rafmacalaba/data-use-mentions-extended huggingface dataset:
|||||||||
|1993|25590|6391|63.3|11882|5090|50.4|21771|206|2.0|
|1995|31693|440|50.6|35894|306|35.2|64075|55|6.3|
|Savannah||||||||||
|1993|15257|1026|10.2|8728|1434|14.3|26965|2729|27.1|
|1995|7599|111|11.2|9758|127|12.8|32460|164|16.5|

It is highly unlikely that there would be no growth in cocoa revenues in the region of West Forest as a whole, in view of the fact that there was about a 10 percent increase in the real producer price of cocoa, and a 30 percent increase in cocoa production West Forest according to the CSSPA data. Thus, we conclude that cocoa-growing households are likely to have been undersampled in the West Forest.<sup>3</sup> ' If in fact cocoa-growing households were undersampled, it is likely that the households producing as much or more cocoa than the mean that were undersampled, since the increase in mean cocoa revenues among cocoa-growing households seems if anything understated, given the reported increase in production and producer prices.

Even if cocoa growing households were undersampled in the West Forest, however, it would not explain the drop in food shares, controlling for total expenditure, within the population of cocoagrowing households (and other export crop -growing households) that were sampled. Thus, it is an unresolved question as to why food shares dropped so significantly among this group. The drop in food shares seems to result from an underestimation of consumption of home production, leading to a downward bias in total expenditure. If so, then the incidence of poverty as indicated by the survey results would be biased upwards. However, we do
- Preston, Samuel H. 1975. “The Changing Relation between Mortality and Level of Economic Development.” _Population Studies_ 29 (2): 231–48. https://doi.org/10.2307/2173509.

- Tandon, Ajay, Jewelwayne Salcedo Cain, Christoph Kurowski, and Iryna Postolovska. 2018.

- “Intertemporal Dynamics of Public Financing for Universal Health Coverage : Accounting for Fiscal Space Across Countries.” 133115. The World Bank.

- http://documents.worldbank.org/curated/en/639541545281356938/Intertemporal-Dynamics-ofPublic-Financing-for-Universal-Health-Coverage-Accounting-for-Fiscal-Space-AcrossCountries.

Unesco. 2019. “UIS Statistics.” 2019. http://data.uis.unesco.org/.

- World Bank Group. 2018a. “Human Capital Project.” Text/HTML. World Bank. 2018. http://www.worldbank.org/en/publication/human-capital.

- ———. 2018b. “World Development Report 2018: LEARNING to Realize Education’s Promise.” http://www.worldbank.org/en/publication/wdr2018.

- ———. 2019. “ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity.” 2019. http://datatopics.worldbank.org/aspire/indicator/social-expenditure.

- World Health Organization. 2018. “Global Health Expenditure Database.” 2018. http://apps.who.int/nha/database.

- ———. 2019. “Cost Effectiveness and Strategic Planning (WHO-CHOICE).” WHO. 2019. http://www.who.int/choice/en/.

26
# **<u>Table A2: converting estimates from the paper to impact from 0 to 100% coverage</u>**

|Study|Estimate<br>from the<br>paper|Treatment variable|Percentage points<br>increase in maternal<br>work/employment<br>if all eligible<br>children get a spot<br>(percentage points)|
|---|---|---|---|
|Berlinski and Galiani (2007)|0.142*<br>(0.075)|New stock: number of preschools, multiplied by<br>50, normalized by cohort size.|14.2|
|Calderon (2014)|0.015***<br>0.0003|Number of spots per 10 eligible children|15|
|Halim, Johnson, and Perova||||
|(2021)|0.048***<br>(0.017)|Number of preschools per 1,000 eligible children|32*|
|De La Cruz Toledo (2015)|0.1591<br>(0.089)|Enrollment in preschool (fraction of children of<br>eligible age enrolled)|15.9|

* we multiply the estimate by the number of preschools needed to offer spots to 1,000 children, assuming one preschool serves 150 children

1
The second competitiveness area deals with human capital because a healthy and educated labor force is pivotal to national competitiveness. Pillars 5 and 6 therefore concern health and skills. In this study, these components are indistinctly proxied by human capital (Penn World Table). This index is based on average years of schooling from Barro and Lee (2013), weighted by an assumed rate of return to education based on Mincer equation estimates around the world (Psacharopoulos 1994).

