rafmacalaba/data-use-mentions-extended huggingface dataset:
standards on maternal dietary diversity, the WHO (2013) recommendations on child immunizations schedules, the WHO and UNICEF (2006) guidelines on drinking water and sanitation, as well as the 1990 UNICEF strategy on improved nutrition of children and women. In Bolivia and Peru country specific immunization practices are used. These immunization schedules follow closely the WHO guidelines.

The prevalence of adequacies varies across countries and resource access, but in most contexts children were most likely to be adequate in care and least likely to be adequate in environment and health. Adequate care was the facet which was most likely to be met in all countries with the exception of Indonesia, where the prevalence of adequate food was greater than the prevalence of adequate care (Figure 5a, 6a and 7a). In all countries it is adequate environment and health that is most lacking. With the exception of Bolivia, in the most recent survey for each country only 10 % or less of the children lived in a household where the housing infrastructure and health opportunities were adequate (Figure 7a). However, for Bolivia no information on sanitation was collected and thus the measure is not comparable to the other countries where sanitation measures were included.

# **3.2.1 Adequate food security**

In most countries either adequate dietary diversity or adequate meal frequency is the component of food security which is least prevalent among children. In four of the seven data set with comparable information we find meal frequency to be the least prevalent component and in three it is dietary diversity (Figure 5a). The two South American countries have above 80% of the children consuming foods from at least four of the seven groups. Indonesia also has more than 80% of children with diverse diets, however, the measure is not comparable to the others since it is based on household food consumption and not child‐specific food consumption. Except for Ethiopia and Zimbabwe, more than half of the children under 6 months are exclusively breastfed. The highest prevalence of exclusively breastfed children are
on forward GVC participation shares in Table 6 imply that such factors have more negative or smaller positive e¤ects on forward GVC trade than on traditional exports.

# 5.3 Cross-Country Cross-Sector Di¤erence-in-Di¤erence Analysis

The cross-country regression models described in previous sections use IVs to establish causality between determinants and GVC participation. However, there may be a concern that the IV strategy is not su¢ cient given that some other determinants, such as factor endowments, institutions, and connectivity may also be endogenous. As an alternative approach, we use country-sector data with year-to-year variability and apply the country-sector di¤erence-in-di¤erence regression model inspired by the Rajan and Zingales (1998) framework. This framework relies on interactions between a country’s factor endowments and the intensity with which a sector uses a particular factor and has been used to link countries’ sectoral trade performance to various sources of comparative advantage: factor endowments (Romalis, 2004), institutions (Levchenko, 2007; Nunn, 2007; Costinot, 2009), and ...nancial development (Beck, 2003; Manova, 2008).<sup>37</sup> We use this di¤erence-in-di¤erences regression model to estimate the role of several determinants in explaining GVC participation and exports at the country-sector level: where c; s and t stand for country, sector, and year subscripts, respectively. Y is a measure of GVC participation in levels or a measure of gross exports, X is a vector of country factor endowments, m is a set of sectoral intensities of use of these endowments from a benchmark country (the US), Z is a vector of country-sector trade policies, which include tari¤s imposed at home on imports of ...nal goods, and on imports of intermediate inputs, and tari¤s imposed by destination markets and "; is an i.i.d. error.<sup>38</sup> The speci...cation controls for country-year ..
%||
|Access to finance|What percentage of the start-up capital of the business came from each of the|9.5%||
||Share of investment financed externally||12.6%|
||Whatpercentage ofyour sales are made on credit?|17.9%|30.9%|
|Connectivity to global knowledge|Does your firm use the internet?|20.5%|45.4%|
||Has the firm used a consulting service in the last 3 years?|8.9%||
||Whatpercentage ofyour sales are made to the following:{Foreign firms}|1.4%||
|Sample size||610|609|

Source: 2009 SLLSE (Booster Sample) and 2011 WBES. Some questions differ slightly across survey-years.

