The civil conflict that erupted in 2015 resulted in the cancellation of the second year of the program, effectively halving our intended sample size and reducing our statistical power. It also required the follow-up survey to be conducted more quickly after the end of the program that we had planned, and for a shorter survey instrument to be used. Moreover, the armed conflict in 2015 has resulted in large-scale internal displacement, more than 80 percent of the population in need of humanitarian assistance, shortages of fuel, electricity, and water, and infrastructure damage (European Commission, 2015). As a result, employment opportunities for the population in this study are likely to have been severely affected, potentially reducing any longer-term impact. Nevertheless, we believe it important to share the results available given the paucity of evidence on internship programs in developing countries.

# **References**

Attanasio, Orazio, Adriana Kugler, Costas Meghir (2011) “Subsidizing Vocational Training for Disadvantaged Youth in Colombia: Evidence from a Randomized Trial”, _American Economic Journal: Applied Economics_ 3(3): 188-220.

Blattman, Christopher and Laura Ralston (2015) “Generating employment in poor and fragile states: Evidence from labor market and entrepreneurship programs”, Mimeo. Columbia University.

Bruhn, Miriam and David McKenzie (2009) “In pursuit of balance: Randomization in practice in development field experiments”, _American Economic Journal: Applied Economics_ 1(4): 200-32.

Card, David, Pablo Ibarraran, Ferdinando Regalia, David Rosas-Shady, and Yuri Soares (2011) “The Labor Market Impacts of Youth Training in the Dominican Republic”, _Journal of Labor Economics_ 29(2): 267-300.

European Commission (2015) “YemenECHO Factsheet”, <u>http://ec.europa.eu/echo/files/aid/countries/factsheets/yemen_en.pdf</u> [accessed August 14, 2015].

Galasso, Emanuela, Martin Ravallion and Augustin Salvia (2004) “Assisting the Transition from Workfare to Work: A Randomized Experiment”, _Industrial and Labor Relations Review_ 57(5): 128-42.

Groh, Matthew, Nandini Krishnan, David McKenzie and Tara Vishwanath (2013) “Do Wage Subsidies Provide a Stepping Stone to Employment for Recent College Graduates? Evidence from a Randomized Experiment in Jordan”, Mimeo. World Bank.

13
Random Forest performs better for women compared to men, with out-of-bag errors of approximately 17 percent, compared to the male Random Forest models where the out-of-bag errors are approximately 30 percent. The performance of the Random Forest models with all 301 variables is similar to the restricted one with only top 30 predictors, suggesting high predictive power of the top 30 predictors.

**Figure 6. Out-of-bag error by number of iterations**

Source: Authors’ calculations from DESMIGRATIF (2018). Note: the out-of-bag errors are obtained from separate Random Forest models in which different number of iterations are included. All the models contain the 301 variables.

However, the predictive power of the Random Forest as suggested by this internal error measurement is much lower than suggested earlier in Table 5, using logistical regressions. Indeed, the sensitivity levels are around zero for women and approximately 6 percent for men, which mean that the model does not predict well who is interested in migrating in the testing sample. Note that the sensitivity level is 100 percent in the training sample. This poor performance could be due to the model being over-trained or the small testing sample. To gauge which reason is potentially driving our poor performance, we compared the sensitivity levels for: (i) different numbers of iterations and (ii) with a different split of testing and training samples (50 percent of randomly selected observations in each group). The performance of the models do not improve when implementing these changes (Appendix Table 3). We also implemented Random Forest models with different depth (number of variables) and 31
jective, our study differs in two ways. First, our research design allows us to overcome some of the limitations of audit studies by fully controlling for all agent characteristics. Second, our experiment uses real workers in jobs they will continue to have after the experiment is over, which factors in the selection processes for hiring workers and worker incentives (a disadvantage of this approach is that we did not choose the sales agent pool we study).

# **2 Context**

## **2.1 Service sector in Sub-Saharan Africa**

We study consumers’ discriminatory behavior when engaging with online sales agents in Sub-Saharan Africa. As service-sector jobs increase, customer-facing roles are increasingly common across the continent. For example, the share of working-age individuals employed in services throughout Sub-Saharan Africa rose 12% from 2011 to 2019 (WDI). Women largely drove these trends: the share of working-age women employed in services increased by 16% over the same period—currently at 39.7%. While one might assume that discrimination is lower in sectors where female labor force participation rates are high, women may choose to work in these sectors precisely because they have a comparative advantage in this space.

