rafmacalaba/data-use-mentions-extended huggingface dataset:
work with an institution such as a chamber of commerce, NGO, or training organization that has pre-existing relationships with firms, and has thus observed firm performance over time, and developed a sense of which entrepreneurs are more likely to succeed. Information on entrepreneurs with potential for growth might also be solicited among neighbors (Hussam et al., 2017), an approach that has the advantage of using local knowledge, but which will be dependent on how useful the local knowledge is. An additional advantage of micro-lenders, chambers of commerce, and similar organizations is that they may have social capital with local firms. The investor may benefit, in terms of better reporting and repayment, if firms find value in maintaining relationships and reputations with these local organizations.

A third approach is to have firms apply for funding through a business plan competition or similar competitive mechanism. McKenzie (2017) shows that the firms that apply for such programs tend to be positively selected, and that winners grow rapidly after receiving capital. An alternative approach is to rely on large amounts of data and prediction models to predict which firms will be good investments. McKenzie and Sansone (2017) find machine-learning methods have very little power in distinguishing growth paths among semi-finalists in a business plan competition. But such models may be better at distinguishing between subsistence and growth-oriented firms among microenterprises. For example, Hussam et al. (2017) find machine learning helps predict which Indian firms have higher returns; Arráiz et al. (2017) find that the algorithm used by the Entrepreneurial Finance Lab is able to use psychometrics to identify borrowers without credit histories who borrow more when given the chance, and who repay at similar rates to other borrowers; and Björkegren and Grissen (2018) find mobile call record data help predict which borrowers will have better repayment trajectories.

The choice between these methods will depend on who the investor is, and on scale. These methods will differ in terms of their feasibility and cost-effectiveness, and a micro-equity investor may be able to fine-tune this selection
, in the weekly surveyed household, they may represent cumulative or gross data.

Table 7, panel B shows a clear gap in the reporting on plots in the recalls, wherein roughly half the active agricultural plots are reported as in the weekly surveys. To track the addition and subtraction of plots over time, we turn to the weekly data. In the recall survey, plots are reported in the postharvest end line survey (July–September 2014), whereas weekly surveyed households first list plots in the baseline survey (January 2014). For these weekly surveyed households, the plot roster is then updated each week for the duration of the season. This allows for changes to the original plot listing, for instance, because it is necessary to add plots that were mistakenly forgotten at baseline, to add plots that are brought into cultivation after the previous weekly survey, or to drop plots that households planned to cultivate and, subsequently, decided not to cultivate. Figure 2 shows the increase over the course of the season in the cumulative number of plots reported by each group. Over the course of the weekly data collection, the number of plots per household in the weekly visit group grew from an average of 3.4 to 5.1 in the first 20 weeks of interviews.<sup>21</sup>

> 21 Part of the change in plots reported over the season could be the phrasing or context of the survey. In the baseline, households were asked “Please list all plots anyone in this household currently owns or cultivates.” Over the subsequent weeks, the survey was clearly (by default) referencing the 2014 long rainy season. In the end line survey, recall households were asked “please list all plots anyone in your household owned or cultivated during the 2014 long rainy season (masika season).” Nonetheless, because our standard for the inclusion of a plot in the data depends on whether labor was actually reported on the plot during the masika, misreporting based on a misunderstanding
of poverty in the latter. The objective is to summarize the approaches and insights offered by the diverse strands of research that are currently being conducted. While a number of the most important findings are reviewed, it must be said at the outset that the field is undergoing rapid evolution and has yet to reach a consensus on many key issues.

Not surprisingly, a variety of methodologies have been proposed to analyze the various trade/poverty linkages, which suggests that the range of findings will also be diverse. The most obvious methodological gulf is between researchers who come at these issues from a tradition of measuring poverty using detailed household expenditure data, and those who are of a trade background and more accustomed to dealing with economy-wide, national accounts data. One might 2
# EMPLOYMENT AND EARNINGS DIFFERENTIALS

Vietnam is predominantly a rural country, and most Vietnamese make their living off the land. Two-thirds of all workers are in agriculture. Other large sectors include manufacturing (12 percent) and wholesale and retail trade-including restaurants and hotels (11 percent) (World Bank 1997).

