rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8019

# **Abstract**

Two long-established stylized facts in the urban and development economics literatures are that: (a) a country’s level of economic development is strongly positively correlated with its level of urbanization; and (b) a country’s level of urbanization is strongly negatively correlated with the size of its agricultural sector. However, countries in the Latin America and Caribbean region appear to depart significantly from the rest of the world in these two basic relationships. Although Latin American countries appear to be significantly more urbanized than predicted based on these global relationships, Caribbean countries appear significantly less urbanized. However, analyses involving cross-country comparisons of urbanization levels are undermined by systematic measurement errors arising from differences in how countries define their urban areas. This paper reexamines whether Latin America and Caribbean countries differ from the rest of the world in the basic stylized facts of urbanization, development, and structural transformation. The analysis makes use of two alternative methodologies for the consistent definition of urban areas across countries: the Agglomeration Index methodology and a methodology based on the identification of dense spatially contiguous clusters of population. Both methodologies rely on globally gridded population data sets as input. There exist several such data sets, and so the paper also assesses the robustness of the findings to the choice of input population layer.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region and the Social, Urban, Rural and Resilience Global Practice Group. 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 mroberts1@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 9.1**

## **Table 1: Agents and branch staff characteristics, BSN**

||**Banking Ag**|**ents(N=6)**|**MM Agen**|**ts(N=251)**|**Branch st**|**aff(N=13)**|
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD|
|Male (1=Yes)|0.83|0.41|0.61|0.49|0.00|0.00|
|Age|24.50|6.83|31.60|7.22|31.92|5.60|
|Married (1=Yes)|0.33|0.52|0.45|0.50|0.69|0.48|
|Household size|7.50|4.23|8.36|3.94|5.83|3.04|
|Years of education|14.00|3.29|11.68|4.44|16.38|1.26|
|Monthly household income (USD)|767.53|315.71|487.44|250.06|837.95|300.75|
|Financial knowledge index (self-reported, 0-3)|1.83|0.75|1.37|1.05|2.38|0.87|
|Financial knowledge index(questionnaire,0-3)|1.33|1.03|1.69|0.92|1.85|0.55|

Notes: This table reproduces Appendix Table OA2 from Buri (2021). MM agents are mobile money agents. Monthly household income is evaluated in US dollars of January 2015. Household size corresponds to number o people living in the HH where the respondent lives. Self-reported financial knowledge index takes value 0 for no knowledge, 1 for basic knowledge, 2 for medium knowledge and 3 for advanced knowledge. The financial knowledge index based on a questionnaire corresponds to the number of correct answers given by the respondent and varies from 0 to 3.

16
<!-- Start of picture text --> wo<br>N<br>N<br>Ww<br>ec<br>oO<br>><br>Vio<br>on<br>©<br>a£ “7 ~ ~SL<br>ae “ en<br>Cc . “7 -<br>5 ae<br>a<br>o 7<br>-* -— — ~ Le Lk.<br>ol-- eee -<br>2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>——— Allevents —-—-—-- Property<br>— — — - Violence<br><!-- End of picture text --> <!-- Start of picture text --> ad<br>w<br>c<br>ao<br>>@.<br>re)<br>o<br>a<br>=<br>Zio a<br>co pom ne \4e Ne<br>=>@ Zz y See7a ~ ~ ~<br>~|==s----- 0-00<br>2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>———— Insurgent —-—--- Criminals/bandits<br>— — —-- Pastoralists/nomads<br><!-- End of picture text -->
in an effort to control for differences between PROSPERA and non-PROSPERA children in the absence of the program. Given the higher poverty rate of PROSPERA children, we expect any remaining biases to have the effect of reducing the estimated impacts of PROSPERA on enrollment and on learning.

