rafmacalaba/data-use-mentions-extended huggingface dataset:
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Typical Wage in Past<br>Three Months|V|Husband|No|4|Typical wage the husband reports earning in<br>the past three months. It takes on a value of<br>zero if the husband earned nothing.|
|=1 if Currently Employed|V|Husband|No|4|=1 if husband currently employed.<br>The husband's sexual activity and marital<br>fidelity standardized index is constructed from|
|Sexual Activity and|||||three variables: number of sexual partners|
|Marital Fidelity<br>(Standardized)|V|Husband|No|4|ever, number of sexual partners in the past 12<br>months and an indicator for concurrent<br>multiple partners. Constructed so that higher<br>values are better.<br>=1 if husband does not suffer from|
|=1 if Does Not Suffer<br>from Psychological<br>Distress|V|Husband|No|4|psychological distress. Psychological distress<br>is equal to one if the summed General Health<br>Questionnaire- 12 score is equal to three or<br>higher, and is zero otherwise.|
||||||=1 if husband is HIV positive. Biomarker data|
|=1 if HIV Positive|V|Husband|No|4|for HIV was collected through home based<br>voluntary counselling and testing (HCT).|
|Attitudes Towards<br>Women's Empowerment<br>Super Index<br>(Standardized)|S9|Husband|Yes|4|Standardized super index of five indicators<br>listed below: preferences for children's<br>education,
related to the probability of being a Nini for men, but not for women. Several of these findings are also corroborated using data from Argentina and Brazil, in particular, the importance of previous decisions on time use and their relevance for current decisions.

The most important lessons from this paper are: (i) once youth aged 15 to 18 leave school it is very unlikely that they will return; (ii) being a Nini is a highly persistent condition; (iii) marriage of young women (perhaps as a result of teen pregnancy) increases the probability of girls leaving school and raising children by themselves, which increases their likelihood of becoming Ninis and also the probability of their male children being Ninis in the future, potentially creating an intergenerational transmission of Nininess.

These results seem to validate the predictions of the theoretical model, although in this paper we do not interpret them in a causal way. However, they are indicative of strong relationships between youth decisions and the variables of interest. In all analyses presented, moreover, we have seen the inadequacy of the definition of _Nini_ to capture non-labor market work (our definition of Nini refers only to work performed in the labor market), in particular for women. The analyses should thus be interpreted with this in mind.

The patterns shown in this paper for different socioeconomic groups suggest a place for policies that target education access programs particularly at young boys between 15 and 18 years of age, as well as including girls in anti-dropout programs and facilitating schooling opportunities for young women with children. The first dropout is crucial for the future of Ninis and as such is important that policy responses target youth at risk of dropping out for the first time. In addition, the high concentration of female Ninis who have started a household is an intuitively different phenomenon to that of male Ninis. The former could potentially reflect a rational decision to partake in domestic work and reducing the number of Ninis in this category, if deemed desirable, would likely involve intrinsically different policies probably oriented
in the IE-LFS surveys. As expected, the sample of phone owners comprises households more likely to be urban, with higher expenditure levels and lower poverty rates (Table 5, panel b). To adjust for this sampling bias, the AWMS R3 relies on a reweighting process to adjust sampling differences between the AWMS and the original full IE-LFS 2019/20 surveys. Panels a and c show that, to a great extent, the reweighting process narrows sampling differences in the spatial distribution, asset ownership, and dwelling conditions of the AWMS R3 and the IE-LFS sample. Additionally, Panel b shows that the weighted AWMS R3 sample closely reflects the expenditure distribution of the full IE-LFS 2019/20 sample.

