rafmacalaba/datause-encoder-data huggingface dataset:
# Shoring Up Economic Refugees: Venezuelan Migrants in the Ecuadoran Labor Market

Sergio Olivieri, Francesc Ortega, Ana Rivadeneira & Eliana Carranza<sup>_∗_</sup> _The World Bank Group_ & _CUNY, Queens College_ _JEL Classifications_ : 1720, 4060, 5050, 5150 _Keywords_ : Migration, Venezuela, Skills, Exodus, Ecuador

> _∗_ We thank Carlos Vayas and José Muñoz from _Telefonica Ecuador_ and Juan Muñoz and Ana Aguilera for their advice and knowledge on sampling design. We also thank Leticia Arroyo Abad, Michael Clemens, Marianne Fay, Jesus Fernandez-Huertas and Tanjia Goodwin for their helpful comments and Oliver Balch for expert editing advice. All errors are our own. The affiliation for Eliana Carranza, Sergio Olivieri and Ana Rivadeneira is _The World Bank Group_ . Francesc Ortega is the _Dina A. Perry Professor of Economics_ at Queens College (CUNY). Contact e-mails: solivieri@worldbank.org and fortega@qc.cuny.edu.
trade openness is effective in promoting an expansion of income in countries that are not excessively regulated. They argued that resource allocation towards the most productive sectors and companies is more difficult in highly‐regulated countries.

# **3. Data and Methodology**

## **3.1 The Data**

We have initially collected a panel dataset of 136 countries organized in 5‐year non‐overlapping observations over the period 1970‐2010, with each country having at most 8 observations. The list of countries in our sample is presented in Table A.1.<sup>4</sup> Given that the availability of data is different across variables, we have an _<u>effective</u>_ sample of 99 countries with at least 4 consecutive observations for all variables involved in our analysis. This sub‐section describes the construction and sources of the data used in our empirical analysis. The focus of this paper is to examine the growth effects of trade openness and the role of complementarities between the latter and other structural factors in promoting growth.

Our dependent variable is the average annual growth rate in real GDP per capita within the 5‐ year period, which is computed as the simple average of log differences in real GDP per capita over the 5‐year period. Real GDP per capita is expressed in 2005 international dollars (adjusted by PPP) from Heston, Summers and Aten (2009).

Our set of control variables includes the (log) level of real GDP per capita at the beginning of the 5‐year period to test for the existence of _transitional convergence_ . A negative coefficient estimate for this variable would imply that poorer countries may grow faster than richer countries —i.e. consistent with the neoclassical model. The rest of variables that conform our set of long‐run growth determinants follows Loayza, Fajnzylber and Calderon (2005): human capital, financial depth, institutional quality, lack of price stability, infrastructure, financial openness and our variable of interest, trade openness.

> 4 Note that we have collected information for some variables since 1960. However, the information on holdings of foreign assets and liabilities restrict our effective regression sample to start since 1970.

6
1

## **<u>WEB APPENDIX NOT FOR PUBLICATION</u>**

# **A Calibration: Distributions of Schooling and Mortality**

Here we discuss some details of the parameterization of the accounting framework. We calibrate the economy to match a representative LIC, MIC and HIC.

We begin with the mortality function _δ_ ( _·_ ). United Nations (2019) reports _δ_ only for ages zero, 1, 5 and for five-year intervals henceforth. As a result, we need to interpolate _δ_ ( _·_ ) for other ages. We develop a smooth function _δ_ ( _·_ ) by computing the step function implied by taking the UN’s reported values of _δ_ ( _·_ ) and assuming the unreported years equal the nearest lowest value (e.g. _δ_ (2) = _δ_ (1)), and then smoothing the step function with the Hodrick-Prescott filter. We used a parameter of _λ_ = 10, a value that was both low yet smooths out the “steps” in the step function.<sup>1</sup> Then, we set _a_ ̄ = 99 as the population aged 100 and above is very small everywhere.

_−_ We also match the distribution of schooling, based on the I2D2 data (1990 2016; see Section 6.1. for details on the data). We assume that agents may only accumulate up to 25 years of schooling because some countries only record schooling up to 25 years. See Web Appx. Fig. A.1 for the resulting distributions of mortality and schooling.

