rafmacalaba/data-use-mentions-extended huggingface dataset:
all had female partners. All conversations were recorded and transcribed with the previous authorization of the participants.

Table 3. Profile of Communities Visited

|_Community_|_Municipality,_<br>_Department_|_Extreme poverty_<br>_(%)* _|_Main economic activity_|
|---|---|---|---|
|_Palo Marcado_|Lepaterique, Fco.<br>Morazán|57.5|Vegetables - subsistence|
|_Las Tablas_|Lepaterique, Fco.<br>Morazán|57.5|Vegetables -<br>underemployment|
|_Las Crucitas_|Choluteca,<br>Choluteca|44.9|Precarious and temporary<br>wage work|
|_Coffee Cooperative Belen Limited,_<br>_COCABEL_|Belén, Lempira|78.0|Coffee cooperative|
|_Coffee Cooperative Capucas,_<br>_COCAFCAL_|San Pedro de<br>Copán, Copán|56.5|Coffee cooperative|
|_Female Association of Coffee_|La Labor,|60.9|Coffee cooperative|
|_Producers of La Labor, AMPROCAL_|Ocotepeque|||
|_Cooperative of United Agricultural_<br>_Producers, COPRAUL_|Sinuapa,<br>Ocotepeque|60.4|Vegetable seedlings<br>cooperative|

Source: Authors’ tabulations. Extreme poverty rates is the percent of households living in extreme poverty in 2014, based on World Bank estimates using the Honduran Population Census and Household Surveys.

Table 4. Number of Interviews by Community

|_Communities_|_Number of individual_<br>_interviews - Women_|_Number of individual_<br>_interviews - Men_|_Focus Group_<br>_Discussions - Women_|
|---|---|---|---|
|_Southern Zone: 3 communities (Palo_|4 Palo Marcado|5 Palo Marcado|2 Palo Marcado|
|_Marcado, Las Tablas & Las Crucitas)_|8 Las Tablas|4 Las Tablas|1 Las Tablas
Peri, Giovanni, and Chad Sparber. 2011. “Highly Educated Immigrants and Native Occupational Choice,” _Industrial Relations: a journal of economy and society_ , 50 (3): 385-411.

Piracha, Matloob and Florin Vadean. 2013. “Migrant educational mismatch and the labour market.” In A. F. Constant & K. F. Zimmermann (Eds.), _International Handbook on Economics of Migration_ (pp. 176-192). Edward Elgar Publishing Ltd.: Cheltenham, UK.

<mark>Premji, Stephanie, Patrice Duguay, Karen Messing and Katherine Lippel. 2010. “Are immigrants, ethnic and linguistic minorities over‐represented in jobs with a high level of compensated risk? Results from a Montréal, Canada study using census and workers' compensation data.”</mark> _<mark>American journal of industrial medicine</mark>_ <mark>,</mark> _<mark>53</mark>_ <mark>(9): 875-885.</mark> <mark>Rojas, Felipe Lozano, Xuan Jiang, Laura Montenovo, Kosali I. Simon, Bruce A. Weinberg, and Coady Wing. 2020. “Is the cure worse than the problem itself? Immediate labor market effects of COVID-19 case rates and school closures in the US.”</mark> _<mark>National Bureau of Economic Research</mark>_ <mark>Working Paper No.27127.</mark> Rich, Judith. 2014. “What Do Field Experiments of Discrimination in Markets Tell Us? A Meta Analysis of Studies Conducted since 2000.” _Institute for the Study of Labor (IZA) Discussion Paper_ 8584.

Sanromá, Esteve, Raúl Ramos and Hipólito Simón. 2015. “Portability of Human Capital and Immigrant Overeducation in Spain,” _Population Research and Policy Review_ , 34 (2): 223-241.

