rafmacalaba/datause-encoder-data huggingface dataset:
that half the time that men work on the basis of the data means for SNA activity. If market consumption is assumed to be shared equally, they would be seen as recipients of a substantial transfer within the household while enjoying long hours of leisure. In reality, they may be the source of transfers to other family members due to their longer total hours of work.

To estimate a labor supply/domestic work model on these data, information on individual wage rates and non-labor incomes is required. Here, again, we run into problems with the SSA TUS2000. The survey failed to collect separate information on individual earnings and non-labor incomes. The income variables reported are the “usual total monthly income” of the household and of each respondent in the household. There is also a question on source of income in each case. However, the latter required only a “Yes/No” answer. Thus it is not possible to work out how much income is earned and how much is from non-labor sources. Experience suggests that the latter information is rarely of any use in modeling the effects of policy. It could argued that this deficiency can be overcome by instrumenting for the wage, since the survey was designed to contain questions found in other SSA surveys questionnaires to allow matching. However, cross section data typically yield very poor instruments for wage rates and also for non-labor incomes. There is a further problem with the SSA TUS2000 income data. Quite extraordinarily, the categories of income that were summed to obtain individual incomes included transfers from other family members. This makes the information on individual incomes essentially unusable, since the inclusion of transfers from other family members leads to “double counting” at the household level.

In contrast, the EMNV98 contains detailed data on labor incomes for each individual and it reports separately non-labor incomes at the level of the household. The file also contains detailed data on outside child care which, together with the information on domestic time use, allows the estimation of a combined labor supply and market/domestic child care demand model as outlined in Section 4.5. The results of such a model could, for example, be used to analyze issues raised in the World Bank (2000) 53
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We will then compare the bootstrap estimates of the drought parameter across the candidate models. The model with the least bootstrap variance would be selected. Thus, bootstrapping can be considered as a test of internal validity. The relevant internal validity question can be phrased as: given a model that has been estimated using a nationally representative sample, if we resampled many times, would we estimate a stable relationship?

Note here that either method (bootstrapping or k‐fold) can be used to validate the parameters of an estimated model.

# **<u>Outputs from the analysis</u>**

The shortlisted functions of D(.) in the conceptual section are candidate models 1‐4 outlined above. We proposed that the control variables should be selected on the basis of economic theory/empirical findings/common sense. The interactions should also be selected on that basis, however the number of interactions, and the functional form is to be decided using the statistical analysis. The next step in the exercise is to test the performance of the candidate models in predicting consumption in different contexts (or out of sample predictions) using different values of X, than those based on which the parameters for the D(.) functions were estimated (i.e. minimizing or reducing “test” MSE).

# **How to select candidate functions of D(.)?**

Recall that D(.) comprises the hazard **_I_** and household characteristics **_X_** . There are two issues: a) Specifying the functional form for D( **_h_** ) whilst holding **_X_** constant and b) Which characteristics enter the vector **_X_** ?

For a) we investigate vulnerability functions specific to household “types” as noted in the description of the regression model above. We also investigate ii) whether the vulnerability function for all types should be non‐linear by including squared and cubed terms in the model.

For b) we use economic theory to choose the characteristics of households and communities that enter the function. These will be constrained by the availability of data in the household survey datasets.

- **d. How significant is the variation of the derived functional forms across geographical regions within Ethiopia?**

The first round of testing discussed above involved random division into k parts. However, also split groups according to characteristics that vary across the dataset, but that we have not explicitly identified as determinant of vulnerability, to test _external validity, or generalizability across ecological contexts_ . We use non‐random sampling for the k‐Fold Cross Validation method, separating the k groups according to geographical area (e.g. Woreda) but controlling for differences expected from changes in agro‐climatic conditions. This will tell us if, when we separate out households according to what we have determined to be the key vulnerability characteristics, the relationships hold across geographies.

However, we have reasons to believe that the relationship between rainfall, crop losses and consumption differs considerably in pastoral areas as discussed above so we exclude the pastoral regions from the analysis.

