rafmacalaba/data-use-mentions-extended huggingface dataset:
influential observations Croatia (2016) and Estonia (2011) discussed in the text. GDP growth (as a control) is winsorized at the 90% level for NUTS2 regions and at 98% for country level regressions (though investment data is not winsorized). *, ** and *** stars indicate significance at the 10%, 5% and 1% levels (respectively). CEE is Central and Eastern Europe. See Equation 1 (OLS/IV second stage) and Equation 6 (IV first stage).

Table 2 shows that EU funds tend to boost investment, with all-Europe and CEE total investment multipliers often being close to 1 (against smaller and insignificant investment multipliers for non-CEE countries). This suggests that we are capturing actual changes in investment driven by EU funds. At the NUTS2 level, we find that an extra 1€ of EU funds spending in CEE countries boosts total investment by around 1€ (OLS and IV) with both being statistically significant at the 1% and we are unable to reject an investment multiplier of 1. At the country level, CEE OLS multipliers are also close to 1, but IV multipliers are imprecisely estimated due to weak instruments. For the all-Europe sample the subnational- and county-level investment multiplier is significant and usually around 1 for both the OLS and 2SLS specifications except for an OLS NUTS2 multiplier which is smaller at around 0.6 (though still significant). For non-CEE countries, investment multipliers are smaller and sometimes insignificant (IV of 0.57 significant at 5% being larger than insignificant 0.14 of OLS), and for OLS are significantly smaller than CEE investment multipliers. This is not surprising as most EU structural fund spending is focused on Central and Eastern Europe.

_Robustness._ We conduct three robustness tests of the investment multiplier results: (i) using raw (rather than modeled) payments, (ii) splitting total investment into public investment and private investment portions (which is only available at the country level) and
8 (in its parametric IV form) is also delivering an estimate of the marginal gain from a program, namely the gain when the program is expanded (or contracted) by a small change in the eligibility cut-off point.

A deeper understanding of the factors determining outcomes in _ex post_ evaluations can also help in simulating the likely impacts of changes in program or policy design _ex ante_ . Naturally, _ex ante_ simulations require many more assumptions about how an economy works.<sup>64</sup> As far as possible one would like to see those assumptions anchored to past knowledge built up from rigorous _ex post_ evaluations. For example, by combining a randomized evaluation design

> 64 For a useful overview of _ex ante_ methods see Bourguignon and Ferreira (2003). Todd and Wolpin (2006) provide a number of examples, including for a schooling subsidy program, using the PROGRESA data.

58
Policy Research Working Paper 7879

# **Abstract**

Most research on the economic payoffs of skills has used individuals’ level of schooling attained—typically years or level of education or training received—as a key proxy for skills. Such research has consistently found that individual returns to schooling are positive and that returns tend to be higher in low- and middle-income countries than in higherincome countries. However, years in school is only one proxy for skills—are these returns still observed using other measures as proxies? This study uses data from the STEP Skills Measurement Survey to examine the extent to which there is an independent association between cognitive and noncognitive skills and earnings in low- and middle-income countries. The study uses measures of reading proficiency and complexity of on-the-job computer tasks to proxy cognitive skills, and personality and behavioral measures to proxy noncognitive skills. The results demonstrate that even when controlling for schooling and background factors, these skills pay off in the labor market. This is particularly the case for the measures of cognitive skills, while noncognitive skills show some significant, but small, effects on earnings. The findings also suggest that there is significant heterogeneity across countries in how skills are valued in the labor market.

This paper is a product of the Education Global Practice Group. 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 authors may be contacted at Avalerio@ 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
_Figure 22: Gaps between expected and actual education premiums by country, gender and levels of education_ <!-- Start of picture text --> 120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>-20% Indonesia Lao PDR Mongolia Philippines Vietnam<br>-40%<br>-60%<br>-80%<br>Secondary vs. primary Male Secondary vs. primary Female<br>University vs. secondary Male University vs. secondary Female<br><!-- End of picture text --> Source: CGD–World Bank 2022 survey for envisioned premiums and Patrinos and Montenegro (2014) for a compilation of the actual premiums.

# 7 How is this research relevant for EAP education reform and globally?

Survey responses of officials from twelve middle-income EAP countries about their perceptions of education highlight that these officials, in many cases, have surprising views on key areas of policy interest. Officials in EAP prioritize increasing school completion over improving learning, underestimate learning poverty by a higher margin than other regions and believe implementation capacity is the greatest barrier to improving student learning. If officials think children know more than they do, and prioritize increased years of schooling over increased learning, it seems less likely that they will take the necessary steps to ensure that all children are learning.

