rafmacalaba/datause-encoder-data huggingface dataset:
|Reintegration of Ex-combatants Project|Abderrahim Fraiji|
|---|---|
|Reintegration of Ex-combatants Project|Abderrahim Fraiji|
|Reintegration of Ex-combatants Project|Abderrahim Fraiji|
|IRAQEITI Implementation Support.|Carlos Alberto LopezQuiroga|
|IRAQEITI Implementation Support.|Carlos Alberto LopezQuiroga|
|Harmonization of Price Statistics & National Accounts in LAC Cou|nt Maurice Nsabimana|
|Ethiopia: NationalQualityInfrastructure Development Project|Karen Grigorian|
|Ethiopia: NationalQualityInfrastructure Development Project|Karen Grigorian|
|Ethiopia: NationalQualityInfrastructure Development Project|Karen Grigorian|
|Ethiopia: NationalQualityInfrastructure Development Project|Karen Grigorian|
|Ethiopia: NationalQualityInfrastructure Development Project|Karen Grigorian|
|Ethiopia: NationalQualityInfrastructure Development Project|Karen Grigorian|
|Ethiopia: NationalQualityInfrastructure Development Project|Karen Grigorian|
|Sierra Leone Agro-ProcessingCompetitiveness Project|NancyChen|
|Sierra Leone Agro-ProcessingCompetitiveness Project|NancyChen|
|Sierra Leone Agro-ProcessingCompetitiveness Project|NancyChen|
|Sierra Leone Agro-ProcessingCompetitiveness Project|NancyChen|
|Mexico Higher Education Project|Robert J. Hawkins|
|Mexico Higher Education Project|Robert J. Hawkins|
|Mexico Higher Education Project|Robert J. Hawkins|
|Mexico Higher Education Project|Robert J. Hawkins|
|Odisha Higher Education Program for Excellence and Equity|Kurt Larsen|
|Odisha Higher Education Program for Excellence and Equity|Kurt Larsen|
|Odisha Higher Education Program for Excellence and Equity|Kurt Larsen|
|Odisha Higher Education Program for Excellence and Equity|Kurt Larsen|
|Odisha Higher Education Program for Excellence and Equity|Kurt Larsen|
|Odisha Higher Education Program for Excellence and Equity|Kurt Larsen|
|Rural and Urban Access Improvement Project|Roger Gorham|
|Rural and Urban Access Improvement Project|Roger Gorham|
|Rural and Urban Access Improvement Project|Roger Gorham|
|Rural and Urban Access Improvement Project|Roger Gorham|
|Rural and Urban Access Improvement Project|Roger Gorham|
|Rural and Urban Access Improvement Project|Roger Gorham|
|Rural and Urban Access Improvement Project|Roger Gorham|
|Rural and Urban Access Improvement Project|Roger Gorham|
|EnergySector Support and Investment Project|Miarintsoa VonjyRakotondramanan|
|EnergySector Support and Investment Project|Miarintsoa VonjyRakotondramanan|
|EnergySector Support and Investment Project|Miarintsoa VonjyRakotondramanan|
|EnergySector Support and Investment Project|Miarintsoa VonjyRakotondramanan|
|EnergySector Support and Investment Project|Miarintsoa VonjyRakotondramanan|
|Innovation in Solar Power and Hybrid Technologies|Surbhi Goyal|
|Innovation in Solar Power and Hybrid Technologies|Surbhi Goyal|
|Innovation in Solar Power and Hybrid Technologies|Surbhi Goyal|
|StrengtheningOAG capacities inprocurement audits|SidyDiop|
|Zambia ScalingRenewable EnergyProgram Investment Plan|Tigran Parvanyan|
|Ethiopia Electrification Program(ELEAP)|Lara Born|
|Ethiopia Electrification Program(ELEAP)|Lara Born|
# **References**

Acharya, V., Almeida, H., Campello, M.(2007). Is Cash Negative Debt? A Hedging Perspective on Corporate Financial Policies. _Journal of Financial Intermediation_ , _16_ , 515554.

Almeida, H., Campello, M., Weisbach, M. (2004). The Cash Flow Sensitivity of Cash _. The Journal of Finance,_ 59(4), 1777-1804.

Arikan, A., Stulz, R. (2011). Corporate Acquisitions, Diversification, and the Firm’s Lifecycle. _Fisher College of Business WP 2011-03-018_ .

