rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Note on attrition weight construction_

We calculated inverse probability weights (IPW) separately for the four groups used in the panel cohort analyses, i.e., children under or over three years old in migrant or non-migrant households, using an approach similar to Molina Millán and Macours (2017). First, we constructed approximately 40 indicators based on the 2008 survey. In addition to the variables in Tables 1a, 1b, 4 and Appendix Table 1, these included further measures of household characteristics (electricity, dirt floor, block walls, metal roof and home ownership), whether household members were in religious or other (e.g., sports) community groups, and measures that could facilitate recontact, including whether the household address, phone number or a neighbor’s name/address had been fully completed. <u>Second, we ran bivariate</u> regressions on an indicator of being interviewed in 2012 for each variable (separately for each age cohortmigration status sample). We retained for potential inclusion in the weight construction all variables significant at 10% in any model. Third, we estimated the probability of re-interview in 2012 with this set of baseline predictors for each age cohort-migration sample to calculate separate weights for each group. To account for collinearity between predictors, the baseline predictor set was further limited by conducting stepwise selection of variables with backward elimination and using the adjusted R<sup>2</sup> as the information criteria.

Controls for a quadratic of age, municipality fixed effects and an indicator for male child were fixed in the regressions. At each step, the iterative procedure removes from the model the predictor that most improves the information criterion until there is no variable whose removal improves it. We implemented this using ‘vselect’ in Stata (Lindsey and Sheather 2010). Fourth, using the final model for each subsample, we predicted for each observation the probability of having been re-interviewed and constructed the IPW. Sample weights ranged from 0.78 to 5.56
In the surveys, two variables were used to measure cargo dwell time: (a) the average dwell time measured in days and (b) the distribution of dwell time, by length of time: 0–5 days, 6–10 days, 11–20 days, 21–40 days, 41–70 days, and more than 70 days.<sup>6</sup> The average cargo dwell time (measured in days) by country, weighted by the number of imported containers, is shown in Figure 10. South Africa has the shortest dwell time, as expected, close to what is identified as ―normal‖ in Europe or Asia. Nigeria has the longest. The average dwell time of the total five countries sample is around eight days.

These figures should be viewed with caution, because they are not necessarily representative and are less reliable than customs data. Data collected in firm surveys should only be used as a complement to customs data on dwell time.

**Figure 10: Cargo Dwell Times in Select African Countries, Weighted by the Number of** **<u>Imported Containers, 2011</u>**

<!-- Start of picture text --> 16<br>14.11<br>14<br>12<br>10 8.71<br>8.12<br>7.41 7.64<br>8<br>6<br>3.93<br>4<br>2<br>0<br>Kenya Nigeria South Africa Uganda Zambia Total<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

It is also interesting to analyze the distribution of dwell time by country (Figure 11). In South Africa 93 percent of imported containers have a dwell time between 0 and five days, which is expected, since the average is very low (3.93 days
correlations using data on Chinese start-ups. Hu et al. (2019) also find that relationship-oriented personal initiative increased entrepreneurial intentions. According to Frese (1997), although not identical, personal initiative and entrepreneurship are related and both imply the use of constructive, creative and active strategies to overcome challenges in cases where they occur. This suggests an empirical connection between personal initiative and entrepreneurial performance. Indeed, recent work by Amankwah et al. (2024) highlights the important role played by both gender role attitudes and personality traits in shaping individuals' decisions on labor market participation and employment choices. Crucially, the authors establish that the effects of personality traits tend to be mitigated or even reversed in the presence of greater traditionalism.