The characteristics of input and product markets are also crucial for productivity and growth. Pillars 7 to 10 therefore look at markets as sources of growth: Pillar 7 deals with the efficiency of product markets and healthy market competition, both domestic and foreign, as drivers of business productivity. For domestic markets, the top marginal income and payroll tax rate (Fraser 2016) and the regulatory quality indicator (WGI) are used as proxies. Regulatory quality captures perceptions of the government’s ability to formulate and act on sound policies and regulations that promote private sector development. In terms of foreign markets, openness is proxied either by the trade-weighted average applied tariff rate (WEF) or by the trade-to-GDP ratio (WEO). Pillar 8 refers to the flexibility of the labor market to quickly shift workers from one economic activity to another at low cost, and to allow for wage fluctuations without much social disruption. This is proxied by the labor market regulation index developed by the Cambridge Business Research (CBR) Center, including the subcomponent related to regulation of work time. The index measures the extent of protection and regulation of work time in the labor code.<sup>5</sup> Pillar 9 deals with efficient financial markets and financial development, proxied here by the ratio of domestic credit to the private sector to GDP (WDI). Finally, Pillar 10 refers to market size (which could be proxied by either GDP or volume of imports), a variable that either coincides or is highly correlated with the dependent variable and was therefore dropped as a
£3 : :<br>c<br>go \<br>Boyo a ' '<br>'<br>-2 0 2<br>SPEI anomaly<br>—— Linear ———— Quadratic ————— Cubic<br><!-- End of picture text -->

<!-- Start of picture text --> Landed households, maize<br>' '<br>2 }<br>2 H i<br>8 1 |<br>=. | '<br>2<br>: |<br>Co }<br>& \ \<br>'<br>-2 0 2<br>Total rainfall anomaly<br>—— Linear -———— Quadratic — Cubic<br><!-- End of picture text -->

<!-- Start of picture text --> Landed households, highland<br>“4' \'<br>So] |<br>e | |<br>=] ! !<br>So | \<br>= \<br>=©Sewso |H' 'i!<br>g |<br>8a = If I'<br>a 1 1<br>2, {7 !<br>-2 0 2<br>Total rainfall anomaly<br>——— Linear -———— Quadratic —— Cubic<br><!-- End of picture text -->

<!-- Start of picture text --> Landed households, roots & lowland<br>\ \<br>c<br>2 | |<br>2 |<br>E \ I<br>ge<br>8 |! '<br>£©2 |\ \\<br>o | |<br>
## **High-Skilled Migration from Myanmar: Responses to Signals of Political and Economic Stabilization**

Yashodhan Ghorpade, Muhammad Saad Imtiaz, and Theingie Han

**Abstract:** In recent years, Myanmar has witnessed considerable economic and political instability, leading many young people, particularly the higher-skilled, to consider migrating abroad for improved prospects. This paper employs an innovative method to quantify migration intentions among high-skilled youth by analyzing the take-up of migration at different wage premia. A randomized survey experiment then evaluates how hypothetical political and economic stabilization scenarios impact these intentions. The findings show that 35 percent of the respondents would be willing to take a similar job abroad for pay equal to their current income. Randomization within the survey indicates that political stabilization would potentially reduce high-skilled workers’ desire to migrate by about 15 percent, especially among men, those living in high-conflict areas, and persons with lower absolute income but higher perceived relative income. In contrast, prospects of economic stabilization do not have a significant effect on migration intentions. In the absence of political stability and a reduction in conflict, economic stabilization is unlikely to reduce talent outflows among the young.

# 1. Introduction

Emigration from Fragile and Conflict-Affected States (FCAS) is driven by political and security considerations in addition to the standard economic drivers of labor mobility. Economic theory postulates migration as an effective means for labor to realize higher returns arising from spatial differences in income earning opportunities. Classical models have described migration as an outcome of wage differences, which in turn is determined by wages, costs of migrating, and the probability of finding a job (Todaro, 1969; Harris and Todaro, 1970; Fields, 1975). The New Economics of Labour Migration posits three main motivations for economic migration: (i) relative deprivation, whereby poorer individuals migrate to increase household income; (ii) investment: as migration aims to enhance household investments through remittances directed towards agriculture or enterprises; and (iii) insurance, where migration serves as a means to safeguard against risks through
Eichengreen, B., and P. Gupta. 2016. “Managing Sudden Stops.” In Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures, edited by E. G. Mendoza, E. Pastén, and D. Saravia, 9-47. Santiago: Central Bank of Chile.

