rafmacalaba/datause-encoder-data huggingface dataset:
We use both discovery filters described in section 3 on the HS 4-digit and HS 6-digit data during the 1990s. In addition, we examine the SITC data in the same manner, first with a crosssection of discoveries in the 1990s, and with a conditional fixed-effects negative-binomial regression on discoveries since 1972 (including year dummies)<sup>8</sup> . The results are shown in Table 4.

**<u>Table 4: Stages of Discovery</u>**

||**HS 4-Di**<br>|**git 1990s**<br>|**HS 6-Dig**<br>|**it 1990s**<br>|**SITC**<br>|**3-Digit**<br>|
|---|---|---|---|---|---|---|
||**Filter 1**|**Filter 2**|**Filter 1**|**Filter 2**|**1990s**|**1972-1991**<br>**(FE)**|
|Coefficient: ln<br>GDPpercapita|15.65175<br>(5.68)|13.95415<br>(3.72)|15.72022<br>(5.89)|12.06436<br>(4.57)|22.38211<br>(4.38)|19.53553<br>(7.47)|
|Coefficient: ln<br>GDP per capita<br>squared|-.9280328<br>(-5.92)|-.821797<br>(-3.85)|-.9114158<br>(-6.03)|-.695039<br>(-4.65)|-1.32140<br>(-4.50)|-1.16821<br>(-7.64)|
|Maximum Point|$4595|$4866|$5564|$5878|$4765|$4278|
|Pseudo R-<br>Squared|.1515|.0980|.0719|.0549|.1487||
|Discovery<br>Count|332|150|1710|865|93|1114|
|Sample Size|50|49|50|49|67|76|

Note: brackets indicate z-statistics. Source: Author’s calculations According to the data, discovery activity is low among the poorest countries, but rises quickly and reaches a maximum when countries earn between $4200 and $5500 per capita. After that point, discovery activity tends to fall, and is low as countries reach a relatively high level of development. Notice that the coefficients fall when moving from filter 1 to filter 2. This is expected, as the more restrictive filter has fewer discovery counts. What matters, however, is that the relationship (that is, signs of the coefficients and the level of GDP per capita at which the expected discovery count curve reaches a maximum) is consistent.

> 8 On the fixed effects negative binomial estimator, see Hausman et. al. (1984)

22
example, arise when handwritten records are not fully digitized, reported with error, or records are not passed on by all health care providers (Shapira et al. 2021). In the case of COVID-19, evidence from a survey of 42 USAID country offices has documented that such issues have been commonplace (Rosenbaum and Waugaman 2022).

We provide indicative evidence of such flaws and weaknesses, which had previously also been documented in the case of routine immunizations (Lim et al. 2008; Sandefur and Glassman 2015; Cutts et al. 2016; Galles et al. 2021) and quantify their potential effects on reported COVID-19 vaccination rates. Survey data can thus serve as a complementary source to countercheck the official administrative figures and inform policy.

To this end, phone surveys can provide flexible, cost-effective, and rapidly deployable tools for high-frequency monitoring. Phone surveys also facilitate iterative experimentation with design choices to ascertain the robustness of the evidence compiled. This would either be prohibitively expensive or outright impossible at similar scale in the case of in-person surveys or administrative data.

Both monitoring and experimentation was possible because of the existing survey infrastructure building on longitudinal in-person households, which allowed for broad population coverage and comparatively low non-response rates (Gourlay et al. 2021) as well as providing a nationally representative benchmark for the phone surveys.

# 6 Conclusion

Studies from before the COVID-19 pandemic have found substantial discrepancies in LMICs between routine childhood immunization rates reported in survey and administrative data, hence creating a dilemma for research and policy (Galles et al. 2021; Danovaro-Holliday et al. 2021; Cutts et al. 2016; Burton et al. 2009; Lim et al. 2008). In this study, we show that such misalignment is also widespread in the context of COVID-19 vaccinations. In a sample of 36 LMICs, the survey data statistically significantly exceeds figures reported in administrative sources in 35 out of 57 survey rounds and by 47% on average. This pattern is particularly striking and consistent in Sub-Saharan Africa.

