rafmacalaba/data-use-mentions-extended huggingface dataset:
Excel file) to all matches and validate each match only if names, activity and neighborhood were consistent in both databases.

The “reclink” command allows inputting different weights to match on each of the three variables used (surname, first name and gender). In order not to rely on the weights used, we reiterate the process with different weights until no additional matches were found.

Since it is possible that the first name in the program data corresponds to the second name in the GUFE data, we also reiterate the whole process for all combinations of first to fifth names. Surname and first name were inverted in one of the two databases. So we also reiterate the process with other combination of surname and first names.

# **Checking that the matching method is working:**

To assess whether our matching method is working efficiently, we used the following methods:

1. First we looked at whether we could find additional matches using a more usual method of matching. That is looking at the two lists (sorted by surnames) and trying to find each business of the GUFE data in the program database. So it means looking mainly at businesses with surnames starting with the same letters. We were not able to find any additional matches.

2. Secondly, we looked at the proportion of business which formalized with the _entreprenant_ status during the first 3 months after program launch. Indeed, most businesses which formalized with the _entreprenant_ status should be also in the program data (in theory they are the only businesses aware of this new status). We matched 78% (119/153) of the newly registered _entreprenants_ .

3. We then took the 34 businesses which formalized with the _entreprenant_ status and were not matched with the program data and we tried harder to match these businesses. We took the program data and looked at all the businesses in the same neighborhood as the unmatched businesses. We were able to find 6 new matches. These 6 matches are very imperfect matches with surnames somewhat different
13</sup> These results are qualitatively consistent with Furman and Orszag [2015], Koller et al. [2017] and the Council of Economic Advisors [2016], all of which were produced using a proprietary dataset of US firms from McKinsey & Co.

# **1.1 Role of Human Capital**

Firms differ in the complexity of tasks that they perform and the product market may reward certain capabilities more than others. To address this issue, we construct industry-level indices of the composition of tasks firms perform and assess how they affect the likelihood of a firm from that industry becoming a star firm. In creating these indices, we draw on a large labor market

> 12In particular, if we do not subtract _GDWL_ from _INTAN_ we would run the risk of capitalizing future monopoly rents reflected in high acquisition premiums, thereby incorrectly attenuating the relation between ROIC and pricing power when one firms buys another.

> 13In an early version of the paper, we find much higher increases in ROIC (over 190%) for the 90th percentile without the sample restrictions to large firms and firms with positive invested capital.

9
36|5.32|
|Rate of Urbanization 4|67.8%|77.3%|77.3%|77.0%|
|Self-employed as share of Labor Force <sup>4</sup>|27.03%|26.20%|26.19%|27.21%|
|Share of Formal Employment <sup>4</sup>* 6|57.76%|37.97%|36.41%|31.51%|
|Mean Household Per Capita Income 4,_5_|265.10|239.08|243.15|276.46|
|Inequality (Gini) 4|0.595|0.561|0.576|0.591|
|Inequality (Theil - T) 4|0.760|0.610|0.657|0.694|
|Poverty Incidence (R$ 30/month) 4|0.0681|0.0727|0.0758|0.0922|
|Poverty Incidence (R$ 60/month) 4|0.2209|0.2149|0.2274|0.2176|

Notes: *) Annual figures.

1) Percent, from January to December. Based on the IGP-DI for 1976, and on the INPC-R for all other years.

2) Based on the IBGE Metropolitan Unemployment Index.

3) For all individuals 10 years of age or older, in urban areas.

4) Calculated from the PNAD samples by the authors. See Appendix 1.

5) Urban only, monthly and spatially deflated. Expressed in constant 1996 Reais.

6) Defined as the number of employees 'com carteira' as a fraction of the sum of all wage employees and selfemployment workers.

5 See Table 1, and Appendix I for details.
26. Figure 6 shows the non-linear relationship between oil income per capita (on the horizontal axis) and the annual conflict rate from 1990 to 2006 (on the vertical axis). As a country‘s oil income rises from zero to about $1000 – Russia‘s average level during this period – its conflict rate increases. Yet above this level, it drops back towards zero. Strikingly, Libya‘s oil income is well above the previous cut-off point for conflict in oil producers; its civil war is an exceptionally rare event.

**Figure 6. Oil Income and Conflict rates, 1990-2006**

_Source:_ calculated from Gleditsch et al. (2002), and Ross oil dataset.

27. There are many arguments about why oil wealth makes low-income countries more conflict-prone. The evidence is relatively strong for two arguments; two other arguments are more speculative but are worth considering for their potential significance in the MENA region.

