rafmacalaba/data-use-mentions-extended huggingface dataset:
Y|Y|Y|Y|Y|
|Population 1991|Y|Y|Y|Y|Y|Y|
|Socio-Economic Development 1991|Y|Y|Y|Y|Y|Y|
|Region FE||Y|Y||Y|Y|
|Province FE|||Y|||Y|

**Notes** The table presents OLS regression estimates measuring the effect of the share of refugees in 1923 on the presence of sport club in the early 2000s, at the community level. Standard errors are clustered at the province level. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the 1928 population census, 1923 refugee census and General Secretariat of Sports’ inventory.

Table A.8: Sport clubs and Albanians immigrants

|Dep. var:||Presen|ce of at le|ast one spo|rt club||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Share of Albanian immigrants in 2001|0.123|0.135|0.131|0.134|0.141|0.050|
||(0.143)|(0.140)|(0.148)|(0.098)|(0.090)|(0.099)|
|Share of Albanian immigrants in 2001 * small community||||-0.015<br>(0.188)|-0.008<br>(0.185)|0.112<br>(0.188)|
|_R_<sup>2</sup>
br>**itures, % of**<br>**GDP**<sup>**b**</sup>|**Domestic**<br>**Tourism**<br>**Expenditures,**<br>**% of GDP**|
|---|---|---|---|---|---|---|
|Panama|9,392|71%|452–642<br>[+313]<sup>c</sup>|3.8|5–7 %<br>[+3%]<sup>c</sup>|0.04%|
|Bocas del<br>Toro|165|4%|17–31|0.9|11–19%|0.57%|
|Chiriqui|1,036|12%|53–94|4.3|5–9%|0.41%|
|Rest of|3,413|13%|57–100|8.8|2–3%|0.26%|
|Panama|||||||
|Total|14,005|100%|579–867<br>[+313]|17.8|4–6%<br>[+3%]|0.13%|

a. For details about the estimation procedure and the meaning of the ranges, see text footnote 15.

b. Expenditures in the national accounts and the survey data in this table exclude the cost of international travel by air and land.

c. In addition, tourists in direct transit (that is, tourists that spend less than four hours in Panama and do not leave the airport) have spent a further US$313 million in 2006. This constitutes an additional 3 percent of
of the relation between the corresponding indicator and per capita GNP, based on the data for some 125 countries in 1993; the last two columns are authors' computation, following procedures explained in the text.

a/ Population aged 6-1 1 as percent of total population.

b/ As ratio of the per capita GNP.

c/ This item is equal to the product of the components on the left-hand-side of equation (1"'), i.e. the product of the first two items in column 2, and the inverse of the third and fourth items in the column.

d/ In the framework of equation (1"'), country B's advantage is computed, for the first two items listed, as the ratio of its indicator to that of A; and for the last two items, as the inverse of its indicator to that of A. For the third item, the calculation is explained in the text.

We turn next to consider the other sources of country B's advantage in resource availability for education. A lighter demographic burden is an important source of advantage because it means more can be spent per school-age child.<sup>2 4</sup> Table 3 shows that whereas the share of the school-age children in the population is 23 percent in country A, it is only 17.5 percent in country B. With a smaller school-age population country B can allocate more resources for education per child. If A and B have the same aggregate budget for education as well as the same population size, the resources available per child of school-age would in fact be 31 percent (= (23.0/17.5) x 100) greater in country B.

Finally, because teachers are a key input in the production of education services, differences in the relative cost of teachers also influence the resources that are effectively available for education. As countries grow richer in the course of economic development the prices of most goods and services tend to rise
|
|Number of people working in business|2|2|2.1|2|0.2|
|Number of owners|1.1|1.1|1.1|1.1|1.9|
|Owner started business|89.7|88.7|91.9|91.2|1.4|
|Owns space where operates business|36.2|31.1|32.6|35.3|1.5|
|# new products introduced past 12 months|0.6|0.8|0.7|0.5|1.4|
|Business with access to electricity|26.9|25.3|28.1|25.1|0.8|
|# of competitors|14.1|13.8|15.2|14.6|0.2|
|<br>Time to nearest bank (minutes)|21.3|18.9|20.5|21.3|3.3**|
|**_Individual Characteristics_**||||||
|Owner age|33.6|32.8|33.3|34|1.8|
|Married / Living with someone|78.9|79.9|80|81.5|0.5|
|HH decision making index (0-100)|83.5|83.7|84.3|84.9|0.6|
|Main provider of income to household|77.6|76|78|74.7|1|
|Literate|92.7|92.8|90.7|91|1.1|
|Primary school completed is max education|34.8|34.8|35.9|36.5|0.2|
|Secondary school completed is max education|23
trends of the middle class over time, produces outcomes that don‘t align with our general conceptual definition of the middle class. Applying the thresholds defined by Ravallion and B&D to Chile, Mexico and Peru we observe that the middle class has decreased over the whole period of time analyzed in this paper. Conversely, the middle class has increased in times of economic downturn moving in parallel with poverty levels.

