rafmacalaba/data-use-mentions-extended huggingface dataset:
flooding, caused by storm surges and high tides in coastal areas. Country-level pluvial and fluvial flood data are from the 2019 version of the Fathom Global 2.0 flood hazard dataset (Sampson et al., 2015). These were combined with a separate coastal flood hazard map generated using the LISFLOOD-FP hydrological model (Rentschler et al. 2023). While Rentschler, Salhab, and Jafino (2022) provide gridded exposure headcounts to flood risk by admin area, we ignore the existing population count in their dataset and use only the information on inundation depths for 100-year return period flooding. We merge the data for all 187 countries to create a global dataset at a 3-arcsecond resolution and consider the following inundation depths as thresholds for being exposed to floods: 15cm, 50cm and 150cm.<sup>10</sup> It is important to note that the dataset used does not account for artificial flood protection structures. Although the use of the undefended flood maps may lead to an overestimation of exposure in areas with flood protection systems, there is evidence that many low-income lower-middle income countries do not have any flood protection system against even light flooding (Hallegatte et al., 2017; Rozenberg and Fay, 2019; Rentschler, Salhab, and Jafino, 2022).

> 8 The maximum misalignment using nearest neighbor resampling at this resolution is approximately 45 meters. 9 Smith et al. (2019) use the High-Resolution Settlement Layer (HRSL) population density map (at 1 arcsecond) and observe that current estimates of flood exposure using GHSL may overstate flood risks in rural regions while potentially underestimating these risks in urban areas. Other alternatives such as WorldPop an LandScan also overestimate the number of people exposed to floods. However, the HRSL is only available for the year 2015.

> 10 The flood hazard maps are available from <u>https://datacatalog.worldbank.org/search/dataset/0062763/Global-FloodExposure--Gridded-exposure-headcounts-by-country. The inundation depth is the maximum from the three types of flooding</u
16 30. WHO. Guidelines for Drinking-water Quality. World Health Organization; 2004. 31. Pickering AJ, Null C, Winch PJ, Mangwadu G, Arnold BF, Prendergast AJ, et al. The WASH Benefits and SHINE trials: interpretation of WASH intervention effects on linear growth and diarrhoea. Lancet Glob Health. 2019;7: 1139–46.

32. Mertens A, Arnold BF, Benjamin-Chung J, Boehm AB, Brown J, Capone D, et al. Effects of water, sanitation, and hygiene interventions on detection of enteropathogens and hostspecific faecal markers in the environment: a systematic review and individual participant data meta-analysis. Lancet Planet Health. 2023;7: e197–e208. doi:10.1016/S25425196(23)00028-1 33. Bain R, Johnston R, Khan S, Hancioglu A, Slaymaker T. Monitoring Drinking Water Quality in Nationally Representative Household Surveys in Low- and Middle-Income Countries: Cross-Sectional Analysis of 27 Multiple Indicator Cluster Surveys 2014–2020. Environ Health Perspect. 2021;129: 097010. doi:10.1289/EHP8459 34. Cumming O, Arnold BF, Ban R, Clasen T, Esteves Mills J, Freeman MC, et al. The implications of three major new trials for the effect of water, sanitation and hygiene on childhood diarrhea and stunting: a consensus statement. BMC Medicine. 2019;17: 173. doi:10.1186/s12916-019-1410-x 1
14. Chaney, Thomas (2005) “Distorted Gravity: Heterogeneous Firms, Market Structure, and the Geography of International Trade,” mimeo, University of Chicago.

15. Coe, David, Arvind Subramanian, and Natalia Tamirisa, with Rikhil Bhavnani (2002) “The Missing Globalization Puzzle,”IMF Working Paper 02-171.

16. Demirguc-Kunt, Asli, and Ross Levine (2001) “Financial Structure and Economic Growth: A Cross-Country Comparison of Bank, Markets, and Development,” Cambridge, MA: MIT Press.

17. di Giovanni, Julian, Andrei Levchenko, and Romain Ranciere (2005) “Trade and Growth at Industry Level,”unpublished.

18. Do, Quy-Toan, and Andrei Levchenko (2004) “Trade and Financial Development”, World Bank Working Paper 3347.

19. Eichengreen, Barry, and D.A. Irwin (1998) “The Role of HIstory in Bilateral Trade Flows,” in Je¤rey Frankel (ed.), The Regionalization of the World Economy, Chicago: University of Chicago Press.

