rafmacalaba/data-use-mentions-extended huggingface dataset:
covered, only a small number of these messages by chance occurred in the week preceding an interview in the L2T survey.

Columns 1-3 in table 6 provide simple pooled regressions, as before, adding more control variables moving from left to right. The coefficient for receiving an official SMS reported in column 1 suggests those who received messages in the week preceding the interview report about .3 more reported behaviors on average, significantly higher reporting than those who do not report a message. Adding more control variables to this basic estimation (columns 2 and 3) does materially affect the coefficient of interest. The sign and significance of the control variables suggest that people living in urban areas report more risk reducing behaviors on average, as do higher income people (columns 2 and 3). However, the inclusion of controls for month of interview absorbs the differences in reporting in the basic descriptive OLS regression (column 3). This suggests that in a descriptive sense, reporting differences cannot be distinguished from a time trend.

Focusing again on changes in a fixed effects framework in columns 4 and 5 of table 6 shows that messages in the past week lead to about .47 additional behaviors. Controlling for income (column 5) does not materially affect the coefficient of interest. Thus, for both definitions of treatment, the results suggest that messages significantly increased reporting risk reducing behaviors.

**Table 6: Receiving Official SMS in Previous Week vs. Behaviors Reported**

|||y=number of<br>|behaviors repo<br>|rted (out of 9)<br>||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Gov. SMS past week|0.306***|0.458***|0.091|0.467***|0
two were chronic disease. By 2017 none were infectious/neonatal and two were violence.<sup>6</sup>

> 4 CONAPO. Demographic Indicators in Mexico 1990 to 2050. Consulted on 09/15/2018 in http://www.conapo.gob.mx/work/models/CONAPO/Mapa_Ind_Dem/index_2.html

> 5 World Bank. Data from the World Bank. Consulted on 09/15/2018 in https://datos.bancomundial.org/indicador/sp.dyn.le00.in?end=2016&start=1997&year_low_desc=true

> 6 Global Burden of Disease Study. Results for Mexico. Consulted on 09/15/2018 in https://vizhub.healthdata.org/gbdcompare/

4
reserves the right to adopt measures related to the list of products and export and import licensing. These limitations do not appear in their respective AFAS schedules.

Vietnam has pre-committed to liberalize retail distribution. It will phase out in five years after the date of entry into force of the agreement the Economic Needs Test (ENT) to which it currently subjects the establishment of outlets for retail services (beyond the first one) (Annex I of Vietnam, page 6).<sup>16</sup>

**Figure 4: STRI for Retail Distribution - Doha Offers, ‘Best PTA’, TPP Commitments and Applied Policy of 2015**

<!-- Start of picture text --> Retailing: Doha, PTA, TPP and applied policy<br>BRN MYS CHL SGP MEX CAN VNM USA PER JPN AUS NZL<br>Doha PTA<br>TPP Policy '15<br>Note: Absense of a bar means there are no restrictions (STRI score is zero)<br>100<br>75<br>50<br>25<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Source: The World Bank TPP Database, 2016

# **Transportation**

Transport services are among the most protected services in many countries (Borchert et al., 2014). One of the ironies of services trade negotiations is that these services which are most in need of coordinated liberalization, have, de jure (air transport) or de facto (maritime transport), often been carved out of the scope of the GATS and other earlier trade agreements. The TPP too has not fully grasped this nettle. Although the TPP expanded the coverage of auxiliary air transport services to include ground handling, airport operations and specialty air services, the cross-border air traffic services are excluded. Thus we read in Article 10.2.5 that: “This Chapter [on cross-border trade in services]
an area where the use of new technologies and data sources can be highly beneficial. In general, augmenting panel survey data with new types of data like digital trace data would allow the collection of information in domains that are normally arduous to cover in household surveys due to respondent burden and recall issues, such as geographic moves and migratory events.<sup>32</sup>  Complementary to (longer-term) efforts for more and better tracking, the collection of (full or partial) **migration histories** of selected household members is an effective – and more immediate – solution for increasing the availability of migration data in panel household

> 30 This includes the adoption of new best survey practices introduced as part of these experiences such as the systematic elicitation of contact information of all household members which is a key prerequisite for implementing remote follow-up surveys. 31 In a recent working paper exploiting LSMS-ISA panel data to study the effects of cumulative climate shocks on long-term migratory flows in Sub-Saharan Africa, Di Falco et al. (2022) point out that the information about the motives of moving is only available for two of the five countries included in their analysis ( _i.e._ , Ethiopia and Nigeria).

