rafmacalaba/data-use-mentions-extended huggingface dataset:
16

# **7. Performance**

The product is expected to be piloted in the next season – the Yala season (May - Sep 2019), which depends more heavily on irrigation. To improve its efficacy, it is important to track its performance in terms of pay-outs and compare these pay-outs to the actual experience of farmers in terms of production losses and reasons for those losses for at least two years. It is also important to use additional historical storage data, if possible, and to complement the dataset with other sources of data, including satellite data sources.

Based on the actual rainfall and irrigation tank level data<sup>25</sup> in Anuradhapura, Figure 5 below presents simulated historical pay-outs. For the Maha season, the years of 2003, 2004, 2008 and 2017 were particularly bad years, when crop losses were incurred due to dry spells. The simulated pay-outs show that the pure rainfall index insurance product (blue bars) would have triggered pay-outs in all these years. However, it would have also triggered pay-outs in other years, such as 1995, 2009 and 2015, when pay-outs would not have been necessary for farmers with access to major irrigation. Hence, a pure rainfall index product would have triggered more pay-outs than necessary, leading to a more expensive product and not addressing the lower risk profile of irrigated farming. Used together, the rainfall and irrigation index (red bars) would have triggered pay-outs only in the particularly bad years, which would be a more relevant and affordable product for farmers with access to major irrigation.

**Figure 5. Product Simulated Pay-outs for Anuradhapura (in LKR)**

<!-- Start of picture text --> 12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>Rainfall index Rainfall and Irrigation index<br><!-- End of picture text
available in a second data set representative of the same population, this second data set can be used to estimate the missing variable in the first data set. A prerequisite is that the two data sets share a set of regressors that are sufficiently correlated with this missing variable.

Consider the following standard linear model for household log expenditure: where _x_ denotes a vector of independent variables (e.g. variables on demographics, education, employment, housing conditions, asset ownership) including the constant, _u_ denotes a zero expectation error term, and where the subscripts _i_ and _t_ indicate household _i_ and time _t_ . The superscript ‘T’ indicates matrix transpose.

We have two types of data sets: Household Expenditure Survey (HES) data and Labor Force Survey (LFS) data. Both types of surveys contain the regressors _x_ but only the HES contains the household expenditure _y_ , and only for selected years. In our case, we will consider the period 2001-2010 for which we have two years of the HES (2001 and 2007) and the full period of the LFS. The objective is to use 8
2.466)||(4.186)|(3.368)|
|Rule of Law|||11.160***|||8.782**|
|(lagged 1 year)<sup>a</sup>|||(3.457)|||(3.669)|
|R-squared|0.2652|0.4013|0.6338|0.3580|0.4503|0.5823|
|N|20|20|20|20|20|20|

_Notes:_ See Annex 1 (Table A1) for details on explanatory variables. Constant terms are not reported. Countries covered: AT, BE, BG, DE, DK, EE, ES, FI, FR, HU, IE, IT, LV, NL, PL, PT, SE, SI, SK, UK. (*) , *, **, *** - estimates significant at 11%, 10%, 5%, 1% level, respectively.

_Sources:_ Dependent variable: Riedmann and Fischer (2008), based on _Special Eurobarometer_ 284. Explanatory variables - see Annex 1 (Table A1) _._ Model estimates - own calculations.

47
compute, for each farm, the area cultivated with winter and summer crops annually from 2019 to 2023.<sup>23</sup> Comparing cropped area before and after receipt of program funds between PSG recipients and eligible SAR registrants who did not receive PSG resources then can provide an estimate of program impact.

To illustrate the locations of farms in our data, we use the centroid of all parcel centroids as a proxy for the farm’s location. Figure 1 panel A shows the map for all farms registered in SAR with a size below 120 ha. Successful applicants in blue and unsuccessful ones in yellow. Areas with yellow dots only correspond to conflict-affected zones identified by Government that were not eligible to benefit from PSG. Excluding these provides us with a sample of 43,003 farms (19,660 treated and 23,343 control) that cultivate land exclusively in non-conflict affected areas. Panel B of figure 1 maps these farms, illustrating that, apart from conflict zones, they cover Ukraine’s entire territory.