**Figure 1: People living on $2-10 a day in Chile, Mexico and Peru (B&D’s measure of middle class)**

_Percentage of households_ <!-- Start of picture text --> 60.6<br><!-- End of picture text --> <!-- Start of picture text --> 56.7<br><!-- End of picture text --> <!-- Start of picture text --> 56.7<br>51.8<br><!-- End of picture text --> <!-- Start of picture text --> 36.5<br><!-- End of picture text --> _Source_ : Author‘s calculations based on data from CASEN, ENIGH and ENAHO cross-sectional databases.

**Figure 2: People living on $2-13 a day in Chile, Mexico and Peru (Ravallion’s measure of middle class)**

_Percentage of households_ <!-- Start of picture text --> 64.3<br>62.9<br>61.0<br><!-- End of picture text --> <!-- Start of picture text --> 49.9<br><!-- End of picture text --> _Source_ : Author‘s calculations based on data from CASEN, ENIGH and ENAHO cross-sectional databases.

As figures 1 and 2 show, the size of the middle class increased in Mexico during
international migrations), what are the information needs? And how to prioritize? What are the opportunities offered by the household survey data collection? What would it be possible to achieve with slight modifications to the current data collection instruments utilized by popular, long-established household survey programs, such as the World Bank’s Living Standards Measurement Study (LSMS)?

This critical review aims to shed light on the issues above. Given the vastness of the topic and the huge body of work in the field, a few preliminary clarifications on the scope of this review are in order: first, this review has an explicit orientation toward the _empirical_ and _data_ perspective on the climate changemigration links. Hence, we look at retrospective studies using historical data, we assume as given the state of the art of the theoretical underpinnings of the current empirical investigation, and we do not consider studies on projections or predictions of future migratory flows; second, since it is now widely recognized that developing countries, which are both hotter and poorer, will be disproportionately affected by climate change (Auffhammer, 2018; Dell, Jones and Olken, 2014; Nordhaus and Moffat, 2017; Tol, 2018), the focus of this work will be on migration originating within and/or from these countries; third, we will focus on the more _recent_ empirical advances. This means we limit our exploration to studies and works published in the last decade (specifically, from 2010 on);<sup>2</sup> fourth, in our assessment of data gaps in household survey data, we will look specifically at the Living Standards Measurement Study (LSMS) collection by the World Bank. The LSMS has been the World Bank’s flagship household survey program since 1980, and it is currently a global leader in the methodological development of rich and extensive multi-topic questionnaires designed to study various aspects of household welfare. <mark>Within the broader LSMS program, we restrict our attention to the LSMS-ISA household survey program, the explicit a</
we obtain a tenure security variable at household level that includes tenure perception provided by Prindex and ownership status provided by the most recent household consumption and expenditure survey.<sup>10</sup>

# **_<mark>3.3 Summary Statistics of Adequacy Dimensions</mark>_**

This section explores housing adequacy at dimension level. Table 1 below provides summary statistics of the seven adequacy dimensions embedded in the _AHI_ . Across all 64 economies, except for five countries,<sup>11</sup> all seven adequacy dimensions are available and harmonized across various variables according to the definitions provided in Section 3.1. The mean of each dimension

> 9 For a concise overview on the calculation of the wealth quintile, consult <u>https://dhsprogram.com/data/Guide-toDHS-Statistics/Wealth_Quintiles.htm.</u>

