rafmacalaba/datause-encoder-data huggingface dataset:
- IOM (2018b). _Displacement Tracking Matrix (DTM). Libya’s Migrant Report (Round 23). November - December, 2018_ . International Organization for Migration, Geneva.

- IOM (2019). _Living and Working in the Midst of Conflict: The Status of Long-term Migrants in Libya_ . International Organization for Migration, Geneva.

- Irhiam, H. R., M. G. Schaeffer, and K. E. Watanabe (2023). _The Long Road to Inclusive Institutions in Libya: A Sourcebook of Challenges and Needs_ . International Development in Focus series. Washington, DC: World Bank.

- Marchesi, M. (2021). Can migration flows be controlled? the effects of the italian 2017–2018 policy against illegal immigration. In E. Savona, R. Guerette, and A. Aziani (Eds.), _The Evolution of Illicit Flows: Displacement and Convergence among Transnational Crime_ . Springer.

- McKenzie, D. and H. Rapoport (2010). Self-selection patterns in Mexico-US migration: The role of migration networks. _Review of Economics and Statistics 92_ (4), 811–821.

- Meltzer, C., J. Eberl, N. Theorin, T. Heidenreich, J. Str ̈omb ̈ack, H. G. Boomgaarden, and C. Schemer (2021). Media effects on policy preferences toward free movement: evidence from five eu member states. _Journal of Ethnic and Migration Studies 47:15_ , 3390–3408.

- Micallef, M. (2017). _The Human Conveyor Belt: Trends in human trafficking and smuggling in post-revolution Libya_ . Global Initiative against Transnational Organized Crime.

- Mixed Migration Centre (2019). _What makes refugees and migrants vulnerable to detention in Libya? A microlevel study of the determinants of detention_ . MMC Research Report, December.

- Mixed Migration Hub (2015). _Detained Youth: The Fate of Young Migrants, Asylum-Seekers, and Refugees in Libya Today._

- Murphy-Teixidor, A., A. A. Bonfiglio, and V. Leigh (2020). Migration and risks: smuggling networks and dynamics on the central mediterranean route. In F. Fargues and M. Rango (Eds.), _Migration in West and North Africa and across the Mediterranean: Trends, risks, development and governance_ . Berlin: IOM GMDAC.

- Pesando, L. M., V. Rotondi, M. Stranges, R. Kashyap, and F. C. Billari (2021). The internetization of international migration. _Population and Development Review 47_ (1), 79–111.

- Rahman, A. and M. Di Maio (2020). _The private sector amid conflict: The case of Libya_ . The World Bank. Washington D.C. (USA).

- Raleigh, C., A. Linke, H. Hegre, and J. Karlsen (2010). Introducing acled: an armed conflict location and event dataset: special data feature. _Journal of peace research 47_ (5), 651–660.

- Ribeiro, F. N., M. Ara ́ujo, P. Gon ̧calves, M. Andr ́e Gon ̧calves, and F. Benevenuto (2016). Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods. _EPJ Data Science 5_ , 1–29.

22
Table 7a: Trade, Legal Origins <u>and Institutional Quality, Relative Explanatory Power</u> Dep. Var.: log of average trade flow

|A.<br>|(1)<br>1995-99|(2)<br>1984-99|(3)<br>1984-99|(4)<br>1984-99|(5)<br>1995-99|
|---|---|---|---|---|---|
|Different Legal Origin|-0.14|-0.13|-0.1|-0.08|-0.25|
||[3.02]|[2.76]|[2.19]|[1.70]|[5.96]|
|Bureaucratic Quality|0.16|0.26|0.38|||
||[7.76]|[14.90]|[26.20]|||
|Control of Corruption|0.06|0.14||0.27||
||[4.81]|[11.22]||[25.11]||
|Protection of Property Rights|0.2||||0.31|
||[11.07]||||[21.43]|
|Observations|23609|77594|77594|77594|29772|
|R^2|0.74|0.67|0.67|0.67|0.72|
|B.||Be|ta Coefficien|ts||
|Different Legal Origin|-0.020|-0.018|-0.014|-0.011|-0.035|
|Bureaucratic Quality|0.071|0.126|0.182|||
|Control of Corruption|0.032|0.082||0.154||
|Protection of Property Rights|0.091||||0.137|

Notes: Panel A reports OLS estimates. t-values in brackets. Standard errors are calculated taking into account country-pair clusters. The following variables are included in the estimation but their coefficients are not reported: log distance, log product of GDP, log product of land area, no. of landlocked, common border, common border, common language, currency union. A constant and year dummies are not reported as well. Panel B reports beta coefficients estimated for the same specification as panel A.

