rafmacalaba/datause-encoder-data huggingface dataset:
A World Bank study (World Bank, 1983) on the higher efficiency of small versus large farms in Kenya, found that output per hectare was 19 times higher and employment per hectare was 30 times higher on holdings under 0.5 hectare than on holdings over 8 hectares. At the national level, this meant that a 10% reduction in average farm size would increase output by 7% and employment by over 8%. Binswanger _et al_ (1993) report similar results for many other countries. Chavas and Aliber (1992) found virtually no scale economies in dairy production, and the very limited initial scale economies they observed were attributable to lumpiness of certain inputs.

Evidence is also available at the macro-level, but only in terms of physical yields - an imperfect indicator of efficiency. Prosterman and Riedinger (1987) using data from 117 countries, show that 11 of the top 14 countries in terms of grain yields per hectare are countries in which small-scale, family farming is the dominant mode of production.

However, studies by Feder (1985) and Caner and Kalfayan (1989) demonstrate that the existence of market imperfections which tend to favor large farms (e.g. capital and insurance markets) may negate the inverse relationship between farm size and productivity. Carter (1994) finds that certain financial market disadvantages may render small farms non-competitive. Hence, whereas the small-scale farming strategy holds considerable promise from an efficiency perspective, this does not mean that its implementation is easy or can afford to ignore critical policy issues, such as resolving the usually constrained access of small farmers to credit markets.

# _Related Issues: Mechanization, Labor Organization and Farm Size_

Also underlying the establishment and maintenance of large-scale farms is the misguided perception that there is a relationship between mechanization and large farms. This has been clarified in the literature (see Johnson and Ruttan, 1994). Capital intensity is explained by the substitution of capital for labor because of high wages. This substitution process, brought about by changes in relative factor prices (Peterson and Kislev, 1991), indirectly caused larger farns. Machinery allows farmers to work progressively larger units of land (Hayamni and Ruttan, 1985).

In this respect, the work of Brewster (1950) on the influence of machinery on farm size is enlightening: Mechanization in industry involves stationary machinery, which implies that the number of workers can be increased substantially without increasing labor supervision costs. In agriculture, labor and machines are both mobile, making supervision expensive and increasing management costs. In addition, agricultural tasks are sequential in nature due to the annual cycle of production. This limits the Opportunities for specialization and division of labor, which creates few advantages to expansion beyond the size of owner-operator.

7
_The spouse left behind gains from migration of the spouse, in terms of shares of income spent on his own private consumption, only when the migrant spouse is “generous enough_ ” _Children always receive a larger share of income when none of their parents migrates, because either of them has incentives to shift resources onto own private consumption when alone._ These results should not be interpreted as suggesting that, for example, children are always worse off when either of their parents migrates, or that the spouse left behind would prefer no migration to take place, because the allocations compared are in terms of shares of the total household budget available, which is generally larger than the budget available in the case of no migration, as suggested in proposition 1. Yet they highlight how, in the absence of a moral hazard problem, children of migrant parents would instead receive a larger share of the household income.

# II. DATA

The empirical analysis is based on data from the Indonesia Family Life Survey (IFLS), an ongoing longitudinal survey of Indonesian households, which started in 1993, was repeated in 1997, 2000, and 2007, and contains a sample that is representative of about 83% of the total Indonesian population, including over 30,000 individuals living in 13 of the 27 provinces of the country.<sup>11</sup> By tracking individuals over time, these data allow detecting migration. Indeed, for all individuals who appeared in the first wave of the survey the IFLS roster provides information on where they currently are (if they are not in the household anymore), why and when they left and how much they earned in the past twelve months.

I define migrants as those adult people who have left the household and are reported to having done so for work reasons or explicitly to help the family. Table 1 shows that from the time of the first interview in 1993, fourteen years later more than one household out of four had at least one member that had migrated and not come back while, among individuals, migrants represented about 9%.

**Table 1.** Migration in the IFLS.

|Wave|Individuals Migrants %<br>Households|Migran~~t~~%<br>Households|
|---|---|---|

> 11 For a more detailed description of the dataset see Thomas et al. (2012).