Table 2b: Descriptive Statistics of Beneficiaries and Non-beneficiaries 6th Graders 2008 Enrolled in Indigenous Schools.

||Non-PROS|PERA<br>|PROSPE|RA<br>|
|---|---|---|---|---|
|Variable|Mean|Std.<br>dev.|Mean|Std.<br>dev.|
|_Student and Household characteristics_|||||
|Age|12.097|0.856|12.004|0.837|
|# siblings|4.334|2.515|4.781|2.534|
|Have health problems|0.157|0.364|0.159|0.366|
|Spanish is first language|0.485|0.5|0.419|0.493|
|Currently works|0.399|0.49|0.392|0.488|
|HH has DVD|0.51|0.5|0.45|0.497|
|HH has TV|0.664|0.473|0.629|0.483|
|HH has MP3 player|0.32|0.466|0.286|0.452|
|HH has cell phone|0.344|0.475|0.275|0.446|
|HH has computer|0.145|0.352|0.109|0.312|
|HH has internet|0.077|0.266|0.076|0.266|
|HH has calculator|0.826|0.379|0.811|0.392|
|Aspires to attend college|0.385|0.487|0.315|0.465|
|Studies English|0.117|0.321|0.098|0.298|
|_School Characteristics_|||||
|Student teacher ratio|25.203|7.363|24.927|7.251|
|Has computer|0.485|0.5|0.497|0.5|
|More than 1 grade per classroom|0.26|0.439|0.27|0.444|
|Proportion of teachers with university|0.313|0.274|0.304|0.278|

Source: Authors calculations: ENLACE 2007-2008 merged with context questionnaires and school characteristics.

# _B. Descriptive variables on outcomes_

Table 3 shows the proportion of students enrolled after sixth grade, which we take as a baseline for our study. We present the proportion enrolled in seventh grade, ninth grade and twelfth grade, by gender and for the samples of schools in medium to very high marginalized areas and for students in indigenous primary schools during sixth grade. The Table shows enrollment, proxied by the proportion of students taking the ENLACE in that year. According to the Table, about 60% of males and 64% of female sixth graders are captured in the seventh grade ENLACE applied in the next academic year and about 30% of males and 32% of females are captured as taking the twelfth grade ENLACE. As mentioned before, this is an underestimate of enrollment 16
**Table 2. Characteristics of estimates in the analyzed sample**

||||All e|stimates|||Excl.|CCDRs||
|---|---|---|---|---|---|---|---|---|---|
|||The p<br>more e|oor are<br>xposed|The poor<br>adversely|are more<br>affected|The p<br>more e|oor are<br>xposed|The poo<br>adversel|r are more<br>yaffected|
|||Share|SD|Share|SD|Share|SD|Share|SD|
|Outcome||0.679|0.468|0.805|0.397|0.585|0.495|0.762|0.426|
|Type of<br>|Climate change|0.103|0.305|0.177|0.382|0.035|0.185|0.097|0.296|
|shock|Natural disaster|0.245|0.431|0.182|0.386|0.303|0.461|0.210|0.408|
||Temperature|0.152|0.360|0.312|0.464|0.148|0.356|0.376|0.485|
||Heat|0.071|0.257|0.007|0.082|0.056|0.231|0.000|0.000|
||Typhoon/cyclone/hurricane|0.016|0.127|0.018|0.133|0.014|0.118|0.019|0.138|
||Rainfall|0.016|0.127|0.004|0.067|0.007|0.084|0.003|0.053|
||Flood|0.234|0.424|0.083|0.276|0.246|0.432|0.075|0.263|
||Drought|0.152|0.360|0.056|0.230|0.169|0.376|0.033|0.179|
||Earthquake|0.000|0.000|0.002|0.047|0.000|0.000|0.003|0.053|
||Landslide|0.022|0.146|0.011|0.105|0.014|0.118|0.008|0.091|
|Country or|Global|0.207|0.406|0.296|0.457|0.268|0.444|0.365|0.482|
|region|United States|0.087|0.283|0.063|0.243|0.113|0.317|0.077|0.268|
||Sub-Saharan Africa|0.228|0.421|0.173|0.378|0.183|0.388|0.110|0.314|
||South Asia|0.103|0.305|0.224|0.418|0.070|0.257|0.246|0.431|
||East Asia and Pacific|0.130|0.338|0.135|0.342|0.099|0.299|0.141|0.348|
||Latin America and Caribbean|0.168|0.375|0.065|0.247|0.190|0.394|0.047|0.212|
||Europe and Central Asia|0.022|0.146|0.016|0.124|0.014|0.118|0.006|0.074|
||Middle East and North Africa|0.027|0.163|0.027|0.162|0.028|0.166|0.006|0.074|
|Level of<br>|Country|0.538|0.500|0.386|0.487|0.451|0.499|0.337|0.473|
|analysis|Subnational|0.179|0.385|0.265|0.442|0.211|0.410|0.282|0.450|
||Household|0.185|0.389|0.265|0.442|0.211|0.410|0.298|0.458|
||Individual|0.103|0.305|0.085|0.279|0.134|0.342|0.083|0.276|
|Channels|Income reduction|-||0.276|0.447|-||0.326|0.469|
||Expenditure reduction|||0.038|0.192|||0.036|0.186|
||Welfare loss|-||0.009|0.094|-||0.000|0.000|
||Asset loss|-||0.027|0.162|-||0.022|0.147|
||Human capital loss|-||0.166|0.372|-||0.166|0.372|
||Food insecurity|-||0.058|0.235|-||0.055|0.229|
||Poor health|-||0.056|0.230|-||0.061|0.239|
||Education loss|-||0.016|0.124|<br>-||0.017|0.128|
||Mortality|-||0.063|0.243|-||0.077|0.268|
|Number of o|bservations|1|84|44|6|1|42|3|62|
|_Note:_CCDR<br>on which esti<br>characteristic<br>mutuallyexc|=_Country Climate and Developm_<br>mates are based shrinks to 33 studi<br>. Country or region indicators, typ<br>lusive.|_ent Report._<br>es. Each r<br>e of shock|Estimates<br>ow represe<br>indicators,|are from 7<br>nts the shar<br>level of ana|0 studies_._ <br>e of estima<br>lysis indic|When CC<br>tes in ou<br>ators, an|DRs are<br>r sample<br>d channe|excluded, t<br>with the sp<br>l indicators|he sample<br>ecific<br>are not|