Finally, we proceed similarly to obtain the distributions by gender. Web Appx. Fig. A.2 then shows the distributions of schooling for male and female workers separately.

# **B COVID-19, Mortality and Human Capital**

The focus of this study is on education and work experience. Mortality is unlikely to be an important driver of the COVID-19 pandemic’s impact on human capital. COVID mortality among the working-age population is only a small share of that population.

For the U.S., the CDC provides data on the number of deaths associated with COVID19.<sup>2</sup> Web Appx. Table A.4 indicates that the share of deaths from COVID-19 is a nontrivial (10% or above) share of deaths for the population aged 40 and above. However, as a share of the population within a given age group, COVID-19 deaths are below 0 _._ 5 percent of the population, and only increase for the 65-74 age group. In contrast, returns to experience are an order of magnitude larger per year of work experience for all survivors. Consequently, welfare losses due to lost schooling and work experience of survivors should be far larger than any losses through increased mortality (and also smaller than any apparent increase in GDP per capita from the decline in population).

Bauer et al. (2021) examine a sample of European countries as well as the U.S., and

> 1 Ravn and Uhlig (2002) recommend a smoothing parameter of 6 _._ 25 for annual business cycle data. However, business cycle data are fluctuations around a trend, whereas we smooth a step function, so we prefer a higher coefficient to ensure a smooth hazard rate that does not appear like a step-function. 2“Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1.” See https://www.cdc. gov/nchs/nvss/vsrr/covid ~~w~~ eekly/index.htm, last checked 11/20/2021
- For the lower estimate, we introduce a decline in the LPFR based on the latest ILO‐ modelled estimates and projections that in turn draw on the most recent UN population estimates and regional GDP growth.<sup>19</sup> Other possible alternatives for this, though probably less satisfactory, are to: (i) assume the same decline in the participation rate as in the previous decade, but this would have likely been an unsustainable path; or (ii) use an earlier average growth, namely for 1991‐2000 (‐0.6%), which would be quite arbitrary.

- For the upper estimate, we use a higher I/Y of 23.5% since the IMF data presented in Section 2.1 indicates roughly a 2 ppt increase in average gross capital formation (from 23.1% over 2001‐2010 to 25.2% over 2011‐2016) and we assume the same for gross _fixed_ capital formation. We also use a higher long‐term average growth rate of 1.5% for human capital based on both CSL and BL data.

**<u>Table 2: No‐Conflict Simulation –</u> Upper and Lower Estimates for Projected Variables**

|**Variable**<br>**Note**|**Input value**|
|---|---|
|**Lower estimate onprojected variables(average, 201**<br> <br>|**1‐2030)**|
|Labor forceparticipation rate, growthg�<br>_(1)_|‐0.003|
|**Upper estimate onprojected variables(average, 2010**<br> <br>|**/11‐2030)**<br>|
|Investment‐to‐output ratioI/Y<br>_(2)_|0.235|
|Public investment‐to‐output ratioI<sup>�</sup>/Y<br>_(2)_|0.094|
|Private investment‐to‐output ratioI<sup>�</sup>/Y<br>_(2)_|0.141|
|Human capitalgrowthg�<br>_(3)_|0.015|
|*Multiply by 100 to obtain parameter/variable values in percent sh<br>terms (%).<br>Note (see Section 2.1 for further discussion):<br>_(1)_<br>Calculated using female and male LFPRs from ILO‐modelled<br>projections, May 2018. We carry out linear interpolation fo<br>based on the ILO‐reported values of series end points (2010<br>turning points (2020‐21 for the male participation rate) so<br>smoothened. Female and male shares of working‐age po<br>United Nations (2017) are kept unchanged at 2010 levels.<br>_(2)_<br>I/Yis based on IMF estimations suggesting higher GCF (% of G<br>The assumption of public investment share of total invest<br>used in the central projection is maintained.<br>_(3)_<br>Long‐term average based on calculations using both Cohen,<br>and Barro and Lee average years of schooling data respectiv<br>4(a)‐(b).|are or growth<br>estimates and<br>r annual values<br>and 2030) and<br>that trends are<br>pulation, from<br>DP) after 2010.<br>ment of 40% as<br>Soto and Leker;<br>ely. See Graph|

