rafmacalaba/data-use-mentions-extended huggingface dataset:
the private sector to GDP (WDI). Finally, Pillar 10 refers to market size (which could be proxied by either GDP or volume of imports), a variable that either coincides or is highly correlated with the dependent variable and was therefore dropped as a determinant.

Finally, the extent to which an economy operates as an innovation ecosystem also matters **.** Pillars 11 and 12 relate to business dynamism and innovation capability; dynamism is proxied here by the cost of starting a business (Doing Business) and innovation by the availability of the latest technology (WEF).

# Methodology

The first part of the analysis relies on the supply-side methodology introduced by Bouis and Duval (2011), Barnes et al. (2013), Johansson et al. (2013) and further elaborated by Egert and Gal (2016). The supply-side framework is based on a production function approach and has the advantage that GDP growth can be expressed as the sum of separable and independent supply-side components. This makes it possible to study separately the link between structural variables and each supply-side channel: labor, capital, and productivity.

Assume a standard Cobb-Douglas production function,

> 5 See Annex **_6_** for details on the CBR labor market regulation index.

8
_I) Introduction_ One aspect of poverty that emerges strongly from people's descriptions of their experience is the notion of vulnerability (Beard, 1998). Many households, while not currently in poverty, recognize that they are vulnerable and that events could easily - push them into poverty a bad harvest, a lost job, an unexpected expense, an illness, a lull in business.' This aspect of vulnerability is not captured when poverty is defined as a function of the shortfall of _current_ consumption expenditures from a "poverty line" (current consumption expenditures deficit or CCED concept).<sup>2</sup> We propose a simple expansion of these static poverty measures to include vulnerability. Vulnerability is defined as the risk a household will fall into poverty in the future, and we propose a simple empirical measure that allows the setting of a "Vulnerability to Poverty Line" and thereby estimates of the "Headcount Vulnerable to Poverty Rate," which is commensurate with traditional headcount poverty rate. While the proposed quantitative measure does not begin to capture all of the complex, multifaceted, dimensions of the concept of vulnerability, this measure at least begins to put vulnerability on a par with static poverty measures in analytic and policy interest.

In the developed countries context, Goodin _et al_ (1999) find that much income instability is strongly associated with ordinary lifecourse events. In addition to traditional lifecourse events driven by age, people now have increasingly to cope with new ones, in particular separation, divorce, and early retirement.

2 Examples of CCED measures of poverty are the FGT poverty indices from Foster, Greer, and Thorbecke (1984) as elaborated in Ravallion (1994) and many World Bank Poverty Assessments.

2
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|Subcomponent|Open defecation (%)|1 if no defecation facility used and 0<br>otherwise|
|---|---|---|
|Subcomponent|Self-reported handwashing with<br>soap/ash (%)|1 if respondent washed their hands with<br>soap/ash at least once since previous day<br>0 Otherwise|
|Subcomponent|Frequency of latrine cleaning over past<br>2 weeks|Number of times the latrine has been<br>cleaned in thepast 2 weeks|
|Subcomponent|Water pot is clean and covered (%)|1 if the pot has clean water and is<br>covered<br>0 Otherwise.<br>This indicator is measured via actual<br>observations|
|Subcomponent|Water treated for consumption, any<br>method (%)|1 if drinking water stored in household is<br>treated with any product/method for safe<br>consumption<br>0 otherwise|
|**Secondary**<br>**Outcome**|**Life satisfaction & self-esteem index**|Summary index of 11 questions<br>The 11 questions include the (1) life<br>satisfaction question as defined by the<br>World Values Survey and (2) the 10<br>questions as defined in Rosenberg's Self-<br>Esteem Scale.|
|Subcomponent|Life satisfaction (WVS)|All things considered, on a scale of 1 to<br>10, how satisfied are you with your life as<br>a whole?<br>1 means completely dissatisfied<br>10 means completelysatisfied|
|Subcomponent|Feel I am person of worth (Rosenberg)|I feel I am a person of worth, at least on<br>an equal plane with others.<br>Tell me to what extent you: Strongly<br>agree,
NA|NA|NA|
|1979|<br>79 fr||84|24|11|19|
|1984|<br>91|85<br>|91|34|36|45|
|1986|100|ite-|100|27|24|39|

