rafmacalaba/data-use-mentions-extended huggingface dataset:
and savings among talented individuals by directly reflecting reward to effort, thus stimulating growth (Ferreira 2005, Marrero et al. 2013). Empirically, the strong negative relationship between IOp and growth has been documented in various settings (Marrero et al. 2013, Bradbury et al. 2016; Carranza 2020).<sup>3</sup> Therefore, singling out the extent and sources of IOp and adequately addressing it is key to fostering much needed economic growth and poverty reduction, especially in SSA where poverty and inequality are high and economic growth is slowing (Wu et al. forthcoming).

The measurement of IOp revolves around the extent to which disparities in outcomes are due to circumstances beyond a person’s control. This typically involves using income or consumption data as the outcome variable and birth characteristics as circumstances, such as region of birth or parental education and occupational background. Because of the heavy data requirements, empirical work has compiled estimates of IOp across many upper-income countries, while estimates in low- and middle-income settings, especially in SSA, are rare.

Until now, the main effort for the SSA region had been Brunori et al. (2019), which estimates IOp across 10 countries using data from 2000-2013. They find that IOp in SSA explains on average 47 percent of inequality in consumption with estimates ranging between 40-56 percent across countries. Though reflecting the data availability and methodological advancements available at the time, the analysis is limited to 10 countries and only a subset of variables is available in each country.<sup>4</sup> Because it is not feasible to observe all characteristics that are fixed at birth and outside an individual’s control, IOp estimates are often seen as a lower bound estimate of true IOp (Ferreira et al. 2011). As often pointed out, when the set of observed circumstances is limited, the degree of underestimation is large and lower bound estimates of IOp end up being of limited use for policy (Kanbur & Wagstaff 2014).

3
--|---|
|Per capita Consumption|USD in consta|nt 2011 PPP|%|
|All Population|8,971|8,354|‐6.88|
|Bottom 40|3,619|3,395|‐6.20|
|Top 60|12,539|11,660|‐7.01|
|Poverty headcount|Headcoun|t ratios (%)|% Points|
|2.5 $ a day line|0.25|0.33|0.07|
|5 $ a day line|3.14|3.77|0.64|
|10 $ a day line|19.06|21.98|2.92|
|Gini coefficient|41.06|40.86|‐0.20|

Source: HBS data and micro‐simulation results. Note: per capita consumption is expressed in 2011 US PPP $; poverty headcounts and Gini in percentages The impact of the oil shock along the full welfare distribution of the Russian population can be seen in the growth incidence curve (GIC) of Figure 1. For all percentiles, per capita consumption is measured with and without the oil price shock and the difference between these two situations is plotted in the graph. The welfare distribution obtained from the household survey of 2011 is assumed to represent the situation without the oil shock. The microsimulation model is used to generate the counterfactual distribution with 14
/sup> Because many households in developing economies are producers as well as consumers, barriers to the reduction of unsustainable debts affect not only consumption but also have consequences for investment incentives and productivity (see Myers, 1977; Krugman, 1988).

There are also important differences in the institutions for debt resolution available to households in advanced and emerging economies. Many advanced economies offer some form of personal bankruptcy as a rules-based form of debt relief for households in financial distress. In the US, for example, households obtain more than $450 billion in debt relief through the personal bankruptcy system each year, with nearly one in ten households having filed for bankruptcy at some point (Keys, 2018; Dobbie and Song, 2015). Where personal bankruptcy is available, it functions as an important social safety net. When households lack insurance against adverse events such as job loss and illness, the option to discharge debt in bankruptcy acts as a de facto source of insurance. Estimates from the US find that the insurance value of bankruptcy is large, equaling 58.4% of annual consumption for the marginal bankruptcy filer (Indarte, 2023). In contrast, most developing economies lack an effective personal bankruptcy system. More than half of all emerging economies have no laws that provide for the discharge of an insolvent individual’s debt and even where personal bankruptcy laws exist, overburdened courts often make it difficult for households and small businesses to obtain debt relief.