Data on registered area and crop cover can be used to assess targeting of PSG resources in two ways, namely by identifying if (i) farms that cultivated more than 120 ha (or that did not cultivate at all) received PSG grants and (ii) self-declared area matches with the total area of parcels to which farmers have a registered right that are cultivated. Table 4 illustrates that of 20,445 farms with parcel data (including those with land in conflict areas), 31 had zero cultivated area based on satellite imagery while 46 had registered and cultivated area greater than 120 ha implying that, based on remotely sensed data, they would not have been eligible to receive PSG resources. Also, with 19.15 ha, mean self-reported area is 0.33 ha larger than what was obtained using remote sensing. Despite a strong incentive to over-report area cultivated, this suggests mis-targeting was rather limited, especially if compared to widespread reports about irregularities in earlier farm support programs in Ukraine
**Table 3. Variance predictions across estimation methods**

|||_Household surv_<br>|_ey_<br> <br>|_C_<br>|_ensus_<br>|
|---|---|---|---|---|---|
|_Indicator_||_Günther and Harttgen (2009_|_)_<br>_SAE_|_SAE_|_SAE_|
|||_Total sample_|_Total sample_|_Communes in household survey_|_Communes not in household survey_|
|||_(1)_|_(2)_|_(3)_|_(4)_|
|Communes,|number|540||540|11|
||_Sample mean_|0.346|0.349|0.343|0.336|
|Predicted|5|0.217|0.277|0.259|0.292|
|variance,|25|0.305|0.312|0.328|0.333|
|_selected_|50|0.354|0.348|0.344|0.334|
|_percentiles_|75|0.395|0.375|0.357|0.334|
||95|0.447|0.424|0.426|0.395|

_Sources:_ Calculations using EHCVM (Enquête Harmonisée sur le Conditions de Vie des Ménages 2018–2019; Harmonized Survey on Household Living Standards 2018–2019), Living Standards Measurement Study, World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/4292; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics and Demography, Dakar, Senegal, <u>https
we have used micro-data from 38 developing countries to estimate the mortality effects of water and sanitation, and to quantify the degree to which improvements in water and sanitation infrastructure could contribute to achieving the mortality objectives established in the Millennium Development Goals. We have shown that the mortality gains from improved water and sanitation are large: on average, access to improved water and sanitation lowers the odds of a child dying before the age of five by about 5 to 8 percent, while private access to piped water and toilets connected to a septic tank or sewage system can reduce the odds of early child mortality by about 16 percent.

> 21 This figure excludes India, Nigeria, Bangladesh and Indonesia; given that these countries receive very low development aid per capita, their inclusion would have led to misleadingly high ratios of water and sanitation investment costs relative to international development aid.

> 22 OECD Statistics: http://stats.oecd.org/Index.aspx?DatasetCode=ODA_RECIP#

> 23 Note that the cost data from Hutton and Haller (2004) reflect estimates from 2000.

28
2012 in Peru can be attributed to non-communicable diseases such as cardiovascular diseases, cancers and diabetes. In response to these findings and their negative implications for the country’s economic development, the Peruvian government has implemented several interventions. Yet, these are mostly intended to eliminate undernutrition, whereas overnutrition receives less attention (Acosta and Haddad 2014). At the same time, 65 percent of all urban households in Lima that include at least one adult consume food away from home (FAFH) on a daily basis. According to data of the National Household Survey 2013 (Encuesta Nacional de Hogares, ENAHO) and Farfan et al. (2017), Peruvian households spend an average of 27 percent of their food expenditures on eating outside the home. Because the nutrition transition is a rather recent development in Peru and the consumption of FAFH is high across urban areas, gaining a better understanding of the determinants of overweight and obesity is highly relevant.