Surveyed senior public officials in EAP tend to have positive views on utilizing education technology as a tool for equitable learning, supporting access for students with disabilities, supporting gender equality, and opposing violence against children. Despite this support for inclusion, they tend to select teacher training or other investments over additional teachers for children with disabilities when given the choice.

Surveyed EAP officials tend to overestimate public financing for education and question the effectiveness but not the need for additional education spending. Officials tend to believe distance learning was effective during COVID-19-related school closures and underestimate the risk of increased
statistically significant. The magnitudes and signs of other coefficients are similar to those shown in the top panel of table 3.

Next, we investigate the heterogeneity of the effects of the electoral cycle on public expenditures by the degree of political competition. Several recent studies demonstrate that political budget cycles are positively correlated with electoral competitiveness. Using data on 27 countries in the European Union for 1997–2008, Efthyvoulou (2012) shows that tighter electoral competition is correlated with a higher probability of observing a political budget cycle. Analysis of the composition of public expenditures in 69 developing countries from 1975 to 1990 demonstrates that in competitive elections, incumbent politicians are willing to incur the economic costs of changing the composition of public spending (Block 2001). <u>Prichard (2018)</u> and Vergne (2009) show similar results in a sample of 42 developing countries over the period 1975–2001. <u>Eibl and</u> - <u>Lynge Mangueira (2017)</u> show the mediating effect of electoral competitiveness on the political budget cycle on a sample of 112 countries from 1960 to 2006.

To test the effect of political competition on public spending during the electoral cycle in the pandemic, we split countries in our sample into two groups based on the political competitiveness index. The 53 countries in the first group have an index of political competition with a value of 8 or less; the index for the 72 countries in the second group is greater than 8. A value of 8 in the political competition index represents a transitional situation between factional/restricted competition (values 7 and below) and institutionalized electoral competition (values of 9 and 10).<sup>7</sup> <u>Figure 2 shows the semiparametric graphs of the relationships between the share of the social</u> protection stimulus budget spent on cash and in-kind transfers and the number of months since the last election for these two groups of countries.

The share of social assistance and
Rural Household Survey in Dercon and Porter (2014) suggests the observed height gains in the treatment group would result in approximately 3 to 4 percent increases in annual earnings as an adult.<sup>2</sup> To better understand the underlying mechanisms behind the effects, we simultaneously collected measures of fine airborne particulate matter with a diameter of 2.5 micrometers or less (PM2.5) in a random subsample of study households. These concentrations of matter, averaging approximately 1250  g/m<sup>3</sup> , or 50 times more than the World Health Organization’s (WHO) threshold for healthy exposure (WHO, 2006b), decreased by 10 percent on average for all households in the _Mirt_ treatment arm, relative to the control group.<sup>3</sup> Meanwhile, households with young children, which also cook more frequently, experienced reductions of 24 percent on average. When examining whether measured health outcomes relate directly to particulate matter, we demonstrate a clear gradient between reduced household air pollution and increased child growth, although we do not find a statistically significant or otherwise meaningful relationship between the level of particulate matter and respiratory symptoms or adult physical functioning.

These results advance our understanding of the health impacts of hazardous air pollution, while also refining design and implementation options for interventions geared toward mitigation in similarly situated environments. Health benefits for adults and older children do not arise in the data, but, given the link between early-life health and education, cognition, adult health, and earnings in later life, the demonstrated advancement for young children represents a substantial and meaningful impact.

# **2. Study Design**

# **2.1 Ethiopian context and Mirt improved cookstove technology**

Despite enjoying economic growth over the preceding decade, Ethiopia is known as a “hotspot” for forest depletion, and it is one of the top four countries in the world to simultaneously maintain a high level of nonrenewable fuelwood consumption per capita and disease
# **I. Introduction**

One of the more commented upon facts about the Indian economy is its small and dwindling importance in world trade. At independence, India accounted for over two percent of world exports and imports, and by the early 1 990s, this share had declined to about half a percent (Chart 1 below). A plausible case can be made that India's economic fortunes have been related to this de facto international disengagement which is attributable, at least in part, to India's inward looking economic and trade policies. Protectionist polices, in turn, have shaped India's attitude to participation in the multilateral trading system, both in the old GATT and its successor, the World Trading Organization (WTO). India's stance in the GATT/WTO has always tended to be defensive, seeking freedom to use restrictive policies, which has been one of the two prongs of the so-called special and differential (S&D) treatment embraced by developing countries as a whole. Since the early 1990s, India has embarked on serious trade and economic reform, without, however, a corresponding change in the nature of its multilateral engagement. This paper examines whether India should adopt a proactive role in the multilateral arena founded on its recent adoption of more open policies.