Source: Annual Bank Statistics, various issues.

move, however, was costly to the borrowers: free credit carried extremely high real interest rates (see below) while linked credit was usually supplied at "civilized" rates. Restricted to export financing, directed short term credit was 100 percent linked in 1985 and 1986.

# 4. Term Structure

- It was repeatedly shown that agriculture relied on short term

- credit to finance production assets (Lowe 1955; Sadan and Kadishay 1967; Sarig Committee 1979). It was also often claimed that the mismatch in the term structure of investment and its finance and the creation of the "financing gap"~ (between terms of credit and the productive life of assets) was the result of government policy of

29
Herfindahl index)_|_(0.000)_|_(0.000)_|_(-0.803)_|_(0.000)_|_(0.000)_|_(0.000)_|_(-0.143)_|_(0.000)_|
|<br>Product concentration|<br>-0.014<sup>***</sup>|<br>-0.010<sup>***</sup>|<br>-0.020<sup>***</sup>|<br>-0.015<sup>***</sup>|<br>-0.018<sup>***</sup>|<br>-0.014<sup>***</sup>|<br>-0.018<sup>***</sup>|<br>-0.015<sup>***</sup>|
|_(Herfindahl index)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|
|**Natural Resource Exports**|||||||||
|Natural resource exports|..|0.008<sup>***</sup>|..|0.006<sup>***</sup>|..|0.004<sup>***</sup>|..|0.005<sup>***</sup>|
survey data provide the only sales and export data available.

Our final source of follow-up data comes from a detailed scoring exercise we undertook on the digital presence of firms in December 2021, also one year after the program started (with a more basic version collected at baseline, and used as control variables). Improvement of digital presence to attract new customers was a key area emphasized by the program, and took on more importance during the COVID-19 pandemic as other opportunities to interact in-person with potential customers were limited. These data were collected by two independent consultants with linguistic knowledge of the prevalent languages in the region, who were blinded to treatment status. An additional advantage is that these data can be collected for all 225 firms in the experiment.

We measure three elements of digital presence, capturing the extent to which firms are employing modern marketing methods to be attractive to domestic and international customers. The first is based on Google searches, and reflects whether the firm has used search engine optimization techniques to make it easy for potential customers to find them. Our consultants score the firm on seven features of this search, including their page rank, whether there is a description in an English search, whether google provides a right menu address and contact information, and whether it is clear from the search result what the company does. The second measure examines the firm website, and scores in based on 12
or a sub-sample of plots, crops, or individuals, thus allowing for short phone interviews. Sub-sampling has implications for the representativeness of the data so this approach may not be suitable to replace an extensive agricultural sample survey in full but may be used to complement inperson data collection.

# _Survey timing and seasonality_

In addition to questionnaire design, survey timing is a critical design choice in agricultural data collection, particularly because of the highly seasonal nature of agricultural production and the varying frequency 15
and by smoothing out resource consumption across different locations.

One of the better treatments of this subject can be found in Bossert and Beauvais (2002) who characterize the degree of decentralization by examining the amount of “decision space” accorded to local and central actors. The authors map decision space according to whether the “range of choice” for local decision-makers is viewed as narrow, moderate or wide, assessing the degree to which ministries of health and education prescribe how services are to be delivered. Bossert and Beauvais then survey indicators of finance,

> 10 The literature on this difference is most extensive in the United States. However, studies comparing the US and other wealthy countries (e.g. Mobley and Magnusson 1998) and a systematic review that includes developing countries (Hollingsworth 2003) demonstrate that the hypothesis of a difference between forprofit and non-profit organizations should be tested.