We investigate a number of potential explanations for this discrepancy, focusing on possible errors of representation and errors of measurement in phone survey data from five Sub-Saharan African countries. The gap between both data sources shrinks when accounting for selection bias at the respondent level but remains substantial in most cases. While we cannot experimentally probe the administrative data in the same way as the survey data, we present indicative evidence pointing to some weaknesses and inaccuracies in administrative data.

Our findings make several substantial contributions.

First, we show that substantial misalignment between survey-based and administrative vaccine coverage rates plagues COVID-19 data, the largest vaccination effort in history. Our study is the first to document these discrepancies in a cross-country sample and a low- and lower-middle 30
**Table 26 Headcount Index of Poverty Among Farmers, Corrected for Seasonal Variation** _(Poverty line = 75, 000 CFAF per year in 1985 CFAF)_

||_Seasonally_|_unadjusted_|_Seasonall_|_y adiusted_|
|---|---|---|---|---|
|_Region_|_1993_|_1995_|_1993_|_1995_|
|Other Cities|||||
|Export crop farmer|0.334|0.071|0.296|0.071|
|Food crop farmer|0.540|0.645|0.523|0.645|
|East Forest|||||
|Export crop farmer|0.407|0.406|0.400|0.363|
|Food crop farmer|0.414|0.567|0.429|0.557|
|West Forest|||||
|Export crop farmer|0.340|0.515|0.352|0.561|
|Food crop|0.503|0.594|0.516|0.604|
|Savannah|||||
|Export crop farmer|0.524|0.406|0.531|0.406|
|Food crop farmer|0.532|0.577|0.552|0.603|

One problem in using the coefficients derived from the 1985 -1988 LSMS data to correct the 1993 and 1995 surveys is that the total expenditure aggregate for many food and some nonfood items is based on the average of the two week (annualized by multiplying by 26 weeks) and the normative estimate of monthly food expenditure multiplied by typical number of months of purchase. Unless there is seasonal recall bias, one would expect that there would be no seasonal variation in the normative estimate. Thus, the average of the two estimates would exhibit less seasonal variation and could result in a downward bias in the seasonal correction parameters.

One way of assessing the possible extent of this bias is to compare the difference in the seasonal variation in the two different estimates of food expenditure. One estimate is based on the two week recall, while the other is average of the two week and the normative estimates. The JuneNovember time period was split into two sub-periods: June-August, and September-November, corresponding more closely to the pre and post-harvest periods, which may have some bearing on the seasonality in food purchase. With a few exceptions, the coefficients are generally of the same magnitude. The averaged estimate does not appear exhibit less pronounced seasonal variation than the two week estimate alone (table 27). This suggests that there may also be some seasonal bias in the annual recall estimate of food expenditure in the same direction as the two week estimate. We opted to use the average of the two week-annualized estimate and the normative estimate as the dependent variable in the regressions used to produce the seasonal adjustment variable, as the expenditure aggregate was the only cleaned expenditure aggregate variable available to us in the 1985-1988 data file.

30
market (returns to education) and school. The next section provides a discussion on the data sets used for the empirical analysis. Section 4 reports and discusses the estimates of the investment and mobility equations for the urban households, along with an analysis of how the estimated parameters can help sort out the mechanisms driving the observed pattern of educational investment and intergenerational mobility. Section (5) then discusses the corresponding results for the rural households. Section (6) concludes with a summary of the main findings and the methodological contributions of the paper.

# **(2) Related Literature**

The literature on intergenerational mobility in developed countries is well-developed, with many fundamental theoretical and empirical contributions. For excellent surveys of the literature, please see Solon (1999), Black and Devereux (2011), and Bjorklund and Salvanes (2011). The focus of this literature has been on intergenerational (permanent) income persistence, and a lot of effort has been devoted to understanding the biases that arise from measurement error and life-cycle effects. Also, most of the studies deal with father-son linkages, and, as noted above, research on women, and in particular on gender bias is lacking.