Most workers in Vietnam (79 percent) are self-employed, and 11 percent are employed in private firms. The public sector, broadly speaking, accounts for 9 percent of the labor force (World Bank 1996). Estimates here of the returns to schooling are limited to the wage labor force, thus excluding the large self-employed sector for which the VLSS does not collect earnings information. The fact that this analysis relates only to 20 percent of the labor force must, therefore, be kept in mind.

The Vietnamese wage labor force is relatively well educated, especially for a low-income country (Table 1). The mean age of the sample is 31 years. The average number of school years completed is 7.9, years of experience 17 and hours worked per week 46. About a quarter (22 percent) of the sample have no education or incomplete primary, and half have completed just the primary cycle. Only 8 and 12 percent of the labor force have secondary academic and secondary vocational education, and 7 percent have completed university. Over 40 percent of those in the wage labor force are employed in the public sector broadly speaking (which includes government, state companies, cooperatives and social organizations). Mean monthly earnings are about VND 152,000 ($14).

9
1), in line with the notion that large farms fail to significantly contribute to generation of paid jobs.<sup>19</sup> The point estimate suggests that being located 10 km closer to the next town increases the propensity to engage in paid work by some 0.9 percentage points, of almost 20% of the baseline level of 4.8%. We find that, as expected distance to towns does not affect the propensity to take up (agricultural) casual work but neither does distance to large farms (table 8, col. 2 and 3), implying that, even in their immediate vicinity, such farms have no perceptible effect on casual labor demand. Using large farm area cultivated within different distance bands as an independent variable supports the notion of large farms having no impact on local labor markets but, at the same time suggests that large farm area 50-100 km away is associated with higher demand for casual agricultural labor, possibly because temporary or permanent labor is imported. More detailed labor data from large farms that are currently being collected are likely to provide the detail needed for a more definitive answer to this issue.

# **4.3 Resilience to climatic shocks**

> 19 The short time period covered by our panel implies that there is little intertemporal variation in distance to towns. We thus report regressions that include zonal fixed effects in table 8, noting that coefficients on the variables related to distance to farms are virtually unchanged if woredaor household-level fixed effects are included.

14
season, leading to wage growth and female empowerment. Impacts of newly established productive infrastructure on agricultural productivity, although a defining characteristic of MNREGS as compared to, say, a simple cash transfer scheme, have been less well studied. In fact, critics argue MNREGS may displace existing jobs rather than generate new ones; that poor quality of assets built under the program may undermine its contribution to higher productivity; and that program-related labor demand may push up agricultural wage rates, undermine mobility, and encourage premature mechanization. In a worst case scenario, this could depress rural labor demand and create dependency on the program not as an employer of last resort in crises but as a permanent feature of rural India’s institutional landscape.

To explore short-term effects of MNREG on labor markets, risk taking, and agricultural productivity, we use unique national panel data from 1999/2000 and 2007/08 that offer three advantages. First, as they cover the same households in 1999, i.e. before the program had been conceived, and in 2007/08 when some but 2
glow (Andreoni, 1990) as the willingness-to-pay for another farmer whom the participant does not know and who produces no spillovers from the participant’s standpoint. I assume that spillovers related to either contagion or knowledge externalities decay to zero beyond a certain plot or home distance threshold respectively. At the 95th percentile in plot and home distance (for a home distance larger than 1.7 miles and an average plot distance larger than 35 walking minutes), the median willingness-to-pay for a farmer whom the participant does not know is zero, pointing to a lack of warm-glow giving behavior; the same holds true at the 90th percentile.