# **3. Data and descriptive statistics**

# **3.1. Data**

Data from the 2022 Ghana Informal Sector Measurement Study (GISMS) was used for the analyses in this paper. The GISMS is a multi-topic household survey with detailed information on household ownership of non-farm enterprises and other household, individual (including personality traits and gender role attitudes), and geographic characteristics. The GISMS adopted a list-based, multi-stage sampling approach to select respondents from two regions in Ghana – Ashanti and Northern. In the Ashanti region, the Kumasi Metropolitan Assembly and six surrounding municipalities were purposively selected, while in the Northern region, the Tamale Metropolitan Assembly and all districts that are within 40 kilometers from the center of Tamale were also purposively selected. Following the purposive selection of the districts, municipalities and metropolitan areas, 67 Enumeration Areas (EAs) were selected at random (using probability proportional to size (PPS)) in each region in the first stage. A full household 7
-----`|<br>`67`<br>`-----------`|<br>`66`<br>`-----------------`|<br>`39`<br>`--------------------`|

``` Notes: This table presents five regressions, where each column reports the findings from an instrumental variables regression.
``` ``` The dependent variables, measured in 1999, are:Private Monitoring Index measures the degree to which regulatory policies encourage and facilitate private sector monitoring of banks; Official Supervisory Power Index measures the extent to which official supervisory agencies have the authority to take specific actions to prevent and correct problems; Entry Restrictions Index measures the number of specific legal requirements for obtaining a banking license; and Restrictions on Activities Index measures regulatory restriction on the degree to which banks can engage in securities, insurance, and real estate activities plus restrictions on banks owning and controlling nonfinancial firms. Government Owned Banks equals the percentage of the banking system’s assets held by banks that are 50% or more government owned.
``` ``` The explanatory variable of focus is Voice & Accountability, which measures the degree to which average citizens have voice in the political process and the degree of accountability on the part of politicians (Kaufmann, Kraay, and Zoido-Lobaton, 1999).
``` ``` The unreported, though included in the second-stage instrumental variables, arethe legal origin of each country (that is, we include three dummy variables for Common, French civil, and German civil law countries respectively).
``` ``` The excluded instrumental variables are the latitude of the capital, the percentage of years since 1776 that the country has been independent, the fraction of the population that is
toward poverty eradication in the EU, an absolute measure is recommended to track changes over time against a fixed benchmark.

# **Furthermore, distinguishing between transient and persistent poverty is crucial, as it allows**

**for a policy focus on preventing long-lasting poverty.** Though cross-sectional analysis of poverty is extremely important, moving beyond isolated snapshots of monetary deprivation in a single period is critical. In 2023, persistent at-risk-of-poverty rates<sup>6</sup> varied significantly across the EU. Croatia, Bulgaria, Estonia, and Romania reported rates between 14% and 20%, highlighting entrenched challenges, while Hungary and Czechia had rates below 4.0%. The persistence of poverty is a complex issue that requires a multifaceted approach.

**Finally, there are challenges to properly identifying some at-risk-of-poverty groups in the EU and designing policies to properly address poverty and social exclusion, particularly ethnic minority groups such as the Roma.** While official household surveys in European countries show that several identifiable groups face higher rates of poverty, chronic poverty, or at-risk poverty and social exclusion **,** in most EU countries, there are no official measures of poverty among the Roma population, as they cannot be accurately identified in household surveys. Ethnicity is not broadly collected, and if included in the survey instruments, they tend to be undercounted in censuses and under-sampled in household surveys, so poverty cannot be reliably measured (Annex 1).<sup>7</sup> Official data from countries with available ethnic identifiers, such as Bulgaria, indicates extremely high monetary and multidimensional poverty rates among this group. However, comprehensive estimates for this group are not universally available across all EU countries. The absence of data on poverty within this group may result in a lack of tailored policy responses, as policymakers may not be aware of the extent of their poverty, whether they are persistently poor or move in and out of poverty frequently, or the overlapping deprivations they face.

**Unofficial
businesses and left the consumer credit system in great difficulty. The remaining 35 finance companies and 15 commercial banks were required to make higher provisions, and their percentage of non-performing loans are expected to increase as the economic downturn deepens.<sup>16</sup> EVOLUTION OF INTERBANK RATE AND INFLATION

**16**

Based on the latest available data from the Central Bank, the non-performing loans of the banking system represented 8.3% of total loans as of June 1997. By December they were estimated to have reached 18% and are expected to peak 25-30% in the middle of 1998. In May 1998, a further 13 finance companies were closed.

32
# **Figure 1 Living Arrangements of China’s Rural Elderly**

<!-- Start of picture text --> .8<br>.6<br>.4<br>.2<br>0<br>1990 1995 2000 2005<br>Year<br>Live with Adult Child (and Others) Live with Spouse (or Others)<br>Live Alone<br>Share of Elderly<br><!-- End of picture text --> Source: China Health and Nutrition Survey (CHNS).

28
Both the welfare ratio WR defined in equation 15, and the money metric utility MMU defined in equation 10, satisfy the properties identified at the beginning of this section, and therefore represent strong candidates for proxying the living standards based on household budget survey data.

