rafmacalaba/datause-encoder-data huggingface dataset:
7 up to 28 days of age. Postneonatal mortality deaths are those that occur from age 28 days up to age 12 months. Neonatal and postneonatal mortality rates for a cohort sum up to the infant mortality rate (World Health Organization 2005).

True cohort life table infant mortality estimates calculated with the sample as described above are shown in Figure 1 and Table 2. These estimates differ from the published DHS estimates for the periods 1999-2003 and 2004-2008, because both the time period and the methods differ. The broad trends, however, are similar. The cohort life table estimates show the overall decline in infant mortality was entirely the consequence of a decline in postneonatal mortality and was partially offset by a small and statistically insignificant increase in neonatal mortality. Neonatal deaths increased from 34 to 38 deaths per 1000 live births. Postneonatal mortality fell from 47 deaths to 22 deaths per 1000 live births. Infant mortality declined from 81 to 60 deaths per 1000 live births. These changes correspond to an increase of 12 percent and declines of 53 percent and 26 percent, respectively.<sup>4</sup> A number of changes took place between 2003 and 2008 in Kenya in factors that could explain the drop in infant mortality. The use of iron supplements during pregnancy, immunization rates, intake of anti-malarial drugs during pregnancy, ITN ownership and usage, and access to improved sanitation and improved source of drinking water rose substantially, as shown in Table 3. A particularly notable change was the growth in ITN ownership. The fraction of households owning at least one ITN increased from 8.3 percent to 60.4 percent nationally.

A change in ITN usage by children would be expected to have an effect on postneonatal mortality but little or no effect on neonatal mortality. This is because the incidence of severe malaria in newborns is low (Snow and Marsh 2002).<sup>5</sup> The basic pattern of neonatal and

> 4 This analysis does not closely examine differences in infant mortality by sex of the child. Overall, infant mortality rates are higher for boys than for girls. The trends for boys and girls over time are similar: a large decline in postneonatal mortality but no change in neonatal mortality.

> 5 Snow and Marsh (2002) cite past research which suggests that newborns are less susceptible to malaria due to some combination of  protection afforded by passively acquired maternal immunoglobulin G, the physiological protection  offered  through  the  continued  presence  of  fetal  haemoglobin, and  riboflavin deficiency. ITN ownership could affect neonatal mortality via ITN usage by pregnant women. According to Snow and Marsh (2002), malaria in pregnancy is associated with low birth weight, which increases both neonatal and postneonatal mortality.
**Table C.3:** SURE Model: District-level characteristics and Temperature z-scores

|Standardized outcome|(1)<br>Coefcient|(2)<br>SURE|
|---|---|---|
|variable at district level|_βdp_|p-value|
|HH head Age|-.61|0|
|HH head literate|-.10|.39|
|Rural|-.25|.04|
|HH head female|.00|.98|
|HH size|-.19|.87|
|Covid restrictions|-.01|.46|
|Private water connection|-.49|0|
|Electricity connection|.49|0|
|Use clean fuel|-.06|.61|
|Distance to hospital|-.07|.51|
|Distance to PHC|-.04|.73|
|School in community|-.16|.19|

_Note_ : This table reports the coefficient from equation 5) from district level regression of standardized district demographic variables on temperature anomalies at the district level.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

49
53

# **Figure la: Macroeconomic instability in Brazil: Inflation**

<!-- Start of picture text --> 1,800 - -1706-<br>1,600 - 1509<br>1,400 _ _ _ _<br>1,200 1 -- -- -<br>1,000 -- _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>800 _ _ __ _ _ _ _ _ _ _ _ _ _ _ __ _ - _ _ _ __ 703_ _ _ _ _<br>600 - _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>400 ___ - __-__ 350 ____<br>420- 36 38 6158 174 190<br>42 2 841 57 20 9<br>1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996<br><!-- End of picture text --> <!-- Start of picture text --> Years<br><!-- End of picture text --> Source: Fundacao Getulio Vargas (1999) and IBGE (1999).
observation. Bliss and Stern published a book based on their investigations (Bliss and Stern, 1982), which has a primary focus on the 1974-5 survey year.

