rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Alkire, S., 2015. Choosing Dimensions: The Capability Approach and Multidimensional Poverty. Palgrave Macmillan. DOI: 10.1057/9780230592407_6</mark> <mark>Amador, M., Phelan, C., 2021. Reputation and Sovereign Default. Econometrica, 89 (4): 1979-2010. https://doi.org/10.3982/ECTA16685</mark> <mark>Amador, M., Phelan, C., 2021a. Reputation and Partial Default. accepted for publication at AER: Insights.</mark> <mark>Andreasen, E., Sandleris, G., Van der Ghote, A., 2019. The political economy of sovereign defaults. Journal of Monetary Economics. Volume 104, Pages 23-36. https://doi.org/10.1016/j.jmoneco.2018.09.003</mark> <mark>Arellano, C., Mateos-Planas, X., Ríos-Rull, J.V., 2022. Partial Default.</mark> <mark>Arellano, C., Bai, Y. Mihalache, G., 2018. Default Risk, Sectoral Reallocation, and Persistent Recessions. Journal of International Economics. https://doi.org/10.1016/j.jinteco.2018.01.004</mark> <mark>Arellano, C., Ramanarayanan, A., 2012. Default and the maturity structure in sovereign bonds. J. Pol. Econ. 120, 187</mark> <mark>Arkhangelsky, D., Athey, S., Hirshberg, D., Imbens, G., Wager, S., 2019. Synthetic Difference in Differences. American Economic Review, 111 (12): 4088-4118. DOI: 10.1257/aer.20190159</mark> <mark>Arteta, C., Hale, G., 2008. Sovereign debt crises and credit to the private sector. J. Int. Econ. 74, 53–69.</mark> <mark>Asonuma, T., Trebesch, C., 2016. Sovereign Debt Restructurings: Preemptive or PostDefault. Journal of the European Economic Association, Volume 14, Issue 1, 1 February 2016, Pages 175–214, https://doi.org/10.1111/jeea.12156</mark> <mark>Benjamin, D., Wright, M., 2018. Deconstructing delays in sovereign debt restructuring. Oxford Economic Papers, Volume 71, Issue 2, Pages 382–404,</mark> <mark>https://doi.org/10.1016/j.jinteco.2018.01.004</mark> <mark>Benjamin, D., Wright, M., 2009. Recovery Before Redemption: A Theory of Delays in Sovereign Debt Renegotiations. SSRN Electronic Journal. DOI:10.2139/ssrn.1392539</mark> <mark>Baird, S., Friedman, J., Schady, N., 2011. Aggregate Income Shocks and Infant Mortality in the Developing World. The Review of Economics and Statistics, 93(3), 847856.https://doi.org/10.1162/rest_a_00084</mark> 35
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE In fact, the lack of competition is argued to be one of the potential causes of Air Botswana domestic reputation for poor service with a very high index of delays and cancellations. This lack of reliability is commonly cited as a major obstacle to better performance in the tourism sector.

**Figure 9. Evolution of seats and city pairs in Botswana**

<!-- Start of picture text --> a. Seats b. City pairs<br>900,000 16<br>800,000 14<br>700,000<br>12<br>600,000<br>10<br>500,000<br>8<br>400,000<br>6<br>300,000<br>200,000 4<br>100,000 2<br>- 0<br>2,001 2,004 2,007 2009 (Est)<br>2,001 2,004 2,007 2009 (Est)<br>Total<br>International Total International<br>Intercontinental excluding flights between NA and SSA<br>Intercontinental Domestic<br>Domestic<br>City pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems.

# Water resources

Botswana has scarce water resources compared to countries occupying similar climatic zones. The renewable water resource per capita is estimated at about 6,819 cubic meters (m<sup>3</sup> ) per year (including cross-border flows), below the Sub-Saharan African average of 7,000 m<sup>3</sup> per year. Rainfall levels vary considerably across regions and during the course of the year, averaging a low 416 millimeters annually.

Botswana’s population growth (1.5 percent annually) has put significant pressure on its water resources. Demand for drinking water has increased over time due to the increasing rate of urbanization, and some 95 percent of the population is reported to have access to improved drinking water resources. Domestic use accounts for 34 percent of water withdrawals. Need for agricultural production adds to the stress. Irrigation and livestock account for 47 percent of water withdrawals. Industry accounts for around 20 percent of total water demand, particularly the mining sector.