16
using a difference-in-differences design. We compare locally-managed schools in the municipalities whose local authorities extended children’s winter break (treatment group) with locally-managed schools in the municipalities that did not change the school calendar (comparison group). For locally-managed schools, student-level proficiency in _Prova Brasil_ has been available since 2005.<sup>12</sup> However, this proficiency assessment does not include all the primary education schools, mostly due to their size.<sup>13</sup> A key assumption of the DiD design is that in the absence of the policy, the trajectories in proficiency in treated and comparison groups would be the same. To test the plausibility of the parallel trends assumption, we compare the proficiency of both groups before the school closures (2005 and 2007). Due to data limitations, the test is restricted to a subset of treated (10 out of 13) and comparison municipalities (469 out of 629) (Table 2). Because the estimation takes the municipalities as clusters, we use wild bootstrap to estimate standard errors to account for the small number of treated clusters.

To estimate the impact of school closures on state-managed schools, we explore a within-municipality variation. For the same municipality, we can compare test scores in both state and locally-managed schools in which students experienced the same shocks at the municipality level. We restrict the sample to municipalities with at least one state and one locally-managed school offering primary education. This is the case of 112 municipalities where the local authority did not extend the winter break (a group that we classify as _G_ = 1) and for 10 out of the 13 municipalities that did ( _G_ = 0). For schools in _G_ = 1, we compare students’ outcomes of 929 state-managed schools that extended winter break with 1,334 locally-managed schools that remained open before and after the H1N1 shock. For schools in _G_ = 0, we compare students’ outcomes of 868 state-managed schools against 759 locally-managed schools, all closed before and after the shutdown. The triple difference leverages these two double differences in G0 and G1 respectively. In doing so, it accounts for any differences in the proficiency trajectories of state and locally-managed schools within and across municipalities (Table 2).

> 12Proficiency data at the school level for state-managed schools are available starting in 2007.

> 13As described in Section 3, to participate in the assessment, the class size has to have at least 20 students in grade 5.