Newly available data on household balance sheets shows that despite these differences, there are nonetheless some striking similarities in the finances of households in advanced and emerging economies. Households in advanced and emerging economies are, for example, remarkably similar in

> 2 See, for example, Bertrand and Morse (2011) and Skiba and Tobacman (2019) on debt traps and payday lending in the US.

3
, time between marriage and age at first birth, and son preference. The literature has identified these variables as strongly influenced by social norms. Clearly, some variables have a more direct link with social norms, such as the age of marriage and IPV, while others, such as educational achievements, are determined by multiple factors, and their links with social norms may be less direct. Lastly, we look at a list of subjective attitudes around gender that are expected to have a strong correlation with underlying gender norms: gender attitudes towards education, economic empowerment, IPV, and fertility. While attitudinal data remains an imperfect and likely incomplete proxy for underlying social norms, cross-country data on influential social norms are not available. Table 1 summarizes these indicators across the different domains.

# **4. Methods**

_Pseudo-panel from repeated cross-sections_ Using repeated cross-sections from the Demographic and Health Surveys and the World Value Surveys, we create pseudo panels, where individuals from specific birth cohorts are followed as they age. Using

> what is acceptable and appropriate as an action for women and men in a given group or society (Cislaghi and Heise, 2020).

7
<mark>in other contexts, poverty predictions for refugees can be expanded at scale, with good prospects for the improvement of targeted programs.</mark> <mark>The poverty estimates used in this paper do not reflect the official poverty estimates monitored by the government and the international community. The interest of this paper is to test a cross-survey imputation methodology, and, for this purpose, we use a subsample of UNHCR refugee data that are not nationally representative of the refugee population in Chad. By contrast, the official poverty statistics require national consumption surveys conducted by the national statistical office with samples that are nationally representative. At the time of writing this paper, these national data were not available, but they are expected to be published before the end of 2020. This will provide another opportunity to validate the cross-survey imputation method proposed in this paper.</mark> <mark>The paper is organized as follows. Section II outlines the country context. Section III presents the data and analytical framework. T</mark> he estimation results are presented in section IV, and section V evaluates the <mark>targeting strategy</mark> used in Chad and our targeting method in light of the global experience. Section VI discusses the limitations of the study, and section VII concludes.

# **II. Country Context**

Chad is one of the poorest countries in the world. According to the latest national consumption survey, which was administered in 2011, 29% of the population falls below the food poverty line and 47% falls below the national poverty line (World Bank 2018). The past decade has been a decade of instability for Chad, with negative consequences on household well-being. Pe <mark>r capita gross domestic product decreased by 15% between 2015 and 2017, from an average of US$963 in 2015 to US$823 in 2017 (in 2010 purchasing power parity (PPP)). In terms of overall development, Chad is</mark
is a need to develop a response framework that explains these differential effects and covers various types of shocks.

The lack of knowledge about an optimal social protection response to shocks extends beyond the COVID-19 pandemic. The literature on shock-responsive and Adaptive Social Protection (ASP) has mainly focused on operational features to enable a response through social protection programs to various types of shocks, such as setting out program procedures in advance (assessment, enrollment, payments, etc.), prepositioning financing to fund an increase in program size, and the use of social registries or geolocation to identify affected populations for program support (Bowen et al. 2020; O’Brien et al. 2018). Beyond the focus on building the capacity of a social protection program and its delivery systems to respond to future shocks, in countries with more robust social protection systems lies the question of

> 2 Globally, social assistance makes up 61 percent of these response measures, followed by supply-side labor market programs (20 percent) and social insurance (19 percent).

> 3 Reviews of the social assistance response to the COVID-19 pandemic mostly examine the target population, coverage rates, adequacy of benefits and speed of the response and highlight gaps in coverage and weaknesses in delivery systems. See for example Bastagli and Lowe (2021). Gentilini (2022) also discusses trends within the global cash transfers response to the COVID-19 pandemic.

> 4 Autor et al. (2022), for instance, look at the “Paycheck Protection Program” in the US and find that while the program was deployed swiftly and had widespread take-up, its lack of targeting implied that only 23-34 percent of the spending went to workers who would have lost their jobs otherwise: similar results may thus have been obtained at a fraction of the cost.

