rafmacalaba/data-use-mentions-extended huggingface dataset:
TABLE A11: Impact of text messages on individual intermediary outcomes: preventive health

||**ITT**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Mean**<br>**Control**<br>(4)|**Obs**<br>(5)|
|---|---|---|---|---|---|
|**_Health_**||||||
|**Health index**|0,07|( 0,04)|0,07|0,00|2.512|
|Insist on brushing teeth|0,01|( 0,01)|0,41|0,02|2.512|
|Ensure a healthy diet|0,03|( 0,01)|0,02|0,06|2.512|
|Keep child away from stove|-0,00|( 0,00)|0,93|0,00|2.512|
|Ensure child’s good hygiene|-0,01|( 0,02)|0,46|0,21|2.512|
|Wash child’s clothes properly|0,01|( 0,02)|0,60|0,12|2.512|
|Avoid giving coffee|-0,00|( 0,00)|0,32|0,00|2.512|
|Make child wash hands|0,00|( 0,02)|0,86|0,27|2.512|
|Smoke at a distance from child|-0,00|( 0,00)|0,74|0,00|2
poverty rate from 7 percent in 2016 to an official rate of 5.6 percent in 2022 appears to slightly underestimate the improvements in living conditions for the economically disadvantaged population segments.

Figure 17. Poverty and extreme poverty rates using SWIFT <!-- Start of picture text --> a. 2016 b. 2010<br>33.0 15.0 18.0<br>40.0<br>37.08<br>13.0 16.0<br>28.0 26.46<br>35.0 34.77<br>25.86<br>11.0 14.0<br>23.0<br>9.22 30.0<br>9.0 12.18 12.0<br>18.0<br>7.00 7.0 25.0 10.0<br>9.58<br>13.0 5.0 20.0 8.0<br>Current approach Swift Current Swift<br>approach<br>Poverty Extreme poverty (right axis) Poverty Extreme poverty (right axis)<br>Poverty Poverty<br>Extreme poverty Extreme poverty<br><!-- End of picture text --> Source: Own estimations using the presented approach and SWIFT, based on 2010, 2016 and 2022 HIES

- c. The GDP-poverty elasticity with a neutral distribution

Another approach involved scaling the 2022 observed consumption distribution by a fraction of the real GDP per capita growth, known as the pass-through rate (Mahler, Castañeda Aguilar & Newhouse, 2022). In this case, the exercise involves backcasting instead of nowcasting, yet the procedure remains identical. Data from HIES revealed that between 2010 and 2016 (comparable rounds), the mean per capita consumption increased by 13 percent in real terms. Concurrently, the World Development Indicators (WDI) Database from the World Bank shows that the real GDP per capita growth during
relatively poorly, with only 70 percent covered by a network.<sup>3</sup> Also according to the ITU, the number of mobile cellular subscriptions per 100 people equals 86 in Dominica (2021) and 64 in Haiti (2021).<sup>4</sup> In Saint Lucia we found that when 68.2 percent of individuals have a cell-phone, the share of households with a cell phone is over 95 percent. We therefore consider the share of households with access to a phone to be close to 100 percent in all countries, perhaps with the exception of Haiti where coverage may be a bit lower.

This study relies on a wave of survey data collected in the fourth quarter of 2021 jointly by the World Bank and the U.N. Development Programme (UNDP).<sup>5</sup> The response rate in this survey wave was 49.2 percent in Belize, 47.2 percent in Dominica, 43.9 percent in Haiti, 43.6 percent in St. Lucia, and 34.2 percent in Suriname. Survey weights were adjusted to account for non-response.

# **2.2 Disaster risk survey module**

The survey wave referenced above included a specific module to measure disaster risk awareness and preparedness for Belize, Dominica, Haiti, Saint Lucia, and Suriname. The survey module was adapted from a disaster risk reduction survey guide developed by the Red Cross Societies and Johns Hopkins Bloomberg School of Public Health (2013). It was further adapted for the purpose of the analysis presented in this paper and refined through the solicitation of extensive feedback from numerous development partners in the region.

