rafmacalaba/data-use-mentions-extended huggingface dataset:
WPS3784 coffee farmers typically have a high propensity to spend on other products produced in the rural communities. Increasing returns to commercial farmers could have substantial multiplier effects within rural communities. The challenge is to provide an infrastructure and framework by which rural markets can develop and flourish. Given the current level of development in Rwanda it is clear that this will require a significant response and engagement from the international institutions and donors.

# **References**

Balat, J. and G. Porto (2005) ‘Globalisation and Complementary Policies: Poverty Impacts in Rural Zambia’, NBER Working Paper 11175.

Besley, T. and A. Case (1994). “Diffusion as a Learning Process: Evidence From HYV Cotton,” mimeo, Princeton University.

Cadot, O., L. Dutoit and M. Olarreaga (2005) ‘Subsistence Farming, Adjustment Costs and Agricultural Prices: Evidence from Madagascar’, mimeo, World Bank.

Conley, T. and C. Udry (2004). “Learning About a New Technology: Pineapple in Ghana,” mimeo, Yale University.

Dabalen A., S. Paternostro and G. Pierre (2004). “The Returns to Participation in the Nonfarm Sector in Rural Rwanda” World Bank Policy Research Working Paper 3462.

Deaton A. (1989). Rice prices and Income Distribution in Thailand: A non parametric Analysis”. _Economic Journal_ 99 (395) (supplement), 1-37.

Deaton, A. (1997). _The Analysis of Household Surveys. A Microeconometric Approach to Development Policy_ , The World Bank and The Johns Hopkins University Press.

Delgado, C. J. Hopkins, and V. Kelly (1998). _Agricultural Growth Linkages in SubSaharan Africa._ Research report 107, IFPRI.

Foster, A. and M. Rosenzweig (1995). “Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture,” _Journal of Political Economy_ , 103, 1176-1209.

Haggblade S, J. Hammer and P
# _Flat Returns_

While skill supply increased, has is kept up with demand? If demand is outpacing supply, one would expect to see the wage gap between workers with skills and without skills to increase. Figure 24.A (all wage workers) and Figure 24.B (monthly wage workers only) suggest that, first, more education is associated with higher wages at any point in time; and, second, real monthly wages trended upward until 1996, fell during the crisis, and remained roughly constant since, especially for secondary or higher education levels. Wage data from the national Socio-Economic Surveys (SES) from 1986 to 2002 for wage workers confirms this pattern, although the actual wage time series differ somewhat (Figure 24.C).

Combining wages and education, it is possible to estimate returns to education over time using Mincer regressions. In its most basic form, log hourly wages are regressed on education, controlling for age, aged squared, gender, province and urbanization.<sup>21</sup> Figure 24.D shows the coefficients on education levels from a series of such cross-sectional regressions for wage workers. Education is divided into six attainment levels (less than primary, primary, lower secondary, upper secondary, vocational and university) and the returns are estimated relative to workers with less than primary education, the omitted category. The returns relative to less than primary education are flat over time and there is no trend towards higher wage differentials across skill levels. If anything, there is a small downward shift of returns to university post crisis.

A robustness check is to restrict the sample to workers on a monthly payroll, in case rising returns are restricted to workers in regular employment. Again, there are no signs of rising returns to skills (Figure 24.E), apart from modest increases for primary and vocational education. The wage premium to university is even higher, but again, the returns to education look fairly stable over time. Using years of schooling instead
modernization is deemed vital to capitalize on the opportunities presented by the green transition.

# **6. References**

- Alatas, H.; Granata, J.; and Posadas, J. (2020) Indonesia’s Occupational Tasks and Skills: from occupational employment demand to tasks and skills requirements. World Bank.

- Apella, I.; Rovner (2021) Cambios ocupacionales y formación a lo largo de toda la vida laboral. Aportes del sistema de información ocupacional de Uruguay (O*NET UY). Working Draft.

- Acemoglu, D.; Autor, D. H. (2011): Skills, tasks and technologies: Implications for employment and earnings (Chapter 12). In: Ashenfelter, A.; Card D. (Eds.) Handbook of Labor Economics, 4B, 10431171.