8
# **Table 6:** Income and Other Perceptions

||Income Distribution<br>Perceived as Unfair<br>(1)|Generalized<br>Trust<br>(2)|Economy is<br>Improving<br>(3)|Democracy is<br>Better<br>(4)|Satisfed with<br>Democracy<br>(5)|
|---|---|---|---|---|---|
|||Panel<br>|A: Full Sample<br>|<br>||
|NTL Density at Municipality (in logs)|-0.066*|-0.050|0.064**|0.067*|0.008|
||[0.035]|[0.036]|[0.027]|[0.040]|[0.034]|
|Observations|12,580|12,128|11,433|11,542|11,817|
|Number of Municipalities/Waves|166/13|166/13|163/13|166/12|166/13|
|R-squared|0.481|0.054|0.046|0.064|0.100|
||Panel B: Individ|uals Living in|Municipalities|with Weak State|Capacity|
|NTL Density at Municipality (in logs)|-0.017|-0.018|0.019|0.005|0.016|
||[0.047]|[0.055]|[0.039]|[0.047]|[0.050]|
|Observations|4,470|4,288|4,116|4,004|4,130|
|Number of Municipalities/Waves|100/13|100/13|99/12|100/13|100/13|
|R-squared|0.445|0.068|0.070|0.077|0.123|
||Panel C: Individu|als Living in|Municipalities w|ith Strong Stat|e Capacity|
|NTL Density at Municipality (in logs)|-0.068|-0.119**|0.073*|0.096|-0.053|
||[0.056]|[0.052]|[0.043]|[0.075]|[0.055]|
|Observations|8,110|7,840|7,317|7,538|7,687|
|Number of Municipalities/Waves|66/13|66/13|64/13|100/13|100/13|
|R-squared|0.502|0.052|0.036|0.061|0.093|

Notes: All regressions include individual-level controls (gender, age, age squared, unemployment status, and small town dummy), homicide rates, FODES transfers in previous periods, municipality and year fixed effects. Robust standard errors clustered at the municipality-year level in brackets. FODES refers to Development Municipal Fund Economic and Social transfers. A municipality is defined as weak (strong) state capacity if the average share of household with access to electricity and water services is below (above) 80 percent.Statistical significance: *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1.

(column 1), generalized trust (column 2), views on country’s economic situation (column 3), and attitudes towards democracy (columns 4 and 5). Panel A shows the results for the whole sample whereas panels B and C show the results for weak and strong state capacity municipalities, respectively.

Result from column 1 in Panel A of Table 6 suggests that individuals living in municipality experiencing improving economic conditions in the current year tend to be less likely to report that income distribution in the country is unfair. This effect is remarkably absent in locations with weak state capacity (column 1 of Panel B) and exclusively explained by individuals living in location with strong state capacity (Panel C). The association is not economically large though. To convey the main magnitude better: one-standard deviation increase in our proxy for economic development at the municipality level is associated with 6 percentage points (0.12 standard deviation of the outcome variable) decrease in the probability of assessing income distribution as unfair.

Unlike findings for the Russian Federation documented by Ananyev and Guriev (2018), I do not find that income is positively associated with generalized trust (column 2 in Panel A). If any19
Ambler et al (2014b) find that these discounts lead to large increases in the number of transactions and total amount remitted (measured using administrative data from the partner money transfer company). These effects of the discounts persist up to 20 weeks after migrants are no longer eligible to receive them. These are real increases in remittances: follow-up survey responses help rule out that migrants are sending remittances on behalf of others or shifting from other remittance channels.

An important question that arises from the findings of both these studies is why migrants send so much more in total remittances in response to relatively small changes in remittance fees. Ambler et al (2014b) suggest that the pattern of results could be generated if remittance recipients in the home country have reference dependent preferences regarding their expected level of remittances, which evolve slowly as remittance levels change, and if migrants are partially naïve about the extent to which recipients’ preferences exhibit such reference dependency. With this characterization of preferences, migrants might respond to the remittance fee discounts by sending more remittance transactions during the discount period, intertemporally substituting for remittances later (post-discount) remittances. But once migrants increase their remittance frequency during the discount period, recipients in the home country raise their reference point for remittance receipts (now expecting higher total amounts per time period). Migrants, however, do not fully anticipate this change in recipients’ reference points. Once the discount ends, migrants find that recipients expect them to maintain the level of remittances (in total dollars per time period) that they experienced during the discount period. Migrants therefore do not immediately return to their previous level of remittances, but only do so in a gradual fashion. This can explain the persistence of higher remittance levels for some time after the end of the discount.