11
_Table 3: Regression results for unrestricted education equation (control group = age 24 years in 1974)_

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|**_Region fixed effects_**|||||
|Sumatera|1.583|1.5888|1.6151|1.4379|
||(0.2472)**|(0.2703)**|(0.2982)**|(0.2954)**|
|Java|0.3463|0.3381|1.046|0.887|
||(0.2302)|(0.2329)|(0.2488)**|(0.2491)**|
|Jakarta|2.3503|2.289|2.1338|0.0000|
||(0.2928)**|(0.2934)**|(0.3043)**|(0.0000)|
|Bali|0.2558|-0.0337|1.5491|1.5587|
||(0.3567)|(0.3599)|(0.3789)**|(0.3768)**|
|Nusa|-1.0689|-0.9848|0.5742|0.4938|
||(0.3134)**|(0.3184)**|(0.3347)|(0.3336)|
|Kalimantan|0.0633|0.1268|0.7769|0.7123|
||(0.3547)|(0.3639)|(0.3812)*|(0.3765)|
|**_Cohort of birth fixed effects_**<sup>**_(a)_**</sup>|||||
|Cohort 1: 1-6yrs in 1974|3.6932|4.9285|3.1104|2.9743|
||(0.3467)**|(1.5993)**|(1.5580)*|(1.5969)|
|Cohort 2: 7-12yrs in 1974|2.1566|4.9693|3.0418|2.958|
||(0.3761)**|(1.7852)**|(1.7226)|(1.7838)|
|Cohort 3: 13-18yrs in 1974|1.6457|2.0099|-0.0552|0.614|
||(0.3663)**|(1.7359)|(1.6598)|(1.6923)|
|Cohort 4: 19-23 yrs in 1974|1.4196|-5.7985|-6.9493|-7.0145|
||(0.4168)**|(2.0532)**|(1.9685)**|(2.0309)**|
|**_Programme intensity x Age in 1_**|**_974 (interaction t_**|**_erms)_**|||
|Age=1|-0.1201|-0.2707|-0.4113|-0.3547|
||(0.1358)|(0.1389)|(0.1298)**|(0.1526)*|
|Age=2|-0.2046|-0.3464|-0.5403|-0.4778|
||(0.1246)|(0.1253)**|(0.1540)**|(0.1640)**|
|Age=3|<br>0.064|<br>-0.0677|<br>-0.2235|<br>-0.177|
||(0.1344)|(0.1388)|(0.1335)|(0.1546)|
|Age=4|-0.0406|-0.2017|-0.379|-0.3229|
||(0.1144)|(0.1277)|(0.1249)**|(0.1524)*|
|Age=5|0.1077|-0.0022|-0.2031|-0.1507|
||(0.1250)|(0.1277)|(0.1271)|(0.1495)|
|Age=6|<br>-0.5426|<br>-0.6883|<br>-0.7858|<br>-0.7202|
||(0.1486)**|(0.1518)**|(0.1475)**|(0.1687)**|
|Age=7|<br>0.1949|<br>0.1437|<br>0.0092|<br>0.0688|
||(0.1433)|(0.1442)|(0.1504)|(0.1692)|
|Age=8|0.3843|0.3372|0.2406|0.3007|
||(0.1479)**|(0.1521)*|(0.1506)|(0.1715)|
|Age=9|-0.2524|-0.3957|-0.4474|-0.3792|

34
- 17 -

secondary city included in the study, compared to 9 percent in Jakarta. Similarly, the capital share for the firms in Chiang Mai (the secondary city included in the study) was 22 percent compared 9 percent in Bangkok and 6 percent in the five metropolitan provinces surrounding Bangkok. The share for the small firms in Chiang Mai was as high as 51 percent according to the survey data. Tables 3.6 and 3.7 present more detailed information across regions for generators and boreholes. The opportunity costs foregone by sustaining such a substantial amount of capital investment in private provisions should be very high to the firms and to the society. More formal quantitative analysis is conducted in Report No. 2 and Report No. 3.

