rafmacalaba/data-use-mentions-extended huggingface dataset:
charge people for services that should be free, thus dissuading them from using them; Some people in the community do not trust immunizations, believing there is a plot by another identity group to infect the local people with a virus; Traditional birth attendants are more accessible, particularly for rural communities than skilled birth attendants in the health facilities and they offer more services (they will cook you pepper soup); There is no transport available for taking pregnant women to secondary health facilities; Health workers do not come to work because of security issues and doctors having been kidnapped in the past; It is culturally problematic for health workers to come into a person’s home, particularly if they are of a different gender, thus limiting the ability to monitor or advise on mosquito bednet utilization; People get upset if we test them for HIV but then we do not have drugs available for treatment so some health workers avoid testing.

10 Like the DHIS data, which are collected at the facility rather than household level, the PIGEON data are known to have a poor correlation with state level household survey data; as such, their use by ADG (as providing an 6
# **1 Introduction**

With over half of the world’s population engaged in social media (We Are Social, 2023), governments and organizations increasingly leverage these platforms in their social marketing campaigns to influence individuals’ beliefs, attitudes and real-world behaviors.<sup>1</sup> Typically, the effectiveness of these campaigns is assessed using online engagement and direct response metrics, such as likes, clicks, or website activity, along with “Brand Lift” surveys that measure short-term effects (Athey et al. 2023; Shawky et al. 2019). However, while these methods may be adequate to evaluate ads’ impact on purchase intent and sales (e.g., Bart et al. 2014; Johnson et al. 2017), they fall short when assessing the longer-term objectives of social marketing campaigns, such as those promoting public health. These campaigns require outcome data that go beyond what can be measured on ad platforms and may need offline data, as their goal is to influence offline behaviors. Moreover, even when ad content is proven effective, campaigns may fail to reach the intended audience due to the ad delivery optimization algorithms used by social media platforms (Lambrecht and Tucker 2019; Sapiezynski et al. 2022). Additionally, recent changes in privacy policies by regulators and tech companies, which restrict access to third-party data that facilitates targeting, can further hinder both the success and assessment of these advertising efforts (Goldfarb and Tucker 2011 _b_ ; Gordon et al. 2021; Johnson et al. 2020).

This paper addresses these measurement and targeting challenges by employing a newly-developed experimental methodology and toolkit (Rao et al. 2020) to evaluate the impact of a nationwide malaria prevention ad campaign and its heterogeneous treatment effects across sub-populations in India. By linking survey responses and administrative records with (potential) ad exposure data, our design enables us to assess the causal impact of online public health messages on offline behaviors and real-world outcomes, such as consistent bed net usage, timely
in the wide (narrow) band. Clearly, then, the productivity impact of temperature shocks greater than half a degree must be inferred by extrapolation out of our sample. Yet, most climate change scenarios for India over the next 30 years predict two and a half times this much warming.

With this caveat in mind, we proceed to the panel estimates in Table 4. The first two columns report annual weather shock impacts on gross revenue per hectare controlling for, respectively, a linear year trend (quadratic and cubic terms were not significant) and for year dummies. There is no compelling reason to choose one of these specifications over the other a priori, since macro shocks affecting land productivity are unlikely to be correlated with weather (recall that, by construction, our gross revenue measure is invariant to intertemporal crop price movements). Be that as it may, the estimates show that a 1<sup>_◦_</sup> C increase in annual temperature reduces gross productivity per hectare by 24 percent with a year trend and by 31 percent with year dummies.

