rafmacalaba/data-use-mentions-extended huggingface dataset:
Croke et al. (2016) WHO recommends MDA deworming, the estimated treatment effect is 0.148 kg [95% CI: 0.039,0.258; p=0.008]. In areas with more than 50% prevalence, where the WHO recommends multiple doses annually, the estimated effect is 0.182 kg [CI: 0.070,0.293; p=0.001].

Our full sample estimate has more statistical power than TMSDG. Using the same approach as above, we find an MDE of 0.15 kg in the full sample, roughly half the MDE of 0.28 kg using the TMSDG sample. Using the random effects estimate of 0.13 kg, we find that the full sample analysis has 72% statistical power to detect a significant effect at 95% confidence. This compares to 18% statistical power to detect an effect of 0.08 kg, or 31% statistical power to detect an effect of 0.13 kg using the TMSDG sample (see Figure 3).<sup>25</sup> This full sample effect remains robust (at p<0.05) when any individual trial estimate is dropped from the meta-analysis, as shown in Table 4. The level of significance with which the null hypothesis that the mean effect is zero can be rejected remains high even when simultaneously dropping any two of the 22 estimates: among the 231 possible combinations of two studies that could be dropped simultaneously, in 96% of cases the estimated effect remains statistically significant at p<0.05, and the largest p value is just 0.067 (not shown). The results are similarly robust to dropping any one or two studies in

> Cochrane pre-analysis plan (Dickson, Awasthi, and Demmellweek, 1997). Examining all studies conducted in environments in which the WHO recommends MDA while excluding studies where the WHO does not recommend MDA would enhance policy relevance.

> 25 Using an effect of 0.
result, there is considerable interest in understanding when these gaps in child development appear. Recent studies from developed countries have examined the emergence of gender gaps in cognitive and non-cognitive outcomes in the first year of schooling (Anderson 2008, Cornwell et al. 2013, DiPrete and Jennings 2012, Fryer and Levitt 2010, Janus and Duku 2007). Evidence from nationally representative data of kindergarten students in the United States shows that while there are no mean differences in math scores between boys and girls in kindergarten, girls lose ground to boys by more than 0.2 standard deviation (SD) by fifth grade (Fryer and Levitt 2010). Other studies using the same data set also 3
49)|(0.50)|
|Distance of comm. to bus station (km)|4.81|3.75|
||(7.14)|(7.01)|
|Prop. of land with technical irrigation|0.07|0.08|
||(0.17)|(0.18)|
|Asphalt road in the comm (Yes=1)|0.74|0.78|
||(0.44)|(0.41)|
|Village has sewerage (Yes=1)|0.55|0.52|
||(0.50)|(0.50)|
|Village has piped water (Yes=1)|0.56|0.55|
||(0.50)|(0.50)|

Notes: Calculated from IFLS2 and IFLS3 data 30
31 Appendix C). For these firms, we are interested in knowing if they repeatedly post for the same position or not. Such behavior could express, for instance, repeated needs (e.g. growth of a sales department) or the consequences of hire-and-fire policies and high replacement rates. Our analysis, however, suggests that the number of firms repeatedly posting the same ads is small.<sup>31</sup> The large majority of jobs posted online are for full-time positions (98.8 percent); part-time jobs are rare in Kosovo (Figure 34, Appendix C) and only a handful of postings are for internships. Of the 5,272 job descriptions, only 23 refer to an internship position. According to job platforms’ management, internships are not perceived as equivalent work opportunities by firms, which would rather advertise through other (free) channels such as social media or private networks. The absence of internships on main job portals is of concern for youth searching for experience in a labor market dominated by informal networks.

Most job advertisements do not specify whether a position is temporary or permanent, but they are almost exclusively temporary given the prevalence of such contracts in the labor market. Less than 0.5 percent of job descriptions indicate that a position is temporary, likely because a minority of jobs are permanent in Kosovo. This is consistent with evidence from survey data showing that over 70 percent of the employed population in Kosovo are in a temporary contract in 2017 (Figure 35, Appendix C).

Surprisingly, job portals exhibit low cyclicality of job postings over the year (Figure 37). Minor dips in the posting activity occur in January and July only.

Regarding the dynamics in the market, most vacancies in 2018 remained online for two weeks (Figure 36). Almost a quarter of vacancies expired within the first 10 days (26.91) and only around one-fifth of vacancies (19.44 percent) were posted for a period between 25 and 40 days
observe realizations of the infection process and therefore does not know her HIV status. Assuming she knows the mortality process associated with HIV infection, however, survival during each additional period gives her information about her status. Specically, she reduces her subjective probabilities of having become infected in each of the past periods based on the fact that she is still alive. According to these probabilities and given the mortality process, she updates her survival expectations. HIV infection also increases child mortality probabilities through mother-to-child transmission. In the model, the woman also updates expectations about the survival of each of her children depending on the probability assigned to her having been infected at the time of birth.