# C. Te Priyate Cost as A Measure of Willingness to Pa for Reliable Services

As documented above, manufacturers incur high capital cost in installing own facilities for providing their own services. In the case of electric power generation, the firms with own generators tend to install standby capacity sufficient to run the entire plant during a period of power interruption. We found that in most cases the standby generators can supply 100 percent of the power need for production plant operation. Therefore, the proportion of electricity consumption from own generators with respect to the total consumption including the public supply should be a good approximation of the capacity utilization of own generators during a time period such as one year. Table 3.2 shows the proportion of electric power supply from own generators. For the firms having generators, the proportion was 37 percent in Nigeria while it was 67 percent in Indonesia. The Indonesian manufacturers rely more on self-
|Social SafetyNet Project|Rebekka E. Grun|
|---|---|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|Senegal Inclusive and Sustainable Agribusiness Development Projec|Nicolas Ahouissoussi|
|IN: Development PolicyLoan(DPL)to Promote Inclusive Green Gro|wCharles Joseph Cormier|
|ID-TF INTEGRATED MIS(SIMPADU PHASE II)FOR PNPM|Alexander BambangSetiaji|
|ID-TF INTEGRATED MIS(SIMPADU PHASE II)FOR PNPM|Alexander BambangSetiaji|
|ID-TF INTEGRATED MIS(SIMPADU PHASE II)FOR PNPM|Alexander BambangSetiaji|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|Tanzania Productive Social SafetyNet|Michele Davide Zini|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|ML-StrengtheningReproductive Health|Aissatou Diack|
|Additional Financingfor the Decentralized Rural Development Proj|eWinston Dawes|
|Additional Financingfor the Decentralized Rural Development Proj|eWinston Dawes|
|Additional Financingfor the Decentralized Rural Development Proj|eWinston Dawes|
|Additional Financingfor the Decentralized Rural Development Proj|eWinston Dawes|
**Table O.3** African and other countries seeing structural change in the 21st century

||Labor<br>|productivity growth (pe<br>|rcent)<br>|
|---|---|---|---|
|Country|Total|Within|Structural|
|Botswana||||
|1990–2000|1.1|1.7|–0.6|
|2000–2010|2.7|4.9|–2.2|
|Ghana||||
|1992–2000|1.0|–0.9|2.0|
|2000–2006|4.5|6.0|–1.5|
|2006–2010|2.7|0.0|2.6|
|Nigeria||||
|1996–1999|–0.8|–7.1|6.3|
|1999–2005|4.4|6.2|–1.8|
|2005–2009|4.1|–1.6|5.7|
|Nigeria, excluding oil and gas||||
|1996–1999|0.8|–2.0|2.8|
|1999–2005|4.8|9.4|–4.6|
|2005–2009|7.0|2.6|4.4|
|Zambia||||
|1991–2002|–2.0|0.0|–2.0|
|2002–2010|3.6|1.8|1.8|
|India||||
|1990–1999|2.9|1.7|1.3|
|2000–2004|6.5|6.2|0.3|
|Vietnam||||
|1990–2008|5.1|1.9|3.1|
|1990–2000|5.2|1.0|4.2|
|2000–2008|4.9|2.7|2.2|
|Brazil||||
|1995–2005|0.8|0.6|0.2|
|1990–2005|0.8|0.8|–0.0|
|1993/1995–2007/2008|0.5|0.3|0.2|

Source: _Botswana_ —Value-added and employment data are from the Groningen Growth and Development Centre Africa Sector Base; _Ghana_ —Economic Survey of Ghana 1961–1982; population and housing censuses 1960, 1970, 1984, 2000, and 2010; Ghana Living Standard Survey 1991–1992 and 2005–2006; Singal and Nartey (1971); Androe (1981); Ewusi (1986); GSS (2010); and World Bank (2010); _Nigeria_ —Output data are from the Nigerian Bureau of Statistics. Employment data are from the Nigeria General Household Survey (GHS) [1996–2011]; Zambia—Data are from the Central Statistics Office [1993, 2004, 2011, and 2012.]; _India_ —Value-added and employment data are from the Groningen Growth and Development Centre; _Vietnam_ —Employment, gross domestic product (in constant 1994 prices), and labor productivity (also in constant 1994 prices) data are from the General Statistics Office of Vietnam; _Brazil_ —For the period 1950–2005, value-added and employment data are from the Groningen Growth and Development Centre. For the period 1993/1995–2007/2008, data are from Pesquisa Nacional por Amostra de Domicílios. Note: _Botswana_ —Data are disaggregated at 10 sectors, as in McMillan and Rodrik (2011); _Ghana_ —Data are disaggregated at 9 and 14 sectors; _Nigeria_ —Data are disaggregated at 9 sectors; _Zambia_ —Data are disaggregated at 9, 10, or 3 sectors; _India_ —Data are disaggregated at 10 or 9 sectors. _Vietnam_ —Data are disaggregated into 19 economic sectors; _Brazil_ —Data are disaggregated at 10 sectors, as in McMillan and Rodrik (2011).

