rafmacalaba/datause-encoder-data huggingface dataset:
Quisumbing (2011). We calculate such inverse probability weights for all outcomes and then use them in all estimations that are discussed from now on.

Apart from attrition, households are also subject to split-offs over time. In most of these cases, household members grow up, marry, and leave their households after the initial survey to form new households. Thus households surveyed in the first round may spawn one or more new households by subsequent surveys. In our analysis we have treated these households as separate units who are related by their same initial (first-round) characteristics.<sup>12</sup> <u>4. Validating the earlier estimates of microcredit effects using third round survey data</u> Earlier results of microcredit programs using the cross-sectional survey of 1991/92 in Pitt and Khandker (1998) and the panel survey of 1991/92-1998/99 in Khandker (2005) used the amount of cumulative borrowing over the past five years as a measure of intervening variable. Following Pitt and Khandker (1998), let us consider the following reduced-form demand equation for the amount of credit by women ( _Cijft_ ) and men ( _Cijmt_ ) of _i-_ th household in _j-_ th village in period _t_ as<sup>13</sup> : where _X_ is a vector of household (e.g., sex, age and education of household head, landholding, and so on) and village (e.g., extent of village electrification and irrigation, availability of infrastructure, prices of consumer goods, etc.) characteristics, β is a vector of unknown parameters to be estimated, _η_ is an unmeasured determinant of the credit demand that is time-invariant and fixed within a

> 12 This is different from what was done in Khandker (2005), which analyzed the two-point panel data (consisting of 1991/92 and 1998/99 surveys) by aggregating the split-off households at the second round after testing to make sure that such households can be aggregated without incurring bias in the estimation process. Since we have treated the spawned households as separate units we do not have to go through such tests.

> 13 Unlike Pitt and Khandker (1998), we do not allow the credit effect to vary by program such as Grameen Bank, BRAC, and other MFIs on the assumption that programs do not have heterogeneous effects as the program design is very similar for all MFIs in Bangladesh.

11
Table 5. Marginal effects of the ordered probit and probit models: the probability of psychological distress (anxiety and depression) over the last 2 weeks of survey <u>(based on PHQ scale)</u>

||<br>|Ordered Probit Est<br>|imation(full scale<br>|)<br>|Probit<br>|Estimation<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||No<br>psychological<br>distress|Mild<br>psychological<br>distress|Moderate<br>psychological<br>distress|Severe<br>psychological<br>distress|Has anxiety<br>(Subscale)|Has depression<br>(Subscale)|
|_Ref: Cismen_|||||||
|Ciswomen|-0.095***|-0.013|0.031***|0.077**|0.204***|0.041|
||(0.034)|(0.010)|(0.010)|(0.033)|(0.052)|(0.051)|
|Transgender|-0.044|-0.003|0.015|0.032|0.082|0.036|
||(0.041)|(0.005)|(0.014)|(0.033)|(0.057)|(0.057)|
|Non-binary|-0.064***|-0.006|0.022***|0.048***|0.094***|0.054*|
||(0.020)|(0.004)|(0.006)|(0.017)|(0.028)|(0.028)|
|Age|0.010***|0.0002|-0.004***|-0.007***|-0.010***|-0.014***|
||(0.002)|(0.0001)|(0.001)|(0.002)|(0.003)|(0.003)|
|Age-Squared|-0.0001**|-0.00001|0.00002*|0.00004**|0.00004|0.0001**|
||(0.00003)|(0.000001)|(0.00001)|(0.00002)|(0.00004)|(0.00004)|
|_Ref: Completed less than 6 years of education_|||||||
|Between 6 and 12 years|-0.020|-0.001|0.007|0.014|0.031|0.042|
||(0.025)|(0.001)|(0.009)|(0.017)|(0.032)|(0.033)|
|Trade school or vocational training|-0.006|-0.0002|0.002|0.004|-0.016|0.018|
||(0.026)|(0.001)|(0.009)|(0.018)|(0.032)|(0.033)|
|Some university but no degree|-0.011|-0.0004|0.004|0.008|-0.016|0.021|
||(0.024)|(0.001)|(0.008)|(0.016)|(0.030)|(0.031)|
|University degree or more|0.014|0.00001|-0.005|-0.009|-0.021|-0.005|
||(0.022)|(0.001)|(0.008)|(0.015)|(0.028)|(0.029)|
|_Ref: Single_|||||||
|Married registered|0.056***|-0.001|-0.020***|-0.036***|-0.040*|-0.096***|
||(0.018)|(0.001)|(0.006)|(0.010)|(0.021)|(0.021)|
|Non-registered relation|0.053***|-0.001|-0.018***|-0.034***|-0.037**|-0.063***|
||(0.012)|(0.001)|(0.004)|(0.007)|(0.015)|(0.015)|
|Others|0.005|0.0002|-0.002|-0.003|0.001|-0.004|
||(0.021)|(0.001)|(0.007)|(0.015)|(0.027)|(0.028)|
||-0.077***|-0.001|0.027***|0.052***|0.083***|0.077***|

