rafmacalaba/datause-encoder-data huggingface dataset:
# **IV. Econometric Results**

Results from econometric estimation demonstrate that, even in an environment where there are little observed outcomes from operation of rental markets that are fully efficient, significant barriers to entry or friction in their operation could imply that whatever adjustment is brought about through the operation of such markets falls short of the potential. The hypothesis of sharecropping leading to inefficient use of resources is not rejected. This adds to the magnitude of friction that would, according to our estimates, prevent rental markets from attaining a first best allocation of resources.

# **_A. Evidence on the Inefficiency of Sharecropping_**

Table 3 reports results from the household fixed effect estimates of yield and input intensity equations at the plot level for the most common crops. As discussed earlier, we use semilog equations conditional on plot characteristics (soil quality, topography, years of possession and availability of irrigation) to allow interpretation of the coefficients on the ownership dummy as percentage deviations of output and input intensities of owner-cultivated relative to sharecropped plots. After controlling for the effects of soil and other plot level characteristics, the results in column 1 of table 3 show a statistically significant difference in yields between owned and sharecropped plots that can be attributed to the rental arrangement. The point estimate suggests that, after adjusting for other factors, yields are 7% higher on owned as compared to sharecropped plots for the same household, about half the yield differential in semiarid India (Shaban 1987). This supports our hypothesis of ‘Marshallian inefficiency’, i.e. lower output on sharecropped as compared to owned plots.

To complement the above results, columns 2-6 of table 3 report results of fixed effect regressions for intensity of input application on owned and sharecropped plots by the same household. Except for oxen power where descriptive statistics also showed few differences, we find statistically significant gaps in input use between owned and sharecropped plots throughout. For example, intensity of family labor and total labor (including hired labor) are estimated to be 17 and 16 percentage points lower on sharecropped as compared to owned plots (columns 2 and 3). Similar conclusions emerge from random effect tobit regressions for intensity of fertilizer application (columns 5 and 6); conditional on fertilizer use, owned plots receive about 38% more fertilizer than sharecropped ones. Overall, and consistent with findings for yields, results point towards moral hazard in tenants’ supply of labor or effort that is not compensated by more intensive use of purchased inputs. Thus, despite generally close social relationships, principals are unable to induce tenants to apply optimum effort levels. The fact that parties enter into contracts that are less efficient than fixed rental can be explained by capital market imperfections or social norms.