20. Evans, Carolyn (2003) “The Economic Signi...cance of National Border E¤ects,”The American Economic Review, 93(4), 1291-1312.

21. Feenstra, Robert C., Robert E. Lipsey, Haiyan Deng, Alyson C. Ma, and Hengyong Mo (2005) “World Trade Flows: 1962-2000,”NBER Working Paper No. 11040.

22. Frankel, Je¤rey, and David Romer (1999) “Does Trade Cause Growth?” American Economic Review, 89, 379-399.

23. Frankel, Je¤rey, with Ernesto Stein, and Shang-Jin Wei (1997) “Regional Trading Blocs in the World Economic System,”Washington DC: Institute of International Economics.

24. Heston, Alan, Robert Summers, and Bettina Aten, (2002) “Penn World Table Version 6.1," Center for International Comparisons at the University of Pennsylvania (CICUP
# **7 Conclusions**

This paper studies the diffusion of a capital intensive, indivisible technology and analyzes the dynamics of income inequality during this process. We find evidence that credit constraints are the key factor that explains the pattern of delayed adoption observed in the data. During the diffusion process, inequality follows Kuznets’ well-known inverted U-shaped curve. Consistent with the model and results, a redistributive policy favoring the poor results in accelerated economic growth and a shorter duration of sharpened inequality, although the quantitative impact of such a policy is small.

One advantage of this paper over other studies about the impacts of globalization, is that context is well understood. Thus, the specific channel in which wealth matters for adoption can be disentangled.

We conclude, like Platteau (1984), that overall our village in Tamil Nadu experienced a success story of globalization: according to the simulated data, the diffusion for the entire sample is completed in less than five years and it results in substantial income gains for everyone.

# **References**

- [1] Bandiera, O. and I. Rasul, 2004. Social Networks and Technology Adoption in Northern Mozambique, unpublished manuscript, London School of Economics.

- [2] Bell, C. and T. N. Srinivasan, 1989. Some aspects of linked product and credit contracts among risk-neutral agents. In P. Bardhan, editor, _The Economic Theory of Agrarian Institutions_ . Oxford: Clarendon Press.

- [3] Besley, T. and A. Case, 1993. Modeling Technology Adoption in Developing Countries, _American Economic Review_ 83, 396-402.

- [4] Besley, T. and A. Case, 1994. Diffusion as a Learning Process: Evidence from HYV Cotton, Princeton University Department of Economics Working Paper 5/94.

29
surveys include socioeconomic and demographic information, but do not include any information on consumption or assets at the household level.

Given that our focus is on college graduate workers, we restrict the sample to individuals aged 23-65 years old (inclusive). For employment patterns, we use the full sample and define ‘work’ as the positive number of weekly hours of work in the week of reference. For the wage sample, we use full-time earnings defined as earnings of those with more than 30 hours of work in the week of reference. When calculating hourly wages, we divide the monthly earnings by hours of work times 4.33. We drop earnings below and above the 1st and 99<sup>th</sup> percentile within education groups from the sample.<sup>6</sup> To calculate the hourly wage statistics for all workers, we use the expansion factor in the survey times hours of work.

Table 1 shows the descriptive statistics for three years in the data: 2000, 2007 and 2014. Panel A includes the full sample and Panel B includes only the working sample (those with positive hours of work in the week of reference). This distinction is important due to the selection into the labor force mainly by women. There are three large changes in the Mexican population and workforce in the 2000-2014 period. First, there is a substantial increase in the female labor force participation. From 40 percent in 2000, it increased to 48 percent by 2014. This increase has been on hold since the macroeconomic crisis in 2008. Second, the proportion of individuals with a university diploma has increased, while at the same time the proportion of individuals with primary or less has declined substantially. This is satisfied both in the full and the working samples. In the case of women, there is positive selection into the workforce by education. Higher educated women are more likely to enter the labor force than less educated women. Third, average incomes declined substantially after the macroeconomic crisis of 2008
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE percent of the market. Two other operators each hold over a quarter of the market, however, so that the overall market concentration is quite good with a Herfindahl index of 0.31.

