rafmacalaba/data-use-mentions-extended huggingface dataset:
risk tolerance, and skills, which are all factors positively associated with migrating, and the income earned from self-employment could fund the costs of migrating.

This new and causal evidence on the be- paper provides descriptive relationship tween self-employment and internal and international migration. We begin by outlining how migration theory offers uncertain predictions for both selection into migration on the basis of attributes that also determine the self-employment decision, as well as for the likely causal effect of self-employment on subsequent migration. We then put together panel data on almost 140,000 individuals from eight countries to descriptively examine the association between self-employment and migration. Contrary to several theoretical predictions and the priors of many academics,<sup>4</sup> we find that the selfemployed are less likely to migrate than either wage workers or the not employed, and that this holds conditioning on age, gender, education and income. This relationship is stronger for internal migration than international migration, in part reflecting the much lower likelihood of international migration.

We then attempt to put together data from as many randomized experiments that increase self-employment as possible and measure the causal impacts of these programs on migration. We were able to do this for seven different experiments, exhibiting a wide range of increases in self-employment (between 2 and 41 percentage points), and enabling migration to be measured over time horizons of up to 10 years. In we do that which more did aggregate, find programs generated self-employment reduce migration. However, the impacts are typically modest, are restricted to internal migration, and require several years for impacts on migration to materialize. One reason for the limited impacts is that these programs have not been expressly targeted at those with high likelihoods of migration, and in some settings, there is very little migration among the control group to deter. This is particularly the case for international migration, which is an extremely rare event.<sup>5</sup> Nevertheless, we find no evidence of programs that increase self-employment subsequently increasing migration. The results therefore
the number of years between the final and initial year; the figure is multiplied by 100 to convert to percentage terms (Haltiwanger method). 2) The figures are a partial scatter plot of the residuals of employment growth rate (Y axis) plotted against residuals of informal competition (X-axis). The residuals are obtained by regressing the employment and informal competition (separately) on industry fixed effects, year fixed effects, initial level of employment (logs) and (log of) GDP per capita. Informal competition variable equals the proportion of firms in the country (excluding own firm) that report competing against informal firms. 3) Group averages (mean values) of the residuals are shown. Close to the median value of the informal competition residuals is used to categorize the two groups in Figure 2A. The cut-off value used for the two groups in Figure 2B is close to the 75<sup>th</sup> percentile value of informal competition residuals.

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highlighting women’s direct and indirect contribution to long-run economic growth (Galor & Weil, 1996; Klasen, 1999).

A range of modeling approaches have been used to quantify the economic loss caused by structural and systemic differences between men and women. Following the seminal work of Galor and Weil (1996), an overlapping generations framework by Agénor (2012) estimated the monetary valuation of a decline in the cost of childrearing (particularly sons), a decline in gender biases in the workplace, an increase in women’s bargaining power, and increased mothers’ time allocated to daughters. This model finds that policy regimes that increase public spending may contribute to increase in women’s productivity, women’s time allocation to work, and economic growth, particularly if investments were made to improve infrastructure (such as roads and access to water and sanitation). A simulation approach by Cavalcanti and Tavares (2007) examined differences in output and income by varying the degree of gender-based wage discrimination, taking as endogenous savings, fertility, and labor force participation rates. The authors found that a 50 percent increase in the gender wage gap led to a 35 percent decrease in income per capita, noting that up to 60 percent of the gap in output per capita between countries could be attributed to continued gender inequality. In Niger, a Computable General Equilibrium (CGE) model based on a LINKAGE model<sup>2</sup> was applied to estimate the economic opportunity from gender equality and to isolate the monetary impact of gender differences.<sup>3</sup> The study concludes that reduced gender gaps have the ability to increase GDP in 2030 by 12.6 percent under scenarios of reduced fertility and close to universal education, and by 8.9 percent if increased labor force participation and productivity are modeled (World Bank, 2019). Pennings (2022) constructs the Employment Gap Index (GEGI) that translates the inequalities in employment between men and women into the potential monetary gains (in
10 even though the cash component of earned income is usually the largest. Most unearned income on the other hand is in cash, with relatively few households receiving unearned income in kind, especially in Inner Mongolia. The China income data do not allow further disaggregation by gender.