A fourth resurvey of Palanpur took place in 1983-4 when Jean Drèze and Naresh Sharma, assisted by V.K. Sharma and S.S. Tyagi Jr. (whose brother S.S. Tyagi Sr. had been in charge of data collection during the 1957-8 survey round) lived in the village for fifteen months, once again collecting data for the entire village population and liasing closely with Stern. The further re-survey of the village, once again by Drèze and Sharma, was conducted in 1993. This survey was carried out over a shorter period and is consequently somewhat less comprehensive. The shorter duration of the 1993 survey prevented collection of the detailed economic information necessary to construct an income measure for 1993 which is comparable to that of the earlier survey years. In the period between May, 2008 and April 2010, Himanshu of Jawaharlal Nehru University in Delhi led a team of researchers to resurvey Palanpur for a sixth time. The fieldwork was organized in close consultation with Nicholas Stern, Jean Drèze, Peter Lanjouw and Naresh Sharma and was structured and carried out in such a way as to maximize comparability with the earlier waves of data collection. Data from this most recent round of fieldwork are now available and underpin the present discussion of non-farm diversification and its distributional impacts. While the 2008-10 data have largely been entered and processed, analysis of these data is still ongoing and the results presented should thus be regarded as preliminary.

In early 2008 Palanpur had a population of 1,270 persons, divided into 236 households (Table 2). In this year, Hindus represent 85.2 per cent of the village population, and Muslims the remaining 14.8 per cent. Hindus are divided into six main castes, with a few additional castes numbering three households or less. The shares of Hindus and Muslims in the total population, and the relative sizes of the main castes, have remained fairly stable since the start of the Palanpur project in the late 1950s.

# **Table 2:  Palanpur Village Profile 1993** **<u>and 2008</u>**

|||1993|2008|
|---|---|---|---|
|Location||13 kilometers north of Cha<br>Moradabad district; 31 kilom<br>Morada|ndausi a small town in<br>eters south of the city of<br>bad|
|Population||1,133|1,270|
|Number of Households||193|236|
|Average Household Size||5.93|5.42|
|Female/Male Ratio||0.85|0.98|
|Main Hindu Castes|T|hakur,Murao,Dhima,Gadara|ia,Passi,Jatab|
|Main Muslim Castes||Dhobi,Teli||
|Proportion of the<br>population in different<br>castegroups|Thakur|25.0|22.9|
||Murao|25.9|24.4|
||Muslim|12.5|14.8|
||Jatab|11.7|16.2|
||Other|24.9|21.7|
|Main economic activities|Agricul|ture,livestock,wage employm|ent outside the village|

11
# **Appendix 3: The Mapping between the 2006’ Nigerian SAM, GTAP Sectors and the aggregated 21 sectors**<sup>**Ω**</sup>

|||Nigerian 2006’s SAM|GTA|P database version 8.1|Aggrega|ted 21 GTAP sectors|
|---|---|---|---|---|---|---|
|No.|Code|Description|No.<br>Code|Description|No.<br>Code|Description|
|1|crice|Rice|1<br>pdr|Paddyrice|1<br>PaddyRice|Paddyrice|
|2|cwhet|Wheat|2<br>wht|Wheat|2<br>Wheat|Wheat|
|3|cmaze|Maize|3<br>gro|Cereal grains nec|3<br>OCereals|Other cereal grains|
|4|csorg|Sorghum|||||
|5|cmilt|Millet|||||
|6|ccass|Cassava|4<br>v_f|Vegetables, fruit, nuts|4<br>VegsFruits|Vegetables, fruit and nuts|
|7|cyams|Yams|||||
|8|ccyam|Cocoyam|||||
|9|cpota|Irish potato|||||
|10|cspot|Sweet potato|||||
|11|cplan|Banana and plantain|||||
|16|cveg|Vegetables|||||
|17|cfrt|Fruits|||||
|18|ccoco|Cocoa|||||
|24|cnuts|Nuts|||||
|25|ccash|Cashew|||||
|13|cgnut|Groundnuts|5<br>osd|Oil seeds|5<br>ProcdFood|Processed food, oilseeds, rice<br>and beverages|
|14|csoys|Soybeans|||||
|15|cosed|Beniseed|||||
|21|cpalm|Oil palm|||||
|41|cofod|Processed food products (excluding|21<br>vol|Vegetable oils andfats|||
|||beverages)|23<br>pcr|Processedrice|||
||||25<br>ofd|Food productsnec|||
|40|cbevg|Beverages and tobacco products|26<br>b_t|Beverages and tobacco<br>products|||
|22|csuga|Sugar and sugar cane|6<br>c_b<br>24<br>sgr|Sugarcane, sugarbeet<br>Sugar|6<br>Sugar|Sugar cane and sugar beet|
|20|ccott|Cotton|7<br>pfb|Plant-basedfibers|7<br>Cotton|Plant-basedfibers, Cotton|
|12|cbean|Beans|8<br>ocr|Cropsnec|8<br>OCropsFrst|Othercropsincludingforestry|