Even though some cases of obesity and overweight are linked to genetic and metabolic factors, the obesity epidemic itself is related to behavioral determinants and the social and physical environment (Sturm and An 2014; Cannuscio et al. 2014; Katare and Beatty 2018). There is growing agreement among experts that the food environment is a key driver of rising trends in excess body weight (Steeves et al. 2014).<sup>1</sup> Increased

> 1 This comprises the types of food sources that are accessible to an individual and to which consumers are exposed in those environments (the availability of healthy and unhealthy foods, food prices and so on).

2
<sup>9</sup> To investigate the presence of a secular trend in the probability of having each tenure duration, we proceed to the second-stage estimation, which consists of linear regression with the marginal effects of each year dummy ( _πt_ ) as a dependent variable and a linear time trend as the single independent variable (equation 3). We bootstrap the standard errors of the second-stage estimations to account for the small sample error heteroskedasticity:<sup>10</sup>

> 8 We use the two-digit occupation classification because in 2010, the labor force surveys transitioned from the ISCO 88 classification to the ISCO 08 classification. A correspondence between both classifications is possible only at the two-digit level.

> 9 For the second-stage regression, we could also use the probit estimated coefficients (πt). However, using the marginal effect simplifies the interpretation of our results.

> 10 We estimate the standard errors of the second-stage regression using 500 bootstrapped sample draws (Imbens and Kolesar 2016).

12
- Erickson, C. A., Owusu-Nantwi, V., & Owensby, F. (2015). The government spending multiplier: Evidence from county level data. The Social Science Journal (Fort Collins), 52(3), 358-363.

Eskesen, L.L., & Lueth, E. (2009). "Republic of Korea: Selected Issues," IMF Country Report 09/263.

- Espinoza, R. A., & Senhadji, A. S. (2011). _How strong are fiscal multipliers in the GCC?: An empirical investigation_ . Washington, DC: International Monetary Fund.

- Estache, A., Ianchovichina, E., Bacon, R., & Solomon, I. (2012). _Infrastructure and employment creation in the Middle East and North Africa_ . Herndon: World Bank Publications.

- <mark>Eusepi, S.</mark> & <mark>Preston, B. (2018a). Fiscal foundations of inflation: Imperfect knowledge,</mark> _<mark>American Economic Review</mark>_ <mark>, vol. 108, 2551– 89.</mark>

- <mark>Eusepi, S.</mark> & <mark>Preston, B.  (2018b). The science of monetary policy: An imperfect knowledge perspective,</mark> _<mark>Journal of Economic Literature</mark>_ <mark>, vol. 56, pp. 3– 59.</mark>

- Fatas, A. & Mihov, I. (2001). _The effects of fiscal policy on consumption and employment: theory and evidence_ . CEPR Discussion Papers, 2760.

- Fazzari, Steven M., James Morley & Irina Panovska. **(** 2015). “State-Dependent Effects of Fiscal Policy.” _Studies in Nonlinear Dynamics and Econometrics_ 19(3): 285–315.

- <mark>Fernández‐Villaverde
The second set of findings focuses on the magnitude and persistence of the parenthood penalty across various subgroups in Indonesia. These results offer important insights for policymakers seeking to target interventions effectively. First, we found that households with an extended family member living in the household experienced the fastest recovery from the birth penalty. For these households, the penalty became insignificant by the second year after childbirth. In contrast, for households without extended family members, the penalty persisted until year seven. Second, we observe significant differences in the magnitude and persistence of the birth penalty based on education level. Highly educated individuals faced larger penalties (approximately 30% in the first year) and for a longer period compared to their less educated counterparts (around 20% in the first year). Furthermore, we find that rural women experienced the highest birth penalty in year three, after which the trend began to reverse. In contrast, urban women saw this reversal begin in year four. These results suggest that expanding access to childcare could significantly reduce the persistence of the birth penalty, particularly for highly educated urban women, who experienced the largest and most persistent penalties. This subgroup represents an important target for policy interventions, where childcare provision could have the greatest impact.

The rest of the paper is structured as follows. Section 2 provides an overview of the Indonesia Family Life Survey (IFLS) data and the institutional context of the Indonesian labor market. Section 3 outlines our empirical strategy, including the DID methodology and the identification assumptions. Section 4 presents our main findings, including a detailed heterogeneity analysis and robustness checks. Finally, Section 5 discusses the policy implications of our findings and concludes with suggestions for future research.

