rafmacalaba/data-use-mentions-extended huggingface dataset:
i.e. by the effective protection denominator. That is: ESC = (VAD + NS) / VAR where, ESC = Effective subsidy coefficient VAD = Value added at domestic prices VAR = Value added at reference price NS = Net subsidies on non traded inputs The rationale for the ESC concept is that the subsidy per unit of the commodity is treated _as if_ it is equivalent to an increase in the price of the commodity. For example, if the combined value of the subsidies on irrigation water, electricity and credit is equivalent to 10 percent of the domestic selling price of the commodity, the ESC pretends that the same incentive effect to the farmer would result from a 10 percent increase in his selling price.<sup>13</sup> The largest non-traded subsidies are generally received by crops grown in irrigated areas. The average level of subsidies received by some crops which are predominantly not irrigated -in particular maize and sorghum- was very low. Partly for this reason, data on non-traded subsidies for these crops was also not available, and so in these cases ESCs were not calculated.

> 13 Insofar as changes in input prices would lead to substitution among them, the incentive effects are clearly not the same. But this qualification also applies more generally to effective protection indicators, which implicitly assume fixed input-output coefficients. There is a considerable literature which argues that empirical EPC estimates are useful indicators of incentives despite this qualification.

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Our methodology is conceptually quite different from previous epidemiological estimates on preventable child deaths through investments in water and sanitation infrastructure (Clasen et al., 2007; Pruess et al., 2002; Pruess-Uestuen et al., 2008). While these existing studies share our objective of quantifying the mortality improvements achievable with water and sanitation investment in developing countries, the way of deriving these estimates differs greatly. Rather than directly deriving the marginal mortality improvements generated by access to water and sanitation infrastructure across a large set of national representative surveys, most existing studies start from an estimated total distribution of mortality causes and combine them with local experimental intervention studies about the impact of water and sanitation on fecallytransmitted diseases. The implicit assumptions are that intervention studies have external validity and are scalable, and also that the mortality impact of improved water and sanitation can be derived directly from related morbidity numbers.

The main advantages of the approach chosen in this paper are twofold: first, as mentioned before, the average treatment effects estimated in this paper allows us to avoid external validity and scalability concerns surrounding intervention studies. Second, we can directly estimate mortality reductions without having to rely on a theoretical framework to translate diarrhea prevalence into actual mortality outcomes. That is, by analyzing the aggregate mortality impact we can also capture the impact through diseases for which no intervention studies on water and sanitation exist (Pruess-Uestuen et al., 2008).

Notwithstanding these advantages, it is clear that the framework applied in this paper also rests on a large set of assumptions: by estimating the effects of water and sanitation in a pooled sample, we implicitly impose that the marginal impact is constant across countries and age groups. While this assumption is clearly an oversimplification, it should not bias our results in any specific direction, and greatly facilitates the benefit-cost calculations in the following section. Another problem of our methodology could be endogeneity leading to an overestimation of the impact of water and sanitation on child mortality.

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However, it is also important to note that many of the techniques designed to capture sensitive information have been used in more traditional settings to address issues not able to adequately be covered by a wide range of traditional data collection. However, as mentioned in the Introduction, fragile and conflict settings both increase the potential need to capture sensitive information and pose significant additional challenges beyond those experienced in traditional settings. For example, embedding individuals in communities to ask about personal involvement in the conflict is potentially dangerous for enumerators and limits the ability of any amount of trust enabling individuals to admit opinions and actions that could place them in trouble with authorities (e.g., Human Rights Watch 2021). Additionally, many of the indirect methods have primarily been utilized in relatively stable environments, and some have suggested that sudden shocks and crises might limit the ability of these methods to precisely infer sensitive viewpoints (e.g., Kramon and Weghorst 2019; etc.).

Given the difficulty in validating and eliciting sensitive opinions and the added difficulties of collecting such information in fragile and conflict settings, it is important to rely on a number of different methods. In the remaining sections of the paper, we try and validate anonymous internet-based surveys that, if they prove valuable, offer advantages in conflict settings over other approaches and can be used alongside these other techniques to provide policy makers and stakeholders a more complete characterization of important, sensitive issues.