25
<!-- Start of picture text --> Chart 7. Dependency Ratio In Developing Countrles, 1950-2025<br>(Youth and elderly as percent of working age population)<br>130 130<br>Projection<br>120 120<br>110 110<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>1950 1960 1970 1980 1990 2000 2010 2020<br>Souwuw U.N. populatbn data and prjectIons<br>Chart 8. Saving in Developing Countries by Region<br>(Gross national saving as percent of GDP; 1985-94 average)<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0 - - \ - CI - -A6,0<br>Source. W4bf Bank dMat.<br><!-- End of picture text -->
e¤ects of malaria on the quality of institutions emphasized in cross-country literature (Acemoglu et al, 2001) are not relevant for our identi...cation scheme, because we exploit the variations in historical malaria intensity within the same district, and it is not possible to have variations in the quality of institutions at the sub-district level that can be traced back to historical malaria.

Although most of the districts were a¤ected by the spread of malaria beginning from the 16th century, malaria infestation was most severe in the districts in the dry zone. More important, there were substantial variations in the degree of malaria prevalence even within a district (Newman (1965)), and we use this within-district variations for identi...cation. The malaria infestation led to exodus of households, especially the Sinhalese population, from the a¤ected areas (Peebles (2006), De Silva (1981)). Most of the land in the malaria infested areas was taken over by the government, and was designated as ‘crown land’ during the colonial period. The crown land was later distributed to the Sinhalese population from other parts of the country through land settlements after independence in 1948. The land settlements were part of a political movement to recapture the ‘cradle of ancient Sinhalese civilization’, and the LDO restrictions were imposed on the settlement land. The extent of land restrictions in an area was thus primarily determined by the availability of crown land. The amount of crown land in a DSD is a positive function of the severity of malaria infestation from 16th century to early 20th century because of out-migration and abandonment of land. The variations in the intensity of malaria across di¤erent DSDs thus constitute a plausible instrument for identifying the e¤ects of proportion of LDO land in a DSD, especially because Sri Lanka implemented a successful nationwide malaria eradication program starting from 1947 (our survey year is 2002).<sup>10</sup> However, unfortunately, we do not have data on historical malaria prevalence at the appropriate level of disaggregation (i.e., at DSD level); the available data are at the district level (average over the years 1937-41). The district level malaria data are not suitable for identi...cation of the e¤ects of LDO restrictions at the DSD level for at least two reasons. First, the district level data do not provide us with enough variations for identi...cation of the LDO restrictions at the DSD level, as there are 243 DSDs in our data set, but we have data on average malaria prevalence before the start of the malaria eradication program

> 10 Note also that the historical malaria prevalence in the DSD of current residence cannot a¤ect the health of most of the households under LDO restrictions in any signi...cant way as they were resettled from relatively malaria free areas.

