rafmacalaba/datause-encoder-data huggingface dataset:
Berry R. and W. Cline 1979. _Agrarian Structure and Productivity in Developing Countries_ , Baltimore: Johns Hopkins University Press.

Feder, G. 1985. The Relation Between Farm Size and Farm Productivity, _Journal of Development Economics_ , 18:297–313.

Foster, A.D. and M.R. Rosenzweig, 2010, Is There Surplus Labor in Rural India? Yale University.

- -------------, 2011, Are Indian Farms Too Small? Mechanization, Agency Costs and Farm Efficiency,

Brown University.

Hayami, Y., and V.W. Ruttan, 1985, _Agricultural development: An international perspective_ , Johns Hopkins University.

Hayashi, F. and E.C. Prescott, 2008, The Depressing Effect of Agricultural Institutions on the Prewar Japanese Economy, _Journal of Political Economy_ 116, 573-632.

Hicks, J.R., 1932, _The Theory of Wages_ , London: Macmillan.

Otsuka, K. 2013, Food Insecurity, Income Inequality, and the Changing Comparative Advantage in World Agriculture, _Agricultural Economics_ , 44: 7-18.

Otsuka, K., Y. Liu and F. Yamauchi, 2013, Factor Endowments, Wage Growth, and Changing Food SelfSufficiency: Evidence from Country-Level Panel Data, _American Journal of Agricultural Economics_ , 95: 1252-1258.

Yamauchi, F., 2011, Spatial Connectivity, Labor Markets and Returns to Human Capital: Evidence from Rural Indonesia, Manuscript, International Food Policy Research Institute, Washington D.C.

16
Given that the aim of this paper is to examine variation in overall levels of corruption, the most valid and reliable measure of corruption is the one that is most likely to capture those elements of corruption associated with both politicians and the broader public sector.

From this, it is possible to argue that the CPI, CC, GCB, and Ticket data are most likely to satisfy these criteria, since these indicators appear to focus, to varying degrees, on general of corruption. Discriminating between these indicators is more difficult, because the way the indicators are constructed generates different costs and benefits. Narrowly focused indicators – such as the GCB and the Ticket data – are more likely to be less noisy indicators of one dimension of corruption. However, by focusing on a narrow range of actions or respondents, it may be the case that such indicators fail to capture the multi-dimensional nature of corruption. Conversely, the CPI and the CC have the advantage of combining multiple sources to provide a more comprehensive assessment of corruption, but may also be noisy as they rely on perceptions.

In fact, as the next section shows, using the CPI, CC, Tickets and GCB as dependent variables of interest does not alter the results, an unsurprising finding given the fact that these indicators are highly correlated (see next section). Despite this, it is possible to argue that the composite subjective indicators (the CPI and the CC) are the best starting point of any empirical analysis, as they are more comprehensive and thus more likely to capture all elements of corruption.

# **_III.  Do the Indicators Measure an Underlying Level of Corruption? An Empirical Assessment_**

The discussion above, as well as much of the literature regarding the validity of measuring corruption, suggests that both objective and subjective measures of corruption should be measuring the same underlying activity. The indicators are constructed using similar questions and the oversight capacity of politicians suggests that even indicators focused on different activities will be highly correlated. While correlation is not a measure of validity and cannot ascertain causality, a robust positive 23
the meeting was not directly explained to the invitees except to say that they have been randomly selected in their community to participate in a research study by the CSS, and that it is not related to market research.

To compensate participants for their time, the invitation letter explained that all participants would receive a fuel voucher of JD 5 (equivalent to about US$7.50) as a show-up fee and that there would be a chance to keep JD 10 more in such vouchers, depending on the outcome of the meeting. Each invitee who agreed to participate was left with two receipts, which they were encouraged to bring to the meeting to exchange for real vouchers: one for the show-up fee of JD 5, and the other for JD 10.

If the person selected by the Kish table was not present at the time of enumerators’ first visit, enumerators would schedule an appointment and visit the household again in the evening to make the invitation in person. Based on extensive piloting, protocols were designed to replace households whose members refused the invitation and to ensure that two groups of 10 randomly assigned individuals could be constituted in each PSU. To ensure that 20 participants would show up at the set time to the next day’s meeting, enumerators invited 30 individuals per PSU, emphasizing that it is very important to show up on time. At the start of the meeting, all present participants signed a consent form and were randomly assigned to either the treatment group classroom or the control group classroom. Despite the appeal to show up on time, the team had to delay the start of the experiment virtually every time to fill the quota of 20 participants. After 20 participants had assembled, randomization into control and treatment groups occurred. Any latecomers were assembled into a mock group, which received the show-up fee and completed a questionnaire.