# **6.3 The Social Benefit of the Training Is Large and Heterogeneous**

The willingness-to-pay data allow me to evaluate three policy-relevant objects: the value of the per capita externality generated by training one farmer; the social benefit of the training; and how different targeting criteria for the training generate different levels of social benefit. In what follows I will show how to calculate the per capita externality and social benefit of training one farmer, and then compare two targeting criteria. The first allocates the training according to a _price criterion_ : the farmer with the highest willingness-to-pay gets the training. The second allocates the training according to an _externality criterion_ : the farmer producing the highest per capita externality gets the training. While it is trivial to foresee that the externality criterion will produce a higher social value than the price criterion, farmers’ valuations allow us to fine tune this intuition by quantifying the change in social benefit when externalities are accounted for in targeting decisions.

I use farmers’ willingness-to-pay for others to calculate the externality generated by a trained farmer _t_ in village _v_ : Where _Nj_ is the number of sampled farmers _j_ = _t_ in village _v_ .

Column (2) in Table 2 shows the mean, standard deviation, median, maximum and minimum value of the per capita externality, calculated using equation (11); it ranges from 500 to 24,250 UGX and has a median
of source district (the district of immediate last residence) and destination district (the district of current residence and Census enumeration), this data set contains the number of individuals who have moved from the former to the latter, dis-aggregated by duration of stay in the destination district, sex, age, education and the reason for migration (but not by urban/rural parts of source/destination). We obtained these custom tables for the 2001 and 2011 Census. The dis-aggregation by the duration of stay in the destination district distinguishes between individuals who migrated at most five years ago and those who migrated six years ago or earlier. We supplemented these data with census data on district population, also dis-aggregated by sex, age and education.

To construct the dependent variable, we obtain the number of out-migrants for a given age/sex category for a given source district by summing up all migrants within this category who moved in the past five years to all destination districts. We distinguish between within-district and out-of-district migrants.<sup>12</sup>

# **Data challenges and limitations**

**Harmonizing districts** Our dataset is a district-level panel. Districts units represent the third level of administrative units in India, and are commonly used for data collection and dissemination purposes. Some district boundaries and names altered over the period of our study. Defining 2001 as the base year, we first checked for changes in names and renamed districts post 2001 using the 2001 name. In the case of district splits, we mapped the post-2001 district back to its parental district in 2001. This process automatically resulted in a reduction of sample size as there were fewer districts in 2001 than in later years (Kumar and Somanathan, 2015).

> 12It is possible that a person moved twice or more in the past ten years prior to the census. In this case, only his last residence would be mentioned, and we would rely on this information to construct the measures.

11
the median rent of US$53/ha & year that, as per the last household survey, smallholders are willing to pay,<sup>1</sup> could generate annual income of some US$50 million, equivalent to almost 5% of Malawi’s public revenue.

Beyond failing to live up to expectations in terms of productive development, estate development also does not seem to have reduced tenure insecurity: with 22% of farmers concerned about losing their land and 21% fearing encroachment, tenure insecurity is high. It is particularly pronounced among the poor, households not born in the village or engaged in land transactions or who had a land dispute in the past, and in villages close to an estate with no more land to be distributed. By comparison, impacts of matrilineal inheritance, a factor emphasized in the recent literature, seem modest at best. Including fear of land loss in a production function suggests that insecure tenure is associated with a 12% point output reduction for women but not for men. In principle, comparing the present value of productivity gains due to program-induced reductions in tenure insecurity to program costs would allow to decide if systematic regularization can economically justify an alternative to demand-driven estate development. This implies that if a tenure regularization program as implemented in Rwanda at a cost of US$5 to US$6 per parcel (Nkurunziza 2015) could eliminate insecurity in Malawi, its benefits would be more than sufficient to recoup the costs. Carefully designed and evaluated pilots that pay particular attention to women’s rights and overlap with estate leases could help not only to deal with the issue of estate lease renewal, but also inform implementation of recently adopted Land Bills. If success can be demonstrated and regulations to implement at larger scale passed, they could provide significant economic benefits.