<mark>Smith, Peter M., Agnieszka A. Kosny, and Cameron A. Mustard.
|Constant|(0.097)<br>0.298***|(0.158)<br>0.330|(0.470)<br>-2.032***|(0.550)<br>-2.791***|(0.622)<br>-2.144***|(0.718)<br>-1.588***|(0.646)<br>-3.303***|(0.639)<br>-3.341***|
|---|---|---|---|---|---|---|---|---|
||(0.098)|(0.241)|(0.494)|(0.463)|(0.352)|(0.384)|(0.484)|(0.538)|
|Number of observations|618|468|629|472|678|521|553|426|
|r2_w|0.293|0.420|||||||
|r2_b|0.079|0.069|||||||
|r2_o|0.072|0.064|||||||
|rho|0.678|0.718|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the Georgia COV-ES data. Size information used as the interaction terms is taken from the total number of full-time equivalent workers in the baseline ES. Columns 3 through 8 report coefficients from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, **
destinations for each of the years where coverage is complete (2006 to 2009) as well as averages (mean and median) for the number of products per firm, the number of destinations per firm and the number of firms per destination. As mentioned in section 2, we analyze the entire population of legally exporting firms, meaning we know nothing about illegal, informal or non-exporting activities.

While the total figures show increases in almost every respect, the averages are more or less stable, even showing occasional decreases. The number of firms is highest in 2009 (608 exporters), but there are slightly fewer destinations than in 2008 (70 vs. 67 countries). The number of destinations seems surprisingly low, especially in 2006 (25), even for a landlocked country like Lao PDR.

The bottom part of Table 1 shows similar descriptive statistics for six other countries, in years where Lao data is available, in order to put this new data into perspective. Comparisons can only be made where equivalent data is available: unfortunately this precludes comparisons with regional competitors to Laos. A high degree of heterogeneity is observed: high-income countries have many more firms active in exporting (Portugal has over 40 times the Lao average), but so does Malawi when compared to Mali, even though both are land-locked African countries with similar-size populations; Portuguese firms export an average number of products within our small cross-country sample, while the Dominican Republic produces ~2.5 times as many. Portugal has twice as many destinations as Senegal, Malawi or Mali. Laos appears to have comparable average figures, except for the mean number of destinations served by firms – although its figure is not far from that of Portugal, it is less than a third of that for Senegal. This seems to suggest there is no obvious linear evolution of these numbers with a country‟s GDP, and could instead depend on policy, historical ties, geography or other such factors.

**Table 1:** Descriptive statistics - Lao PDR yearly
attorneys’ client accounts are covered in full. _Sources_ : Own survey of deposit insurers, IADI Survey: Denmark (2002), Institute of International Bankers (1996).

**Dominican Republic.** ( _Savings Account Insurance, National Housing Bank Law_ ) The deposit insurance scheme of Dominican Republic was established in 1962 and it only covers the savings and loan associations. Membership to the system is not compulsory. The system is jointly administered and funded. The government can fund the DIS through savings and loan associations. Foreign currencies are covered, whereas inter-bank deposits are not.

_Sources_ : Garcia (1999), Kyei (1995).

**Ecuador.** ( _Deposit Guarantee_ Agency) The DIS in Ecuador was established on December 3<sup>rd</sup> , 1998 after a major financial crisis and failure of the biggest bank Filanbanco. The system is government legislated and administered. It excludes the deposits of owners, current or recent directors or managers. The fund can borrow, but it is not clear from whom. Both inter-bank and foreign currency deposits are covered. The

64
affected regions. These findings are further corroborated by a lack of correlation between violence in the district of residence and school attendance for the entire sample of households.

And second, we demonstrate that other conditions associated with conflict aside from the location of violence can have a large impact on access to education. The distrust of the IRG, insufficient salaries for teachers, and overall poor conditions in schools led to a sudden and voluntary teacher strike in regions controlled by the IRG during the survey period. The strike was directly in response to factors that have been caused by the conflict and have impacted the entire country to a differing degree, but the strike was not directly related to any specific violent event. Importantly, the event is further entwined with the conflict as it has been reported that southern secessionists had been exploiting these frustrations to help incite the strike.4 The duration of the strike was longer than many of the rapid escalations of violence experienced during the conflict, and like many incidents of violence, the shock impacted the ability of schools to remain open.