6
**<u>Table 5. The district fixed-effect RDD-DD regressions of income of bottom-income households and poverty</u>**

||||Dependen<br>|t variables<br>|||
|---|---|---|---|---|---|---|
||Log of per|Log of per|Log of per|Income poor|Bottom 20%|Bottom 40%|
|Explanatory variables|<br>capita income<br>(the sample of<br>the poor)|<br>capita income<br>(the sample of<br>the bottom 20%<br>income<br>households)|<br>capita income<br>(the sample of<br>the bottom 40%<br>income<br>households)|<br>households<br>(using the<br>constant<br>poverty line in<br>2020)|<br>households<br>(using the<br>bottom 20%<br>income<br>threshold in<br>2020)|<br>households<br>(using the<br>bottom 40%<br>income<br>threshold in<br>2020)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|P * P id|0.0528|0.0490|0.0347|-0.0463|-0.0528|0.0141|
|rogram ost-program pero|(0.0436)|(0.0434)|(0.0515)|(0.0604)|(0.0603)|(0.0523)|
|(Poverty rate – 50) * Post-|-0.0040|-0.0036|0.0012|-0.0025|-0.0017|-0.0021|
|program period|(0.0055)|(0.0055)|(0.0062)|(0.0057)|(0.0057)|(0.0055)|
|Program * (Poverty rate – 50) *|0.0033|0.0029|-0.0027|0.0054|0.0046|0.0057|
|Post-program period|(0.0057)|(0.0057)|(0.0065)|(0.0063)|(0.0062)|(0.0058)|
|Age of household heads|0.0059***|0.0059***|0.0098***|-0.0143***|-0.0141***|-0.0148***|
||(0.0014)|(0.0014)|(0.0015)|(0.0013)|(0.0014)|(0.0014)|
||-0.0001***|-0.0001***|-0.0001***|0.0001***|0.0001***|0.0001***|
|Age squared of household heads|(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|
|Gender of household head|0.0249**|0.0270***|0.0299***|-0.0115|-0.0092|-0.0126|
|(male=1, female=0)|(0.0096)|(0.0097)|(0.0087)|(0.0092)|(0.0085)|(0.0111)|
|Ehi iii =1 Kih=0|-0.0853***|-0.0820***|-0.1585***|0.2422***|0.2403***|0.3138***|
|tnc mnortes (yes, n)|(0.0164)|(0.0165)|(0.0202)|(0.0202)|(0.0197)|(0.0259)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.3926***|8.3845***|8.4786***|0.8696***|0.8577***|0.9889***|
||(0.0421)|(0.0415)|(0.0495)|(0.0442)|(0.0447)|(0.0468)|
|Observations|16,857|15,389|24,891|40,468|40,468|40,468|
|R-squared|0.159|0.157|0.196|0.252|0.252|0.274|
|The mean value of the dependent<br>variables before the program|8.560|8.495|8.732|0.546|0.472|0.741|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

44
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A further contribution is the use of novel geo-localized displacement data collected by the International Organization for Migration (IOM) that allow us to examine the arrival of IDPs in host communities as a channel through which the spatial spillover effects of conflict potentially operate. The literature on the effects of forced displacement on host communities has studied labor market outcomes (Ruiz and Vargas-Silva, 2013; Ruiz, Isabel and Vargas-Silva, Carlos, 2015; Alix-Garcia _et al._ , 2018; Ayenew, 2021; Verme and Schuettler, 2021), rent and food prices, and overall well-being of host-community members (Verme and Schuettler, 2021). A more recent literature seeks to better understand the extent to which displacement accentuates gender vulnerabilities among the IDPs themselves— for example, by looking at risks of gender-based violence and intimate partner violence among displaced people (Kelly _et al._ , 2021a; Ekhator-Mobayode _et al._ , 2021; Kelly _et al._ , 2021b). It also examines the role of gender norms in shaping outcomes such as school enrollment and the prevalence of domestic work among refugees (Krafft _et al._ , 2021), and it analyzes how gender norms themselves may change with displacement (Rubiano Matulevich, 2021). Our paper differs from this literature by elucidating the influence of IDPs on gender outcomes of host-community members.