Graph 7 shows the trajectory for the level and growth of GDP in Syria based on the central calibrations in Table 1 and the calibrations for the upper and lower estimates from Table 2. Note that the lower estimate shows a slight upward trajectory – this is because of the ILO‐modelled estimates and projections, which show the LFPR for men gradually rising from 2021 onwards, after declining in the previous decade.

> 19 The estimates and projections do not fully account for the conflict. UN projections of population decline are captured but not GDP growth developments. See footnote 10 for further details.

11
- Rosenzweig, Mark R. 1996, When Investing in Education Matters and When It Does Not, _Challenge_ 39(2) 22-29

- Savadogo,-Kimseyinga; Reardon,-Thomas; Pietola,-Kyosti 1995, Mechanization and Agricultural Supply Response in the Sahel: A Farm-Level Profit Function Analysis, Journal-of-AfricanEconomies;4(3), December 1995, pages 336-77.

- Schiff,-Maurice; Montenegro,-Claudio-E. Aggregate Agricultural Supply Response in Developing Countries: A Survey of Selected Issues, _Economic-Development-and-Cultural-Change;45(2):393-_ 410.

- Tembo, S, Nkomeshy, A., Ntalasha, H., Kalumbi, J. 1995, Pre-ASIP Study of the Status of smallholder farming and agricultural services in Sinazongwe and Zimba, Southern Province of Zambia, Institute for African Studies, University of Zambia, Working Paper 2, Lusaka

- World Bank 1992, Zambia Agriculture Sector Strategy: Issues and Options, Washington DC, mimeo
Another strand in the growth literature considers the influence of economic freedom and political freedom on economic growth. A survey of the empirical literature by Wu and Davis (2003) found economic freedom to be a robust variable in explaining economic growth and development. The empirical results on the influence of political freedom on economic growth were conflicting. Mahmood (2003) however makes a distinction with regard to different stages of the developmental process. Using conceptual models of technological imitation and innovation, Mahmood shows that a government can spur economic development through centralization of economic and political control. However, as the economy approaches the technological frontier, the range of choices narrows. Political and economic freedoms are necessary at this stage as technological innovation depends on spillovers facilitated by networks of firms and individuals, rather than on centralized planning.

41
These data can be used to identify nutritionally vulnerable women and children. The DHS also include a household wealth index based on a household’s assets and living conditions. We use this index as a proxy for household wealth. However, aggregate consumption may well be a better predictor of individual welfare (and nutritional status) than the DHS wealth index, which (for example) may not respond quickly to shocks. Wherever possible, we complement the DHS data with good-quality nationally-representative household consumption surveys from the World Bank’s Living Standards Measurement Study (LSMS).

We acknowledge that nutritional status is not all that matters to individual welfare; our findings may not hold for other dimensions of individual poverty. Yet, in the absence of better individual poverty measures, and given considerable evidence of unequal intra-household allocation of resources, it is important to investigate this issue. Our results are also relevant to policy makers who are specifically interested in reaching undernourished individuals viewed as having a health deprivation. There are various forms of direct interventions with the aim of improving nutrition, including direct nutrition supplementation and promoting better health practices.<sup>9</sup> Many of these are implemented through health clinics and delivery points other than the household. However, there is a growing interest in doing so more through household-based policies—by integrating nutrition programs within anti-poverty policies more broadly. We throw light on whether this might work.

Our principle finding is that, although the incidence of undernutrition tends to be higher in poorer households, the nutritional deprivations are spread quite widely through both the wealth and consumption distributions, such that the joint probability of being an underweight woman or child and living in the poorest household wealth quintile is low. This also holds when we use an augmented regression to control for various individual- and household-level factors which may influence nutritional outcomes. Our results point to the need for broad coverage in efforts to address undernutrition and, by extension, individual poverty, rather than subsuming this problem within household targeted antipoverty interventions. Data availability limits how far we can go in explaining our findings, but we point to evidence suggesting that intra-household inequality may well be a major factor.