Other non-experimental work comes to similar conclusions that reductions in remittance fees would raise remittances. Gibson et al. (2006) provide evidence on remittance responses to fee reductions from survey hypotheticals. Tongan migrants in New Zealand who they surveyed say that they would send substantially higher remittances in response to reductions in the fixed cost component of the remittance fee. The estimated elasticity of remittances to changes in the fixed cost component of the remittance fee is -0.22. Freund and Spatafora (2006) use cross-country 19
Table 4: OLS and IV Results Using Different Threshold Levels for the Regulation Dummy

||Number of regressions|where interactive term F<br>significant<sup>1</sup>|DI*Regulation Dummy is|
|---|---|---|---|
|Percentage of most regulated<br>countries for the cut-off point<br>of the regulation dummy|OLS<br>(8 regressions)|IV (2SLS)<br>(8 regressions)|Total<br>(16 regressions)|
|Top 20 percent|6/8 (6 out of 8)|8/8 (8 out of 8)|14/16 (14 out of 16)|
|Top 30 percent|6/8|4/8|10/16|
|Top 40 percent|4/8|0/8|4/16|
|Top 50 percent|0/8|0/8|0/16|

Note:<sup>1</sup> 10 percent significance level or better. The interactive term always has a negative sign.

So far, we have used the aggregated regulation index for the computation of the regulation dummy. In a further set of regressions, we identify those regulation sub-components that drive our results by applying them individually. Out of the five sub-components, starting-abusiness shows the strongest results if we focus on the top 20 percent most regulated countries. In comparison to the aggregated regulation index, the significance levels for the new interactive term, where the dummy is based on regulations on starting a business only, are even slightly higher: For 15 out of 16 OLS and IV regressions, we obtain a negative and significant coefficient for the interactive term (Table 5).<sup>17</sup> For this sub-component, the outcome is very similar if we select a higher threshold level for the dummy, as significance levels of the interactive term drop considerably for the top 30, 40 or 50 percent most regulated countries.

Table 5: OLS and IV Results Using Sub-components for Regulations

|Percentage of most regulated|Number of regr<br>negative and sig<br>|essions where interact<br>nificant out of a total<br>|ive term FDI*Reg<br>of 16 regressions (<br>|ulation Dummy is<br>8 OLS and 8 IV)<sup>1</sup><br>|
|---|---|---|---|---|
|<br>countries for the cut-off point<br>oftheregulationdummy|Starting a<br>business|Labor market<br>regulations|Closing a<br>business|Contract<br>enforcement|
|Top 20 percent|15/16|6/16|0/16|4/16|
|Top 30 percent|9/16|13/16|0/16|13/16|
|Top 40 percent|0/16|7/16|0/16|13/16|
|Top 50 percent|0/16|1/16|0/16|3/16|

Note:<sup>1</sup> 10 percent significance level or better.

> 17 Detailed results for all sub-components are not shown due to space constraints. Like all other results, they are available upon request.

19
immediately after the transfer? By this measure, the preferred geographic targeting indicator directs limited resources to areas where they would have the greatest short-term impact on poverty. More complex formulations, which might model the expected long-term returns<sup>from</sup> transfers to different districts, are beyond the scope of this paper (see the comments by Binswanger 1989, cited in Ravallion 1993).

In what follows I use three poverty measures from the Foster-Greer-Thorbecke (FGT) family--the headcount index, the poverty gap, and the P2 measure (Foster, Greer, and Thorbecke 1984). The FGT family of poverty measures follows the general formulation below: where y is income, z the poverty line, and a is a parameter which represents the aversion to inequality. When a=O, Po corresponds to the headcount index--the number of people below the poverty line; when oa= 1, P1 corresponds to the poverty gap--a sum of the individual shortfalls in income for those below the poverty line, as a fraction of the poverty line itself. As a increases, the measure gives a greater weight to the poorest poor, and at very high values of a P,, approaches a "Rawlsian" measure of welfare which gives weight only to the poorest household. The P2 measure corresponds to a value of a=2.

Finally, one could look at changes in the entire distribution of log PCE, rather than just at changes for those below the poverty line. I use non-parametric (kernel) density estimates for this purpose.

# _Allocationformulas_

When there is no targeting, districts are simply allocated resources according to their share of the total population in the country, and everyone is assumed to receive the same per capita transfer. This no-targeting scenario serves as a benchmark to measure additional reductions in poverty that could be achieved when geographic targeting is conducted on the basis of some welfare indicator.

To compare targeting indicators, one must develop a formula which allocates resources across districts. I consider one such formula, which is relevant for Peru because it has been<sup>the</sup> basis of targeting by FONCODES, the program which made the most significant early advances developing targeting indicators in Peru. FONCODES ranks all districts by its poverty index. It then allocates resources to each district according to the following formula: 6