**Table 10: Firm-level Innovation and the Business Environment in Sri Lanka**

||**(1)**|**(2)**<br>**In the last three years**<br>**itdd**|**(3)**<br>**, has this establishment**<br>**itdd**|**(4)**<br>**:**<br>**itdd**|
|---|---|---|---|---|
|**Dependent variable (0/1 indicator):**<br>****Probit coefficients displayed**|**introduced new**<br>**products or**<br>**services?**|**nrouce any new or**<br>**significantly improved**<br>**methods of manufacturing**<br>**products or offering**<br>**services?**
We combine administrative data from the bank with six waves of detailed survey data – to study how commitment impacted respondents’ financial behavior both at the partner bank and more broadly. First, we find robust demand for commitment. Seventy-two percent of individuals in the treatment group signed up for the product, and just 13 percent of those who signed up dropped out before the 18-month savings cycle ended. Second, the product more than doubled balances at the partner bank both during and after the commitment period. We also observe a marginally significant 6 percentage point (18 percent) decline in overdrafts in the six months following the release of the commitment amount. In the longer run, however, treatment effects on overdrafts disappear.

Our third main result underscores the importance of taking a broader view of consumers’ finances when evaluating savings products. Although the commitment product did not crowd out other cash savings, we do observe a significant increase in the share of respondents who took on new debt during the commitment period. Point estimates on debt stocks are roughly equal to point estimates on total savings, and as a result our treatment estimates on net savings (savings less debt) are close to zero and not statistically significant.

This interpretation changes dramatically when studying heterogeneity in treatment effects by baseline overdrafter status, however. “Heavy overdrafters” (those with above median overdraft histories at baseline) did save more at the partner bank during the commitment period, but this treatment effect disappears after the commitment amount is released. Moreover, the product significantly _decreased_ heavy overdrafters’ other formal sector savings during the commitment period while increasing the rate of debt taking. These results are consistent with the hypothesis that heavy overdrafters are naïve hyperbolic discounters, who end up undoing their commitment by borrowing. “Light overdrafters” (those with belowmedian histories) exhibited a very different response: the commitment product significantly increased other formal sector savings both during and after the commitment period, with no significant impact on debt. This suggests that light overdrafters benefited from the savings product through a
selection bias because we are only observing firms with more than 20 employees it appears to be making it harder to reject cleansing.

Finally, to address the concern that exit is a protracted process and that by focusing on

> 11 Our preferred specifications do not control for capital because controlling since this would introduce sample selection bias as the response rate for capital is far lower for measures of the capital stock than for other variables. It is also missing in the 1996 data, requiring it to be estimated for that year (see Appendix).

23
of social protection systems.

**_The informal sector comprises both workers preferring formal sector employment (involuntarily informal) and those preferring the qualities of unregulated entrepreneurship._** In motivation surveys from Brazil and surveys of workers leaving formal employment in Mexico, 60-70% report preferring informal self employment for reasons of independence and higher earnings.<sup>6</sup> Though difficult to measure job characteristics make wage comparisons _faux amis_ for establishing segmentation, numerous studies have established higher earnings among informal workers, particularly the urban self employed.<sup>7</sup> Recent studies for Brazil and Bolivia that attempt to control for selectivity into the sector find that predominantly informal salaried workers at the bottom of the salary scale within any skills set face a significant wage disadvantage vis-á-vis formal jobs.<sup>8</sup> Attempts to compare welfare more directly find a premium to being informally self employed in Bolivia (Arias and Sosa 2004), consistent with the sociological evidence from Mexico finding self employment is often considered a step up from salaried work.  As discussed below, women appear to

> 5  See background papers by Maloney and Saavedra

> 6, Cunningham and Maloney (2002), Maloney (2004)

> 7 See Yamada (1996), Marcouiller, Ruiz and Castilla and Woodruff (1997), Maloney (1999), Saavedra and Chong (1999), Gong and van Soest (2002).

> 8 Tannuri and Pianto (2003) and Tannuri, Pianto and Arias (2004).

