rafmacalaba/data-use-mentions-extended huggingface dataset:
068***|-0.257***|
||(0.003)|(0.072)|(0.017)|(0.014)|
|Rural|0.012***|0.120**|-0.034**|-0.014|
||(0.002)|(0.061)|(0.016)|(0.011)|
|Oil prices|0.014***<br>(0.000)||||
|Diplomatic exchange||-0.431***<br>(0.066)|||
|Confict|||0.118***<br>(0.020)||
|Illegal status||||-0.337***<br>(0.046)|
|Legal status||||0.129***<br>(0.040)|
|Observations|28,982|3,927|3,920|16,323|
|Year fxed efect|Yes|Yes|Yes|Yes|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=1.|911; Illegal mig|.=1.445; Legal|mig.=2.040||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : We rely on pooled cross-sectional data from the 2012 and 2018 ELMPS. Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return
could also be problematic when the literacy level of respondents is low. Yet, many researchers consider the diary to be the ‘gold-standard’ for collecting time use data.

Another method for collecting time use data in large-scale surveys is the _stylized questions_ approach where respondents are asked to recall the amount of time they allocate or have allocated to a certain activity over a specific period. Stylized questions are believed to be the least expensive way to measure time use, mainly because they require fewer questions and less effort to implement than time diaries. However, evidence suggests that data collected with this method might have a degree of error. For instance, stylized questions can understate the time women spend on childcare because people may classify it as work or because it is reported only when it is done as a main activity (Craig, 2002). Respondents may also have a difficult time recalling what they did over the time period that the question references and they might overreport activities that are perceived as socially “good” or acceptable. Another drawback to stylized questions is that questions must be worded so that respondents understand the types of activities for which they are to report. Getting contextual information under this method, such as where and with whom the person was during each activity, can impact negatively on the response rate. This implies that activities need to be classified within the question.

Regardless of the instrument, self-reporting the amount of time allocated to the different activities is far from being objective. For example, the various types of activities that constitute housework are variable. “Cooking” can refer to anything from micro-waving food to a large dinner in which everything is made from scratch. “Doing the laundry” might or might not include putting the clothes away. At the same time, most time use studies focus only on primary activities, ignoring the fact that individuals often perform two or more tasks at the same time. This implies that the time allocated to activities such as childcare and housework which are also performed as secondary activities is underestimated
1989-2018 US Census imports data and showing that the low elasticity further decreases over time. To this goal, we first estimate the sea-distance elasticity of ad-valorem and unit-cost maritime transport costs _τodkt_ at the HS6 level _k_ : where subscripts _o_ , _d_ , _k_ , _t_ denote country of origin, destination port, product and year, respectively; _Y_ , ( _LANG_ ), ( _FTA_ ) are time-varying gravity equation variables from the “Dynamic Gravity Dataset" denoting GDP, common language, and free trade agreement, respectively; ( _SeaDist_ ) is the CERDI country level bilateral sea distance;<sup>17</sup> _WgtIModktodkt_<sup>istheratioofimportcharges(CIF)toimportweight</sup> measured in kilograms; and _θt_ and _νk_ are the year and HS6 product fixed effects, respectively. To explore how it varies over time, we then added the distance-year interaction terms, ln( _SeaDistod_ ) _× θt_ . The Chow-Test confirmed that it is time-variant since the interaction terms significantly improve the model. The full set of estimates is reported in Appendix Table 6.

Figure 9 illustrates the results with the year-specific elasticities. The left panel demonstrates that the average decline in the distance elasticity of ad-valorem transportation cost is approximately _−_ 0.003 per year or _−_ 0.09 over the entire thirty-year interval. The downward trend was even more pronounced for the distance elasticity of the per-unit transportation cost. As demonstrated by the right panel, this elasticity fell, on average, by _−_ 0.007 per year or _−_ 0.21 over the same three decades. To the best of our knowledge, the decreasing trends in these elasticities and the negative distance elasticity of the transportation costs have not been documented before. This trend is paralleled by the increasing share of low-income and remote countries in global trade. Since low-income and remote countries specialize in lighter goods (Lashkaripour, 2020), their growing share may cause the distance elasticity to decline. A more comprehensive explanation of the decreasing trend requires further
. After accounting for firm pricing heterogeneity, I find that over a six-year period, the difference in productivity growth between export entrants and their non exporter counterparts is about 11 percentage points. The estimates indicate that about 15 percent of the differences in productivity growth is explained by imports of intermediate inputs, while investment in R & D explains about 85 percent of this difference in productivity growth. Thus, investment in R & D is an important driver of productivity growth even in developing countries like India.

The rest of the paper is organized as follows. Section 2 describes the environment the firm operates in and the production technology of the firm. Section 3 presents the empirical production function where I discuss the measure of productivity that is used. Section 4 is on the estimation strategy employed. Section 5 describes the data. I discuss the findings in Section 6 and Section 7 concludes.