# References

Acemoglu, D., T. Hassan, and A. Tahoun (2014): "The Power of the Street: Evidence from Egypt’s Arab Spring." National Bureau of Economic Research, Working Paper 20665, Cambridge, MA.

Alesina, A. and P. Giuliano (2011): Preferences for Redistribution. In Handbook of Social Economics, A Bisin and J. Benhabib (eds.), pp. 93-132, North Holland.

Alesina, A., and E. Glaeser (2004): Fighting poverty in the US and Europe: A world of difference, Oxford: Oxford University Press.

Alesina, A., and E. La Ferrara (2002): "Who Trusts Others?" Journal of Public Economics, 85 (2), pp. 207—34.

– – – (2005): "Preferences for Redistribution in the Land of Opportunities," Journal of Public Economics, 89 (5), pp. 897—931.

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<!-- Start of picture text --> Figure 13: Change in Workers over the Last Two Years<br>Decreased<br>Informal<br>Unchanged<br>Formal<br>Increased<br>0 20 40 60 80 100 120<br>Percent of Enterprises<br>Source: UES, 2004.<br><!-- End of picture text --> 35. Employers were also asked about their plans for hiring in the coming year. On this question, the answers of formal and informal sector employers show considerable divergence, with formal sector employers overwhelmingly indicating that they plan to hire, while informal establishments – which account for the large bulk of employment - for the most part remain uncertain about their hiring plans (see Table 6). Such uncertainty is not surprising given the various shocks to which urban Timorese labor markets have been exposed since the late 1990s. However, given the demographics of sustained net inflows to the labor force, the data on hiring plans gives cause for concern about the capacity of Timor to create employment for urban youth.

**<u>Table 6: Hiring Plans of Urban Enterprises for the Next Year in 2004</u>**

||**F**<br>|**ormal**<br>|**Inf**<br>|**ormal**<br>|
|---|---|---|---|---|
||N|Percent|N|Percent|
|Plans to hire the next year<br>(reporting yes)|108|84.4|56|28.6|
|Don'tknow|20|15.6|140|71.4|
|Total(Responded)|128|100|196|100|

Source: UES, 2004 36. (b) <u>Hiring Practices in Urban Enterprises:</u> In addition to hiring history/plans, the survey asked about preference in hiring between younger and older workers and recruiting methods.

37. _(i) Age:_ The survey asked questions on perceptions of respondents (usually owners or managers) about hiring younger as compared to older workers. Concerns in Timor Leste about younger workers being less likely to get jobs are strong, and youth unemployment rates in urban areas are high. However, employers of both formal and informal employers overwhelmingly reported that they make no distinction based on age in hiring decisions (Figure 14). To the extent that they make a distinction, there is a preference for younger workers. These findings need to be interpreted cautiously given the lack of recent hiring, but are nonetheless of interest.

38. If there really is no age-based discrimination in hiring, and it is younger workers who dominate self-employment, further research needs to address both the macro and individual levels determinants of youth unemployment in Timor-Leste, which may be due to reasons of skill gaps, motivation, and other barriers to entry into the workforce. For example, emerging research indicates that it is more difficult for 15-24 youth group to find a paid job as compared to older 18
with the quintile for one group: young men in the 19 to 24 age group. For all other groups, the poorest (quintile 1) are often less likely to be Ninis than those in quintile 2, and for women also than those in quintile 3.

Table 1 also shows substantial heterogeneity in the probability of being a Nini for women but not for men: while for men the probability is roughly constant across the two age groups (between 11.6 percent and 13.6 percent for those in quintile 2), for women in the same quintile the probability is 31.1 percent for those in the 15 to 18 age group and 52.8 percent for those in the 19 to 24 age group. This result applies for all quintiles.

Similarly, differences in Ninis rates for urban and rural youth are high for women, and much less so for men. As Table 1 shows, Ninis rates by urban-rural status among men range between 9.5 percent and 10.0 percent. In contrast, for women rates vary greatly from 17.4 percent for younger women (those aged 15 to 18) to 61.3 percent for older women in rural areas. The latter suggests the inappropriateness of the standard definition of Ninis in capturing the dynamics of family formation and the definitions of gender roles within the household, and their interactions with the participation of women in the labor market.

