rafmacalaba/data-use-mentions-extended huggingface dataset:
We also estimate the sensitivity of poverty to the incremental price increase experienced by the 1st decile relative to the overall inflation as measured by the CPI for the period 2011-2019. The results are summarized in Table 1. The values are high, ranging from 1.41 in 2012 to 3.28 in 2015, underscoring the high vulnerability of households to the negative effects of fast price increases on purchasing power, particularly for families right above the poverty line when inflation is pushed by price hikes of food, non-alcoholic beverages, housing, education and health. The average value of this sensitiveness check for the period analyzed is 1.98. In other words, the poverty rate increases nearly twice as fast as the price increases above the CPI that are experienced by the bottom decile and are not offset by income (labor and non-labor) growth.

Figure 6: Household income Gini coefficient (2011 - 2019) <!-- Start of picture text --> 44.0<br>43.41<br>43.5<br>43.0<br>42.42<br>42.5 43.02 42.20<br>41.98<br>41.78<br>42.0<br>41.48<br>42.06<br>41.5 41.88 41.94<br>41.01<br>41.52<br>41.0<br>41.26<br>40.42<br>40.5 40.92<br>40.77<br>40.22<br>40.0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019<br>Nominal Real - Decile Specific Inflation<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK

||Table1:Pove<br>|rtyrate and s<br>|ensitivity of<br
effect of MDA is different from the effect that might expected, given deworming’s effects on those known to be infected. The hypothesis of a common zero effect of multiple-dose MDA deworming on child weight at longest follow-up is rejected at the 10% level using the TMSDG dataset, and with a p value < 0.001 using the updated sample. In the full sample, including studies in settings where prevalence is low enough that the WHO does not recommend deworming, the average effect on child weight is 0.134 kg (95% CI: 0.031, 0.236, random effects). In environments with greater than 20% prevalence, where the WHO recommends mass treatment, the average effect on child weight is 0.148 kg (95% CI: 0.039, 0.258). The implied average effect of MDA on infected children in the full sample is 0.301 kg. At 0.22 kg per U.S. dollar, the estimated average weight gain per dollar is more than 35 times that from school feeding programs as estimated in RCTs. Under-powered meta-analyses are common in health research, and this methodological issue will be increasingly important as growing numbers of economists and other social scientists conduct meta-analysis.

### JEL classifications: I10, I15, I38, H43.

Keywords: deworming, nutrition, meta-analysis, cost-effectiveness

**Acknowledgments** : We are grateful to the Campbell Collaboration deworming team, especially Vivian Welch, for generously sharing data and information on their work. We thank Harold Alderman, Shally Awasthi, Don Bundy, Serene Joseph, Chengfang Liu, Scott Rozelle, and Walter Willett for help interpreting their studies, searching for original data, or supplying us with additional results. We thank Egor Abramov, Kamran Jamil, Spencer Ma, Cole Scanlon, Wen Wang, and Kevin Xie for research assistance. We thank Amrita Ahuja, Harold Alderman, Sarah Baird, Matthew Basilico, Peter Hotez, Rachael Meager, Antonio Montresor, and Dina Pomeranz for
respectively in the year of implementation through to the second year following implementation.<sup>12</sup> These are economically sizable numbers.<sup>13</sup> One potential problem with our analysis is confounding policy reforms that coincide with the adoption of e-filing. To address this concern, we use two variables to proxy for reforms and the overall business climate in a country. One measure is the first principle component of the time to enforce a contract, the time and number of procedures to start a business, and the time and number of procedures to register a property (denoted as the “doing business” variable in the Table B.1.). We take these components from the _Doing Business_ database. Higher values of the constructed variable reflect a larger burden on business. The second measure is the business freedom index from the Heritage Foundation, where higher values represent more economic freedom for businesses. If these variables capture country-level reforms, and e-filing implementation is only a consequence of these reforms, then the effects of e-filing should be much lower after controlling for either of these proxies. Table B.1 in the Online Appendix reports the results of estimating specification (1) without lags and leads. In columns (I) and (II) only standard controls are includes, in columns (III) and (IV) the first principal component of the set of doing business indicators is added, and in columns (V) and (VI) the business freedom index is included. Both proxies are highly correlated with our tax compliance costs measures; however, the coefficients on the e-filing dummy variables remain qualitatively the same. This suggests that e-filing implementation has an effect independent of policy reforms and the business environment.

