rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

The tourism sector has grown rapidly in Latin American countries over the last decade and has become an important source of foreign exchange and an impetus for overall economic growth. Tourist arrivals rose by about 68 percent worldwide over the 1995– 2007 period and by about 50 percent in Latin American countries (Fayissa et al. 2009). The tourism sector makes an increasingly large contribution to the overall economy and foreign exchange earnings in the region, and it has rapidly grown in Panama in the last decade. The international arrivals in Panama have grown from around1.3 million visitors in 2006 to 1.7 million in 2010.<sup>2</sup> Given the sector’s growing importance, the Panamanian Tourism Authority ( _Autoridad de Turismo de Panamá,_ ATP) carried out a series of surveys between 2006 and 2008 to better understand the direct and indirect economic impacts of tourism and develop pilot satellite accounts. According to the 2006 data on national and international tourism and tourists’ expenditures, including tourists in transit that never leave the airport, international tourist arrivals in Panama reached 2.4 million—a number equivalent to 70 percent of the country’s population. The expenditures by foreign tourists during this period totaled around $960 million, equivalent to 6 percent of Panama’s 2006 GDP. Of the total of 2.4 million visitors, about half of the visits are direct transit<sup>3</sup> and a quarter is for recreation; the former account for one-third of total expenditures by foreign tourists and the latter for over 40 percent. Panama is also becoming a popular destination for retirees from North America and Europe—not reflected in these statistics on tourism arrivals, which contributes to the real estate boom in the metropolitan area of Panama City, Bocas del Toro and Chiriqui.

Recent studies show that growth of the tourism sector has contributed to overall economic growth and development in Latin American countries. A cross-country econometric study of the determinants of economic
and average wages becomes positive. In other words, foreign firms that employ a higher share of women pay higher average wage rates which could relate to cultural norms adopted in those firms. Increasing the female labor share by 10 percentage points is associated with average wage rates of foreign firms being 2.6 percent higher. Interestingly, the correlation between the female labor share and average wages is even higher for FDI firms that offer training to their employees compared to FDI firms that do not train their workforce.<sup>13</sup>

> 12 While this type of analysis can be performed with firm‐level data only, it cannot control for worker characteristics which might also explain part of the gender wage gap.

> 13 These findings are in line with the literature on FDI spillovers, which suggests that foreign investors can help domestic firms upgrade their technological capabilities directly through sharing of production techniques and product design and assisting with technology acquisition (Paus and Gallagher, 2008), and indirectly through personnel training, advance payment, leasing of machinery, provision of inputs, help with quality assurance, and organization of product lines (Lall, 1980; Crespo and Fontoura, 2007; and Javorcik, 2008).

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|Urbanization (%)<br>WBL Index||||||1.682***<br>(0.422)<br>17.365**<br>(8.736)|
|---|---|---|---|---|---|---|
|Women can work the same<br>night hours as men Y:1 N:0||||||2.816|
|||||||(2.830)|
|Women can work in the same<br>industries as men Y:1 N:0||||||-1.293|
|||||||(2.066)|
|Ratio of Female to Male||||||2.502***|
|Population|||||||
|||||||(0.567)|
|Fertility Rate||||||-16.125***<br>(3.677)|
|Starting a Business (DTF)||||||0.079**<br>(0.039)|
|Inflation (%)||||||0.057<br>(0.065)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry fixed|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>effects|||||||
|Constant|28.879***|-93.348**|-94.276**|-113.860**|-110.443**|417.745**|
||(1.998)|(44.156)|(46.013
TFP growth, and in our projections of sector productivity terms in eqn. (A3) we initially set all the  _j_ 's to the same value, 0.018. These are then adjusted to match actual GDP growth rates in the initial years for which we have actual data.

The value share parameters of the production functions (  _Kj_ ,  _Lj_ , etc.) are set to the values in the 2010 IO table in the first year of the simulation. For future periods we change most of these parameters so that they gradually resemble the shares found in the US input output table for 1997. The exceptions to this are the coal inputs for all the sectors, this is set to converge to a value between current Chinese and US1997 shares.<sup>25</sup> The rate of reduction in energy use is set at a modest level relative to the rapid improvements in the recent Chinese history. We assume that the share of energy in industry output is reduced gradually to 60% of the 2005 levels in 40 years. This is conservative compared, for example, to the performance in the electric power industry during the 1990-99 period. In that time the thermal output grew 88% whereas coal input only rose 61%, a rate of improvement of some 1.5% per year.<sup>26</sup> _C_ The  _it_ parameters of the consumption function are set in a similar way. That is, for the first period they are equal to the shares in the 2010 Social Accounting Matrix, and for the future periods they gradually approach US 1997 shares except for coal. This implies a higher projected demand for private vehicles and gasoline than that assumed in most other models of China. The coefficients determining demand for _I G_ different types of investment goods (  _it_ ), and different types of government purchases (  _it_ ), are projected identically.