> 6National Science Board. 2014. Science and Engineering Indicators 2014. Arlington VA: National Science Foundation (NSB 14-01)

4
br>required to use<br>the data. (1 =<br>little to no effort<br>is required, 3 =<br>extensive effort is<br>required)|The submitters tell us their use case and the<br>steps they took to make the data usable<br>(example: “I have to reformat the data”).|Not<br>scored|The question is a subjective assessment. Furthermore,<br>usability depends on context and the purposes for which<br>a person wants to use the data.|

Table A8: Weights of Features of the MENA Micro Access Indicator (MENA MAI)

|**Element**|**Feature**|**Weight in overall score**|
|---|---|---|
|User experience|English translation|1/6<br>1/2|
||Micro data tab/dashboard/library|1/6|
||Survey calendar|1/6|
|Data openness|Firm data|1/14<br>1/2|
||Price data|1/14|
||Consumption data|1/14|
||Labor Force data|1/14|
||Health data|1/14|
||Population Census|1/14|
||Economic Census|1/14|

52
citizens directly connected to the government, compared to -0.24 s.d. for those with three or more degrees of separation (see Appendix Table A.23).

Appendix Table A.21 shows that electoral turnovers lead to greater alignment between bureaucrats and citizens, including those with fewer social connections to village officials. In Section 4.3, we showed that turnovers improve bureaucrats’ understanding of citizens’ priorities. When we disaggregate these effects by social distance, we find that the gains in alignment are, if anything, more pronounced for citizens with weaker ties to village officials. In turnover villages, bureaucrats are more likely to identify investment priorities that align with socially distant citizens (columns 1–2) and to receive complaints about services prioritized by these citizens (columns 3–4). Additionally, village governments are more likely to act on the priorities of these most socially distant and marginalized constituents (columns 5–6).

Finally, socially distant citizens drive the improvements in perceptions of service access and quality following a turnover (columns 7–8). While turnovers improve perceptions across all subsamples, the largest and most precisely estimated gains (at the 1% level) occur among those least connected to the administration. Consistent with village bureaucracies becoming less beholden to local elites, these results suggest greater responsiveness to the preferences of more marginalized constituents.

**Robustness Checks.** Appendix Table A.22 reports robustness checks for the results in Table 7, including removing election-year dummies, adding region fixed effects, using a third-degree polynomial in the running variable, varying the RD bandwidth, and excluding villages with Suharto-era elites. In column 1, although the smaller administrative data sample ( _N_ =378 villages that held their last election

> 13See Appendix Table A.3 and Appendix Table A.4, discussed in Section 3.3.

19
Despite these concerns, nightlights have several advantages as a data source. They are globally available, providing coverage even in regions where economic data might be scarce or unreliable. Nightlights also have a standard spatial resolution and time intervals, which allows for consistent comparisons over time and across different geographic areas. When processed and interpreted correctly, taking into account the potential limitations and biases, nightlight data can indeed serve as a useful proxy for the intensity of economic activity (Gibson et al., 2021).

2. Relationship between disaster and conflict-affected population traction over the past decade (Siddiqi, 2018), specifically focusing on co-location and causation debates associated with climate-related hazards, violent and armed conflict, and insecurity (Gemenne et al., 2014; Gleditsch, 2012). Often, previous studies have focused on the impacts of disasters on conflicts – whether they exacerbate existing conflicts, ignite new ones, or in some cases halt ongoing conflicts (Nel & Regharts, 2008; Schleussner et al., 2016; Slettebak, 2012; Ghimire et al., 2015; Nardulli et al., 2015). Due to such uncertain impacts of disasters and disaster recovery effort on conflicts, other studies explore how disaster risk reduction and recovery measures should be done differently in conflict contexts (Brzoska, 2018; Peters et al., 2019; World Bank, 2016).

attention has been given to understanding and quantifying the influences of conflicts on disaster impacts – the additional economic impacts of disasters should they take place in conflict areas and its effect on conflict-affected population – as well as the causal pathways and mechanisms behind such additional impacts. The absence of comprehensive economic data and ground truth data to validate disaster impacts, coupled with the complexity of defining conflict-affected populations are among the scientific challenges prohibiting analyzing the influence of conflicts on disaster impacts. This paper seeks to address this gap and support further quantitative analyses on the additional impact on households’ welfare and nations’ economic growth in countries experiencing these compounded crises.

# Empirical strategy

# Data

density
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 19. Liberia has done relatively well at capturing private investment in infrastructure**

# Power ICT Transport Water

Note: Average annual private sector commitments to infrastructure projects for the period 2002/07. Source: Derived from World Bank, Private Participation in Infrastructure Database, 2008.