The common structure of the two measures facilitates their interpretation and practical implementation: they are both defined as the ratio between nominal total household expenditure to a true-cost-of-living index. They can both be approximated by replacing TCLI with a price index, a Paasche index in the case of MMU, a Laspeyres index in the case of the WR. The choice of the index (whether Paasche or Fisher) implies a different choice of the underlying price measure. In this sense, the question “which index is the best choice” is not a good question: No ‘best’ index exists. By choosing the price index, the analyst chooses the welfare measure: Paasche implies a MMU, Laspeyres implies a WR, Fisher, Törnquist and other indexes imply different welfare measures.

The analysis reviewed in this section hopefully helps to clarify the recommendation put forward in Deaton and Zaidi (2002) and confirmed by Mancini and Vecchi (2022), and sets the stage of the analysis carried out in the next section. The aim is not to contribute to the debate on the best welfare measure (MMU versus WR), but rather to focus on different price adjustment procedures, SI versus MI, _taking as given that the appropriate welfare concept has been chosen_ .

# **4.2 Can the SI and MI methods be ranked?**

Section 4.1 discusses two welfare concepts, _MMU_ and _WR_ , which we use here as benchmarks for assessing whether the practice of using one deflator (SI) is preferable than the practice of using two deflators (MI). Consider first the MMU approach. According to equation 9, the money-metric utility function for household _h_ , MMU<sup>h</sup> , can be written as the ratio between household _h_
of fem_|_ale leaders_|||||||
|Fraction of female leaders||||0.908***|0.902***|0.900***|0.885***|1.018***|
|in close elections||||(0.096)|(0.099)|(0.098)|(0.098)|(0.094)|
|Observations||||65,292|65,292|62,122|43,981|18,141|
|Outcome mean||||0.087|0.087|0.089|0.089|0.089|
|First stage _F_-stat||||89.238|83.740|83.818|81.159|116.175|
|State fxed efects|x|x|x|x|x|x|x|x|
|District characteristics||x|x||x|x|x|x|
|Individual characteristics|||x|||x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. Columns (1)–(6) show estimates for the full sample of women, while columns (7) and (8) restrict the sample to those living in rural and urban areas, respectively. Columns (1)–(3) present OLS estimates and columns (4)–(8) present 2SLS estimates as specified in equations 1 and 2. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion,
results show that well-being has an effect on political actions taken by individuals and hence underscore the importance of assessing the effect of policies on subjective well-being. Second, using subjective well-being data we find evidence for a grievance-based model to nonviolent uprisings. This contradicts previous conclusions that there are no systematic models of civil resistance; our model can help not only explain, but also predict uprisings in the future. Third, whereas many analyses of the causes of conflict focus on factors that change little over time (e.g. resources, ethnic fractionalization, institutions, geography), the level of subjective wellbeing can change relatively quickly, which allows us to exploit short-term dynamics in conflict. Hence, we exploit the within-country variation in uprisings rather than using a cross-sectional design. Accordingly, our results shed light not only on _whether_ conflicts occur, but also _when._ This study is in part limited by data availability. Our dependent variable represents the number of peaceful protests and does not capture the intensity of these events. As a robustness check, we redo our analysis with an alternative data source only capturing major uprisings, which confirms our results. However, future research could consider the relationship between subjective well-being and the intensity of non-violent uprisings. The non-violent and violent campaigns and outcomes (NAVCO) data set provides information on the number of protestors in these events, but it currently covers fewer than 30 countries and good estimates of the number of participants are often lacking.

Future research could analyze how the effect of subjective well-being on both nonviolent and violent rebellion depends on contextual factors not covered in this paper. A decrease in subjective well-being might be a necessary condition for conflict to occur; however, it is unlikely to be a sufficient one, as the decision to engage in rebellion depends on the benefits and the costs of doing so (Klandermans, 1996). Although this study finds no evidence that political opportunities or the ease of mobilization affect the relationship between subjective well-being and nonviolent uprisings, there might be other factors that influence the costs
of rural population, population growth rate, overall literacy rate and female literacy rate interacted with year indicators. We also control for a state-specific time trend to control for state specific time-varying unobserved heterogeneity, such as discretionary state-level education funding. We allow for a differential trend for government and private schools over time by interacting school type with year indicators.

Our identifying assumption is that the outcomes in districts that received the program in different phases are not trending differentially prior to treatment after controlling for trending program criteria. We test this assumption on a sub-sample of states for which we have pre-treatment data. We show that growth in school enrollment in districts that received the program in different phases is similar prior to the program. We also show that the within-school results are invariant to including changes in enrollment from 2003 to 2005. We do not have data from 2003-2005 for 10 small states and union territories. We verify that excluding these 10 states in our empirical analysis does not influence the results to rule out selection into the sample.