The import and export elasticities are set to the values in GTAP v4. The base share of exports
specific questions about incomes, wealth or consumption, since these are more sensitive. This issue is called item non-response. Like unit non-response, this can be missing completely at random, or it may be related to other individual characteristics or income levels themselves. If the latter, then this is a form of selection bias, like the potential bias of unit nonresponse.

Bollinger et al. (2019) used linked survey and administrative earnings data from the US to show that item non-response is not missing completely at random- it is highest at low and high earnings. Such studies require substantial resources and are thus unfortunately uncommon in LMICs. However, Flachaire et al. (2022) did obtain such linked survey and income tax data from Uruguay. The authors show that item non-response was around 10% in the surveys and that it was highest at the bottom of the income distribution, a surprising result that, taken at face value, suggests that item non-response may not be as much of a concern when measuring the top of the income distribution as one might expect.

6
results that are not systematically biased. When the estimated results are compared with those directly estimated on the original sample, the bias is negligible. Furthermore, the estimates of inequality indices for the years in which only labor force data are available seem to be consistent with Moroccan economic trends.

In the future, it would be interesting to explore the implementation and estimation of HCR for two or more areas where the poverty line stands at 60% of the national median. An alternative approach would be to identify the starting points to estimate the parameter and its variance. Other distributions of consumption should be tested in order to reduce the margin of error. The authors are working on the log-student-t distribution. For this paper, it is evident that bias reduction, realistic rebuilding of the values, and the economic interpretation of results represent a good starting point.

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a panel of _T_ choices for each subject, as long as preferences are stable during the administration of the MPL, so that inconsistencies in expressed preferences within the same MPL are the result of choice errors. Both terms are assumed here to have a normal distribution, but other distributions are in principle possible; the most obvious being a logistic distribution for _eit_ which leads to a random effects logit.

The distributional assumptions on _ui_ and _eit_ discipline the distribution of WTP within each WTP interval. In the probit, the assumed distribution of latent indifference points across MPL intervals is normal with mean _α_ . The location of the sample average will thus partly determine where the mass of indifference points within a given MPL interval is placed. If this seems restrictive, note that any method deriving point estimates for WTP from MPL data must make distributional assumptions. For example, when assigning the

> 19For datasets with multiple MPL per subject, we recommend clustering at the subject-by-MPL level.

19
.51|(0.50)|1,609|
|<br>Attended mosque (=1)|0.09**|(0.04)|[0.19]|0.25|(0.43)|1,609|
|Went to market (=1)|0.01|(0.06)|[0.93]|0.46|(0.50)|1,609|
|Left village (=1)|0.03|(0.06)|[0.71]|0.22|(0.41)|1,609|

**_Notes_** :

- The table shows the regression results on index items using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to all indices components.

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To begin we produce a score for each dimension within a given pillar, which, unless otherwise stated, is an unweighted average of the indicators within that dimension.

where S c c is an indicator _i_ (e.g. population census score) in dimension _d_ , pillar _p_ , time cc period _t_ , and country SS. SSIIDD _c_ , and is the number of indicators in dimension _d_ . For instance, the score II for the Standards and Methods dimension is obtained by taking the unweighted average of all II indicators in this dimension, including the indicators for the system of national accounts in use, national accounts base year, classification of national industry, CPI base year, and classification of household consumption (Appendix A, Table A1).

After computing a score for each dimension, a score for each pillar is computed as the average score of the dimensions in in that pillar. For pillars 1, 2, 4, and 5, the unweighted average of the dimensions within each pillar is taken. For pillar 3 on data products, we take a weighted average of the dimensions, where the weights are based on the number of SDGs in each dimension (6 SDGs in dimension 3.1 on social statistics, 6 SDGs in dimension 3.2 on economic statistics, 2 in dimension 3.3 on environmental statistics, and 2 in dimension 3.4 on institutional statistics).<sup>10</sup> This reflects a perspective that all SDGs are of equal importance, and therefore the dimensions are weighted accordingly. Additionally, for Pillar 4 on data sources, censuses and surveys are given separate weights, so that censuses, surveys, admin data, and geospatial data each receives a weight of 1/4. While censuses and surveys are in the same pillar in the framework, and therefore each would typically only receive a weight of 1/6 in this dimension, because of their importance in

> 10 SDG 14 - Life Below Water - is omitted because land-locked countries do not
The largest portion of each workday is devoted to household agriculture. Figure 1 gives an overview of the hours per day across activities as reported in the weekly visits. Figure 1a averages across all people ages 10 or above for all days, and figure 1b excludes weekends and days the person was ill. Roughly a third of the total of 3.6 to 4.2 working hours, respectively, are devoted to agricultural activities. These data obscure important distributional differences, to which we return in later sections. Finally, figure 1c shows the allocation of time on days when at least some household farm activity was reported. On average, 5.8 hours were spent across all activities, of which 78 percent was spent on household farming. The remainder of the time was made up largely of collecting water, tending to livestock, and attending school.