Bates, T., Kahle, K., Stulz,, R.(2009). Why do U.S. firms hold so much more cash than they used to?. _The Journal of Finance,_ 64(5), 1985-2021.

Baum, C., Caglayan, M., Ozkan, N., Talavera, O. (2006). The Impact of Macroeconomic Uncertainty on non-financial firms` demand for liquidity. _Review of Financial Economics,_ 15 _,_ 289-304.

Baum, C., Caglayan, M, Stephan, A., Talavera, O (2008). Uncertainty Determinants of Corporate Liquidity. _Economic Modelling ,_ 25, 833-849.

Brown, G., Kapadia, N. (2007). Firm-specific risk and equity market development. _Journal of Financial Economics, 84,_ 358-388.

Campbell, J., Lettau, M., Malkiel, B., Xu, Y. (2001). Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk. _The Journal of Finance_ , 56(1), 1-44.

Campello, M., Graham, J., y Harvey, C. (2011), The real effects of financial constraints: Evidence from a financial crisis. _Journal of Financial Economics,_ 97, 470–487.

Custodio, C., Ferreira, M., Laureano, L. (forthcoming), Why are US firms using more short-term debt?, Journal of Financial Economics, in press.

DeAngelo, H., DeAngelo, L., Skinner, D. (2004). Are dividends disappearing? Dividend concentration and the consolidation of earnings. _Journal of Financial Economics,_ 72, 425456.

Denis, D., y Osobo, I. (2008). Why do firms pay dividends? International evidence on the determinants of dividend policy. _Journal of Financial Economics, 89_ , 62-82.

Ellis, L., Smith, K., (2010), The Global Upward Trend in the Profit Share. _Applied Economics Quarterly, 56( 3),_ 231-256 Fama, E., French, K., (2001). Disappearing dividends: Changing firm characteristics or lower propensity to pay? _Journal of Financial Economics,_ 60, 3–43.

Fama, E., French, K., (2002). Testing tradeoff and pecking order predictions about dividends and debt. _Review of Financial Studies, 15_ , 1–33.

Fazzari, S., Hubbard, G, Petersen, B (1988).Financing constraints and corporate investment, _Brooking Papers on Economic Activity 1_ , 141-195.

13
findings from our interviews with maize growing households, who reported that they do not generally have a difficulty in finding a buyer for their maize when they are ready to market it.

The second factor is volume. Remote households also tend to be more vulnerable and resource poor, particularly since they have limited access to infrastructure and social and economic facilities. If these households are also less productive, they may be making smaller volume transactions when it comes to selling their maize. In addition, small transactions are less attractive to buyers and may attract lower prices.

To explore this potential effect, we examine the farm size of households in relation to their distance from market in table 5 below. We use farm sizes as an indicator of the volumes of maize produced by farmers<sup>18</sup> . Table 4 shows that relatively remote households do not tend to have smaller farm sizes than households located nearer to the marketplace. For instance, in the southern Iringa and central Dodoma regions, households in the middle tercile tend to have the largest farms whilst in the northern Arusha region, farm sizes tend to be similar irrespective of distance to market. This leads us to discount the possibility that small volume sales are another casual factor that has not been accounted for.

Besides competition and volume, other factors such as the quality of maize are unlikely to play a relevant role here. Production related quality issues can be significant but are likely to affect households irrespective of their distance to market. Storage related quality issues are also important, particularly since only four percent of maize growing households make use of effective storage facilities. On the whole, households do not tend to have adequate storage amenities and post-harvest losses can be large. Of the maize growing households that do store maize, only two percent do so to sell it later in the season<sup>19</sup> . Their main reason for storing maize is for household consumption.

_Table 4: Household farm size by distance to market_ _<u>(terciles)</u>_

||Iringa|Arusha|Dodoma|
|---|---|---|---|
|Lowest|3.1|2.8|4.9|
|Middle|6.9|2.6|7.1|
|Highest|4.0|2.7|6.1|

Source: Author‟s calculations from HBS 2007.

> 18 Data of the quantity of maize produced by household was not available.

> 19 Tanzania Sample Census of Agriculture 2002/03.