14
Table 10. Main features of global expansions (by country group)

|||Global|expansio|ns||Global|expansions|(excluding|initial ye|ars)|All|
|---|---|---|---|---|---|---|---|---|---|---|---|
||1976-81 1|983-90 199|2-2008 2|010-19 A|verage|1977-81|1984-90 19|93-2008 2|011-19 A|verage|years|
|**Advanced economies**||||||||||||
|Output|3.3|3.7|2.5|1.9|2.9|3.0|3.8|2.5|1.8|2.8|3.3|
|Output per capita|2.6|3.1|1.8|1.4|2.2|2.3|3.2|1.8|1.4|2.2|2.4|
|Industrial production|3.5|3.5|2.0|1.8|2.7|2.7|3.7|2.1|1.2|2.4|3.0|
|Trade|5.6|6.4|6.1|4.3|5.6|4.6|6.9|6.2|3.4|5.3|5.9|
|Unemployment rate|0.2|-0.2|0.0|-0.3|-0.1|0.3|-0.3|-0.1|-0.4|-0.1|0.0|
|Output (PPP)|3.4|3.8|2.6|2.0|2.9|3.1|3.8|2.6|1.9|2.8|3.3|
|Output per capita (PPP)|2.6|3.1|1.9|1.5|2.3|2.3|3.2|2.0|1.4|2.2|2.5|
|Credit|3.5|6.8|3.4|1.2|3.7|3.5|7.1|3.5|1.5|3.9|4.7|
|Government expenditure|3.9|3.3|2.9|1.1|2.8|4.1|3.3|2.8|1.2|2.8|4.2|
|Policy rate|1.0|-0.3|-0.3|0.0|0.1|1.2|-0.1|-0.3|0.0|0.2|0.0|
|**EMDEs**||||||||||||
|Output|4.0|3.0|4.7|4.9|4.1|3.5|3.2|4.9|4.6|4.0|4.7|
|Output per capita|2.0|0.9|3.2|3.5|2.4|1.4|1.1|3.4|3.3|2.3|2.8|
|Industrial production|...|...|5.6|5.1|5.3|...|...|5.8|4.4|5.1|5.0|
|Trade|4.2|2.3|8.1|5.5|5.0|4.1|2.8|8.6|4.7|5.0|5.6|
|Unemployment rate|-2.0|0.0|0.1|0.0|-0.5|-2.0|0.1|0.1|0.0|-0.4|0.0|
|Output (PPP)|4.0|3.2|4.8|5.1|4.3|3.4|3.3|5.1|4.9|4.2|4.8|
|Output per capita (PPP)|1.9|1.1|3.3|3.8|2.5|1.4|1.2|3.6|3.6|2.4|2.9|
|Credit|6.1|0.8|10.4|10.1|6.9|6.1|1.9|10.1|9.9|7.0|7.8|
|Government expenditure|6.8|1.4|7.1|5.7|5.3|5.8|2.4|7.2|5.7|5.3|5.8|
|Policy rate|0.3|0.3|-0.3|0.0|0.1|0.3|0.4|-0.2|0.0|0.1|0.0|
|**LICs**||||||||||||
|Output|1.3|2.5|4.0|5.9|3.4|1.4|2.6|4.3|5.8|3.5|3.8|
|Output per capita|-1.1|-0.4|1.1|3.0|0.6|-1.1|-0.3|1.5|2.9|0.7|1.1|
|Trade|1.7|4.5|7.4|8.8|5.6|2.5|5.0|8.2|8.2|6.0|6.0|
|Output (PPP)|1.6|2.6|4.2|6.1|3.6|1.6|2.8|4.5|6.0|3.7|3.9|
|Output per capita (PPP)|-0.9|-0.3|1.3|3.1|0.8|-0.8|-0.1|1.6|3.0|0.9|1.2|

Note: All variables show percent changes, except in unemployment rates and policy rate, in which percentage-point changes of these variables are reported. See footnote of Table 9.

58
informal economy and remittances are much larger than is generally acknowledged.

Nightlight intensity has also been used to study long-term economic growth. Using inequality measures based on income predicted by nightlights, Lessmann and Seidel (2017) find that close to 70 percent of all countries experience sigma convergence. Similarly, in India, nightlight data from 2000 to 2010 provides evidence of both absolute and conditional convergence among rural areas (Chanda and Kabiraj 2016). Nightlight data also suggests that there is convergence at the district and state level (Tewari and Godfrey 2016). And in Pakistan, nightlight data have been used to identify convergence, albeit slow, between the richest and poorest provinces of the country (Mahmood, Majid and Chaudhry 2017).

Finally, another line of research has focused on the consequences of economic shocks. A study on the impacts of the 2015 earthquakes and trade disruption in Nepal, for example, argues that the aggregate impact of the earthquakes was modest (Galdo et al. 2017).

# **III. DATA**

Global nightlight data is derived from satellite measurements collected by the Defense Meteorological Satellite Program (DMSP), a meteorological initiative of the US Department of Defense. Since 1976, this program has included a weather sensor, the Operational Linescan System (OLS), which measures light intensity. The DMSP satellites follow an oscillating orbit at a mean altitude of approximately 833 kilometers, with nightlight captured daily between 8:30 pm and 9:30 pm. The OLS sensor has the unique capability of detecting city lights, gas flares, shipping fleets, and fires. An algorithm developed by the National Geophysical Data Center (NGDC) of the National Oceanic and Atmospheric 4
# **7. Omnibus Effects and Multiple Hypothesis Testing**

Our analysis so far has followed the existing literature and tested for the impact of migration on particular outcomes on a one-by-one basis. An advantage of this approach is that we can directly compare our results for any particular outcome, for example, the impact of migration on business ownership, to those from other studies examining the same outcome. However, in total over Tables 4 through 10, we are examining the impacts of migration on 62 different outcomes, and for each outcome, we consider the result with and without controls. This raises questions about multiple hypothesis testing. In this section, we examine which of our results are robust to different corrections for multiple testing.