Table 2. Persistence of Nini status for Mexican youth (first and fifth interviews only), <u>by gender and age group</u>

|||_Male_<br>|||_Female_<br>||
|---|---|---|---|---|---|---|
||_(1)_<br>_15 to 18_|_(2)_<br>_19 to 24_|_(3)_<br>_15 to 24_|_(4)_<br>_15 to 18_|_(5)_<br>_19 to 24_|_(6)_<br>_15 to 24_|
|_Variable_|_y.o._|_y.o._|_y.o._|_y.o._|_y.o._|_y.o._|
|All|35.0|31.4|33.1|67.5|72.8|71.2|
|_Quintiles of H_|_H income per_|_capita_|||||
|Quintile 1|32.1|27.9|29.4|68.2|71.8|70.7|
|Quintile 3|36.2|32.6|34.4|66.6|73.6|71.6|
|Quintile 5|29.0|25.7|26.9|62.2|67.2|66.1|
|Urban|35.1|31.5|33.1|62.2|67.8|66.2|
|Rural|34.7|31.1|33.0|75.0|82.1|79.7|

_Notes_ : Reported figures are the percentage of Ninis in the first interview who continue to be Ninis at the fifth interview (a year later) in the corresponding cell (not taking into account their status in the three interviews in the middle). It employs a pooled sample of nearly 360,000 observations (persons) that had complete information on the variables of interest for the first and fifth interviews of the rotating panel (see text for description of data). Weights are used in the estimation. A person is considered a Nini if s/he is not working nor studying. _Source_ : Authors’ calculations based on the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), 2005:Q1 - 2013:Q4.

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Table 5. Earnings Regressions in 2004 By Selected Sub-Samples (OLS regression coefficients; t-statistics in parentheses)

||(1)<br>All workers|(2)<br>All workers|(3)<br>All workers|(4)<br>All male workers|(5)<br>All young workers<sup>1</sup>|(6)<br>All young male workers<sup>1</sup>|
|---|---|---|---|---|---|---|
|Male|0.1959***<br>(5.40)|0.1821***<br>(5.14)|0.1671***<br>(5.08)||0.0067<br>(0.07)||
|Age|0.0204*|0.0224*|0.0305***|0.0287*|||
||(1.72)|(1.94)|(2.95)|(1.92)|||
|Age-squared|-0.0002*|-0.0003**|-0.0004***|-0.0003*|||
||(1.69)|(1.98)|(2.87)|(1.95)|||
|Single|0.03|(0.00)|(0.00)|(0.03)|0.2913**|0.33|
||(0.74)|(0.02)|(0.03)|(0.43)|(2.46)|(1.35)|
|**Educational attainment**|||||||
|Vocational|0.1178***|0.0674|0.0451|0.0575|0.2069*|0.2991**|
||(2.62)|(1.51)|(1.06)|(1.19)|(1.70)|(2.39)|
|High School|0.1667***|0.1151**|0.1027**|0.1059|0.0638|0.0806|
||(2.90)|(2.17)|(2.06)|(1.56)|(0.45)|(0.49)|
|College|0.3218***|0.2903***|0.2905***|0.1394|0.0705|0.123|
||(4.42)|(3.76)|(3.91)|(1.51)|(0.55)|(0.99)|
|University|0.6900***|0.6332***|0.6283***|0.5494***|0.5445***|0.5307***|
||(9.26)|(9.45)|(9.29)|(5.70)|(3.83)|(3.24)|
|Ever unemployed (2001-02)|-0.1185***|-0.0985**|-0.1016**|-0.1380**|-0.089|-0.0627|
||(2.60)|(2.16)|(2.39)|(2.59)|(1.07)|(0.57)|
|Constant|5.4312***|5.1655***|5.0999***|5.5426***|5.7227***|5.6215***|
||(21.02)|(18.61)|(21.08)|(16.29)|(30.97)|(21.31)|
|Entity (location) dummy|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry dummies|No|Yes|Yes|No|No|No|
|Sector dummies|No|No|Yes|No|No|No|
|Observations|1,816|1,798|1,797|1,208|305|205|
|R-squared|0.16|0.22|0.28|0.13|0.15|0.14|

* significant at 10%; ** significant at 5%; *** significant at 1% Standard errors are corrected for stratification and intra-cluster correlation. 1Those who were in the 15-24 age bracket in 2001.

