rafmacalaba/datause-encoder-data huggingface dataset:
they have positive demand effects and act as a “big push” (Magruder, 2013) coordinating wage setting at a higher wage and employment equilibria. Studies of the impact on minimum wages in developing countries typically find evidence of both such effects and wage compression, with those at the bottom of the distribution gaining most (Gindling and Terrell, 2007; Lemos, 2009; Maloney and Nuñez-Mendez, 2004).<sup>iii</sup> The employment effects of minimum wages, which critically depend on market structure, may also vary by gender (see Comola and de Mello, 2009). While the largest impacts are expected on the lowest paid workers, the empirical evidence on the employment impacts of minimum wages is mixed (see e.g. Neumark and Wascher, 2008, for a survey of the evidence).

# **2.2 Hypotheses**

The studies discussed above suggest the following hypotheses. Minimum wages are likely to have the largest impact on those workers for whom they are binding. Consequently, one would expect workers with less human capital and women to experience the largest wage increases, with the least educated women benefitting the most. However, this result may not obtain if minimum wage changes have a “lighthouse effect” and induce all workers to demand wage increases commensurate with the change in minimum wages or if compliance with minimum wage changes is imperfect. One may also expect the workers that gain the most from minimum wage increases in terms of wage growth to bear a larger share of the brunt of employment adjustments made in response to minimum wage changes, as they are likely to become less attractive to employers since minimum wages render them more expensive both in absolute terms, and relative to workers whose wages did not increase as much.

7
# Charter School Entry and School Choice: The Case of Washington, D.C.

Maria Marta Ferreyra Grigory Kosenok The World Bank New Economic School mferreyra@worldbank.org gkosenok@nes.ru

> ∗We thank David Albouy, Joe Altonji, Stanislav Anatolyev, Steve Berry, Marcus Casey, Rajashri Chakrabarti, Estelle Dauchy, Pablo D’Erasmo, Dennis Epple, Fernando Ferreira, Steve Glazerman, Brett Gordon, Bryan Graham, Phil Haile, Justine Hastings, Dan McMillen, Aviv Nevo, Javier Penia, B. Ravikumar, Stephen Ryan, Tim Sass, Holger Sieg, Fallaw Sowell, Che-Lin Su, Chris Taber, Jacques-Francois Thisse and Patrick Wolf for useful conversations and comments. We benefitted from comments by seminar participants at Boston College, Carnegie Mellon, Cleveland Fed, Cornell, ECARES, High School of Economics, Maryland, McMaster, New Economic School, New York Fed, Notre Dame, Philadelphia Fed, St. Louis Fed, U. of Illinois-Urbana/Champaign, Yale and the World Bank, and by session participants at numerous conferences. Ferreyra thanks the World Bank and the Berkman Faculty Development Fund at Carnegie Mellon University for financial support. Kosenok acknowledges the support of the Ministry of Education and Science of the Russian Federation, grant No. 14.U04.31.0002, administered through the NES CSDSI. We are grateful to Steve Glazerman, the staff from FOCUS and 21st Century School Fund, and the charter school principals we interviewed for answering our questions on charter schools in D.C. We thank Jeremy Fox, Benjamin Skrainka and Che-Lin Su for conversations on MPEC and SNOPT. We owe special thanks to Michael Saunders for his assistance with the SNOPT and MINOS algorithms. For their help, we thank our research assistants Nick DeAngelis, Nathalie Gonzalez Prieto, Gary Livacari, Sivie Naimer, Djulustan Nikiforov, and Hon Ming Quek. Jeff Reminga assisted us with the computational aspects of the project, and Bill Buckingham from the Applied Population Lab at the University of Wisconsin-Madison provided Arc GIS assistance. All errors are ours.
respectively and for electricity they are 30.1% and 42.2%. These are, of course, very gross estimates based on the price shocks described in Table 14. If the government decides, for example, to change electricity tariffs for the production sector, the effects on household welfare may be very different.

**Table 15 – Indirect Effects of Subsidies Elimination (%, selected products)**

||**Diesel**|**Sugar**|**Electricity**|
|---|---|---|---|
|Quintile 1|99.55|1.03|30.10|
|Quintile 2|98.87|1.22|30.31|
|Quintile 3|96.48|1.48|30.52|
|Quintile 4|93.43|1.67|33.89|
|Quintile 5|81.33|2.84|42.17|
|Total|87.79|2.00|36.55|

Source: Authors estimations using SUBSIM.