5
the second Sustainable Development Goal (SDG) of zero hunger. Most of those studies employ panel methods to quantify the impacts of specific events on poverty outcomes. However, to the best of our knowledge, no study has further exploited these results with the objective of effectively predicting the welfare impacts of weather shocks at sub-national levels. This is mainly due to data limitations for investigating a highly complex and heterogeneous relationship between environmental conditions and welfare. Moreover, it is now well-established that both idiosyncratic and covariate shocks, including weather shocks, induce some degree of inter-temporal variability in households’ welfare and measuring poverty at one point in time can be misleading; the non-poor today may be the poor of tomorrow (Hill and Porter, 2017; Skoufias et al., 2021). As a result, another strand of literature has moved the focus away from event-specific impacts to grasp a broader understanding of welfare volatility by developing methods for quantifying the risk of being poor, usually referred to as “vulnerability to poverty”<sup>2</sup> (see Gallardo

> 1Source: World Bank, World Development Indicators and World Bank (2020).

> 2In this paper, we revise the concept of vulnerability and provide a definition that is consistent with the notion of vulnerability used in catastrophe risk models. We define vulnerability at the household-level as the relationship between rainy season conditions and food consumption in the following lean season.

3
# **Comparison of Replacement Households**

|||**Provinc**<br>**Mass**|**e level inte**<br>**media trea**|**rvention**<br>**tment**|||**Commun**|**ity treatmen**|**t sample**|**District level i**|**nterventio**|**n**<br>**School**|**componen**|**t sample**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Contr|ol mean|Treat|ment mean||Contr|ol mean|Treatm|ent mean||Con|trol mean|Treat|ment mean||
||N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|
|**Average HH size**|153|4.588|132|4.719|-0.131|153|4.588|179|4.587|0.002|122|4.852|115|5.130|-0.278|
|**Percentage of HH heads that are male**|152|0.921|131|0.931|-0.01|152|0.921|179|0.860|0.061|122|0.877|114|0.860|0.017|
|**Percentage
We broaden our presentation of the statistical results by summarizing the evidence obtained from the multi-variate econometric specifications, where a fuller set of controls in multiple specifications explaining service performance were included. As per description of the simple model and legend with the variables presented in the previous section, we performed econometric tests through OLS specifications with the various service performance outcomes as dependent variables, and with city characteristics, city and country governance and globalization indicators, as well as country characteristics, as independent variables.

Table 4 below presents the combined synthesis of the results of all econometric specifications. This synthesis presents the magnitude values (+,0,- on the left) and significance (with asterisks, on the right), which we obtain by averaging the magnitude and significance of the regression results of 21 different OLS specifications, for each city performance indicator.<sup>17</sup> From the implementation of the fuller econometric model, for which the results are summarized in Table 4 above, we obtain a number of salient results. There is an effect of City Globalization on performance using both the Taylor (Global City variable) and the UN (water, electricity, telephone, and sewerage access variables) databases. This effect is relatively modest in some cases yet present, though with varying levels of significance depending on the type of service, controls used, and model specifications. The variable that is most correlated to city globalization (global city (3)) is the quality of electricity.

Other city globalization proxies, such as city web portal with information on steps to start a firm (business dummy), and whether the budget is transparently available on the web (budget dummy), show positive and significant results, but these results are not robust across all service variables. In particular, access to cell phones (as opposed to telephones), and the overall quality of infrastructure are significant, while the other service variables, such as access to water, electricity and sewerage, and to telephone lines show less significance
<br>Remote sensing data to be used to target the<br>number of CCEs to be conducted at harvest in<br>insurance units, and to monitor CCE reports.|
|Involvement<br>of private<br>sector|No private sector involvement|Domestic insurance companies authorized to<br>compete with AICI to offer mNAIS. Private sector<br>reinsurance capacity. Possible use of private<br>sector in conducting or auditing CCEs.|
|Basis risk|Basis risk, whereby a claim<br>payment to an individual<br>farmer does not adequately<br>reflect yield experience.|Reduce the size of the insured unit from the level<br>of the administrative block to the individual<br>village Panchayat for major crops.<br>Expand coverage to include cover for prevention<br>of sowing, replanting, post-harvest losses and<br>localized risk, such as hail losses or landslide.|
|Adverse<br>selection|Adverse selection caused by<br>non-borrowing farmers being<br>able to purchase cover well<br>into the growing season when<br>pre-existing drought<br>conditions are known.|Uniform sales cut-off dates to be introduced in<br>advance of the sowing season for all farmers.|