> 10 As we are averaging perceived tenure security across income groups (HH level), rural / urban areas (country-level), and ownership status (HH level) in both data sets, the difference between the individual-level data that Prindex presents and the HH-level data of the HCES housing modules does not present an issue. 11 Kazakhstan, Russian Federation, Ukraine, Uzbekistan, and Vietnam.
The results in Table 4 suggest that disease environment effects (the current monsoon rainfall shock) are more salient for boys in the first two years of life. This apparent disadvantage of boys, however, could just as well reflect the interaction of biological and environmental factors than any differences in behavioral responses to child illness on the part of parents. At any rate, based on the coefficients on last completed monsoon rainfall deviations, we do not find much evidence of “discrimination” against girls; the point estimates across columns 2 and 3 are statistically indistinguishable. Indeed, once we net out the larger disease environment effects for boys – using our arguments from Section 4 – the pure _income effects_ are extremely close for boys and girls.

# _Robustness_

As support for our argument that excess rainfall in the current or ongoing monsoon is associated with the disease environment, we examine the determinants of diarrhea episodes in our sample children. DHS surveys record the incidence of diarrhea in the two weeks preceding the day of the interview; 20% of children in our sample reported a bout of diarrhea. Results in Table 5 indicate a significant positive relationship between excess rainfall and contemporaneous diarrhea among children. Insofar as diarrheal morbidity inhibits nutrient uptake and thereby decreases body weight in the short-run, we take this evidence as corroboration that the current monsoon rainfall shock operates through a disease environment channel.

Turning next to the rainfall shock in the last completed monsoon, we perform two robustness checks. First, if our interpretation is correct, then we should see that the implied income effect is much smaller, if not nonexistent, for children residing in urban areas (see also Maccini and Yang, 2009). After all, urban households’ purchasing power is likely to be much less sensitive to the vagaries of the harvest than that of rural households. Indeed, the results reported in Table 6 suggest as much. Using the coefficients in the last column, we can bound the income effect of monsoon rainfall shocks for urban children at no greater than -0.014
|**Table 3:**<br>**Confirmation perce**<br>**to teacher utility.**|**ntages of various educa**<br>|**tional inputs sorted b**|**y direct importance**|
|---|---|---|---|
||1<br>| Number of studies|Positive and<br>significant relation|Confirmation<br>Percentage|
|**Primary Schools:**||._.||
|Teacher's salary level|11|4|36.4|
|School teacher pupil ratio|26|9|34.6|
|Teacher's years of schooling|18|9|50.0|
|Teacher's experience|23|13|56.5|
|Class instructional time|17|15|88.2|
|Class frequency of homework|11|9|81.8|
|School library|18|16|88.9|
|School textbooks|26|19|73.1|
|**Secondary schools**||||
|Teacher's salary level|11|2|18.2|
|School teacher pupil ratio|22|2|9.1|
|Teacher's experience|12|1|8.3|
|Class instructional time|16|12|75.0|
|School textbooks|13|7|53.8|
|,Source: Fuller and Clarke (1994)||||

If marginal product per dollar were equalized, there is no compelling reason to believe that the confirmation ratios would not have been roughly equal across inputs. While this evidence is far from airtight, as it is possible the differences in precision could have affected the pattern of statistical significance, the most straightforward way to read this evidence is that studies that use
namely **by** taking **q** in (3.4) to equal the replacement cost of capital. 12 **In** turn, we value physical gold stocks (taken from the IMFs **IFS)** at current market (London) prices. **13** Using these estimated wealth stocks, we construct the foreign wealth **/** domestic wealth ratios of country _i_ as the sum of wealth across all sample countries other than _i_ divided **by** the wealth of country _i._

# **3.2** Measures of return and risk

Apart from wealth ratios, the two key explanatory variables in our model of net foreign asset positions are the measures of anticipated risk and return for each country. In practice, these likely depend on a large variety of variables reflecting relative prices, profitability, transaction costs, property rights, tax regimes and so on. However, degrees-of-freedom considerations prevent us from including a large number of variables in the empirical estimation. In order to reconcile the need to consider all relevant variables with the requirement to maintain a sensible number of degrees of freedom, we summarize the information provided **by** pertinent variables in a few indices. These indices correspond to the categories introduced in equation **(2.6).** That is we construct, respectively, indices for expected returns _(REl)_ and perceived risks _(RIiy)._ We construct each index as a weighted average of the principal components of its respective underlying indicators, where the weights are given **by** the share of the indicators' overall variance explained **by** each principal component. The principal components are derived from the indicators data pooled for all countries in the sample. The resulting index for each country would then be relative to the world and time sample average.