# **6. The political economy of reforms**

The political economy of subsidy reforms in Morocco has largely been driven by the global prices of strategic commodities and by the increasing cost of subsidies to the state’s budget. From an equalization fund with own resources sufficient to conduct its mission of stabilizing prices of basic commodities over short periods of time, the CDC transformed over the years into a permanent subsidy fund relying heavily on budget transfers. With rising world prices of basic commodities, especially of fuels, the burden on the budget of the subsidy system has been increasingly heavy. This has been particularly the case for fuels, given that Morocco depends totally on imports. While the share of fuels in total subsidies was relatively small before the first oil shock in 1974, it started to rise steadily over time to reach almost 90% in 2012. With respect to GDP, subsidy outlays rose from less than 0.5% over the first decades after independence to almost 2% by end of the 1990s. As shown in Figure 4, the trend in the amount of subsidies followed rather closely the international price of crude oil.

# **Figure 4 - Subsidies changes have been correlated with the variations of oil prices (Oil prices in MAD/bbl., and subsidies in % of GDP)**

<!-- Start of picture text --> 1,000.0 8.0<br>900.0<br>7.0<br>800.0<br>6.0<br>700.0<br>5.0<br>600.0<br>500.0 4.0<br>400.0<br>3.0<br>300.0<br>2.0<br>200.0<br>1.0<br>100.0<br>0.0 0.0<br>Crude oil prices Subsidies<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Source: The Ministry of Finance, the CDC, and WDI 21
# **COVID-19 and Corporate Balance Sheet Vulnerabilities:**

# **A Simple Stress-Test Approach**

Erik Feyen Fernando Dancausa Bryan Gurhy Owen Nie<sup>1</sup> Originally published in the <u>Policy Research Working Paper Series on</u> _July 2020._ This version is updated on _April 2022._ To obtain the originally published version, please email prwp@worldbank.org.

JEL Classification Numbers: F34, F65, G01, G30 Key words: COVID-19, Corporate Vulnerability, Non-financial Sector, Debt