Working with year dummies, we next disaggregate temperature and rainfall into their seasonal averages (or totals in the case of rainfall). Since kharif and rabi season temperature shocks are much less intercorrelated than the corresponding seasonal temperature normals, it should be easier to distinguish seasonal temperature effects with panel data than with cross-sectional data. In the event, we find that higher kharif temperatures reduce annual productivity by nearly twice as much as higher rabi temperatures (column 3 of Table 4). Together with the fact that the value of kharif output is typically double that of rabi output, this result implies that the temperature sensitivity of kharif and rabi production are roughly equal.<sup>23</sup> Our evidence also shows that rabi precipitation, relatively minor as it is, has no significant productivity impact, hence its exclusion from all subsequent specifications. Quadratic terms, shown in column 4, are also not significant for seasonal temperature, but there
with three and more children leave them with only limited possibilities for expenditure reduction and pushes adults towards a more active search for additional employment income. Single parent households are another vulnerable group that tend to rely more on search for additional earnings, and on help from relatives, compared to other households. Retirees, on the other hand, predominantly rely on cutting expenditures, as other strategies are often not available for them. In the next section we explore self‐reported coping strategies in detail, exploiting micro data on incomes and expenditures.

# 5. Effects of Separate Coping Strategies

## 5.1. Non‐labor income: Role of public transfers and in‐kind income

As we have noted before, the vulnerable and the lower‐income population groups were among the most hurt by the crisis. By default these groups are the target for social policy. However, they do not report that they can rely on the state when their financial well‐being deteriorates and data on households’ income in the form of government transfers supports this thesis. It appears that government transfers did not help mitigate the effects of the crisis.

Over 95 percent of the average decline in cash income in 2014‐2016 can be explained by the decline in earnings – wages, entrepreneurial income and other earned income. Government transfers and other types of income (including capital income like rent income and dividends) account for less than 5 percent on average, although they may be more important for certain groups. For those vulnerable to poverty, the drop 17
Gini coefficient|0.402|0.416|0.413|0.414|+3.48|+2.70|+2.94|
|Predicted mean per capita household<br>expenditure (including remittances) in<br>Ghanaian cedis|1,424,153|1,523,462|1,460,281|1,534,467|+6.97|+2.50|+7.46|
|N|5,998|5,998|5,998|5,998||||

Notes: Column (1) uses predicted income equations to measure the situation excluding remittances for all 5,998 households. Column (2) measures the situation for all households when only internal remittances (from Ghana) are included in predicted household expenditure. Column (3) measures the situation for all households when only international remittances (from African or other countries) are included in predicted household expenditure. Column (4) measures the situation for all households when both internal and international remittances are included in predicted household expenditure. Poverty calculations made using poverty line of 684,401 Ghanaian cedis per person per year, which is equivalent to poverty headcount index of 39.5 percent that is cited as Ghana poverty line in Ghana Statistical Service (2000: Table 2).

In 1999, US$ 1.00 = 2,394 Ghanaian cedis.

Source: Calculated from 1998/99 Ghana Living Standards Survey (GLSS 4).
# _Demand subsidies_

# <u>Interest Rate Subsidies on Mortgages Distributed by the Housing Bank.</u>

As noted above, this corresponded to interest rate subsidies on mortgages distributed by the CIH. The amount of the subsidy was 2 points of interest for mortgages taken out on dwellings valued at less than MDH 350,000 ( _régime général_ ), and 6 points for mortgages taken out on dwellings valued at less than MDH 200,000 ( _régime spécial_ ). All loans issued by the CIH for the construction of units valued at under MDH 500,000 were also exempt from VAT on loan interests (at a rate if 7%)<sup>45</sup> . In 1992, expenditures amounted to MDH 50 million under the _régime general_ , and MDH 130 million under the _régime special_ .

Although these subsidies were transparent and commercially simple to explain to buyers, they were not simple to manage because the State was obliged to keep track of every single loan and reimburse the CIH annually on the basis of the loan interest rates which varied. A special unit in the Treasury managed this system. On the incentive side, however, the existence of thresholds for eligibility under the _régime général_ and _régime special_ created strong incentives for developers to understate the value of the units in order to fall into advantageous brackets. As the same limits applied to various tax deductions and exemptions, it was common to understate the value of dwellings.<sup>46</sup> For the reasons mentioned above, interest rate subsidies were not adequately covering homebuyers, nor were they well-targeted. Moreover, since these subsidies were aimed at formal housing mortgage owners, they neglected those in the lower brackets of the income distribution.<sup>47</sup> A World Bank Study in 1995 based on documents from CIH showed that 80% of beneficiaries of _régime spécial_ loans declared annual incomes above MDH 60,000, which corresponds to deciles 6 and above in the income distribution scale. Subsidies under _régime spécial_ loans
Policy Research Working Paper 5976