- ASEAN and ILO (2021). Regional Study on Green Jobs Policy Readiness in ASEAN. Final Report. ILO Thailand. ISBN 978-623-6945-15-5

34
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# Table 2: **Summary Statistics** – Bank Data

This table reports summary statistics for all accounts in the sample frame based on bank data. “Total overdue” is the total amount of outstanding and overdue agricultural credit as of December 31, 2007. “Eligible debt relief” is the total amount of debt eligible to be waived under the program, which is 100% of outstanding debt for households with _≤_ 2 hectares of pledged collateral and 25% conditional relief otherwise. Treatment effects at the program cutoff _τ_ ˆ _RD_ are estimated using the preferred specification with a local linear control function. Robust standard errors, in brackets, are clustered by bank and district. +p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01

||N|Mean|Median|StDev|Min|Max|ˆ_τRD_|SE|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Total overdue|5,524|52,915|41,427|48,538|0|882,806|127.12|[4149.9]|
|Principal overdue|5,514|40,627|30,000|43,351|0|830,000|533.7|[3802.1]|
|Interest overdue|5,414|12,595|7,193|17,009|0|319,810|1,607|[1333.5]|
|Loan type=crop loan|5,554|0.77|1.00|0.42|0|1.00|
7 28.0 f 8 23.3 g 9 16.9 h 10 11.6 i

*Using Duncan's test on the average count of NTMs faced. Averages tagged with the same letter of the alphabet are not significantly different.

Similarly, the applied MFN tariffs in the United States and China (taken from the WTO Integrated Database based on the Member's own data notification) tend to be higher for goods produced by poorer workers (based on average wage data from the UNIDO database). Goods produced by workers in the fifth, or richest, quantile face tariffs that are markedly lower than on goods produced by workers from the first, or poorest, quantile across all three countries (Table 8). While the average tariff falls consistently across income groups in China and India, the average US tariff for sectors corresponding to the richest quantile (3.7%) is close to that of the second-poorest quantile (3.8%). However, this appears to be driven by the very high tariff on tobacco.<sup>11</sup> Since the US tariffs on in-quota tobacco imports from selected partners are lower than the tariffs quoted here, the actual applied average duty is lower. Excluding tobacco, the average tariff for the richest quantile is only 0.8%, less than a fifth of the average tariff of the poorest quantile. These results reinforce the view that there is a coordination problem – the "poor" are disadvantaged in terms of market access because trading partners appear to impose trade restrictions disproportionately on sectors that employ poor households.

Table 8. Import tariffs by wage or income quantiles for selected countries

|Wage/Income<br>|Average M<br>|FN for indicat<br>|edyear(%)<br>|
|---|---|---|---|
|Quantile|USA 2008|China 2010|India 2012|
|Q1|4.5|12
coefficient movements but also movements in R-squared values when including additional independent variables. The _“bias-adjusted estimate”_ is calculated as: ) ∗<sup>RRm</sup> (5)<sup>xx−RRcc</sup> where ββ<sup>∗</sup> = ββ<sup>cc</sup> −(ββand<sup>uucc</sup> − ββ are the “<sup>cc</sup> _controlled estimate_ ” and the of the regression from which the RR<sup>cc</sup> − RR<sup>uucc</sup> “ _controlled estimate_ ββ<sup>cc</sup> RR<sup>cc</sup> ” was obtained respectively; and and RR<sup>2</sup> are respectively the coefficient estimate and of the uncontrolled regression, the regression in which the variable of interest is ββ<sup>uucc</sup> RR<sup>uucc</sup> the only independent variable. RR<sup>2</sup> m is the of a hypothetical regression in which both xx observables and unobservables are controlled for, which is clearly unknown. Oster (2015) suggests RR RR<sup>2</sup> that m , 1}. The from our models are such that > 1, suggesting that we xx choose RR m= min{2.2RR= 1 based on Oster (2015). Meanwhile Gonzalez and Miguel (2015) argues that<sup>cc</sup> RR<sup>cc</sup> 2.2RR<sup>cc</sup> xx m of 1 or close to 1 is likely to be too high. Based on relatively high-quality US data, Gonzalez RR xx and Miguel (2015) suggested that RR m should not be greater than 0.9. An m of 0.89 was xx xx therefore chosen for our analyses. RR RR
tend to be more sensitive to prices (Duranton, 2014).<sup>6</sup> Third, developing countries are characterized by considerable regional inequality, where modern economies in larger cities coexist with subsistence economies in remoter regions, and improved transport connectivity is often offered as an important way to integrate and transform the isolated areas (Grover, Lall and Mal1y, 2022). Yet, literature on the impacts of transport connectivity as well as trade shocks on