_Does the scarring effect vary with individual characteristics?_ The overall results from the previous analysis show that education and experience shelters workers form unemployment and improve earnings. The results also show that on average those who experience unemployment spells tend to have their wages and employment opportunities lower than others who have similar age and education characteristics but have more favorable employment histories.

To what extent there are differences across skills and age in the impact of previous bad outcomes on current employment and wage outcomes among workers in Bosnia and Herzegovina could be also be tested. The results in Tables 4 and 5 and Appendix Tables 4 and 5 imply that these effects are higher for adults than youth. This possibly reflects a larger loss of specific human capital among adults compared to young workers as previously found in the literature.

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Notice that the trade balance (Xj – Mj) is deflated by the PPP for domestic final goods, to evaluate real GDP on the expenditure side. In contrast to (10), suppose that we have reference je prices for final goods,<sup>π</sup> ei<sup>, i=1,...,M as well as for the traded exports and imports,</sup> π ix and<sup>π</sup> im<sup>,</sup> for i=M0+1,...,M+N. The consider the following definition of real GDP on the output side: jo where the second line follows from (4) using (yij – zij) = qij for non-traded final goods i=1,...,M0, while the intermediate inputs have qij = 0 so that (yij – zij) = (xij – mij), i=M+1,...,M+N.

The first line of (11) is similar to nominal GDP measured on the production side in (6), but using reference prices in (11) rather than nominal prices. In principle, the first line of (11) relies on domestic reference prices for intermediate inputs (that is,<sup>π</sup> io<sup>for i=M0+1,...,M+N). But</sup> the second line of (11), where we re-write real GDP using the trade balance evaluated with jo reference prices, shows that the domestic reference prices for intermediate inputs are not need after all! Essentially, the use of the international reference prices π ix and<sup>π</sup> im<sup>gives us a short-cut</sup> method for evaluating real GDP on the output side. jo To evaluate the reference prices used in (11), consider the augmented-GK system: and, 10
charge 37 percent per year or more. Banks, at the median, charge just 13 percent per year at the median, and 19 percent or more for the top quartile.

When compared with Compartamos’s 90+ percent average interest rate in 2007, these kinds of charges seem eminently reasonable, though they are apt to surprise newcomers to the field. Our data show the logic for why the highest fees for borrowing in microfinance are not typically being charged by the banks, the institutions most focused on profits. The highest fees are being charged by the institutions most focused on social missions, while the commercial microfinance institutions offer relatively cheap credit. Their cost structures explain the relationships.

Some institutions, like BRAC and ASA of Bangladesh, grew to serve millions of customers while constituted as nongovernmental organizations, but they are exceptions. The third row shows that the typical bank in fact has many more borrowers per institution. A comparison of the median nongovernmental organization versus median bank yields a ratio of roughly 1:3 in the number of active borrowers. Scale, though, proves to be a limited route to cost reduction. The sense among microfinance experts is that returns to scale through expanding the customer base have been hard to find; a regression study of 1000 institutions, for example, finds that scale economies disappear after about 2,000 customers (Gonzalez 2007). After that, gains must be found by pursuing the intensive margin through serving existing customers with larger loans and more services. This is where the action is. The larger loans made by banks translate into lower costs per dollar lent, as seen in the sixth column. The median bank spends 12 cents on operating costs per dollar of loans outstanding, while the median nongovernmental organization spends 26 cents.

The result holds despite the fact that the average operating cost per borrower for the median nongovernmental organizations vs. banks is $156 versus $299 for the median microfinance bank (as elsewhere in the table, the dollar figures are in purchasing power parity adjusted dollars to approximate their value in local currency). The nongovernmental organizations are keeping costs down, in part by giving lower quality services, but it is not enough to compensate for the diseconomies of transacting small loans.

17
below poverty line despite the rich resources that the country possesses (World Factbook, 2012). The farming infrastructure is poorly developed, and the costs of farming inputs (other than labor) increase with distance away from urban centers while prices of rural agricultural produce decrease with distance away from these centers (Cortez, et al. 2010).