The dynamic fertility model is estimated using the Malawi Diusion and Ideational Change Project (MDICP) dataset, a rich longitudinal dataset collected in rural areas of three dierent districts of the country. The data contain extensive information on more than 4,000 individuals at the individual and household level. The three sampled regions vary signicantly in several aspects that are potentially relevant for the analysis, such as HIV prevalence rates, polygamy rates, and schooling levels. A unique feature of the MDICP data is that they include measures of subjective expectations regarding a range of outcomes, including the likelihood respondents assigned to being HIV-infected at the time of the interview. The expectations data were collected using a novel bean-counting method, developed by Delavande and Kohler (2009), which is appropriate for populations with low levels of numeracy. Delavande and Kohler (2009) nd that the reported subjective expectations follow basic properties of probabilities and that the assessments of HIV-infection vary meaningfully with observable characteristics associated with dierent levels of HIV prevalence. I use a subsample of 1006 married women who were interviewed at least once during the 2006 and 2008 rounds, when family rosters and subjective expectations were collected.

Since 2004, each round of data collection included HIV testing of respondents and the prevalence rate was found to be about seven percent.<sup>7</sup
Policy Research Working Paper 8596

# **Abstract**

The shares of manufacturing in value added and employment across a range of developing economies peaked at lower levels of per capita income compared with their high-income, early-industrializer precursors. Based on the statistical analysis of input-output tables and firm-level data, the paper contributes to the discussion on whether this “premature deindustrialization” matters by showing that: a) the premature declining share of the manufacturing sector is largely not driven by a statistical artifice whereby what was earlier subsumed in manufacturing value added is now accounted for as service sector contributions; b) Some features of manufacturing that were thought of as uniquely special for development, such as scale economies, exports, and innovation, are increasingly shared by services sector firms. Yet, a given service subsector is unlikely to provide opportunities for productivity growth and job creation for unskilled labor simultaneously; c) Some high-productivity services serve final demand or derive demand from several sectors, while others are more closely linked to a manufacturing base.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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/research. The authors may be contacted at gnayyar@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
to alleviating food insecurity (Smith et al., 2017a; 2017b) and the role of local decentralized governance structures.

# **4. Conclusion**

Trust in institutions is crucial during crises like the COVID-19 pandemic since a lack of trust can impede governments’ responses and slow emergency and recovery efforts (OECD, 2013; Bavel et al., 2020; Siegrist and Bearth, 2021). Interpersonal trust is a public good (Ostrom, 1997) based on people’s willingness to follow norms and follow institutional health guidelines to prevent pandemics (Johnson et al., 2020). Trust in friends and family is essential for risk-sharing and social networks that protect against shocks, especially in developing countries (Townsend, 1994; Ambrus et al., 2014; Barrett et al., 2022).

Crime, corruption (Clausen et al., 2011), and inequality are well-studied trust factors, but food insecurity is not. We fill this gap in the literature using data from the 2014–2017 Gallup World Poll Survey. In a global sample of 387,385 adults from 134 countries, food insecurity is negatively associated with both vertical (trust in national institutions) and horizontal (trust in friends and family) measures of trust. The probability of trusting in national institutions is 2.7 percentagepoints lower among the food insecure. The probability of trusting in friends and family is 11.7 percentage-points lower among the food insecure. The magnitude of the association of food insecurity with trust in friends and family is larger than any other explanatory variable. These relationships also vary significantly by GNI per capita country rankings. As a robustness check, we employ an entropy matching strategy to control for the possible endogeneity of food insecurity. Results remain robust and sensitivity analyses using other measures of trust support our results.

Several limitations are worth mentioning. Since the data are cross-sectional, we are unable to control for unobserved individual heterogeneity and thus some hidden bias may remain. While a robustness check confirms that food insecurity is
**Figure 5:  Contribution of Education to Productivity**