19
Table 5: Import Uncertainty and Export Dynamics: South-South Trade

||Firm Entry<br>Rate<br>(1)|Firm Exit<br>Rate<br>(2)|First yr.<br>Survival<br>(3)|Second yr.<br>Survival<br>(4)|Third yr.<br>Survival<br>(5)|
|---|---|---|---|---|---|
|Log import uncertainty|-0.014<br>|-0.022<br>|0.020<br>|0.029<br>|-0.025<br>|
||(0.012)|(0.015)|(0.030)|(0.000)|(0.000)|
|Log direct imports (%)|0.004|0.008|-0.036|-0.061|-0.209|
||(0.022)|(0.030)|(0.059)|(0.000)|(0.000)|
|Log direct exports (% sales)|-0.030***|-0.048*|0.084|0.133|-0.060|
||(0.010)|(0.026)|(0.056)|(0.000)|(0.000)|
|Log experience|0.047*|0.012|0.308***|0.420|0.172|
||(0.024)|(0.034)|(0.070)|(0.000)|(0.000)|
|Log nb. of employees|-0.042**|-0.006|-0.017|0.033|0.178|
||(0.018)|(0.017)|(0.039)|(0.000)|(0.000)|
|Log TFP|0.040|0.008|0.012|0.082|0.323|
||(0.044)|(0.039)|(0.080)|(0.000)|(0.000)|
|Common border|-0.022|-0.011|0.089|0.125|0.311|
||(0.034)|(0.040)|(0.083)|(0.000)|(0.000)|
|Log distance|0.127***|0.059***|-0.115***|-0.205|-0.130|
||(0.016)|(0.022)|(0.044)|(0.000)|(0.000)|
|Trade agreement|-0.082***|-0.084***|0.176***|0.104|0.335|
||(0.026)|(0.031)|(0.064)|(0.000)|(0.000)|
|Log tarifs|0.015|0.014|-0.034|-0.057|-0.080|
||(0.009)|(0.012)|(0.023)|(0.000)|(0.000)|
|Log exporter GDP cap.|-0.493**|0.204|-0.412|-0.742|-0.796|
||(0.230)|(0.271)|(0.407)|(0.000)|(0.000)|
|Log importer GDP cap.|0.052|-0.015|-0.023|0.364|0.547|
||(0.094)|(0.120)|(0.236)|(0.000)|(0.000)|
|Log exporter entry cost (% GNI)|-0.194***|-0.193**|-0.473***|-0.683|-0.672|
||(0.072)|(0.090)|(0.170)|(0.000)|(0.000)|
|Log importer entry cost (% GNI)|0.012|-0.002|-0.071|-0.144|-0.581|
||(0.027)|(0.039)|(0.083)|(0.000)|(0.000)|
|Constant|2.851|-1.814|5.351|-8.910|-7.117|
||(2.065)|(2.419)|(3.763)|(0.000)|(0.000)|
|Observations|4,113|3,308|3,317|2,894|1,390|
|R<sup>2</sup>|0.205|0.192|0.139|0.131|0.168|
|Country Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Destination Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Sector Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Log direct imports (%), Log direct exports (% sales), Log experience, Log nb. of employees and Log TFP computed over the sample of exporting firms, except in Column 1. The dummy variable equal to 1 if the exporter and importer were in a colonial relationship post-1945 was dropped due to insufficient observations for which Colony = 1. The description and source of variables are provided in Table A3 in the Appendix.

18
household endowments and contexts influence the access to and use of the VUP and other programs and how this in turn shapes asset accumulation. A number of factors come into play here such as geographic factors, i.e. accessibility and transport to the public works site (not shown here) or a number of household characteristics such as the total labor capacity of the household, alternative work opportunities and care requirements of the households.

The household characteristics contextualize welfare trajectories at two stages: First, they affect access to the program(s); second, they shape the utilization of the income transfer and other program support for productive and risk management purposes.

For stage 1, while access to the VUP is formally restricted by the eligibility criteria outlined above, households identified a number of additional characteristics which influenced their ability to access the VUP and also their use of other programs, notably other asset transfer programs and subsidized health insurance. These characteristics include: the sex of the household head or potential VUP beneficiary, their education level, the total labor capacity and care obligations within the household and initial endowments in assets and land. In all these aspects, we noted a strong interplay with the sex of the household head which emerges as another contextual factor related to gender dynamics across all the identified factors. In exploring each of these factors in the quantitative data, we find that typically education levels are low. Very few of the beneficiaries have received any form of formal education which in turn shapes economic opportunities. Low literacy levels are more pertinent among the female respondents; male respondents more frequently reported to be able to read and write. The qualitative data reveal a strong gender divide with respect to the control of resources ( _“As the husband, I control the income”_ (male, PW-M1)) and initial asset base with women reporting being historically traditionally excluded from inheritance and thus subject to lower land ownership compared to their male counterparts ( _“My parents passed away a long time ago. The land they had was divided among my other siblings and I did not inherit anything, as I was a girl”_ (female, DS-F1K)). More clearly, gendered divisions come to the forefront when 8
So the change in currency shares is given by The first term is the valuation effect, and the second term is the change in the ratio of holdings of currency _i_ to the total, when currency _i’s_ holdings are valued at 1999 exchange rates. Thus, the column marked “other” will show a decline if reserve accumulation in a particular currency, at 1999 exchange rates, does not keep pace with total reserves.