> 32 This type of applications would require consent from survey participants and possibly participation in additional tasks with the risk of increasing panel dropouts (Trappmann et al., 2022), a risk that, however, could be outweighed by the potential gains in using these data.

20
require much more individualized intra-household information or they will need to be nearly-universal in coverage.

This dispersion of undernourished individuals across the distributions of household wealth and consumption entails that countries with a higher overall incidence of undernutrition tend to be countries where a larger share of the undernourished are found in non-poor families. This suggests that the need for broad coverage in social policies (rather than policies finely targeted to poor households) is especially great in countries with a high incidence of undernutrition. Rather than folding nutrition schemes into household-targeted antipoverty programs in such countries, emphasis should be given to nutritional interventions with near universal coverage, such as comprehensive school feeding (with explicit nutrition supplementation), maternal health care and universal sanitation services.

In addition to documenting the limitations of relying on household poverty data to reach nutritionally deprived individuals, we throw some light on why those limitations are so severe. For the subset of countries for which we also know adult male BMI, we have shown that the extent of intrahousehold inequality entails that the bulk of underweight women and undernourished children are found in households where the male head appears to be adequately nourished. In exploring the cross-country patterns, we find that richer countries (in terms of GDP per capita) within Africa tend to have child stunting more concentrated among the wealth-poor, suggesting greater scope in those countries for targeting wealth-poor households as a means of reaching children with longer-term nutritional deficiencies. But this is not so for child wasting. In countries with a higher female literacy rate one tends to find a greater concentration of underweight women in poor wealth strata. By contrast, female literacy has little power for predicting whether children’s undernutrition is more concentrated among the wealth poor. There is no sign that countries with lower average access to improved water and sanitation tend to have undernourished women and children more concentrated among the wealth poor; while there is little doubt that improved water and sanitation makes for better nourished people, intra-household inequalities appear to be a more plausible explanation for our main findings on the relationship with household wealth than
>4%<br>3%<br>2%<br>1993 1995 1997 1999 2001 2003 2005<br>Annuity Rate (RV-04) Central Bank Bonds Corporate Bonds<br><!-- End of picture text -->

# **_6.2. Comparisons with Other Estimates for Chile_**

This section compares the MWRs estimated in this study with those estimated by James, Iglesias and Martinez (2005), who compute MWRs using data on quoted annuities from 4 insurance companies in March 1999 and March 2003. The MWRs are calculated using 3 alternative mortality tables, 2 different discount rates, and 2 different premium levels.
.GDP.MKTP.KD)|
|Exports of copper|8.2%|% of initial PC GDP|2021|UN-Comtrade|
|Capital-to-GDP ratio|3.6|[0, inf]|2021|PWT 10.01|
||||Equalize the VPMK across sectors in||
|Non-resource sector|3.15|[0, inf]|2019<br>Equalize the VPMK across sectors in||
|Copper sector|0.46|[0, inf]|2019||
|Reserves of natural resources|||||
|Copper sector|81|Million of Metrics tons|2017|U.S. Geological Survey|
|C. Assumed paths of standard drivers of grow|th, 2021-50||||
|Total Investment|22.4%|% of GDP|2004-2019 average|WEO total investment - WEO public investment|
|Private, Investment|17.7%|% of GDP|2004-2019 average|WEO total investment - WEO public investment|
|Public, Investment|4.7%|% of GDP|2004-2019 average|WEO total investment - WEO public investment|
|TFP, non-resource sector|0.6%|Annual growth rate|LTGM-TFP|Aggregate TFP growth from PWT 10.01|
|TFP, mining sector|-0.5%|Annual growth rate|TFP average growth in Chile in 2000-17|Productivity Commission Chile (2016)|
|Human Capital|0.2%|Annual growth rate|LTGM-HC|PWT 10.01, Human Capital index (hc)|
|Demographics:|||||
|Population|0.7%|Annual growth rate|2021-2050, average|ILO's estimates, total population|
|Working-age population|65.2%|% total population|2021-2050, average|ILO's estimates, population aged 15-64|
|Population, Male|49.2%|% total population<br>
four different surveys conducted in Morocco:

