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Dewatripont, Mathias, Jewitt, Ian and Tirole, Jean, (1999), The Economics of Career Concerns, Part I: Comparing Information Structures, Review of Economic Studies, 66, issue 1, p. 183-198.

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Donato, Katherine, Grant Miller, Manoj Mohanan, Yulya Truskinovsky, and Marcos Vera-Hernández. 2017. "Personality Traits and Performance Contracts: Evidence from a Field Experiment among Maternity Care Providers in India." American Economic Review, 107 (5): 506-10.

Elbers, Chris; Lanjouw, Jean O.; Lanjouw, Peter. (2002). Micro-level estimation of welfare. Policy, Research working paper; no. WPS 2911. Washington, DC: World Bank. http://documents. worldbank.org/curated/en/362131468739473297/Micro-level-estimationof-welfare.

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38
# **Heterogeneous Returns to Income Diversification: Evidence from**

# **Nigeria**

Eleonora Bertoni<sup>ac</sup> , Paul Corral<sup>a</sup> , Vasco Molini<sup>_∗_a</sup> , and Gbemisola Oseni<sup>_†_b</sup>

> aPoverty-Global Practice, The World Bank, Washington, D.C., USA

> bDevelopment Data Group, The World Bank, Washington, D.C., USA

> cUniversità di Torino, ITA

<mark>November, 2016</mark>

**Key words:** Income Diversification, Off-farm, Heterogeneous returns, Nigeria. **JEL classification:** C14, D31, J43, Q12.

> _∗_ Corresponding author: Vasco Molini. Telephone: +1 202 473 4891. E-mail: vmolini@worldbank.org

> _†_ We aknowledge comments from Carlo Azzarri and Alberto Zezza on previous versions of this paper.
started operations in an area of intervention once the clinic submitted the first bill for payment to Plan Nacer for services provided to beneficiaries.

Figure 1 plots the timing of incorporation of clinics into the program. Overall, the incorporation is smoothed over the five-year period. The first billing of services took place during the last quarter of 2004 in the province of Tucumán. The remaining provinces started billing from 2005 onwards. By the end of 2008, the proportion of clinics that had billed at least once to Plan Nacer was close to 100 percent.

**Figure 1: Timing of Clinics Enrolling in Plan Nacer**

<!-- Start of picture text --> 1<br>.8<br>.6<br>.4<br>.2<br>0<br>2004q3 2005q3 2006q3 2007q3 2008q3<br>Quarter<br>c.d.f. Normal c.d.f.<br><!-- End of picture text --> _Source: Plan Nacer clinic billing and payment administrative records_ _Note:_ The figure plots the cumulative proportion of clinics incorporated by quarter-year. The quarter-year of incorporation refers to the date the clinic submitted the first bill for services provided to Plan Nacer beneficiaries.

In contrast to service provider enrollment, beneficiary enrollment of eligible mothers and children took significantly more time (figure 2). In practice, the 9
How much are poor customers penalized by fixed charges and block structures? As noted above, over 50 percent of the sample countries have incorporated fixed charges in their tariff schemes. Fixed charges in African countries range from US$0.43 cents to US$5.00 per month (figure 23a). Furthermore, these charges constitute a large portion of the aggregate monthly bill particularly at subsistence levels of consumption (figure 23b). At 50 kWh/month—subsistence consumption—the fixed charge represents more than 40 percent of the monthly bill in over 30 percent of the countries. At higher levels of consumption—75kWh/month and 100 kWh/month—the weight of the fixed charge is less prominent. This indicates the disproportionate weight of the fixed charge in the bills of households consuming at the subsistence level.

<!-- Start of picture text --> Figure 23. Fixed portion of residential tariffs<br>a. Residential fixed charge per month ($) b. Fixed charge as a share of monthly bill (%)<br>Niger<br>Nigeria 70%<br>Ghana<br>Côte d'Ivoire 60%<br>Ethiopia 50%<br>Malawi<br>Uganda 40%<br>Burkina Faso<br>30%<br>Zambia<br>Tanzania 20%<br>Botswana<br>Kenya 10%<br>Mozambique 0%<br>Madagascar<br>Cameroon 50 kWh/mo 75 kWh/mo 100 kWh/mo<br>Congo, Rep.<br>1%-20% 21%-40% >40%<br>- 1 2 3 4 5<br>Percentage of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database.