Liberia’s mobile footprint (the percentage of the population living within range of a mobile signal and hence able to subscribe to service) grew substantially between 2003 and 2009, from 18 percent to 32 percent. At the start of the period, there were many more potential subscribers than actual subscribers, with penetration rates of less than 0.1 percent in 2003 (figure 11). This shortfall took some time to bridge, but by 2008 penetration rates were around 20 percent and had almost caught up to the mobile footprint. The latest figures for 2009 suggest that penetration has exceeded the population within the signal area. This is possible if individuals subscribe to more than one service, which is not unusual in Africa. The evidence based on industry statistics is corroborated by Liberia’s 2007 Demographic and Health Survey, which found that 29 percent of households had a mobile telephone—a coverage level many times higher than the electrification rate for the country. In fact, Liberia’s mobile penetration rates are now on par with other low-income countries in Africa (table 10).

**Figure 11. Liberia’s mobile footprint has grown steadily while penetration has burgeoned**

<!-- Start of picture text --> subscribers within range of a GSM signal<br>% of population<br><!-- End of picture text --> Source: Mayer and others 2008.

While no official statistics on mobile prices could be found for Liberia, there is evidence to suggest that prices have fallen over time. The cost of a SIM card, for example, is reported to have fallen from $65-85 in 2001 to $3-5 at present. The average revenue per user (ARPU) for the main operator fell from $43 per month in
13 with secondary or university education – are not statistically significant. These outcomes may reflect something of the nature of the employment market in Ghana, where educated people face large levels of under- and unemployment.

The most important finding in Table 3 is that the two selection control variables are statistically insignificant. Both the insignificant t-values on the selection control variables and the fact that the other coefficient estimates in the table are generally similar in the two specifications suggest that the subsample of households not receiving remittances is randomly selected from the population. This means that under the assumptions imposed, the bias resulting from estimating the equations by ordinary least squares without selection controls would be small.<sup>17</sup> This finding of “no selection bias” is similar to the one reported by Barham and Boucher (1998) in their examination of selection bias among migrant households in Nicaragua. However, since this finding runs contrary to the common assumption in the literature that migrants are a “select” group (with respect to education, income, skill),<sup>18</sup> it is important to list some of reasons for this no selection bias finding in Ghana. The first reason for the finding has already been broached, namely, that households receiving internal and international remittances in Ghana are not positively selected with respect to education.<sup>19</sup> The results of the choice function model in Table 2 show that households with the most educated members – secondary and university education – do not always have the highest propensity to receive remittances. The second reason for the no selection bias finding relates to the nature of the data set. The Ghana data are based on information collected from households in a labor-sending country, and thus they include data on households which are producing both legal and illegal international migrants. It is likely
In the first stage of the analysis, OP is regressed on several human capital indicators for each migrant in the relevant sample. These are (i) work experience (proxied by age and age squared), (ii) general assimilation effect (represented by a dummy variable indicating whether migration took place in the first or the second half of the decade),<sup>10</sup> and (iii) level of education. This is captured by six dummy variables that group the years of education into the following categories: less than five, five to nine, ten to twelve, high-school level, some college, Bachelor’s Degree or higher. Finally, we add country dummies for each migrant.

The estimation samples include only migrants who earned their final degrees before migration to the United States. Since a variable indicating the place where education was obtained is not directly available in the census, we construct it using the number of years of education and the age at migration.<sup>11</sup> Age restrictions ensure cohort comparability across censuses.<sup>12</sup> Regressions for each of the three OP indices are weighted and run _separately_ for each census year _t_ (where _t_ =1980, 1990 or 2000) and decade of arrival _d_ (where _d_ = 1970-1979 or 1980-1989). The first cohort (1970-9 arrival) is observed three times (in each census) whereas the second cohort (1980-9 arrival) is observed only twice (1990 and 2000 censuses). These _five_ estimations are performed separately for each OP measure, resulting in a total of _fifteen_ estimations. The estimated equation is given by: 10 Usage of intervals is justified by the fact that Census 1980 and Census 1990 do not provide the exact individual years of migration, but only the year interval.

11 A person is considered “US educated” if he arrived in the US before he would have normally acquired the declared education level. For example, if a university graduate arrives at the
the cumulative total. Based on poverty data for 2015.

A “Highest” indicates quartile of EMDEs with the strongest regulatory quality (based on data for year with latest poverty data). “Lowest” indicates quartile of EMDEs with the weakest regulatory quality. The back data for regulatory quality has been taken from the Worldwide Governance Indicators. The data is for 2017.