**Chart 1: India's Share in World Imports and Exports**

<!-- Start of picture text --> 3.5 3.5<br>3 . -3<br>2.5 -A /Export Sha- 2.5<br>22<br>1.5 1.5<br>0.5 0.5<br>1947 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995<br>Year<br><!-- End of picture text --> _Source:_ IMF's International Financial Statistics The collapse at Seattle of the negotiations to launch a new round provides time and
13 to the three types capital stocks that are assumed to influence growth. The second set of variables on the right hand side of the equation are included in the regressions to capture the impact of differences in efficiency of public capital utilization and in the financing of public capital, as explained in more detail below.

The parameters of the growth equation are estimated with data for 46 developing countries over the period 1970-1990, from the data set in Easterly and Rebelo (1993) and various issues of the World Bank's World Development Report.14 (Accordingly, note that since y(.) is expressed in natural logs, y(T) - y(0) represents a growth rate over a 20-year time interval, T.) Private and public investment rates (expressed as shares of output) were averaged for each country over the period 1970-1990 and then deflated by the average effective depreciation rate over that period to generate proxies for the private and public capital stocks. It is pertinent to note that public capital as defined here does not correspond directly to the definition of infrastructure capital. In most countries, the bulk of transportation, power and other public infrastructure facilities are publicly owned, but in several countries, some of these facilities are partly privately owned. Human capital is proxied by secondary education enrollment rates averaged over the same 20-year period.

Hulten (1996) drew attention to the importance of taking into account the efficiency with which the public capital stock is being used, along with the absolute amount of that stock. To capture this effect, a continuous measure of public capital efficiency across countries has been constructed on the basis of the four indicators used in Hulten (1996) and obtained from the 1995 World Development Report. The four indicators are (i) mainline faults per 100 telephone calls for telecommunications, (ii) electricity generation losses as a percent of total electricity output, and (iii) the percentage of paved roads in good condition and (iv) diesel locomotive utilization as a percentage of the total
EUSILC and tax data (5% and over 20%, respectively<sup>17</sup> ), we focus only on full-time employees. The outcome of this comparison is presented in Table 5.

**In July 2020, a relatively large proportion of all full-time employees in the tax administration dataset earned a minimum wage or less compared to the EU-SILC.** In tax admin data, in July 2020, only a tiny About 24.7% fraction (2.7%) of all full-time employees earned less than 95% of the minimum wage. earn between 95% and 105% of the minimum wage. Thus, the share of minimum wage earners is at 27.4%. In the EU-SILC data, the share of minimum wage earners is significantly lower, at 11.95%. In the imputed EU-SILC, the share of minimum wage earners is between EU-SILC and tax data, which equals 23.04%.

**Results presented in Table 8 suggest that the share of minimum wage earners estimated based on tax data may be biased upwards.** However, even if the actual share of minimum wage earners is lower than in tax data, it is still among the highest in the European Union. The lower share of minimum wage earners in EU-SILC data and imputed EU-SILC data may also be explained by the fact that income is measured annually. Thus, some employees who earned minimum wage for part of the year are not in the minimum wage threshold for the whole year.

**Our estimates on the share of minimum wage earners in the survey data are close to estimates based on other surveys.** For example, Eurostat, based on EU-SILC 2018, estimated the share of minimum wage earners in Romania to be 13% for men and 18% for women. Based on the Structure of Earnings Survey 2018, Eurostat estimated the share of minimum wage earners to be 13%.<sup>18</sup>

> 17 The higher share
While the draft module found in annex 5 is prepared as a questionnaire that can be used directly to measure empowerment or modified to add an empowerment dimension to other questionnaire-based household surveys, under ideal conditions more interactive forms of enquiry such as semi-structured interviews, case studies, participatory appraisal tools and focus groups would be used to deepen understanding of sensitive issues and causal links. The feasibility of using these approaches to data collection on a sub-sample of the national survey sites will be reviewed and, if appropriate, the preparation of field guidelines for such “deep-drill” sites initiated. In addition, options to develop a mixed-methods approach under certain survey conditions—such as where a PPA is undertaken at the same time as an LSMS survey is applied—will be explored.