# _4.1.2. Firm-level evidence_

Table 2 shows the results of the estimation of specification (2). According to column I, the coefficients _α1_ and _α2_ on the e-filing indicator variables are negative and highly significant. The probability of being inspected by a tax
# 3. **Data**

We have selected our sample of countries and banks as follows. We want to include a number of Asian countries to see whether the gross safety net subsidies are different between countries that have been heavily affected by the East Asian financial crisis of 1997 and countries that have not. As a second control group we include a number of highly developed Western countries, which are expected to provide us a benchmark for a low degree of risk taking. To assess the impacts of the crisis we need data for the crisis years 1997-98 as well as for some years before the crisis. Within each country we restrict our sample to the number of exchange listed banks, since we need data on bank market capitalization and dividend yields.

We have collected data on daily stock market capitalization and annualized dividend yields from Datastream. Total deposits at year end, net loans at year end and ownership data are taken from BankScope. For missing observations we have consulted Bloomberg. Ownership data is collected as follows. We distinguish between four concentrated ownership forms. State-owned (the state, treasury, military or other government-institution owns shares in the bank), family-owned (a family or individual owns shares in the bank), company-owned (a manufacturing company owns shares in the bank), and owned by another financial institution (another financial institution owns shares in the bank). Banks that have no concentrated owners (dispersed ownership) are classified as being widely held. We define a number of ownership dummnies that are related to the above classification of ownership and that are based upon different thresholds of shareholdings. Our threshold for a majority shareholding is 50% of shares, and our threshold for a major shareholding is 20% of shares.

We have also collected country-specific data. GDP per capita figures and inflation rates are taken from International Financial Statistics (IFS) of the International Monetary Fund (IMF). As a proxy for the quality and enforcement of the legal system of a country we have collected figures from the Law and
6 Cereal prices and climate**

Since the main cereals produced and consumed in India, rice and wheat, are traded on both domestic and world markets, it would be nonsensical to use the cross-district correlation between current prices and climate (let alone weather) to forecast the effect of climate change on cereal prices 30 years hence. Even under the arguable proposition that, up to now, domestic grain markets have not been well integrated so that districts are for all practical purposes autarkic, such a district-level analysis would tell us little about how cereal prices would change in the future. This is because, despite short-run government attempts to 17
on NREGA is that the program did lead to modest increases in private wages (Azam 2012; Berg et al. 2018; Imbert and Papp 2015; Muralidharan et al. 2017; Zimmermann 2012). Notably, the income gain due to higher private wages is not limited to program beneficiaries but also non-beneficiaries. For example, Muralidharan et al. (2017) note that the general equilibrium effect of NREGA is “a much more important driver of poverty reduction than the direct income provided by the program ”.<sup>2</sup> First, I estimate the impact of the employment policy on agricultural wages. Since agricultural wage data is available at annual frequency, I control SGRY by redefining “treatment” as spending in a district under public workfares. Analogous to the study on Minimum Legal Drinking Age (MLDA) laws in the US by Angrist and Pischke (2014), I use program expenditure as a measure of “exposure to the policy” and use the within and across district variation in program expenditure to estimate the effect of public workfares on private wages. A limitation of this approach is that program spending may correlate with local labor market conditions, which can influence wage rates in the private sector. I partially address this issue by controlling for district and year fixed effects and other relevant time-varying district controls. I also control for ‘potentially endogenous’ changes in program expenditure at a district-year level that represent under (or over) utilization of funds made available by the government.<sup>3</sup> The critical point is the extent of underestimation in the results when we ignore SGRY. Controlling for expenditure under SGRY, the results indicate public workfares to have increased agricultural wages between 0 _._ 4 _−_ 1 percent per annum. However, if one assumes no expenditure to have occurred under the older program, this underestimates NREGA’s impact on agricultural wages by 43%, making it insignificantly different from zero. This null result without SGRY is consistent with the findings
This approach has a number of important strengths, but is not without its weaknesses as well. A significant advantage of this methodology is in the simple and powerful policy implications it produces. The analysis identifies which socioeconomic groups are benefiting the most from various fiscal policies, and highlights how these policies impact the poor. Conceptually, however, the incidence analysis rests on strong operational assumptions: for instance, the approach assumes that publicly provided services are homogeneous across all consumers, yet quality may vary enormously. Incidence analysis also assumes that benefits received by individuals are equal to the costs of service provision; a perfect translation of taxes to consumers with no significant distortions arising from illegal behavior. Additionally, since data are more often available at the household rather than the individual level, additional assumptions must be made about the distribution of resources within the household. A common practice is to assume equitable intra-household allocations and to rely on per capita measures as the household‘s representative welfare measure. Finally, incidence analysis says nothing as to why the results are the way they are and rarely provides insights about how a certain program or policy influences behavior of beneficiaries (or nonbeneficiaries).

