rafmacalaba/datause-encoder-data huggingface dataset:
Table 2. Descriptive Statistics

|Variable|Obs.|Average|SD|Min|Max|
|---|---|---|---|---|---|
|_Panel A: Total Crime Reports (ratesper 100,000)_||||||
|Homicides (per 100K)|1,080|1.99|1.81|0.00|18.97|
|Threats (per 100K)|1,080|12.00|8.46|0.00|52.82|
|<br>Domestic Violence (per 100K)|1,080|20.82|13.42|0.00|98.67|
|<br>Thefts (per 100K)|1,080|83.80|30.95|12.69|193.44|
|Sex Crimes (per 100K)|1,080|7.95|4.61|0.00|31.16|
|<br>Total Crimes(per 100K)|1,080|127.17|40.56|40.49|266.15|
|_Panel B: Crime Reports by Venezuelan Victims (ratesper 10_|_0,000)_|||||
|Homicides (per 100K)|1,080|0.08|0.42|0.00|4.88|
|Threats (per 100K)|1,080|0.04|0.24|0.00|3.07|
|<br>Domestic Violence (per 100K)|1,080|0.15|0.49|0.00|9.18|
|<br>Thefts (per 100K)|1,080|0.44|0.90|0.00|14.65|
|Sex Crimes (per 100K)|1,080|0.08|0.42|0.00|6.12|
|Total Crimes(per 100K)|1,080|0.79|1.63|0.00|19.59|
|_Panel C. Crimes Perpetrated by Venezuelans (ratesper 100,_|_000)_|||||
|Homicides (per 100K)|150|0.46|0.92|0.00|5.94|
|<br>Threats (per 100K)|150|0.26|1.02|0.00|9.48|
|Domestic Violence (per 100K)|150|1.39|4.47|0.00|48.84|
|<br>Theft (per 100K)|150|3.71|7.12|0.00|37.23|
|Sex Crimes (per 100K)|150|0.66|2.48|0.00|19.54|
|Total Crimes(per 100K)|150|6.48|11.65|0.00|78.15|
|_Panel E: Treatment and Other Migration Variables_||||||
|PEP-RAMV holders|1,080|4,839.60|7,686.24|13.00|35,729|
|PEP holders per 100K (ages 10-64)|1,080|217.98|375.02|3.33|1,767.00|
|Venezuelan population 2005|1,080|676.83|1,171.58|0.00|4,578|
|<br>Totalpopulation 2005|1,080|644,783|1,252,964|12,897|6,778,691|
|_Panel F: Control Variables (at baseline)_||||||
|Night Light Density (2009)|30|12.73|14.47|0.03|53.98|
|Total Mun. Income (COP, Billions) (2009)|30|628.79|1,513.32|9.06|8,131.36|
|<br>Mun. Public Expenditures (COP, Billions) (2009)|30|617.83|1,434.27|10.52|7,553.10|
|<br>Total Central Gov. Transfers (COP, Billions) (2009)|30|222.60|381.46|4.98|2,048.44|
|Number of Financial Institutions (1995)|30|24.23|47.62|1.00|252.00|
|Number of Tax Collection Offces(1995)|30|8.87|18.93|1.00|99.00|
|<br>Homicide Rate (Per 100,000 Indv.) (2009)|30|34.88|21.45|7.00|109.91|
|<br>N. of Terrorist Attacks (1995)|30|0.07|0.37|0.00|2.00|
|Unsatisfed Basic Needs (UBN % Households) (2005)|30|27.46|17.76|9.16|89.51|
|<br>Informal Labor* (% Households) (2005)|30|88.33|5.54|77.16|96.51|
|<br>GDP Agriculture (COP, Billions) (2009)|30|53.79|42.21|4.50|145.92|
|<br>GDP Industry (COP, Billions) (2009)|30|2,138.95|4,368.66|8.44|22,970.06|
|<br>GDP Services (COP, Billions) (2009)|30|4,444.15|13,266.97|87.69|72,695.80|
|<br>PEP1 (August 2017-October 2017)|30|1,777.50|5,133.56|0.00|27,703.00|
|<br>PEP2 (February 2018-June 2018)|30|2,880.43|8,737.14|0.00|47,389.00|
|Inverse Distance to Closest Border Crossing|30|0.13|0.35|0.00|1.00|

_Notes:_ The table depicts the summary statistics of the main variables used in our study.