This trend will likely persist as internet connectivity spreads across the continent and service-sector jobs increasingly interface with clients online. In 2010, only 8.3% of the population in Africa had internet access. By 2017, internet access had increased to 22.3% (WDI). Online shopping, in particular, has increased 18% annually between 2014 and 2017 (UNCTAD, 2018) and estimates suggest that almost 50% of digital buyers in Africa are female (Statista, 2019). The COVID-19 pandemic has likely accelerated these trends as consumers increasingly head online. Reflecting this growth and importance: in 2020, the value of African e-commerce was estimated at 20 billion USD—a 42% increase over 2019 (IFC, 2021).

## **2.2 Company and study details**

We evaluate an experiment at an online travel agency with offices located across Sub-Saharan Africa—we work primarily with an East African field office. For confidentiality reasons, we cannot provide any identifiable information about the company. The company sells flights and hotels to customers that come from different parts of the continent (the average price of a flight/hotel conditional on making a sale is 110 euros). The company employs local sales agents who answer customer questions, field complaints, and encourage purchases (using phone calls and online chat interfaces).<sup>10</sup> In the company overall, approximately two-thirds of the sales agents are women, and these women account for 83% of chats. The experiment included six agents, all of whom were female. These sales agents are full-time company employees, and receive an annual wage.<sup>11</sup>

> 10 Approximately 40-50% of chats with any customer response relate to purchases.

> 11 We do not have access to other information about agents’ demographics or wages within the company.

6
is fixes the scale for both factors and elasticities since the product invariant by and .

# **_Iterations_**

The E-PCA (Annex 3) generates a series of predicted matrices with an increasing number l of dimensions/variables, beginning with the “flat world” no-comparative advantage value: Each stage adds dual sets of factors and elasticities that provide the best fit with the original trade matrix. The estimator with l number of already determined latent variables is and should minimize: The information improvement brought by the variable l is the improvement in log-likelihood from l-1 to l, or

# **_The iteration from step l to l+1 proceeds as follows._**

The impact on trade and comparative advantage of the first l variables comes from the coefficient: , with for l= 0 and . is known from the previous iteration up to stage l, and the predicted value at stage l+1 is The problem is formally quasi-identical to the determination of the FSE in section 5, except that and have to be determined simultaneously. This is achieved at each stage l+1 by:

- Seeding a randomly distributed centered and normalized (zero mean and unit variance).

- Computing the corresponding elasticities as per the algorithm in section 5, and centering them.

- From the elasticities, computing with the same algorithm a new iteration of

- .

20
# _Centralized Control, Firm Size, and Capital Intensity_

In giving different controls to SOEs, the government sacrifices differential benefits in terms of control amenity.'<sup>9</sup> Implementation of different reforms, moreover, incurs different governance costs. These considerations will partially determine how the incentive structure and control rights are related to a firm's size and capital intensity.

In deciding on MRS status, the government sees more control amenity in more capital-intensive firms, because it cares for capital maintenance and accumulation besides profitability. When capital-intensive firms become more independent, the government has to worry more about their tendency to abuse equipment or not accumulate capital in single-minded pursuit of short run profits. The analysis of Holmstrom and Milgrom (1991) 20 is relevant to our discussion: when the principal pursues multiple goals (for example, profit and capital accumulation in our case), a stronger reward (MRS with pay sensitivity in our context) for one objective (profit in this case) might lead the firm to sacrifice others. The principal, therefore, may opt for low-powered incentives (no MRS or MRS with lower pay sensitivity). One might naturally assume that, by the same line of reasoning, more capital-intensive firms would also be less likely to have production autonomy. This is not, however, true. First, the granting of production autonomy was not associated with strong incentives for one objectives versus the other(s), while MRS is

> 19 See Demsetz and Lehn (1985), in H. Demsetz (1988), _Ownership, Control and the Firm,_ New York: Basil Blackwell.

20 See Bengt Holmstrom and Paul Milgrom, "Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design," _Journla of Law, Economics, and Organization_ 7(1991): 2452.

13