# **e. Are there any other kinds of hidden subsidies?**

Besides tariffs, there are also other less explicit mechanisms by which policy makers subsidize consumption. For instance, when policy makers overlook, tolerate, or even promote certain operational inefficiencies, they are in practice transferring resources from one sector of the economy to another, from the producer to the consumer, from future taxpayers to current customers, and so on.

Tolerance of nonpayment is an implicit tax on utilities (and/or a transfer to consumer). Tolerance of pilferage—one of the main causes of transmission and distribution losses—is an implicit subsidy to customers and an implicit burden on future taxpayers. Acceptance and promotion of over-employment represents an untargeted transfer of resources from the utility to the society. These inefficiencies can be empirically quantified and prove to be substantial relative to GDP (Briceño-Garmendia, Foster, and Smits 2008). For most countries, over-manning as well as collection inefficiencies amount to less than 0.2 percent of GDP, whereas transmission and distribution losses tend to be much larger in value amounting to around 0.2-0.6 percent of GDP (figure 24a). These operational inefficiencies also look very large in 34
**<u>Table 2: Extreme Shocks Effects on Consumption –Robustness</u>**

|**DV**||**Food Consumption**||**H**|**ousehold Consumpti**|**on**|
|---|---|---|---|---|---|---|
||(1a)|(1b)|(1c)|(2a)|(2b)|(2c)|
|Extreme Shocks|-24278.43|-27516.18|-37100.35*|-47548.94*|-58201.20*|-69858.20**|
||(13535.18)|(16264.08)|(18650.48)|(22554.52)|(28638.90)|(27692.16)|
|All 23 Strategies|-|Yes|-|-|Yes|-|
|Savings (26.1%)|||-4308.88<br>(5197.246)|||-7531.36(5689.09)|
|Do Nothing (20.8%)|||4327.13(3080.73)|||8013.45*(3873.04)|
|Spiritual (12.1%)|||1522.13(7615.587)|||175.92(14049.06)|
|Sale Animals (8.8%)|||-2206.21(4773.69)|||-441.01(6952.14)|
|Social Networks (6.9%)|||-6683.79(4722.68)|||-4723.99(7013.27)|
|Adjust Consumption (5.4%)|||7288.43(5876.81)|||10530.61(8187.44)|
|R-/Pseudo -squared (%)|4.88|6.15|5.31|0.87|3.95|2.01|
|Observations|3,059|3,059|3,059||||
|Fixed Effects|Households+ Year|Households+Year|Households+Year|Households+ Year|Households+Year|Households+Year|
|Model|Linear|Linear|Linear|Linear|Linear|Linear|

**Notes:** Table reports estimates from regressions of two per capita consumption expenditure on extreme shock variables, and vectors of shock-coping strategies. Frequencies of strategy-use are shown in parentheses. Shock realizations are based on estimated shock quantities in section 2 which range between -1.12 and 7.83. Extreme shocks correspond to realizations above 80th percentile of shock estimated shocks. The columns are differentiated by outcome variables and implied regression models. All columns include both household level and year fixed effects. All standard errors are clustered at the region level and shown in parentheses. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

16
increases (slightly) the importance of tariff preference erosion.

For Bangladesh and Turkey, the benefits change very little as σ1 rises, rather than following the general pattern of rising substantially. In these cases there appears to be a balance of competing forces on the market access side, Bangladesh and Turkey benefit from preferential access to the European market, where it faces the same problem as Chile. Bangladesh and Turkey differ importantly from Chile, however, in benefitting from reform of non-preferential tariffs in major markets such as the United States.