The precision of an incidence analysis depends on the quality and disaggregation of the available data. For example, an incidence analysis on health care in Zambia would ideally require that public healthcare expenditures be disaggregated at the provincial level by type of provision: hospitals (levels 1 to 3), clinics and health posts, and type of attention, i.e. inpatient versus outpatient. However, it is only possible to obtain data at the provincial level for combined expenditures on level 1 and 2 hospitals and combined expenditures on clinics and health posts. Additionally, only nationally aggregated expenditure data on level 3 hospitals are available. This limits the precision of the estimates.<sup>5</sup> In addition, households are assumed not to change their behavior after they receive (or fail to receive) benefits. All households are also assumed to value the transfers
.

However, in light of accumulated evidence from household survey data, the basic premise of this hypothesis can be questioned. Empirically, we see a far more mixed picture, with inequality decreasing roughly as often as it increases in growing economies.<sup>12</sup> These findings have been mainly from research on developing countries. However, a similar result is found when one includes high-income countries; in the global data set we use below, the correlation coefficient between the first difference of the log Gini index and the first difference of log GDP per capita is -0.09.<sup>13</sup> So, from what we know already, this hypothesis can also be dismissed.

> 9 <u>Han (2021)</u> finds that in authoritarian regimes that hold elections, redistributive policies are implemented in the runup to elections even when election results are predetermined.

> 10 See, for example, World Bank (2006) and Milanovic (2016).

> 11 We measure the ratio of the mean to the median, with both measured from the household surveys, as processed in the World Bank’s _<u>PovcalNet</u>_ site. While the ratio of the mean to the median is not strictly skewness (the third moment of the distribution), it is an intuitively appealing indicator. The ratio exceeds unity in all countries as one would expect.

> 12 <u>Ravallion (2016a, Chapter 8)</u> reviews the evidence on this point. Note that we refer here to relative inequality; absolute inequality tends to rise with growth; on this distinction and the evidence see Ravallion (2021).

> 13 For the levels regression with country fixed effects the partial correlation coefficient is -0.03.

9
contributions which based their estimations on 1990s household survey data (Panizza and Quiang, 1999; Panizza, 2001) found evidence of a public sector premium in Latin America, particularly for female workers. Empirical evidence of a public sector premium has been found also outside Latin America. Heitmueller (2006) found evidence of a public sector premium for women in Scotland by using a switching regression model. Gibson (2009) found a public sector premium in Australia, Canada, Great Britain and United States by using propensity score matching. According to the theoretical literature, public sector premia might be the result of different factors, including: (i) a higher level of unionization in the public sector (Holmlund, 1993); (ii) softer budget constraint faced by the public sector (Gregory and Borland, 1999); (iii) tighter no-shirking constraint in the public sector, given the higher firing costs (Panizza, 2001).