31
# Methodology

## Distribution Based on Rural Population

The methodology for distributing rural population used in this analysis is relatively straightforward. First, as with all the methodologies compared here, the agricultural GDP is aggregated from the municipio level up to the microregion level. Then, the rural population grid was produced using the Gridded Population of the World (GPW) version 4 (CIESIN 2017) for 2010 adjusted to the United Nation’s World Population Prospects (UN WPP), and masking out urban area using the Global Human Settlement grid for 2015 (Pesaresi and Freire 2016).<sup>7</sup> Once these steps are done, it is straightforward to aggregate population to the 5 arc minute pixel used by all three methodologies, then for each microregion, distribute the agricultural GDP per rural person to each pixel based on the rural population in each pixel.

## Spatial Allocation Using Cross‐Entropy

<mark>The methodological framework is a spatial allocation model that estimates the subnational agricultural GDP to 5 arc minute pixels (or any other spatial resolution desired). The probabilistic model includes the control (constraint) of national or subnational agricultural GDP available from various sources of national ministries by country or reports. The five components of agricultural GDP (crops, livestock, fisheries, timber, and non‐timber forest products) are used to provide a prior estimate of agricultural</mark> GDP at the pixel level.

<mark>We define our spatial AgGDP allocation problem in a cross‐entropy framework following You et al. (2014). The first step is to transform all real‐value parameters into a corresponding probability form. Let S</mark> _i_ <mark>be the percentage of the total agricultural GDP allocated to pixel</mark> _<mark>i</mark>_ <mark>within a certain country (e.g. Country X) except the crop value for that pixel.</mark> _<mark>AgGDP</mark>_ i <mark>is the agricultural GDP allocated to pixel</mark> _<mark>i</mark>_ <mark>in Country X while</mark> _<mark>TotalAgGDP</mark>_ <mark>is the total agricultural GDP for that country. Therefore:</mark> <mark>Let</mark> _<mark>PreAlloc</mark> i_ <mark>be the prior allocation of AgGDP share from our best guess. One possibility for the first approximation is from pixel level components of AgGDP where we have gridded data sets:</mark> PreAgGDP� �Crop� �Livestock� �Forestry� �Hunting� �Fishing� <mark>As we have the major components of AgGDP, and the sum of these component should be close to the official value. At least, we make sure that the official AgGDP value (</mark> _<mark>TotalAgGDP)</mark>_ <mark>should be no less than the sum of all five components of agricultural GDP. Therefore, we first sum them up</mark> 7 We select GHS Settlement grid as an example of a geospatial definition of urban population to exclude from the model. The definition of urban is an active topic of research (e.g. Dijkstra and Poelmann 2014; Roberts et al. 2017; Angel et al. 2018; Bosker et al. 2018).

4
The unexpected time and localized nature of the violence make the 2002 Gujarat riots a suitable setting to study the effects of communal violence. We will carefully discuss our choice of treatment and control groups in the following section and show evidence of parallel trend below, an essential identifying assumption for difference-in-difference.