This paper combines different geo-referenced databases. We use cross-sectional household survey data from the Nigeria Demographic and Health Surveys conducted in 2013 and 2018. We complement this database with information on geo-referenced violent events taken from the Armed Conflict Location and Event Data Project (ACLED), which uses data gathered through text-recognition technology. In addition, we use displacement data from the Displacement Tracking Matrix data project of the IOM. We use this database to calculate the number of IDPs residing within different distance radii from village centroids.

Our empirical strategy exploits the distance from the village centroids to the location of violent attacks in Nigeria occurring during the period 2007 to 2018. Specifically, we count the number of violent events occurring beyond a radius of 50 km from a village. This distance is the largest within which, according to the literature (e.g., Bendavid _et al._ , 2021), nondisplaced women and children are still considered to be living dangerously close to armed conflict, i.e., we take a rather conservative approach. We regress different 6
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 10

# **Table 1 Urban definitions and rural proportions according to national statistical offices**

Sources: Rural population from World Development Indicators, based on United Nations (2002); Urban definitions from United Nations (2002)

|||**_Urban_**|**_definition_**|**_based on:_**||
|---|---|---|---|---|---|
|**Country**|**Rural**|**Size t**|**hreshold**<br>|**Administrative**|<br>**Other**|
||**popula**<br>**percen**<br>**2002**|**tion**<br>**tage,**<br>**for loc**|**alities**<br> <br> <br>|**centers**<br>**of**<br>**municipios,**<br>**cantons,**||
|||||**provinces**||
|Argentina||8.0|2000|||
|Belize||51.8|||Not available|
|Bolivia||36.6|2000|||
|Brazil||17.8||X||
|Chile||13.7|||Populated centers with urban|
||||||characteristics|
|Colombia||24.0|1500|||
|Costa Rica||40.0||X||
|Cuba||24.3|2000||**or**<br>with<br>specified|
||||||infrastructure services.|
|Dominica||28.3|500|||
|Dominican Republic||33.5||X||
|Ecuador||36.1||X||
|El Salvador||37.6||X||
|Grenada||61.0|||two urban parishes|
|Guatemala||59.7||X|plus<br>entire<br>municipio<br>of<br>Guatemala|
|Guyana||62.9|||three cities|
|Haiti||63.0||X (communes)||
|Honduras||45.5|2000||**and**with a set of specified<br>infrastructure services|
|Jamaica||42.9|||selected towns|
|Latin<br>America|&|23.8|||not applicable|
|Caribbean||||||
|Mexico||25.2|2500|||
|Nicaragua||43.1||X||
|Panama||43.1|1500||**and**with a set of specified<br>infrastructure services|
|Paraguay||42.7||X (districts)||
|Peru||26.5 >100 d|wellings|X|(satisfies<br>dwelling<br>or<br>administrative criteria)|
|St. Kitts and Nevis||65.5|||Basseterre and Charlestown|
|St. Lucia||61.7|||Castries|
|St.<br>Vincent<br>and|the|43.0|||Not available|
|Grenadines||||||
|Suriname||24.7|||Greater Paramaribo|
|Trinidad and Tobago||25.1|||selected towns|
|Uruguay||7.7|||officially defined cities|
|Venezuela, RB||12.6|2500|||
the extent to which the relationship between income inequality and future income growth is channeled through inequality of opportunity.

We find that it is inequality of opportunity that is negatively correlated with future income growth of the poor. Using weak instrument-robust inference confirms the robustness of this finding. While inequality of opportunity is also found to be positively correlated with growth at the top end of the income distribution, this effect is not robust. Our research advances the earlier studies by Marrero and Rodríguez (2013) and van der Weide and Milanovic (2014). The relationship between inequality of opportunity, inequality of effor ~~ts~~ and growth in average income uncovered by Marrero and Rodríguez (2013) is found to describe the income growth of poor households better than growth of the average household. The relationship van der Weide and Milanovic (2014) found between total inequality and growth of the poor versus growth of the rich appears to be driven mostly by inequality of opportunity.