2
_percentages of how much weight you placed on each criterion. (Please make sure it adds up to 100%!) – [Growth opportunity, Investment opportunity, Improvement made during program]._ Given that investors do not accurately explain the criteria that are important to them (Petty and Gruber 2011), we followed recent field experiment research and conducted semi-structured interviews to provide more insight into mechanisms driving results (e.g., Dimitriadis and Koning 2022). The first author interviewed all 15 trainee investors in one region to further examine how they decided to give a startup a high or low score – what investors were paying attention to during evaluation.

To assess the effect of systematizing inquiry on individual investors’ evaluation, we compared how investors in the treatment and control group evaluated startups using a set of criteria, by founder gender, across three phases of evaluation. We used the same regression model, but changed the dependent variable to each criterion score i.e., score for “business model”. We then reran the regression to assess how investors re-evaluated startups from their first impressions at baseline.

**6.5. Results:** The balanced panel dataset after attrition was 1,530 investment decisions made by 65 investors on 69 startups over three time periods (510 decisions per period). Attrition was largely due to Covid-related absences. As shown in Table 4, there was no significant difference in observable characteristics between treatment and control groups – using raw numbers, percentages, or the p- value taken when regressing each characteristic on treatment.

To assess the baseline, we used trainee investors in the control group. Similarly to Stage 1, investors asked startups represented by females more prevention-focused questions and awarded lower scores than startups represented by all males. However, during the program, we observed this effect lessening, observing only directionally different effects by gender (see Appendix D). This suggests that the Vilcap program itself reduced gender disparities in the control group. The “Vilcap effect” could
with proper consideration of the impact any such decision will have on design and ratemaking. Removal of a statistically significant trend is likely to increase the threshold yield and reduce the commercial premium rate, resulting in a better value product for the farmer.

One potential approach would be to remove the same percentage of any statistically significant best estimate trend for each crop. This percentage could be set to zero for crops which do not appear to

> 3 These may be calculated using MS Excel functions as follows.  Suppose cells A1:A10 contained ten yield data years (e.g. the numbers 1998, 1999, ... , 2007) and cells B1:B10 contained weighted average yields from year 1 to year 10.   The linear trend, assumed to be calculated in cell C1, is given by the formula

> .  The two sided t-value, assumed to be calculated in cell D1, is then given by

> .  Finally, the p-value is given by

> .

8
well-being most likely varies across individuals, countries, and time (Deaton et al., 2009; Radcliff, 2001). While for some individuals or groups in a society a lack of civil liberties is a key source of grievances, others might put more weight on poverty-related issues. Hence, because grievances are broadly-defined and essentially value- and preference-based, subjective measures might be more relevant and valid than objective indicators for predicting uprisings.

Second, objective indicators of growth also tend to raise expectations and aspirations (Tocqueville, 1856), which, if unmet, can decrease well-being. The ‘tunnel effect’ describes this phenomenon using the parable of a traffic congestion in a tunnel (Hirschman, 1973). If one of the lanes starts moving while the other lanes are still jammed, those who are stuck initially experience hope as the end of the traffic jam seems to be in sight, but unless they start moving forward, their hope will give way to envy and frustration. The tunnel effect ties in with the modernization theory (Inglehart & Welzel, 2005; Lipset, 1959), which posits that income growth leads to changes in values and fosters expectations of political and civil freedoms. When these expectations are unmet, this can lead to feelings of great disappointment and resentment, which can in turn mobilize citizens to demand changes within the political system (Gurr, 1970). In these cases, objective indicators reveal signs of optimism, whereas subjective indicators could indicate that anti-government sentiments are on the rise. This mechanism was at work during the Arab Spring, which was preceded by a period of ‘unhappy development’ in many Arab countries, especially the ones where the uprisings were most intense (Arampatzi et al., 2018).