We check for differential trends in women’s age at marriage and the rate of child marriages by estimating the following event-study equation: where _Y eard_ are year-specific dummies and the omitted year is the year 2002; _βt_ represent the coefficients on interactions between the year dummies and the _Riots_ dummy. Another advantage of estimating the difference-in-differences coefficients by year is that it enables us to investigate how women’s marital outcomes evolved over time after the riots.

# **5.3 Choice of Treatment and Control Groups**

As discussed in section 5.1, we take Gujarat as the treated state and the rest of India or neighboring states or a synthetic amalgamation of states as the control. In principle, one can think of a within Gujarat analysis where we analyze whether women’s marriage market outcomes are correlated with the intensity of riots. There are two major issues with such an analysis: The first is the high mobility of women across districts within the same state, specifically for marriage. According to the 2001 Indian Census data, while the leading cause of migration for men is work, it is marriage for women: 70% of all women who migrate do so for marriage (Kone et al., 2018). In table 2, we see that, among those women who migrate for marriage, more than 70% cross district borders within their states; for Gujarat, this number is more than 80%. Since the available data only records the residence district of the women after marriage, a within Gujarat district-level analysis will be unable to account for the spillover from treated to control districts and vice versa.

The second issue is the widespread prevalence of riots across Gujarat. According to contemporary news articles (Outlook India, 2002, 2022), 16 of 24 districts in Gujarat were affected, namely, Ahmadabad, Anand, Banas Kantha, Bharuch, Bhavnagar, Dohad, Gandhinagar, Junagadh, Kheda, Mahesana, Panch Mahals, Patan, Rajkot, Sabar Kantha, Surat, and Vadodara. Varshney (2020) highlights that the cities of Ahmadabad, Vadodara, and Godhra (in the Panch Mahal district) experienced the worst violence. Given the widespread violence across districts of Gujarat and the high inter-district within state mobility of women for marriage, considering the whole of Gujarat as the affected region offers the most credible 9
# Figure 9 (continued)

(e) Inter-industry wage inequality (controlled for collective bargaining and level of income) and average tariff rate <!-- Start of picture text --> 0 10 20 30 40<br>unweighted average tariff rate from WB sources<br>resid Fitted values<br>.06<br>.04<br>.02<br>0<br>resid/Fitted values<br>-.02<br>-.04<br><!-- End of picture text --> (f) Change in average tariff rate and change in inter-industry wage inequality <!-- Start of picture text -->
-40 -20 0 20 40<br>change in WB mean tariff rate<br>dtheil Fitted values<br>20<br>10<br>0<br>dtheil/Fitted values<br>-10<br>-20<br><!-- End of picture text -->

But the relationship between levels may not necessarily be indicative of the relationship between changes. And in effect, inspection of Figure 9 (panel f) does show that there is a mild negative relationship between changes in average tariffs and changes in the Theil index of inter-industry inequality. In Table 6 we look at whether this relationship holds for poor and rich countries. We easily notice that for rich countries a decrease in protection is associated with an increase in inter-industry wage inequality; and the reverse for the increase in protection. This in turn indicates that the protected sectors tended to be sectors with lower average wage (that is, less skilled) as we indeed postulated in the model. An increase in protection, on the contrary, is associated with lower inter-industry wage differences implying again that higher tariffs will tend to protect sectors with lower average wage (presumably, less skilled too). The same pattern, on average, holds for poor countries although there the average changes are much less clear and the standard deviation much greater. Yet the fact that the same pattern is 46
Table 3: Savings Functions for the Panel Data of Industrialized Countries, 1960-90