- Demographic and Health Survey (DHS) of 2003/04

- Multiple Indicator Cluster Survey (MICS) of 2006/07

- National Population and Family Health Survey (ENPSF) of 2011

- National Human Development Observatory (ONDH) panel baseline round of 2012<sup>8</sup>

- All these surveys are relatively similar in nature. They all have (a) a household

component that allows for assessing the circumstances in which children live and (b) an individual component that collects data from women and children on ECD.

The DHS of Morocco for 2003/04 was conducted by the Ministry of Health (MOH) in collaboration with the Measure DHS program, ORC Macro, and the Pan Arab Project for Family Health (PAPFAM). The survey includes detailed information on fertility, maternal and child health, the nutritional status of children and mothers, family planning, child mortality, maternal mortality, disability, chronic diseases, and sexually transmitted infections (STIs) and HIV. The survey collected information at the household, woman, and child level. The resulting data are nationally representative after the application of sample weights. The 2003/04 round was fielded from October 2003 to February 2004. In fielding, 11,513 households and 16,798 women aged 15–49 years were interviewed successfully. Anthropometric data were also collected directly through measurement of children.

> 8 Statistics presented in this paper may be different than those in the reports for the various surveys because of differences in the universe (children versus mothers or households) or other elements of data treatment.

3
productivity differences. Our approach to constructing an instrument that represents historical sub-district level malaria incidence is to find exogenous sub-district characteristic(s) that can essentially be used as “weights to recover the variations in malaria prevalence across different sub-districts from the district average malaria data. A large literature on malaria in tropical countries identify a few ecological characteristics that can potentially be used to generate the sub-district level historical malaria estimates. Among the candidate ecological variables, rainfall is perhaps the most reliable predictor of spatial malaria variation in the specific context of Sri Lanka (Briet et al., 2003, 2008). We thus use rainfall in a sub-district as the relevant exogenous characteristic to uncover the incidence of historical malaria across sub-districts. The effects of rainfall on the incidence of malaria, however, can be different in different countries.<sup>10</sup> In Sri Lanka, the relationship between malaria and rainfall is negative across geographic space, as higher rainfall washes out the breeding grounds of _Anopheles Culicifacies_ , and _Anopheles Subpictus_ , the main malaria vectors in Sri Lanka (Clemesha, 1934; Rustomjee, 1944; Briet et al., 2008). An interaction of rainfall with historical malaria is used as an instrument in our empirical analysis. As we discuss in the empirical strategy below, all regressions control for rainfall directly to capture any productivity effect of rainfall.

The HIES 2002 also collected information on education, age, gender, ethnicity and religion. The individual and household level explanatory variables are defined from the HIES 2002. HIES 2002 however did not collect information on health status of the household members. We draw information on the chronic illness of household heads from HIES2006 data (Table A.20, p.99 in the final report on HIES 2006/7). The information on anemia prevalence rate among nonpregnant women is drawn from Demographic and Health Survey 2006/7 (Table 6, p.19, DHS report (2009)).<sup>11</sup> The area characteristics including rainfall,
1. World Bank 2008a.