**Table 4 Full Liberalization: Sensitivity Analysis for σ1**

|||**Real Inco**|**me, %**||**T**|**erms of T**|**rade, %**||
|---|---|---|---|---|---|---|---|---|
||σ1=0.5|σ1=1|σ1=2|σ1=4|σ1=0.5|σ1=1|σ1=2|σ1=4|
|**World total**|**0.67**|**0.73**|**0.88**|**1.17**|**0.00**|**0.00**|**0.00**|**0.00**|
|**_Low and middle income_**|||||||||
|**_countries_**|**_1.00_**|**_1.07_**|**_1.30_**|**_1.75_**|**_-0.20_**|**_-0.18_**|**_-0.08_**|**_0.04_**|
|Bangladesh|-1.03|-1.02|-1.03|-1.02|-2.08|-2.09|-2.12|-2.17|
|Brazil|1.62|1.97|3.18|3.63|1.22|1.51|2.52|2.96|
|Chile|0.26|0.20|0.06|-0.06|0.07|0.02|-0.08|-0.19|
|China|0.01|0.05|0.17|0.76|0.16|0.22|0.42|0.64|
|Egypt|1.60|2.12|5.01|8.31|-0.26|-0.26|-0.27|-0.30|
|India|1.26|1.25|1.24|1.25|-0.15|-0.20|-0.28|-0.47|
|Indonesia|0.56|0.56|0.69|1.37|0.18|0.16|0.18|0.71|
|Pakistan|-0.50|-0.44|-0.34|-0.10|-0.48|-0.48|-0.49|-0.51|
|Thailand|2.83|3.03|3.49|4.93|-0.01|0.01|0.09|0.81|
|Mexico|0.24|0.27|0.35|0.51|-0.53|-0.54|-0.55|-0.58|
|Nigeria|5.53|5.76|6.30|7.10|-1.99|-2.00|-1.96|-1.86|
|Turkey|2.16|2.18|2.21|2.23|0.81|0.79|0.76|0.70|
|Rest of Asia|4.03|4.29|4.68|6.19|-0.63|-0.61|-0.63|-0.01|
|Rest of LAC|0.68|0.62|0.44|0.42|0.02|-0.03|-0.18|-0.25|
|Morocco & Tunisia|4.94|5.22|5.82|6.94|-1.74|-1.76|-1.79|-1.91|
|SACU|1.74|1.80|2.25|3.15|-0.09|-0.08|0.00|0.13|
|Rest of Sub Saharan Africa|-0.32|-0.37|-0.55|-0.79|-1.90|-1.96|-2.13|-2.42|
|Rest of the World|1.19|1.30|1.54|2.03|-0.76|-0.76|-0.72|-0.61|
|**_High income countries_**|**_0.58_**|**_0.63_**|**_0.76_**|**_1.02_**|**_0.06_**|**_0.05_**|**_0.02_**|**_-0.01_**|
|Australia and New Zealand|0.53|0.54|0.59|0.89|0.38|0.39|0.40|0.37|
|Rest of Europe|2.80|3.07|3.56|4.45|0.67|0.74|0.88|0.94|
|EU 27|0.63|0.70|0.96|1.37|-0.10|-0.12|-0.18|-0.24|
|United States|0.12|0.13|0.13|0.16|0.04|0.04|0.03|0.03|
|Canada|0.14|0.18|0.24|0.24|-0.30|-0.31|-0.30|-0.22|
|Japan|0.86|0.88|0.94|1.20|0.34|0.34|0.34|0.32|
|Korea and Taiwan, China|3.22|3.56|3.95|4.99|0.27|0.22|0.14|-0.18|
|Hong Kong SAR, China and|||||||||
|<br>Singapore|1.23|1.25|1.28|1.27|1.19|1.20|1.22|1.18|