# **Section 3. The Increased Need to Collect Sensitive Information in the Republic of Yemen’s Conflict**

We analyze the validity of anonymous internet surveys in the midst of the Republic of Yemen’s conflict. Importantly, this is a setting that illustrates how traditionally sensitive behaviors become more prevalent and how a wide range of routine data can become sensitive in a conflict setting. These sensitivities arise from the complex nature of the conflict, the resulting humanitarian crisis, and the large humanitarian response.

> their responses are not anonymous to everybody. Although these approaches have led to
becomes innovative.

# **3.5 Descriptive Statistics**

Table 3 reports summary statistics of key variables comparing innovative - those with at least one PoTec worker - with non-innovative firms in 2017 for all sectors of the economy and excluding public sector institutions. Innovative firms represent only 3% of total firms in Brazil but 43% of private employment in 2017. Innovative firms are significantly larger than non-innovative. The median innovative firms have more than eight times the size of the median non-innovative firm; which is mainly a micro firm. Innovative firms have younger and more educated workers, have a larger share of male workers and also employ a larger share of high-skill occupations.

Table 3: Summary statistics of private sector firms by innovative status, 2017

||**Innovative**|**Non-Innovative**|
|---|---|---|
|Log of hourly wage|2,4|1,9|
|Employment (mean)|187,4|7,3|
|Employment (median)|25,0|3,0|
|Age|35,4|35,9|
|Share of female workers|34,1%|45,4%|
|Share of high-skill workers|67,4%|40,2%|
|Share of college graduate|33,2%|7,3%|
|Average worker tenure|45,1|42,4|

Innovative firm is defined as those with at least one PoTec workers in 2017. Employment is the number of workers by innovative status. Tenure is measured in months, and age shows the average wage of workers.

These basic descriptive statistics highlight the fact that innovative firms tend to be much larger than non-innovative firms and employ a more educated labor force. Thus, given the stylized facts established by the LFWP summarized above and the extensive Mincerian literature on the returns to education, one should expect that innovative firms also pay higher wages. This is
in NO2 that many countries’ GDP in 2020 understated the extent of ongoing activities, many of them unmeasurable otherwise.

# **7 Conclusion**

In this paper, we have presented a global analysis of cyclical economic activity using NO2, and shown that this signal may be used to compute improved measures of economic activity in comparison with GDP alone. In contrast with the exceedingly short list of existing globallyavailable signals like night lights, NO2 uniquely has reliable explanatory power for off-trend business cycle fluctuations. It also has information content beyond GDP for all countries.

**Limitations.** As in any single study, there are a number of possible limitations deserving further insight. First, heterogeneity in production technologies across countries could mean that NO2 is a better signal of economic activity in some countries than others. We have found by formal statistical test that the relationship between NO2 and GDP is stable across countries, supporting our presumption of a fixed growth elasticity (Table 3). Yet, investigating dimensions of heterogeneity may be an important concern in other applications. Second, production

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# **ENDNOTES**

1 Likewise, the increasing number of available impact evaluations that study child labor as one of their outcomes of interest in areas as diverse as microfinance, training and education, conditional cash transfers, and public works, rely mostly on proprietary data that is collected mainly from the program beneficiary or head of household in the targeted households (Dammert el at. 2018).

2 There is a wealth of evidence on measurement error on adult labor statistics in developed countries, particularly in the U.S., where labor statistics from household surveys is compared to administrative data (e.g., Bound et al 2001).

3 In contrast to other developing settings, there are no social stigmas or negative perceptions of child labor in Ethiopia, as child labor historically has been viewed positively as training for children (Bass 2004).

4 In Sidama, we worked with two Fairtrade cooperatives, each with around 1,500 active smallholder farmer associates. One cooperative reported a yearly average production of 1,122 kgs of coffee crops per associate, while the other reported average production of 789 kgs in 2014. In Jimma, we also worked with two Fairtrade cooperatives. The first cooperative had 800 active farmer associates and yearly average production of over 1,600 kgs of coffee per associate, while the second cooperative had 1,100 active farmer associates and yearly average production of 600 kgs of coffee per associate in 2014.