One approach sometimes used with multiple outcomes is to aggregate them into particular groupings to examine whether the overall impact of the treatment on a family of outcomes is different from zero.<sup>29</sup> This is useful if the intention is to see whether the global impact of a particular treatment is generally positive. This is not our focus here, since we are interested in the individual channels through which migration impacts family members left behind. For example, we are interested in whether household labor earnings and subsistence earnings go down with migration and remittances go up, more than whether the average effect over all types of income is positive or not.

We instead consider the significance of individual coefficients when viewed as part of a family of _n_ hypotheses. For example, we could consider all outcomes related to diet as a family. The family-wise error rate is then defined as the probability of at least one type I error in the family (Shaffer, 1995). Then, we can maintain the family-wise error rate at some designated level α, such as 0.05 or 0.10, by adjusting the p-values used to test each individual null hypothesis in the family. The simplest such method is the Bonferroni method, which uses as critical values α/ _n_ . Thus, with 10 outcomes in a family, we would need to use a cutoff of a p-value less than 0.01 when testing each individual outcome to maintain the family-wise error rate at 10 percent.

30 Several refinements to the Bonferroni method offer greater power. Ranking the _n_ outcomes in increasing order of their p-values for testing a null effect, so that _p_ 1 ≤ _p_ 2 ≤ ... ≤ _pn_ . Then, the Holm’s (1979) sequentially-rejective bonferroni method is applied as follows. In the first step, a null effect for outcome 1 is rejected if _p_ 1 ≤ α / _n ._<sup>If we</sup> cannot reject this outcome, we cannot reject null effects for all other outcomes. Otherwise,

> 29 See, for example, O’Brien (1984) and Kling and Liebman (2004)

> 30 The description of methods here is based on Shaffer (1995).

25
|Other|0.0262|(-0.03)|
|---|---|---|
|Main source of lighting|||
|(baseline=Electricity/Gas)|||
|Kerosene|-0.0832***|(-0.016)|
|Candles/Battery|-0.0583***|(-0.0222)|
|Other|-0.122***|(-0.0234)|
|Main heat source for cooking|||
|(baseline=Firewood)|||
|Charcoal|0.0625|(-0.0486)|
|Kerosene/Oil|0.130***|(-0.0197)|
|Electricity/Gas|0.283***|(-0.0392)|
|Other|0.339***|(-0.0501)|
|Type of garbage disposal<br>|||
|(baseline=Bins)|||
|Compound|-0.0722***|(-0.0233)|
|Empty Lots/streets/rivers|-0.0560***|(-0.0191)|
|**Assets**|||
|TV|0.0408***|(-0.0155)|
|Fridge|0.0259|(-0.0167)|
|Stove|-0.00359|(-0.0161)|
|Bicycle|-0.0800***|(-0.015)|
|Car|0.0593***|(-0.0208)|
|Iron|0.00704|(-0.0134)|
|Constant|11.33***|(-0.0847)|
|Observations|21,271||
|R-squared|0.473||
|Robust standard error|s in parenthes|es|
|*** p_<_0.01, ** p_<_|0.05, * p_<_0.1||

Source: Authors’ estimations based on GHS and ACLED Data 36
**Figure 2.** Change in enrollment in private schools as a share of total enrollment

<!-- Start of picture text --> 6-10 year olds 11-15 year olds 16-18 year olds<br>Level, 2000,2005, Bangladesh Change, 2000-2005-2010, Bangladesh Level, 2003, Bhutan Change, 2003-2007, Bhutan<br>Level, 2004/05, India Change, 2004/05-2009/10, India Level, 1995/96,2003/04, Nepal Change, 1995/96-2003/04-2009/10, Nepal<br>Level, 1998/99,2006/07, Pakistan Change, 1998/99-2006/07-2010/11, Pakistan<br>Note: Data available for two periods for Bangladesh, Nepal and Pakistan only<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>Change in share of private enrollment (percent)<br>0<br>Bangladesh Bhutan India Nepal Pakistan Bangladesh Bhutan India Nepal Pakistan Bangladesh Bhutan India Nepal Pakistan<br><!-- End of picture text --> _Source:_ Authors, based on household survey data.