Given the wide range of competing sources of demand (agriculture, water supply, and industry) and frequent droughts, it is essential to have a clearly defined basis for allocating water rights among sectors so as to enhance efficient water use and to maximize its development impact. Furthermore, water rights are also determined by international riparian rights. The most important drainage basins are the South 17
# **Table 1: Higher South-North migration flows can lead to income per capita in 2030 being higher than in the baseline**

Percent difference from baseline; difference from baseline in world GDP (last row) in US$ billion (2007 prices); <u>difference in global GDP per capita (last row) in US$ 1000 (2007 prices)</u>

||**R**|**eal GD**|**P**|**Real G**|**DP per**|**capita**|
|---|---|---|---|---|---|---|
||**2020**|**2025**|**2030**|**2020**|**2025**|**2030**|
|Developed,migrant-receiving|||||||
|Australia, New Zealand and Other Oceania|1|2|2.1|-0.6|-1.2|-1|
|Canada|0.8|1.7|1.9|-0.9|-1.6|-1.3|
|European Economic Area|0.9|1.7|1.9|-0.8|-1.5|-1.2|
|High-Income Asia|0.7|1.4|1.5|-0.8|-1.5|-1.3|
|High-Income Middle East|0.5|1.1|1.4|-1.2|-2.2|-1.9|
|Russian Federation|0.6|1.2|1.5|-1|-1.9|-1.6|
|USA|0.9|1.9|2|-0.7|-1.3|-1.1|
|Developing,migrant-sending|||||||
|Bangladesh|-0.1|-0.2|-0.2|0|0.1|0.1|
|Brazil|-0.6|-1.2|-1.3|1|1.7|1.3|
|Central Asia|-0.1|-0.2|-0.3|0|0.1|0.1|
|China|0|-0.1|-0.1|0|0.1|0.1|
|India|0|0|-0.1|0.1|0.1|0.1|
|Indonesia|0|0|0.1|0.1|0.2|0.2|
|Low-Income Sub-Saharan Africa|-0.5|-1|-1.1|1.1|2|1.8|
|Mexico|-0.1|-0.2|-0.2|0.4|0.8|0.8|
|Middle-Income Middle East and North Africa|-2|-4|-4.4|1.9|4.2|3.9|
|Other Former Soviet Union and Other Eastern Europe|-0.5|-1|-1.1|0.4|0.7|0.6|
|Other Latin America and Caribbean|-0.1|-0.3|-0.3|0.3|0.5|0.4|
|Other Low-Income South and East Asia|-0.1|-0.2|-0.2|0.5|0.9|0.8|
|Other Middle-Income Asia|0|0|0.1|0.1|0.3|0.2|
|Other Middle-Income Sub-Saharan Africa|-0.1|-0.2|-0.1|0.2|0.3|0.3|
|Pakistan|-0.3|-0.5|-0.5|0.1|0.2|0.1|
|South Africa|-0.7|-1.5|-1.7|1.2|2.2|1.7|
|Turkey|0.5|0.9|0.9|0.5|0.9|0.9|
|**World**|0.5|0.9|0.9|0.5|0.9|0.9|
|**ΔWorld GDP**|386.3|843.7|974.7|0.05|0.104|0.115|

Source: Authors’ simulations 12
- Long, J. and J. Ferrie (2013). “Intergenerational Occupational Mobility in Great Britain and the United States since 1850”. _American Economic Review._ 103(4) pp. 1109-37

Lopez-Calva, L.F. and E. Ortiz-Juarez (2011). “A Vulnerability Approach to the Definiiton of the Middle Class”. _Policy Research Working Paper 5902. The World Bank_ , Washington DC.

Ravallion, Martin (2010). “The Developing World’s Bulging (but Vulnerable) Middle Class”. World Development. 38(4):445-454 Sen, Amartya. (1983). “Poor, Relatively Speaking”. _Oxford Economic Papers_ . 35(2): 153-169 <mark>Shorrocks, A. F. (1978). “The measurement of mobility”.</mark> _<mark>Econometrica</mark>_ <mark>. 46(5): 1013-1024</mark>

- Tiwari, Sailesh, Hanan Jacoby and Emmanuel Skoufias (2017). “Monsoon Babies: Rainfall Shocks and Child Nutrition in Nepal”, fortcoming in the _Economic Development and Cultural Change_ .