> 6 The discrepancies between estimates from annual data using distance as a measure of transport cost and ne grained transaction-level data are noted already in literature (see, Wong for a discussion) and discussed in more detail in section 4.

5
When considering the viability of a publicly supported transfer mechanism, such as a base level non-contributory pension, concerns frequently arise over whether public transfers may crowd out private transfers, and as a result offer a lower than intended cushion against falling into poverty. The paper assesses the importance of this concern by examining the responsiveness of private transfers to low income in households with elderly residents. The analysis suggests that a noncontributory pension might partially crowd out private transfers, even at very low levels of income per capita, but that it would still nonetheless raise the living standards of elderly who might otherwise find themselves in poverty. To some extent, support for younger members of the current elderly under the new rural pension responds to this concern.

The paper proceeds as follows. Section 2 presents descriptive statistics from the 2005 one percent population sample on sources of support for China’s elderly, with particular attention to contrasts between elderly living in rural and urban areas. It next presents updated evidence on changes in living arrangements which lie behind concerns that the rural elderly are at increasing risk of falling into poverty. Finally, the section concludes with a review of trends in rural-tourban migration in China and reviews debates over whether migration may be leading to problems of elderly abandonment. Section 3 introduces a conceptual framework for analyzing the responsiveness of transfers to the income levels of the elderly, and then introduces the data source to be used in the analysis. Results of the analytical exercises are presented in Section 4 and a final section discusses results in light of new rural pension experiments that are due to be expanded nationally by the end of the 12th Five Year Plan in 2016.

4
***|-2.48***|-.894***|-1.65***|-1.71***|-2.30***|
||(.403)|(.452)|(.443)|(.585)|(.310)|(.474)|(.479)|(.372)|
|**Treatment Effect in Low Disruption**|-1.11***|-.602***|-1.11***|-2.43***|-.746***|-1.32***|-1.05***|-2.05***|
||(.240)|(.211)|(.233)|(.334)|(.250)|(.327)|(.214)|(.285)|
|**Difference [β3, p-value]**|{.507}|{.012}|{.385}|{.942}|{.696}|{.543}|{.206}|{.580}|
|**Control Mean at BL**|6.57|2.52|6.35|10.8|6.40|7.70|5.77|11.0|
|**Observations**|2,379|2,379|2,379|2,379|2,400|2,400|2,400|2,400|

**Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. Leisure time allocation data was collected both at Baseline and Endline in a similar way. The leisure categories recorded were:
Box 1: **District re-organisation in Orissa** Orissa became a separate state in 1936 after its separation from the province of Bihar and Orissa, which was itself separated from the province of Bengal in 1912.

On its formation in 1936, the state of Orissa comprised six districts: Cuttack, Puri, Balasore, Sambalpur, Ganjam and Koraput. By 1949, the 24 princely states were also integrated with the State of Orissa, which then comprised 13 districts: Cuttack, Puri, Balasore, Ganjam, Koraput, Sambalpur, Dhenkanal, Sundargarh, Keonjhar, Balangirpatna, Boudh-Khonmandal, Mayurbhanj, and Kalahandi. These 13 districts are now commonly referred to as the 'undivided districts'.

In 1973, a Committee was established to consider the question of district/subdivision re-organisation in Orissa. No decision was taken on the recommendations of the Committee until 1990, apart from the renaming of Boudh-Khondmals district as Phulbani in 1986. In 1992, four new districts were declared (Gajapati, Malkangiri, Nowarangpur, Rayagada), followed by 10 more in 1993 (Khurda, Nayagarh, Sonepur, Bargarh, Kendrapara, Jagatsinghpur, Jajpur, Nuapara, Angul, and Bhadrak), and a further three in 1994 (Jharsuguda, Deogarh, and Boudh), bringing the total number of districts in Orissa to 30. The new district boundaries are shown in the Map together with those of the former, undivided districts.