- 5 The size effect considered for power analysis is based on a conservative approach to the survey design results found by Dammert and Galdo (2013) in Peru and Janzen (2018) in Tanzania.

- 6 In the first survey, the compliance rate is around 97% as proxy respondents provided the information for 27 children in the self-report group, while other household members provided

30
the table could include overlapping industries which could affect the results. Although there are industries that have been included in multiple groups, their numbers are not large with a possible exception of industries with highest input and output tariff drops. 50 percent of firms that belong to industries in the top quartile of input tariff drop also exist in industries that are in the top quartile of output tariff drop. The overlap of industries among other groups is much smaller.

In India, there are institutional differences across states on labor regulations. These differences can affect the relationship between licensing reform and innovation. Besley and Burgess (2004) show that pro-worker labor regulations in Indian states resulted in lower output, employment, investment, and productivity in formal manufacturing sector. Following their work, Aghion et al. (2008) find that when licensing reform occurred, industries in states with pro-worker regulations experienced less increase in output and employment relative to pro-employer states. These differences across states can affect firms’ innovative performances. Aghion et al. (2008) use a dataset at industry level obtained from ASI which prevents them from analyzing the dynamics of firm evolution. To see the effects of labor regulations on innovation performances of firms, I include an interaction term between the licensing reform indicator and a dummy variable which is set to one for pro-worker states. The result is presented in the first column of Table 7. In this specification interaction term is treated as endogenous in addition to _Log(Sales)_ and R&D variables. The result is in accordance with that of Aghion et al. (2008) although their dependent variable is log of sales where here it is product growth rate. In pro-worker states, the contribution of licensing reform on firm innovation rates is almost six percent less compared to proemployer states. Regulations that favor worker rights hinder job flows in firms by raising the costs of

> 13 The wage data is from 1987 Annual Survey of Industries database.

15
- Deaton, Angus (2005). Measuring poverty in a growing world (or measuring growth in a poor world). _Review of Economics and Statistics_ , **87,** number 1, 1–19.

- Deaton, Angus and Dreze, Jean (2002). Poverty and inequality in india: A reexamination. _Economic and Political Weekly_ 3729–3748.

- Deaton, Angus and Kozel, Valerie (2005). Data and dogma: The great indian poverty debate. _World Bank Research Observer_ , **20,** number 2, 177–199.

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- Edochie, Ifeanyi Nzegwu, Freije-Rodriguez, Samuel, Lakner, Christoph, Herrera,
the end of 2008 and the beginning of 2009 due to the international financial crisis -, volumes grew by 5.3%, as a result of the lower grade of the mineral extracted from existing mines and few important new mines coming on stream. This has given rise to a relative stagnation of the volumes in recent years, despite the significant price increase (see Figure 5, mobile average of 6 months on the black line).

> 11 / The analysis can only be performed with data starting in 1994, as the data from the Central Bank of Peru has disaggregated data on the prices and volumes of traditional and non-traditional exports as from that date. A study on this same topic is that of Banco Central de Peru (2008).

10
find an association between male LFP and having a washing machine. The variables capturing social norms and attitudes towards women and public policies are, in general, not statistically significant. Exceptions include the share of women in high-paying positions in Central Government, the logarithm of public spending in health interacted with having a family member 60 years old or more, and the municipality road accessibility measure.

# **5. Robustness Checks**

Our results are robust to different samples. First, we expand the sample to include women aged 25 through 55 years old. Table 2 presents results when controlling for individual and household characteristics, local labor market drivers, and the model that interacts the normalized number of public preprimary centers with having kids in the 4-6 age range, respectively. Findings are aligned with previous estimates: (i) there is a negative association between having children and FLFP and the penalization is larger the younger the kids, and (ii) a larger number of public preprimary centers in the municipality where a woman lives is positively related to FLFP of women having children in the 4-6 age range.