# **2 Institutional Context, Data, and Descriptive Statistics**

## **2.1 The Indonesia Family Life Survey**

The Indonesian Family Life Survey (IFLS) is a longitudinal multitopic household survey that is representative of 83% of the population and covers 13 of the 27 provinces in Indonesia. Individuals
future small-scale farm growth and farm consolidation” (Sitko and Jayne 2014, p. 199).

> 13 The estimate by Jayne _et al._ (2014a) is based on binned DHS data. Sitko _et al._ (2015) noted that “Data on state lands (which include farm blocks) are from a spatial boundary dataset in wide circulation within the Ministries of Agriculture, Finance, Planning, and other governmental and nongovernmental entities, although the provenance of this dataset cannot be determined with certainty. To evaluate the validity of these datasets, we confirmed that mapped boundaries conformed to knowledge held by managerial and operational personnel within the relevant ministries” (p.13). 14 Land relations in Zambia are governed by the 1995 Land Act. After long deliberations, a National Land Policy was adopted in May 2021. Studies have long pointed out that limited capacity and local presence of relevant state institutions makes implementing this ‘replacement’ paradigm (Bruce & Migot-Adholla 1994) difficult, implying that relevant laws may have little effect on local realities (Atwood 1990; Pinckney & Kimuyu 1994).

7
( _yc_<sup>_∗_)canbeanyindicatorfunction,suchasoneoftheFGTpovertyindicators.</sup> 4. Use the model parameter estimates obtained in step 1 to obtain new sample welfares, _ych,s_<sup>_∗_,for</sup> every location _c_ as follows: ln( _ych,s_<sup>_∗_) =</sup><sup>_xch,sβ_ˆ0+</sup><sup>_η_</sup> _c_<sup>_∗_+</sup><sup>_e∗_</sup> _ch,s_<sup>_._</sup>

- (a) The estimates _β_<sup>ˆ</sup> 0 come from step 1.

- (b) _ηc_<sup>_∗_isthesameonegeneratedinstep2.Specifically,alllocationspresentinthesurveyare</sup> matched to the census locations and the same value of _ηc_<sup>_∗_that was simulated for that location</sup> in step 2 is applied to the survey households within the same location.

15
Poverty measurement for FDPs remains at a very early stage. One should remember that the global poverty measurement spearheaded by the World Bank in the 1980s has required several decades to develop into a structured system of microdata collection at the country level and a theoretical and empirical body of knowledge that could make use of these data. Microdata collection for FDPs can build on this infrastructure but also requires its own specific surveys, measurement methods, and theoretical and empirical adjustments. Multilateral organizations such as the World Bank and UNHCR have started this process and this has encouraged the research community to follow, but this is only the beginning of a long process.

22
# **4.2 Comparison of Different Measures and Policy Analysis**

We begin by comparing the three data sources visually and exploring the correlations between them. We then demonstrate how the data sources compare when used to measure the impact of a particular policy. The focus is on the set of policies that were implemented in 2020 as a response to the COVID19 pandemic. As in many other countries, the Government of Senegal instituted a number of policies starting on March 14, 2020, with the goal of limiting mobility and thus curbing the spread of COVID19.<sup>15</sup> Literature from other contexts has documented important externalities from these types of policies, including large decreases in pollution (Venter _et al._ , 2020). Therefore, it is possible to use the timing of these policies in Senegal to study how the different sources of data measure this externality in Dakar. Additionally, this is a time for which we have data from all three data sources, which makes it possible to compare measured impact across all three.