World Bank (2015). “A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals”. _Policy Research Report_ . http://doi.org/10.1596/978-1-46480361-1 21
- [7] Bagwell, K., Staiger, R. (2011). “What Do Trade Negotiators Negotiate About? Empirical Evidence from the World Trade Organization.” _American Economic Review_ 101(4): 1238-73.

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- [11] Broda, C., Limao, N., Weinstein, D. (2008). “Optimal Tariffs and Market Power: The Evidence.” _American Economic Review_ 98 (5): 2032-65.

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- [14] Caves, D, Christensen, L., Diewert, E. (1982). “Multilateral Comparisons of Output, Input, and Productivity using Superlative Index Numbers.” _Economic Journal_ 92: 7386.

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25
92 2009; _Saigon Times_ 2009; Vietnam News Brief Service 2011a and 2011b; VietBiz24 2012; Thai News Service 2010a, 2011f, 2011h; Asia Pulse 2010; ESMAP 2009; government, utility, and national oil company Web sites and their announcements.

- a. √ under P signals 20 percent or more of power generation from petroleum products.
The individual migration data for sons and daughters is used to construct various measures of migration capital. Family migration capital is estimated for each son or daughter based on the sum of all family migrants at each point in time, excluding the son and daughter themselves. Also, aggregation of the migration data at the community level, excluding ego’s household migration capital, provides a measure of time-varying community migration capital. All community migration data were also separated by gender in order to test for gender-specific forms of migration capital.

A rare perspective on migration incentives and constraints is collected in a module where households report on severe household-level shocks occurring each year since 1990. Shocks were categorized into one of four categories: a job loss; a major illness or death; a large loss of property; or an income shock relating to the collapse of the pyramid saving schemes. The annual nature of the shock data provides a time-varying indicator of their influence on both male and female migration patterns.

The remaining core variables are built in a standard fashion. Individual level education is constructed by categories (completed up to 8 years of schooling, at least some high school education, or at least some university education), while age is categorized in 5-year groups. A wealth proxy, based on a principal components analysis of durable goods owned by the household in 1990, is used in order to avoid endogeneity with subsequent migration. Finally, the region of residence is composed of Tirana, the capital, and then Mountain, Central and Coastal regions, each disaggregated into urban and rural sectors.

# **5. Methods**

We employ discrete time hazard models using logistic regression to estimate the hazard of firstmigration. The hazard analysis has several advantages in this setting because persons are observed over the course of up to 14 years and some but not all make transitions – i.e. move abroad for the first time – in any particular year. In many cases, no out-migration ever occurs within the observed time span. These cases are right-censored because they may well end up migrating but only after the observation is completed (post-2003). Individuals enter our eligible sample at age 15. Many sons and daughters will not have reached 15 by 1990, meaning both their exposure and migration are not counted until they reach the cut-off age. The hazard analysis 9
 Quality standards for warehouse receipts  Performance guarantees on warehouse operator  Receipt negotiability _6) Getting credit_  Security interest granted to movable assets and future assets  Collateral registry  <u>Credit information from non-banks institutions</u> MFIs play an important role in promoting financial inclusion. They substitute for moneylenders and help provide financial services to those who are excluded from the traditional banking system (Dev, 2006). Accessing microfinance credits has significant impacts on improving the livelihood of the poor, by enabling them to smooth consumption, better manage skills and diversify their economic activities (Bakhtiari, 2011). The microfinance regulator’s challenge is to adopt a regulatory framework with appropriate prudential and non-prudential regulations to balance the multidimensional goals of financial access, financial stability, and consumer protection (Christen et al., 2003). The MFI indicator measures the regulatory environment for deposit-taking microfinance institutions, including prudential regulations such as minimum capital adequacy ratios and provisioning rules imposed on those MFIs, as well as consumer protection requirements focusing on interest rate disclosure and enrollment in a deposit insurance system.

Financial cooperatives serve large numbers of low-income customers; however, constrained by obsolete governance structure, low capacity, lack of an appropriate regulatory framework, and poor supervision, financial cooperatives in most developing countries are underdeveloped and not fulfilling their potential to serve the unbanked population (Nair and Kloeppinger-Todd, 2007). This indicator measures the existence and content of financial cooperative regulations, including the minimum requirements to establish a financial cooperative, prudential ratios and consumer protection requirements.