The disaster risk survey module begins by asking respondents about perceived exposure to 12 types of disaster risks, that can be classified across four disaster risk categories as follows: meteorological (tropical cyclones / hurricanes, severe storms, and extreme rainfall), geophysical (earthquakes, volcanic eruptions, and tsunamis), climatological (heat waves, droughts
households report using illegal fishing methods, such as spear gun fishing, poison fishing, and dynamite fishing. As with any household survey data, there are always concerns that households under report illegal activities for fear they or their community may be adversely affected as a result. Another reason households may under report these types of illegal fishing activities may be related to the frequency they occur. Through discussions with fisher groups and MPA management staff on the use of illegal fishing methods, we learned that these methods— particularly dynamite fishing—tend to be used during specific times of the year (holidays, beginning of the school year) when households often need extra cash. As such, households may fail to report the use of these illegal fishing methods because they do not normally use them or simply because the last time they used such methods falls outside of the recall period.

15
.48%|
|<br> <br>PM 2.5(at x=40)<br>|-0.0479<br>|-8.064<br>|64.78%<br>|
|<br> <br>Note. The coefficients are for<br>different values for x, with th<br>coefficient forPM 2.5<sup>2</sup>, andδ|the African regions.<br>e following equation:<br>3the coefficient for|That is, they are based on Tab<br> δ1+ 2δ2x+ 3δ3x<sup>2</sup>, whereδ<br>PM 2.5<sup>3</sup>.|le 9. They have been calculated at two<br>1is the coefficient forPM 2.5,δ2the|
|<br>This table reports the associa<br>changes by one standard devi|<br>ted change (in percen<br>ation (in Africa).|<br> <br>t of one standard deviation o|<br>f the outcome in Africa) when pollution|

The impact is even more shocking when looking at pollution levels (Table 11). After relatively low PM 2.5 concentration levels, the effects turn negative, with a huge impact on both, employment growth and labor productivity growth. For example, at 40 μ , a reduction in one standard deviation of PM 2.5, approximately 8 units, is associated with an increase in /mm<sup>3</sup> employment growth in 93% of its standard deviation (0.172), for African firms. This huge effect translates in an increase of 16.03% in employment growth rate. At the mean level, the annual employment growth rate in Africa will go from 4.1% to 20.1%. For labor productivity,
# **3. Data and Methodology**

# **(1) Data**

In light of the significant relevancy of aid effectiveness issues in FCS relative to non-FCS, a critical question is whether aid is more effective in FCS than non-FCS. In order to measure _relative_ aid effectiveness performance in FCS vis-à-vis non-FCS, this paper uses the results of the PD survey due to its three advantages.

First, it is the most comprehensive survey on aid effectiveness, with 15 indicators (including subindicators) covering the five aid effectiveness principles agreed in the Paris Declaration<sup>6</sup> , namely ownership, alignment, harmonization, managing for results, and mutual accountability<sup>7</sup> . Second, it has had a significant and increasing participation from both partner countries and development partners. The number of participating countries increased from 33 in the 2006 baseline survey to 55 in the 2008 survey and 78 in the 2011 survey<sup>8</sup> . More than 50 development partner participated in the 2011 survey. The 2011 survey captured US$70 billion of aid which was about three quarters of the core aid provided to developing countries worldwide (OECD, 2011, p.18). Third, it was conducted three times, in 2006 (for 2005 baseline data); 2008 (for 2007 data); and 2011 (for 2010 data). Three surveys allow us to conduct a comparison between 2005 and 2010 (although each round of the survey had different country coverage).

Among the 15 indicators, 10 indicators are relevant for the analysis of this paper, as they measure the management and delivery of aid while the remaining 5 measure quality of country systems etc. (Table 1).