To study the policy impact, a difference-in-differences style design is used. Air pollution data right before and right after the policy is compared to air pollution data during those same pre- and post-policy periods in a different year. This strategy makes it possible to control for seasonality, which plays an important role in PM2.5 levels. The specification estimated is: where _PMyt_ is PM2.5 at hour _t_ of year _y_ . _I_ 2020 _y_ is an indicator equal to one if the year is 2020 and zero otherwise. _Postt_ is an indicator equal to one for all hours after the policies were put in place (March 14 to May 12) and zero before the COVID-19 policies (January 23 to March 13). _Xyt_<sup>_′_isavectorofcontrols</sup> that includes weather variables (temperature and humidity) and indicator variables for hour of the day, the day of the week and week of the year (i.e., an
||||||
|Chinese competition|0.222**|0.195|0.232*|0.196*|0.193*|0.183|
||(0.103)|(0.124)|(0.122)|(0.110)|(0.103)|(0.125)|
|_R_<sup>2</sup>|0.73|0.73|0.76|0.77|0.77|0.77|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects.

Source: Authors’ calculations using data from ENTIC, INEGI 16
Table A.21: Analysis of enumerator bias distribution

|VARIABLES|Brazil|Vietnam|Senegal|Bangladesh|
|---|---|---|---|---|
|Share of Signifcantly Diferent Interviewers|0|0.09|0.08|0.11|
|Number of Signifcantly Diferent Interviewers|0|13|2|4|
|Number of Interviewers|8|145|25|37|
||Ghana|India|Korea|Kenya|
|Share of Signifcantly Diferent Interviewers|0|0|0|0.2|
|Number of Signifcantly Diferent Interviewers|0|0|0|2|
|Number of Interviewers|44|18|9|10|

Note: Data from the Firm-level Adoption of Technology (FAT) surveys in Brazil, Vietnam, Senegal, Bangladesh, Ghana, India, and Korea. Significantly different interviewers are identified from the regressions of employment on interviewer dummies with controlling for stratification information (e.g., sector, size, and region). For each country, the share of significantly different interviewers is computed by dividing the number of interviews conducted by significantly different interviewers by the total number of interviews.

we re-interview 100 randomly selected firms with a short version of the questionnaire. For those firms, we randomly select three business functions and ask about the presence of the relevant technologies.<sup>39</sup> Both the original and back-end interviews in the pilot are conducted by phone by different interviewers.

Despite using phone interviews which are subject to greater measurement error than faceto-face interviews, comparison of answers from the pilot reveals that 73% of the answers were the same across the two interviews.<sup>40</sup> We estimate a probit model to assess the likelihood of consistent answers between the original and the back-check interviews, controlling for firmlevel fixed-effect. Reporting the use of a technology in the back-check interview is associated with 80.6% of likelihood
How much international variation in child can height sanitation explain?

Dean Spears<sup>_∗_</sup> First circulated: 10 December 2012 This version: 17 January 2013

**Abstract**

Physical height is an important economic variable reflecting health and human capital. Puzzlingly, however, differences in average height across developing countries are not well explained by differences in wealth. In particular, children in India are shorter, on average, than children in Africa who are poorer, on average, a paradox called “the Asian enigma” which has received much attention from economists. This paper provides the first documentation of a quantitatively important gradient between child height and sanitation that can statistically explain a large fraction of international height differences. This association between sanitation and human capital is robustly stable, even after accounting for other heterogeneity, such as in GDP. I apply three complementary empirical strategies to identify the association between sanitation and child height: country-level regressions across 140 country-years in 65 developing countries; within-country analysis of differences over time within Indian districts; and econometric decomposition of the India-Africa height difference in child-level data. Open defecation, which is exceptionally widespread in India, can account for much or all of the excess stunting in India.

> _∗_ Princeton University. Wallace Hall. Princeton, NJ 08540. dspears@princeton.edu. I am grateful for helpful suggestions from Luis Andres, Robert Chambers, Juan Costain, Jean Dr`eze, Marianne Fay, Ariel Fiszbein, John Newman, Doug Miller; from seminar participants at LBSNAA, Princeton, and the World Bank and WSP; and especially from Anne Case, Diane Coffey, Angus Deaton, Michael Geruso, and Jeff Hammer. Errors and interpretations are my own. Portions of this paper have previously circulated as “Sanitation and open defecation explain international variation in childrens height: Evidence from 140 nationally representative household surveys” and “What does the NFHS-3 (DHS) tell us about rural sanitation externalities?,” both of which this working paper supersedes.

1