# **4. RESULTS**

## _4.1. Main results_

### **Overreporting hours on the farm**

To examine the implications of survey design on the reporting of household farm labor, we start from the lowest unit: the number of hours each household member spent on each household plot over the course of the entire season (henceforth, person-plot hours).<sup>14</sup> Throughout the analysis presented here and unless otherwise specified, plots refer to plots on which any household member was reported to have worked at any point during the season. This measure of plots depends on the actual incidence of labor (rather than on the stated use of the plots) and so does not include plots held fallow, rented out, and so on, for which no household labor was reported. The analysis is restricted to household members ages 10 or older who reported they worked on any household plot during the season (a “person”).<sup>15</sup> By this definition, then, any specific person-plot hours could total zero.

Panel A of table 3 reports the results at the
7 each farming activity (land preparation, sowing, weeding, fertilizer application, pesticides application, and harvesting), disaggregated by gender (male, female, and children), both for both household and hired labor. This approach to measuring labor input in agriculture follows the latest recommendations (Sagesaka et al., 2021). Information on the type of equipment used in land preparation (e.g., manual, draught animal, tractor, etc.) was also collected.

To have a broader picture of the household living standards, detailed information regarding household assets and food insecurity was asked. Food insecurity is measured using the food insecurity experience scale survey module (FIES-SM) (Cafiero et al., 2018). The household head and/or his/her spouse were asked to answer eight questions about the experience of food insecurity in the past twelve months. These detailed questions about the food experience allow to calculate the food insecurity experience scale score. To proxy for the level of village development and access to household labor for agricultural development, we also include geospatial population density values in the analysis, extracted from NASA Socioeconomic Data and Applications Center (SEDAC). The 2019 available data were merged, with the resolution of one square kilometer per pixel, to each household in our survey data, based on the GPS coordinates. Overall, the household interviews were conducted with the household head or the person responsible for farming activities. Table 1 lists the key outcome variables used in the analysis.

**<u><mark>Table 1: Definition of outcome variables</mark></u>**

|**Variable**|**Definition**|
|---|---|
|Yield|Rice output per hectare of rice land (kg/ha)|
|Variable cost|Cash expenses of seeds, fertilizer, herbicides, land preparation and<br>hired labour per hectare of rice land (FCFA/ha)|

the respondent is
estimates.<sup>2</sup> Originally intended to be used to estimate maternal mortality, these modules record the survival status of all main female respondents’ siblings, and include the current age if alive, and record the year of death, and age at death, in the event that a sibling has died. Subject to various assumptions and adjustments (described in more detail below) the responses can be extrapolated to form an estimate of national-level mortality.

# **2.1)  Estimating mortality based on sibling reports**

At one level using sibling reports to estimate adult mortality is straightforward. The nationally representative DHS identifies all women aged 15-49 as respondents.<sup>3</sup> These women list all of their siblings born to the same biological mother, the birth dates of those siblings, report on the survival status of those siblings, and on the age and date at death if relevant. For any timeframe, therefore, we can establish which siblings were alive at the beginning of the timeframe, and whether or not they died during that timeframe. The number of deaths during the timeframe, divided by the population at risk at the beginning of the timeframe, determines the timeframe-specific mortality rate. To be more concrete, consider the timeframe of 1995-1999. We establish the population at risk at the beginning of 1995 (P1995), calculate the number of deaths between 1995 and 1999 (D1995,1999), and estimate mortality as (M1995,1999) the ratio between the two. The generic form of this relationship is given in equation (1) where _t_ is the beginning of the timeframe of interest. We are able to cover the periods 19751979, 1980-1984, 1985-1989, 1990-1994, 1995-1999, and 2000-2004 using these data. Not all surveys provide information on all the periods as we require that the full period be captured by the survey for it to be included (that is, a survey conducted in 1997 will not contribute to the estimate of mortality in the 1995-1999
challenges associated with attrition, typical of longitudinal studies, are exacerbated in the context of the COVID-19 crisis due to mobility restrictions, in times of higher likelihood of business closures, and potentially larger opportunity costs of responding to a survey rather than working when restrictions are lifted. Such idiosyncrasies may systematically influence the availability of certain types of firms to respond to the follow up surveys.