<!-- Start of picture text --> Contribution of Education to Relative Productivity<br>SGP<br>JPNKOR<br>FIN HKG<br>CUBSRB RUSHRVKAZLVAPOLHUNPRTESTLTUCYPSVNCZEISRNZLESPITAFRAGBRCANAUSTWNBELDNKDEUSWEISLAUTNLDUSACHEIRLNOR MAC<br>VNM UKR BGR SYCSVKMLT<br>MNGCHNMUSCHLGRC BHR ARE LUX<br>MYSTTO<br>BDI MWISLEMOZCODMDGCOMTGOETHGINGMBHTIBFARWAAFGZWEUGABENKIRTLSSLBYEMNPLTZASENLSOCMRVUTKENTJKBGDKHMTUVKGZGHAZMBCIVPNGSDNSYRCOGPSEHNDNICPAKMDANGATONINDMMRLAOGTMGUYPHLMARJAMAGOSWZSLVPRYARMXKXGEOJOREGYTUNIDNECUBIHLKANAMALBPERZAFCOLDZAIRQLBNBRAMKDDOMVENGABTHAAZECRIMNEBWAMEXIRNARGTURURYPANROU OMNSAUKWT QAT<br>MRT<br>LBRNER MLITCDSSD<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>.9<br>.8<br>.7<br>.6<br>.5<br>Productivity Relative to Benchmark<br>.4<br><!-- End of picture text --> Notes: This graph reports the contribution of cross‐country differences in learning‐adjusted years of school to cross‐ country differences in worker productivity. The vertical axis measures the productivity of a worker relative to the benchmark of complete education. Differences in years of school are converted to productivity differences using estimates of the returns to school detailed in Appendix A2. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)) , with missing countries filled using data from World Bank estimates of GDP at PPP. Graph shows the most recent data for all countries.

14
of PTA depth. Based on this first data set, several research papers then looked, respectively, at the impact of deep trade agreements on trade, global value chains, foreign direct investment, and the effect of breaking up such agreements.<sup>6</sup> The data have also been extensively employed for policy advice by the World Bank in several developing countries in Africa, Latin America, East Asia and the Balkans.

The new data that we briefly review in this paper and that are analyzed in detail in the individual chapters of the Handbook offer insights into a different dimension of PTAs depth. They capture the detailed commitments to establish and preserve the rights to economic integration, and the procedures, institutions and enforcement mechanisms that countries set up to make deep integration work. The focus is therefore not on the extensive margin of integration (number of policy areas that are covered by the agreement), but on its _intensive margin_ (the specific commitments within a policy area).

While there are a number of individual studies that have documented the deepening of PTAs in specific areas, two major data collection projects—Dür et al. (2014) and Acharya (2016)—also aimed at documenting the specific commitments for a group of policy areas covered in PTAs. Both efforts have important merits. Dür et al. (2014) covered a large set of PTAs, including those that have been notified to the WTO but are no longer in force. Acharya (2016) provided a series of databases on the content of PTAs that go beyond specific policy areas and cover emerging issues such as e-commerce or the rules on dispute settlement in PTAs. Relative to these data collection projects, the new data set is more comprehensive, both in terms of the number of policy areas covered and in terms of the information on detailed disciplines in each area.

> 3 See, e.g., Freund and Ornelas 2010, Limao 2017. 4 See, e.g., Baldwin 2010, Mattoo et
# **I.   Introduction**

Recent research in law and finance has examined the legal determinants of external finance. LaPorta et al. (1997) find that legal rules and origin are important in determining the size of a country’s capital markets, especially its equity market. However, the role of legal factors in determining the size of debt markets is less clear. Using firm-level data LaPorta et al. find that the similarity of the ratio of _total debt to sales_ across countries of different legal origins is “... remarkable, and suggests ... a potentially important conclusion: large publicly traded firms get external debt finance in almost all countries, regardless of legal rules” (p. 1148). They conjecture that state intervention in the banking system may be the reason for the similarities.

Other research has examined legal determinants of firms’ capital structure decisions. For example, Demirguc-Kunt and Maksimovic (1999) study _debt-to-capital_ ratios for publicly traded firms in 30 countries and find “that reliance on long-term debt by large firms is clearly higher in countries with an effective legal system” (p. 325), but they also find “no evidence that the index of creditor rights helps predict either short-term or long-term leverage or debt maturity” (p. 329).<sup>1</sup> In addition, Demirguc-Kunt and Maksimovic find that in countries without the right of secured creditors to be paid first in bankruptcy, firms use relatively _more_ short-term debt—evidence consistent with the monitoring hypothesis of Diamond (1991). Similarly, Giannetti (2003) finds that firms in countries with stronger creditor rights have higher long-term debt. Recent work by Fan, Titman, and Twite (2003) examines leverage and maturity across 47 countries finding that a common law system is unrelated to leverage, but that weak legal system integrity, as measured by the corruption level, is associated with high leverage and more short-term debt. In sum, while there seems to be some evidence positively linking _long-term_ debt
# Poorer than Adults and Deprived in Almost All Counts: Welfare Status of Children in Nigeria

_Caroline Schimanski, M Abul Azad_

**Keywords:** child poverty, monetary poverty, multi-dimensional poverty, deprivations, Nigeria, intergenerational mobility, chronic poverty

**JEL Codes:** O1; O15; O55; O12

Acknowledgements: The authors thank Aibek Baibagysh Uulu for providing the consumption estimates for the GHS data sets wave 1-4.
# **6 Simulations**

This last section of the paper uses the estimated coefficients from table 6 to compute, for each student, the total _test-score gain_ attributable to peer effects. The aim is to analyze how the average performance and the gap between the poorest and the wealthiest group change as the degree of segregation varies. The change in mean test score will depend on whether the losses incurred by poor students are compensated by the gains accruing to the rich. Two elements are key: (1) the coefficients on peer variables for each group, and (2) the proportion of students in each socioeconomic group in the whole distribution. I will construct a (fictitious) sample of students which have the same proportion of pupils in each category of socioeconomic status as found in Argentina. Each student is then assigned randomly to a school of size equal to the median class size in the country (27). In the present exercise, each school has only one class. For each student, I compute the proportion of peers belonging to each socioeconomic group that results from the random assignment.