**Table 5: Sample composition AWMS rounds**

|**Panel a. Sample size and hous**|**ehold spatial distribu**|**tion of IE-LF**|**S and AWM**|**S R3 round**||
|---|---|---|---|---|---|
||**Full IE-LFS 201**|**9/20**|**Quarter 3**|**IE-LFS 2019/20**|**AWMS R3**|
|Fieldwork|October19-Septemb|er 2020|June-A|ugust 2022|April-June 2023|
|**Households**|Unweighted<br>W|eighted|Unweighte|d<br>Weighted|Unweighted<br>Weighted|
|Central|22.2|24.6|23|.1<br>28.2|27.1<br>26.2|
|East|11.7|9.2|11|.7<br>10.8|14.9<br>11.2|
|North|13.6|13.8|14|.6<br>15.
|14%|<br>14%|32%|32%|44%|44%|55%|54%|<br>60%|<br>59%|84%|73%|
|Uruguay|40%|<br>40%|45%|45%|46%|46%|59%|58%|<br>59%|<br>58%|60%|58%|
|Venezuela|11%|<br>10%|23%|22%|34%|31%|39%|34%|<br>44%|<br>37%|62%|45%|
|_LCR Totals_|_13%_|<br>_13%_|_26%_|_25%_|_39%_|_36%_|_46%_|_42%_|<br>_50%_|<br>_45%_|_64%_|_52%_|

Source: Population and density are from CIESIN Gridded Population of the World 3. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total population as given by the tabulated cells, and excluding those few cells with omitted country codes.

- 4)  "Remote" means that the gridcell is located an hour or greater away from a city of 100,000 people.
the fact that top 20 participation was still significant in sectors that were relatively hard hit,<sup>22</sup> may attenuate the (potential) regressive gradient in work stoppages, especially in times of stringent mobility restrictions. Nonetheless, sector of employment alone does not appear to explain differences between the top 20 and the bottom 20 in the likelihood to stop working during times of higher mobility. Table 1 in the Appendix shows that the coefficient on quintile 5 does not change significantly once we control for the most recent sector of employment. One possible explanation for this result is that the industry classification available in the HFPS is not granular enough to pick up differences across quintiles in employment sector. These results suggest that differences across the welfare distribution _within_ sectors (e.g., occupation, ability to work from home, job formality, job security) likely have contributed more to observed differences among the top 20 and bottom 20.

> 22Across countries in the sample, an average of 46 percent of workers in quintile 5 worked in manufacturing, transportation, retail, or other services pre-pandemic.

16
# **Table A8: The effects of temperature on poverty and inequality – Country level analysis**

# **using alternative data from WDI and SWIID**

# _Panel A: Poverty data from WDI_

|Poverty:|$1.90|/day|$3.2|0/day|$5.5|0/day|
|---|---|---|---|---|---|---|
||Panel FE|Long<br>differences|Panel FE|Long<br>differences|Panel FE|Long<br>differences|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Temperature|0.629***|0.217***|1.020***|0.381***|1.344***|0.501***|
||(0.098)|(0.028)|(0.153)|(0.042)|(0.220)|(0.055)|
|Precipitation|-0.019|0.047|-0.017|0.008|-0.010|0.060|
||(0.016)|(0.233)|(0.029)|(0.325)|(0.036)|(0.339)|
|Country FE|Yes|No|No|No|Yes|No|
|Year FE|Yes|No|No|No|Yes|No|
|Mean dependent var.|7.288|7.288|15.399|15.399|26.593|26.593|
|Number of countries|134|95|134|95|134|95|
# **Introduction**

The explosive growth of telecommunication networks in developing countries is yielding an unprecedented wealth of highly granular real-time data, and governments have only begun to leverage the enormous potential of this new information source. This paper explores analytic methodologies for using aggregated and encrypted cell phone call data to map the distribution of poverty in Guatemala. To estimate poverty rates, Guatemala, like many other developing countries, relies on conducting and analyzing household surveys and population censuses that are both expensive and administratively demanding. By contrast, analyses based on cell phone data and machine-learning technology have the potential to generate reliable and timely information on the spatial distribution of household poverty at a far lower cost than traditional household surveys or population censuses.