The conditions as described above are markedly different from especially industrialized countries where it does not matter where foods and other goods and services are produced, they will generally reach the rest of the country efficiently and the costs of those foods, goods and services will generally be the same throughout the country. This is one very important factor which makes biofuels versus other competing interests a location specific issue rather than a global issue.

Globally, there is a direct link between petroleum prices and the food prices (GRAF, 2011). This is true in Zambia too -- increases in prices of food and other commodities whenever there are increases in petroleum prices (Chanda, 2011). The impacts of increased petroleum prices can be shielded if petroleum products are substituted with domestically produced biofuels. It is expected that biofuels industry could enhance household/national income in Zambia, especially for rural communities where feedstocks are to be grown. If the expenditure on petroleum imports is spent on domestic biofuel industry, it would significantly spill over throughout the economy, particularly, rural and low income households (Urbanchu, 2011; Neves _et al_ ., 2010).

Production of biofuels not necessarily decreases food supply is also demonstrated in other countries. Take an example of Brazil where productions of food as well as biofuels are ever increasing since 1990. The FAO crop statistics show that Brazil ranks top in producing several food crops. The development of biofuels industry in Zambia should learn from Brazilian experience (Sinkala and Mulenga, 2008; Pioneer, 2007).

# **8 Conclusions and Final Remarks**

Zambia’s climatic and agronomic conditions can accommodate a large number of tropical and subtropical crops which can be exploited for production of biofuels without negatively affecting food supply. This study shows that production of biofuels is economically attractive compared to imported gasoline and diesel in Zambia. The production costs of biofuels from most feedstocks are competitive to current prices of petroleum products in Zambia. Another comparative advantage of Zambia on biofuels is that the country does not have land constraints. A small fraction of agriculture lands could produce biofuels to meet the entire national demand thereby substituting petroleum products which is entirely imported otherwise.

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# **Table A2: Geographical disaggregation of available data**

|**Country**|**Geographical**|**Number**|**Popula**|**tion**||**Land are**|**a(km^2)**||**Relation to islands**|
|---|---|---|---|---|---|---|---|---|---|
||**level**|**of regions**|**Min**|**Max**|**Average **|**Min**|**Max**|**Average **||
|Fiji|Enumeration<br>area(EA)|1602|27|1400|522.64|0.013|285.93|13.05|Islands include multiple Eas<br>EA can include multiple Islands|
||Tikina|86|104|144526|9735.71|3.17|884.33|243.01|Tikina can include multiple islands;<br>Island can include multiple Tikinas|
||Province|15|2002|231760|55818.07|44.15|3250.78|1393.28|Island can include multiple provinces<br>Province can include multiple islands|
|Solomon<br>Islands|Enumeration<br>area(EA)|1344|0|2036|383.83|0.004|394.77|21.57|Islands include multiple Eas<br>EA can include multiple Islands|
||Ward|183|115|14806|2818.96|0.074|1082.98|158.43|Ward can include multiple islands,<br>Island can include multiple Wards|
||Constituency|50|2334|25018|10317.40|6.02|2781.11|579.87|Constituency can include multiple islands,<br>Island can include multiple Constituencies|
||Province|10|3006|136384|51587.00|23.52|5611.15|2899.33|Province include multiple islands|
|Vanuatu|Enumeration<br>area (EA)|621|0|7111|368.57|0.029|331.97|21.47|Islands include multiple Eas<br>EA can include multiple Islands|
||Island|67|0|64327|3416.16|0.017|3996.23|199.04|Data by island|
||Area Council|66|341|43276|3467.92|0.956|1035.4|202.06|AC can include multiple islands,<br>Island can include multiple Acs|
||Province|6|9189|77047|38147.17|879.35|4271.67|222.63|Province include multiple islands|
|Kiribati|Enumeration<br>area(EA)|265|31|2311|388.90|0.01|4.59|0.27|Island include multiple EAs|
||Village|180|31|15755|572.54|0.02|4.59|0.39|Island include multiple Villages|
||Island(Atoll)|23|31|50182|4480.78|0.2|443.42|37.44<sup>13</sup>|Island(Atoll)include multiple islands|
|_(continued_|_over)_|||||||||

> 13 Total area for Kiribati at EA and Village Level is 70.93 km2, at Island(Atoll) Level is 861.192 km2. Because the EA and Village Level map file does not cover the whole land area of the country.

29