28. The first argument is that when oil is extracted from a region populated by an ethnic or religious minority, it increases the benefits of secession and hence the likelihood of a secessionist rebellion. Table 5 lists 16 separatist conflicts that broke out in petroleum-rich territories between 1960 and 2006.<sup>14</sup> These are the separatist conflicts that broke out between

> 14 Eight of these conflicts began in  ̳oil states,‘ meaning they produced at least $100 per capita in oil and gas income in the conflict‘s first year.  In six of these eight cases, petroleum extraction was already under way in the secessionist region (Angola, Iran-Kurdistan, Iran-Arabistan, Iraq, Nigeria-Niger Delta, and Russia); in two cases, extraction was occurring in other parts of the country, and about to begin in the region where the rebellion took

3
of assets to store value (stocks, bonds, real estate, bank accounts, gold, antiques etc.). For the choice of store of value, return, risk and diversification expectations guide the investor. For the choice of unit of account tradeoffs between credit and exchange rate risk may guide contract design. For the means of payment liquidity considerations are key.

# VII. **Conclusion**

**_Thefinancial systew in_** _2025._ The scenario presented above assumes that current ffiorts to find a globally agreed framework for financial markets fail. Likewise efforts to render monetary authorities independent are assumed to fail over longer periods of time. Meanwhile the advent of low cost financial transaction technology effectively erases limits to trade in financial instruments of whatever denomination. As individuals, corporations and nations ry to grapple with the consequences we may start to see the emergence of a new global financial system.

By 2025 key elements of the new system may already be in place. In a number of countries, where monetary discipline was too hard to introduce and where citizens took move and more advantage of their capital flight option, currency systems have imploded. In some of'tLese countries governments turned to formal commitment mechanisms like currency boards or unions."<sup>3</sup> In others, citizens routinely use "foreign" currencies to settle transactions. The sr read

> 13 Both in currency unions and among "dollarised" zones balance of payments statistics become of less use. A.s iin Hong Kong today, full balance of payments statistics may no longer be produced and thus may not forn the basis of macro-economic policy-making anymore.
OZIER - REPLICATION REDUX – p. 30 of 41 but what can others take from this, in their efforts toward transparency and reproducibility? And where, again, does this leave deworming?

# 4.1. How should replication work?

Citing several examples including this one, Clemens (2017) wrote about the broader category into which this situation falls, regarding what separates a replication from a robustness check: _“Confusion in the meaning of replication harms research... anyone can find ‘plausible’_ _ways to change someone else’s regression so that coefficient estimates change... A well‐_ _known form of this problem is that it is a simple matter to change any result into a null_ _result by running modified versions of the same test that are underpowered by construction.” (p. 334)_ Indeed, in this case, changing the definition of treatment, splitting the sample, and applying uneven weights does reduce the power of the study by construction.<sup>26</sup> What possible benefit is there in approaching a study in this way? Two recent examples provide some insight. Galiani, Gertler, and Romero (2018) point out (via a survey of editors) that academic journals are part of this: while all editors surveyed indicated interest in publishing a replication study that overturned the results of the original, only a quarter were interested in replications that upheld the original result.<sup>27</sup> Replication teams, sometimes perceived as setting out simply to overturn others’ work, have even been called “replication bullies” for this (Meyer and Chabris, 2014). This may seem pernicious, but journals are intended to print new findings; a successful re‐running of someone’s code just isn’t very much new information

> 26 To “underpower,” or to “reduce power,” in this context, means making estimates less precise, and thus less likely to be statistically different from zero even when
consider the treatment as an improvement of any type, the market access measure allows us to use the changes in speed limits associated with road upgrades. We use a long-difference strategy to understand how changes in market access are associated with changes in outcome variables. To uncover the causal impact of market access on outcome variables, we control for baseline levels of market access, local economic activity, and pre-trends in selected variables. In addition, to further account for non-random road placement, we instrument market access with a version of market access that excludes immediately surrounding localities when constructing the market access measure. As a robustness check on these results, we also implement a panel data model that controls for time-invariant characteristics.

These different empirical approaches each come with their own set of advantages and limitations. For example, the difference-in-difference approach enables us to leverage the granular nature of both the satellite imagery and the road data and to test whether locations were endogenously targeted based on pre-trends. However, to temporally fix the time of “treatment,” this approach considers treatment to begin the first year 3
|Price of a call to United States|US$ per minute|0.7|1.6|0.4|0.4|0.6|
|Price of an inter-Africa call|US$ per minute|0.9|1.9|0.4|0.4|1.1|

_Source_ : Adapted from AICD, OTH, ART, SOCATEL, Orange and World Bank ICT at-a-Glance. — = data not available The country has benefited from a huge expansion in coverage and access of mobile communications. Mobile penetration rose from 1 subscription per 100 people in 2000 to 47 in 2008, almost twice the average penetration in low-income countries. However, with only 43 percent of the population under GSM coverage, Benin remains behind the levels of comparable Sub-Saharan peers (table 14). Analysis suggests a large efficiency gap that could be reduced with further regulatory reform that would allow 99 percent of the population to be reached by a GSM signal on a commercial basis (figure 18).