Agent banking allows agents to provide financial services on behalf of a financial institution in areas where physical branches do not reach. It provides the poor with more economical options of accessing financial services, as they do not need to spend out of pocket to reach a bank branch (Barasa and Mwirigi, 2013). Strong legislation fosters a positive customer experience that creates trust in the system. At present, the agent banking indicator examines the extent to which countries have enacted some good legal/regulatory practices to better enable third-party agents to provide financial services on behalf of financial institutions. It includes the minimum standards to qualify and operate as an agent, type of contracts that can be signed between financial institutions and agents, the range of financial services agents can provide and financial institutions’ liability for agent actions.

Nonbank e-money issuers (NEMI) can play an important role in providing an array of financial services — particularly payments, transfers, and savings—for those who are currently excluded from the formal financial system (Lauer and Tarazi, 2012). It is important for governments to adequately supervise non6
Table A5: Malawi - Educational attainment of children and their parents by five-years birth cohort

||Average years<br>of schooling|No schooling|Primary|Secondary|Tertiary|
|---|---|---|---|---|---|
|Son||||||
|1942-1946|3.7|33.2|51.7|13.1|2.0|
|1947-1951|4.5|22.5|54.2|21.1|2.2|
|1952-1956|4.8|22.8|50.6|24.1|2.5|
|1957-1961|5.0|19.1|54.1|23.3|3.5|
|1962-1966|5.3|19.4|49.2|27.6|3.9|
|1967-1971|5.6|15.8|50.7|28.5|5.1|
|1972-1976|6.0|14.1|48.6|33.2|4.1|
|1977-1981|6.6|10.3|45.1|40.9|3.7|
|1982-1986|7.0|7.8|44.0|44.4|3.8|
|1987-1991|6.8|6.9|45.1|46.5|1.5|
|Daughter||||||
|1942-1946|1.8|61.2|35.7|2.5|0.6|
|1947-1951|2.1|56.3|38.2|4.8|0.8|
|1952-1956|2.5|51.4|38.6|9.6|0.4|
|1957-1961|2.7|48.5|40.1|10.9|0.5|
|1962-1966|3.2|38.1|50.1|10.2|1.6|
|1967-1971|3.5|38.4|46.7|11.7|3.3|
|1972-1976|3.7|31.8|53.1|14.2|0.9|
|1977-1981|4.7|22.6|53.6|21.4|2.4|
|1982-1986|5.4|14.8|54.6|28.5|2.2|
|1987-1991|5.7|12.3|53.5|32.9|1.4|
|Mother||||||
|1942-1946|0.2|97.8|0.2|2.1|0.0|
|1947-1951|0.1|98.3|0.3|1.3|0.1|
|1952-1956|0.2|97.7|0.4|1.9|0.0|
|1957-1961|0.3|96.7|0.4|2.9|0.1|
|1962-1966|0.5|95.1|0.4|4.3|0.3|
|1967-1971|0.5|94.2|1.1|4.4|0.3|
|1972-1976|0.7|91.6|1.5|6.6|0.4|
|1977-1981|1.0|88.4|2.4|8.3|0.9|
|1982-1986|1.3|83.5|5.3|10.4|0.8|
|1987-1991|1.8|73.8|13.4|12.0|0.8|
|Father||||||
|1942-1946|0.5|94.4|0.2|5.3|0.2|
|1947-1951|0.5|94.1|0.0|5.8|0.1|
|1952-1956|0.9|90.0|0.0|10.0|0.1|
|1957-1961|0.9|90.3|0.0|9.3|0.4|
|1962-1966|1.2|88.0|0.1|10.9|1.0|
|1967-1971|1.3|86.7|0.3|12.4|0.7|
|1972-1976|1.6|83.0|0.5|15.2|1.3|
|1977-1981|2.2|78.1|1.4|18.3|2.3|
|1982-1986|2.7|72.1|3.6|21.8|2.6|
|1987-1991|3.1|63.7|10.2|23.4|2.8|

37
exchange rate are not significant predictors of greenfield FDI ceteris paribus.<sup>14</sup> This third specification is our preferred one as it estimates the “direct” effect of political instability on greenfield investment inflows net of its possible impact through economic variables.