Eickmeier, S., and T. Ng. 2015. “How do US Credit Supply Shocks Propagate Internationally? A GVAR Approach.” European Economic Review 74: 128-145.

Federal Reserve Board. 1958. “Federal Reserve Bulletin.” October, Board of Governors of the Federal Reserve System, Washington, DC.

Feenstra, R. C., R. Inklaar, and M. P. Timmer. 2015. “The Next Generation of the Penn World Table.” American Economic Review 105(10): 3150-3182.

Ferrara, L., and C. Marsilli. 2019. “Nowcasting Global Economic Growth: A Factor-Augmented Mixed-Frequency Approach.” World Economy 42(3): 846-875.

Feyen, E., R. Letelier, I. Love, S. M. Maimbo, and R. Rocha. 2014. “The Impact of Funding Models and Foreign Bank Ownership on Bank Credit Growth.” Policy Research Working Paper 6783, World Bank, Washington, DC.

Forbes, K. J., and F. E. Warnock. 2012. “Capital Flow Waves: Surges, Stops, Flight, and Retrenchment.” Journal of International Economics 88(2): 235-251.

Fosu, A. K. 2013. “Impact of the Global Financial and Economic Crisis on Development: Whither Africa?” Journal of International Development 25(8): 1085-1104.

Francis, N., L. E. Jackson, and M. T. Owyang. 2018. “Countercyclical Policy and the Speed of Recovery after Recessions.” Journal of Money, Credit and Banking 50(4): 675-704.

Frankel, J. 2014. “China is Not Yet Number One
Table 2. Distribution by Income Classification: COVID-19 Deaths, All-Cause Deaths and Total Population (percent)

|||Balance<br>(160 co|d group<br>untries)|Unbala<br>(all c|nced group<br>ountries)|
|---|---|---|---|---|---|
||Share in<br>COVID-19<br>deaths|Share in<br>expected<br>deaths from<br>all causes|Share in<br>population<br>|Share in<br>expected<br>deaths from<br>all causes|Share in<br>population|
|HICs|78.9|18.1|15.7<br>|18.6|16.2|
|UMICs|17.8|34.5|36.0<br>|32.7|34.1|
|LMICs|3.1|38.9|40.1<br>|38.3|39.7|
|LICs|0.2|8.5|8.1<br>|10.6|10.0|

Source: Johns Hopkins University; World Health Organization; World Population Prospects. Note: COVID-19 data for 160 countries (balanced group) and world (unbalanced group).

The starkly differential impact creates the impression of rich and poor countries being subjected to ‘two pandemics’. HICs currently claim 79 percent of recorded deaths globally due to COVID19 (Table 2). This leaves 21 percent for the developing world, which comprises 85 percent of the global population. Some 18 percent of the global death toll is claimed by UMICs, whereas just 3.1 and 0.2 percent, respectively, by LMICs and LICs. In other words, the two poorest groups of countries collectively represent almost half of the global population but
the distribution of elicited WTP.

Of course, some of the patterns discussed earlier indicate that there likely _is_ error in individual choices; such as MSB, dominated choices (e.g., choosing not to buy a voucher that can be exchanged for cash), and implausible WTP distributions, such as a mass of never-switchers at both extremes of the MPL. Formally, the subject will weakly prefer option A if The terms _εijmt_ describe any disturbance to the preference for option _j_ in choice _t_ of MPL _m_ . Because of this term, the subject may sometimes choose option A even though _WTPi < vBt − vAt_ and option B even though _WTPi > vBt − vAt_ . This could lead to multiple switching as well as randomly switching “too early” or “too late”. Going forward, we will use the value difference term ∆ _t_ = _vAt − vBt_ . This is the inverse of the difference in values on the right hand side above, and so subjects will tend to choose option A when ∆ _t_ is large and option B when it is small (incl. negative).

Potential reasons for individual error, _εijmt_ , include inattention and incomprehension. For example, Yu et al. (2021) have shown in the case of risk preferences that greater attention reduces MSB in final MPL choices. Cason and Plott (2014) also provide evidence that unexpected behavior in a BDM occurs when subjects do not understand the payoff structure of the game created by the experiment, leading to behavior that looks irrational. Incomprehension may be a particular concern in populations with low literacy and numeracy. For example, Dave et al. (2010) have shown that subjects with a math ability score more than one standard deviation below the sample mean make inconsistent choices about four times more often than do other participants. On the other hand, Allcott and Kessler (2019) report only 2.4% inconsistent choices in a mail-in survey with US energy customers.