13
- Meng, Xin, and R.G. Gregory. 2002 (b). “Exploring the Impact of Interrupted Education on Earnings: the Educational Cost of the Chinese Cultural Revolution.” Processed, Australia National University.

- Migozzi, Jacques. 1973. _Cambodge. Faits et Problèmes de Population._ Paris : Editions du Centre.

- Prescott, Nicholas, and Menno Pradhan. 1997. “A Poverty Profile of Cambodia.” World Bank Discussion Paper No. 373. World Bank. Washington, D.C.

- Sliwinski, Marek. 1995. _Le Génocide Khmer Rouge: Une Analyse Démographique_ . Paris : L’Harmattan.

- Verwimp, Philip. 2003 (a). “The Political Economy of Coffee, Dictatorship and Genocide.” _European Journal of Political Economy_ 19: 161-81.

- Verwimp, Philip. 2003 (b). “Testing the Double Genocide Thesis for Central and Southern Rwanda.” _Journal of Conflict Resolution_ 47: 423-42.

- Verwimp, Philip. 2004 (a).  “Death and Survival during the 1994 Genocide in Rwanda.” _Population Studies_ 58: 233-45.

- Verwimp, Philip. 2004 (b).  “An Economic Profile of Peasant Perpetrators of Genocide.” _Journal of Development Economics_ . Forthcoming.

Vickery, M. 1984. _Cambodia, 1975-1982._ Boston: South End Press.

19
**Figure 7: Motor vehicle premiums versus ratio of cars per 1000 persons**

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>JOR<br>1 MAR<br>TUN<br>BHR<br>0.5 OMN ARE<br>S YR DZA QAT<br>EGY LBY KWT SAU<br>0<br>0 100 200 300 400 500 600 700<br>Cars per 1000 people<br>Motor premiums/GDP<br><!-- End of picture text --> Source: Axco, WDI

**There are four main factors limiting motor premium revenues in MENA: lack of compliance, understatement of provision for outstanding claims, price controls in some jurisdictions, and predatory price competition** . The level of compliance with compulsory motor insurance seems low.  Large segments of the population do not seem to understand the importance of this insurance and regard it as a tax<sup>7</sup> .  While most countries require evidence of insurance before registering motor vehicles, this has not been a successful mechanism for raising compliance levels in MENA.  False policy documents not issued by insurers, poor validation procedures, and misclassification of vehicles all contribute to avoidance strategies by drivers, who prefer to “settle in the street”, no matter how expensive relative to the formal insurance process.

**A second reason for inadequate motor premium income is the chronic understatement of motor third party claim provisions by insurers combined with price controls** . Claims provisions feed directly into the calculation of premium levels and if understated will result in inadequate pricing.  This is particularly evident in the countries where the legal profession plays a significant role in the claims negotiation process and claims can take many years to settle.

**This understatement of claims costs (and hence technical premiums) is an even more serious issue where MTPL rates are approved by the government (often the Ministry of the Interior) and have a political aspect** .  Examples of rates not being adjusted for many years can be found in numerous countries, including Kuwait and, until recently, Egypt.

**The fourth major cause of inadequate motor third party premiums in several countries is predatory price competition sometimes in the belief that investment returns will cover underwriting losses** .  Motor third party insurance is cash rich and funds can be invested quickly. This attracts entrepreneurs who understate claims provisions and often adopt poor claims

> 7 Anecdotal evidence suggests that some drivers throw their policies away, considering the payment to be a tax.