41
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the measurement errors in self‐reported crop production estimates and their implications for the inverse scale‐productivity relationship (Desiere and Jolliffe, 2018 in Ethiopia; Gourlay et al., 2017 in Uganda).

Less common but also well‐established is to measure crop yields by physically harvesting a sub‐ section of a farmer’s plot in a so‐called crop cut (Fermont and Benson, 2011). Crop cuts provide a more objective way to measure grain production for a part of the plots, but heterogeneity within a plot can lead to sensitivities of crop cut yields to the precise location and size of the crop cut sub‐plot vis‐à‐vis the entire plot (Fermont and Benson, 2011). An alternative is to harvest the entire plot, which avoids most of the problems of the prior methods, and therefore, is frequently considered the “gold standard” (Casley and Kumar, 1988; Fermont and Benson, 2011). However, full plot harvests require a substantial amount of labor and coordination with farmer harvest schedules, which makes them costly and difficult to scale.

Given the limitations of existing approaches, recent work has explored the ability of satellite data to track crop yields. Burke and Lobell (2017) (hereafter BL17) showed that 1m resolution data from Terra Bella’s Skysat sensors were useful for mapping maize yields for farms in western Kenya. This usefulness was measured both by correlation of satellite‐based yield estimates with traditional ground‐based yield measures, as well as by the ability of satellite‐based yields to detect positive yield responses to fertilizer and hybrid seed inputs. This latter aspect was considered especially important since (i) ground‐based yield measures are inevitably imperfect themselves, and (ii) detecting response to inputs or some other aspect of farm management is a common motivation for collecting plot‐level yield data in the first place.

The primary objectives of this paper are to assess the ability of satellite‐based approaches to measure plot‐level maize yields on African smallholder farms and to gauge the sensitivity of production function estimation to the choice of ground‐ versus satellite‐based maize yield variant. The analysis uses data from the 2016 round of MAPS: Methodological Experiment on Measuring Maize Productivity, Soil Fertility and Variety, which was implemented during the first rainy season of 2016 (June‐October) in 45 enumeration areas within a 400 square kilometer area spanning Iganga and Mayuge districts of Eastern Uganda; the leading maize‐producing region of the country. The analysis extends the work presented in BL17 in at least three substantial ways.

First, the Ugandan maize systems are considerably more subsistence‐focused and heterogeneous than the Kenyan counterparts in BL17, with generally smaller plot sizes, lower input use, and greater prevalence of under‐canopy intercrops such as beans and groundnuts, and frequent occurrence of over‐canopy intercrops such as cassava and bananas. Thus, Uganda represents a 3
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# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS883 Malara: The Impact of Treated Bed-<br>Nets on Childhood Mortality in the<br>Gambia|Pedro L. Alonso<br>Allan G Hill<br>Patricia H. David<br>Greg Fegan<br>Joanna R M. Armstrong<br>Andreas Francisco<br>K. Cham<br>Brian M Greenwood|April 1992|0. Nadora<br>31091|
|WPS884 Intercommodity Price Transmittal<br>Analysis of Food Markets in Ghana|Haiold Alderman|April 1992|C. Spooner<br>30464|
|WPS885 The Impact of EC-92 on Developing<br>Countries' Trade: A Dissenting View|A. J Hughes HalletI|April 1992|A Kitson-Walters<br>33712|
|WPS886 Factors That Affect Short-Torm<br>(Commercial<br>Bank Lending to<br>Deveioping Countries|Sudarshan Gooptu<br>Maria Soledad Martinez Pe|April 1992<br>ria|L Dionne<br>31426|
|WPS887 Power Sharing and Pollution Control<br>Coordinating Policies Among Levels<br>ot Government|William Jack|April 1992|P Pender<br>37851|
|WPS888 Transformation of Agriculture in<br>Central Eastern Europe and the Form<br>USSR: Major Policy Issues and<br>Perspectives|Csaba Csaki<br>er|April 1992|C. Spooner<br>30464|
|WPS889 On-th-Job Improvements in Teacher<br>Competence: Policy Options and<br>Their Effects on Teaching and<br>Learning in Thailand|S.ephen W Raudenbush<br>Suwanna Eamsukkawat<br>Ikechuku Di-lbor<br>Mohamed Kamali<br>Wimol Taoklam|April 1992|C Cristobal<br>33640|
|WPS890 Population, Health. anci Nutrition.<br>F:scal 1991 Sector Review|Denise Vaillancourt<br>Janet Nassim<br>Stacye Brown|April 1992|0. Nadora<br>31091|
|WPS891 Public Institutions and Private<br>Transactions: The Legal and<br>Regulatory Environment for Business<br>Transactions in Brazil and Chile|Andrew Stone<br>Brian Levy<br>Ricardo Paredes|April 1992|G Orraca-Tetteh<br>37646|
|WPS892 Evaluating the Asset-Based Minimum<br>Tax on Corporations An Option-<br>Pricing Approach|Antonio Estache<br>Sweder van Wijnbergen|April 1992|**A**Estache<br>81442|
# **_England and Wales, 1688_**