**Table 1: Ten PD Survey Indicators**

|**Principle**|**Indicators**|**How Measured**|
|---|---|---|
|Alignment|**Ind
# **3 Results**

## **3.1 Determinants of the gender gap in 2008**

Using the 2008 data, we first run a simple Ordinary Least Squares (OLS) regression of logged values of agricultural productivity in XOF/ha on female household headship. In Table 3, we find a 21 percent agricultural productivity gap to the detriment of female household heads (column 1), which is slightly attenuated to 17 percent once introducing region fixed effects (column 4).<sup>3</sup> Although we find a negative and significant gap of 23 percent for export crops, which are defined as cashews, cacao, coffee, cotton, rubber and palm oil, this gap disappears when adjusting for cross-regional differences through the introduction of fixed effects (column 5). The gender gap in food crop productivity remains unchanged at 24 percent (column 6).

**Table 3. OLS regressions of agricultural productivity without control variables (2008)**

|||**Log [Sel**<br>|**f-reported Pr**<br>|**oductivity (**<br>|**XOF/ha)]**<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||All crops|Export crops|Food crops|All crops|Export crops|Food crops|
|Female-headed households|-0.234***|-0.264***|-0.279***|-0.182***|-0.079|-0.270***|
||(0.054)|(0.094)|(0.056)|(0
**Table 7. Expenditures, Savings, Insurance and Loans**

|||**Midlin**<br>|**e Survey**<br>||||**En**<br>|**dline Surve**<br>|**y**<br>|
|---|---|---|---|---|---|---|---|---|---|
|||Control|Treatm|ent|- Control||Control|Treatmen|t - Control|
|||<br>Group|<br> <br>||<br>||<br>Group|<br> <br>|<br>|
||Obs|Mean|Coeff.||_p-value_|Obs|Mean|Coeff.|_p-value_|
|**Per capita HH expenditures last 30 days (FDJ)**||||||||||
|Total|914|13,462|-649||_0.428_|879|10,106|675|_0.274_|
|Durables|914|274|-153||_0.231_|879|93|139|_0.118_|
|Non-durables|914|13,188|-496||_0.534_|879|10,013|536|_0.377_|
|Food|914|7,502|-339||_0.618_|879|4,666|46|_0.778_|
|**Ln of per capita HH expenditures**||||||||||
|Total|914|9.22|0.09|_**_|_0.021_|879|9.04|0.04|_0.293_|
|Durables|
-13.256|-14.190*|
||(9.325)|(8.860)|(8.929)|(9.591)|(9.441)|(7.679)|
|F-frst stage IT|7.5|7.1|5.9|5.9|5.0|5.1|
|F-frst stage IT*competition|12.1|12.1|11.1|11.8|10.6|9.7|
|Sargan|0.17|0.13|0.15|0.15|0.15|0.17|
|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|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

33
Johnson, and Robinson, 2001; Frankel and Romer 1999). Our study relies on two separate IV strategies, obtained through the combination of several different trade instruments: “lagged air transport freight capacity,” “the mean GDP of top five trade partners,” and “the mean distance to the world top five largest traders.” The use of these instruments is justified on both an intuitive and technical grounds below.

# _The IV1 strategy – Integrating Air transport freight capacity_

The economic intuition for the study’s choice of instruments is predicated on refinements of the Frankel and Romer (1999) instrument by Feyrer (2018). <mark>Trade by air represents a material component of trade.</mark> While <mark>over 80% of the volume of international trade in goods is carried by sea,</mark><sup>23</sup> <mark>air cargo transports over US $6 trillion worth of goods, including those where transport speed is critical, accounting for a material 35% of world trade by value.</mark><sup>24</sup> <mark>F</mark> eyrer (2018) incorporated both sea and air distance to create an instrument. However, he relied on flight distance as a proxy for air-channeled trade. This instrument includes passenger flight and introduces the contribution of non-trade growth factors (e.g., tourism, migration) into the analysis, and distorts the coefficient estimating the effect of trade on GDP growth. Feyrer (2018) considers this inflated coefficient as a ‘globalization effect’ which comprises pure trade effects, and other effects associated with changes in factors such as technology transfer or foreign direct investment (FDI).