# **3 A Theoretical Account of Mental Accounting**

When there is some pain associated with spending hard earned money on less basic or more frivolous goods or when there is less joy when spending unearned money on basic goods, households may mentally put earned and unearned income in different accounts, allowing them to evaluate the utilities derived from different consumption goods differently, depending on whether they are financed by earned or unearned income. The core intuition of a household’s consumption decision when facing such an (emotional) form of mental accounting is captured in the following one period optimization model: where are the amount spent on basic consumption goods (e.g. staples) using unearned and earned income respectively, and the amounts spent on non-basic goods using unearned and earned income respectively. , are unearned and earned income respectively. (>0) captures how the household weighs the utilities from consuming basic and non-basic consumption goods. Though not endogenized here, is likely also dependent on income with poorer households attaching more weight to the consumption of basic goods (smaller ) than richer ones, as per Engel’s and Bennett’s Laws.

The parameter reflects the loss in pleasure derived from spending unearned income on basic consumption goods ( ) following, for example, from the break in a person’s “feel good” mode after his acquisition of unearned income; reflects the additional pain/guilt derived from spending earned income on non-basic consumption goods ( ), due, for example, to the failure to mitigate the negative emotional tag associated with earned incomes. Differences in the parameters and could for example reflect the differential impetus for coping with negative and positive feelings, with the impetus for the former usually stronger than for the latter, implying a
4|
|Average duration outages (hours)|3.3|5.3|4.6|12.6|**3.9**|2.9|0.5|8.2|6.2|6.6|
|Losses due to outages (% of sales)|5.8|8.9|5|6|**1.8**|1.6|2.5|8.9|5|6.4|
|% of firms owning/sharing generator|11.6|39.8|6.5|26.6|**23.8**|28.6|24.8|85.7|55.4|43.1|
|Electricity from generator (%)|10.9|4.6|15.1|29.5|**16**|9.2|10.5|60.9|24.7|30.6|

Source: Enterprise Survey database available online at http://www.enterprisesurveys.org, year of the survey in brackets.

Mali successfully introduced a rural electrification program in 2003 with the creation of AMADER<sup>2</sup> . At the time, rural electrification was only about 3 percent of the population, and average household monthly spending on energy was as high as $4 to $10. As of 2008, the program had granted access to 36,277 households (box 2). However, AMADER has been hindered by insufficient and uncertain funding for providing capital cost grants, which are essential for the success of the program given the high cost of connections ($776).

> 2 Agence Malienne pour le Développement de l’Énergie Domestique et l’Électrification Rurale, or Malian Agency for the Development of Domestic Energy and Rural Electrification

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4 it is an indicator that equals 1 if true, and in column 5 it is the ideal number of children. Skilled/Professional jobs include professional, clerical, and skilled manual job groups; the other categories exactly describe the occupation groups included. Ideal number of children is censored at 20; no women in the Demographic and Health Survey report having more than 18 children, and � non-numerical responses are assigned the maximum value. Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure, _Izy_<sup>FPE</sup> . All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. Standard errors are clustered at the zone level, and Anderson and Rubin (1949) confidence sets of the 2SLS estimate are shown along with the _p_ -value from the associated chi-squared test, given in square brackets. The tobit model with clustered standard errors in column 5 is not compatible with confidence set calculations from Finlay et al. (2013).

Table B.4: Effect of Schooling and Reforms on Beliefs Regarding Domestic Violence

|Beating justifed if wife:|Goes Out|Neglects|Argues with|||
|---|---|---|---|---|---|
||w/out Permission|Children|Husband|Refuses Sex|Burns Food|
||(1)|(2)|(3)|(4)|(5)|
|�<br>Years of Schoolingizy|-0.045|-0.061|-0.013|-0.099|-0.109|
||(0.051)|(0.072)|(0.042)|(0.055)|(0.061)|
||[0.380]|