|Social group|Number of<br>people|Percentage of<br>people|Income per<br>capita (in £)|Income in terms<br>of per capita<br>mean|
|---|---|---|---|---|
|Temporal lords|8000|0.14|151.5|15.83|
|Baronets|12800|0.22|93.8|9.80|
|Merchants on land, greater|19584|0.34|66.7|6.97|
|Spiritual lords|520|0.01|65.0|6.79|
|Knights|7800|0.14|61.5|6.43|
|Esquires|30000|0.53|56.3|5.88|
|Merchants by sea, greater|16000|0.28|50.0|5.23|
|Artisans and handicrafts|26980|0.47|50.0|5.23|
|Gentlemen|120000|2.11|35.0|3.66|
|Merchants by sea, lesser|48000|0.84|33.3|3.48|
|Merchants on land, lesser|78342|1.38|33.3|3.48|
|Persons in offices, greater|40000|0.70|30.0|3.14|
|Law|56434|0.99|22.0|2.30|
|Persons in offices, lesser|30000|0.53|20.0|2.09|
|Naval officers|20000|0.35|20.0|2.09|
|Military officers|16000|0.28|15.0|1.57|
|Clergymen, greater|10000|0.18|14.4|1.50|
|Freeholders, greater|192976|3.39|13.0|1.36|
|Science and Liberal Arts|64490|1.13|12.0|1.25|
|Freeholders, lesser|482450|8.48|11.0|1.15|
|Clergymen, lesser|50000|0.88|10.0|1.05|
|Shopkeepers and tradesmen|457668|8.04|10.0|1.05|
|Farmers|516910|9.09|8.5|0.89|
|Manufacturing trades|732883.5|12.88|8.4|0.88|
|Common soldiers|70000|1.23|7.0|0.73|
|Common seamen|150000|2.64|6.7|0.70|
|Building trades|328581|5.78|5.6|0.58|
|<br>Laboring people & outservants|997489.5|17.53|4.3|0.45|
|Miners|64080|1.13|3.3|0.35|
|Cottagers and paupers|1017845|17.89|2.0|0.21|
|Vagrants|23489|0.41|2.0|0.21|
|_Total_|_5689322_|_100_|_9.57_|_1_|

**Income distribution data** . The source is the Lindert-Williamson revision of Gregory King’s social table (available at

<u>http://www.econ.ucdavis.edu/faculty/fzlinder/King1688revised.htm). The data which</u> were originally presented on per household basis are transformed on per capita basis (each individual is assigned per capita income of his/her household).

**Population and area.** Current territory of England and Wales. Population: obtained directly from King’s numbers.

50
# **SUMMARY**

_This pqper presents a detwled bilateral comparison of GDP between China and the United States with 1986 as a reference date, following the purasing power parity (PPP) appoach formulated by the United Nations International Companson Program (ICP). The finings of the study include the following._

- _(a) China's per capita GDP in 1986 international dollars is between 1S770-1,044, depending on the different assumptions made in the compaiso of comprisonresiskant service sectors and the quality adjustments made in dte calculation of PPPs in a number of selected are_

- _(b) Based on the impact of iflation in the United Staes on ihe pardzsing power panty and grwth rates computedfmm national carency GDP data in consat price for China China's per capita GDP in 1991 irderntional dollars hows out to be between 1 1,227-1,663._

_The specffic issues in the comarisons of the housing and dte compisn-isisant rwice categories are dsused and some pproaches like te estimation of the shadow rent are tried The possible errors in the bilatera comparisons are analyzed_ _rwice_ Di
24 from 0.45 (for the most rapidly monetising economy) to -0.18 (for a rapidly demonetising economy with low savings rates). For the average country, 14% of the savings flow is financialized as measured here.