|||Depe<br>(t-|ndent Variab<br>Statistics in p|le: Savings rat<br>arenthesis)|e||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|_(5)_|(6)|(7)|(8)|
|GDP per capitagrowth|2.318<br>(2.92)|3.365<br>(4.60)|2.793<br>(4.50)|3.569<br>(5.71)|2.359<br>(1.98)|4.273<br>(5.42)|0.020<br>(0.01)|4.146<br>(4.94)|
|Lagged GDPgrowth per capita||0.273<br>(0.42)||0.109<br>(0.18)||0.174<br>(0.24)||0.003<br>(0.01)|
|Dependency ratio|-0.028<br>(-0.25)|0.020<br>(0.26)|-0.010<br>(-0.15)|0.031<br>(-0.60)|-0.150<br>(-0.81)|0.087<br>(1.24)|-0.434<br>(-1.72)|0.090<br>(1.06)|
|Lagged Savings Rate||0.273<br>(5.38)||0.336<br>(5.20)||0.442<br>(9.66)||0.449<br>(11.08)|
|Overall Education Stock|-2.623<br>(-3.99)|-0.756<br>(-1.58)|||||||
|Lagged Overall Education Stock|2.882<br>(5.79)|0.865<br>(5.38)|||||||
|Primary Education Stock|||-1.448<br>(-1.63)|-0.690<br>(-1.43)|||||
|Lagged Primary Education Stock|||2.087<br>(3.19)|0.861<br>(2.47)|||||
|Secondary Education Stock|||||-0.791<br>(-2.34)|0.069<br>(0.47)|||
|Lagged Secondary Education Stock|||||0.806<br>(2.57)|-0.012<br>(-0.09)|||
|Tertiary Education Stock|||||||-0.445<br>(-2.82)|-0.018<br>(-0.24)|
|Lagged Tertiary Education Stock|||||||0.367<br>(3.16)|0.048<br>(0.78)|
|AdjR<sup>2</sup>|0.587<br>|0.741<br>|0.611<br>|0.746<br>|0.194<br>|0.727<br>|0.194<br>|0.730<br>|
|DW|1.63|2.16|1.86|2.15|1.35|2.23|1.23|2.24|
|Observations|110|110|110|110|110|110|110|110|

All variables are expressed in log (with the exception of the GDP growth rate) and in 5 year average. The TSLS have been used in all regression and fixedeffects have been introduced to account for countries' characteristics.
**TABLE 2: Attrition (Child Level) - CONTINUED**

|||**18-Month**|**36-Month**|
|---|---|---|---|
|||**Follow-Up**|**Follow-Up**|
||**Variable**|**(1)**<br>**(2)**|**(3)**<br>**(4)**|
||T3 x Leiter Sustained Attention|-0.025|-0.022*|
|**bles**||(0.019)|(0.012)|
|**ria**|T3 Pbd Pit Vbl Tt|-0.016|-0.022|
|**d va**|x eaoy cure ocauary es|(0.030)|(0.017)|
|**hil**|T3 x Malawi Developmental Assessment Tool: Language|0.024|-0.006|
|**y x C**|Skills|(0.023)|(0.015)|
|**m**|T3 x Malawi Developmental Assessment Tool: Fine Motor|-0.002|0.013|
|**Dum**|/ Perception Skills|(0.021)|(0.019)|
|**T3**|T3 x Male|-0.011|0.002|
|**ns:**||(0.037)|(0.031)|
|**ctio**|T3 x Ae (months)|-0.006|-0.002|
|**tera**|g|(0.004)|(0.003)|
|**In**|T3 HihfA|0.007|0.029|
||x egt-or-ge z-score|(0.021)|(0.018)|
|**s**|T4 x Leiter Sustained Attention|0.015|-0.029**|
|**ble**||(0.022)|(0.013)|
|**aria**|T4 x Peabod Picture Vocabular Test|-0.022|0.005|
|**d v**|y y|(0.022)|(0.016)|
|**Chil**|T4 x Malawi Developmental Assessment Tool: Language|0.017|0.002|
|**y x**|Skills|(0.024)|(0.021)|
|**mm**|T4 x Malawi Developmental Assessment Tool: Fine Motor|0.010|0.030*|
|**Du**|/ Perception Skills|(0.022)|(0.017)|
|**T4**|T4 x Male|0.007|-0.010|
|**ns:**||(0.035)|(0.027)|
|**tio**|T4 A h|-0.006|-0.004|
|**erac**|x ge (monts)|(0.004)|(0.004)|
|**Int**|T4 HihfA|0.016|0.007|
||x egt-or-ge z-score|(0.040)|(0.022)|
||Mean (standard deviation) of dependent variable for the|0.062|0.046|
||control group|(0.242)|(0.210)|
||Joint F-test of Baseline Controls (minus interactions) - p-<br>value|0.314|0.023**|
|||with T2:<br>0.363|0.338|
||Joint F-test of Interactions - p-value|with T3:<br>0.793|0.180|
|||with T4:<br>0.178|0.147|
||Number of observations|2,035<br>2,035|2,120<br>2,120|