The remainder of the paper is organized as follows. Section two puts Malawi’s land tenure system in context by describing its evolution, highlighting questions to be analyzed, and drawing out their relevance for recent
- 17 -

having a relatively high income elasticity of demand. However, important differences between the two approaches still exist. Conventional measurements of income elasticity use the mean of the income distribution as a reference point and are indifferent to income inequality. The partial test for welfare dominance, on the other hand, measures the income elasticity at each point along the concentration curve and is sensitive to the income position of the spender.6/ Moreover, the usual argument for luxury excises cannot easily distinguish on welfare grounds which of two income-elastic commodities would be the most suitable target for excise taxation.

In the analysis of the expenditure data for the C6te d'Ivoire that follows, weighted income elasticities are compared for different pairs of commodities. The weights employed are those associated with the calculation of the extended Gini coefficient. Values for these weights are permitted to vary between 1.5 and 5 and some of the welfare implications of this range of values are depicted in Table 1. Higher values for these weights attach more importance to the lower end of the income distribution. A weight of unity implies complete indifference to inequality while an infinitely large value would assign zero weights to everyone except the household with the lowest income. Thus a range of values between 1.5 and 5 encompasses a reasonable diversity of views about the importance of income inequality.

# C. Welfare Dominance and ET taxes in the C6te dl'voire

Data for the C6te d'Ivoire originate from a random sample of 1,600 households who were surveyed over a period of a year starting in Hess114j/We1faro Dominanco & **the** Dmsi&n of **Excise** Taxation **in the** Coto **_D'!voir/lWTir h:aIb/5/18/I_**
data is not available in the Lakner an Milanovic (2015) dataset, we observe that average GDP growth of the bottom billion group during the New Normal is revised upwards by 0.1 (Figure 24).

The implication of this is that limitations on country coverage may _bias upwards_ the result for the world’s poorest countries – and therefore lead us to be more sanguine, and less concerned about their economic plight. By using wider country coverage, we revise downwards the estimates. If such data limitations systematically bias availability away from the poorest and slowest growing countries, we are likely over-estimating convergence and overlooking the important policy questions posed by continued divergence.

# **Figure 23. Cross-country and temporal data availability**

Panel a. Share of countries with less than 2 benchmark years of data Panel b. Average benchmark years with nonmissing data 30
- Fiszbein, A., N. Schady, Ferreira, F., P. Olinto, and E. Skouffias. 2009. “Conditional Cash Transfers: Reducing Present and Future Poverty.” World Bank Policy Research Report 47603.

- Fortin, Nicole, Thomas Lemieux and Sergio Firpo. 2011.  Decomposition Methods in Economics.  In: Ashenfelter Orley and Card David (eds) Handbook of Labor Economics, Vol. 4A , pp. 1-102. Northolland, Amsterdam.

- Foster, James, J. Greer, and Eric Thorbecke. 1984. “A Class of Decomposable Poverty Measures.” _Econometrica_ 52 (3): 761–65.

- Juhn, Chinhui, Kevin Murphy, and Brooks Pierce. 1993. “Wage Inequality and the Rise of Returns to Skill.” _Journal of Political Economy_ 101 (3): 410–442.

- Kolenikov, Stanislav, and Anthony Shorrocks. 2005. “A Decomposition Analysis of Regional Poverty in Russia.” _Review of Development Economics_ 9 (1):25–46.

- Kuznets, Simon.1955. “Economic growth and income inequality.” _American Economic Review_ 65 (1): 1–29.

- Ravallion, Martin. 2001. Growth, Inequality and Poverty: Looking Beyond Averages. _World Development_ 29 (11): 1803–1815.

- Ravallion, Martin, and Shaohua Chen. 1997. “What can new survey data tell us about recent changes in distribution and poverty?” _World Bank Economic Review_ 11 (2): 357–382.