______________________________________________________________________________________

-23-
**Table 1 Dependent and Explanatory Variables Influencing Private Investment in Renewable-based Energy**

|**Vribl**|**Dfinitin**<br>**E Sin **|
|---|---|
|**aaes**|**eo**<br>**xp. g **|
|**Itdti f fdi**|**SECTORALGOVERNANCE VARIABLES**|
|**nroucon o ee-n**<br>**tariffs**<br>_FIT_|= 1 since the year of establishment of a feed-in tariff system<br>= 0 otherwise<br>**+**|
||**ECONOMYWIDEGOVERNANCE VARIABLES**|
|**Control of Corruption**|= measures the perceptions of the extent to which public power is<br>exercised for private gain, including both petty and grand forms of<br>corruption, as well as "capture" of the state by elites and private<br>interests.(Kaufmann and KrayWorld Governance Indicator)<br>**+**|
|**Degree of democracy**<br>**_Polity2_**|= Revised Combined Polity Score (Polity 2): This variable is a modified<br>version of the POLITY variable to facilitate the use of the POLITY<br>regime measure in time-series analyses. It ranges from +10 (strongly<br>democratic) to -10 (strongly autocratic) based on 6 indicators<br>**+**|
||, .<br>**SHORT RUN FINANCIALCRISES**|
|**East Asian and Russian**<br>**Crisis**<br>_d_FC1_|= 1 for the 3 years after 1997<br>= 0 otherwise<br>-|
|**Latin American Crisis**<br>_dFC2_|= 1 for the 3 years after 2001<
|||||
|-European<br>Centre<br>for<br>Medium-Range<br>Weather<br>Forecasts<br>(ECMWF) ERA5|1979-current|0.28 deg|hourly|mean temp|K|
|-Modern-Era Retrospective analysis for Research and Applica-<br>tions, version 2 (MERRA-2) statD|1980-current|0.625x0.5 deg|daily|mean temp|K|

_Note_ : The table summarizes the remote sensing sources and related details for precipitation and temperature data.
lower in terms of general education and tertiary education. This is quite atypical for developing countries, where in general women have higher educational levels than men.

The actual work experience of women (about 1.6 years) is much lower than that of men (about 7.1 years). Note that, as explained in the data section, this measure takes into account also the number of children of men and women, subtracting some years to potential work experience to take into account maternity factors. This is the first evidence of the dramatic gender differences emerging in the labor market as a consequence of the commitment of women in reproductive activities in the country.

In the SWT survey data, the self-employed account for only a small share of the overall sample and about 11.9% of total employment. It is 12.5% in rural areas and 11.1% in urban areas.

The migration status is very similar by gender suggesting that migration is neither common nor an individual choice but rather a family choice. Only few individuals in the sample migrate to study or seek employment.

According to the SWT survey data, about 54.7% of young people found their jobs through friends and relatives while only 3.9% found theirs through Public Employment Service (PES) offices. The second most important channel is direct hiring (17.8%, which could be also associated with 8
variation in average household demographic attributes across countries. Two coarse indicators, average age and household size, reveal significant variation across countries (Figure 1) and regions (Table 1). In terms of average household size, the data used in our analysis indicate that average household size within regions ranges from 2.4 people in high-income countries to 5 in Sub-Saharan Africa and 5.1 in the Middle East and North Africa.<sup>4</sup> Across countries, average household size ranges from 2 in many high-income countries to above 9 in Senegal and Mali (based on the data used in this paper). In terms of variation in age, 20 percent of the population in upper-middleincome countries was 14 years of age or less in 2021; this proportion doubles (41%) when considering low-income countries.<sup>5</sup> Given that this age category on average needs fewer calories to meet basic needs, the per capita assignment differentially treats countries in a manner that is systematically correlated with income level.