> 1 We thank Alfonso Garcia Mora, Anderson Caputo Silva, Yira Mascaro, Mahesh Uttamchandani, Igor Zuccardi, Ezio Caruso, Matei Dohotaru, and Diego Sourrouille for valuable discussions and suggestions.
|**Table A1.2: First Sta**|**ge- IV- Column**|**s 4 and 5**|||||
|---|---|---|---|---|---|---|
|**Column in IV**|**4**|**4**|**5**|**5**|**5**|**5**|
|Dependent variable:|**Inputs per**<br>**worker**|**Program**<br>**Dummie**|**Inputspw**|**Program**<br>**dummy**|**prog*inputspw**|**prog*capitalw**|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**_Factors_**|||||||
|Capital-per-worker|0.153**|-0.011|0.033|0.022|0.147|0.018|
||(0.06)|(0.02)|(0.13)|(0.04)|(0.37)|(0.33)|
|**_Activities_**|||||||
|PCB|0.529|0.329***|0.463|0.278**|3.637***|2.775***|
||(0.36)|(0.12)|(0.37)|(0.11)|(1.07)|(0.93)|
|HCB|0.912***|-0.009|0.848***|-0.045|0.245|-0.399|
||(0.30)|(0.10)|(0.30)|(0.09)|(0.87)|(0.72)|
|Gravel|-2.044**|0.080|-2.162***|0.281*|-0.676|2.649*|
||(0.71)|(0.23)|(0.72)|(0.17)|(2.10)|(1.40)|
|Wood|-0.326|0.189**|-0.264|0.149*|1.547*|0.995|
||(0.30)|(0.10)|(0.30)|(0.09)|(0.88)|(0.72)|
|Wall|-0.490|0.079|-0.485|0.158|0.573|2.039|
||(0.53)|(0.17)|(0.52)|(0.14)|(1.53)|(1.18)|
|Structural|0.847|0.060|0.767|-0.354|0.894|-2.272|
||(1.84)|(0.59)|(1.83)|(0.35)|(5.33)|(2.90)|
|Electrical|0.149|0.049|0.169|0.220|0.274|1.253|
||(0.70)|(0.22)|(0.69)|(0.17)|(2.01)|(1.46)|
|Sanitary|-0.887|4.42e-06|-0.879|0.020|-0.035|-0.142|
||(0.57)|(0.18)|(0.56)|(0.16)|(1.64)|(1.31)|
|Finishing|-0.101|-0.138|-0.114|-0.097|-1.309|-1.088|
||(0.63)|(0.20)|(0.63)|(0.16)|(1.83)|(1.31)|
|Production-inputs|<br>1.423***|<br>-0.270**|<br>1.423***|<br>-0.282**|<br>-2.298*|<br>-2.719***|
||(0.41)|(0.13)|(0.41)|(0.12)|(1.19)|(1.00)|
|Other|0.503|-0.149|0.514|-0.115|-0.949|-1.171|
||(0.47)|(0.15)|(0.47)|(0.12)|(1.36)|(0.99)|
|**_Instruments_**|||||||
|Inputprice|0.119***|-0.002|0.049|-0.010|-0.175|-0.103|
||(0.03)|(0.01)|(0.05)|(0.02)|(0.15)|(0.14)|
|Labor-at-start|-0.063|0.159***|-0.654|0.301*|2.055|-0.004|
||(0.15)|(0.05)|(0.56)|(0.17)|(1.62)|(1.38)|
|Labatstart*inpprice|||0.065*|0.011|0.219*|0.108|
||||(0.04)|(0.01)|(0.12)|(0.10)|
|Labatsrat*cappw|||<br>0.067|<br>-0.018|<br>-0.135|<br>0.125|
||||(0.07)|(0.02)|(0.20)|(0.17)|
|Constant|7.175***|0.199|8.19***|-0.040|0.184|1.448|
||(0.61)|(0.20)|(1.05)|(0.33)|(3.05)|(2.73)|
|N|<br>148|<br>148|<br>148|<br>169|<br>148|<br>169|
|R2|0.382|0.382|0.398|0.386|0.39|0.392|
|Adjusted R2|0.317|0.317|0.325|0.321|0.315|0.328|

Note: *, **, *** indicate significance at the 10%, 5% and 1% level respectively

-23-
# **Table 16: Unorganized Sector Log (Real Output): Lag Specification**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||RHS Lag= 2|RHS Lag= 2|RHS Lag= 4|RHS Lag= 4|RHS Lag= 9|RHS Lag= 9|
|||_Ot_|_her-sector Outp_|_ut Spillovers_|||
|Horizontal Output Spillovers|0.0435|0.0493|-0.0583|0.0477|-0.0477|-0.0217|
|from Organized Sector|(1.48)|(1.71)|(-1.86)|(1.65)|(-1.41)|(-0.66)|
|Forward Output Spillovers|0.439|-0.471|1.568*|1.446|2.815***|1.179|
|from Organized Sector|(0.62)|(-0.29)|(2.15)|(1.29)|(4.68)|(1.08)|
|Backward Output Spillovers|1.378**|1.437*|-0.217|0.634|0.260|10.76***|
|from Organized Sector|(3.09)|(2.05)|(-0.44)|(0.51)|(0.46)|(8.43)|
|||_Other_|_-sector Produc_|_tivity Spillove_|_rs_||
|Average TFP of Horizontal-linked|0.464|0.262|0.380|-0.103|-0.108|-0.236|
|Organized Plants|(1.48)|(0.86)|(1.28)|(-0.37)|(-0.37)|(-0.85)|
|Average TFP of Forward-linked|0.486|-2.654**|-1.047|1.729|-1.160*|0.277|
|Organized Plants|(1.34)|(-2.69)|(-1.84)|(0.45)|(-2.33)|(0.20)|
|Average TFP of Backward-linked|2.041***|4.860*|-0.128|-4.783|-4.086***|-16.73***|
|Organized Plants|(3.96)|(2.41)|(-0.23)|(-1.31)|(-6.42)|(-14.57)|
|||_O_|_wn-sector Outp_|_ut Spillovers_|||
|Forward Output Spillovers|1.477***|4.500***|1.326***|-1.313|1.320***|6.846***|
|from Unorganized Sector|(4.83)|(4.43)|(4.40)|(-1.01)|(4.56)|(8.43)|
|Backward Output Spillovers|1.147***|0.831*|1.203***|-0.0268|1.307***|3.657***|
|from Unorganized Sector|(5.16)|(2.18)|(5.33)|(-0.04)|(5.81)|(7.04)|
|Constant|-70.83***|-118.8***|-64.79***|8.592|-88.78***|-392.0***|
||(-6.43)|(-5.24)|(-4.23)|(0.22)|(-6.10)|(-12.02)|
|N|5888|5888|5888|5888|5888|5888|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Columns 2, 4 and 6 contain state-year and industry-year fixed effects while Columns 1, 3, and 5 do not Standard errors are clustered at the state-industry level. Significance at the 10% level is indicated by *, at the 5% level by ** and at the 1% level by ***._ 45
# **_4.1_ .** **_Data Availability_**