## **_Poverty in Guatemala_**

Poverty rates in Guatemala are higher than in other comparable middle-income countries, and the distribution of poverty reflects a set of overlapping regional, rural/urban and ethnic dimensions. Contrary to the trend observed in other Latin American countries, poverty rates in Guatemala have increased in recent years. The poverty rate<sup>2</sup> rose from 55 percent in 2001 to 60 percent in 2014, as the number of people living below the poverty line increased by 2.8 million. Poverty rates vary dramatically by administrative department. In 2014, Guatemala’s poorest department, Alta Verapaz, had a poverty rate of 83 percent and an extreme poverty rate<sup>3</sup> of 54 percent. Meanwhile, the poverty and extreme poverty rates in the country’s wealthiest department, where the capital of Guatemala City is located, were far lower at 33 percent and 5 percent, respectively. Moreover, while urban areas are now home to a majority of the country’s poor, poverty rates remain substantially higher in rural areas. In 2014, 35 percent of the rural population was living in extreme poverty, compared with 11 percent of the urban population. Poverty rates are also significantly higher among Guatemala’s indigenous population.
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- help prospective suppliers (domestic as well as foreign) to set up production facilities;

- provide technical assistance or information to raise the quality of suppliers' products or to facilitate innovations;

- provide or assist in purchasing of raw materials and intermediaries;

- provide training and help in management and organization; and

- assist suppliers to diversify by finding additional customers.

In his empirical study, Lall (1980) examines two Indian truck manufacturers (one MNC and one joint venture) and finds significant backward linkages of all five types mentioned above. In particular, he notes that the truck manufacturers had been active in the establishment of supplier firms: of the 36 sampled supplier firms, 16 had been launched by the principals.<sup>7</sup> Behrman and Wallender (1976), who examine the operations of General Motors, ITT, and Pfizer in several host countries, find similar linkages. They emphasize the ongoing character of the contacts and information flows between MNCs and their local suppliers. Evidence on the development of linkages is also provided by e.g. Watanabe (1983a, 1983b) and UNCTC (1981).8 Apart from demonstrating various types of linkages that create a potential for spillovers, these studies also suggest that the local content in MNC production is one of the determinants of the strength of linkages. Reuber et al. (1973), in a comprehensive survey of MINC affiliates in developing countries, note that over a third of the total value of goods and services purchased 1970 by all affiliates included in their survey were provided by local firms. However, there were systematic differences in local purchases depending on the affiliates' market orientation, the parent's nationality, and the host country. Local-market oriented affiliates purchased more from local firms than did export-oriented affiliates (perhaps because import licenses are easier to obtain ifor exporters); European MNCs relied more on local firms than US or Japanese firms (perhaps because they are generally older and have already built up local supplier networks); and
- 906 S. Bernstein and M. Hoffmann. The politics of decarbonization and the catalytic impact of subnational 907 climate experiments. _Policy Sciences_ , 51(2):189–211, June 2018. ISSN 0032-2687, 1573-0891. doi: 908 10.1007/s11077-018-9314-8. URL `http://link.springer.com/10.1007/s11077-018-9314-8` .

- 909 T. Besley and T. Persson. The Political Economics of Green Transitions. _The Quarterly Journal of_ 910 _Economics_ , 138(3):1863–1906, June 2023. ISSN 0033-5533, 1531-4650. doi: 10.1093/qje/qjad006. 911 URL `https://academic.oup.com/qje/article/138/3/1863/7000849` .

- 912 S. Black, A. Liu, I. Parry, and N. Vernon. IMF Fossil Fuel Subsidies Data: 2023 Update. IMF Working 913 Paper 23/169, International Monetary Fund, Washington, DC, 2023.

- 914 B. Bollinger and K. Gillingham. Learning-by-Doing in Solar Photovoltaic Installations, Feb. 2019. URL 915 `https://papers.ssrn.com/abstract=2342406` .

- 916 S. Brunner, C. Flachsland, and R. Marschinski. Credible commitment in carbon policy. _Climate Policy_ , 917 12(2):255–271, Mar. 2012. ISSN 1469-3062, 1752-7457. doi: 10.1080/14693062.2011.582327. URL 918 `http://www.tandfonline.com/doi/abs/10.1080/14693062.2011.582327` .