- Ravallion, Martin, and Shaohua Chen. 2003. Measuring Pro‐Poor Growth. _Economic Letters_ 78: 93–99.

- Ravallion, Martin, and Shaohua Chen. 2007. “China’s (Uneven) Progress Against Poverty.” _Journal of Development Economics_ , 82 (1): 1–42.

- Ravallion, Martin, and Monika Huppi. 1991. Measuring Changes in Poverty: A Methodological Case Study of Indonesia during an Adjustment Period. _The
lockdown. Our main findings are the following: First, in the early months of the lockdown, labor force participation and unemployment fell, while there was very little change in employment levels. Second, job search activity declined, both because of continued attachment to employers who had suspended operations and because of almost no hiring activity. Third, by the end of June, we estimate that (registered) employment was 11.9% less than it should have been, based on trends from the previous two years. Fourth, this lost employment was entirely due to the sharp decline in hiring activity in the first few months of the crisis. This is evident from both the administrative and online vacancy data. As noted above, the early months spanned a period when seasonal activities, especially related to tourism, would normally be expanding and this needs to be taken into consideration in assessing the impact of the crisis. Most of the “missing” jobs thus far in 2020 have been in accommodation and food, which reflects the pandemic’s effect on tourism. Fifth, and somewhat unexpectedly, separations to the end of May were _lower_ than would be predicted based on the trends from recent years. This almost certainly was due to the government’s measures to protect existing employment relationships. This was done through a prohibition of layoffs in industries affected by the crisis and by tying the major form of income support to the maintenance of jobs.

To sum up, the analysis points to the important role that policy has played in determining how the Greek labor market has adjusted to the pandemic and lockdown. The measures put in place by the government to mitigate the effects of the crisis on employers and workers emphasized job protection. The decreased labor demand, then, translated into a downturn in hiring rather than increases in separations that would lead to higher unemployment. In this respect, Greece has been similar to some other European countries that have adopted measures to avoid layoffs. This stands in contrast to some other countries, like the United
1790, Germany from 1870 and the US starting with its first official, foreign loan in 1917. In addition to providing an important consistency check, these sources allow us to capture within-empire (country) transactions, e.g. lending to colonies, for which no inter-governmental treaties exist.

_Reports and datasets from international organizations:_ Starting from the inter-war period, publications and data by international organizations become an additional, highly valuable source. We make extensive use of regular research reports on international capital flows by the League of Nations, the BIS, the IBRD, the UN, the OECD and the IADB. We also use a wide range of creditor specific sources, such as recently declassified CIA reports on foreign lending by Sino-Soviet Bloc countries and the annual reports of a large number of bilateral and multilateral creditor institutions. Moreover, and most importantly, we supplement this hand-coded data with a comprehensive data-extract on bilateral lending from the World Bank's Debtor Reporting System (DRS), which was shared with us on a confidential basis. Specifically, the DRS records all bilateral lending transactions of all member states reporting to the World Bank, starting in the 1960s. For aid flows and grants, our main sources are the 9
it more likely to be also self-employed after moving. This also helps address the question of whether the self-employed can simply move their job with them.

Figure 1 puts together evidence from six countries for which we have information on type of occupation at destination for the majority of migrants.<sup>10</sup> After dropping respondents who do not move between survey waves, we are left with a sample of more than 15,000 distinct migrants aged 18 to 65 who were self-employed, wage workers, or not working at home.

> somewhat similar migration rates to the self-employed in most countries.

> 9The CFPS and ELMPS do not track international migrants, and it is likely that many of the attritors from the different panels may be migrants. In the absence of alternative information, our implicit assumption is that the self-employed are not more likely to attrit when they migrate than other occupations. Under this assumption, we may understate the magnitudes of migration, but attrition should not bias the signs of associations in the data.

> 10Occupation type after move is missing for a large part of the sample in the Mexican data and in all the Nigerian data.

8