29
# Table 7 Average and Minimum Wages in Ukraine

|Date||At Current Pri|ces||At Constant P|rices|
|---|---|---|---|---|---|---|
||Minimum<br>Wage|Average<br>Wage Total|Average Wage<br>Industry|Minimum<br>Wage|Average<br>Wage Total|Average Wage<br>Industry|
|1990||247|-|0|216.7|-|
|1991||474|548.4|-|221.9|256.7|
|1992|-|6423.2|7885|-|198.7|243.9|
|Jan 91|80|-|-|67.7|-||
|April 91|145||-|64.9|-||
|Oct91|185|-|-|74|-||
|Dec91|400|-|-|134.5|-||
|Jan 92|400|1655.9|2280.8|38.2|158|217.7|
|Feb|400|1847.4|2622.9|31.1|143.6|203.9|
|March|400|2068.6|3031.7|28.2|145.6|213.4|
|April|400|3068.1|4529.1|24.8|190|280.4|
|May|900|3572.4|4508.4|45.4|180.1|227.3|
|June|900|5464.4|5696.7|39.5|239.6|249.8|
|July|900|5379.7|6715.6|30.9|185|230.9|
|Aug|900|5705.9|7096.5|26.8|169.9|211.3|
|Sep|900|7689.8|8789.1|23.3|199.3|227.7|
|Oct|900|8715.9|10414|18.6|180.1|215.2|
|Nov|2300|11314.3|14436.3|38.8|190.7|243.3|
|Dec 92|2300|20072.5|25385.3|27.8|243|307.4|
|Jan 93|4600|15802|19180|28.2|97|117.7|
|Feb|4600|20192|23060|22|96.6|110.3|
|March|4600|24857|28491|18.3|98.7|113.1|
|June|6900|38169|-||||

19
9 above the elasticity of import demand in the country.

The data used in construction of the model were obtained from two sources: World Bank National Accounts data for GDP, exports and imports of goods and nonfactor services; and the UN trade matrix system (Campano 1989) for data on bilateral trade in manufactures and miscellaneous goo(ds (SITC 5-9) and total merchandise trade. Since both sources contain data on an fob basis, the differences between total National Accounts imports/exports and the trade matrix estimates for merchandise trade were used to represent imports and exports of nonfactor services which were then allocated to specific bilateral trade flows via a simple probability model. For consistency, all data were for 1987, the latest year for which the full trade matrix data set is available. Unfortunately, this data base did not allow manufactures trade to be distinguished into consumption goods and industrial inputs, thus requiring the specification of all manufactures either as intermediate inputs or as final goods. Since the choice is somewhat arbitrary and should not affect the results of the experiments reported in this paper, the consumption goods specification of de Melo and Robinson was followed.

Estimates of average tariff rates for the regions included in the model were based on preliminary assessments of tariff rates in developing regions prepared by Roland-Holst (1991) and should be treated as indicative of a very rough lower bound estimate of prevailing protection levels expressed in tariff equivalent form. To save space, the estimates are presented in Table 1. While the tariff estimates are undoubtedly approximate, their general ranking, with South Asia having the highest protective barriers and Latin America and the
30 Privacy – building on OECD and APEC principles. These steps could pave the way ultimately for mutually binding obligations on source and destination countries as in the CPTPP.

Apart from any bilateral or plurilateral approach, there may also be scope for multilateral discussions, for example under the recent initiative on electronic commerce. Such discussions may help to gradually forge a broader consensus on both standards and mechanisms to ensure compliance.

VII. Bibliography

- APEC (Asia Pacific Economic Cooperation). 2014. Joint work between experts from the Article 29 Working Party and from APEC Economies, on a referential for requirements for Binding Corporate Rules. <u>https://www.apec.org/~/media/Files/Groups/ECSG/20140307_Referential‐BCR‐CBPR‐ reqs.pdf</u>

- APEC (Asia Pacific Economic Cooperation). 2017. “Privacy Framework.” <u>file:///C:/Users/CConstantine/Downloads/05_ecsg_privacyframewk.pdf</u>

- Appellate Body Report, Brazil‐Measures Affecting Imports of Retreaded Tyres, WT/DS332/AB/R, adopted 17 December 2007.

- Basu, Nayanima. 2013. “Data Adequacy Grant to India Non‐Negotiable, Says EU envoy.” The Business ‐ ‐ ‐ ‐

- Standard. 17 May. http://www.business <u>standard.com/article/economy policy/data adequacy grant‐to‐india‐non‐negotiable‐says‐eu‐envoy‐113051700013_1.html</u>

- Bauer, Matthias, Fredrik Erixon, Michal Krol, Hosuk Lee‐Makiyama, and Bert Verschelde. 2013. “The Economic Importance of Getting Data Protection Right: Protecting Privacy, Transmitting Data, Moving Commerce.” European Center for International Political Economy. <u>https://www.uschamber.com/sites/default/files/documents/files/020508_EconomicImportance_ Final_Revised_lr.pdf</u>