Although attrition of firms in our sample is not large, it is not trivial. On average, the attrition rate is 28% (see Appendix Figure B3 for attrition rates by country). Moreover, attrition is not random. For instance, firm characteristics such as size and sector determine the follow-up responses. In particular, micro firms, those in the retail sector, the ones that had larger declines in sales (more than 50% or 90%) in wave 1, and those that were already in arrears or expecting to fall into arrears at the time of wave 1 surveys were less likely to respond to the wave 2 survey (see Figure B4 of Appendix B.4). To avoid a reduction in sample size, attriting firms were replaced with other firms with similar characteristics, as per the original sample design, such as stratification by size and sector. Thus, we observe a balance between waves in terms of general characteristics of the firms, such as size and sector (Figure B5 of Appendix B.4). However, we cannot rule out differences in unobserved performance of the firm during the first months of the pandemic.

# _Estimation Methodology_

To address the heterogeneity in the sample across countries, we conduct our analysis through regressions that controls for such differences in survey implementation. Following a similar approach used to analyze the BPS data from the first wave (Apedo-Amah et al., 2020), our basic specification controls for pre-pandemic firm size, sub-sector, country fixed effects, and the constructed measure of the severity of the crisis faced by firms at the time of the survey. More specifically, we estimate the following
monthly average drops in export and import values, respectively, according to Arenas et al. (2022). We closely follow the two studies with a focus on Indonesia and in turn make three contributions to this literature.

Our first contribution is that our paper incorporates non-tariff measures (NTMs) in the analysis, allowing the paper more policy relevance. This is highly relevant for Indonesia, as NTM incidence is among the highest in the region, and their use was prevalent in the pre-COVID period (Cal`ı & Montfaucon, 2021). The novel feature of this contribution is the use of the most updated time-varying data on non-tariff measures, an area in trade policy that has not received much attention in this literature, especially due to the lack of reliable data on NTMs. This allows us to link to the timeline of the pandemic and assess how various lockdown policies differently affected import trade of products subject to specific NTMs.

Our second contribution is methodological. We employ an event-study in our study. This approach is preferred over impact-evaluation estimators such as the difference-in-differences (DiD) and Propensity Score Matching (PSM), and a gravity-model. The event-study approach addresses concerns of lockdown anticipation. Since East Asia was the first epicenter of COVID-19, most countries in the region started imposing restrictions earlier than the rest of the world (Arenas et al., 2022), which could have triggered the reaction of exporters and importers in Indonesia in anticipation of the imposition of lockdowns in partner countries. The event study also enables us to observe the reaction of international trade to lockdown policies at high frequency (months), which often changed depending on the spread/wave of the virus in a country.

Finally, we add to the literature on COVID-19’s impact on trade in a country case study by using more recent and more detailed trade data in a comprehensive analysis of trade for Indonesia, separating impacts of domestic and external lockdowns on trade. This is unlike other studies on Indonesia such as Eschachasthi (2022), Ing and
# **3. Methodology**

This section first presents the method used to construct indicators for measuring accessibility, availability and safety of the public transport network (these are the facets of the public transport system that are of interest to us). It then presents the empirical model used to estimate the effect of the constructed transport measures on labor market outcomes.

## **_3.1. Measuring accessibility, availability and safety of the public transport network_**

In this section, we present the definitions for each measure used to capture different aspects of the public transport system as well as the data sources and the methodology used to compute them. It also provides descriptive statistics and maps of the transport measures for each of the three cities. Details for each of the indicators can be found in Annex A.

### _a. Measuring accessibility_

Accessibility focuses on measuring the spatial accessibility of job opportunities throughout the city using either public transport or walking. For each residential location, we compute the percentage of total jobs that are accessible within 60 minutes during peak hours (8am to 9am).<sup>21</sup> The measure of accessibility combines the time taken to walk to a public transport stop, time spent in the public transport vehicle, and the time needed to walk to a job opportunity.

Three sources of data are used to measure accessibility for each household in our sample. First, we use a layer of the street composition for each city. All street grid data is taken from OpenStreetMap. For the three cities, there are no pedestrian restrictions, meaning all streets and links in the network are considered accessible to pedestrians. A pedestrian walking speed of 3.6km/h is used, and the threshold of 20 minutes is used as the maximum walking time _per leg_ of the trip when public transport is being used. Second, we use the public transit network that has been collected. The collected frequency of departure, and speeds by which the public transport vehicles travel on