As previously stated, several countries do not compile data on newly-created businesses or on firms that went out of business, much less on re-registered businesses (i.e. businesses that register existing businesses because of changes to firm names, ownership, sector, etc.) A second challenge refers to some countries, excluded from this survey, which have in fact collected data on enterprise creation, but simply do not have the tools or resources to process them. In some cases, decentralized business registries make aggregation to the national level extremely difficult. In other cases, the data are archived only in paper format.

# **_4.2 Data “Purity”_**

Time series data should be used with caution because the levels of total and newly registered businesses might be inflated due to recent legal or economic reforms. For instance, Algeria issued a new law requiring all existing businesses to reregister, in order to bring their status up to new sectoral requirements. As a result, the number of businesses doubled from 1997 to 1998 (Figure 1).

**Figure 1: Legal Reforms that Affect Business Statistics, the Algerian Case**

<!-- Start of picture text --> 20000<br>18000 Law n° 97 -42 of 1-18-1997. Obligated all businesses to “re--register”<br>16000<br>14000<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>1995 1996 1997 1998 1999 2000 2002 2003<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database, 2007.

# **_4.3 Limitations Regarding Data on Firm Closures_**

As previously stated, although approximately 80% of surveyed countries require businesses to report closures; a significantly lower number were actually able to report the 13
|Years since baseline|Pooled sample|BENINFORM|EGYPTMACROINS|GHANAFLYP<br>IFLS|KENYAGETAHEAD|MALAWIFORM|MXFLS|NGLSMSISA|NGYOUWIN|SLKFEMBUSTRAINING|SLKINFORMALITY|SLLSE<br>SLMS|TOGOINF<br>TTHAI|UGWINGS|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|9|83%<br>[67%,86%]||||||||||||83%*<br>[68%,86%]*||
|10|66%|||||||||||39%|87%*||
||[58%,70%]|||||||||||[37%,43%]|[73%,89%]*||
|11|65%|||||||||||39%|90%*||
||[57%,70%]|||||||||||[36%,43%]|[75%,92%]*||
|12|90%||||||||||||90%*||
||[74%,91%]||||||||||||[75%,92%]*||
|13|91%||||||||||||92%*||
||[75%,93%]||||||||||||[75%,93%]*||
|14|92%||||||||||||93%*||
||[76%,93%]||||||||||||[76%,94%]*||
|15|77%|||76%|||||||||94%*||
||[53%,84%]|||[52%,84%]|||||||||[78%,95%]*||
|16|96%||||||||||||97%*||
||[80%,96%]||||||||||||[80%,97%]*||
|17|100%||||||||||||100%||
||[80%,100%]||||||||||||[80%,100%]||

# Notes:

Death rate bounds accounting for missing information on survival/death, including attrition, are given in parentheses.

* indicates that multiple rounds are available for this combination of survey and time horizon, so that death rates, lower and upper bounds shown are averages of all their respective round-specific values available for this particular horizon.