11
|11|SRH Vouchers,<br>Nicaragua, 2000|User satisfaction higher among voucher receivers vs. non-<br>recievers, especially amonggirls not yet pregnant (91% v.85%); <br>88%voucher receivers satisfied with care at reception vs.80%<br>for non-receivers; no statistically significant difference in<br>perception about doctors’ explanation (83% ws.80% Tor<br>receivers v. non-receivers). Priorto voucher implementation,<br>half the simulated patients (SP) leftthe doctors’ office without<br>contraception; reported decision on contraception beingmade<br>by doctor; with voucher, higher rate of contraception<br>distributed, also respondents more frequent mutual decision<br>makingon contraception. However, SPs with vouchers stated<br>that their wait timeswere longer in some cases, and there may<br>have been somegaming ofthe system by providers. Also,<br>some quality indicators improved during intervention but<br>dropped once itwas stopped|Voucherreceivers had significantly higher<br> |levels ofknowledge aboutmodern<br>|contraceptives (OR 1.3), STIs (OR 2.6) and the<br>ways to preventSTIs (OR 1.2).|
|---|---|---|
|jongt|ermesh—[CSSCO|OOOCSCSC*d|
|12|Family Allowance<br>Program (PRAF)'s BMI <br>Voucher, Honduras,<br>1990 onwards|Poorindividual counselling on nutrition reported, as well as<br> |gaps in information to beneficiaries|Evaluation offirstphasefound no evidence of<br>health or nutritional gains, perhaps because of<br>low level of payments|
|13|Opportunidades,<br>Mexico, 1997|Oportunidades beneficiaries received 12.2% more prenatal<br>procedures compared with non-beneficiaries (adjusted mean<br>78.9,95% Confidence Interval (Cl): 77.5-80.3; P<0.001). Higher<br>perceived quality of care among users - which may be result of <br>greater sense ofempowerment achieved through the overall<br>program.Some small-scale studies suggesthoweverthat<br>publicfacilities were not always able to cope with the increase <br>in demand (e.g. running out of drugs)|Oportunidades beneficiary statuswas<br>|associated with 127.3 g higher birthweight<br>|among participatingwomen anda 4.6<br> |percentage point reduction in low<br>|birthweight. Also lower incidence of illness<br>amongst beneficiary children, reduced<br> |stunting and increased growth. Maternal and<br>child mortality delined in programme areas,<br>but this is based on administrative data alone<br>anduncontrolled|
Policy ReseaRch WoRking PaPeR 4820

# **Abstract**

This paper proposes that individuals care about the relative income of proximate reference groups. Making use of self-reported life satisfaction as a proxy for unobservable utility, the relative income of siblings is tested for relevance as a reference point for new sample data from Venezuela. Having greater perceived income than one’s siblings is found to be positively linked to individual life satisfaction. This evidence supplements the scarce economic research on reference groups, supporting the hypothesis that individuals with proximate characteristics and resembling opportunities in life serve as points of comparison.

This paper—a product of the Development Economics Vice Presidency—is part of a larger effort in the department to analyze policy-relevant topics rigorously with the best available information to support decision making. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at akuegler@ 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 entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 3: Baseline results following equation (1)

||**(1)**<br>**Digital+**|**(2)**<br>**Digital**|**(3)**<br>**Publishing**|**(4)**<br>**Telecom**|**(5)**<br>**IT & Info**|**(6)**<br>**Business**|
|---|---|---|---|---|---|---|
|MODEL**CB**|0.021|0.097|-0.169|0.101|0.065|-0.146|
||(0.816)|(0.524)|(0.349)|(0.335)|(0.737)|(0.352)|
|Obs|4096|4096|4096|4096|4096|4096|
|P-R2|0.983|0.962|0.958|0.961|0.928|0.968|
|MODEL**DR**|-0.131|0.038|-0.418**|0.165|0.264|-0.171|
||(0.172)|(0.796)|(0.019)|(0.209)|(0.158)|(0.281)|
|Obs|4096|4096|4096|4096|4096|4096|
|P-R2|0.983|0.962|0.959|0.961|0.927|0.967|
|MODEL**CB**|0.129|0.165|0.125|0.072|0.069|0.002|
||(0.145)|(0.296)|(0.349)|(0.483)|(0.729)|(0.988)|
|MODEL**DR**|-0.223**|-0.046|-0.524***|0.097|0.238|-0.225|
||(0.031)|(0.788)|(0.005)|(0.470)|(0.242)|(0.193)|
|Obs|4096|4096|4096|4096|4096|4096|
|P-R2|0.983|0.963|0.959|0.961|0.930|0.968|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp+ imp|Yes|Yes|Yes|Yes|Yes|Yes|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex Table A2. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter and importer are applied. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A4.