23
Recent research that has used the D&B data includes Harrison, Love, and McMillian’s (2004) analysis of capital ‡ows, Black and Strahan’s (2002) study of entrepreneurial activity in the United States, Acemoglu, Johnson, and Mitton’s (2009) cross-country study of concentration and vertical integration, and Alfaro and Charlton’s (2009) analysis of vertical and horizontal activities of multinationals.

We impose a number of requirements in cleaning the data. First, we drop records that lack primary industry classi...cation and starting year information. Second, we restrict the data to establishments that report sales information for all years. Moreover, we include only establishments with positive sales and employment in 2005. Requiring positive employment helps to exclude establishments registered exclusively for tax purposes as well as self-employment businesses. These types of establishments constitute a signi...cant share of businesses in countries such as the United States. Finally, we drop agricultural and mining industries as well as countries with fewer than 100 observations. These criteria result in a ...nal sample of approximately 12 million establishments in 53 countries.

We use four categories of information for each establishment: (i) industry information including the 4-digit SIC code of the primary industry in which each establishment operates and, for most countries, the SIC codes of as many as ...ve secondary industries, listed in descending order of importance; (ii) ownership information including the establishments’ domestic and global parents, status (joint-venture, corporation, partnership), and position in the hierarchy (branch, division, headquarters); (iii) location information; (iv) operational information including sales, employment, and export status.

Establishment performance is measured in this paper by the annual percentage change of sales. An establishment is considered foreign owned if its parent ...rm is based in a di¤erent country. There are 43,513 foreign owned subsidiaries in the ...nal sample.

# 3.2 Production Linkages

To measure the production linkages between subsidiaries and foreign parent ...rms, we examine the input-output relationship between each subsidiary’s primary product category and the parent ...rm’s ...nal goods.

First, we follow Acemoglu et al. (2009) and Alfaro and Charlton (2009) in considering two main types of MNC subsidiaries: horizontal and vertical. Horizontal subsidiaries refer to subsidiaries that duplicate parent ...rms’...nal-good production. We identify these subsidiaries by comparing their primary product category with the ...nal product categories of their parent ...rms, all reported at SIC 4-digit level. If the subsidiary’s primary product category is listed as one of the parent ...rm’s ...nal products, the subsidiary is considered as horizontal.<sup>9</sup> The

> 9 We also separately considered subsidiaries that produce parent ...rms’core ...nal product and those that produce secondary ...nal products and did not ...nd the two types of horizontal subsidiaries to have di¤erent performance.

9
Appendix Table 3: Balance for Supplementary Treatments

||Control<br>Group|Wage<br>Subsidy|Wage Subsidy<br>& Savings|Wage Subsidy<br>& Training|Training<br>Only|Savings<br>Only|F‐test<br>p‐value|
|---|---|---|---|---|---|---|---|
|_Re‐randomized Variables_||||||||
|Number of Paid Workers|0.19|0.16|0.20|0.15|0.23|0.25|0.507|
|Education (Years)|10.35|10.25|10.32|10.54|10.52|10.43|0.659|
|Raven Test Score|3.34|3.34|3.32|3.12|3.28|3.35|0.687|
|Digitspan Recall Score|6.42|6.36|6.35|6.39|6.27|6.21|0.681|
|Total Assets|239893|250563|232635|203654|236665|258261|0.430|
|Total Assets<1500LKR|0.06|0.02|0.03|0.03|0.01|0.05|0.135|
|Total Assets>935000LKR|0.05|0.06|0.04|0.03|0.04|0.05|0.743|
|Monthly Profits|13862|14552|14010|12947|13282|13861|0.491|
|Profit Data Missing|0.03|0.02|0.03|0.03|0.02|0.02|0.624|
|Monthly Profits<2000LKR|0.06|0.03|0.03|0.04|0.03|0.04|0.896|
|Monthly Profits>30000LKR|0.04|0.06|0.06|0.04|0.06|0.01|0.002|
|Business Practices Score|8.27|8.76|8.24|8.59|9.24|8.35|0.440|
|From booster sample|0.52|0.53|0.54|0.56|0.54|0.46|0.435|
|_Stratification Variables_||||||||
|Retail Sector|0.39|0.38|0.38|0.38|0.38|0.38|1.000|
|Colombo|0.47|0.44|0.47|0.46|0.49|0.47|0.898|
|Kandy|0.47|0.48|0.47|0.47|0.48|0.47|1.000|
|_Additional Variables_||||||||
|Any paid worker at baseline|0.12|0.10|0.13|0.09|0.15|0.12|0.385|
|Monthly Sales|41175|52435|49142|41785|46768|34496|0.013|
|Owner's Age|35.43|35.16|36.19|34.91|34.28|34.38|0.049|
|Business is Registered for Taxes|0.31|0.32|0.32|0.31|0.31|0.40|0.478|
|Weekly hours worked|57.94|59.31|60.77|60.41|57.20|59.78|0.496|
|Sample Size|286|250|297|298|141|112||