In contrast, research on intergenerational economic mobility in developing countries remains relatively neglected. This partly reflects the data constraints. Good quality income data for long enough time periods to calculate permanent income remain rare. As a result, the focus of the research on developing countries has been on intergenerational educational persistence. For a discussion on the methodological challenges and data constraints in research on intergenerational mobility in developing countries, see Emran and Shilpi (2019). Recent surveys of this literature include Iversen et al. (2019), Behrman (2019) and Torche (2019). For cross-country evidence, please see Hertz et al. (2008), Narayan et al. (2018) and Bhalotra and Rawlings (2013). The recent studies on India include Azam and Bhatt (2015), Azam (2016), Emran and Shilpi (2015), Asher et al. (2018), Maitra and Sharma (2010), and Ahsan and Chatterjee (2017). On intergenerational mobility in China, see, among others, Fan et al. (2019), Golley and Kong (2013), Emran and Sun (2015), Gong et al. (2012), Park and Zou (2017), and Knight et al. (2011). Most of the studies on India 5
**Business Cycles Accounting for Paraguay**<sup>**1**</sup>

By Viktoria Hnatkovska<sup>2</sup> and Friederike (Fritzi) Koehler-Geib<sup>3</sup> Keywords: Paraguay, volatility of GDP growth, external factors, domestic factors, structural VAR estimation, business cycles accounting, agricultural GDP JEL classification codes: E13, F43, F44

> 1 We thank Guillermo Cabral for excellent research assistance. We are also grateful for excellent comments and feedback from Cesar Calderon, Oscar Calvo-Gonzalez, Mehmet Caner, Rodgigo A. Chaves, Julie Dana, Edgardo Favaro, Norbert Fiess, Jesko Hentschel, Auguste Kouame, Aristomene Varoudakis, Zafer Mustafaoglu, 2 University of British Colombia

> 3 Macroeconomic and Fiscal Management Global Practice, The World Bank
characteristics such as age and gender. While more detailed data are required to gain a more indepth understanding of parents’ preferences and behaviors, limited demographic information can sometimes help researchers better understand parents’ preferences. Several recent studies have shed light on these. In Brazil, Duryea et al. (2007) studied the impact of unemployment of household heads and found that older girls are the most likely be pushed into the labor market to help support the family when the head of the household is unemployed. In studying the Indonesian financial crisis, Thomas et al. (2004) found a decline in educational spending in poor households that had more young children and suggested that poor households tended to protect their investment in older children’s education. Similarly, Beegle et al. (2006) found a decrease in school enrollment among younger children during the Indonesian financial crisis in 1998, also suggesting that households chose to protect their investment in their older children’s education by keeping them in school.

There are a number of studies examining the impact of the 2008 food crisis on children’s education; most of them are simulation-based studies. The effects of a food crisis on child labor depend not only on child and household characteristics, but also on social and community factors. Thus it is difficult for cross-sectional or repeated cross-sectional data to capture all of these factors. This study used a panel survey from the Pakistan Standard Living and Measurement (PSLM) 2008 and 2010 and exogenous geographic variations in wheat price changes to identify the impact of the food price increase on children’s activity status (attending school, working, or idle).

This paper fills in the literature by analyzing the heterogeneous effects of a food price increase on child labor, including within-household and cross-household differences. It examines how children’s age, sex, and pre-crisis activity status are associated with the observed impacts. The same emphasis was placed on children being idle (neither working nor attending school) as on working and attending school because idleness is one major status for children in Pakistan, particularly for girls. The reasons for this are not just economic; due to a strong social stigma against girls’ schooling in some areas in Pakistan. Heterogeneous effects of the food crisis on agricultural and non-agricultural households were also examined.

3
# **7.  Incorporating Biodiversity**

We develop a variety of biodiversity indicators to identify concentrations of diverse species and their vulnerability to encroachment and habitat loss in road corridors.

# **7.1  Species Density**

We identify resident animal species in each cell using thousands of digital range maps (shapefiles) provided by the International Union for the Conservation of Nature (IUCN) and BirdLife International. We measure species density as the species count for each cell.

# **7.2  Species Vulnerability**<sup>**8**</sup>

Species density provides critical information for assessing ecological risks, but at least three other elements are needed: (1) **_Geographic vulnerability_** , which can be proxied by **_endemicity_** : the proportion of each species’ range that lies within each cell. Species that reside in very few cells may be particularly vulnerable to habitat encroachment. Endemicity treats all species equally at the global level, since each species has a total count of 1. Total endemicity for each cell -- the sum of its species endemicity measures -- assigns higher values to cells inhabited by species whose ranges are relatively limited. By implication, forest clearing in higher-value cells may be particularly destructive for remaining critical habitat.