Table 4 shows the results when considering each of the three data models separately. In this case as well, the two components of CB and DR for each model are reported separately first, and then the two components are entered together. A first result in column 1 is that the conditional model is the only model for which a positive and significant coefficient result for Digital+ is found, when the CB component is entered separately; whereas the limited model comes out negative and significant. The results are similar when moving from column 1 till 4, albeit not always significant for the conditional model. Looking at the second panel, a strikingly consistent results is that the open model reports negative and significant results for most sector classifications when the DR component is 27
|**Vegetable & Fruits**|||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|World|17,778|39,368|51,115|74,334|13,181|32,446|45,122|67,314|4,597|6,922|5,993|7,019|0.3|0.2|0.1|0.1|
|Industrial Countries|10,093|23,500|27,887|39,872|10,308|27,590|35,918|53,944|-215|-4,090|-8,031|-14,072|0.0|-0.2|-0.2|-0.2|
|Middle-income, All|7,180|14,879|21,173|31,442|2,213|4,370|8,075|11,713|4,967|10,509|13,098|19,729|1.2|1.3|0.8|0.7|
|Mid Income--Oil Exporters|442|1,345|2,546|3,474|520|960|2,276|4,074|-78|385|270|-600|-0.1|0.4|0.1|-0.2|
|Mid Income--Civil Conflict|151|179|140|148|75|235|240|291|76|-57|-99|-143|0.6|-0.3|-0.6|-0.5|
|Small Island Economies|71|245|111|103|41|90|102|138|31|154|9|-35|0.5|1.7|0.1|-0.1|
|Other Middle-income|6,516|13,111|18,376|27,717|1,578|3,084|5,458|7,210|4,938|10,026|12,917|20,507|1.7|1.5|0.9|0.8|
|Low-income, All|436|908|2,028|2,965|172|387|898|1,423|264|520|1,130|1,543|0.5|0.8|0.8|0.6|
|Low-income--Oil Exporters|50|97|249|349|80|61|85|131|-29|36|164|218|-0.1|0.3|0.9|0.6|
|Low-income--Civil Conflict|23|27|38|47|6|22|39|79|17|6|-1|-32|0.3|0.1|0.0|-0.3|
|Other Low-income|362|783|1,741|2,570|86|305|774|1,213|277|478|966|1,357|0.9|1.1|0.9|0.7|

Note: See classifications of country groups and product categories in Appendix Tables 1-3.

Source: Based on partners data from UN COMTRADE Statistics.

26
transfers, India was able to provide aid during the pandemic that reached the majority of urban and rural households. Despite documented successes, observers have cautioned against rapid scaling up of DFS without adequate regulations and safeguards and have acknowledged the potential for DFS to increase inequality (Agur et al., 2020).

Regarding the drivers of DFS prior to COVID-19, evidence from smaller samples of countries than the one used here indicates that multiple factors were at work.<sup>3</sup> Focusing on digital credit provided by fintech firms for a sample of 61 countries, Claessens et al. (2018) find that fintech credit volumes were increasing with per capita income and declining with the competitiveness of the banking sector, presumably because fintech firms found it harder to compete with banks. Fintech credit volumes also declined with the stringency of banking regulation, perhaps because “fintech regulations are also more liberal in jurisdictions where banking regulation is more liberal,” or because new lending activities may be harder to undertake in countries with strict prudential and bank licensing regimes.<sup>4</sup> For a sample of 52 countries in 2014 and 2017, Sahay et al. (2020) find that the prevalence of digital payments was positively linked to GDP growth. Based on the existing literature and interviews with policy makers, regulators, fintech companies, and banks, those authors also posit that the safe development of digital financial inclusion is influenced by the financial/digital literacy of DFS users and regulations and digital systems that instill trust through digital identification, consumer protection,

> 3 Efforts to identify the drivers of financial inclusion more broadly were forerunners of these more recent studies on drivers of DFS. For example, Rojas-Suárez (2016) and Rojas-Suárez and Amado (2014) show that poor institutional quality (as reflected in weak rule of law and a lack of reliable contract enforcement) and uncompetitive banking sectors were associated with less financial inclusion in Latin America (measured as owning an account at a formal financial institution). Using Global Findex data for 2011 and 2014, Dabla-Norris et al., (2015a,b) also find financial inclusion to be positively linked to per capita income, rule of law, and competition in banking. Similarly, using Findex data from 2014, Deléchat et al. (2018) show that individuals were more likely to be financially included in countries with high levels of per capita income and general financial development. 4 Claessens et al. (2018), p. 38.