# The selected districts are:

**Sambalpur,** originally belonging to the Central Provinces land revenue system. Located on the border with Madhya Pradesh in the north-west, it consists of a wide expanse of fairly open country, fringed by forest-clad hills and a series of low hill ranges of irregular shape (Sarap, 1991). Over half the total area of the district is classified as forest land, compared with the state average of around a third of total land area. Sambalpur district has a total population of just over 800,000 (1991 census) of which nearly 75 percent live in the rural
always adapting to varying levels of uncertainty and opportunity, adding a layer of complexity to understanding the association between behavior change intentions and actions. For African women, Johnson-Hanks (2007) argues that the ability to reproduce is a resource in that it provides opportunities that might otherwise not have arisen, and therefore whether or not a woman has a child is dependent on many more things than simply her intentions to do so. Similarly, sex could be considered such a resource, and uncertainty faced in the daily lives of those at risk for HIV infection may influence how intentions are formed and acted upon and how pursuit of one life goal is selected over pursuit of another. Characterizing this uncertainty by gaining a clearer understanding of the reasons for discrepancies between intentions and actions in a particular cultural context may guide design of interventions that are perhaps more effective in reducing risky sexual behavior.

To better understand what factors might intervene between intent to change risky sexual behavior and actual behavior change, we use panel data from the baseline and 4-month follow-up visits of participants enrolled in the Tanzania RESPECT trial. These data provide an opportunity to explore how a cash incentive might influence the consistency or lack thereof between reported intent to change sexual behavior at baseline and reported actual behavior change at follow-up. To our knowledge, this study is unique in its ability to characterize both stated intention and realized actions in panel data before and after a behavioral change intervention such as this.

# _Conceptual Framework and Hypotheses_

Previous research can lend guidance in developing a conceptual frame and corresponding hypotheses relating to sexual behavior change intentions and actions for study enrollees. To understand factors associated with behavior change, authors have studied the reciprocal relationship between selfefficacy, risk perception and behavior change (Bandura, 1977; Ajzen & Fishbein, 1980; Weinstein & Nicholich, 1993; Smith & Watkins, 2005), the effects of receiving STI and HIV test results on subsequent behavior change (Kamali et al., 2003; Thornton, 2006; Wolitski et al., 1997; Boozer & Phillipson, 2000
the fact that, owing to informal rotation, some beneficiary households worked fewer days than intended in program design, leading to an underestimation of the welfare impacts of the program. Because of informal rotation, actual program impacts may have been diluted given that the same total transfer amount was distributed across a larger number of beneficiaries.

The data collected during the unannounced site visits indicate that, on average, 13.1 percent of beneficiaries on site at the time of the visits were not registered beneficiaries. Similarly, time sheet records show that, on average, 13.6 percent of the workers listed on the time sheets were not registered beneficiaries. On average, there were 65 registered beneficiaries per subproject and 10 nonregistered beneficiaries listed on time sheets. This is not definitive evidence of informal rotation because these nonregistered beneficiaries may have simply been alternates, that is, replacements from the same household who had been nominated to work in the event of absence by the beneficiaries. Nonetheless, at least a portion of these additional workers were likely to be from households not captured by the IE. Discussions with administrative staff confirm that this was a practice at least in some sites despite attempts to prevent it.