# (2) **Density of endangered and critically endangered species** .  Risk assessment is

particularly critical for species listed as endangered by IUCN. We measure endangered species density as the count of endangered and critically endangered species in each cell.

- (3) **A measure of** **_extinction risk_** that adds the insights of the international scientific

community. We convert Red List status codes to extinction probabilities using the methodology

> 8 Much of the index design work described in this section was first undertaken in a collaboration with Richard Damania for the Congo Basin countries.  See Damania and Wheeler (2015).

39
_._ _Table A2.5 Weighted TOT and ATE estimates of the “training and grant” vs “training, but no grant” on main socio‐economic outcomes_

|||(1)<br>TOT<br>(no<br>controls)<br>|(2)<br>TOT<br>(controls)<br>_Main ou_<br>|(3)<br>TOT<br>(controls +<br>geography<br>controls)<br>_tcomes – Socio_<br>|(4)<br>LATE<br>(no<br>controls)<br>_‐economic_<br>|(5)<br>LATE<br>(controls)<br>|(6)<br>LATE<br>(controls +<br>geography<br>controls)<br>|
|---|---|---|---|---|---|---|---|
|Employment<br>|Training,|0.090|0.095*|0.059|‐0.011|0.011|‐0.016|
|index|no grant|(0.126)|(0.093)|(0.593)|(0.957)|(0.958)|(0.956)|
||Training|0.040|0.051|0.019|0.277|0.262|0.165|
||and|||||||
||grant|(0.717)|(0.649)|(0.903)|(0.506)|(0.503)|(0.664)|
|Consumption<br>|Training,|0.009|0.004|‐0.060|‐0.434**|‐0.374**|‐0.280|
|index|no grant|(0.889)|(0.953)|(0.605)|(0.019)|(0.038)|(0.372)|
||Training<br>and|0.194**|0.174*|0.134|1.145**|1.017**|0.986**|
||grant|(0.029)|(0.052)|(0.340)|(0.026)|(0.027)|(0.021)|
|Savings,|Training,|0.200***|0.186***|0.107|‐0.194|‐0.207|‐0.283|
|investment<br>and debt<br>index|no grant<br>Training<br>and|(0.000)<br>0.460***|(0.001)<br>0.444***|(0.203)<br>0.360***|(0.219)<br>1.349***|(0.218)<br>1.356***|(0.327)<br>1.161***|
||grant|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.002)|
|Business skills<br>|Training,|‐0.038|‐0.038|‐0.025|‐0.122|0.002|0.098|
|index|no grant|(0.524)|(0.524)|(0.690)|(0.528)|(0.993)|(0.727)|
||Training<br>and|0.294***|0.294***|0.274***|0.275|0.050|0.004|
||grant|(0.000)|(0.000)|(0.001)|(0.453)|(0.901)|(0.991)|
|Observations||1,500|1,495|1,474|1,500|1,474|1,474|
|F‐stat|||||20.62|18.22|34.41|

_Note: Observations are weighted by their inverse likelihood to be in the final sample, based on who was easy to reach during the phone survey. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict events between baseline and endline. Column (3) also controls for geographic features since the estimation strategy relies on the distance to the closest KCB bank branch that might correlate with other geographic characteristics. Geography controls include distance to the closest city, distance to the closest road, average land gradient and their respective interactions with selection to the original treatment group, and the interaction between conflict exposure and the original treatment group. P‐ values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level._ 41
**Table 4.** Regression Estimation of the Impact of Women’s Decision-making Power on Caloric Availability