The following section considers relevant arguments and evidence from the literature. Section 3 outlines a simple theoretical model to help understand the relevant aspects of the joint distribution of household poverty and individual undernutrition. Section 4 then reviews the data we shall be using.

> 9 See for example the package of nutritional interventions described in Bhutta et al. (2013).

4
Policy Research Working Paper 5547

# **Abstract**

Besides superior productivity, what other firm characteristics are associated with export success? This empirical study identifies the effects of signaling tools (foreign technical license, International Standards Organization certification, and review of financial statements) and Internet tools (email and website) on export frequency and intensity of firms in developing countries. Using data from the World Bank’s Enterprise Survey, the author finds that productivity, size, foreign ownership, International Standards Organization certification, and the use of Internet tools have positive effects on the probability of exporting and on the intensive margin of trade. International Standards Organization certified firms are 22 percent more likely to be exporters, whereas firms that use their own website to communicate with clients and suppliers increase the likelihood they export by 11 percent. Among exporting firms, those that are International Standards Organization certified sell 41 percent more abroad than firms that are not certified. Firms that use email sell 31 percent more in foreign markets than exporting firms that do not.

This paper is a product of the Trade and Integration Team, Development Research Group. It is related to the Trade Costs and Facilitation Project with support of the Multi-Donor Trust Fund on Trade. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at eferro@brandeis.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table S2.6 Relative Supply of Workers with 20 or More Years of Experience

|||Evolution of the 20 or more<br>years of experience ratio|
|---|---|---|
|||20 or more years of<br>experience / total|
|Country|1995|2002<br>2015|
|Argentina|0.528|0.502<br>0.490|
|Bolivia|0.507|0.498<br>0.496|
|Brazil|0.565|0.559<br>0.574|
|Chile|0.486|0.511<br>0.531|
|Colombia|n.a.|0.519<br>0.523|
|Costa Rica|0.500|0.537<br>0.548|
|Ecuador|n.a.|0.503<br>0.506|
|Guatemala|n.a.|0.522<br>0.534|
|Honduras|0.529|0.506<br>0.525|
|Mexico|0.474|0.508<br>0.535|
|Nicaragua|0.521|0.507<br>0.496|
|Panama|0.460|0.492<br>0.532|
|Peru|0.490|0.491<br>0.544|
|Paraguay|0.525|0.528<br>0.492|
|El Salvador|0.528|0.520<br>0.523|
|Uruguay|0.599|0.584<br>0.569|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : The ratio represents the share of workers with 20 or more years of experience in the total of employed individuals. The level of experience of workers is calculated as the age minus the number of years of completed education minus six. The sample was restricted to individuals aged 18 to 65.

57
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WPS3310

# **BUNDLING OF SERVICES AND HOUSEHOLD WELFARE IN DEVELOPING COUNTRIES:**

NOVEMBER 2003 Using panel data for Perú for the period 1994-2000, we find that when households receive two or more services jointly, the welfare increases of the household, as measured by changes in consumption, are larger than when services are provided separately of each other. Such increase appears to be more than proportional, as F-tests confirm on the coefficients of the corresponding regressors confirm. Thus, we find that bundling of services may help realize welfare effects.

JEL Classification: O1, O2 Key Words: Bundling, Expenditures, Public Services, Welfare, Panel Data Word Count: 1,747

> * Chong: Inter-American Development Bank; Hentschel: The World Bank, Latin America and Carribbean Region; Saavedra: The World Bank, Latin America and the Carribbean Region. We are grateful to Vajeera Dorabawila, Virgilio Galdo, and Pablo Suárez, for excellent research assistance. The views and opinions in this paper should not be attributed to the Inter-American Development Bank, the World Bank, or their corresponding executive directors. The standard disclaimer applies. Correspondence: Chong, Research Department, Stop W-0436, Inter-American Development Bank, 1300 New York Avenue, NW, Washington, DC 20577, USA. Tel. (202) 623-1536; Fax (202) 623-2481. E-mail: _<u>albertoch@iadb.org</u>_
deficit areas. This function is facilitated by some of the large wholesalers, aggregators, and processors, especially millers.

