rafmacalaba/data-use-mentions-extended huggingface dataset:
of the results for time use—namely, women in the control group allocate 67 percent of daily time to household tasks and to caring for other household members. Working time makes up, on average, only 8 percent of women’s daily time, which is consistent with their low level of employment.

According to the midline survey results, women significantly reduce the time they spent on chores when offered public works. They do not replace the time they spent on chores completely with the time they spent doing public works, which indicates that they perform their chores during a modest “second shift.” Women who participate in public works increase the time they spend on paid work, which was approximately 3 hours (the previous day). This is consistent with the daily hours of the program (4-5 hours, with breaks) and when factoring in both the reduction in self-employment activities and the program take-up by 77 percent of women among the 92 percent households reported in Table 4. The increase in time employed is also marginally offset by a reduction in personal care, which includes sleep, and substantially offset by a reduction in time spent on chores.

The public works program has no effects on the time husbands spent on household chores (see Table 5, Panel 2), but it does produce a modest increase in the time men dedicated to work. This finding is consistent with the 6 percent take-up of public works among husbands, 20
Ravallion (2022) argues that for some research on the top of the distribution, survey data will be adequate. The evidence we have discussed above suggests that in South Africa and Côte d’Ivoire, a divergence between survey and tax data starts quite far down the distribution and thus that tax data are required to accurately measure the top tail. Further work comparing tax (both new and already existing) and survey data will shed light on the extent to which surveys can be reliably used to measure top incomes.

Making use of other types of administrative data may also be useful. Further work could use house price data and improve the methods of Van der Weide et al. (2018). This is likely to be available only in urban areas in low-income countries, and many middle-income countries as well. House price data could be complemented with land record data for rural areas, where this exists. The extent to which the top tail is comprised of those who live in rural areas in low-income countries could then be investigated more comprehensively, a topic that there has not been much research on.

A second useful part of a research agenda using new data would link survey and tax microdata at the individual level, to provide further insight into the reasons for differences across tax and survey data, specifically the role of unit non-response, measurement error and under-reporting. We are only aware of one study in a LMIC that uses linked survey and tax data – Flachaire et al. (2021) for Uruguay. The links were only available for couples with children aged 0-3 so the extent to which this generalizes to the adult population in Uruguay is unclear. Further research would thus be extremely useful.

A third part of the research agenda would be to undertake future household surveys asking about incomes or wealth that oversample high-income or high-wealth households or areas. Oversampling rich households using tax or other forms of administrative data is not common, even in high income countries, probably because it requires coordination between various public agencies (for example national statistical
the fuel vs. food demand for agricultural commodities, land reallocation, and land rent. To assess the distributional and poverty impacts, we feed the CGE results to a Global Income Distribution Dynamics (GIDD) model, a model that is based on detailed household surveys, regularly carried out by the World Bank. We find that large-scale expansion of biofuels would improve wages for unskilled labor and land rent. However, it would also increase food prices, especially in low-income countries where food share in overall household expenditure is higher. The effects on poverty are negative (i.e., increase in poverty) in some countries, while positive in others. Globally, these differences balanced out to a slight increase in poverty.

3
taged women in these countries.

# **4.3 Decomposing the Childcare Law Effect**

To this point, we have presented the positive effect of childcare enactment on FLFP rate. However, what specific element of the childcare attributes regulated in the law is driving this positive impact? Existing evidence documenting the positive impact of childcare availability and affordability on women’s labor market outcomes is compelling and broad for both higher and lower-income countries. Moreover, although the literature highlights childcare as a factor that determines decision to use childcare ser- quality key parental vices, this has not been rigorously evaluated yet.

Exploiting the information on childcare availability, affordability, and quality addressed by the childcare laws in the WBL dataset, we assess the contribution of each of these three pillars to women’s labor market outcomes. Specifically, we create standardized scores for each of the pillars and replace _Postct_ with these scores one by one in specification (1), which we estimate on the stacked dataset by law enactment cohort.

The results on the impacts of each childcare legislation pillar on FLFP rate are presented in Table 3. Columns (1) to (3) present the results including all years; columns (4) to (6) exclude the year of law enactment, and columns (7) to (9) exclude the year of enactment and the year after. Consistent with previous literature, the effect of the regulation of childcare availability on FLFP rate is positive and statistically significant across different samples, ranging from 0.53 pp (when we include all years) to 0.86 pp (when we exclude the year of law enactment and one year after). The estimated effects of regulating child19
.4<br>Share of new hires (<12 months) per occupation<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>Share of income-risk jobs per occupation<br>.1<br>0<br><!-- End of picture text --> Source: EU-LFS (2018).