4
**Figure 4: Volatility of Output Gap vs.  Regulation Indices**

<!-- Start of picture text --> Overall Regulation Product Market Regulation<br>Correlation: 0.41*** SLE Correlation: 0.43*** SLE<br>TGO TGO<br>PNGIDN MWI IDN MWIPNG<br>THA THA<br>MYS KORTUR JORZWEMAR HTI TURKORMYS JORMARZWE HTI<br>ZMB ARG ZMBARG<br>PER NERVEN COG PER VENNER COG<br>URY CRI MEXNICCIV ECU SYR URY MEXCRI NICCIV ECUSYR<br>USAGBR CHECANAUSDNKIRLNLDISL FINSWENORAUT JPNBELCHLJAMESPBWAISRPRTITAFRAZAFGRCTTOLKAKENCOLSLV GTMGHABGDDOMPHLPAKINDHNDPANTUNSENNGABOLPRYEGYBRAGMBIRNMDG BFA GBRNLDUSACANDNKFINAUSSWEIRLCHEBELISLNORAUTJPNESPITAPRT GRCISRFRACHL JAMZAFBWACOLLKATTOSLVKENPANTUNGTMPAKGHAINDPHLEGYBRABGDSENHNDDOMBOLPRYNGAIRNGMBMDGBFA<br>.2 .3 .4 .5 .6 .7 0 .2 .4 .6 .8<br>Overall Regulation Index Product Market Regulation Index<br>Labor Regulation Fiscal Burden<br>Correlation: 0.16 SLE Correlation: -0.25** SLE<br>TGO TGO<br>PNG IDN MWI IDN PNG MWI<br>THA THA<br>MYSZWE KORMAR TUR JORHTI HTI MYS KORJOR TURMAR ZWE<br>ZMB ARG ARG ZMB<br>NER COGPERVEN NERPERVEN COG<br>URYSYR CIV ECU CRINIC MEX ECU URYCRI NICMEX CIV SYR<br>USA KENGBRBWATTOJAMGHAAUTCANZAFJPNISRCHEDNKAUSNGALKA BGDIRNINDDOMFRACHLSENNORIRLPAKHNDCOLTUNNLDBELPHLEGYGTMSWEBFA BOLGRCITAESPSLVPRYMDGFIN PANBRAPRT PRYSLVDOMBOLGTMCHLBGDMDHNDNGA G JAMMBPHLCHE COLBWALKABFAPANTTOKENPAKBRAISLUSAIRLGHASENGBRIND NORAUSIRNESPGRCZAFPRTFINTUNSWEEGYCANAUTJPNDNKNLDISRFRA ITABEL<br>0 .2 Labor Regulation Index.4 .6 .8 0 .2 Fiscal Burden Index.4 .6 .8 1<br>.06 .06<br>.04 .04<br>Volatility of output gap .02 Volatility of output gap .02<br>0 0<br>.06 .06<br>.04 .04<br>Volatility of output gap .02 Volatility of output gap .02<br>0 0<br><!-- End of picture text --> _Note: *, **, and *** denote significance at the 10%, 5%, and 1% level respectively._ 36
Table 4: Comparing Village Heads to Civil Society Leaders

|Village Head|Civil SocietyLeader|
|---|---|
|Demographics,Economic Status and Partisanship (All|Villages)|
|Female<br>0.07<br>(0.25)|0.58<br>(0.49)|
|Average Age (Years)<br>64.1<br>(15.7)|51.7<br>(14.2)|
|Sign for Ruling Party<br>0.30|0.16|
|(0.46)|(0.37)|
|Finished Primary Education<br>0.69|0.86|
|(0.46)|(0.35)|
|Average Income ($)<br>959.7<br>(1537.5)|1023.7<br>(1806.8)|
|Average Cattle Wealth<br>2.98<br>(3.69)|4.30<br>(28.45)|
|Related to Village Head|0.57<br>(0.50)|
|Knowledge and Opinions(Control Villages Only)||
|Dare Should Have More Power<br>0.73|0.47|
|(0-1)<br>(0.45)|(0.50)|
|Average Support for Individual Rights<br>2.86|2.98|
|(1-4)<br>(0.40)|(0.41)|
|Average Knowledge of Law<br>0.66|0.69|
|(0-1)<br>(0.23)|(0.21)|

Table reports means with standard deviations below in parentheses. The means in the top panel aggregate data from 247 village heads and 196 community leaders across all villages. The means in the bottom panel aggregate data from 128 village heads and 95 community leaders in the control villages.