The underlying indicators for each index are listed below.'<sup>4</
phone's location is computed as the average of centroids of antennas in that cluster, weighted by the number of events used by each antenna. Finally, the phone is assigned to the PSU where that point belongs.

# II.4 Extrapolating to the overall population

The above process determines how many of Telefonica's phones reside in each PSU, and how many of them are likely to be Venezuelan. The figures were adjusted by canton-wise adjustment factors based on Telefonica's market shares (known from other sources), to estimate the total number of phones from all companies and the total number of Venezuelan phones from all companies in each sector. Another adjustment factor based on the fraction of the population that uses mobile phones was used to make the figures consistent with the total number of households reported by INEC in each sector at the time of the 2010 Census.

# II.5 Results

At the country level, the total number of Venezuelan migrants estimated in 2019 by Telefonica de Ecuador was 470,095. This population represented about 3 percent of the total population in Ecuador in 2019 and

> 12 The workplace was also determined by monitoring cellphones’ activity from 9 am to 5 pm. The idea was to see whether Venezuelans move far away from their homes to seek for a job or not.

5
developing countries.<sup>7</sup> We also leverage new data on robots types – e.g. laser guided cutting vs mechanical cutting – to examine whether certain robots are more crucial for quality upgrading than others.

Export quality has attracted a great deal of attention because of its importance for the development and growth (Hirschman 1958; Grossman and Helpman 1991; Hidalgo et al 2007). At the firmlevel, businesses that produce higher quality goods have higher revenue and employment, pay higher wages, are more productive and sell their goods to a greater variety of markets (Verhoogen 2008; Manova and Zhang 2012; Crozet et al 2012). Producing high quality exports is also often a

> 6 We also add control variables for (log) industry employment and real value added per worker.

> 7 Including developing countries in our sample necessitates using data from 2000, whereas for advanced economies data is available from 1993 (as in Graetz and Michaels, 2018).

5
the Arab Spring (2007-2010) and the period after the Arab Spring (2010-2012). For the regional analysis, we construct the synthetic panel using data from 16 countries, including the Arab Spring countries (Egypt, Libya, Syria, Tunisia, and Yemen) and other countries (Algeria, Bahrain, Iran, Iraq, Jordan, Kuwait, Lebanon, Morocco, Qatar, Saudi Arabia, and United Arab Emirates), but for the country-level analysis we focus on only nine countries with larger sample sizes (i.e., Algeria, Egypt, Iraq, Jordan, Lebanon, Morocco, Syria, Tunisia, and Yemen).<sup>13</sup> Life satisfaction in the Gallup Poll was measured using a question known as the “Cantril Ladder” or “Self-Anchoring Striving Scale” (Cantril, 1965). The question is stated as follows: _“Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”_ The higher the given score is, the closer the respondent’s life is to his or her “ideal” life.<sup>14</sup>

# **III.2. Setting Welfare Thresholds**

Table 1 displays the country-specific thresholds for the bottom 40 percent and the middle 40 percent for both objective measures and subjective measures, based on our first definition of the welfare categories. As discussed above, these thresholds are obtained from the data in the first survey period and are then kept fixed in the second period for the welfare dynamics analysis. This table illustrates our earlier discussion that the bottom 40 percent represent the poor and the vulnerable in a country but to different extents across countries. For example, the bottom 40 percent in Egypt includes all the poor and
done in the studies cited above, results in substantial gender differences, even after firm and manager characteristics have been controlled for.

The finding that women operate smaller scale firms begs the question why. One possible explanation is that they sort into industries which have a lower optimal scale, although this only pushes the question another step backwards. Another salient explanation is that they lack access to finance. Evidence from developed countries on this issue is mixed.<sup>6</sup> Furthermore, cultural norms may militate against women being in power or engaging in certain activities. Alternatively, successful female firms, which tend to be larger, may be more likely to be “captured” by husbands. Women entrepreneurs could also face different constraints. However, using investment climate survey data from Africa Bardasi and Sabarwal (2009) find little evidence for differences in self-reported constraints once firm characteristics are conditioned on.