- 918

- 919 Y. Cai and T. S. Lontzek. The Social Cost of Carbon with Economic and Climate Risks. _Journal of_ 920 _Political Economy_ , 127(6):2684–2734, Dec. 2019. ISSN 0022-3808, 1537-534X. doi: 10.1086/701890. 921 URL `https://www.journals.
cities earn less controlling for household characteristics, overall city scale economies are very important for them. While people in the core have a density elasticity of 0.45 those in the fringe have a big extra kick and total elasticity of 0.75. Column 2 tells us that it is more overall city density that matters not density in the core. Column 3 then adds a local density measure. While people in the fringe still get an extra kick relative to those in the core from overall city density, we can not establish that local effects for those in the fringe are greater than those in the core. Column 4 reruns column 3 showing a similar core versus fringe differential response to overall city density, but it imposes the same effects in the fringe and core for local density.

The puzzle is the bigger role of city density externalities for fringe than core city residents. We thought this was because of sorting based on migration status, where migrants lack information and might benefit more from say information spillovers. Hence, we expected that the fringe would have a greater proportion of migrants (defined by having moved within the last 5 years) than the core within the LSMS sample. However, there is only a tiny difference: 16% vs 15%. Moreover, coefficients on migration status interacted with scale effects are insignificant and have no impact on the fringe results. So that leaves a puzzle. It certainly says that including the fringe of a city is important, but we do not have a ready answer for why economic density effects may be greater for people in the fringe. There certainly must be sorting on some dimension, where people in the fringe earn less for the same observables, but somehow this disadvantaged population benefits more from greater city density, even though by construction they live in less dense neighborhoods.<sup>15</sup>

# **6 Looking at firm productivity within a city: Kampala**

This section will highlight a critical aspect of the work in this paper. What
**Pension contributions are fairly flat.** Their incidence ranges from 0.7 (decile 1) and 2.6 (decile 8) (Figure 7-1a). Their concentration is lower than the indirect and direct taxes in decile 10, with a concentration share of 24 percent, versus 28-29 percent for the indirect and direct taxes, respectively (Figure 7-1b).

**Figure 7-1 Of all taxes, the Customs Tax and VAT have the largest incidence across the distribution**

Taxes and contributions: Relative incidence and concentration shares <!-- Start of picture text --> a. Incidence: taxes & contributions<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>PIT Property tax Pension contributions<br>VAT (direct effect) VAT (indirect effect) Customs (direct effect)<br>Fuel excise (direct effect) Fuel excise (indirect effect)<br>Source: Authors' estimates based on PECS 2016/2017; LFS 2017.<br>Notes:<br>[1] Incidence is a measure of size and distribution relative to a reference income.<br>[2] Transfers are shown as a positive bar and taxes are shown as a negative bar.<br>[3] Households are ranked over Market income<br>Incidence (% of Market income)<br><!-- End of picture text --> 49
Policy **Research Working Paper Series**

|**T**it3e|Author|**Date**|Contact<br>for paper|
|---|---|---|---|
|WPS2328 Social Transfers and Social<br>Assistance: An Empirical Analysis<br>Using Latvian Household Survey Data|Branko Milanovic<br>|April 2000|P. Sader<br>33902|
|WPS2329 Improving Russia's Policy on Foreign<br>Direct Investment|Joel Bergsman<br>Harry G. Broadman<br>Vladimir Drebentsov|May 2000|S. Craig<br>33160|
|WPS2330 Reducing Structural Dominance<br>and Entry Barriers in Russian<br>Industry|Harry G. Broadman|May 2000|S. Craig<br>33160|
|WPS2331 Competition, Corporate Governance,<br>and Regulation in Central Asia:<br>Uzbekistan's Structural Reform<br>Challenges|Harry G. Broadman|May 2000|S. Craig<br>33160|
|WPS2332 Financial Intermediary Distress in<br>the Republic of Korea: Small Is<br>Beautiful?|Paola Bongini<br>Giovanni Ferri<br>Tae Soo Kang|May 2000|K. Labrie<br>31001|
|WPS2333 Output Fluctuations in Latin America:<br>What Explains the Recent Slowdown|Santiago Herrera<br>? Guillermo Perry<br>Neile Quintero|May 2000|C. Palarca<br>35328|
|WPS2334 Sex Workers and the Cost of Safe<br>Sex: The Compensating Differential<br>for Condom Use in Calcutta|Vijayendra Rao<br>Indrani Gupta<br>Smarajit Jana|May 2000|P. Sader<br>33902|
|WPS2335 Inflation and the Poor|William Easterly<br>Stanley Fischer|May 2000|K. Labrie<br>31001|
|WPS2336 Endogenous Enforcement and<br>Effectiveness of
In order to characterize the local densities of the intervention (in the neighborhoods), we combine information from the program administration, indicating which localities were eligible for the program at the end of 1998 and 1999, with information from the 2000 population census and the annual school census. The population census provides the geographical coordinates (latitudes and longitudes) for all the rural localities in Mexico while the school census provides the coordinates of all secondary schools. The georeferenced data further allows us to identify the locations of the evaluation localities.<sup>6</sup> As in many rural regions of Latin America and elsewhere, the topography of the area covered by the program consists of clusters of villages with a quasi-continuum of dwellings rather than isolated villages. On average, there are 22 localities with an overall population of roughly 6,400 inhabitants within an area defined by a five-kilometer radius from each evaluation village. This proximity favors the interactions between inhabitants of neighboring villages.