The aim of this paper is to understand the implications of relaxing the per-capita allocation assumption for the profile of global poverty and the identification of poor people by contrasting it with a commonly used alternative allocation rule (i.e., dividing household resources by the square root of household size). We show that the regional profile of extreme poverty is relatively insensitive to changing the household-resource-allocation rule, but the within-country identification of who is poor is highly sensitive to the change (reclassifying the poverty status of 270 million people across the world). We also provide evidence that the probability of being poor as identified by the square-root allocation is more strongly negatively correlated (compared to

> 4 Other data sources indicate even greater dispersion in household size. UN-DESA (2019a) estimates that countries range from slightly more than 2 people to slightly more than 8 people. These differences have clear geographic patterns with the average person living in a household with 6.9 people in Sub-Saharan Africa, compared to 3
the paper compares measures derived from the HFPS data to measures published by the International Monetary Fund (2020) and the International Labour Organization (2020) to understand the early labor market effects of the crisis in the context of official macroeconomic and labor market data. The comparison reveals that labor market outcomes based on the high frequency phone survey data differ markedly from standard macroeconomic measures of growth, especially in Sub-Saharan Africa.

# **2.2 Background: The COVID-19 Pandemic**

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). During the first half of 2020, COVID-19 spread globally: by November 2020, 216 countries and territories have reported cases. COVID-19 is highly infectious. Its symptoms include fever, cough, shortness of breath, difficulty breathing, chills, muscle pain, headache, sore throat, and reduced sense of taste or smell. It can cause a wide spectrum of diseases ranging from mild illness to moderate and severe pneumonia, respiratory failure, and death. By mid-January 2021, there have been 93 million cases and 2 million deaths worldwide.

Columns 5-8 of Table 1 show the number of confirmed cases, the number of confirmed cases per million, the number of deaths, and the number of deaths per million on May 31 (the midpoint of our surveys) in the countries included in our data. The table suggests that measured by the number of deaths, the pandemic was not very severe in the East Asia & Pacific and the Sub-Saharan Africa countries included in our data. It was most severe in some of the Latin America & the Caribbean countries, particularly in Ecuador, Peru, and Mexico, while the Europe & Central Asia countries in our data experienced moderate outbreaks, apart from Romania which was hit particularly hard.

10
electricity, in amplifying or reducing the effects of access to high-speed Internet in attracting FDI. In other words, is access to high-speed Internet enough to drive FDI, or the effect conditional on having complementary (hard) infrastructure (road and electricity)?

To address this question, we revisit the baseline analysis and leverage spatial data on access to road and electricity grid network: two critical infrastructure that supports the productive sectors of the economy by aiding transport and providing needed energy for the production of goods and services. Specifically, we compute the density of electricity grid and road networks at the district level and classify districts into high and low (grid/road) density based on the sample median. Thus "high electrification (high road density)" is a dummy equal to 1 if the electricity grid (road) density is above the sample median and 0 otherwise. The density of the electricity grid is used as a measure (proxy) of the availability of electricity and hence the cost of electricity connection (supply). Places with high grid intensity plausibly have low connection costs, all else equal. Similarly, high road density is used as a proxy for transport cost, i.e., high road density implies low transport cost and vice versa.

15
poverty over time when prices are changing is to identify particular goods that have linear Engel curves that are fairly insensitive to changes in prices.

We use LSMS data from Rwanda, Tanzania, and Uganda and devise a simple way to identify goods with (nearly) linear Engel curves using data in an initial period. We form a linear sub-aggregate of such goods, and then ask whether this sub-aggregate can be used to accurately measure poverty in a subsequent period. Theory suggests that this will not work if there are significant changes in relative prices. Unfortunately, 2. The “upper bound” method described by Ravallion (1994).