B. “Highest” indicates quartile of EMDEs with the highest share of account ownership at a financial institution (greater than 59 percent) in 2017. “Lowest” indicates quartile of EMDEs with the lowest shares (less than 21 percent). India is excluded from the “Best” category.

C. “Highest” indicates quartile of EMDEs with the highest 2019 Ease of Doing Business score (above 67.5). “Lowest” indicates quartile of EMDEs with the lowest 2019 Ease of Doing Business score (below 51.6).

D. “Highest” indicates quartile of EMDEs with the highest 2019 Trade Across Borders score (above 78.1) in the Ease of Doing Business survey. “Lowest” indicates quartile of EMDEs with the lowest 2019 Trade Across Borders score (below 57.1).
# **Figure 3a: Value of Crop Output per ha        Figure 3b: Agriculture value added per worker**

<!-- Start of picture text --> 1200 1900<br>1000 1800<br>800<br>1700<br>600<br>1600<br>400<br>1500<br>200<br>1400<br>0<br>1300<br>Timor-Leste<br>Small Island Developing States 1200<br>Low Income Food Deficit Countries 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>Constant (2004-2006) int $<br>1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013<br>Value added per worker (constant 2010 US$)<br><!-- End of picture text --> _Source:_ FAOSTAT This paper uses data from the 2007 and 2014 waves of the Timor-Leste Survey of Living Standards to address the following questions: To what extent does agricultural productivity affect poverty? What are the drivers of agricultural productivity? What are the constraints to increasing agricultural productivity? What can be done to increase agricultural productivity? These questions are in line with the country’s Strategic Development Plan 2011–2030 (SDP), which highlights the relevance of agriculture for poverty alleviation, economic diversification, employment, and economic growth. In committing to deliver this SDP, the Ministry of Agriculture and Fisheries developed the Strategic Plan 2014–2020, which seeks to support the transition from subsistence to commercial agriculture, improve rural incomes and livelihoods, and reduce poverty. Thus, answers to these aforementioned questions will provide guidance on how agricultural policy can help reduce poverty in the country.

Agricultural productivity affects poverty levels both directly through the _“food and income” pathway_<sup>3</sup> and indirectly through the _“wage” pathway_ .<sup>4</sup> Since hired farm labor is usually supplied by poor households, an increase in
589|-1.326|-1.365|-0.692|
|suvy y|2016|(S.E.)|(0.663)|(0.844)|(0.683)|(0.722)|(0.767)|
|||Comp. mean|10.24|9.008|11.16|10.06|10.43|
|||Obs.|2,894|1,289|1,605|1,834|1,060|

p<0.001***; p<0.01**; p<0.05* Note: Each cell-block is the result of a separate regression. Standard errors clustered at the village level are in parentheses. “Comp. mean” refers to the comparison group mean for the outcome variable. Column (1) regressions control for child characteristics (age, household size, household wealth asset index, parenting practices, mother's education and gender) and baseline child development measures (BMI, cognitive, fine motor, gross motor, and language). Columns (2) and (3) regressions use the same controls as column (1) except they exclude household wealth. Columns (4) and (5) regressions use the same controls as column (1) except they exclude parenting practices.

Next, we turn to results on child development outcomes. Table 3 presents the impact estimates on the EDI at age 5 (2013) and age 8 (2016). Overall, we find a few positive impacts of the project on children’s developmental outcomes at age 5 but no positive impacts at age 8. At age 5, we estimate a 0.208 S.D. increase in scores on the physical health and well-being domain and a 0.115 S.D. increase in scores on the emotional maturity domain for the overall sample. However, these effects
Processing C.Manuf. D.Other<br>LAC countries Other countries LAC countries Other countries<br>.3 .15<br>.2 .1<br>.1 .05<br>0 0<br>Size of New Destin. Rel. to Incumbent Destin.<br>Size of New Destin. Rel. to Incumbent Destin.<br>Ecuador Peru South Africa Costa Rica Chile Bangladesh Nicaragua Guatemala El Salvador Dominican Rep Cambodia Egypt Mexico LAC countries Ecuador Peru South Africa Colombia Costa Rica Chile Guatemala Bangladesh Nicaragua El Salvador Dominican Rep Cambodia Egypt Mexico LAC countries<br><!-- End of picture text --> Notes: in Panel A the average for Ecuador covers 2006-2007. Colombia does not enter in Panel A due to lack of data. Brazil does not enter in the figure due to lack of access to the exporter-level customs data.