_Source: Authors’ computation. LINKAGE Model._ 22
<!-- Start of picture text --> Female Ratioof Age-slope Age-slope Female Ratioof Age-slope Age-slope Female = Ratioof Age-slope Age-slope<br>mortalitymale-to-femaleof female —_ of male mortalitymale-to-femaleof female of male mortalitymale-to-femaleof female —_ of male<br>rate per mortality mortality mortality rate per mortality mortality mortality rate per mortality mortality mortality<br>100,000(1) (2) (3) (4) 100,000(1) (2) (3) (4) 100,000(1) (2) (3) (4)<br>lexpecserheaae Excess deaths:2020 Excess deaths:2021<br>Kyrgyzstan<br>Nicaragua<br>Uzbekistan<br>Bolivia (Plurinational State of)<br>Guatemala<br>Cuba<br>Iraq<br>Tunisia<br>Mongolia 0.35<br>Egypt} —<br>Peru] 2<br>Ukraine s<br>Paraguay | —<br>Republic of Moldova<br>South Africa 061 092<br>Albania<br>Azerbaijan<br>Iran (Islamic Republic of)<br>Brazil<br>Georgia<br>Colombia<br>Bosnia and Herzegovina<br>Thailand 0 0 2698 «849038 8619 (0.84<br>Dominican Republic<br>Serbia<br>Mexico<br>Costa Rica<br>Argentina 176.2 2.86 1.94 2.25<br>Uruguay<br>Kazakhstan<br>Bulgaria<br>Chile<br>Malaysia j oy<br>Russian Federation] 2<br>Greece s<br>Croatia 4<br>Panama<br>Latvia<br>Romania<br>Slovakia<br>Oman<br>Poland<br>Hungary<br>Portugal<br>Estonia<br>Lithuania<br>Czechia<br>Israel<br>Spain<br>Republic of Korea<br>Japan<br>Italy<br>The United Kingdom<br>Canada j e9<br>Australia} 2<br>FinlandFrance} 4 § 4910254 a 425<br>Belgium<br>Kuwait 086 084<br>Sweden<br>Netherlands<br>Austria<br>Germany<br>USA<br>Switzerland<br>Note: Columns 1-4 are regression coefficients based on the country-specific Poisson model described in the text applied to the WHO Global Excess<br>Deaths dataset. Column1 is the predicted female mortality per 100,000 at age 65. Column2 is the predicted ratio of male-to-female mortality at age<br>65. Columns 3 & 4 is the increase in mortality risk for women and men respectively associated with an additional 10 years in age. In 2020, for all age<br>groups at or above 55, excess death estimates for males in Thailand are negative, since these values were bottom coded to 0 the sex-ratio at age 65 is<br>0 and the association of excess deaths with age for males is also 0. Countries are sorted by GNI per capita (PPP)<br><!-- End of picture text -->
Policy Research Working Paper 10773

# **Abstract**

Can alternative survey methods address the underreporting of women’s and youths’ labor market outcomes, and thus improve the measurement of the underlying gender- and age-based gaps? This paper addresses this question using a survey experiment in El Salvador that compares two alternative survey methods—a list of activities survey module and enforced self-responses—against a traditional household survey, which consists of proxy responses without a list of activities module. The findings show that including the list of activities module yields higher work and employment rates for the average respondent compared to the standard household survey. Notably, when using the list of activities module, the reported work gap between men and women falls by 8.1 percentage points. Moreover, when using enforced self-responses, the male age gaps in employment and work rates fall by 13.9 and 12.3 percentage points, respectively. The paper provides evidence that the prevalence of peers’ informal employment or social norms for domestic obligations drive these results.

This paper is a product of the Development Data and the Development Research Groups, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at icontreras@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 11046

# **Abstract**

This paper investigates the role of gender bias in artificial intelligence–driven analyses of citizen participation, using data from the 2023 Latinobarómetro Survey. The paper proposes that gender bias—whether societal, data driven, or algorithmic—significantly affects civic engagement. Using machine learning, particularly decision trees, the analysis explores how self-reported societal bias (machismo norms) interacts with personal characteristics and circumstances to shape civic participation. The findings show that individuals with reportedly low levels of gender bias, who express political interest, have high levels of education, and align with left-wing views, are more likely to participate. The paper also explores different strategies to mitigate gender bias in both the data and the algorithms, demonstrating that gender bias remains a persistent factor even after applying corrective measures. Notably, lower machismo thresholds are required for participation in more egalitarian societies, with men needing to exhibit especially low machismo levels. Ultimately, the findings emphasize the importance of integrated strategies to tackle gender bias and increase participation, offering a framework for future studies to expand on nonlinear and complex social dynamics.