This paper tests whether subjective well-being data can explain variation in nonviolent conflict going beyond what is captured by the typical objective measures. We propose a hybrid model, which captures the main features of the grievance, resource-mobilization, and politicalopportunity theories of conflict, and in which we include additional subjective
Davies and<br>Caves (1987)|3 98<sup>(cef)</sup>|<sup>60 percent of UK value addeded</sup><br>61 percent of US value addedU|UK/USA|1977|
|Van Ark (1988)|167-197|38 percent of Brazilian value<br>added, and 29 percent of UK<br>value added|Brazil/UK|19;5|
|Maddison<br>and Van Ark<br>(present study)|171-372<br>192-342<br>200-157|33 percent of value added in<br>Brazil, 39 percent in Mexico, 20<br>percent in USA|Brazil/USA<br>Mexico/USA<br>Mexico/Brazil|1975<br>1975<br>1975|
|(a) Galenson inc<br>(b) In the absen|ludes three<br>ce of infor|mining induste'i-s<br>(coal, iron ore<br>mation from the &.ithors,<br>these are|, oil and natura<br>rough estimates|l gas).<br>.|

(c) Information supplied by the authors.

(d) West does not say how big his sample is, but we derived this figure by comparing the industry codes he uses (pp. 59-61) with 1963 information in the General Summary volume of the 1977 Census of Manufactures.

(e) Refers to number of 'matches' instead of number of matched products.

(f) These figures refer only to directly derived PPPs.
This process, together with coal washing used to remove impurities from coal pre-combustion, requires large quantities of water. Thus, a second factor in the choice of location for coal-fired power plants is the proximity to a body of water.

Naturally, these general considerations were also relevant for the choice of location of the power plants in our sample. Chile has few coal deposits, which have mostly been depleted over the years. The majority of the coal used in electricity production has been imported, hence the usefulness of the proximity of coal plants to a port. The ports of Antofagasta and Huasco are close to local copper mines, that are heavy users of electricity. Valparaiso, also a port, is close to Santiago, a big demand center. Coronel is in the proximity of the main coal mines in Chile. These have been mostly depleted by now and the power-plants in this area are being phased out. In summary, while both access to inputs, such as coal and water, and transmission costs related to supplying electricity to demand centers are important, the location of coal-fired power plants in Chile appears to be mainly in order to minimize production costs, see, for example, Smith (1973). Coal-fired power plants are among the most polluting. While the amounts of pollutants released in the atmosphere depend on the grade and quality of the coal used, emitted pollutants always include significant amounts of nitrogen oxides (NOx), sulfur oxides (SOx), as well as particulate matter (PM2.5), in addition to greenhouse gases like carbon dioxide (CO2) and methane (CH4). We will concentrate on particulate matter (PM2.5) in what follows.

# **3.2 Coal-fired power plants as a valid instrument**

Our analysis focuses on city-level pollution and productivity data from Chile. As noted by Graff Zivin and Neidell (2012), several issues emerge when identifying the causal relationship between pollution and labor productivity. First, selection issues arise from how workers allocate their labor
Although the estimates of gender differences in agricultural productivity (henceforth referred to as the gender gap)<sup>9</sup> across sub-Saharan Africa range widely from 4 to 40 percent, the majority cluster around 20 to 30 percent. The studies that compare productivity outcomes on female- vs. male-managed plots across and within households provide further support for the presence of systematic and persistent gender differences in agricultural productivity in the region (Akresh, 2005; Alene et al., 2008; Gilbert et al., 2002; Goldstein and Udry, 2008; Moock 1976; Peterman et al., 2011; Oladeebo and Fajuyigbe, 2007; Quisumbing et al., 2001; Saito et al., 1994; Tiruneh et al., 2001; Udry, 1996; Vargas Hill and Vigneri, 2011). The major reasons for the observed gender gap have been identified as gender differences in (i) access to and use of agricultural inputs, (ii) tenure security and related investments in land and improved technologies, (iii) market and credit access, (iv) human and physical capital, and (v) informal institutional constraints affecting farm/plot management and marketing of agricultural produce.<sup>10</sup> Regardless of whether the comparisons are made across or within households, the common thread across the relevant literature is that the gender gap disappears or diminishes significantly once the researcher controls for the factors discussed above.