Our second robustness explores alternative measures of access to preprimary centers using information from the census. Using census data, we compute a measure for each woman in our sample indicating whether at least one of her children ages 4 to 6 attends a preprimary center. Column 3 in Table 2 presents the results of our main specification when including this variable and restricting the sample to women that are mothers of children aged 4 to 6 years old. Results indicate that access to a preprimary center is still strongly and positively associated with FLFP. Considering that most preprimary centers are public, we interpret this finding as reinforcing our previous results. It also suggests the importance of public infrastructure to facilitate the entrance of mothers into the labor market.

We also propose different measures of social norms and attitudes towards women in the society using data from 2018 Latinobarómetro. The first captures the share of males considering that a team of women and men will have better results
introduced some bias in the survey, to the extent that the data on those firrmis were subject to more scrutiny and corrections. Since there were no other reliable sources of inrduLstrial data in either Georgia or Moldova to make comparisons possible at the time of our sLurveys, we could not conjecture which way the bias would have gone.

We first study the ownership structure of the sampled firms. We distinguish between two eroups of privatized firms, since the primary focus is on the differences between firms bought by mna.agers, and firms which were either given to managers for free or were _defacto_ controlled by i-ailagers since ownership was not exercised (Table 1). Only a tenth of the firms in the Georgian ,;arple can be classified as management buy-outs (MBOs), while about a quarter of the firms in 8
11 Combining the 2013 and 2016/17 nationally representative Integrated Household Panel Surveys (IHPS) from Malawi with newly compiled local food composition data for Malawi, human nutrient requirements, and monthly market food prices across 25 markets, we are able to calculate monthly lower and upper bound least-cost nutrient-adequate diets for all households from January 2013 to July 2017. The household data provide the necessary information to identify individual nutrient needs (age and sex for all household members, occupational data), geographic identifiers to match households to markets, and all requisite expenditure information to calculate annualized household food spending and total expenditure following the methods used for poverty calculation in Malawi (National Statistical Office (NSO) [Malawi] and World Bank Poverty and Equity Global Practice 2018; National Statistical Office (NSO) [Malawi] 2017).

We use the sample of rural households from the IHPS since the food price data set to which we have been given access only covers markets in the rural districts of Malawi. The National Statistical Office (NSO) does collect prices in Malawi’s four urban centers with locations stratified by the general income level of the clientele served but does not share these data. Further, although there is an earlier round of the IHPS data, the price data only contain more nutrient dense food items beginning in January 2013. Since the surveys are representative of both urban and rural strata nationwide, our results can be considered representative of the rural population.

We use monthly prices for 51 food items collected between January 2013 and July 2017 by the NSO in 29 markets across Malawi. We identified households in 25 of the 29 markets for which price data are collected (Supplementary Table A). The markets were purposively selected and are in the main district or trading towns in the rural districts outside of Malawi’s four largest
|**Table 1. Medium-Term Income Mobility, RLMS 1994-2015 (perce**<br>**Unconditional**|**ntage)**<br>**Conditional**|
|---|---|
|**Panel A: 1994-2004**||
|Upward mobility<br>29.2|39.6|
|Immobility<br>49.5|49.5|
|Downwardmobility<br>21.3|36.3|
|**Panel B: 2004-2015**||
|Upward mobility<br>27.8|39.6|
|Immobility<br>47.0|47.0|
|Downwardmobility<br>25.2|38.8|

**Note** : Estimation results are obtained based on total household income per capita. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 4941 panel individuals from the 5th and 13th rounds of the RLMS and 3719 panel individuals from the 13th and 24th rounds of the RLMS.

34
Policy Research Working Paper 5635

# **Abstract**

This paper investigates whether the Pesticides Initiative Program has significantly affected the export performance of Senegal’ shorticulture industry. The authors apply two main microeconometric techniques, difference-indifferences and matching difference-in-differences, to identify the effect of the Pesticides Initiative Program on exports of fresh fruits and vegetables. They use a unique firm-level dataset containing data on sales, employment, and exports by product and destination markets, as well as firm enrolment year, over 2000–2008. The results suggest that wile the program had no significant effect on exports pooled over all products and destinations, it had a positive effect when considering fresh fruits and vegetables exports to the European Union.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. 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 author may be contacted at ocadot@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