The structure of the note is the following: after this introduction, section 2 describes the data and the variables used; section 3 explains the methodology adopted; section 4 shows the results of the estimations; finally section 5 concludes.

3
# **4.2   Descriptive Statistics**

Peru is a middle-income country with an estimated per capita income of US$5,195 in 2010.<sup>31</sup> Peru has a high human development index (HDI) score of 0.725 based on 2011 data (the index for Latin America and the Caribbean is 0.731 and for the world is 0.682).One-third of the population is poor (31.3 percent) and 9.8 percent is extremely poor.<sup>32</sup> The wealthiest region of the country is Lima, Peru’s capital city, which is excluded from this study. The mean years of schooling for adults older than 25 is 8.9 years and is increasing over time. Most of the children attend school (93 percent primary school and 76 percent high school) according to 2010 data.<sup>33</sup> According to the baseline survey, the households analyzed averaged 5.3 members, with 1.4 children under age five.<sup>34</sup> Around one-half of household heads had attained secondary education and most (95 percent) were employed. The average monthly household income per capita was 140 Peruvian nuevos soles (around US$ 54). The population included in the study was poorer on average than the population of Peru, given that poverty is concentrated in the small districts and that we sampled families with at least one child under two years old.<sup>35</sup> Three-quarters of the households had access to improved sources of drinking water, whereas half of the households had improved sanitation. More than 20 percent of all households had no sanitation facilities of any type.

Handwashing with soap behavior was not prevalent among the interviewed households. Although almost all caregivers report having washed their hands with soap at least once during the previous 24 hours, fewer than half confirmed having done so at times of fecal contact (46 percent of
. The data consist of investment‐weighted averages for 18 EMDEs for 1998Q1‐2016Q2. Endogenous variables follow this Cholesky ordering: global financial market or policy uncertainty, EMDE stock price index; EMDE bond price index, and aggregate real output and investment in EMDEs. Exogenous regressors, included with two lags, are: G7 real GDP growth, world stock price index, and U.S. 10‐year bond yields. For the estimation of the impact of EU uncertainty (as measured by the EPU), the sample includes EMDEs in Europe and Central Asia (Bulgaria, Hungary, Poland, Romania, Russia, Turkey).<sup>24</sup> The results are statistically significant within the usual 16‐84 percent confidence bands.

_Domestic uncertainty_ . Country‐specific vector autoregressions are used to estimate the impact of domestic uncertainty on EMDE investment growth. The sample includes data for the same 18 EMDEs as listed above for 1998Q1‐2016Q2. The variables include, in this Cholesky ordering: global financial market uncertainty, domestic financial market or political uncertainty, domestic stock prices, short‐term interest rates, and domestic real investment. G7 real GDP growth is included as an exogenous regressor to preserve degrees of freedom. The regression is estimated with two lags. The model is adapted from the Bloom (2009) U.S. model, with these changes: employment is dropped due to data constraints, global uncertainty measures are added, and quarterly data replaces monthly data. For the full sample of emerging market and developing economies, on average, the impact of domestic uncertainty—whether financial or political in nature—is insignificant throughout the forecast horizon. These results are not reported in the text. Data for the International Country Risk Guide variables are quite smooth; a short‐ term quarterly vector autoregression model therefore struggles to identify any significant correlations. Economic Policy Uncertainty data show more variance for the Brazilian economy; in these cases, the

> 24 A similar estimation for other EMDEs yielded insignificant results,
s _minimum wage_ relative to the median wage.

_Social standards_ deal with a country’s basic welfare system. We use ILO data on the share of _health_ expenditure in total government spending, the share of workers contributing to a _pension_ scheme, and the share of unemployed receiving regular _unemployment_ benefits. Additionally, we assess the role of _total conventions_ of the ILO signed.