Note that Andersen et al. (2008) have argued that
as the gravity is proportional to the mass of two planets and inversely proportional to the distance between them, so are exports positively related to the sizes of the two economies and negatively related to the distance between the two economies. The detailed model is in appendix A.1. Using a full set of bilateral trade flows between all pairs of countries as described in appendix B, we estimate a Poisson Quasi Maximum Likelihood regression model A. described in appendix

**Kenya exports less to China than to economies of similar size** Under the Poisson model in table 1, the coefficients on importer GDP per capita and distance are significant and have the expected signs: distance reduces trade and a higher GDP per capita of the importer increases trade. Distance deflects Kenya’s exports by a factor of 0.18. Kenya’s exports increase by a factor of 10.02 to countries that share a common border with Kenya. The China dummy coefficient is -1.74, so Kenya exports less to China by a factor of 0.18 compared to economies of similar size. If Kenya exports one million USD worth of goods to a country similar to China, it only exports 820,000 USD worth of goods to China. Kenya may export less to China after controlling for other independent variables, but the effect is constant over time —none of the China-year dummy coefficients are significant (table C8).

Table 1: Estimates of Kenya’s Trade Flows (1948-2014). Dependent variable: Kenya’s exports

|Poisson Quasi Ma|ximum Likelihood|
|---|---|
|Constant<br>_−_0.93|(0.41)<sup>_∗_</sup>|
|Kenya GDP per capita<br>0.00|(0.00)<sup>_∗∗∗_</sup>|

14
predictive power and (R<sup>2</sup> of 0.29 or 0.31) as would be expected if these lower resolution images convey less information.

# **5. Conclusion and policy implications**

While many studies investigate effects of conflict on food security, they focus on the demand rather than the supply side. To assess how the war is likely to affect Ukraine’s production and thus global food security, we use Sentinel-2 imagery to construct outcome variables and indicators for the location and extent of conflict activity at different points in time. Although data is currently only available for winter crops, results point towards a reduction of up to 25% with the importance of indirect effects vastly outweighing that of direct ones and small farmers being particularly affected.

Beyond providing estimates of conflict effects, the ability to provide such information in season at high levels of granularity and update it as new data becomes available can help improve decision-making by policy-makers and private parties to minimize war-induced losses. First, losses could be vastly higher if farmers are unable to harvest the standing crop due to shortages of inputs or unwilling to do so as they would not be able to sell it-e.g., because of a lack of storage space. Information on expected output can help guide efforts to anticipate and avoid such bottlenecks. Second, the data generated allow to identify areas or groups that are particularly affected that may require short-term (working capital) support and, if support can be provided, to monitor its use and impact. This could help improve not only welfare for local producers but also make a contribution to greater food security by consumers in countries that are heavily reliant on food imports.

14
the U.S. In both countries, _financial access_ to banks was perceived as a much larger barrier than physical access. Unlike in the U.S. where a large percentage of the unbanked say that they do not need a bank account because they don’t have any savings, only 7% of the unbanked in the Mexico City survey stated that they did not need an account. This difference could be due to the wording of the survey or could reflect the absence in Mexico City of institutions, such as check-cashing outlets, that provide convenient payment services for unbanked individuals. Another difference between the responses in Mexico and the U.S. is that almost 9% of Mexicans in the capital city said that they did not trust the banks, a response that was well under 1% in the U.S. Undoubtedly, this reflects the turbulent history of Mexican banks over the past 20 years.

> 16  Ruiz, as cited in Solo et. al, 2003

43
i ) ∗ln (I ) would indicate a stronger increase in imports from countries with larger population size in the pre-earthquake period. ii jj II(H HHh SSha aa J JJ 2011 IIp aaIIHHIInn IIa Information on country and country-pair characteristics come from different sources. Bilateral distance in kilometers and an indicator variable that captures if two countries share a border are from the CEPII’s GeoDist database (Mayer and Zignago, 2011). Country characteristics related to population and GDP per capita are from the World Bank World Developments Indicators (WDI). Countries’ vulnerability to natural disasters is constructed based on the World Risk Index (Bündnis Entwicklung Hilft, 2011) and it is defined equal to one if the index is greater than 63.3 (i.e., very high risk). The real exchange rate is constructed based on data from the Penn World Tables version 9.1 (Feenstra et al., 2015).<sup>16</sup> We use trade data from WITS to construct an indicator variable equal to one if the exporter was among the top 4 suppliers of importer in HS 6-digit product in the pre-shock period and to compute exporter’s revealed comparative advantage (RCA) index. Data on Free Trade Areas (FTA) are from Mario Larch’s Regional Trade HH kk Agreements Database from Egger and Larch (2008). Finally, we use World Bank’s regional classification to construct an indicator variable equal to one if two countries are located in the same region.<sup>17</sup> To reduce potential endogeneity concerns, we use averages based on the pre-shock period for all the time varying variables, except for the real exchange rate which is lagged.