2. In a few instances surveyors and supervisors reported that respondents were somewhat taken aback at the series of questions, given the personal nature of the topic. This concern was particularly relevant in economies with large security risks, such as Mexico and Zimbabwe, and in economies where personal finances are widely regarded as a private matter, such as Cameroon, Italy, and Portugal. There were also reports from the field that the terminology and concepts used in the survey were entirely new to some respondents. Although efforts were made to include simple definitions of such terms as accounts and debit cards, the unfamiliarity and complexity of the topic were still reported to be a hurdle in several economies, including Afghanistan, Cambodia, Chad, and rural Ukraine. Overall, however, the rate of “don’t know” or “refuse” answers was very low. For the core questions (those not filtered by other questions), “don’t know” or “refuse” responses made up less than 1 percent of the total and no more than 2 percent in any region.

3. The Gallup World Poll has been used in previous academic studies. For example, Deaton (2008) uses Gallup World Poll questions on life and health satisfaction and looks at the relationships with national income, age, and life expectancy. Gallup World Poll questions are also used by Stevenson and Wolfers (2008) and Sacks, Stevenson, and Wolfers (2010) as part of their research to analyze relationships between subjective well-being and income; by Clausen, Kraay, and Nyiri (2011) to analyze the relationship between corruption and confidence in public institutions; by Demirguc-Kunt, Klapper, and Zingales (2012) to study changes in trust in banks over the financial crisis; and by Stevenson and Wolfers (2011) to examine trust in institutions over the business cycle.

4. In some economies oversamples are collected in major cities or areas of special interest. In addition, in some large economies, such as
|6 months|2013|
|2016||Model|✓|✓|6 months|2013|
|2017||Model|✓|✓|6 months|2013|
|2018||Model|✓|✓|6 months|2013|
|2019||Model|✓|✓|6 months|2013|

_Note:_ Own elaboration based on yearly questionnaires and micro-data from EPHPM. ”Self-emp. Ag. module” refers to the existence of additional module on income from agricultural activities from self-employment. ”Imputed Rent” refers to the method used for estimating imputed rent: for the period 2011-2019 it is estimated from a model that imputes rent for owners using characteristics from households that rent and declare rental value; for 2001 - 2010 imputed rent is estimated using average shares of imputed rent, by percentile, from 2011 - 2019. ”13/14vo identifier” refers to the existence of question to workers whether they receive 13th and 14th additional salaries. ”Formality” refers to the existence of a question asking for formality status of workers. ”Self-employment reference period” presents the differences in recall period for income from self-employment. Finally, ”Census weights” show the changes in Census weights used for the survey (* pre-census weights for 2013).

41
Policy Research Working Paper 9868

# **Abstract**

The informal sector is an important source of livelihoods and jobs for a vast majority of people in developing countries. However, there is concern that it may undermine growth and development of the formal sector. For instance, the growth literature indicates that research and development activity and innovation are a key driver of long-term growth. How does the competition that formal sector firms face from informal sector firms affect research and development activity by the formal firms? The present paper attempts to answer this question using firm-level survey data for small and medium-size enterprises in a large cross-section of mostly developing countries. The results show that higher informal competition leads to greater a likelihood of spending on research and development by formal firms. For the most conservative baseline specification, a one standard deviation increase in informal competition leads to an increase of 5.2 percentage points in the likelihood of spending on research and development by formal firms. This is a large increase given that less than 18 percent of the firms in the sample engage in research and development activity. Further, consistent with the “parasite” view of informality, the positive impact of informal competition on research and development activity is magnified when the business environment is less conducive to operating in the formal sector compared with informal sector due to factors such as higher corruption, weaker rule of law, more burdensome business regulations, and a higher tax rate on profits. As expected, there is no impact of informal competition on research and development activity among large firms. The main findings are robust to several controls, alternative specifications, and endogeneity checks.

This paper is a product of the Global Indicators Group, Development Economics. 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://www.worldbank.org/prwp. The author may be contacted at mamin@worldbank.
maps on the spatial distributions of risk determinants.

extinction risk and, thus, the first metric for conservation priority”. This factor has been studied Jenkins et al. 2015; Manne and Pimm 2001; Manne, Brooks and Pimm 1999). It has particular significance because it is a widely-recognized indicator of extinction risk that can be computed for 2.1.2 Formal Protection identifies all areas defined as protected under UNEP-WCMC standards. To compute the formal protection index, we have transformed the shapefile (which includes 283,568 polygons) into a global raster with a resolution of .05 decimal degrees (about 5 km). Each raster cell has value 1 if percent coverage by formal protection.