This paper is a product of the Social Development Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jcuesta@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_Figure 3_ <!-- Start of picture text --> CDFs by stayer or mover and move direction<br>Panel Sample 2005/09<br>Year 2005/06 Year 2009/10<br>9 10 11 12 13 9 10 11 12 13<br>Log of real p.c. expenditure Log of real p.c. expenditure<br>Stayer Moved to rural Stayer Moved to rural<br>Moved to urban Moved to urban<br>Source: UNPS<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Cumulative % of p.c. expenditure .2 Cumulative % of p.c. expenditure .2<br>0 0<br><!-- End of picture text --> _Drivers and welfare impact of out-migration_ Earlier descriptive analysis suggests there are likely poverty differences across time between stayers and movers. To estimate the drivers of moving out – irrespective of the direction – and its welfare impact, we rely on the two-stage regression procedure described previously. Table 7 presents the first stage regression results on the drivers of out-migration, and Table 8 shows the OLS and IV (second stage) regression results on the welfare impact of out-migration.

Results on the drivers of out-migration show that movement out of one’s original residence is driven by demographic-specific traits. We observe that, when compared with non-adults, individuals aged between 25 and 49 are more likely to be movers (Table 7), suggesting that migrants selected for the move tend to be of an economically active age. We also observe that individuals selected for out-migration hold a particular position in the household. While being a head or spouse is associated with a lower propensity to migrate by 1.6 percent, being a child of the head increases the propensity to migrate (Table 7). In addition, individual educational attainment influences out-migration. At a 5% level of significance, a oneyear increase in schooling leads to 0.1 percent increase in the incidence of out-migration. This suggests that further human capital investments may be needed to facilitate out-migration.

Moving out is also driven by violent conflict shocks and distance to the regional capital. A one log-unit increase in the number of fatalities leads to a 0.8 percent increase in the incidence of out-migration, for young individuals of prime age to migrate. The same effect is observed from a one-log-unit increase in the 24
Table 6: Testing for differential pre-course trends

|Outcome:||**Employed **|**[0/1]**||
|---|---|---|---|---|
|Sample|All registrants|MDIC registrants|Unemp. Benefc.|All others|
||(1)|(2)|(3)|(4)|
|Months relative to course|0.000928|-0.001781|-0.001771|-0.000028|
|* course ofer|(0.001117)|(0.001428)|(0.001851)|(0.000532)|
|_R_<sup>2</sup>|0.61|0.60|0.32|0.62|
|_N_|4,794,972|485,327|816,802|3,492,843|

Notes: Table presents estimates from the estimation of equation 3 in the text, adjusted to test parallel trends in pre-course employment differentially across those receiving the course offer and those not. Sample comprised of all periods prior to the start of registrants’ training course. The coefficient for [Months relative to course*offer] gives the differential slope term for those offered a course seat in the pre-course period. Heteroskedasticity-consistent robust standard errors two-way clustered by individual and month*year reported in parentheses. All specifications include an unreported constant term, a primary slope coefficient for months relative to the course, and vectors of individual and month*year fixed effects. Significance indicated by: _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01.

Table 7: First stage estimates, difference-in-differences IV

|Outcome:|**Post-**|**course period [0/1**|**] * Completed co**|**urse**|
|---|---|---|---|---|
|Sample|All registrants|MDIC registrants|Unemp. Benefc.|All others|
||(1)|(2)|(3)|(4)|
|Post-course period [0/1]|0.435062***|0.519792***|0.367063***|0.442523***|
|* course ofer|(0.003203)|(0.006737)|(0.007700)|(0.002292)|
|_R_<sup>2</sup>|0.57|0.61|0.54|0.57|
|_N_|10,243,541|912,053|1,872,837|7,458,651|

Notes: Table presents first-stage coefficients from the estimation of the endogenous variable [Post-course * Completed] in equation 1 as predicted by an indicator for being in the post-course period and having received a course offer. Heteroskedasticity-consistent robust standard errors two-way clustered by class and month*year reported in parentheses. All specifications include an unreported constant term and vectors of individual and month*year fixed effects. Significance indicated by: _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01.

34