Our study further refines the “predicted exogenous trade instrument” by focusing purely on air-freight capacity, which measures only the transportation of freight/cargo and excludes passenger air travel. Instrumenting trade with air freight captures the change in trade logistics, spurred, in part,
ernment consumption minus social security contribution and capital consumption allowances. Government investment spending is de...ned as development expenditure from the Ministry of Finance government spending accounts. The data on tax revenues, Tt, are also taken from the same source, and is de...ned as the sum of total indirect taxes , direct taxes on households, social security contributions and other capital transfers received. Finally Yt is the cyclical component of real output extracted by a Hodrick-Prescott ...lter. Following the de...nition laid out in Stock and Watson (2002), growth below trend is classi...ed as a growth recession.<sup>14</sup> The real GDP data is taken from Abeysinghe and Gulasekaran (2004).

The ordering of the variables in Xt assumes that shocks in tax revenues and real output have no contemporaneous e¤ect on government spending. As argued in Blanchard and Perotti (2002), this identifying minimum-delay assumption may be a sensible description of how government spending operates given that in the short-run government spending may be unable to adjust to spending in response to changes in the ...scal and macroeconomic conditions. On the other hand, Barro and Redlick (2011) argue that the government spending shock in a structural VAR is likely to be endogenous, as a higher GDP leads to higher taxes and therefore to more government spending. However, Barro and Redlick (2011) use yearly data and their argument, thus, is unlikely to hold at the quarterly frequency. Due to decision lags, contemporaneous discretionary government spending is unlikely to respond within a quarter to any news about the economy.

The identi...cation of tax shocks is more problematic in the framework outlined in this paper. As noted in Blanchard and Perotti (2002) identi...cation of tax shocks depend on purging innovations in revenues of automatic responses to output. This could be achieved by imposing a contemporaneous coe¢ cient on the elasticity of revenue with respect
The NRVA surveys are nationally representative, with the 2011 edition providing detailed information on 18,399 households across all but two provinces. The data include household composition, household expenditures, educational attainment, and a range of other socioeconomic indicators. The NRVA surveys also contain information on variables related to fragility, including perceptions on security, revenues from opium, and shocks from violence. In addition, they indicate the household location and report information on the occurrence of local shocks such as severe weather.

The credibility of the proposed approach hinges on the consistency of the data used in the models. Since the databases used in each case are built on unrelated primary sources, disconnects cannot be ruled out. One key indicator that the two models share is the local intensity of conflict. A high correlation between the conflict-related indicators in the two databases should give some reassurance about the consistency of the two models. A further assessment of the reliability of the data used in the analysis comes from its consistency with data from extraneous sources.

The provincial database used to estimate Model 1 includes information on conflict intensity, the number of incidents, and the number of terrorism-related casualties. The NRVA survey used to estimate Model 2 includes data on theft and violence directly suffered by households and on household perceptions of insecurity. This data can be aggregated at the province level using survey weights. To ensure that correlations are meaningful, all seven indicators are scaled by population. Separately, the Asia Foundation Survey of the Afghan people (2015) includes questions regarding the household’s fear for safety and the household concern about bad security conditions. These survey data can also be aggregated at the province level using survey weights.