# **Abstract**

Using a new database on Chinese food standards, this paper estimates the impact of voluntary and mandatory standards on its agricultural and food exports. The dataset covers seven Chinese products from 1992 to 2008. The findings here indicate that standards have a positive effect on China’s export performance. Standards signal to customers that products meet certain quality measures and promote information exchange. The benefits of increased exports outweigh compliance costs. Our results also show that theses positive effects are larger when the standards are consistent with international norms.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at axelmangelsdorf@bam.de, aportugalperez@worldbank.org, and Jswilson@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
instrument, in the absence of panel data we are unable to use firm fixed effects and hence there is always the risk that a firm’s adoption of accounting standards might be correlated with unobservables that affect firm growth. Finally, while the instruments in the countryaverages regressions can be considered to be exogenous since we are using historical institutional variables, there is the possibility of omitted variable bias in the absence of country fixed effects.

# **_Other Robustness_**

In this section, we describe several robustness checks of our main findings. First we investigate whether our results are driven by a few countries or firms. In particular, we investigate two sets of countries: African and Transition economies. Chandra et al. (2001) suggest that firms in African countries may exhibit different responses than the other firms in the sample. A report by the United States General Accounting Office, GAO-04506 (2004) analyzes several firm level surveys on Africa, including the WBES, and concludes that perceptions of corruption levels vary greatly for African countries, proving a challenge for broad-based US Anticorruption Programs. Ayyagari, Demirguc-Kunt, and Maksimovic (2008) argue that Transition economies are fundamentally different from other countries in their perceptions of protection of property rights.

# **Insert Table 5 here**

In the first four columns of Table 5 we run our preferred specification on different samples eliminating Transition and African countries. We find that while Financing and Crime are binding constraints as before, Policy Instability loses significance if we do not include these countries in the sample. These results suggest that the type of Policy 21
Cohen, Aaron J., Michael Brauer, Richard Burnett, and et. al. 2017. "Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015." _The Lancet, Volume 389, Issue 10082_ 1907-1918.

Cole, Matthew A. 2004. "Trade, the Pollution Haven Hypothesis and the Environmental Kuznets Curve." _Ecologial Economics_ 48 (1): 71-81.

Davis, S. J., and J. Haltiwanger. 1999. "On the driving force between cyclical movements in employment and job reallocation." _American Economic Review_ 89: 1234-1258.

Eskeland, Gunnar S., and Ann E. Harrison. 2003. "Moving to Greener Pastures? Multinationals and the Pollution Haven Hypothesis." _Journal of Development Economics_ 70 (1): 1-23.

Fagerberg, Jan. 1996. "Technology and Competitiveness." _Oxford Review of Economic Policy_ 12 (3): 39-51.

Glaeser, Edward L., and J.D Gottlieb. 2009. "The Wealth of Cities: Agglomeration Economies and Spatial Equilibrium in the United States." _Journal of Economic Literature_ 47 (4): 983-1028. Glaeser, Edward L. 1998. "Are Cities Dying?" _Journal of Economic Perspectives_ 12 (2): 139-160. Graff-Zivin, Joshua, and Matthew Neidell. 2012. "The impact of pollution on worker productivity." _The American economic review_ 102 (7): 3652-3673.

Harrell, Frank E. (2010) "Regression Modeling Strategies: with applications to linear models, logistic regression, and survival analysis". _Springer-Verlag New York, Inc._ , New York.

Harrison, Ann, Justin Y. Lin, and Lixin Colin Xu. 2014. "Explaining Africa's (Dis)advantage." _World Development_ 63: 59-77.