Following the strike, there was a more than doubling of households in IRG-held areas that had school-aged children not attending school. In specifications comparing the impacts

> 3Additionally, there is the possibility that selection bias in who is more likely to respond to a phone survey could impact the results. However, the mobile phone survey has been able to detect significant changes in access to education, food security, and access to basic services that have been identified by independent sources. 4For example, see http://aden-tm.net/NDetails.aspx?contid=110777.

3
the average income among the top X% to each individual income. This illustrates that index I(y, y̅<sup>X</sup> ) is more distribution sensitive than the corresponding Kuznets ratio.

# **3. Empirical Applications**

## **3.1 Data**

We use data from the World Bank’s Poverty and Inequality Platform (PIP) (World Bank, 2022a) to illustrate the empirical properties of the new welfare measures proposed in this paper and contrast them with existing ones. The PIP is a large compendium of over 2000 household surveys covering 168 countries and over 97 percent of the world’s population.<sup>18</sup> For most of the analysis in this section, we rely on the “lined-up” version of the PIP data which interpolates/extrapolates between/beyond survey years assuming distribution-neutral growth to create a continuous annual dataset covering 1990-2019.<sup>19</sup> This is the same dataset used by the

> 17 A closely related measure is the Palma ratio, defined as the ratio of the top 10 percent income share to the bottom 40 percent share (Palma, 2011). Setting X= 10% in I(y, y̅<sup>X</sup> ) so that it measures the average factor by which all incomes must increase to reach the mean income of the top 10% of the distribution is similar in spirit to the Palma ratio and moreover gives greater weight to the shortfall of the poorest.

> 18 For documentation on PIP, see pip.worldbank.org and for technical details on the data and estimates used in PIP, see the Global Poverty Monitoring Technical Notes at https://pip.worldbank.org/publication.

> 19 For details on the methodology used for the distribution-neutral shifts to the survey distribution to line up surveys - to a common reporting year, see the PIP methodological handbook (https://datanalytics.worldbank.org
## _Q.3.18: “What was the main reason for moving to the current place of residence?”_

And, among the options, there is the following possible reply: “ _Drought, flood or other weather-related condition_ ”. This is exactly the type of question that would enable more research on the climate-migration relationship. Unfortunately, however, this question disappeared from the latest rounds of the Uganda panel collection and is currently not found in any other collection of surveys across other countries (cf. Table A.1). Our general conclusion, therefore, is that given the current limitations of the LSMS-ISA data sets, there is limited scope for these data to help shed light on some aspects of the climate-migration relationship. However, we believe that a change of perspective on some key issues and relatively straightforward amendments to the questionnaires could greatly enhance the potential of this multi-topic household survey collection in this research field. This is the focus of the next section.

# **5. Adapting LSMS-ISA surveys to collect literature-based migration data**

In this section, we elaborate on what is the opportunity window for: i) current LSMS-ISA surveys to enhance the understanding of the climate-migration nexus; ii) adapting future LSMS-ISA surveys to collect migration data and position itself as a leading data collection program for the field.

In particular, we identify promising areas to which, with minor improvements, LSMS-ISA surveys can greatly contribute, including the issues of migration as adaptation, climate-induced immobility and potential migrants, the role of mediating channels and contextual factors. Finally, we provide an assessment of the potential integration of LSMS-ISA surveys with other non-traditional data sources. Clearly, given the reliance on longitudinal migration information tracking individuals and households over time (and across space), all these data-related opportunities crucially depend on the continuation of existing panels and the launch of new ones, which should go in parallel with efforts towards improving questionnaire design and interoperability with other data sources.

## **_A. Look at (im)mobility
In many urban areas (particularly in developing countries), the prevailing wind direction is regularly recorded at only one location (typically the airport). Taken together, the scarcity of wind direction information and aggregation of capacity measures across dispersed facilities in different microclimates eliminate any chance of incorporating meaningful wind effects. Of course, all locations in some urban areas may be subject to identical wind-direction effects during some periods, in which case radially-symmetric inverse-distance weighting would generate errors for some observations. However, our study calculates daily averages from hourly data, with consequent summation over random changes in wind directions for multiple, widely-scattered points over five annual cycles. Some measurement error probably remains, but we believe that it compares favorably with measurement errors for other variables in our model (e.g., facility capacities, local heating degree days, neighboring fire locations).