Turning to the effects of the Arab Spring, we find a negative and statistically significant association between Arab Spring unrest and greenfield FDI flows, when we do not control for political instability or economic factors (Table 2, column 4). On average, countries experiencing a revolution, civil war, or sustained political disorder associated with the Arab Spring witnessed reductions in greenfield FDI of approximately three-fifths relative to their initial value. In the specification with economic controls, the reduced coefficient estimate on the Arab Spring dummy remains statistically significant (Table 2, column 5). However, once we add political instability to the equation (Table 2, column 6), the Arab Spring dummy loses statistical significance and becomes much smaller. It thus appears that the reduction in FDI flows during the Arab Spring was predominantly driven by intensified political instability.

As a robustness check, Table 3 presents our preferred specification, shown in column 3 of Table 2, re-estimated using alternative estimation methods, notably difference GMM (column 1), systems GMM (column 2), OLS (column 3), LSDV estimates (column 4), and the Han-Philips estimator (column 5).<sup>15</sup> Political instability remains consistently significantly negatively correlated with greenfield FDI regardless of which estimation method we use. Note that the LSDVC and LSDV estimates are very similar, suggesting the magnitude of the Nickell bias is relatively limited. The difference and systems GMM estimations result in quite different coefficient estimates, with the latter being very close to the OLS estimates presented in column 4 of Table 3. As discussed in Roodman (2009) this could be indicative of an overfitting or weak instrument problem.

> 14 We also re-estimated the models using real effective exchange rate data from the database constructed by Darvas (2012), which includes real effective exchange rates for 13 MENA countries for at least part of the sample period. Despite a considerable reduction in the sample size, th estimation yielded qualitatively similar results.

> 15 As an additional robustness check, not presented in the paper to conserve space, but available upon request, we reestimated the models separately for each sector using the alternative estimation methods used in Table 3. These alternative methods yielded qualitatively similar results.

17
between crop mask and the accumulated index was then used as a second set of dependent variables. Two advantages of this procedure are that it covers areas cultivated by non-registered enterprises and that it can be applied even in settings where statistical yield data may be entirely unavailable.

# **3.2 Descriptive statistics**

Table 1 combines data on area cropped from form 29 in 2018 (column 2) with the same information for 2005 (column 1). In 2018, of 27.7 mn. ha of cultivated land captured by the statistical system, 33% was under wheat and barley, 22% under sunflower, 17% under maize and 6% under soybean. Compared to 2005, area expanded by some 3.6 mn. ha and crop composition shifted with quadrupling of the area under soybean, tripling of maize area, and a 75% increase in sunflower. While wheat area remained constant, area under barley declined by 40% and that under root and fodder crops by 30%. These shifts were partly policy-induced as in the case of sunflower for which Ukraine introduced 23% export duty in 1999 that was gradually reduced to 10% in 2014. The associated expansion of sunflower crushing capacity helped Ukraine to develop into the leading global supplier of sunflower oil , creating demand for local sunflower seed and resulting in the area expansion visible in the data. Data on yield, input cost and imputed profit from form 50, an expanded questionnaire for the subset of farms greater than 200 ha, suggest this reflected a move towards relatively more profitable crops. Imputed profits from maize and sunflower are 2.4 and 2.2 times that of barley while soybean and wheat profits exceed those from barley by 56% and 22%.

To assess implications for crop rotation, table 2 combines remotely sensed with statistical information for the four main crops in 2017 and 2018. In 2017, some 20% of cereals, 10% of sunflower, 24% of maize, and 17% of soybean was mono cropped (i.e., grown on the same field where the same crop had also been cultivated in 2016) while figures for 2018 are 17%, 10%, 28%, and 15%, respectively. Availability of data on the last two crops grown in 2018 suggest that 12% and 4.6% of cereal area were cropped in two or three consecutive seasons with analogous figures of 7.4% and 2.5% for sunflower, 19% and 9% for maize, and 11% and 4% for soybean, respectively.

Based on the 2018 figures, cereals were most often preceded by sunflower (33%), others (26%), cereals (17%), maize (14%) or soybean (10%). Most of the area planted to sunflower was previously occupied by cereals (39%) or maize (29%), other crops (15%), sunflower (10%) or soybean (7%). For maize, mono-cropping (28%; 19% with maize grown consecutively for two years and 9% for three years) is the most frequent sequence, followed by rotations where maize was preceded by sunflower (24%), cereals (20%), others (19%) or soybean (9%). Soybean was most often preceded by maize (28%), cereals and others (21% each), sunflower (15%) or itself (15%). In most cases, cultivated area as determined by remote sensing exceeds that reported by state statistics data, with a mean difference 9