Note: Correlation based on the 136 ISCO-08 3-digit occupations that represent more than 0.1% of the total employment in the EU. The size of the circles represents the share of each occupation in the total EU employment.

39
opportunities and motivation for outward exposure, thus limiting trust in strangers (Ermish and Gambetta 2008). Family ties would thus display a positive relationship with generalized trust in the former case, and negative in the latter case.

The paper by Alesina and Giuliano (2009) shows that the relationship between family ties and generalised trust is negative when the strength of family ties is measured on reported scores for the ‘importance of the family in an individual’s life’, the ‘duties and responsibilities of parents and children’ and the ‘love and respect for ones parents’. This result is based on the WVS and has been confirmed by experimental data by Ermish and Gambetta (2008), who add that the negative causality runs from family ties to generalised trust.

Another aspect of the distinction between bonding and bridging social capital which has been investigated is the relationship between _religiosity and generalised trust_ . Guiso et al. (2003), on the basis of the WVS, find positive and significant correlations between generalised trust and some measures of religiosity, i.e. ‘raised religiously at home’, ‘attending religious services’, and ‘believing in God’. This result is confirmed by Helliwell (2008). However, religious denomination matters significantly (Glaeser et al. 2000; La Porta et al. 1997). For example, Guiso et al. (2010) show that the proportion of Catholics in the population is negatively related with trust in strangers. Regarding economic growth, Barro and McCleary (2003) show that this seems to respond positively to the extent of religious beliefs, but negatively to church attendance.

Generalised trust is clearly included in bridging social capital, but the survey question normally used to measure generalised trust in many studies has raised some problems in this regard. The question, which also appears in the WVS, is “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” The answer could
workers is similar to that of native workers, although wage levels are much lower. In addition, informality is associated to lower wages and, in turn, informality is strongly influenced by legal status.

# **5 Occupational Analysis**

## **5.1 Occupations in Ecuador**

Education levels may lack comparability across Ecuador and Venezuela, and this could distort the wage gaps by education level reported in the previous section (as discussed in footnote 13). For instance, if tertiary education has deteriorated in Venezuela to the point that the quality of a college education is substantially lower than in Ecuador, the large migrant-native wage gaps for college graduates reported earlier would not reflect productivity gaps and, hence, a waste of productive skills.

To address this concern we use data on workers’ occupations in Ecuador. More specifically, we rely on the survey question that informs us about the type of employer and classifies workers into the following categories: private-sector employee, self-employed, government employee, day laborer ( _jornalero or peón_ ), manager, domestic employee and outsourced employees ( _empleado terciarizado_ ).<sup>16</sup> The results are collected in Table 8. The top panel reports the occupational distribution for Ecuadoran natives. According to the data, an estimated 2.7 million individuals are private-sector employees and a similar number are self-employed. The highest average hourly wage is $5.59 for government employees, followed by $3.67 for managers. The middle panel reports the analogous information but in reference to Venezuelan migrants. We estimate that about 102,000 Venezuelans in Ecuador are private-sector employees, about 66,000 are self-employed and close to 14,000 are day laborers.

The bottom panel compares the within-occupation characteristics of Venezuelan migrants to native workers by computing ratios for all variables included in the table. The main takeaways are the following. Relative to their population density, Venezuelan migrants are over-represented in three occupations: outsourced workers, domestic employees and private-sector employees. Except for private-sector employment
, information that was collected from treatment group farmers immediately after they received the corrective information of the GPS plot size measure. Behavioral adjustments could cause real adjustment in later season input application rates in response to corrective plot size information, hence our focus on input application that would have all occurred prior to the pre-treatment survey data collection. We focus on labor and fertilizer because they are the most widely used non-land

> 19 Other types of measurement error models may also generate similar spillover effects (e.g., <mark>Schennacha, 2020).</mark> 20 Similarly, measuring labor application in a specific plot can be difficult because of the (staggered) nature of farming activities. In this case, farmers may use the size of the plot to get a sense of the labor needed for some of these farm activities (e.g., land preparation, weeding and harvest).