_Table 4: Country-specific fundamentals_

||(1)<br>|(2)<br>|(3)<br>|(4)|(5)<br>|(6)<br>|
|---|---|-
assess how the vulnerability and resilience of rural households vary by household types, geography, and wealth (Jain, 2007; Thurlow, et al., 2012; Wineman and Crawford, 2017; Al Mamun, et al., 2018; Braimoh, et al., 2018; Alfani, et al., 2019; Ngoma, et al., 2019; Hamududu and Ngoma, 2019). It is equally not well understood the extent to which exposure to different climatic shocks affects vulnerability and resilience at national and subnational levels. There is also a dearth of information on the relative importance of CSA practices such as minimum tillage, improved inputs (fertilizer and seed), and crop diversification to mitigate the effects of climate shocks on livelihoods. This paper contributes to filling these gaps and evaluates the common types of climate shocks Zambian households face and how these shocks affect household vulnerability and resilience. It also assesses the types of strategies households use to cope with shocks, particularly assessing if CSA practices can mediate damaging climate shock effects on vulnerability and resilience.

This paper extends the literature on climate change and poverty in Zambia in four ways:

- (i) It uses detailed longitudinal household-level data to analyze the types of climate shocks households face in rural Zambia, how these shocks differ by geography and household types, and how these shocks affect vulnerability and resilience. This builds on several papers that focus on explaining poverty dynamics in Zambia. See for example Chapoto et al., (2011) covering the period 2001 – 2008, Ngoma et al., (2019) for the period 2012 – 2015 and Diwakar et al., (2020) for the period 2012 – 2019. The last two papers use the same RALS data sets as in this paper.

- (ii) It incorporates unique exogenously defined drought and flood risks to measure vulnerability and exposure to climate risk using detailed three-wave householdlevel panel data.

- (iii) It assesses how exposure to climate risk affects vulnerability and resilience.

- (iv) Finally
## **6.4 Implications for Practitioners**

The policy implication of our mechanisms analysis is clear. Organizations running social media ad campaigns with social objectives should adopt more nuanced targeting approaches to effectively reach their target populations. Specifically, these organizations need to implement _explicit_ targeting strategies to overcome optimization algorithms of social media platforms that prioritize users that are cheaper to reach to maximize overall engagement numbers. In our study setting, for example, these are wealthier users that may frequently check their social media but are at lower risk of contracting malaria.

In our evaluation of the real-world campaign, Malaria No More employed geographical targeting based on states with high incidence rates, and also segmented audiences using various demographic groups. These groups were defined by age, gender, and levels of urbanization, reflecting the personas of interest. Yet, these audiences were likely too broad, and did not adequately consider the higher engagement costs of users living in non-solid dwellings. Our trial shows that more sophisticated targeting tools, like Facebook Lookalike Audience, can enhance the targeting precision of ad campaigns. Our recruitment method via Facebook ads indicates that reaching individuals in non-solid dwellings could be almost 10 times more costly than those in solid dwellings. Therefore, unless we specifically target these more vulnerable groups, the optimization algorithms are likely to concentrate on lower-risk individuals in order to meet engagement metric goals within a set budget.

We propose a three-step approach for practitioners aiming to ensure their social media ads effectively reach and impact populations of their interest. First, during the formative research stage, organizations should identify these target populations where the potential impact of intervention is greatest. Second, practitioners should move beyond the basic targeting options provided by online platforms and employ more sophisticated tools such as Lookalike models to ensure inclusion of these key groups in the campaign’s audience. Third, practitioners should be mindful of the tradeoff between maximizing on-platform engagement and concentrating on vulnerable populations that are most likely to benefit from the campaign. Although the cost to reach and engage these at-risk users may