Table 4 presents the raw numbers for reserves as well as the proportion of reported reserves that were broken down into identified reserve currencies (the rest were not allocated across currencies). Several things are worth noting. First, the proportion of allocated reserves is much higher for industrial countries, while the share of the dollar is much higher for these countries than for developing countries. Second, the proportion of allocated developing country reserves has fallen even lower in recent years, and is currently only a bit over half. Third, developing country reserves have grown enormously since 1999; in that year, allocated reserves were well below industrial country levels (though their _total_ reserves were higher, SDR 760 billion versus SDR 525 billion). In 2006, in contrast, allocated reserves of developing countries were 40 percent higher, and their total reserves more than twice those of industrial countries.

Turning to the changes in shares, 1999 to 2006, a substantial fraction of the decline in the dollar’s share for industrial countries (2.6 percentage points) is due to valuation effects—the depreciation of the dollar against the SDR—and this component more than explains the fall in the dollar’s share. The 3 percentage point increase in the euro’s share is only partly (1.3 percentage points) due to valuation. For developing countries, because the 1999 stock was relatively smaller, the valuation effects account for a smaller drop in the dollar’s share, and the total decline (8.6 percentage points) is mainly due to quantity changes and scale effects (the latter reflect the general rise in the levels of reserves, which reduces the _share_ of an unchanged reserve _stock_ ). Similarly, the rise in the euro’s share in developing countries’ allocated reserves, 9.7 percentage points, is mostly due to quantity changes and scale effects (all but 1 percentage point).

While the new IMF data are an improvement, they require a further key adjustment to get a better estimate of the currency composition of global reserves. The published IMF figures for overall reserve holdings by currency, which give a share of only 25.8 percent for the euro in 2006, are of dubious value given the markedly different extent of reporting 6
Despite numerous theoretical arguments developed on corruption’s impact on social welfare, empirical contributions measuring the widespread of this phenomenon and its relationship with other economic or social dimensions remain quite sparse. Exceptions to the above are studies that have focused (sometimes exclusively) on the unidirectional link between corruption and key economic and social indicators. Treisman (2000) points out that the difficulty to measure the corruption levels in various countries remains one of the major hindrances faced by researchers interested in studying this phenomenon. Although some of these difficulties were over recent years lifted with the development by some international organizations (e.g., Transparency International, World Bank, ICPR) of “perceived corruption” indices based on independent surveys of local agents, yet very few empirical contributions have been carried out to better understand the interaction of this phenomenon with other indicators of the economic or social spheres. In this endeavor, we attempt to contribute to this empirical literature by addressing the causal relationship between corruption and youth unemployment from two different perspectives. First, we discuss how the corruption practices within government institutions that encourage the payment of bribes to access the job opportunities contribute to reducing the efficiency of the resources (labor force) allocations. The resources are diverted from the most productive economic sectors toward those (usually less efficient economic sectors) where self-motivated officials have more discretionary power in selecting the candidates who are less qualified for the job. We examine how bribed bureaucrats are more concerned with their own personal interests at the expense of national welfare when positively reacting to the highest bribe payers. Second, we ask the question why the resulting mismatching between supply and demand in the labor market tends to sustain its underlying causes (i.e., bribes) by giving more incentive to new agents and economic actors to adopt these practices.

By adopting a system GMM approach that simultaneously accounts for the dynamic effect between perceived bribery among officials and the youth unemployment rates, we intend to look at the underlying causal link running between the two variables. To the best of our knowledge, the present study is the first contribution to the empirical literature analyzing this correlation using the two-step system GMM approach (based on first difference and level equation a la Blundell and Bond (1998) to account for endogeneity (reverse causality) that might govern the relationship between these two variables. Overall, our results indicate that after controlling for various macroeconomic and institutional factors, the development of corruption practices tends to increase the unemployment rate among youth and educated job seekers which in turn contributes to sustain those unlawful practices by forcing the latter to bribe rent-seeking government officials in order to secure a job.