> 21 An alternative mechanism could be that information that reveals to a respondent that he mis-estimated plot size may cause him to adjust all self-reports, regardless of whether they relate to plot size or not. Because the plot size information experiment and subsequent questions about input application on the plot were the last module of the postplanting questionnaire, we unfortunately have no variables clearly unrelated to plot size on which to test this alternative hypothesis about the mechanism behind the information spillover effect.

24
# **Supplementary Appendix References**

- 1 World Bank. World Development Indicators. 2019. https://databank.worldbank.org/source/world-development-indicators# (accessed 3 Mar 2020).

- 2 Global Burden of Disease 2017 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 - ScienceDirect. _Lancet_ 2018; **392** :1859–922.

- 3 Global Burden of Disease 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 - ScienceDirect. _Lancet_ 2018; **392** :1789–858.

- 4 Leonard KL, Masatu MC. The use of direct clinician observation and vignettes for health services quality evaluation in developing countries. _Social Science & Medicine_ 2005; **61** :1944–51. doi:10.1016/j.socscimed.2005.03.043

- 5 Bold T, Svensson J, Gauthier B, _et al._ Service delivery indicators: Pilot in education and health care in Africa. Bergen, Norway: : Chr. Michelsen Institute 2011.

- 6 World Health Organization U. _Model IMCI handbook: Integrated management of childhood illness_ . 2005.

- https://www.who.int/maternal_child_adolescent/documents/9241546441/en/

- 7 Peabody JW, Luck J, Glassman P, _et al._ Comparison of Vignettes, Standardized Patients, and Chart Abstraction: A Prospective Validation Study of 3 Methods for Measuring Quality. _JAMA_ 2000; **283** :1715–22. doi:10.1001/jama.283.13.1715

- 8 Mohanan M
**Table 4. Average time (hours per week) spent in homecare activities by economically active children and adolescents**

|**1985**||
|---|---|
|Children<br>(7-14 years)|Adolescents<br>(15-18 years)|
|Very poor<br>12.8|17.6|
|<br>Mid-poor<br>12.3|15.1|
|<br>Non-poor<br>11.8|14.9|
|Abidjan<br>8.2|7.6|
|<br>Other Cities<br>12.7|12.2|
|Rural Areas<br>12.2|16.6|
|All<br>12.2|15.4|
|1988<br>=||
|Very poor<br>11.0|12.7|
|<br>Mid-poor<br>11.9|14.6|
|<br>Non-poor<br>13.8|13.3|
|Abidjan<br>21.0|14.1|
|<br>Other Cities<br>10.5|10.7|
|Rural Areas<br>12.2|14.1|
|All<br>12.1|13.6|

# **3. Child Labor and Schooling in 1988**

The previous section highlighted the importance of child labor for Ivorian households in absorbing the shock of falling incomes during the recession of the **1980s.** As 1988 is the last year for which the detailed data of the CILSS exist, we investigate the determinants of child labor in more detail for that year. In this section, we do so by means of tabulations, focusing on the interplay between work and schooling. The next section will consist of a multivariate analysis.

15
**||||||
|No Education|0.06|0.17|0.04|0.07|0.05|
|Primary|0.10|0.17|0.07|0.11|0.08|
|<br>Secondary|0.54|0.47|0.55|0.54|0.50|
|<br>Higher|0.29|0.17|0.32|0.26|0.36|
|**Outcome variable**||||||
|Currently working|0.15|0.12|15.72|0.14|0.15|
|**N**<br>Source: Authors Calculations - De|52070<br>mographic a|8695<br>nd Health Survey|43375<br>(DHS): Jordan (1990|6088<br>, 1997, 2002, 2007,|4719<br>2009, 2012, 2017)|

<mark>Source: Authors Calculations - Demographic and Health Survey (DHS): Jordan (1990, 1997, 2002, 2007, 2009, 2012, 2017) Note: This table displays full and working sample means for several variables both pre and post Iftida’ divorce reform in</mark> Jordan. For the first three columns, we present full sample means pre and post reform. The remaining columns present working sample means for treatment and control groups. While the table shows means values for treatment and control groups post "treatment", the empirical analysis employs a quasi-experimental framework that takes into consideration values for pre and post reform.

# **5. Effect of Unilateral Divorce Legislation on Women’s Employment**

In this section, we present evidence on the complex relationship between legislative reforms and women’s employment in MENA countries. Tables 5, 6 and 7 present regression results over two
With respect to export delays, only one of the eight African countries performs better than the Philippines and only three perform better than China. Even this is likely to underestimate the relatively poor performance of customs administration in the African countries since the point of exit for most exports from China and the Philippines will be a port and, therefore, the delays will include port delays.<sup>9</sup> In contrast, several of the African countries (including Zambia and Uganda the two best performers) are landlocked—meaning all exports will clear customs at a land border.<sup>10</sup> Further, as noted above, for most of the African countries neighboring countries are the most important export partners for the firms in the Investment Climate Surveys.