3. In this paper, the tracking of monetary poverty is based on the commonly used “Foster-Greer-Thorbecke” (FGT) family of poverty measures described by Foster, Greer, and Thorbecke (1984).
pirate _i_ enters, then _Ti_<sup>_τ_(</sup><sup>_aτ_</sup> _i_<sup>) is given by</sup> Second, high ability pirates are the first to enter, followed by their lower-ability peers: entry in period _τ_ is characterized by a cutoff _<u>a</u>_ <u>(</u> _δi, n_<sup>_τ−_1</sup> _, ν_<sup>_τ_</sup> _, κ_<sup>_τ_</sup> ) such that pirate _i_ enters in _τ_ period if and only if The measure of the set of entrants (i.e. the number of observed attacks) in period _τ_ is thus given by

# **4 Estimation**

## **4.1 Data Description**

The data set that we use in this paper comes from two main sources. The first consists of data on pirate attacks provided by the International Maritime Bureau’s Piracy Reporting Center, thereafter referred to as the IMB data. The Piracy Reporting Center collects information on all self-reported pirate-related incidents around the world.<sup>7</sup> We classify an attack as carried out by Somali pirates if it took place along the coast of Somalia, the Arab Republic of Egypt, Eritrea, the Islamic Republic of Iran, Iraq, Kenya, Oman, Saudi Arabia, Seychelles, Tanzania, the United Arab Emirates, the Republic of Yemen, or anywhere in the Red Sea, Arabian Sea, Gulf of Aden, or Gulf of Oman. We then drop from the data all attacks that are not carried out by Somali pirates. Moreover World Bank (2013) checked local news outlets for information on attacks that took place along the coast of India and in the strait of Hormuz; four incidents were not attributed to Somali pirates and were dropped accordingly from the data. Attacks in our data span from 2000 to 2012,
This concludes the discussion of the theory of occupational choice which I use to study the evolution of occupational change during the last two decades in Costa Rica, El Salvador and Panama. In the next section, I provide details on the data that I use to perform the quantitative analysis for the three Central American economies.

# **3   Data**

The main source of data is the country household surveys. The Socio-Economic Database for Latin America and the Caribbean (SEDLAC) (CEDLAS and the World Bank, 2019) is a repository of the major household surveys conducted in the Latin American and Caribbean countries. For most countries, the available surveys start appearing since the late 1980s. Given that the focus of the analysis is on primeaged workers (25-54 years), the SEDLAC potentially offers lifetime information of the oldest cohort (4554 years) in the present decade. For example, it is possible to know the occupational distribution of this cohort in the 1990s when it was classified as young. Thus, the availability of data over last two decades or so allows me to extract comprehensive information on all current cohorts. The three years that serve as reference points are 1995, 2005 and 2015 because the latter is the latest year for which data are available. The surveys do not correspond to actual panel. Rather, I construct a synthetic panel for each cohort to foster analysis.

The repository harmonizes the data at the country level to make them comparable across years. Still, due to several reasons many surveys remain non-comparable. To fulfill the basic requirement of having data on at least three years separated by a decade, I try bringing non-comparable surveys under analysis subject to them passing quality checks so that they are suitable for use in the study. This effort helps extend coverage to Costa Rica and Panama, as comparable surveys spanning a 20-year span are only available for El Salvador. Furthermore, to add power to the analysis, I pool data from the adjacent year surveys to the reference years whenever possible.<sup>10
(2015)) in a manner that suggests that this DHS wealth index is probably a very rough approximation of our idea of household wealth — in particular, no attempt is made to account for differences in the quality and price of the underlying assets, especially the two most important rural assets (land and livestock). Third, these impacts are averages across countries. However, what we would like to stress is that the magnitude of the impact of a drought on stunting is substantial, especially given that droughts (based on our definition) occur more than a fifth of the time in all five countries.

Table 3, which reports results from Equation 2 below, uses a more complete set of controls, including maternal biological factors _Mi_ — which include the mother’s height, weight and age and the birth order and the birth interval —- as well as all the assets ( _Ai_ ) that are used to construct wealth indices, instead of the quintiles of the wealth index.

> 21As discussed above, this varies by province, we use the cluster here to make the notation simpler.

12