Using the pooled set of averages over 1965-73 and 1974-85, some equations for the two hypotheses are: RFSTS - c + .817 RR RsqAdj - .259 (4.90) (8) RFSTS - c + .694 RR -.150 TSGD -.044 INF +.00003 GNPPE -.031 GROUP (3.82) (-1.02) (-1.27) (1.29) (-1.13) RsqAdj - .271 (9) DYY - c + .113 RFSTS + .058 TSGD -.019 GROUP (4.98) (2.026) (-3.46) RsqAdj - .430 (10) IOCR - c + .280 RFSTS -.098 TSGD - .160 GROUP (2.17) (-.61) (-5.21) RsqAdj - .367 (11) From equations (8) and (9), the rate of savings intermediation, RFSTS, does respond to real interest rates, but the ratio of total savings to GDP, TSGD, inflation, and the level of GDP per head are not significant. This is especially surprising for inflation considering the large Ml component in broad money in most developing countries. The reason seems to be that only some of the low-inflation countries succeed in increasing financial depth, while in the higher inflation countries financial balances have usually already been drawn down to minimum levels so that the degree of further intermediation is zero rather than strongly negative (note that RFSTS measures incremental financial deepening not financial depth). Equat:ons (10) and (11) suggest that the rate of savings intermediation influences the efficiency effect and, indeed, that it is more important than the volume of domestic savings in determining the growth rate. These results seem to be fairly stable to the inclusion or exclusion of other variables.

If we express growth as a function of savings intermediation (a proxy for efficiency or the IOCR) and other variables and then express intermediation itself as a function of the real interest rate and other variables: DYY ( RFSTS ( RR,.) ......

From the regression coefficients,
Therefore, population projections made by the United Nations Population Division are used, as well as administrative information from the IESS in relation to current coverage levels per kind of benefit, number of contributors, wage levels and benefit amounts, all of which per simple age and gender.

Long-term projections are necessary for assessing how a system would react towards expected changes under given economic, political and demographical conditions. These are not predictions "to forecast future", but they allow assessing system perspectives, given certain reasonable hypotheses on longterm evolution of some determining variables. Likewise, this exercise allows assessing the impact of alternative hypotheses and/or reforms to current policies on coverage and social protection financing.

The simulated scenario in this work considers the current rules in terms of model patterns (contribution numbers, contribution rate, minimum legal age of retirement and both passive and active coverage levels). In particular, for simplicity reasons and without compromising generality, this work assumed that retirement age is 60 years of age considering 30 years of contributions. This combination of age and contribution years results in a replacement rate of 75%. Also, the current contribution rate is established to equal 8.2% as personal contribution and 1.3% as employer's contribution. In terms of system performance, it is assumed that the population percentage, by simple sex and age, contributing to social security remains stable in the long term. Finally, benefits are adjusted based on the current policy, depending on simulated inflation in the country.<sup>8</sup> Based on current parameters of the Ecuador pension system, the number of beneficiaries would shift from 340,000 in 2019 to 680,000 by 2040 and to 2.7 million by 2080 (Figure 15). On its part, the number of contributors also shows an increasing trend, though of lower volume, up to the 2060s to remain relatively stable at approximately 5 million. As a result, the system sustainability rate would present a constant decreasing trend from the 2020s shifting from 10 contributors per beneficiary to 2 in 2080. This result is proper of the aging population process taking place in the country, since this process not only implies larger elder population participation but also a lower percentage of working population.

> 8 GDP and inflation projections made by the International Monetary Fund (www.imf.org) as of year 2023 are used. Based on that, an inflation rate of 1% annually and GDP growth rate of 2 % are assumed.

24
inequality in SSA than in the US — suggests that the greater ability to contain the dispersion in consumption in SSA compared with the US must occur through insurance mechanisms other than self-insurance (i.e., own savings).