Looking now at the intervention, figure 1 depicts the geographic scope of the _Progresa_ penetration during the first two years of program roll-out in the seven central states where the evaluation took place. The rural localities targeted by the program in 1998 and 1999 are shown in light and dark grey respectively, while treatment and control localities are reported in red and blue. In order to provide a more in-depth depiction of the areas surrounding evaluation villages, the map features a smaller-scale view of a region in the State of Michoacan in which circles of a five-kilometer radius are drawn around each evaluation village.

As both maps reveal, beneficiary and evaluation villages tend to be geographically clustered with more deprived areas featuring a higher program frequency. These patterns are confirmed by descriptive statistics of the areas surrounding the evaluation sample, 6. We have used official information on the listing of all rural localities receiving the program (broken down by each program component) at the closing of each fiscal year in 1998 and 1999 in order to verify which localities were receiving the program in late 1998 and 1999.
|279,798|223,944|
|Parcel size (ha)|0.18|0.18|0.17|0.13|0.11|0.38|
|.. for residential land|0.13|0.09|0.15|0.11|0.13|0.16|
|.. for agric. land|0.19|0.20|0.17|0.12|0.11|0.42|
|**Share of parcels by claimant type**|||||||
|Natural person|0.80|0.86|0.72|0.83|0.83|0.81|
|Non-natural person|0.05|0.02|0.09|0.03|0.03|0.03|
|Claimant not yet registered|0.16|0.12|0.19|0.14|0.14|0.16|
|**Share of parcels by claimant’s gender**|||||||
|Female only|0.25|0.23|0.26|0.25|0.23|0.24|
|Male only|0.14|0.17|0.19|0.13|0.10|0.14|
|Male and femalejointly|0.61|0.60|0.55|0.62|0.67|0.62|

_Source:_ Rwanda Natural Resource Authority (RNRA), Land Administration Information System (LAIS). _Note:_ Gender of owners is reported only for parcels registered in the name of natural persons.

16
communities. At any rate, the use of hired/ _kuu_ labor is highest in Lofa, Nimba and Bong counties (Table A1.1), known as Liberia’s 'breadbasket'. Some of these counties in northern and western Liberia were also Ebola hotspots, implying a _positive_ spatial correlation between _kuu_ labor and Ebola in the country overall. Indeed, Table A1.3 shows higher shares of hired/ _kuu_ labor in counties designated by FAO as severely affected by Ebola.

# **3. Empirical Strategy**

3.1 Labor Use and Rice Production Qualitative and other evidence cited above suggests that, at its height, Ebola caused significant disruption to Liberia’s rice production system. The main channel of impact was the shortage of labor for land clearing and harvesting. The agricultural calendar and timeline (fig. 2) show that the Ebola outbreak had not subsided, or at least continued to be disruptive, at the time of the 2015/16 rice season, the earliest for which recall data are available.

Our regression model for rice production outcome Y�� of household i in district d is where E� is a continuous measure of district-level EVD prevalence based on the total number of Ebola-related burials per 1,000 district population over the course of the outbreak, X�� is a vector of household demographic, enumeration area (EA), and district level characteristics., and ε�� is a random disturbance term. Standard errors are clustered at the district level.

11