**Figure 18. Efficient market gaps for mobile telephone service in West Africa**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existing access Efficient market gap Coverage gap<br>Percent of population<br><!-- End of picture text --> _Source_ : Mayer and others (2009) using GSM coverage figures for 2005. _Note_ : Existing access represents the percentage of the population covered by voice infrastructure in the third quarter of 2006. Efficient market gap represents the percentage of the population for whom voice telecommunications services are commercially viable given efficient and competitive markets. Coverage gap represents the coverage gap—the percentage of the population for whom services are not viable without
K., A. Dabalen, G. Demombynes, M. Giugale, and J. Hoogeveen. 2012. “Collecting High Frequency Panel Data in Africa Using Mobile Phone Interviews.” Policy Research Working Paper 6097, World Bank, Washington, DC.

- Dabalen, A.L., and S. Paul. 2014. “Effect of Conflict on Dietary Diversity: Evidence from Côte

- d’Ivoire.” _World Development_ 58 (June): 143–58.

<u>https://doi.org/10.1016/j.worlddev.2014.01.010.</u>

- D’Souza, A., and D. Jolliffe. 2013. “Conflict, Food Price Shocks, and Food Insecurity: the Experience of Afghan Households.” _Food Policy_ 42 (October): 32–47.

<u>https://doi.org/10.1016/j.foodpol.2013.06.007.</u>

- Enamorado, T., L.F. López-Calva, C. Rodríguez-Castelán. 2014. “Crime and Growth Convergence:

- Evidence from Mexico.” _Economics Letters_ 125 (1): 9–13.

<u>https://doi.org/10.1016/j.econlet.2014.07.033.</u>

- Ichino, A., and R. Winter-Ebmer. 2004. “The Long-Run Educational Cost of World War II.” _Journal of Labor Economics_ 22 (1): 57–86. https://doi.org/10.1086/380403.

- International Crisis Group. 2017. _Herders against Farmers: Nigeria’s Expanding Deadly Conflict._ Africa Report 252. Brussels: International Crisis Group.

<u>https://d2071andvip0wj.cloudfront.net/252-nigerias-spreading-herder-farmer-conflict.pdf.</u>

- IOM (International Organization for Migration). 2018. _Nigeria: Displacement Tracking Matrix Round XXI Report
the Efficiency-Electricity scenarios. This occurs because, when we do not allow for shared budgets, the government can get less advantage of the infrastructure complementarities, and then the weight of the exogenous productivities in its decision becomes larger.

The correlations between the log of sectoral productivities and the log of road-quality investment are given by the bottom panel of Table 6. Except for agriculture, the sign and relative magnitude of the correlations are similar to the ones in the top panel but smaller in absolute value. This occurs because any improvement in the quality of roads that directly affects two locations can potentially change the least-cost path of other locations, which makes the correlation with its own productivity less meaningful. In the case of agriculture, the modelsolution correlations are smaller than in the data, signaling that other fundamentals, such as geographical barriers and the degree of infrastructure complementarities, can be playing against isolated locations in rural areas.

Taken together, Table 6 says that, in most cases, the quality of infrastructure moves more closely with the exogenous productivities in the model solutions than in the data. Hence, a source of the misallocation is the lack of a correct internalization of the productivity effects.

# **5.2.2 Infrastructure Investment Complementarities**

Next, we turn to another important aspect behind the public capital misallocation: the complementarity of investments. We first ask the question: if the government optimally allocates one type of investment, keeping the other type of infrastructure as observed in the data, what would be the relative gains? The last two columns in Table 7 show the results of this counterfactual experiment. If the government can only reallocate electricity or road investments, the welfare gains are large, 7.52% and 10.97% relative to the benchmark (given by the observed infrastructure data), respectively. We can see also that the average income increases by 3.4% and 10.7%, while for median income, the gains are slightly smaller, 2.85% and 10.25
# **Using Machine Learning to Assess Yield Impacts of Crop Rotation: Combining Satellite and Statistical Data for Ukraine**<sup>**1**</sup>

# **Klaus Deininger**<sup>*****</sup> **, Daniel Ayalew Ali**<sup>*****</sup> **, Nataliia Kussul**<sup>**§**</sup> **, Mykola Lavreniuk**<sup>**†**</sup> **, Oleg Nivievskyi**<sup>**#**</sup>