Finally, we examine the contribution of new destinations to short-run export growth of incumbent exporters in LAC and comparator countries. Figure 10 shows the average annual growth rate in total exports of incumbent exporters for each country in 2005-2007 in Panel A and 2008-2009 in Panel B. The average contribution of the three terms in the incumbent exporters’ export growth decomposition in Eq. (3) -- for new, incumbent, and exiting destinations -- is presented for each country for 2005-2007 in Panel C and for 2008-2009 in Panel D. During the steady growth period, incumbent destinations accounted for the largest share of the growth of 30
**Table S1.1.** Continued.

|**INDEX**|**INDIVIDU**|**AL variables**|**MEAN**|**SD**|**MIN**|**MAX**|**Q25**|**Q75**|**N**|
|---|---|---|---|---|---|---|---|---|---|
||Respondent’s answer to:|Age at which women should<br>start having sex|19.577|3.296|10|30|18|20|4501|
|||Age at which men should<br>start having sex|18.449|3.248|10|30|16|20|4509|
||Respondent is confident he/s<br>tionally ready|he will have sex only when emo|- 1.411|0.776|0|2|1|2|4525|
|Sexual abuse<br>reporting<br>attitudes index|Respondent disagrees with th<br>olence the person must not te|e idea that in case of sexual vi-<br>ll anyone.|0.983|0.131|0|1|1|1|4481|
|variables|Respondent thinks that when|Must tell his/her family|0.713|0.452|0|1|0|1|4502|
||a teenager is suffering from|Must tell the authorities|0.741|0.438|0|1|0|1|4502|
||sexual violence he/she:|In case of rape, must seek<br>medical help|0.596|0.491|0|
were considered active if they were attending ELA clubs at least two times per week if they were enrolled in school or were attending at least three times per week if they

> time of the intervention or had moved away from the study area.

> 8Likely depression is measured using the Patient Health Questionnaire-2 (PHQ-2), where a score of 3 or higher is indicative of depression. The PHQ-2 includes the first two items of the PHQ-9 (Kroenke, Spitzer and Williams, 2003).

> 9Self-Efficacy is measured using the General Self-Efficacy Scale developed by Schwarzer and Jerusalem (1995). A total self-efficacy score that ranges from 10-40 was calculated. We then standardized this score using the mean and standard deviation of the score among females in control communities.

11
productivity _e_<sup>_ω_</sup> , consistently with the misallocation literature.<sup>17</sup> Formally, we characterize this relationship with the following function: The key parameter governing the elasticity between _TFPR_ , the measure of distortions, and _TFPQ_ , the idiosyncratic productivity, is given by _γi_ . Our calibration section discusses how we estimate this elasticity in the Ghanaian firm-level data.

Optimization leads to the following expressions for labor demand and variable profits:

# **5.3 Firm Dynamics, Entry, and Exit in the Formal Sector**

Our characterization of the formal sector features the first-order margins of adjustments through which distortions affect micro and macro-level outcomes. These are an endogenous entry and exit of firms into the formal sector, and an innovation technology that allows for an endogenous component of firm dynamics.

Endogenous firm dynamics in the formal sector arise from a process of technological upgrading and downgrading similar to that in Atkeson and Burstein (2010). Specifically, a firm with current productivity _e_<sup>_ω_</sup> can upgrade to _e_<sup>_ω_+∆</sup> with probability _qt_ ( _ω_ ) and can downgrade to _e_<sup>_ω−_∆</sup> with probability (1 _− qt_ ( _ω_ )). The expected growth rate, given by _qt_ ( _ω_ ), is endogenous, as firms can allocate resources to innovation activities. The variance of the shock process (∆), on the other hand, is exogenous. The labor-denominated cost for attaining the desired probability _qt_ ( _ω_ ) is given by Notice that the innovation cost is scaled by the entrepreneur’s current productivity. This is an important assumption that allows the model to be consistent with the innovation patterns of large firms in the US, our target economy for the calibration. The scale parameter _η_ and the elasticity parameter _φ_ will be calibrated to replicate the properties of the size distribution and the life cycle of firms in the U.S.

The