The higher ability to insure in rural areas is further revealed from direct evidence on selfreported information about shocks, coping strategies, and ability to borrow. Among rural households 71% report being hit by a shock in the past 12 months (most commonly weather-related shocks), and among urban households 39% report a shock (most commonly high food prices). Despite the higher occurrence of shocks in rural areas, of the rural households that report a shock, 51% have some form of insurance to cope with the shock, while this figure is smaller in urban areas, 40%. The ability to insure consumption can also depend on the ability to borrow. In this case, conditional on needing a loan, urban households show higher application rates, 40%, than rural households, 27%.<sup>5</sup> While this urban-rural differential in the ability to borrow suggests urban households might be better able to self-insure, we note that in urban areas borrowing is used 3.6 times more often for start-up capital than for consumption, while this ratio is 1.6 for rural areas. This suggests that such borrowing is used mostly for production and not necessarily for consumption insurance.

In decomposing CIW we find unambiguous signs of a subsistence economy where food consumption requirements can severely limit the ability to save. Specifically, the share of food in total consumption is above 50% throughout the income distribution, except for the top 1% in rural areas and the top 5% in urban areas. Coincidentally, these two groups are the only ones with positive saving rates across the income distribution. The composition of income (mostly agricultural) in rural areas also points towards a subsistence economy. Only in the top 1% of the income distribution in rural areas does the share of agricultural income fall below 50%. The similarity in the wealth portfolio across income groups (except for the top 1%) further reflects an inability to accumulate wealth. The only components of wealth that show signs of accumulation are livestock and non-housing durables.

While the overall power to accumulate wealth is dismal in the SSA countries we consider, the examination of cross-sectional sources of inequality allows us to identify pockets of wealth accumulation. First, over the life cycle, wealth in the rural areas of SSA (i.e., mostly land) follows a hump pattern that increases, in Malawi, by a factor of 2.65, while this increase is by a factor of 7.13 in urban areas, further suggesting a greater ability to accumulate wealth in urban areas than in rural areas. However, again, even the urban increase is small compared to the life cycle behavior of wealth in the United States that increases by a factor of roughly 20. Second, we find

> 5As we discuss below, these figures deal directly with self-selection as households are asked whether they needed a loan and whether they applied or not, independently of the need.

4
Policy Research Working Paper 7513

# **Abstract**

This paper discusses competitiveness-related issues surrounding the design and administration of corporate and value added/sales taxes in four South Asian countries—Bangladesh, India, Pakistan, and Sri Lanka. The paper is based largely on analysis of tax legislation; in addition, data from the World Bank’s enterprise surveys, the Doing Business report, as well as industry studies are used for evidence on tax compliance costs for business. The review of tax regulations in the region shows several commonalities: (1) widespread use of tax incentives to support selected industries, types of firms, and industrial locations; (2) many exemptions from value-added taxes as well as the practice of levying multiple indirect taxes on the same base; and (3) high costs of tax compliance for businesses. The paper discusses the consequences of tax policies for the competitiveness of South Asian producers, describes the main problems in tax administration, and outlines key directions for reforms.

This paper is a product of the Equitable Growth, Finance and Institutions Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at areva@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
25 Table 2: ESTIMATES OF THE UNSKILLED WAGE EQUATION 1/ (Equation 5)

|||Argentina*|Colombia|Chile*|Uruguay|
|---|---|---|---|---|---|
|BO|(Constant)|-.0.187<br>(-0.908)|-0.931<br>(-1.075)|-1.129<br>(-1.872)|-0.349<br>(-0.403)|
|B1|(ln p)|0.029<br>(0.081)|1.010<br>(0.839|2.127<br>(2.408)|0.713<br>(0.516)|
|B2|(ln w)|0.717<br>(1.729)|0.693<br>(2.543)|0.347<br>(2.216)|1.341<br>(2.269)|
|B3|(ln mw)|0.204<br>(0.906)|0.183<br>(1.481)|-0.263<br>(-1.146)|-1.163<br>(-1.780)|
|B4|(N)|0.207<br>(1.098)|0.044<br>(0.770)|-0.081<br>(-0.499)|0.470<br>(1.128)|
|R2||0.96|0.90|0.93|0.59|
|DW||2.7|1.6|2.5|1.3|

1/ The sample period is 1960-85 for Argentina, Chile, and Colombia, and 1965-85 for Uruguay.

* corrected for first-order autocorrelation.

Note: Numbers in parentheses are t-statistics.