The present paper also provides additional robustness checks to Marrero and Rodríguez (2013) and van der Weide and Milanovic (2014). Marrero and Rodríguez (2013) used the PSID database which provides detailed information on individual circumstances (including parental education) but for a relatively small sample of households, and for a limited period of time (1970-2000). They opted to disregard states with fewer than 50 observations for any given decade, which ultimately limited their database to 26 states. Despite the extensive robustness analysis they carried out, the smallness of their survey samples makes their analysis vulnerable to sampling error. Instead, we use an entirely different database (the US community survey) that covers between 1 and 5 percent of the US population which reduces sampling error to a minimum and allows us to consider all US states and the larger time period 1960-2010. Our study also extends the analysis of van der Weide and Milanovic (2014) by considering a different measure of inequality (MLD based on individual income at reference age – aged 30 to 50 – rather than the Gini of income per capita), and above all, by adding an important omitted variable, namely inequality of opportunity.

It remains to be verified whether the findings from this study extend to other countries, including less developed economies. For instance, a developing country could possess a trap in the accumulation of human capital (Azariadis and Stachurski, 2005). If this is the case, an increase in any kind of inequality might be good for growth of the poor 19
**Figure 7:  Gini Coefficient (the latest available) and the Annual Growth** **<u>Rate of Gini (%)</u>**

<!-- Start of picture text --> Gini<br>50 3<br>Growth<br>45<br>2.5<br>40<br>35<br>2<br>30<br>25 1.5<br>20<br>1<br>15<br>10<br>0.5<br>5<br>0 0<br>South Asia East Asia<br>Gini Coefficient (%)<br>Annual growth rate of Gini (%)<br>ladesh a(Rural) Urban) Nepal kistanPa i LankaSr (Rural) Urban) Indonesia Vietnam<br>Bang Indi India( China China(<br><!-- End of picture text --> _Source:_ World Bank staff estimation using household income and expenditure surveys of each country. _Note:_ Source data for Bangladesh - HIES 1991-92 and 2005; Pakistan - PIHS 199899 and PSLM 2005-06; India - NSSO 50th and 55th rounds; Sri Lanka - HIES 199091 and 2002; Nepal - NLLS 1996-97 and 2003-04. Data for all East Asian countries are in the World Bank’s data department; and survey years are 1993-2004 for China; 1999-2004 for Indonesia; 1992-2004 for Vietnam.

**Figure 8: Annual Growth Rate of Regional Inequality and the Pure** **<u>Individual Effect for Selected South Asian Countries</u>**

<!-- Start of picture text --> 20<br>Pure between-<br>18<br>indiv<br>16 Regional<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>esh an ral) an) nka pal<br>Banglad Pakist India (ru India (urb Sri La Ne<br><!-- End of picture text --> _Source:_ World Bank staff estimates using household income and <u>expenditure surveys of each country.</u> 10
as the size and quality of the houses are also relevant according to some of the energy data analyzed.

This may have some repercussions for policy design. In Mexico, gasoline is still highly subsidized and has regressive impacts (UNDP, 2011). In addition, designing a mechanism for fostering the use of different energy sources could take into account the variation in the income levels that was presented here and consider some of the relevant characteristics found in this analysis, such as the geographical location, indigenous status, and the quality of the house, etc.

Further studies could design other surveys for Mexico that include not only the amount spent on energy but also the specific quantities and quality, so the study could be improved with more specific surveys on the topic. In addition, the need to improve subsidies’ mechanisms requires more research on what amounts can be subsidized for households based on the income decile as well as on their inherent characteristics, and, further, on how such policies to foster energy consumption fit into the protocols to reduce global warming.

# **References**

Abid, M. and Sebri, M. (2012). Energy consumption-economic growth nexus: does the level of aggregation matter? International Journal of Energy Economics and Policy, 2(2), 55-62.

Akarka, A., and Long, T. (1980). On the relationship between energy and GNP: A reexamination. Journal of Energy and Development, 5, 326-331.

Barnes, D. F. Khandker, S.R., Samad, H. A. (2011). Energy poverty in rural Bangladesh. Energy Policy, 39(2), 894-904.

29