48
|**Table 1. Population mea**|<br>**ns**||
|---|---|---|
|**National**|**Agricultural**<br>**Households**<sup>a</sup>|**Nonagricultural**<br>**Households**<sup>**a**</sup>|
|1926<br>Nominal value of per capita monthly total|1752|2159|
|(1158)<br>consumption(Afghani)|(934)|(1371)|
|1158<br>Nominal value of per capita monthly food|1134|1189|
|(583)<br>consumption(Afghani)|(528)|(648)|
|2601<br>i il li ik killi|2587|2619|
|(974)<br>Per capta day caorc ntae (ocaores)|(977)|(969)|
|60.95<br>Fd i|61.59|60.07|
|(20.03)<br>oo consumpton score|(19.48)|(20.71)|
|44.87<br>|45.37|44.19|
|(13.78)<br>Age of household head|(13.68)|(13.89)|
|2.09<br>Nb f l|2.19|1.96|
|(1.30)<br>umer o maes|(1.33)|(1.25)|
|2.01<br>|2.09|1.89|
|(1.19)<br>Number of females|(1.22)|(1.13)|
|4.51<br>|4.75|4.18|
|(2.39)<br>Number of children under 16|(2.46)|(2.26)|
|Dummyfor married household heads<br>0.95|0.95|0.94|
|Dummyfor literate household heads<br>0.32|0.30|0.34|
|Dummyforplateau areas<br>0.22|0.27|0.16|
|Dummyfor mountainous areas<br>0.39|0.49|0.27|
|Total observations<br>20,491|11,633|8,858|
|Percentage of full sample<br>100.00|0.57|0.43|
|Note: Estimated population weighted means. Standard deviations in p<br>liter (kerosene).<sup>a</sup>Statistical tests of differences in means (corrected fo<br>faced by Agricultural and Nonagricultural Households (based on loca<br>of significance for all estimated means._Source:_NRVA 2007/08|arentheses. Prices in A<br>r complex sample desi<br>tion) show significant d|fghani per kilogram or<br>gn) between prices<br>ifferences at a 5% level|

26
Table 17: Program Impact on children (6 to 60 months old) nutritional status

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Children|6-60 mont<br>|hs old<br>|Children|12-60 mont<br>|hs old<br>|
||Weight for<br>Age|Weight-<br>for-<br>Height|Height-<br>for-<br>Age|Weight for<br>Age|Weight-<br>for-<br>Height|Height-<br>for-<br>Age|
|School|||||||
|Meals|-0.219|0.005|-0.19|-0.172|0.062|-0.135|
||[0.129]*|[0.157]|[0.180]|[0.132]|[0.160]|[0.186]|
|Dry Rations|0.355|0.291|-0.212|0.376|0.333|-0.189|
||[0.153]**|[0.181]|[0.209]|[0.157]**|[0.187]*|[0.216]|
|Follow-up|0.547|0.161|0.298|0.516|0.166|0.201|
||[0.093]***|[0.112]|[0.133]**|[0.094]***|[0.114]|[0.137]|
|Girl|0.138|0.121|0.116|0.148|0.109|0.123|
||[0.056]**|[0.065]*|[0.075]|[0.058]**|[0.067]|[0.079]|
|Constant|-1.65|-0.098|-0.792|-2.077|-0.437|-0.749|
||[0.277]***|[0.337]|[0.358]**|[0.525]***|[0.625]|[0.758]|
|Observations|3422|3085|3079|3200|2890|2876|
|R-squared|0.15|0.08|0.08|0.16|0.09|0.08|

# Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1%

Dependent variables are z-scores of weight-for-age, weight-for-height and height-for-age.

Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

40
# The Globalization of Refugee Flows<sup>_∗_</sup>

Xavier Devictor<sup>_†_</sup> Quy-Toan Do<sup>_‡_</sup> Andrei A. Levchenko<sup>_§_</sup> _Updated January 2021_ _JEL codes_ : J15, F22, F55 _Keywords_ : refugees, forced displacement, responsibility-sharing, UNHCR

> _∗_ We are grateful to the editor (Andrew Foster), two anonymous referees, C ̧a ̆glar  ̈Ozden, Philip Verwimp, and workshop participants at the Research Conference on Forced Displacement in Copenhagen for helpful suggestions, and to Chau Hai Le for superb research assistance. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent.

> _†_ World Bank: xdevictor@worldbank.org

> _‡_ World Bank: qdo@worldbank.org

> _§_ University of Michigan, CEPR, and NBER: alev@umich.edu
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- 13 -

# m1. DEVELOPMENTS IN THE INTERNATIONAL SEATRADE MARKETS

11. In 1960 the annual volume of international seatrade had reached a level of one billion tons, with equal shares of dry and liquid cargo. During the following years the average annual gr^wth rate of liquid cargo-essentially crude oil and its derivatives-was 11.5 percent. The yearly increments of dry cargo were considerably lower. In the early 1970s the share of liquid cargo in the annual seatrade volumes had increased to about 60 percent. It peaked with 1.87 billion tons (versus 1.35 billion tons for dry cargo) in 1973. During that year the first international oil crisis materialized and had an immediate effect on the volume of liquid cargo trades, which declined to 1.5 billion tons in 1975. Meanwhile the annual volumes of dry cargo continued to increase, caught up with the liquid cargo trades in the mid 1970s, and exceeded the growth of liquid cargo by considerable margins until 1980 (see Figure i below).

<!-- Start of picture text --> F1gure 1 World Seatrade<br>By Main Trade Categories<br>miliion tons million tons<br>4500- 4500<br>4000- -4000<br>3500- 3500<br>3000- 3000<br>2500 -2500<br>2000- 2000<br>1500- 1500<br>1000 1000<br>500 500<br>75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90<br>= Total Seaborne Trsdeo - General Cargo Trade<br>Dry Bulk Trade - Oil Trade<br>Source: Institute of ShiDping Economics enO Logistics. 'Statistical eerbook 1991. eremen<br><!-- End of picture text --> 12. At the beginning of the 1980s the second oil shock occurred. Unlike in 1973, however, the effects of this crisis impacted on both trades, and their volumes declined for several years. By 1983 the total volume of international seatrade had fallen below the level of 1974. But the consolidation phase for dry cargo was relatively short and growth resumed after 1983, whereas the decline in annual liquid cargo volumes continued until 1985. The mild recession in the world economy of the mid 1980s had a temporary dampening effect on both trades. Following a brief respite each segment continued to grow
in their tariffs, a procedure which encouraged them to make larger cuts in their smaller tariffs. The Doha tariff-cutting formulas have the economically desirable feature of making larger cuts in the higher—and hence more costly—tariffs.<sup>1</sup> In line with longstanding practice, developing country cuts in each band are two-thirds those of the industrial countries. The bands are also wider, in part to allow for the fact that many developing countries would otherwise have more tariffs included in the higher bands.

Special provisions apply for tariff escalation products. Here the general principle is that processed products subject to tariffs higher than their raw or intermediate forms are moved to the next higher tier. If they are in the highest tier, the cut is increased by 6 percentage points. If the gap between the processed and unprocessed product is less than 5 percentage points, then the tariff escalation procedure is not used, and the tariff on the processed product should not be brought below the tariff on intermediates.