Notes: .01 - ***; .05 - **; .1 - *; Cross-sectional OLS regressions using 18- and 36-month attrition data and baseline variables. Regressions are at the child level, standard errors are clustered by CBCC, and sampling weigths are used (at the 18-month follow-up, 42 randomly tracked observations are reweighted and 85 randomly untracked observations are not included). All regressions control for district-bin fixed effects. All test scores and Height-for-Age z-score are standardized by using (weighted) means and standard deviations from the control group at baseline. We replaced the missing values of index variables and Height-for-Age z- score with their (weighted) averages at baseline for the overall sample.

44
# _The Geography of Intergenerational Mobility in Latin America and the Caribbean_<sup>_∗_</sup>

Ercio Mu ̃noz<sup>_†_</sup>

**JEL-Codes** : D63, I24, J62.

**Keywords** : _Socioeconomic mobility, Education, Latin America and the Caribbean_ .

> _∗_ I thank Bennett Callaghan, Miles Corak, Christos Giannikos, Marco Ranaldi, Nuria Rodriguez-Planas, Roy van der Weide, and Wim Vijverberg, and several seminar/conference participants for helpful comments; Pablo Vargas and Joaquin Prieto provided outstanding research assistance. I greatly acknowledge the financial support of the Center for Latin American, Caribbean and Latino Studies at CUNY and the Mario Capelloni dissertation fellowship from CUNY. The usual caveats apply. First version: November 2020.

> _†_ Email: emunozsaavedra@worldbank.org.
# **3. Spatial analysis**

It is also of interest to directly analyze the spatial correlation of the error term generated by the model specification using our pseudo-survey data. To this end, we estimate the model for the state on the basis of one (arbitrarily selected) survey and then compare in the census data, the actual household average per capita income against predicted income generated by: ^ ' ~ˆ _r_ log _yach_ = **x** _CENSUSach_ ⋅β _SURVEY_ . A spatial correlation test can be defined by the following: enumeration area level _c_ of the average log per capita consumption, log _y c_ , the average of the predicted log consumption estimated using parameters estimated from the sample, ^ log _yc_ = **x** _CENSUS_ ' _c_ ⋅ β ~ˆ _SURVEYr_ ; and the spatial error component not explained by the model computed on the basis of a local weight matrix W that is derived from contiguity with, or distance from, geographical areas _j_ that neighbor enumeration area _c_ (see below).<sup>12</sup> b) If the model doesn’t capture local conditions well, we would expect significant parameter estimates for all three parameters. On the other hand, if the model is well specified, we ˆ ˆ ˆ would expect to find α 0 = 0 , α 1 = 1 and α 2 = 0 .

Our regression results confirm that EA controls go a considerable way towards removing spatial correlation. Table 5 shows that with no locational controls (Model A), coefficients are always significant suggesting that spatial correlation is present. The null hypothesis test

> 12  Spatial weights refer to the way in which we define neighboring. Rook and Queen Contiguity spatial weights use two different definitions of common boundaries to define neighboring. This sort of weighting matrix need not to be limited to first order contiguity; higher order contiguity boundaries can be set using the algorithm by Anselin and Smirnov (1996).  Rook and Queen Contiguity spatial weighting often leads to a very unbalanced structure. Larger units can have more neighbors and small units a smaller number of neighbors. The solution is to set a unique number of neighbors for all areas by creating a k-nearest neighbor weighting matrix.  When geo-referenced coordinates are available, the spatial weights can be derived from the distance between different points.  Euclidean distance weighting fixes a specified distance and then counts the number of neighbors that fall within that distance.  In this paper we fix the distance to 2.1 km, unless otherwise specified.

19