Observed drops in death rates for a given survey are possible, since some firm closure is temporary (in the sample of unique firms, we observe 6.22 percent as closed at one point in time and open in a later round), and as a result of attrition (1.1 percent of unique firms in our sample are either closed or open in t and t+2, and not observed in t+1). For the MXFLS, the baselines for the horizons over which we observe firm death are different. We use the 2002-round for the 3-year and 8-year horizon and the 2005-round for the 4.5 year horizon.

67
<!-- Start of picture text --> 4 /<br>any i —y j '<br>ae ‘ eae fae t oN <<br>f f - A NR s, P<br>> ae Sy GPE) | y ip Fil<br>| 20-39<br>40-59<br>M60 ‘<br>-79 7<br>Mi 80 - 100 I<br><!-- End of picture text -->
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE As of 2005 power trade flows in the CAPP region were minimal, at 1.8 TWh of exports and 0.1 TWh of imports. These consisted of exports from the Democratic Republic of Congo to its neighbors, the Central African Republic and the Republic of Congo.

Under trade stagnation, negligible amounts of power would be traded. All countries would have to rely on domestic production for power generation. Therefore, the Republic of Congo, Chad, and Gabon would all need to increase their production considerably, with new hydropower plants in the Republic of **-** Congo and Equatorial Guinea and a considerable increase in Heavy Fuel Oil based generation in Gabon.

Under trade expansion, Cameroon would capitalize on its rich hydropower potential and export power to all the countries in the CAPP except the Central African Republic, which would remain self-sufficient. The resulting additional 1,450 MW of hydropower in Cameroon would support exports to Republic of Congo, Chad, Gabon, and Equatorial Guinea (figure 3.3).

**Figure 3.3. Simulated patterns of future power trade in the CAPP, 2015 (TWh)**

<!-- Start of picture text --> 6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>Trade expansion Trade stagnation<br>Source: Rosnes and Vennemo 2009.<br>Note: CAPP = Central Africa Power Pool; TWh = terawatt-hour.<br>Net Power Imports(TWh)<br>Cameroon CAR Chad Congo Equatorial Guinea Gabon<br><!-- End of picture text --> 38
Teacher Incentives in the Kyrgyz Republic (34 percent of the program teacher population). Interviews with “others” not on the list of awardees comprised 2.8 percent of the interviews in Issyk-kul and 3.8 percent of the interviews in Talas.

The typical teacher-awardee was a middle-aged, Kyrgyz-speaking woman with a university degree in education and over 20 years experience teaching; about half had a “higher qualification” level at the outset of the incentive program in 2007/2008 (table 2). Teacher-awardees in Issyk-kul were slightly more likely to hold a higher teaching rank and were slightly less likely to teach elementary subjects than those in Talas, but in other respects the teachers in the two provinces were quite similar. Compared with the national samples of grade 4 and grade 8 teachers surveyed as part of the National Sample-Based Assessment (NSBA), the present sample was less likely to hold a “higher teaching qualification” but was comparable to the NSBA in terms of percent female, education and experience (CEATM 2010).

# 4.2 Questionnaires and Interview Forms

School and teacher questionnaires were prepared in English and Russian and then translated into Kyrgyz (from Russian, with the English versions providing additional guidance for translation). The school interview included 18 multi-part questions, took about 15 minutes to conduct, and asked the principal about the structure and organization of the school, the students and teachers, special programs the school had participated in, and school policies and practices. Six questions addressed the teacher incentive program specifically. The teacher interviews included 40 multi-part questions (some of which were open-ended), took 20-30 minutes to conduct, and asked teachers about: (a) the usefulness and effectiveness of the Teacher Incentive/Performance Management program, (b) the teacher’s participation in the program, and (c) the teacher’s background.

# 5. Results

The incentive program was intended to motivate teachers, build skills and ultimately improve teacher classroom performance, for which teachers would be rewarded. This section presents evidence regarding the opinions and reports from teachers who participated in the program in 2007/2008 and who 11
Using Probabilistic Models to Appraise and Decide. Ley-Borrás and Fox