**_JEL Codes_** O13, Q12, Q15

**_<mark>Keywords:</mark>_** <mark>Machine learning, crop rotation, Ukraine, agricultural productivity</mark>

* Development Research Group, The World Bank

> **§** Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine

> **†** National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

> **#** Kyiv School of Economics

> 1 We thank Olha Halytsya, Siobhan Murray, Andrii Shelestov, and Anna Yailymova for useful inputs and insightful comments. The views presented in this paper are those of the authors and do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent or any of the other institutions they are associated with.
Our export industries are suffering because they are the _first_ to be asked to accept the 10 per cent reduction . . . Our problem is to reduce money wages and, through them, the cost of living, with the idea that, when the circle is complete, real wages will be as high, or nearly as high, as before. By what _modus operandi_ does credit restriction attain this result? _In no other way than by the deliberate intensification of unemployment_ (italics original).

**CFA Zone.** In more recent times, the CFA zone countries in Africa attempted such internal adjustment during the period of their real overvaluation, 1986 to 1994 (Clement _et al.,_ 1996, and Nashashibi and Bazzoni, 1994). This resulted in disastrous consequences for the CFA countries as the economic contractions in some of them were comparable to the Great Depression of the United States. (See l.able 1 below for data on the two largest CFA countries.) Large increases in poverty and other economic problems ensued (see Devarajan and Hinkle, 1994, and further discussion in section IV).

# **Table 1. Comparing Great Depressionis: Cameroon, C8te d'Ivoire and the United States**

All numbers are the percentage decline in per capita GDP.

|Measure of Output Decline \ Country|Cameroon|Cote d'Ivo ire|United States|
|---|---|---|---|
|Purchasing Power Parity a/|31.4|29.1|N.A.|
|Purchasing Power Parity with Terms of<br>Trade adjustmentA'|_38.5_|_34.5_|N.A.|
|Market Pricesb/|41.5|18.8|30.9|

a Author's calculations for 1986-1992 from the Penn World Table Mark 5.6, described in Summers and Heston (1991) and available on the website: http://pwt.econ
Policy Research Working Paper 5652

# **Abstract**

Food price inflation in Brazil in the twelve months to June 2008 was 18 percent, while overall inflation was seven percent. Using spatially disaggregated monthly data on consumer prices and two different household surveys, we estimate the welfare consequences of these food price increases, and their distribution across households. Because Brazil is a large food producer, with a predominantly wage-earning agricultural labor force, our estimates include general equilibrium effects on market and transfer incomes, as well as the standard estimates of changes in consumer surplus. While the expenditure (or consumer surplus) effects were large, negative and markedly regressive everywhere, the market income effect was positive and progressive, particularly in rural areas. Because of this effect on the rural poor, and of the partial protection afforded by increases in two large social assistance benefits, the overall impact of higher food prices in Brazil was U-shaped, with middle-income groups suffering larger proportional losses than the very poor. Nevertheless, since Brazil is 80 percent urban, higher food prices still led to a greater incidence and depth of poverty at the national level.

This paper is a product of the Office of the Chief Economist and the Social Protection Unit, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at fferreira@worldbank.org, afruttero@worldbank.org and pleite@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are
urban population shares.<sup>36</sup> Below, we briefly assess how, compared to the WUP data, these estimates change our impression of LAC’s level of urbanization _vis-à-vis_ other regions of the world. Using the AI and cluster urban population share estimates, we then re-examine the basic empirical relationships between urbanization, development and structural transformation that were discussed in Section 1 with a view to addressing the question of whether LAC countries depart significantly from these relationships. Because the _WorldPop_ data are only available for a much more limited sample of countries, we focus on the results obtained using the _LandScan 2012_ and _GHS-Pop_ data. In doing so, we concentrate on a global sample of 146 countries – including 25 LAC countries – for which we have complete data.<sup>37</sup>

# _4.1. Re-assessing LAC’s level of urbanization relative to the rest of the world_

Figure 2 shows levels of urbanization for both the world and major regions, including LAC, as estimated using both the WUP data and the different permutations of methods (AI and cluster) and input population data sets ( _LandScan_ and _GHSPop_ ).<sup>38</sup> The WUP data show LAC to be far more urbanized than the world overall. Hence, while, based on national definitions of urban areas, the share of the population living in urban areas in LAC in 2014 is estimated to be 80 percent, for the world as a whole it is only estimated to be 53 percent. Furthermore, LAC also appears to be the most urbanized region in the world after North America. However, for the various consistent sets of estimates of urban population shares, the picture is very different. In particular, LAC’s level of urbanization appears much closer to that for the world overall. They also show LAC to be less urbanized than the Middle East and North Africa region in all six measures, less urbanized than South Asia in three measures, less urbanized than Europe and Central Asia