Despite what could be perceived as a well-established evidence base on the extent and proximate causes of the gender gap across sub-Saharan Africa, the overwhelming majority of empirical studies on the topic have used data from small-scale surveys that were limited in terms of geographic coverage, topic, or attention to intra-household dynamics (or, in some cases, all three). With the exception of Akresh (2005), none of the above-referenced papers rely on nationally-representative survey data. Dearth of nationally-representative, methodologicallysound data collected in heterogeneous settings across sub-Saharan Africa has in turn inhibited the computation of externally-valid, rigorous
- ———. 2014. “The Project on Integrated Urban Development Master Plan for the City of Nairobi in the Republic of Kenya (Draft).” Japan International Cooperation Agency.

- Kain, John F. 1968. “Housing Segregation, Negro Employment, and Metropolitan Decentralization.” _The Quarterly Journal of Economics_ , 175–97.

- Klopp, Jacqueline, Sarah Williams, Peter Waiganjo, Daniel Orwa, and Adam White. 2015. “Leveraging Cellphones for Wayfinding and Journey Planning in Semi-Formal Bus Systems: Lessons from Digital Matatus in Nairobi.” In _Planning Support Systems and Smart Cities_ , edited by Stan Geertman, Jr. Ferreira Joseph, Robert Goodspeed, and John Stillwell, 227–41. Cham: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18368-8_12.

- Kumar, Ajay, and Fanny Barrett. 2008. “Stuck in Traffic: Urban Transport in Africa.” _AICD Background Paper_ .

- Lucas, Robert E., and Esteban Rossi-Hansberg. 2002. “On the Internal Structure of Cities.” _Econometrica_ 70 (4): 1445–76.

- Ma, Kang-Rae, and David Banister. 2006. “Excess Commuting: A Critical Review.” _Transport Reviews_ 26 (6): 749–67.

- Melo, Patricia C., Daniel J. Graham, David Levinson, and Sarah Aarabi. 2013. “Agglomeration,

- Accessibility, and Productivity: Evidence for Urbanized Areas in the US.” In _Paper Submitted for the Transportation Research Board 92nd Annual Meeting_ .

- Niedzielski, Michael A. 2006. “A Spatially Disaggregated Approach to Commuting Efficiency.” _Urban Studies_ 43 (13): 2485–2502.

- NORC at the University of Chicago. 2013. “Kenya State of the Cities Baseline Survey.” NORC.

- O Kelly, Morton E., and Wook Lee. 2005. “Disaggregate Journey-to-Work Data: Implications for

- Excess Commuting and Jobs-Housing Balance.” _Environment and Planning A_ 37 (
the uncertainty and expected error up. Figure 2 shows the estimated errors across the World Bank’s geographical regions and four income groups. Evidently, the errors tend to be higher for regions and income groups than for the world as a whole. The only subgroups with lower errors are Europe & Central Asia and High-Income Countries.

# Alternative missingness assumptions

Our analysis so far may provide too optimistic errors if the data are systematically missing in the sense that the correlation between the probability of missingness and the value of the indicator is not zero. This is the case with SDG 1.1.1---the share living below the international poverty line---where less data is associated with higher poverty. Countries with at most five poverty estimates since 1980 have an average poverty rate of 32%, countries with 6-10 poverty estimates have an average poverty rate of 22%, and countries with more than 10 estimates have an average poverty rate of 4%. On the other hand, the errors we have presented so far might be too pessimistic if imputations are used to get proxy values for the countries with missing data.

In this section, we try to address these two issues. First, we assume that all missing values are imputed using regional averages. This is a common way of dealing with missing values in applied work. Second, we order countries by their share of missingness in an indicator in the years without full coverage and delete observations using this order rather than at random. The purpose is to only retain the data for the countries that are most likely to have data in any other year. The results from these two exercises are shown in Figure 3.

Using the empirical missingness from WDI does not systematically make the errors greater. This suggests that the probability of missingness in WDI is not systematically correlated with the indicator values and that our main results are not too optimistic. Yet, if for specific indicators the probability that data is missing is correlated to the values of the indicator, as with