> 27 Grootaert (2003) also discusses indicators that refer to the institutions that influence empowerment and identifies the data sources from which these can be extracted.

> 28 Formal rules can be enacted through both formal and informal institutional contexts. Kabeer explains that the enactment of formal rules in informal contexts is bound up in the societal norms, beliefs, customs, and values (Kabeer 2002, p. 22).

> 29 The empowerment team of PRMPR is available to assist in developing or piloting part or all of this module.

28
_**

Over the past two decades, high-performing NSSs have provided much of the technical expertise to improve developing countries’ statistics. The organization, technology, and data sources of high-performing NSSs are drastically different from those of the rest of the world, however. Highcapacity countries have long been adapting their statistical production processes to accommodate the ever-expanding set of administrative data that have become the foundation of many statistical

> 9 The results of this analysis are available from the authors on request.

15
# 3.3. Results

Table 2 and Table 3 present the main results for this section for events. Table 2 shows results for events with SS equal or higher than 1, while Table 3 for events with SS equal or higher than 3. Results are standardized in order to make all the indexes comparable.<sup>19</sup> Column (1) shows results from equation (4) estimated using OLS. These results have potential issues of serial and spatial autocorrelation. Spatial autocorrelation is a concern given that countries are closely located. Serial autocorrelation is a lesser concern since our study period covers approximately 30 years and bias decreases inversely to time. Therefore, column (2) shows results correcting for serial and spatial autocorrelation in line with Hsiang (2010) and Conley (2008). Column (3) estimates the model using panel data and the control variables. Given that we model it dynamically (with the lagged dependent variable as one of the regressors), it potentially have a systematic bias in our coefficient of interest. Therefore, column (4) shows the panel data results correcting for dynamic models in line with Strobl (2012) and Bruno (2005). Column (2) and Column (4) reports the most credible specifications.

Results across all specifications provide similar results and negative as expected (see Table 2).<sup>20</sup> The magnitude of the effects are economically significant. An increase of one standard deviation in the intensity of a hurricane leads to a decrease in total per capita GDP growth of between ‐0.9 (for MSWS) and ‐1.6 percentage points (for IHD). It is important to highlight that the variation is explained by the different magnitude of a standard deviation according to the different indexes. For example, in the case of the IHD, a standard deviation is equivalent to a hurricane with a sustained wind speed of 74 mph (SS Scale 1) hitting an area with 2.3 percent
15 to lower their costs, at least until the loans turned bad. And this is the problem, of course: the system would be very unstable and ultimately would likely end in a crisis situation.

A key variable used to measure financial stability is the z-score, a variable that explicitly compares buffers (capitalization and returns) with the potential for risk (volatility of returns). The z-score has gained traction as a measure of individual financial institutions‘ soundness (e.g., Boyd and Runkle 1993; Demirgüç-Kunt, Detragiache, and Tressel 2008; Čihák and Hesse 2010). The z-score is defined as z≡( _k_ + _μ_ )/ _σ_ , where _k_ is equity capital as percent of assets, _μ_ is return as percent of assets, and _σ_ is standard deviation of return on assets as a proxy for return volatility. The popularity of the z-score stems from the fact that it is inversely related to the probability of a financial institution‘s insolvency, i.e. the probability that the value of its assets becomes lower _k_ than the value of its debt. The probability of default is given by _p_ (   _k_ )   (  ) _d_  . If μ is   _z_ normally distributed, then _p_ (   _k_ )   _N_ (01,) _d_  , where _z_ is the z-score. In other words, if   returns are normally distributed, the z-score measures the number of standard deviations a return realization has to fall in order to deplete equity. Even if μ is not normally distributed, _z_ is the lower bound on the probability of default (by Tchebycheff inequality). A higher z-score therefore implies a lower probability of insolvency.

The z-scores have several limitations, as well as advantages. Perhaps the most important limitation is that the z-scores are based purely on accounting data. They are thus only as good as the underlying accounting and auditing framework. If financial institutions are able to smooth out the reported data, the z-score