10
<!-- Start of picture text --> Mumbai: Household Distribution according to Income Classes<br>eeClass 0<br>i Annual Income- Range Average Income i Households in i% of<br>USD1=INR46.<br><!-- End of picture text -->

|Mumbai: Househ<br><br>Class|old Distribution according to <br>e<br>|Income Classes<br><br><br> <br>|||
|---|---|---|---|---|
||i Annual Income-Range|AverageIncome<br>i <br>USD1=INR46.|Households in|i%of|
|High Income|iabove 1000,000|2,760,000<br>}|0.22|5.00|
|||759,000|||
|Mean Household|Annual Income|3,263,700||459,457|
|Households mill|ion|||43|
|Note:Outputs of|theStudyareshaded||||
|Source: NCAER <br>Ahmedabad: Ho<br>|(2010)<br>usehold Distribution accordi<br>P|ng to Income Classes<br>|medabad||
||2004-5USD|2004-5inINRat|imillion|:Households|
||<br>:|<br>USD1=INR46||<br>i|
|Low Income|:<3000|138,000|:<br>0.27|;<br>30.00|
|Middle Class<br>|76000 to 30000<br><br>|1,380,000<br>|}<br>0.18<br>|:<br>20.00<br>|
|Mk gs|60<br>nn|ngee|eeu|eee|
|||529,920|||
|Householdsmil|lion|||0.9|

<!-- Start of picture text --> High Income i above 1000,000 2,760,000 } 0.22 5.00<br>759,000<br>Mean Household Annual Income 3,263,700 459,457<br>Households million 43<br>Note: Outputs of the Study are shaded<br>Source: NCAER (2010)<br><!-- End of picture text --> <!-- Start of picture text --> Ahmedabad: Household Distribution according to Income Classes<br>Pfmedabad<br>2004-5 USD 2004-5 in INR at i million : Households<br>: USD1=INR46 i<br>Low Income :<3000 138,000 : 0.27; 30.00<br><!-- End of picture text --> <!-- Start of picture text --> Middle Class 76000 to 30000 1,380,000 } 0.18: 20.00<br>Mk gs 60 nnn gee eeu eee<br>529,920<br>Households million 0.9<br><!-- End of picture text -->
# Debt Financing (1/2)

<!-- Start of picture text --> Debt is usually for 40% of SPV capital, so equity is 20%, of which 51%+ from entrepreneurs<br>fi )<br>COMMON INFRASTRUCTURE a il State<br>A cs<br>)<br>|ia ” |;[Ss aa\%J<br>os PMC 2: 40%SPV | “s, Up to 20% > aio |<br>“<br>=)| , | Loan “s Eau ity | ; Individual ;<br>MoT | BANK Entrepreneurs gf eae<br><!-- End of picture text -->
# Figure 3.  Country correlations for categories of CE versus non-CE, 1980-2020