Some of the correlation coefficients between the various indicators of conflict are quite high (table 1). Not surprisingly, households that report having suffered from theft or violence also have higher perceptions of insecurity. Similarly, conflict intensity is higher in provinces experiencing a larger number of conflict-related incidents. While these two correlation coefficients involve indicators from the same database,
<br>[0.591]<br>[0.609]<br>159,348<br>159,348<br>159,348<br>159,348<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>ed on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data for SG<br>ural Development, Government of India.<br>e order10_−_2. Each column presents results from a separate regression. All regre<br>es are in logs. The sample is composed of all women aged 18 to 60 interviewed <br>dent variable in panel A is the percentage of days in the past seven days that an in<br>percentage of days in the past seven days that an individual spends in private work<br>mates are computed using sampling weights. The standard errors in square brack<br>Yojana. NREGA: National Rural Employment Guarantee Act.<br>_<_0_._01|
|_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_Source_: Author’s analysis bas<br>data is from the Ministry of R<br>_Notes_: All estimates are of th<br>effects. All dependent variabl<br>2007 to June 2008. The depen<br>dent variable in panel B is the<br>2 in the main article. All esti<br>Sampoorna Grameen Rozgar<br>_
This study contributes to three strands of literature. First, it expands the knowledge of the health consequences of environmental hazards in the tropics. Second, it examines the potential role of household resources and practices in mitigating these impacts. Third, the analysis evaluates the past and future role of sanitation infrastructure, particularly toilet ownership, in reducing climate-related health vulnerabilities.

Regarding our first contribution, we add to the environmental health literature by examining the link between weather extremes and the prevalence of diarrhea, respiratory symptoms, and the spread of various diseases. The correlation between diarrhea and temperature has been studied in diverse contexts, finding that an increase in temperature, or prolonged dry seasons, is associated with a higher number of reported diarrhea cases (Amqam et al., 2019; Mertens et al., 2019; Xu et al., 2014; Zhou et al., 2013). At the other extreme, heavy rainfall has demonstrated similar effects. In countries such as India, Mozambique, Indonesia, and Peru, extended periods of intense rainfall have been associated with a higher incidence of diarrhea (Mertens et al., 2019; Horn et al., 2018).

We expand this literature by examining child morbidity in Cambodia, an understudied country. The literature assessing the health impacts of weather shocks in Cambodia is scarce. Choi et al. (2016) conducted a study examining the influence of weather factors on dengue fever incidence in Cambodia, revealing a significant connection between higher temperatures and increased precipitation with elevated dengue fever cases. On the other hand, Saulnier et al. (2018) analyzed the impact of seasonal floods on health using six years of national health data and flood maps. Their study focused on the health consequences of flooding events. While these two studies examined separate hazards, our analysis jointly considers the effects of weather conditions, including temperature and precipitation (potentially flooding), on health outcomes in Cambodia.

We add to the literature on the impacts of fine particulate matter ( _PM_ 2 _._ 5
international prices (terms of trade). An expansion of an unskilled-intensive industry may act as a positive factor demand shifter for unskilled labor. In terms of employment, these trends should be reflected in an increase in the use of unskilled labor and a shift towards sectors employing unskilled workers most intensively. The previous section showed that terms of trade can explain part of the observed evolution of changes in skill wage premia (just for tertiary workers), potentially operating through changes in the structure of production.

The second explanation is related to technological change. Information and communication technology may have required sophisticated skills to operate them in the 1990s and thus could have initially been more complementary to high-skill workers (see Acosta and Gasparini, 2007, for the case of Argentina), but technological diffusion to a larger population group may in part explain the trend reversal in the demand for skilled labor. If this is the case, firms would no longer pay a high premium for technology-related knowledge. The evidence for some countries in the region indicated a generalized increase of the use of skilled labor within all sectors in the 1990s, which can be interpreted as a manifestation of skill biased technological change (see for instance Gasparini and Cruces, 2010, for Argentina).

Finally, it is also possible that skill mismatches in the labor market are playing a role as well. The limited progress achieved in terms of school results as measured by standardized tests in Latin America suggests that the fall in returns to education may be related to deficient educational quality (as measured by international test scores such as PISA) (World Bank, 2011). This would imply that additional years of education did not necessarily improve workers’ general knowledge and skills. Alternatively, the skills provided in the formal education sector may be of relatively good quality but may not be those most needed in the labor market. Employers’ surveys in the region already identify 17