The random allocation of pupils will determine a specific degree of segregation across schools. I use the same segregation index as in the introduction, defined as the proportion of the total variance in number of schools that is ‘explained’ by the variance between schools. This particular degree segregation will be associated with a certain average gain in test scores from peers, and a gap between the average gain for the wealthiest group (upper) and the average gain for the poorest one (lower). I repeat this exercise reallocating students into schools randomly, and computing once again the degree of segregation, the average gain in test score and the average gap in the gains. I do this fifty times, enough to have a sufficient spread in the segregation index. The results are presented in figure 3 and table 9. The vertical lines in the graph are used as reference and indicate the lowest and highest degree of segregation found in
1|Computation|Parameter|%|
|Positive change event|1(Bb)<br>1|33.3|Positive change event|2/3(Bb)+1/3(Cb)|1|25.0|
|Negative change event|0<br>0|0.0|Negative change event|1/3(Ac)|1/3|8.3|
|Stationaryevent|1(Aa)+ 1(Cc)<br>2|66.7|Stationary event|1 (Aa)+1(Ad)+1/3 (Bc)+<br>1/3(Cc)|8/3|66.7|
|Total househ|olds<br>3|100|Total ho|useholds|4|100.0|
|*Continuity rule follows t<br>^Cross-sectional poverty s|he head of household<br>tatus=1, non-poverty status=0||^Cross-sectional poverty|status=1, non-poverty s|tatus=0||

# 3. SAMPLING SETTING AND ESTIMATOR

As anticipated in the introduction, we consider the case where longitudinal and cross-sectional computed using data from a longitudinal survey in which individuals were first observed at estimates referring to the current time (e.g., the year 2024) denoted as time tt are to be intermediate between (e.g., the year 2020) in a refresh of the initial survey (based on well-specified tracking and continuity rules), in which a new sample is also independently selected. This observational time tt<sup>∗</sup> (e.g., the year 2015) and subsequently observed again in year tt<sup>∗∗</sup> setting is very attractive as it summarizes most of the estimation and representativeness tt and tt<sup>∗</sup> problems that can arise in actual large-scale longitudinal studies. The following
Note that the estimation of (8) is not straightforward using panel estimation programs, because of the non-linear constraints on the long run coefficients. In particular, if we estimate then we need to impose the following constraints Estimation can be done in two stages, that is, first estimate the long term regression, (7), and then use the lagged residuals to replace the term in square brackets in (8). We use the two-stage approach for our exploratory regressions that aim to narrow down the set of explanatory variables. However, this method is inefficient since it ignores the effect of short-term dynamics when estimating the long-term coefficients _β_ . Therefore, the PMG estimates impose long-run coefficients in the context of a dynamic panel regression.

When considering variables that may influence short-run dynamics, we include changes in the above long-run determinants and also forecasts of the change in stance of US monetary policy. Thus we first estimate forecasting equations for US interest rates that include as explanatory variables lagged changes in US capacity utilization, retail sales, the producer price index, and M2 data. Then we test whether these forecasts have an additional effect, over and above the long run relationship between US interest rates and emerging market spreads and the contemporaneous change in US rates.

13
2||Equat|ion 3|
||One Inte|raction|Two Inter|actions|||
||Intention|Actual|Intention|Actual|Intention|Actual|
|Treated|-0.003*|-0.038*|||-0.002|-0.030|
||(0.001)|(0.022)|||(0.002)|(0.040)|
|Treated*Price|||||0.013|0.193|
||||||(0.013)|(0.234)|
|Treated*No-contract|0.004***|0.062**|0.002|0.024|||
||(0.002)|(0.027)|(0.001)|(0.015)|||
|Treated*Contract|||-0.003|-0.038*|||
||||(0.002)|(0.022)|||
|No. of Observations|2,925|2,925|2,925|2,925|2,925|2,925|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: All regressions are using the Model 3 specification (see note to Table 2). Robust standard errors clustered at workplace of household head are in parentheses. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 8: Are their Differences in Effects on Employed and Self-Employed Workers?

|**Panel A: With Contract Interaction**|Health|Insurance|Pension|(restricted sample)|
|---|---|---|---