# **3.4 Urban Scale**

How many grid cells should be assigned to each city in our sample? Comparative analysis must confront the difference between arbitrary administrative boundaries and the actual extent of urban economic regions. Any attempt to define the latter will also include an arbitrary element. For all cities in this exercise, we standardize by including grid cells that lie within the same radial distance from their centroids. Then we test the robustness of the model by varying the radial distance. Another possible approach involves identification of a functional urban area (FUA) for each city region, as in Schiavina et al. (2019). FUAs are identified from urban mobility data for the OECD and Colombia, and estimated for other cities using a machine learning algorithm trained from estimated travel times, population distributions and incomes. We provide a further robustness test by estimating the model for FUAs as well.

# **4.  CO2 Measures across Cities and over Time**

# **4.1 Annual CO2 Concentrations**

The global reference standard is provided by CO2 measurement at Mauna Loa Observatory, Hawaii (Keeling et al. 1976; Thoning et al. 1989
when holding the poverty line and the distributions the same.<sup>26</sup> Compared to our survey estimates, on average, country-level poverty estimates at the international poverty line (IPL) of $1.90 are on average 34 percent lower using HFCE, and 61 percent lower using GDP.<sup>27</sup> Figure 5, Panel A, compares survey estimates of poverty at the $1.90 line to corresponding measures using national accounts means combined with distributions from surveys. Only in 11 percent of observations is poverty higher using HFCE rather than using survey estimates, and only 2.5 percent in the case of GDP. This illustrates the much lower level of poverty resulting from using national accounts means in combination with survey distributions and the $1.90 line. Naturally, when aggregated to global estimates, poverty measures using national accounts means is much lower compared to that using survey means, as seen in the right pane of Panel A of Figure 5. Our estimates using national accounts means and survey distributions suggest that the World Bank’s 3 percent global poverty target for 2030 was reached by year 2011 in the case of GDP, and is estimated at 5.7 percent when using HFCE the same year.<sup>28</sup> It is worth noting that with NAS measures being much higher than survey measures at any point in time, any starting point for poverty trajectories based on national accounts are also much lower than for survey estimates. For example, estimates for 1990, the baseline year of the UN Millennium Development Goals, show global poverty measured using GDP at 9 percent, 25 percent with HFCE, and above 35 percent using survey means.

The $1.90 poverty line may not be the appropriate threshold for international poverty measurement when the distribution is adjusted to the level of national accounts means. Since the $1.90 line is itself estimated from national poverty lines originating from household surveys (see Ferreira et al., 2016
091|0.157|0.092|0.114|0.093 0.110|
|Health|Health expenses|0.018|0.020|0.024|0.030|0.010|0.020|0.017 0.052|
|Other|Household durables, Transport,<br>Communications,Legal|0.232|0.266|0.227|0.226|0.238|0.260|0.234 0.225|
|||1.000|1.000|1.000|1.000|1.000|1.000|1.000 1.000|

Notes: N=5301 households. All values are weighted. International remittances include remittances received from spouse, parents and children.

Source: Indonesia Family Life Survey (IFLS), 2000 and 2007.
autocorrelation over the different interval measures. Pointwise confidence intervals for these autocorrelations are obtained via 1000 bootstrap replications. While Figure 1A does suggest some tendency for the autocorrelation to fall as the time horizon increases, the data do not deviate much from the 0.38 level over all time horizons between 1 month and 18 months, and so the assumption of constant autocorrelation seems a reasonable approximation here.

Figure 1B shows the autocorrelations in monthly microenterprise profits in urban Ghana, which were measured at three month intervals in the experiment of Fafchamps et al. (2010b), and Figure 1C shows the autocorrelation in three monthly household expenditures from the same experiment, also measured at quarterly intervals. Horizontal lines are drawn at 0.32, the mean autocorrelation over these horizons for profits, and 0.20, the mean autocorrelation over these horizons for household expenditures. Again both figures suggest the assumption of constant autocorrelation holds approximately over these horizons.<sup>6</sup> Gibson et al. (2003) also provide evidence from Zambia and urban China that the autocorrelation of expenditure does not change very much within a year as one increases the number of months between surveys.