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In addition, there are several prototypical models available today, which may be used as a template for the formulation of new models. Although each model must focus on the specific issues that it is designed to illuminate, most models share accounting identities and equilibrium conditions which may be taken over without much editing. These developments make it possible for the modeler to concentrate on the important tasks of describing behavior and the particular institutional structure of the economy.

# Generation of a Consistent Data Base

Model implementation makes clear in which format data will be used. The next step is to work through the raw data base to produce a set of data that is specific to the model and consistent with its framework.

This is a major task. The raw data base will typically contain some data for one year and other pieces of information for another year. Sector and commodity groupings tend to differ among the input-output table, the household expenditure survey, and foreign trade statistics. Different data sources often use different definitions for households and labor groups. These nbed to be brought together to produce data for a base year (and preferably for more years) with common definitions of sectors, commodities, labor categories, households and other entities identified by the model.

Once data have been adjusted to match common definitions it is usually found that different pieces of data are mutually inconsistent. For instance, supplies and demands do not match for a given sector, _r -he savings-investment identity does not hold. Since general equilibrium mouels must account for all flows in a consistent manner, it becomes necessary to adjust parts of the data to produce a balanced set of numbers. Although algorithms (such as the RAS) are available for this
# **References**

- Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018, January). Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. _Scientific Data_ , _5_ . doi: https://doi.org/10.1038/sdata.2017 .191

- Acheampong, E., Ozor, N., & Sekyi-Annan, E. (2014, 06). Development of small dams and their impact on livelihoods: Cases from northern ghana. _African journal of agricultural research_ , _9_ , 1867-1877. doi: 10.5897/AJAR2014.8610

- Adeoti, A. I., Barry, B., Namara, R. E., Kamara, A. B., & Titiati, A. (2007). _Treadle pump irrigation and poverty in Ghana_ (IWMI Research Reports No. 44520). International Water Management Institute. Retrieved from https://ideas.repec.org/p/ags/ iwmirr/44520.html doi: 10.22004/ag.econ.44520

- Altchenko, Y., & Villholth, K. G. (2015). Mapping irrigation potential from renewable groundwater in africa – a quantitative hydrological approach. _Hydrology and Earth System Sciences_ , _19_ (2), 1055–1067. Retrieved from https://hess.copernicus .org/articles/19/1055/2015/ doi: 10.5194/hess-19-1055-2015

- Aron, D. V., Aguilar, R. A. C., Diaz-Bonilla, C., Betran, M. G. F., Foster, E. M., Fujs, T. H., . . . others (2023). Poverty and inequality platform (pip).

- Aseyehegn, K., Yirga, C., & Rajan, S. (2012, 01). Effect of small-scale irrigation on the income of rural farm households: The case of laelay maichew district, central tigray, ethiopia. _Journal of Agricultural Sciences_ , _7_ . doi: 10.4038/jas.v7i1.4066

- Balana, B. B., Bizimana, J.-C., Richardson, J. W., Lefore, N., Adimassu, Z., & Herbst, B. K. (2020). Economic and food security effects of small-scale irrigation technologies in northern ghana. _Water Resources and Economics_ , _29_ , 100141. Retrieved from https://www.sciencedirect.com/science/article/pii/ S2212428418300744 doi: https://doi.org/10.1016/j.wre.2019.03.001

- Briceno-Garmendia, C. M., & Foster, V. (2009, January). _Africa’s infrastructure : a time for transformation (English)_ (Africa development forum Washington, D.C.) The World Bank. Retrieved from http://documents.worldbank.org/ curated/en/246961468003355256/Africas-infrastructure-a-time -for-transformation

- Burney, J., Naylor, R., & Postel, S. L. (2013, 07). The case for distributed irrigation as a development priority in sub-saharan africa. _Proceedings of the National Academy of Sciences of the United States of America_ , _110_ (30), 12513-7. doi: 10.1073/pnas.1203597110

- Burney, J., Woltering, L., Burke, M., Naylor, R., & Pasternak, D. (2010, 02). Solar-powered drip irrigation enhances food security in the sudano-sahel. _Proceedings of the National Academy of Sciences of the United States of America_ , _107_ , 1848-53. doi: 10.1073/pnas .0909678107

- Chazovachii, B. (2012). The impact of small scale irrigation schemes on rural livelihoods: the case of panganai irrigation scheme bikita district zimbabwe. _Journal of Sustainable Development in Africa_ , _14_ (4), 217–231.

20