The move away from unfunded ex-post government subsidy, combined with planned changes to crop cutting experiments, should allow claim settlement within 1 month of harvest, rather than the current 9-12 months. The addition of an early ‘on account’ payment based on weather indices can allow claim payments to farmers even before harvest, if the weather is sufficiently poor. This combines the strengths of area and weather based index insurance, providing farmers with both the speedy payment of weather-index insurance and the lower basis risk of area yield insurance. Central and state governments will also benefit from improved budget management, where the full cost of government support to mNAIS will be known at the start of each crop season
a shock is studied with a difference-in-differences approach, a 10 percent decline of nighttime lights in the treated group with respect to the control group reflects an economic impact of 6.5 percent of GDP.<sup>30</sup> We concluded the paper with two applications. First, we constructed a nighttime light-adjusted measure of GDP growth and showed that more developed countries and those with higher statistical capacity tend to have smaller measurement errors in their national accounts. Widespread efforts to strengthen statistical offices and systems in EMDEs are hence indeed likely to contribute to better economic measurement. However, we also found that low voice and accountability results in higher overestimation of GDP growth, highlighting a crucial role for public scrutiny in ensuring proper measurement. A proper debate about GDP growth in academia, think tanks, and the media is

> 30To translate changes in nighttime lights to changes in GDP, the inverse elasticity, that is 1 _/β_ , needs to be used.

28
# **Interviewer Design Effects in Household Surveys: Evidence from Sudan**

Alvin Etang, Habtamu Fuje, and Christopher Root<sup>1</sup>

**Keywords** : Survey interviewer, Interviewer effects, Measurement error, Mixed effects regression

**JEL Classification** : C10, C81, C83, C89

> 1 Alvin Etang is a Senior Economist in the Poverty and Equity Global Practice of the World Bank. Habtamu Fuje is an Economist with the Poverty and Equity Global Practice of the World Bank. Christopher Root is an Agricultural Economist who works an independent consultant. The authors would like to thank staff at the Sudan Central Bureau of Statistics who worked on the pilot Agricultural Production Survey project. The authors are grateful to Kristen Himelein (Senior Economist, EEAPV) and Gbemisola Oseni (Senior Economist, DECPM) for peer reviewer feedback and suggestions on an earlier draft, and to Eiman Osman (Consultant, EAEPV) for her support with the study. <mark>The findings, interpretations, and conclusions of this paper are those of the authors and should not be attributed to the World Bank or</mark> its Executive Directors. The authors may be contacted at christopher.n.root@gmail.com; hfuje@worldbank.org; aetangndip@worldbank.org.
pensions and the recipients are not capable to tell how much they have received due to this particular program. Finally, there are some programs targeted at smaller groups of the population that neither can be simulated due to the lack of data, nor are available in the database, e.g. payments to families with foster/adopted children, payments to careers of the disabled relatives, child benefits for families of individuals who are in the military service, etc.