Henderson, J. Vernon. 1974. "The sizes and types of cities." _The American Economic Review_ 640656.

Jones, Charles,
**Figure 2.7: Direct and indirect and foreign services VA in gross exports, by country, 2011**

<!-- Start of picture text --> Direct and Indirect services VA in gross exports Foreign services VA in gross exports<br>28.9 15.7<br>27.5 21.2<br>19.9 21.1<br>32.1 16.0<br>14.4 14.0 15.6<br>23.8<br>20.0 21.8 21.1<br>16.7 17.3 14.7 15.6 13.2 13.5 17.6 15.3 14.2<br>11.4 11.8 10.7 9.7<br>6.6<br>4.8<br>OECD IND BRA TUR POL RUS BGR CHN HUN CZE HUN CZE BGR POL CHN OECD TUR IND RUS BRA<br>Domestic direct Domestic indirect Foreign<br>50 50<br>40 40<br>30 30<br>20 20<br>Percentage of total VA Percentage of total VA<br>10 10<br>0 0<br><!-- End of picture text --> Source: Authors’ calculations based on TiVA database, 2015.

Over time, the direct services value‐added share in gross exports has fallen (left‐hand panel of Figure 2.8), coming close to the OECD average. Turkey’s indirect share of services value‐added has increased somewhat, but has not significantly caught up with the OECD level. On the other hand, the foreign value‐ added share of services is increasing toward OECD levels (right‐hand panel of Figure 2.8).

**Figure 2.8: Direct, indirect and foreign domestic value‐added (VA) in gross exports (1995‐
**Figure A.7. Probabilities of Female-Headed Households Escaping Poverty in Second Year Conditional on Being Poor in First Year (percentage), West Bank and Gaza**

<!-- Start of picture text --> Palestine 2007-2009 Palestine 2009-2011 Palestine 2011-2017<br>FHH wo/children FHH w/children Non-FHH<br>Self-reported Females>0.5 Potential Educated fem. Self-reported Females>0.5 Potential Educated fem. Self-reported Females>0.5 Potential Educated fem.<br>45 45 45<br>40 40 40<br>35 35 35<br>Percentage (%) Percentage (%) Percentage (%)<br>30 30 30<br>25 25 25<br><!-- End of picture text --> Note: Estimates are obtained with synthetic panel data and weighted with population weights where the second survey round is used as the base year. The figure shows the percentage of the population that moves out of poverty in the second year. FHH characteristics are measured in second period. Dashed red lines represent the national average for each period. Household heads' ages are restricted to 25-55 for the first survey round and adjusted accordingly for the second survey round. Standard errors are estimated with 1,000 bootstraps.

66
.

In the context of poverty analyses, imputation methods have found numerous applications to address statistical inference problems across space and time. For example, Elbers et al. (2002, 2003, 2005) combine census and survey data to estimate poverty and inequality for areas considered too small for statistical inference with survey data alone, an exercise known as “poverty mapping”. For an overview of the statistics literature on small area estimation, see Rao (2003). One could alternatively also work with large surveys instead of censuses such as Demographic and Health Surveys (DHSs) to obtain imputationbased poverty estimates. Examples of the latter are Stifel and Christiaensen (2007) and Grosse et al. (2009) who used DHSs to respectively estimate poverty in Kenya and Bolivia. More recently, Christiaensen et al. (2012) used data from Vietnam and China to create a pseudo-panel out of repeated cross-section HESs using imputation methods.

Cross-survey imputation techniques have also been used to resolve problems of comparability between surveys of the same type over time, for example due to changes in the questionnaires. Kijima and Lanjouw (2003) and Tarozzi (2007) have re-estimated poverty rates in India using imputed data in an effort to validate the official figures on poverty during what became known as the “great Indian poverty debate” (see e.g. Deaton and Kozel, 2005).