- Bauer, Matthias, Hosuk Lee‐Makiyama, Erik van der Marel and Bert Verschelde. 2014. “The costs of Data Localisation: Friendly Fire on Economic Recovery, ECIPE Occasional Paper No. 3/2014

- Bennet, Steven C. 2012. The Right to Be Forgotten: Reconciling EU and US Perspectives.” Berkeley Journal of International Law. Vol 30, No. 1. http://scholarship.law.berkeley.edu/bjil/vol30/iss1/4

- Castro, Daniel and Alan McQuinn. 2015. “Cross‐Border Data Flows Enable Growth in All Industries.” The Information Technology & Innovation Foundation (ITIF). http://www2.itif.org/2015‐cross‐border‐ <u>data‐flows.pdf.</u>

- Castro, Daniel. 2013. “The False Promise of Data Nationalism.” Information Technology & Innovation Foundation (ITIF). http://www2.itif.org/2013‐false‐promise‐data‐nationalism.pdf.\

- CBPRs (Cross Border Privacy Rules System). 2015. “For Business.”

<u>http://www.cbprs.org/Business/BusinessDetails.aspx</u>
Normalized utility can be re-expressed in terms of the endogenous variables<sup>14</sup> : At this point, it is interesting to notice that changes in the tax rates do not appear in the righthand side of the equation above. The intuition for this result is that the endogenous quantities already re‡ect variations in the tax rates. In other words, changes in the tax burden are already captured by the changes in consumption, investment and labor supply that they determined.

To make contact with the data, note that the national accounts at market prices de...ne nominal expenditure using prices as perceived from the demand side. Thus, equation (12) can be written exactly as before and still be consistent with standard national accounts data, where sc and si in b b yt = scct + si<sup>b</sup> it are inclusive of indirect taxes (subsidies) on consumption and investment. On the other hand, the national accounts measure factor payments as perceived by ...rms, before income taxes. Hence we can write: where sL and sK are the gross income shares of labor and capital respectively. Thus, the dataconsistent de...nition of the welfare residual with taxes needs to be based on a new de...nition of log PRt, where the shares of labor and capital returns are net of taxes. More speci...cally, equation (15) can be re-written as: In conclusion, after properly rede...ning ybt+s and log PRt, the results discussed in the second and the third section of the paper can be generalized in a model with distortionary time-varying taxes on consumption and investment goods and on the household income coming from labor, capital or ...nancial assets.

# 4.3 Government Expenditure

With some minor modi...cation, our framework can be extended to allow for the provision of public goods and services. We illustrate this under the assumption that government activity is ...nanced with lump-sum taxes. Using the results from the previous subsection, it is straightforward to extend the argument to the case of distortionary taxes.

Assume that government spending takes the form of public consumption valued by consumers. We rewrite the instantaneous utility function as

> 14 See the Appendix for details on the derivation.

17
|Table G2|. Political Instab|ility and|Violence|
|---|---|---|---|
|**Code**|**(Appe ndix1)**|**Row**|**Concepts measured**|
|**Represe**|**ntative sources**|||
|DRI|A 1.3||Risks reduction of GDP by 1-4% due to:|
|||24|Major Urban Riot|
|||20|Major insurgency/rebellion|
|||19|Military coup|
|||21|Political terrorism|
|||22|Political assassination|
|||23|Civil war|
|EIU|A 1.5|1|Armed conflict, war|
|||2|Social unrest|
|||4|Terrorist threat, political violence|
|PRS|A 1.10|4|Intemal Conflict: Political violence and govemance, from no<br>tolerance of arbitrary violence to civil war tom<br>countries (from best to worse score)|
|||10|Ethnic tensions: Based on intolerance and prone to conflict|
|WDR|A 1.13|15|Terrorism as an obstacle to business development|
|||5|Likelihood of unconstitutional govemment changes|
|Non-repr|esentative sourc|es||
|BERI|A 1.1|1|Fractionalization of the political spectrum|
|||3|Fractionalization by ethnic, language and religious groups|
|||5|Restrictive (coercive) measures to retain power|
|||6|Organization/power of radical group|
|||9|Societal conflict: Strikes, Violence, Demonstrations|
|||10|Constitutional Changes, Assassinations, Guerrillas|
|CEER|A 1.2|19|Political stability|
|GCS97|A 1.8|||
|GCS98||3|Likelihood of dramatic change in institutions|
|||28|The highest power is always peacefully transferred|
|GCSA|A 1.8|3|Likelihood of dramatic changes in institutions|
|||68|Government coups or political instability as an obstacle to<br>development|
|||67|Tribal conflict as an obstacle for business development|

54