> 7  WEF’s foreign competition measure could equally be grouped under trade and investment policy.

17
.00011]|Yes|Yes||Yes|Yes|17271|-16068.49<br>|s the estimation|dummy that tak|e 1 if the frm re|
|Pr(IQ=1)|0.000044|[0.00002]***|-0.000021|[0.00001]***|0.00294|[0.0071]|-0.00006|[0.00005 ]|Yes|Yes||Yes|Yes|17271|-16068.49<br>|s table present|variables is a|hat takes valu|
||Know.spillovers||Distance to Innovators|*Know.spillover|International Know.spillovers||Distance to International Innovators|* International Know.spillovers|Firm ’ s characteristics|Firm’ s and Industry-Country’ s|characteristics|Country-Time fxed efects|Industry fxed efects|Num. of observations|Log pseudolikelihood<br>|Note: Thi|dependent|a dummy t|

32
.

One of surveys was performed by the Institute of Labor and Social Affairs in cooperation with CEBOS institute. According to its results out of the total sample of 9,038 persons aged 15+ interviewed 4.8% answered that they performed any unregistered work within 12 months before the interview. Taking into account that according to the LFS, the employment rate in Poland in 2007 was 48.6% it means that the share of shadow employment in total employment amounted to about 9.9%.

The second survey was performed by CASE (Center for Social and Economic Research) in co-operation with Millward Brown SMG/KRC institute. The number of people interviewed during this survey was 18,121. Following the best practice for similar surveys, the respondents of this survey were not asked directly about their shadow activities. The status of their jobs has been identified by the researchers based on a series of questions concerning the kind of working agreement, wage arrangements and tax and social security payments. According to the results obtained, 9.3% have been identified as having fully or partially unregistered jobs either in their main or additional working place. Although the size and the methodology of the two surveys differed, the results obtained have been surprisingly close suggesting that they reveal the actual figures more or less.

This paper presents the results of an attempt to use the results obtained by the CASE survey in order to: (a) analyze the development of the shadow employment in Poland in years 20032008 and, (b) analyze the transition probabilities in and out of shadow employment. This analysis will allow us to verify the hypotheses about the nature of shadow employment. Is it the way to registered employment or a closed avenue leading either to the next shadow
Using a series of linear regressions with country fixed effects, and data from the 2014-17 waves of the GWP/FIES, we find that food insecurity is associated with a decrease in both trust in national institutions and trust in friends and family. These findings also apply in all four World Bank income classes of countries. The association with trust in friends and family is larger than any other explanatory variable. For trust in national institutions, the associations of food insecurity increase monotonically with income ranking. For trust in friends and family, the results reveal nonlinear effects of food insecurity as national incomes rise. There are also differences in the determinants of vertical and horizontal trust. For example, rural/urban location strongly determines trust in national institutions, whereas being an immigrant matters more to trust in friends and family. As a robustness check on the possible endogeneity of food insecurity, we use nonparametric entropy matching methods to isolate the relationship between food insecurity and perceptions of trust. While the results may not be considered causal, they remain robust under alternative model specifications.

# **2. Empirical Strategy**

# **_2.1. Estimation Framework_**

The baseline model that estimates various forms of trust on our main variable of interest (food insecurity) and other control variables, is given by: where _i_ and _c_ are indices for individuals and countries; F represents respondents’ food insecurity status, where represents the correlation of interest; consists of individual- and household-level socioeconomic characteristics; contains country and year fixed effects; and εi,c represents ββ XX unobserved individual heterogeneity. The country and year fixed effects remove the crossγγ sectional variation related to unobserved heterogeneity associated with individuals in each country for each year.

Possible endogeneity may still exist between food insecurity and trust. Food insecurity is not distributed randomly among the population. Observed and unobserved individual factors influence 4