2.2.1 Population Density Other things equal, we would expect species’ extinction risk to increase with population density. We measure density with a spatial raster at 2.5 min resolution (.042 decimal degrees) from the Gridded Population of the World (GPW), v4 (SEDAC/CIESIN, 2023). We overlay the population raster with species occurrence maps and compute the mean cell populations of occurrence areas 2.2.2 Human Settlement Sensitivity Species differ greatly in their sensitivity to human incursion. In North America, for example, bears commonly observed in settled areas. If species occurrence reports provided unbiased information about species populations, a “settlement sensitivity” metric could be derived from statistical analysis of the relationship between the spatial distributions of human and species populations. The metric would capture the degree to which a species’ population density decreases, increases or remains constant as human spatial density increases. Other things equal, a species whose Raster cell populations are equivalent to population densities in this case because all cells have the same
**Comparison with Durand and Espinoza (2021).** Our finding of a lack of a large multiplier at the _country level_ is somewhat surprising given Durand and Espinoza (2021) found all-Europe multipliers of around 1.2 using similar country-level data and empirical methodology.<sup>55</sup> However, the country-level part of our paper still differs from theirs in several dimensions: their sample is longer than ours (including the 1994-99 programming period, whereas we start in 2002 as this is when NUTS2 GDP sample starts, allowing for lags) and they include some extra countries (like the UK, IE and LT), they have several extra controls (lagged change in institutional quality; lagged change in financial risk), they exclude 2009 (we do not), they drop 7 influential observations based on austerity programs and judgement<sup>56</sup> (we drop 2 observations based on one-by-one exclusion regressions), they drop extreme standardized residuals (we winsorize the data), they use a subsample of 5 CEE countries based on similarity to Slovenia (we consider 10) and our instrument construction varies slightly.

To try to understand which of these factors drive the different results we replicated their country-level allEurope results, and then changed modeling assumptions one-by-one from the replication. The first column of Online Appendix Table A4.7 presents their all-Europe results: a contemporaneous GDP country-level of 1.2. Applying their sample (which does not include payments from FEAD and YEI funds), outliers, controls and a similar construction of the instrument, we find a similar multiplier of 1.26, also significant at the 1% level. We are also able to replicate their investment multiplier of 1.5. The remaining columns of Online Appendix Table A4.7 make changes that move towards our sample and specification.

The most important difference seems to be that Durand and Espinoza exclude 2009, whereas we do not. Specifically, in column 3 we include 2009 (by removing their control variable
/sup> At the country level, institutions have often been linked to differences in mean economic performance. However, the fact that strong institutions keep conflict from escalating will obviously also have an impact on volatility. In this aspect our work relates to recent research that shows that growth volatility is higher for countries with weak institutional constraints. Besley and Mueller (2015b) show that countries with weak executive constraints have a chance of experiencing negative growth which is about 50 percent higher compared to countries with strong constraints. Also they are more than twice as likely to have a growth rate below -5% and are over three times more likely to experience a growth rate below -10%. The authors argue that this effect is driven by the fact that weak executive constraints increase the risk of observing a prolonged period of economic contraction. In the absence of strong executive constraints, we expect the misallocation of public goods suggested by Burgess et al. (2015) to translate into cross ethnic group inequalities. These inequalities lead to a higher risk of conflict outbreak as shown in Alesina et al. (2015) which in turn increases the risk of a prolonged period of contraction. The conflict mechanism is valid even when a country does not actually experience two-sided violence. For example, massive investments in state repression capacity by ethnic groups in power might keep the lid on

> 45 They use a panel of 38,427 sub-national regions from 126 countries with yearly observations from 1992 to 2009.

60