<!-- Start of picture text -->
1.0 Agriculture Metal Energy Non-Commodity<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Agriculture average: 0.49***<br>-0.2 Metals: 0.33***<br>Energy: 0.44***<br>-0.4 Non-Commodity = 0.22***<br>-0.6<br>-0.8<br>-1.0<br>AzerbaijanChinaYemenNepalCôte d'IvoireBangladeshUkraine<br>GreeceNicaraguaEcuadorUruguayGuyanaLiberiaArgentina<br>Antigua and BarbudaBrazilNamibiaEquatorial GuineaLebanonNigerPakistanIndiaBoliviaHungaryJordanKenyaBelarusZimbabweFijiRomaniaSerbiaAngolaQatarCyprusRwandaOmanCongo, Rep.Timor-LesteAfghanistanTrinidad and Tobago<br>BahrainCongo, Dem. Rep.BelizeJamaicaPolandBhutanLithuaniaKyrgyz RepublicRussian FederationPapua New GuineaLao P.D.R.VanuatuSyriaCabo VerdeGeorgiaUgandaGrenadaCameroonIndonesiaHondurasMoroccoSuriname TogoSeychellesEstoniaMaltaSri LankaSt. Kitts and NevisDjiboutiSt. Vincent and the GrenadinesIran, Rep. of MadagascarPhilippinesAlgeriaUzbekistanMyanmarBarbadosSouth Africa ParaguayLesothoMicronesiaGhanaBurundiSaudi ArabiaBeninTurkmenistanMontenegroPanamaMozambiqueMarshall IslandsBosnia and HerzegovinaMoldovaComorosThe GambiaCambodiaColombiaTanzaniaLibyaCentral African RepublicCroatiaTurkeyGabonIcelandEthiopiaKiribatiMongoliaTongaSenegalMaliLatviaTuvaluBurkina Faso TajikistanadChadSudan<br>NigeriaPortugalIsraelPeruSouth SudanBulgariaEritreaSolomon IslandsCzech RepublicIraqAlbania<br>Haiti<br>BotswanaVietnamEswatiniSlovak RepublicTaiwan, ChinaSan MarinoArmeniaMaldives<br>SamoaMexicoSt. LuciaSierra LeoneKazakhstanGuinea-BissauMauritaniaCosta Rica<br>SloveniaDominican Republic<br>PalauKuwait DominicaThailandLuxembourgChileNew ZealandZambiaItalyNauruSpainMauritiusTunisiaGuinea<br>SwedenMalaysiaFranceNorwayBelgiumJapanMalawiGermany NetherlandsEl Salvador The Bahamas<br>IrelandAustriaAustralia Guatemala Brunei Darussalam United Arab Emirates<br>Korea<br>Finland<br>North Macedonia<br>EgyptSingapore United Kingdom<br>United States<br>Denmark<br>Switzerland São Tomé and Príncipe<br>Canada<br>Correlation between cyclical components of real  government spending and real GDP 1980-2020 Somalia<br>Hong Kong SAR, China<br><!-- End of picture text -->

Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for different commodity categories and non-commodity exporters. Chad, Sudan, and Tajikistan are also classified as major agricultural exporters (black portion of the bar not shown). *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Sources: Authors’ calculations based on WEO (IMF) data. Commodity categories from World Bank.
Can Temporary Wage Incentives Increase Formal Employment? Experimental Evidence from Mexico Martin Abel, Eliana Carranza, Kimberly Geronimo and Maria Elena Ortega<sup>_∗_</sup> Keywords: youth employment, wage subsidy, formal employment, reservation wages. JEL: J08, J24, J41, J46, J63.

> _∗_ Abel: Bowdoin College (email: m.abel@bowdoin.edu); Carranza: World Bank (email: ecarranza@worldbank.org); Geronimo: Labor Mobility Partnerships (email: kgeronimo@lampforum.org); Ortega: World Bank (email: mortegahesles@worldbank.org). The study was conducted in collaboration with the Mexican Government’s Secretariat of Finance and Public Credit, the National Employment Services, the Secretariat of Education, and the State Government of San Luis Potosi. The Mexican Institute of Social Security provided access to data. This paper benefited greatly from comments by Emily Beam, Rulof Burger, Robert Garlick, Santiago Levy, Mahdi Majbouri, David McKenzie, Margaret McMillan, and Dani Rodrik. We thank Ana Lucia Cardenas for excellent project management, and Daniel Jefte Garcia, Manuel Paredes and Alejandra Rogel for implementation support. Svetlana Pimkina and Jossie Fahsbender provided superb research assistance. We thank the World Bank’s Jobs Umbrella Multi-Donor Trust Fund for funding. Ethics approval was obtained from the Health Media Lab IRB. AEA RCT Registry ID: AEARCTR-0005061.
**Table 1. Response Rates for Baseline and Midline Surveys**

||**Neither Baseline**<br>**nor Midline**|**Baseline Only**|**Midline Only**|**Baseline and**<br>**Midline**|**Total**|
|---|---|---|---|---|---|
|**Treatment**|33|169|45|1026|1273|
|**Control**|26|136|11|596|769|
|**Total**|59|305|56|1622|2042|