18
|Table VI||
|---|---|
|Effect of Elections on<br>[t-statistics in par|State Debta<br>enthesis]|
||z[1]<br>[2]|
|Independent Variables:||
|Scheduled election year|<br>3.59<br>2.88|
||[1.03]<br>[0.93]|
|State domestic product [SDP]|<br>0.06|
||[7.43]|
|Share of agriculture in SDP|<br>-20.35|
||[-1.35]|
|Proportion of rural population|<br>116.79<br>[0.82]|
|Number of Observations|<br>470<br>408|
|R-sq|<br>0.63<br>0.73|

a. State debt and SDP are in 1973 rupees per capita. Regressions include state and year effects, and are reported with robust standard errors.

46
Table 6 summarizes the results of the Curitiba intercept point survey. The majority of the interviews took place at the municipal market and Clube Nikkei, the sports club. The overall refusal rate was 41 percent, very close to the 39 percent found in São Paulo.

**Table 6: Curitiba Intercept Point Survey**

||Number of<br>interviews|Refusal<br>rate (%)|
|---|---|---|
|**_Fixed point locations_**|||
|Mercado Municipal|241|49.2|
|Clube Nikkei|109|16.8|
|Associação Bunkyo|36|28.0|
|Escola Japonesa|3|0.0|
|Associação ABD|3|0.0|
|**Total**|392|40.7|

Table 7 uses the São Paulo data to examine the characteristics of individuals who visit more locations amongst those sampled. Column 1 carries out a parsimonious OLS regression, of the number of locations as a function of gender, age, marital status, education level, employment status, and two key variables of interest for comparing across surveys: whether or not the individual has ever worked or studied in Japan, and whether or not their household receives remittances from Japan. We see that females and older individuals visit more locations. More importantly, we see that return migrants visit more locations. Column 2 then adds additional controls for generation, whether or not a household member reads Japanese newspapers, and for whether or not employed individuals refuse to give a range for income. As we would expect, individuals who are more connected to Japan, by virtue of being first or second generation Japanese, and being in households where Japanese newspapers are read, are found in more locations. Additionally, we see that individuals who refuse to give their income range are found in less locations. Similar results are seen in columns 3 and 4, which use a negative binomial model, to account for the fact that the number of locations visited is a count variable.

24

-  -
Coefficient Std. Err.

Table 9: ELL household (log) consumption regression model

||Coefcient|Std. Err.|
|---|---|---|
|Intercept|-4.3293|(0.0726)|
|STRATA<br>1|0.0265|(0.1417)|
|STRATA<br>2|-0.2575|(0.1435)|
|STRATA<br>3|-0.2739|(0.1332)|
|STRATA<br>4|0.2134|(0.0946)|
|STRATA<br>5|0.2481|(0.064)|
|TA<br>REMOTE100K|-0.062|(0.0262)|

Table 10: ELL heteroskedasticity model

# **References**

- Alderman, H., Babita, M., Demombynes, G., Makhatha, N. and Ozler, B.<sup> ̈</sup> (2002). How low can you go? combining census and survey data for mapping poverty in south africa. _Journal of African Economies_ , **11,** number 2, 169–200.

- Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspirationguidelines for computing crop water requirements. Irrigation and drainage paper 56. FAO, Rome.

- Anselin, L. (2001). _Spatial econometrics_ , A companion to theoretical econometrics, 310330 edn.

- Araujo, M., Ferreira, F., Lanjouw, P. and Ozler, B. (2008). Local inequality and project choice: Theory and evidence from ecuador. _Journal of Public Economics_ , **92,** 1022–1046.

- Aubrecht, C., Gunasekera, R., Ungar, J. and Ishizawa, O. (2016). Consistent yet adaptive global geospatial identification of urban–rural patterns: The iurban model. _Remote Sensing of Environment_ , **187,** 230–240.

- Baird, S., McIntosh, C. and Ozler, B. (2013). The regressive demands of demand-driven development. _Journal of Public Economics_ , **106,** 27–41.

52
# **With the Help of One's Neighbors:**

# **Externalities in the Production of Nutrition in Peru**

## _Harold Alderman_

The World **Bank**

## _Jesko Hentschel_

The World Bank

## _Ricardo Sabates_

Development Studies and the Department of Agricultural Economics University of Wisconsin, Madison Co-authors listed in alphabetical order. Harold Aldernan (halderman(aworldbank.org) and Jesko Hentschel (Jhentschel(&.worldbank.org) are with the World Bank, Ricardo Sabates (Rsabatesa).Students.Wisc.Edu) is with Development Studies and the Department of Agricultural Economics of the University of Wisconsin, Madison.