|Theta|Total Calories<br>-0.037|Calories<br>from<br>grain<br>-0.175*|Calories<br>from<br>vegetables,<br>fruits<br>0.522***|Calories<br>from<br>animal<br>products<br>1.189***|Calories<br>from other<br>sources<br>0.429***|
|---|---|---|---|---|---|
||[0.071]|[0.099]|[0.116]|[0.199]|[0.070]|
|Ln (# of people in HH)|-0.069***<br>[0.008]|-0.016<br>[0.011]|-0.185***<br>[0.014]|-<br>0.083***<br>[0.022]|-0.157***<br>[0.008]|
|Ln (per capita consumption<br>expenditure)|0.185***|-0.009|0.499***|0.863***|0.404***|
||[0.010]|[0.014]|[0.016]|[0.026]|[0.010]|
|Observations|7,919|7,894|7,907|7,078|7,919|
|**Rural**||||||
|Theta|-0.143*|-0.156|0.072|0.673**|0.095|
||[0.085]|[0.116]|[0.146]|[0.267]|[0.082]|
|Ln (# of people in HH)|-0.053***<br>[0.010]|-0.007<br>[0.014]|-0.175***<br>[0.017]|-0.086***<br>[0.029]|-0.140***<br>[0.010]|
|Ln (per capita consumption<br>expenditure)|0.266***|0.099***|0.690***|0.979***|0.505***|
||[0.016]|[0.022]|[0.028]|[0.048]|[0.016]|
|Observations|4,685|4,674|4,675|4,052|4,685|
|**Urban**||||||
|Theta|-0.079|-0.564***|0.900***|1.646***|0.673***|
||[0.116]|[0.163]|[0.177]|[0.285]|[0.118]|
|Ln (# of people in HH)|-0.095***|-0.037*|-<br>0.187***|-0.050|-0.175***|
||[0.014]|[0.020]|[0.022]|[0.034]|[0.014]|
|Ln (per capita consumption<br>expenditure)|0.156***|-0.035*|0.417***|0.807***|0.358***|
||[0.013]|[0.019]|[0.020]|[0.032]|[0.014]|
|Observations|3,234|3,220|3,232|3,026|3,234|

Note: 1. Absolute value of z statistics in brackets. 2. * significant at 10%; ** significant at 5%; *** significant at 1%. 3. Other variables controlled for but not presented include household size, demographic characteristics of household members, , ownership of assets, and regional fixed effect.

28
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rainfall in the cluster, a dummy for whether the child was interviewed during the monsoon and region and survey year fixed effects. The second column adds child-level characteristics, and the third column adds characteristics of the mother, her partner, and household. We take this last set of estimates as the basis for distinguishing impacts of excess rainfall through the income and disease environment channels.

Returning to equation (4), the implied estimate of β is thus 0.15 and that of γ is - 0.02. The former magnitude, for example, implies that a 10% deviation in the past monsoon rainfall from its historic norm leads to a 0.15 standard deviation change in weight-for-age.<sup>10</sup> Disease environment effects induced by excessive current monsoon rainfall, captured by γ, are relatively small in magnitude, but statistically significant and negative, as the epidemiology suggests. Based on our discussion in Section 4, we infer that the income effect of past monsoon rainfall on weight-for-age lies between 0.15 (if there is complete catch-up from illness induced weight-loss) and 0.17 (zero catch-up growth). Fortunately, this is a fairly narrow window.

The last two columns of Table 3 report experiments with controls for proximity to a drinking water source and health posts and the interaction of these variables with current and past monsoon rainfall shocks.<sup>11</sup> Of course, endogenous placement of such facilities may render the estimates of the direct effects of these covariates spurious. Our interest, however, is in the interaction terms; i.e., whether better access to drinking water sources and health facilities mitigates the disease environment effect. Evidently, none of these interactions are significant.

Next we probe heterogeneity of impact across gender and age of children. Evidence from south Asia (see fn. 4) suggests that girls are particularly disadvantaged in intrahousehold reallocations as households respond to economic distress. As for age, while it is generally agreed that the first three years of life are critical for a child’s long term human development, less clear is the particular age “window” of greatest vulnerability to transitory shocks.

> 10 Reported coefficients have been scaled up by a factor of 100 to conform to this interpretation. 11 Rainfall has direct bearing on the availability of potable water in rural Nepal. Access to piped water is limited and households often rely on community taps, wells and sometimes even river water for drinking purposes. Among several basic health services, one of the most important delivered by health posts in rural Nepal is oral rehydration therapy for diarrheal disease.

12