There is also scope for integrating national infrastructure development with substantial privately funded infrastructure development by concessionaires. A number of large multinational companies are taking on the country’s numerous mineral, forestry, and agricultural concessions and will need to invest heavily in the necessary transport and energy platforms to support their operations. It is estimated that power demand from mining, natural resources, and agriculture could climb to 1,000 mega-watts by 2040, about 84 percent of it associated with mining (table 17). A recent study by the Ministry of Planning and Economic Affairs estimates that mining sector investments over the coming years could amount to around $5 billion in railways and $1 billion in power. Liberia has a policy of ensuring that such investments are done in such a way as to benefit the broader national economy, avoiding the formation of enclaves with high quality infrastructure that are insulated from and fail to benefit the broader economy. In the case of transport investments, a principle of open access to roads, railways, and ports developed by private concessionaires has been established, and this will help to ensure that these assets become integrated with the national transport network.

In the case of power, similar issues arise but their resolution is more complex due to the absence of a transmission grid to integrate private power development with the national system. In principle, both the country and concessionaires could benefit from coordinated development of larger, more cost-effective power generation options (including power imports). Capturing these benefits will require a clear national energy plan that explicitly provides for the inter-connection of Monrovia, the key power generation sites, the key mining sites, and the West Africa Power Pool. Private power development could then be
Specific Business Functions (SSBFs) are tasks that are associated with the core production or service provision activity and vary across sectors. The FAT survey in Senegal has specific sets of questionnaires for 8 sectors: i) Agriculture (Crops, Fruits, and Vegetables); ii) Agriculture (livestock); iii) Food Processing; iv) Wearing apparel; v) Retail and Wholesale; vi) Land Transportation; vii) Finance; and viii) Health. Among those, the survey was stratified for and provides a representative sample for firms in agriculture, food processing, wearing apparel, and retail.

The survey asks information on more than 300 technologies associated to almost 50 business functions. To analyze the level of technology adoption and use in a more systematic way, we convert the information for each business function into a technology index. The index, described by Cirera et al. (2020), varies between 1 and 5, where 1 stands for the most basic level of technology been used and 5 reflects the most sophisticated level been used.

9
|
|Agricultural|530,514|961|8|32.1|65.1|
|Indigenous|236,800|310|5.7|90|98.4|
|Total|3,063,474|1851|23.9|16.6|36.8|

* Notes: The figures are computed using the ECV ( _Encuesta de Condiciones de Vida_ ) databases for years 1997 and 2003. The unit of analysis is the household and the welfare measure is consumption per capita. The extreme and moderate poverty lines are equal to 533 Balboas and 953 Balboas, respectively, which correspond to the official poverty lines used by the government of Panama. Informality is defined as the employers and employees in firms with less than 6 workers that do not contribute to the social security system, non-professional self-employed, and household workers. A worker is classified as skilled when he/she completed at least one year of secondary school.

Table 2 shows that the fastest-growing population group in Panama has been nonagricultural informal workers (an increase of 20 percent), while the number of people earning their primary income from formal activities hardly changed. Since wages in informal activities are significantly below formal earnings (Table 1), it is perhaps not surprising that, as the proportion of non-farm informal population rose, the incidence of moderate poverty among non-agricultural workers increased from 15.3 percent to 20 percent during this period. Hence the period was characterized by what Ravallion, Chen and Sangraula (2007) call the _urbanization of poverty_ , with internal migration resulting in reductions in rural poverty, an increase in urban poverty, and little to no overall poverty effects.

# **_2.2. The Canal expansion_**

The expansion of the Canal appears to be a large ̳shock‘ for Panama, with the total cost of the investment project estimated to reach 5,250 million balboas (approximately 40 percent of GDP in 2003).<sup>3</sup> However, the
.93E+08|2.39E+07|1.17E+08|1.41E+08|6.56E+06|3.63E+07|4.28E+07|9768|33,667|43,435|
|Mexico|1.06E+08|1.96E+07|7.30E+07|9.26E+07|4.50E+06|1.84E+07|2.29E+07|5409|19,659|25,068|
|Peru|2.92E+07|9.96E+06|1.83E+07|2.82E+07|2.30E+06|4.42E+06|6.72E+06|8092|12,322|20,414|

_Sources:_ National population from WDI, others from authors’ calculations.

# **Guatemala (ENCOVI 2006)**

The data for this study are taken from the 2006 Living Standards Survey (ENCOVI) administered by the National Statistical Institute of Guatemala. The fieldwork for the survey was carried out between March and October. The survey did not provide information on items consumed but not purchased, except for food. The total non-purchased food was equivalent to 14
We also consider primary and secondary school enrollment rates using data from the United Nations Educational, Scientific and Cultural Organization (UNESCO) Institute for Statistics. Because education is one of the most important opportunity costs of child employment, child labor market changes are likely to be reflected in school enrollment rates. Furthermore, primary school enrollment rates in particular should reflect changes in under 14 employment without potential misreporting due to cultural stigma or legal prohibitions on child labor. School enrollment rates should also capture child labor market changes at both the intensive and extensive margin. Our child employment rates only capture changes in child labor at the _extensive_ margin—entry and exit from employment—while changes in the intensity of child employment might also be reflected in school enrollment rates.