Although customs delays reflect one aspect of the burden associated with trade and customs regulations, they are not the only aspect of trade regulation that is burdensome and costly for enterprises involved in exporting. Further, as noted above, trade regulations that make importing more difficult might also discourage exporting. If trade regulations make it more difficult for exporters to get imported capital and intermediate inputs—and hence to meet international standards in these respects—restrictions on imports will also discourage exporting.

Most firms that use imported goods use clearing agents to help arrange for customs clearance. Over 85 percent of enterprises that used imported inputs reported that they used clearing agents to help with these procedures (see Table 6). The median cost of hiring a clearing agent in Zambia—the only country where the survey asked about the cost—was about 1 percent of the shipment value.

In addition, many companies reported that they had had to obtain (at least one) import license within the two years prior to the survey (see Table 6). In most countries, the average wait to get a license was about a week and in some countries it was close to two weeks. About 10

> 9 In contrast to the African countries, the most important markets
spillovers from the commercial farms included here to those in flowers or high value vegetables and to those from investments higher up in the agro-processing value chain; and (iii) analyzing effects of large farm establishment on input and output prices as an alternative indicator that could also clarify channels through which some of these effects are transmitted.

The paper is structured as follows: Section two motivates by discussing large agricultural investment in Ethiopia and presenting our methodology. Section three introduces the data and provides evidence on differences in performance between large and small farms, changes in smallholder productivity over time, and changes in spatial proximity between small and large farms over time due to new farm establishment. Section four presents results with respect to input use and yields, labor market participation, and resilience to climatic shocks. Section five concludes by summarizing implications for policy and future research.

# **2. Motivation and methodological considerations**

There is little doubt that growth of smallholder agriculture is critical for poverty reduction in Ethiopia. Yet, to fully exploit its land endowments and generate spillovers for smallholders, the country also aimed to attract agricultural investors, based on the premise that doing so could help local smallholders. To test for spillovers from such investment, we argue that physical proximity, measured by either the distance between smallholders and the next large farm or total large farm area cultivated in smallholders’ vicinity, can be used as a proxy. Data from 11 years of a national smallholder survey as well as the universe of large farms and their evolution over time can provide a basis for doing so in Ethiopia.

# **2.1 Large-scale land investment in Ethiopia**

Ethiopia is one of the poorest countries in Africa and the country’s highlands are among the most densely populated regions in Africa. Land constraints are a key determinant of poverty (Jayne _et al._ 2014). After 1990, a strategy of market liberalization and agriculture-led industrialization focusing on small-scale producers was adopted. In the past, the country regularly relied on food aid to
# **2. Analytical Approach and Data**

**This paper presents new global evidence on the key determinants of** public-private partnership ( **PPP) investment in electricity generated by renewable energy** (including hydro, wind, waste and geothermal) based on a panel data analysis  for 105 developing countries over a 16 years period from 1993 to 2008.

**The paper aims at determining the evidence of the effectiveness of sectoral support mechanisms such as feed-in tariffs for attracting private investment in developing countries.** We divide the determinants of PPPs into three channels. These include (sectoral and economy-wide) governance variables and short and long run drivers, represented respectively by financial crises and climate change variables. We also control for macroeconomic, sectoral and regional variables.

**Our study aims to identify the key factors affecting the private investor’s decision to enter the renewable energy market and the amount of investment sunk in this market segment.**

**The analysis of the key determinants of the private investor’s decision to enter is done using the probit model** , where the dependent variable is a dummy equal to 1 if PPPs were introduced in the renewable energy market and 0 if not.

**The treatment of the modeling of the key factors affecting the private investor’s amount of investment sunk in the renewable energy market is based on Heckman’s approach to sample selection** . This distinguishes between (1) the decision on whether to enter the segment of the electricity market (selection equation), and (2) the decision on how much investment to commit to (investment equation).<sup>2</sup> Unlike the Tobit model, the factors that affect the two decisions need not be identical and, where identical, could even be different in the sign of their effect on the decisions. The selection equation relates the choice of whether to attract PPPs (via a zero/one dummy variable) as a function of the governance