Table 1 then considers the evidence for the second part of the assumption, which is that the autocorrelations are the same for treatment and control, using the microenterprise profits data from Sri Lanka and Ghana. The point estimates in most periods are reasonably similar between treatment and control, and the bootstrapped confidence intervals for the difference in autocorrelation between the two always contain zero. Evidence from another setting comes from using the test score data of Banerjee et al. (2007). The verbal test score autocorrelation is 0.58 for the treatment group and 0.61 for the control group; and the math test score autocorrelation is 0.47 for the treatment group and 0.55 for the control group. Therefore in these samples this assumption seems reasonable.

Table 2 provides autocorrelations from other data sets for a range of different economic outcomes. The purpose of
**Figure 3. Growth in Mobile Broadband and Economic Growth in Ethiopia (2000-2017)**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>% smartphone connections<br>% mobile data only connections<br>% mobile internet subscribers<br>% of connections<br>Annual Growth of Connections (%)<br><!-- End of picture text --> <!-- Start of picture text --> 300<br><!-- End of picture text --> <!-- Start of picture text --> 250<br><!-- End of picture text --> <!-- Start of picture text --> 200<br><!-- End of picture text --> <!-- Start of picture text --> 150<br><!-- End of picture text --> <!-- Start of picture text --> 100<br><!-- End of picture text --> <!-- Start of picture text --> 50<br><!-- End of picture text --> <!-- Start of picture text --> 0<br><!-- End of picture text --> <!-- Start of picture text --> 20072008200920102011201220132014201520162017<br>Year<br>Annual growth rate of 3G connections<br>Annual growth rate of smartphone connections<br><!-- End of picture text --> <!-- Start of picture text --> 12<br><!-- End of picture text --> <!-- Start of picture text
(0.353)|(0.562)|
|N|<br>6,107|<br>6,107|<br>6,107|<br>6,107|<br>6,107|<br>6,107|
|R-2|0.024|0.031|0.033|0.057|0.070|0.204|
|Municipalities|2,118|2,118|2,118|2,118|2,118|2,118|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Model with fixed effects by the municipality and by year for 2005, 2010, and 2014, also using the FAIS formulated as the instrumental variable. Weighed results show the same model adding importance by the size of the municipal population. ∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 40
of early childhood nutrition, this is nutrition before a child reaches 18 months. Since school meals take place in schools, they do not reach children under the age of 18 months.

The more recent argument on school participation is a more promising motivation for funding for school meals. Casual observations from international organizations like the World Food Program suggest that subsidized school meals attract a high number of additional children to school.<sup>10</sup> However, it is hard to draw causal inferences or estimate the magnitude of the e¤ect from these observations. Schools that receive subsidies towards school meals tend to be chosen because they are disadvantaged, which makes it hard to compare them to schools that do not receive such subsidies. Similarly, schools that run feeding programs that are paid for by the pupils’parents will

> 7 See for example Jacoby et al. (1996), Grantham-McGregor et al. (1997), Powell et al. (1998) for experimental evidence and Grantham-McGregor et al. (1998), Chandler and Walker (1995), DelRosso (1999) for non-experimental evidence.

> 8 See for example Jacoby (2002), Long (1991) and Powell (1983).

> 9 See for example Glewwe and Jacoby (1995), Glewwe, Jacoby and King (2001), Alderman et al. (1997) and Behrman and Lavy (1997).

> 10 Global School Feeding Report (2002), WFP.

4
which to compare the results of the regional predictions, it is very difficult to assess their accuracy. There is one region for which this can be done, Dar es Salaam. The backwards simulation implies that the headcount rate for Dar was 10.0% in 1992, while according to the 1991/92 HBS survey it was 28.1% Given the wide divergence between poverty incidence based on regional growth data and the HBS data , the regional results should be taken as broadly suggestive rather than indicative of particular regional trends.