Note: A total of 2106 young women were originally recruited for this study and the EPAG program. This table excludes: 39 individuals from the control group who were issued as replacements and 25 exempt from randomization due to pregnancy. Please note that of the 1622 who were interviewed twice, 21 are dropped from the analysis because household information at baseline is missing. This leaves a sample of 1601 individuals for the main analysis in this paper. Of the 305 interviewed at baseline only, nine are missing baseline household information, so they are dropped from the attrition analysis in the “Robustness checks” section.

29
<!-- Start of picture text --> :<br>Ghana Kenya Zambia<br>Happy (to very happy) with 84.7<br>current scheme<br><!-- End of picture text -->

|:|||Gh|ana<br>Ke|nya|Zam|bia|
|---|---|---|---|---|---|---|---|
|Happy (to very <br>currentscheme|happy)|with|||||84.7|
|;<br>;|||Gh<br>Non-|ana<br>Ke|nya|Zam|bia|
|:<br>Proportionoffarm<br><br><br><br><br><br>|ers(%):<br><br>||contract<br>|Contract|Contract||Contract<br>|
|Ha<br>to<br>very<br>ppy<br>(<br>ry<br>currentscheme|ha<br>ppy)|with|58.8|||67.3|88.7|
|/|ME<br>(t|E<br>t<br>her|cn-agreement <br>s|on agreed price<br>[M|EE<br>Seta t|o another buy|er|

<!-- Start of picture text --> ; ;<br>Ghana Kenya Zambia<br>Proportion: of farmers (%): Non- Contract Contract Contract<br>contract<br>Happy (to veryry happy) with 58.8 67.3 88.7<br>current scheme<br><!-- End of picture text --> <!-- Start of picture text --> /<br>MEE tcn-agreement on agreed price [MEE Seta to another buyer<br>(thers<br><!-- End of picture text -->
our approach to analyze program effectiveness, the available data sets and our data sampling technique.

# **3 Methodology**

## **3.4 Study area and time period**

With about 906,000 square kilometers, Mato Grosso is the third largest federal state of the Legal Amazon and Brazil. It is located in the center-west region of the country and has parts of three major ecoregions: Amazon moist forests (52%), “ _Cerrado”_ savanna woodlands (41%) and “ _Pantanal”_ wetlands (7%) (FEMA 2002, 6). All of them are considered to be globally outstanding and of high conservation priority. Yet they are threatened by agricultural expansion, road construction, water projects and pollution (Dinerstein _et al._ 1995).

Colonization of Mato Grosso started already in the 1950s and 1960s when extensive tracts of land were sold for large-scale cattle-ranching projects to entrepreneurs from southern Brazil. Induced by the growth-oriented policies of the 1970s and the export-oriented policies of the 1980s, soybean production expanded rapidly throughout the cerrado of Mato Grosso, making the state Brazilian’s largest soybean producer in the 1990s. In 1995, 24.9% of Mato Grosso was used for agricultural purposes, primarily for large-scale pasture (57.7%) and large-scale annual crop cultivation (18.8%). Less than a quarter of the agricultural land was used for subsistence agriculture (13.9%) in 1995 (SEPLAN 2002). In contrast to some other parts of the Amazon, the colonization process of the Mato Grosso was dominated by private large-scale entrepreneurs who are perceived to be the greatest contributors to deforestation in Mato Grosso. In 1996/97, 62.4% of the total deforestation patches monitored by FEMA were greater than 200 hectares (FEMA 2002).

Although the program was announced in 1999, licensing activities began in 2000, accelerating in 2001 and especially 2002. We expect therefore increased program credibility and deterrent 15