Program impacts have been measured through the endline survey administered simultaneously to treatment and control households shortly before the CfW was launched in control communities. At the time of the endline survey, control communities were aware that they had been approved for a CfW subproject in the coming months (see above). It is plausible that this knowledge could have influenced the survey responses among control households. Specifically, control households might have strategically underreported their incomes and assets to influence the perception that they are poor and, hence, eligible for program participation. If this is the case, the IE may have overestimated program impacts. However, 8
**Table 2: Output means per port authority: 1990-2002**

|**Port Authority**|**Containers**<br>**(tonnes)**|**Liquid bulk**<br>**(tonnes)**|**Other Cargo**<br>**(tonnes)**|**Passangers**<br>**(number)**|
|---|---|---|---|---|
|B. Algeciras|14,209,958|7,356,611|7,722,807|3,800,291|
|Alicante|625,242|385,326|1,610,922|185,242|
|Baleares|1,481,024|2,653,233|5,019,041|3,153,879|
|Barcelona|8,614,954|8,233,942|7,558,250|1,239,828|
|Bilbao|3,233,316|4,076,144|10,581,053|104,084|
|Las Palmas|3,854,494|3,601,112|5,265,526|985,888|
|S.C. Tenerife|2,292,871|7,792,254|4,580,726|3,609,534|
|Valencia|9,214,198|1,513,538|7,861,188|246,620|
|Vigo|964,664|409,051|2,161,053|1,350,561|

Source: Own elaboration from data from Puertos del Estado (some years).

# **5.1.2 The factors**

In order to perform their function as infrastructure service providers, port authorities use
represent only a few days to a couple weeks’ worth of consumption. One way to see this is to compare average total 2-week purchase quantities with _qmin_<sup>_∗_,theminimumquantity</sup> required to pay the lowest unit price. Because price schedules are estimated at the district level, there are up to seven possible values of _qmin_<sup>_∗_(andtheassociatedexpendiurelevel,</sup> _e_<sup>_∗_</sup> _min_<sup>)foreachitem.Table6showsthemean,minimum,andmaximumvaluesofthesetwo</sup> statistics, across the seven districts. In columns 3, 4, 6, and 7 we see substantial spatial variation in the minimum and maximum of _qmin_<sup>_∗_and</sup><sup>_e∗_</sup> _min_<sup>—whatitmeansto“buyinbulk”</sup> varies across districts. Column 1 shows the mean quantity purchased over 2 weeks. Among households that purchase each item, the mean quantity purchased substantially exceeds the average value of _qmin_<sup>_∗_.</sup> To estimate the number of days’ worth of consumption represented by _qmin_<sup>_∗_, we divide</sup> _qmin_<sup>_∗_by the average total quantity purchased (at the district level), and multiply by 14.Figure</sup> 4 shows the histogram of results for the item-district pairs that exhibit bulk discounts (if we include the other items the distribution shifts significantly left). The median is 8.51 days. In half of cases, “bulk purchasing” involves buying roughly a week’s or less worth of consumption. The 75th and 90th percentiles are at roughly 2 and 3 weeks’ worth of consumption. Hence, even though the data are from 2-week transaction diaries, for most items the relevant time frame for storing and consuming a bulk purchase is substantially shorter. Bulk purchasing in this setting involves avoidance of very small
advent of high-speed internet. But this article is silent with respect to unemployment rates, although it does present estimates on local (sub-national) employment rates.

This paper explores the bi-variate and partial correlations between the incidence of the digital economy on frictional unemployment across the world. The empirics rely on international data from 2000-2017. The resulting evidence indicates that, after controlling for informality, the size or incidence of the digital economy – proxied by the share of the adult population that reports using the internet to pay bills – is negatively correlated with unemployment rates across countries. In addition, it is plausible that the negative correlation is stronger among developing economies than among high-income economies.

The rest of this paper is organized as follows. Section II provides a brief review of existing literature. Section III presents the data and discusses the proxy variables used to capture the incidence of the digital economy. Section IV lays out our empirical strategy and presents the results. It begins with non-parametric estimations of the bi-variate relationship between unemployment and the digital economy across countries, followed by a discussion of an apparent omitted variable bias due to the incidence of informality in developing economies, which leads to the specification of simple OLS and IV econometric models to estimate the partial correlation between unemployment and the digital economy. Section V concludes.

# **II. Literature Review**

While there is little literature on the effects of the digital economy on frictional unemployment, new evidence has emerged regarding employment and the role of digital payments, particularly in developing countries. Hjort and Paulsen (2019) studied the gradual arrival of submarine Internet cables on the coast of Africa and investigated the 3