We also include in the Appendix estimates on what we refer to as wage “premia” for these age groups. We calculate these child wage “premia” as the median wage for the age group divided by the median wage over the entire population.<sup>11</sup> As with our relative household equality measures discussed above, these relative wage measures will avoid confounded results driven by cross-sectional income or exchange rate differences. In addition to these child wage premia, we also calculate a skill premium as the median wage for survey respondents who completed secondary education divided by the median wage for respondents who did not complete secondary schooling.

From this pseudo-panel, we construct as our analysis data set a “stacked” country panel with a _±_ 3 year event window around entry into force of any RTAs. To do so we consider every year an RTA enters into force for a country and include three years before and three years after. If a country has multiple RTAs enter into force in adjacent years, each of _±_ 3 year event windows enter into the analysis dataset separately and are “stacked” or pooled.<sup>12</sup> We report summary statistics from this analysis dataset in Table 1.

Our data on RTAs and agreement
|58.46|23.85|76.37|0.81|
|Turkey|79.15|56.24|97.31|65.44|73.74|46.53|92.17|0.64|
|Uruguay|84.21|58.55|97.58|86.56|80.22|49.61|94.42|0.85|
|USA|80.95|64.33|91.84|79.07|74.79|51.79|86.92|0.75|

Source: Authors’ elaboration based on World Development Indicators (2017).

51
provides payment when the average yield in an area, as determined by a random sample, falls below a threshold. Moreover, CADENA also offers traditional and remote sensing index insurance for livestock (Arias et al. 2014). This analysis will focus on the drought index insurance because it has historically been the largest component of the CADENA program, and going forward we will simply refer to it as index insurance. Through this index insurance component, CADENA currently insures farmers growing staple crops on less than 20 hectares of rainfed land (SAGARPA, 2014). The insurance provides coverage during three pre-determined phases that run from sowing to harvesting. If precipitation as measured by the corresponding weather station falls below the threshold in any of the three phases, the insurer makes indemnity payments to the state, which in turn transfers these to eligible farmers in the insured area. Because of restrictions regarding the maximum distance between the weather station and the insured area, a municipality may be insured by multiple policies each linked to a different station.

The data for this evaluation come primarily from four sources. Policy data from SAGARPA include information on the insured crop, rainfall triggers and corresponding stations, area insured, and record of all payouts for each insured municipality for the period 2005 to 2013. Weather data from the National Water Commission (CONAGUA) allow us to calculate the precipitation at each of the weather stations linked to an insurance policy, which in turn is compared to the policy thresholds and used to determine if that policy should have paid out. To determine the effect of insurance payments on yields and area sowed, we use agricultural production data from SAGARPA detailing the annual hectares sowed, hectares harvested, and total production in metric tons at the municipality-crop level. Lastly, to study the economic impacts of the insurance, we use national household income and expenditure surveys (ENIGH), which are carried out every other year with the latest one occurring in 2014. These household expenditure and income surveys are repeat crosssections of households with a rotating sample of municipalities
# **2 Data**

Our sample covers 6 low- and middle-income countries in Africa and Eastern Europe: Kenya, Madagascar, Nigeria, Rwanda, Tanzania and Georgia. In order to apply the methodology developed by Atkin and Donaldson (2015), we require two sets of data for each country. First, we need price data on narrowly-defined product varieties, with observations spanning multiple locations within each country at a monthly frequency over several years. Second, we need the locations in which product varieties were produced or from which they were imported. The latter allows for the identification of trading pairs and the corresponding direction of trade from origin to destination locations. In this section, we briefly describe how we construct these data. Appendix A includes additional details about the data preparation process.

## **2.1 Price data**

Our price data come from CPI microdata provided to us by NSOs in each country in our sample. NSOs collect price data from a predefined list of product varieties (including goods and services), designed to capture the consumption basket of a typical household, at specified locations. Apart from the monthly price quote, the information provided to us by NSOs includes a product description, in some cases including the brand and presentation (e.g., “Coca-Cola, canned, 500ml” or “Body lotion, Nivea, 200ml”), the name of the location where the price was collected, and, occasionally, the name of a producer or the country from where the product was imported.

We restrict the choice of products to narrowly defined varieties, i.e., those for which we can observe a detailed product description and a brand. For most countries in our sample, prices are at the town or city level, with the exception of Rwanda, where prices are collected at the (more aggregated) district level.

The analysis in section 4 uses clean samples of price data, obtained by applying a cleaning algorithm to the raw data. Moreover,