2
malawi-phonesurvey</mark>

- <mark>Nigeria: http://bit.ly/nigeria-phonesurvey</mark>

- <mark>Uganda: http://bit.ly/uganda-phonesurvey</mark>

<mark>The approach to the phone survey questionnaire design and sampling is comparable across countries. It is informed by the template questionnaire and the phone survey sampling guidelines that have been made publicly available by the World Bank. These can be accessed through the following:</mark>

- <mark>Template Questionnaire: http://bit.ly/templateqx</mark>

- <mark>Manual: http://bit.ly/interviewermanual</mark>

- <mark>Sampling Guidelines: http://bit.ly/samplingguidelines.</mark>

<mark>All regressions and results presented in the paper are based on the publicly available data from the World Bank Microdata Library. Data is downloaded from the website and processed using Stata</mark> 15
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especially the informal labor market.<sup>25</sup> / This means that the private sector Is successful In attracting those individuals with 'unobserved' characteristics (ability, motivation, initiative, etc.) which tend to lead to high productivity and earnings. It is remarkable that the informal sector appears most successful at this, but Jt reflects perhaps well on the fact that this sector operates wlth the fewest rigidities and can respond most easily to supply and demand factors.

# Integrating the Regional and Sctoral Dimensions

The general conclusion of the previous sub-section Is that the role of general education and VTE in determining earnings and access to employment differs by type of employment considered. Since section 8 had demonstrated the Importance of the regional dimension as well, ideally, one would want to integrate the two dimensions. Unfortunately, the combination of three locations and three types of employment yields cells for which the CILSS provides only a small number of observations, and estimates of access and earnings equations become increasingly imprecise. Moreover, it becomes necessary to aggregate all types of formal VTE into one category. We therefore limit ourselves to presenting OLS estimates of the earnings equations for some of the combinatinas.. 3ne conclusions have, of course, a more tentative nature.

25/ Since the ptiblic/private sector choice equation in Table 34 was estimated with the private sector as base category ("zero"), the lambda's calculated for the private sector observations are negative. Rence the negative coefficient of lambda in the private sector earnings function indicaites positive selection into that sector. Similarly, the formal/informal sector choice equation used the informal sector as base category, so the negative coefficient of lambda in the informal sector earnings function indicates positive selection.
<mark>Lastly, in addition to the aforementioned, our analysis holds implications for enhancing the design of future longitudinal surveys to produce more and better data for addressing critical gaps in climate migration research. Desired improvements concern the collection of relevant contextual information regarding the ‘migrants’ in order to accurately classify different types of migration and unravel more clearly their drivers and impacts. This includes details about the nature and duration of migration episodes, as well as the characteristics, history, and purposes of migrant individuals. By gathering such additional information and carefully tracking migrant individuals over time, we could create migration variables that go beyond merely measuring the presence or absence of migrants within a household and provide more nuanced evidence to better inform targeted policies and programs.</mark> The rest of the paper is arranged as follows. Section 2 outlines the conceptual framework. Section 3 presents data sources and descriptive statistics, while Section 4 describes the methodology. Section 5 illustrates the results of the empirical analysis and Section 6 concludes.

# **2. Conceptual framework**

Our conceptual framework is placed at the crossroads of the NELM and poverty traps literatures. The distinctive features of the NELM theory, in contrast to the neoclassical counterfactual, lie in its consideration of migration decisions as household-driven rather than individual-oriented in the presence of incompleteness in insurance and credit markets (Stark & Bloom, 1985). Moreover, the objective function is not limited to maximizing expected income, but also includes minimizing risk exposure (Millock, 2015). Within this framework, where formal risk management tools are absent, households manage risk by strategically allocating family workers to diverse and uncorrelated (i.e., geographically dispersed) labor markets with the aim of diversifying income streams (e.g., via remittances). In the context of rational behavior models, households facing higher levels of risk assess the expected remittances against the marginal contribution of each family member to local agricultural productivity under stress. This establishes a connection between migration and agricultural productivity, the direction