Since most of these studies are based on urban enterprises, it is not clear to what extent their conclusions generalize to rural areas, where firms tend to be smaller and firm performance is arguably more intimately intertwined with household- and farm events, and the investment climate is radically different (see World Bank, 2004, Deininger, Songqing and Sur, 2007, Jin and Deininger, 2009, Rijkers, Soderbom and Loening, 2010). Despite their importance as a potential catalyst of growth and an absorber of growing rural labor supply, little is known about the determinants of the performance of non-farm firms and how these may vary with the gender of the manager. In addition, the existing evidence on gender differences in rural non-farm entrepreneurship is overwhelmingly based on household- and labor force surveys, which typically lack detailed information on firm characteristics and the investment climate.

# **(b) Country Context**

The countries in this study, Bangladesh, Sri Lanka, Ethiopia, and Indonesia, were selected to be part of the RICS pilot program because in all of them the non-farm economy is a potentially 5
## **Box 4.5: Major Constraints Listed by the Sampled Enterprises in the Rural FGDs, 2009**

|1. Seasonality of income from agriculture, resulting in business stagnation (especially businesses dependent<br>on farmers‟ seasonal incomes).|
|---|
|2. Poor road infrastructure to the villages (hence high transport cost for goods)|
|3. Low capital which hinders expansion or start-up of businesses.<br>4. Lack of business skills; lack of training|
|5. Poor harvest (due to bad weather), high price of agricultural inputs, poor agricultural markets, poor<br>market information, and lack of irrigation facilities; hence less money from agriculture to invest in<br>business.|
|6. Market competition among entrepreneurs in the same village and/or entrepreneurs from other villages<br>(e.g., auction markets for selling clothes attract traders from several areas, including from the nearby<br>towns).|
|7. Security (risk of theft), coupled with lack of secure places to keep money (e.g., banks which are easily<br>accessible)|
|8. Remote location of some business enterprises|
|9. No saving culture|
|10. Selling by credit with possible risk of default by borrower|
|11. High income tax charged bythe Tanzania Revenue Authority (TRA)|

# **4.5 Coping Strategies**

67. While much of the studies conducted so far have generated a wealth of information about the many constraints faced by HEs, little is known about how they cope with these difficulties. Narratives from the FGD participants shed light on some of the coping strategies HEs adopt to overcome these constraints or mitigate their effects. Some of these findings are corroborated by the results of the recent FinScope survey (Table A1.18).

68. Most HEs survive from a variety of risks and constraints with
household number BMGF/DigitalGlobe (DG) settlement outlines for polygons North Somalia UNFPA/PESS rural population estimates points Pre-war regions boundary polygons Waterbody (OSM) polygons

# **2.4 Data pre-processing**

# **2.4.1 Definition of urban and rural stratum within 18 pre-war regions**

Defining major strata is the first step in generating EAs as the maximum population size and areas constraints of EAs may need to vary in different strata. For example, Urban versus Rural strata typically need very different constraints to account for the differing population densities. In Somalia, urban strata were defined using the previous urban EAs from PESS 2014 (UNFPA 2014). The previous urban EAs were dissolved using the dissolve tool in ArcGIS. The remaining area outside of the urban strata was considered rural. To define and compute the urban and rural strata for each Somalia pre-war regions, urban and rural strata were intersected with the 18 pre-war regions administrative boundary. Based on World Bank recommendations the urban and rural strata in Banadir were merged and considered as urban.

# **2.4.2 Settlement boundaries**

Data on settlement locations and boundaries are needed to inform EA delineation in the ‘merge’ part of the algorithm and are also used to create our refined 100m population density estimates. Recent increased availability of high-resolution satellite imagery and high-power computing resources with adequate image processing algorithm have advanced the development of high-resolution human settlement layers (Vijayaraj et al. 2007; Florczyk et al. 2016; Roy Chowdhury et al. 2018). Examples of recently developed human settlement layers include Global Urban FootPrint (GUF) (Esch et al. 2017), Global Human Settlement Layer (GHSL) ( <mark>Pesaresi et al. 2013</mark> ) and LandScan Settlement Layer (LandScan SL) <mark>(Cheriyadat et al. 2007)</mark> . These data sets either are not available for Somalia or are incomplete for the country or they might not specifically be trained for Somalia since