Figure 1 includes a diagram that illustrates this sequence of simulations in the model. The starting point of simulations in RUSMOD are net market incomes that are reported in survey data. However, 5
|buyers (i.e. those that they sell to)<br>Industrial diversity within a district<br>(higher values mean lower industrial|
||Yes|diversity|District,Year|-|diversity)|
|||Market|||Accessibility indicator measuring|
|**First-Nature**|No|Access|District, Year|+|access to neighbouring regions|
|**Geography**|No|Port|District|-|Travel time (in minutes) to the<br>closestport|
||Yes|Roads|District, Year|+|Road Density per square km|
||||||Proportion of households with|
||Yes|Electricity|District, Year|+|access to electricity|
||||||Proportion of households with a|
||Yes|Telephone|District, Year|+|<br>telephone connection|
|**Infrastructure**|||||Proportion of population with a|
||Yes|Education|District, Year|+|high-school degree|
||Yes|Wages|District, Year|-/+|Non-agricultural hourly wages<br>Proportion of high-income|
||Yes|Wealth|District,Year|+|<br>households|
|||Labour|||Besley and Burgess classification|
|**Institutional**|No|regulations|State|-|(pro-employer=0, pro-labour=1)|
||Yes|Riots|District,Year|-|Incidents of social unrestper capita|

# **3.3 Sources of Data**

Firm-level data on export behavior (i.e. when a firm starts to export and following entry into export markets, the value of exports as a proportion of sales) and on output and inputs is drawn from the Prowess database. Prowess is a corporate database that contains normalized data built on a sound understanding of disclosures of over
variation between years can hide some of the actual weather impacts, extreme conditions in one of the few survey years can bias some of the results. To mediate some of these shortcomings, panel data sets that cover larger time horizons are needed.

# **5. Conclusions**

The results in this paper show that weather variation and income changes over time in rural Vietnam are related. They also warn against an oversimplification of this relationship as there is a breadth of weather impacts, which are highly dependent on socioeconomic groups, climate zones, and income activities, and even the severity of weather conditions. In addition, income effects do not only depend on the intensity of the rainfall or temperature shock, but also on the timing of the shock and the location‐specific optimal rainfall and temperature levels.

Despite this complexity the data allow identifying some general patterns. Warmer temperatures and heat extremes have income‐reducing effects in all climate contexts and for all socioeconomic groups, including poorer households and ethnic minorities. While most income activities are negatively affected by hotter conditions, staple crops, forestry and fishing seem to be less sensitive to temperatures. The income effects of rainfall are more complex. Some findings indicate that more rainfall is beneficial in drier places but harmful in wetter places. Interestingly, the incomes of poorer households seem to be negatively affected by wetter conditions, while those of wealthier households are more impacted by drier conditions. This finding implies that the livelihoods of poor rural people are more vulnerable to severe rainfalls and flooding, which for example occurred at the end of 2013. The data indeed confirm that an increase in rainfall and wet months compared to 2012, which was also a wet year, had a negative impact on income growth between 2012 and 2014.

Bringing the variety of income effects from different types of weather variation together with the uncertainty about how weather conditions will be altered by climate change makes any effort to quantify the income and poverty impacts of climate change extremely challenging – not even considering that the weather sensitivity of
Tabulation on 2000 Population Census of the P. R. China, Table L7-2.

A main expectation underlying the advocacy for town development is to have the towns to absorb excessive laborers who would otherwise head for large cities. In the following two tables, we present the geographic composition of in-migrants to see if towns absorb excessive labor force from the rural areas. In Table 6, we categorize town in-migrants by original place of residence. Data suggest that towns in three provinces have indeed absorbed rural population. In Zhejiang, 38 percent of in-migrants were originally rural residents, living in villages. In Henan and Sichuan provinces, 53 percent and 37 percent came from rural areas respectively. If we include those who were originally rural town residence, then the majority of town in-migrants came from rural areas. Whereas the percentage of in-migrants from city has been small, accounting for 4.6%, 15.6% and 12.4% respectively for Zhejiang, Henan and Sichuan.

Table 6 Town in-migration by original residence place: 2000

|Province<br>Zhejiang|village<br>residence<br>38.49|town rural<br>residence<br>(%)<br>45.19|town urban<br>residence<br>11.69|urban<br>residence<br>4.64|
|---|---|---|---|---|
|Henan|52.99|20.38|11.00|15.63|
|Sichuan|37.34|27.89|22.38|12.40|

Sources: calculated from Tabulation on 2000 Population Census of the P. R. China, Table L7-6.

7