A list of ―tropical‖ and diversification products will be subject to deeper-thanformula cuts. Two lists of products have been considered—one includes highly sensitive products such as rice, sugar and bananas (see Appendix G of the agricultural modalities), and another is the more limited list used in the Uruguay Round. Two alternative treatments have been discussed. Under the first, tariffs below 25 percent would be reduced to zero, and no sensitive product treatment permitted. Under the second, tariffs below 10 percent would be reduced to zero, while higher tariffs would be reduced by 70 percent, except for products already in the top tier, which would be cut by 78 percent. Under the second alternative, sensitive product treatment will not be ruled out. The Uruguay Round set, without sensitive products, is used in the empirical analysis.

Several groups of developing countries listed in Table 2 are allowed smaller tariff reductions. Least Developed Countries (LDCs) are not required to make any reductions. Small and Vulnerable Economies (SVEs)<sup>2</sup> can make reductions 10 percent smaller in each band than other developing members, or may make an average-cut of 24 percent. Recently-acceded members (RAMs) are allowed to: make cuts reduced by 8 percentage points; make zero cuts in tariffs below 10 percent; to delay their reductions until a year

> 1 As conjectured by Falconer (2008) and shown by Jean, Laborde and Martin (2010a), the political costs, and hence the pressure for exceptions, may rise even more rapidly than the economic benefits. 2 Defined in general as countries with less than 0.1 percent of world trade, with some countries such as Congo, Côte d‘Ivoire and Nigeria treated on the same basis in agriculture. See Table 2 for the country list.

5
|**Village Green**<br>**https://www.epa.gov/air-research/village-**<br>**green-project**|US-EPA community-based project aimed at<br>raising awareness on local air quality|
|---|---|
|**AirVisual**<br>**https://www.airvisual.com/**|Aimed at creating a crowd sourced global<br>network for static air quality monitors|
|**PurpleAir**|a US-based low-cost air quality monitoring<br>network|
|**https://www2.purpleair.com/**||

Data collection focused on anticipated emissions from anthropogenic processes within the predefined study areas with a primary focus on Particulate Matter (PM2.5 and PM10), supplemented by NO2 and O3 passive data sets. These pollutants are not only indicative of the ambient air pollution profile in Uganda but are also within the context of WHO-classical and US-EPA-criteria pollutants i.e. pollutants considered to have adverse effects on human health upon inhalation and ingestion (through contaminated food & water), and to a small extent, dermal contact (cited in Kampa & Castanas, 2008) as informed by empirical clinical and epidemiological studies (see table 2).

_Table 2: Classical and Criteria Pollutants (WHO, 2005; EPA, 2018)_

|**Classical**<br>**Pollutants**|**Criteria**<br>**Pollutants**|**Major Sources**|
|---|---|---|
|PM|PM|Automobile-generated particulates, combustion<br>emissions, industrial sources, dispersion and ambient<br>suspension of loose surface particulates by wind, etc.|
|NO2|NO2|Direct combustion of fossil fuel from thermal fixation of<br>atmospheric nitrogen in the combustion environment|
|O3|O3|Series of reactions between NO2and VOCs, CO, CH4,<br>etc. initiated by sunlight|
|SO2|SO2|largely combustion of Sulphur bearing fuels|
||CO<sup>10</sup>|Incomplete combustion of fuels and inadequate<br>ventilation due to limited oxygen supply e.g. tobacco<br>smoke, engine exhaust, etc.|

# **2.2 Geographic Scope (Study Area)**

This study was restricted to predefined geographical project boundaries in selected sub-counties<sup>11</sup> within three of the four macro-regions of Uganda (i.e. Central, Southern and Eastern) defined to

> 10 Only available from US-EPA criteria pollutants.

> 11 Sub-counties are the official administrative units which collectively make up a district, which then are aggregated to form macro-regions (presently four in total).

6