**Figure 4. An example of a structure of objectives for risk reduction and recovery.**

<!-- Start of picture text --> Minimize negative impact<br>from severe natural events<br>Minimize Minimize damage Optimize Minimize negative<br>human impact to infrastructure fiscal expenditure development impact<br>Min. damage to Min. damage Min. short‐term Min. long‐term<br>Minimize Minimize Maximize Maximize<br>transport and to buildings Minimize negative negative<br>human human communication and energy financial cost investment response economic economic<br>suffering life lost efficiency effectiveness<br>infrastructure facilities impact impact<br><!-- End of picture text --> Source: R. Ley‐Borrás The structure shows at the top a global fundamental objective: _Minimize negative impact from severe natural events_ . This objective is explained by dividing it into four fundamental objectives: _Minimize human impact_ , _Minimize damage to infrastructure_ , _Optimize fiscal expenditure_ and _Minimize negative development impact_ . Each of these objectives is further explained to be more precise, for example, _Optimize fiscal expenditure_ is shown being integrated by the objectives: _Minimize financial cost_ , _Maximize investment efficiency_ and _Maximize response effectiveness_ . There is no predefined level of detail the structure of objectives must have: we should only increase detail to add clarity.

3. **Generate a rich set of alternatives and strategies** . The World Bank has developed and helped develop a number of innovative financial instruments (Ghesquiere and Mahul 2010) that can be used directly or used as a starting point for designing new ones. These financial instruments can be part of strategies that aim at achieving the country's fundamental objectives.

4. **Identify and measure key uncertain events** . In the context of protecting the country against natural disasters, identifying and measuring the risks from natural events is essential, but there are also "human made" events that can be as central to the consequences of a disaster as the natural event, and the risks from these other events should also be identified and measured.

These steps will provide quality elements to be modeled with an influence diagram and, together with the appraisal of strategies using probabilistic catastrophe risk models, will lead to good decisions on SDRFI instruments. The four steps are only sketched here; there is a rich set of techniques for performing the steps professionally and, while doing so, getting clarity and value for the decision makers.

**15**
expenditures of Western Europe and North America (Canada and the United States). In all regions, with the exception of LAC, export promotion agencies spend a larger share of their budget for offices abroad in Western Europe.

# **4 The empirical framework**

Our objective is to disentangle the effects of export promotion agencies, their structure, responsibilities, strategies, resources and activities on overall exports in order to understand what works and what doesn’t. The first step is to explore whether there is any correlation between export promotion budgets and exports. Figure 9 provides a plot of exports per capita on EPA budgets per capita. There is a clear positive correlation between these two variables. Figure 9 also provides the predicted value obtained from the corresponding locally weighted regression ( _lowess_ ), which provides us with some _prima-facie_ evidence of which are the agencies that are under-performing in terms of exports per capita given their budgets.<sup>10</sup> For example Rwanda (RWA) would be expected to have a much higher level of exports given the budget of its EPA (under-performer), whereas the Irish agency (IRL) would be expected to have a lower level of exports (over-performer).

An interesting feature is that the curve flattens at very high budgets. Most of the countries among this group of high-budget agencies are developed countries. To test for heterogeneity between developed and developing countries we divided the sample accordingly. Figure 10 shows the two scatter plots. Clearly, the positive correlation is driven by developing country data, and the correlation between exports and the budget of export promotion agencies is unclear within the sub-sample of developed countries.

There are three clear problems with the correlations discussed above. First, the sample might be biased, because it is restricted to the agencies for which we were able to find a local contact. It is further restricted to agencies that answered the survey, even though we had a

> 10An in depth and robust analysis of each agency performance is beyond the scope of this paper and would need to be tackled through agency-specific studies. In this paper, we limit the scope at providing averages across different groups and variables.

12
# **Non-Labor Input Quality and Small Farms in Sub-Saharan Africa: A Review**

**Hope Michelson**<sup>‡</sup> **, Sydney Gourlay**<sup>†</sup> **, Philip Wollburg**<sup>†1</sup>

## **JEL** : O13, Q12, Q18.

**Keywords** : Agricultural inputs, fertilizer, pesticides, household surveys, smallholders, Sub-Saharan Africa.

> 1 ‡ Associate Professor, University of Illinois Urbana-Champaign, Illinois, USA; <u>hopecm@illinois.edu.</u> † Economist, Development Data Group, World Bank, Rome, Italy. This paper was produced with financial support from the 50x2030 Initiative to Close the Agricultural Data Gap, a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030. For more information on the Initiative, visit 50x2030.org.