- We complement these findings with international trade data, which show that many of the value chain products that cross borders stay within Sub-Saharan Africa rather than being exported to other countries in the world.

- Table 5 identifies various products that can be associated to the maize, sorghum, or cassava value chains (or more than one of these). For example, we can identify upstream and downstream products associated to the maize value chain, including maize seed and processed food products, including flour, oil, and bran. In other instances, we can identify products that may use maize as an input, for example, beer. For many of the value chain products identified, exports from Ghana, Kenya, and Zambia are destined to the Sub-Saharan Africa region. There are a few exceptions, including maize from Kenya, maize flour from Ghana and Kenya, sorghum products from Ghana, cassava or cassava flour from Ghana, and other cereal flour and groats/meal of other cereals from Kenya. Zambia’s exports of these GVC products are almost entirely to the continent.

- Of the three countries, Zambia is an important exporter of maize seed and maize, although it exports less of products that require further downstream processing, with the exception of bran. Zambia also exports nonalcoholic beverages made from water with added sugar or other sweeteners, which could use inputs of maize products, such as Maheu. Exports of sorghum products or cassava products are not identified in Zambia’s export data.

- Kenya exports maize seed and maize, as well as sorghum grain and sorghum flour and meal. Kenya also exports unroasted malt, beer from malt, and ethyl alcohol, which could be made from sorghum.

- Of these three value chain crops, cassava exports are the most important for Ghana, which exports fresh or dried cassava and flour from cassava.

35
Not all RFMO databases contain all of the above variables, especially vessel characteristics, and characteristics sometimes differ across RFMOs for vessels with the same identifiers. To include more vessels and vessel characteristics, we combine our RFMO data with data from Rongcheng, a hub of Chinese distant water fisheries (General Administration of Customs, 2021). The municipal dataset of Rongcheng is part of the Shandong Provincial Open Data Platform, which was developed in 2018 and contains 318 distant water fishing vessels registered in Rongcheng (Rongcheng Municipal Government, 2021). We also added 104 vessels that are included in the Rongcheng database but not in the RFMO databases. We standardize all vessel lengths to meters and all engine powers to kilowatts. After removing duplicate vessels, we obtain data for 2,216 Chinese distant water fishing vessels.<sup>11</sup> We match these vessel characteristics data to fishing activity data from the organization Global Fishing Watch (GFW). We are able to match about 70% of vessels because most distant water fishing vessels have Automatic Identification System (AIS) transponders that send their movements to satellite and terrestrial receivers. GFW applies machine learning algorithms to predict fishing activity from AIS vessel movements (Kroodsma et al., 2018). This prediction is possible because fishing involves different movement patterns than non-fishing activities. Note that vessels have no differential incentive to disable their AIS transponders with respect to fuel subsidy thresholds because fuel subsidies depend on VMS rather than AIS pings (Section 2).

From the GFW dataset, we extracted information for all vessels flagged to (registered in) China between 2015 and 2020, including the hours of fishing and vessel characteristics. We focused on China-flagged vessels because this is a requirement for a vessel to receive a subsidy from the Ministry of Agriculture. In contrast to the RFMOs, which can obtain detailed vessel data from member countries based on compliance rules, some GFW vessel characteristics are machine-learning-predicted values (Kroodsma et al., 2018). We believe that RFMO vessel characteristics are more reliable than GFW’s for this reason. We ignore

> 11 We have slightly fewer vessels in our data compared to the 2,705 legally authorized distant water fishing vessels at the end of 2020 mentioned in the Chinese Fisheries Statistical Yearbook. The reason for this is that our dataset only includes distant water fishing vessels from seven RFMOs and Rongcheng; we are missing distant water fishing vessels not present in these data. For example, vessels authorized to fish in another country’s waters, and not in any regions managed by RFMOs, could be authorized by the MOA without being registered in an RFMO database.

10