# **4.2 Estimation Procedure**

We use school level data from 1.13 million schools from 2005 to 2008 to test our hypotheses. Our empirical specification is as follows: _Yidst_ = _α_ 0+ _α_ 1 _MGNREGAdt_ + _α_ 2 _Xidst_ + _α_ 3 _Zds∗Tt_ + _α_ 4 _States∗trend_ + _Tt_ + _Iids_ + _εidst_ (1) where _Yidst_ is the outcome variable for school _i_ in district _d_ in state _s_ in year _t_ . _MGNREGAdt_ is an indicator that takes value 1 if district _d_ in state _s_ has started the MGNREGA program in year _t_ , and 0 otherwise; _Xidst_ is a vector of school level controls including different kinds of incentives received by the students, and the characteristics of the teachers and infrastructure of the school _i_ in district _d_ in state _s_ in year _t_ ; _Zds_ is a vector of district-level controls for demographic characteristics, and these are interacted with year indicators 10
|--|
|.uxembourg|Mar 81|365 J|UNPVSR<br>7/86 (official<br>est.)|Eurostat 1987|Eurostat 1987|
|1acao|Mar 81|242 J|UNPVSR<br>7/86 (official<br>est.)|Based on USBOC<br>1985|Based an USBOC<br>1985|
|Madagascar|74-75|7604 F|Bank projection from census|Bank assessment of|Bank assessment of|
||||and 1960 budget survey|1980 survey|1980 and 1984 surveys|
|Malail|Sep 87|793 F|Bank projection from census.<br>after excluding refugees|Based on 1977 census<br>Analytical<br>Report|U.N. 1988 revision (prelim.)|
|Malaysia|Jun 80|13183 F|Official<br>est.|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Maldives|Mar 85|181 F|U.N. 1988 revision (prelim.)|Bank est.|Bank est.|
|li|Dec 76|63f5 J|BankDroiection<br>from census|Bank assessment of '960-61<br>survey and age data from<br>1976 census|Bank assessment of 1960-61<br>survey and age data from<br>1976 census|
|Lta|Nov 67|316 F|Bank projection from<br>official<br>1982 est.|Based on UNPVSR<br>4/87|Based on UNPVSR<br>4/87|
|rtiniqje|Mar82|327 J|UNPVSR<br>7/86 (official<br>est.)|1980-85, U.N. 1988 revision<br>(prelim.);<br>1985-90, Bank<br>projection|1980-85, U.N. 1988 revision<
**MAGNITUDE AND DYNAMICS**

Measuring levels of violence against women poses many challenges. Estimates vary depending upon how researchers define violence, the questions they ask, the timeframes they explore, and the sample characteristics. Heterogeneous approaches often make it difficult to compare research findings. Another challenge is that surveys are best at measuring discrete incidents of physical violence; rather than the pattern of controlling, violent behavior often referred to as “battering”, which is typically characterized by multiple forms of abuse--physical, emotional, sexual, etc. (Ellsberg et al., 2001a). Such violence often escalates with time and involves a high level of fear among women for their safety and lives.

Sexual violence poses its own definition and measurement challenges. For one thing, sexual violence encompasses a large range of manifestations (from verbal harassment, to unwanted touch, molestation, assault and penetration), settings, and perpetrators. Jewkes (2002) developed a framework for understanding different types of sexual violence against women, ranging from the most visible, namely fatal sexual assault and rape reported to the police, to the most common form and least visible forms of sexual violence that occur within marriage, dating relationships and families. Theoretical and methodological approaches to research on sexual abuse/violence are even more diverse than those focused on intimate partner violence. Typically, researchers use different study designs to measure levels and patterns of child sexual abuse, forced sexual initiation during adolescence, rape among the general population of women, forced sex within marriage, and sexual harassment / abuse within schools.

A serious challenge for all types of research on the prevalence of gender-based violence is that most estimates probably reflect a certain degree of under-reporting. Women are often reluctant to disclose experiences of physical or sexual violence due to shame or fear of reprisals (Koss, 1993). Underreporting appears to be particularly problematic in large-scale surveys designed primarily for other purposes, such 9
Df|47827|Population Size|50604002|
|F-Stat.|255.32|P>F|0.0000|||

_Notes_ : FE = formal employed, IE = informal employed, U = unemployed, and OLF = out of the labor force. Control variables: age, gender, marital status, education, number of children, home ownership, family status (extended family in household), and spatial difference (living in urban or rural area). Coefficients are significant at 1 percent (***), 5 percent (**), 10 percent (*). Avg. X: the average values of explanatory variables. Due to the nonlinear nature of the multinomial logit model, the effects of explanatory variables on dependent variables vary along the explanatory variable vector X. Therefore, we estimated the effect coefficients around the sample average of X.

The initial static analysis indicated that social assistance beneficiaries are less likely to be formally employed and more likely to be informally employed or unemployed. There was no evidence that they are more likely to be out of the labor force altogether, whether or not being in the labor force is correlated with beneficiary status (aggregate) (see table 1). However, disaggregation with respect to benefit types revealed that child assistance beneficiaries are more likely to be out of the labor force; they also have a lower probability of working as formal employed and a higher probability of working informally (see table 2).

We then used all the transitions from the SILC panels over the period 2006–12 (as described in section 4) in order to assess the transition behavior of individuals and how this relates to their social assistance status. In this analysis, we aggregated all social assistance programs delivered by the government. Our main finding from the transition analysis is that individuals are more likely to transition into informal employment if they are recipients of social assistance, no matter what their initial status is.

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