rafmacalaba/data-use-mentions-extended huggingface dataset:
,524 estimates in their meta-analysis on the 50<sup>th</sup> anniversary of Armington's paper. Almost all of these estimates are for developed countries at the sectoral level. Meanwhile, many CGE models of developing countries, including the 1-2-3 model, have been built with the Armington elasticities exogenously specified rather than empirically estimated. The reason is that there has not been sufficient time-series data to estimate these elasticities econometrically (some African countries gained independence only in the 1960s). Yet, as Schurenberg-Frosch (2015) shows in her sensitivity analysis of the Armington elasticity, model results can be highly sensitive to the magnitude of the elasticity. Hilberry and Hummels (2013) put it more bluntly: "It is no exaggeration to say that [the Armington elasticity] is the most important parameter in modern trade theory." Furthermore, apart from a few studies like Devarajan, Go, and Li (1999), there are hardly any direct estimates of export elasticity. A few studies take a country’s exports as imports from its major trading partners, thus estimating it indirectly as export demand from the rest of the world through an Armington function. In the 1-2-3 model, however, the export elasticity derives from a constant elasticity of transformation (CET) of supply, which, as mentioned above, is symmetric with the Armington function for imports in a general equilibrium framework. Thus, estimating it directly is also possible.

In this paper, we estimate the Armington and export elasticities for the 1-2-3 model for 191 countries using data from 1970-2018. Of these, 128 are developing countries, including almost all the countries in Sub-Saharan Africa and many under-studied ones like Benin, the Republic of Congo, Niger, Fiji, Haiti, Kiribati, and Tajikistan. The list includes several microstates, such as Nauru, Tuvalu, and

> 2 de Melo and Robinson (1989) used an aggregate framework that anticipates the 1-2-3 model to derive the theoretical properties of product differentiation in CGE models. Later, Thierfelder and Robisnon
decompose the test score gain from operations management into three effects: teacher selection, teacher incentives and household incentives. If this decomposition is correct, then we should see evidence of these mechanisms in intermediate school outcomes. We develop this argument, and present corroborative evidence from our PISA dataset, in Section 3.4.2.

# **3.4.1 Decomposing the test score gain into teacher selection, teacher incentives, and household incentives**

The difference in expected test scores across intermediate and low management public schools—that is, the impact of operations management holding people management constant—is Letting _a_<sup>_I_</sup> and _a_<sup>_L_</sup> respectively denote household effort in these schools, and using the same decomposition as before, we can rewrite this difference as There is no teacher incentive term because both extrinsic teacher incentives and augmentation of teacher intrinsic motivation depend on people management and this is assumed to be constant across these schools. We can write the teacher selection term as: Again, there are two teacher selection channels. Part of the reason that test scores are higher in schools with good operations management practices is because: (i) the _τ_ -types selected in are intrinsically motivated to exert more effort, and (ii) the _θ_ -types selected in are of greater ability.

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countries for which survey consumption estimates are available in PIP over the period 2000 to 2019. In particular, the annualized growth rate in the survey per capita consumption is regressed on annualized growth in GDP per capita, together with an interaction of annualized growth in GDP per capita and the share of time (if any) that each country spent as FCS between 2000 and 2019. G G s s s = 0.964 ∗G G G G s −0.530 ∗G G G G s mmssm GGmmG GGmm GGmmG GGmm According to this model, for economies that have never been FCS, 96 percent of GDP growth is passed through GGh GGh GGh ∗SShaaGGa aaSS to welfare. However, for economies that have been in FCS throughout the entire period, only 43 percent (0. 64 - 0. 30) is passed through.

The reduced pass-through rate indicates that in economies experiencing conflict, violence, or fragility, changes in national accounts are larger than changes in welfare. In particular, for economies in FCS where GDP per capita declined, this implies that the poverty rates estimated using this adjusted pass-through would be lower compared to those estimated under a full pass-through assumption.
All Southern European countries appear to be heavily informal, with 37% to 53% of economically active and marginally attached population working informally in Israel, Greece, and Cyprus; in Spain, Italy and Portugal this proportion is between 19% and 22%<sup>13</sup> . These six countries together with Ireland (33%), the UK and Poland (22% each), and Austria (20%) constitute the ̳highly informal‖ part of working Europe.

On the other extreme is Lithuania with estimated 6.4% of extended labor force working informally, followed by Latvia, Sweden, and Hungary with 8.0% to 9.4%; Estonia, France, and Belgium feature just slightly higher level of informality around 10%<sup>14</sup> . In other countries covered by the study (Finland, Denmark, Norway, Germany, Netherlands, Switzerland, Romania, Russia, Slovakia, Czech R., Bulgaria, Slovenia, and Ukraine) 11% to 14% of the extended labor force are working informally.

Classifying the Baltic countries and Hungary as low-informality countries based on data referring to the time of crisis, which was much deeper in these countries than elsewhere in the EU, should be taken with care. Indeed, Latvia was among the top ten countries regarding informal dependent employment in 2007, whilst Lithuania was just outside the top 10 in terms of both dependent and total informal employment in 2005 (see Table A3). By contrast, informality rate has been always low in Hungary and, according to most estimates, in Estonia. Furthermore, Latvian State Labor Inspectorate (2011) reports a substantial increase in the incidence of unregistered employment in the post-crisis period (along with falling unemployment).

As a robustness check, in Table A3 we compare ESS-based proportions of employees working without contracts and proportions of all informally employed persons in total employment for 2004-2006 with similar indicators calculated from the Fourth European Working Conditions Survey<sup>15<
_ 0 compared to a survey-only estimate of 36 _._ 4. Our results are in contrast with other studies using different methods of adjusting for top incomes in Egypt (Hlasny and Verme, 2013), which report a more modest effect.<sup>2</sup> Their correction however does not consult a second source of data. If the main problem is that high income earners are simply missing from the survey, then no adjustment that relies soley on the survey will resolve the downward bias in estimates of inequality. The only way to obtain a meaningful correction is to bring in a second source of data that carries the necessary information on top incomes and hence will permit for the consistent estimation of income inequality. This reasoning is shared by Alvaredo and Piketty (2014) who similarly argue that the household survey data by themselves are insufficient to estimate top incomes in Egypt. While they make an appeal for making data on income tax records available, we propose to work with house price data instead. It should be noted that relying on predictors of top incomes rather than actual incomes derived from

> 2The Gini coefficient of household expenditure per capita in 2009 increases from 30 _._ 5 to 31 _._ 8 which is found to be statistically significant, but not economically significant.

3
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fact poor based on the survey data (a mean IER of 0.553, as compared to 0.481 from Table 5). Exclusion errors are also affected, though these errors rose less markedly. Using the Extended PMT we also find a substantial increase in the targeting errors, especially for inclusion, when we allow for lags using Method 1. A similar pattern is found for Method 2.

In Table 17 we give the targeting differentials. Allowing for lags, we find mixed results. Ethiopia and Nigeria have better pro-poor targeting with lags, while Malawi, Tanzania and Uganda see increases in targeting of the non-poor. Malawi in particular has negative TD’s, indicating that the correction for lags now means that a uniform (un-targeted) policy would do better.

The post-transfer poverty rates allowing for lags are provided in Table 18. PMT still brings the poverty measures down, but by about two percentage points less when allowing for lags. For example, allowing for lags achieves an average post-transfer headcount index of 19% instead of 17%. The stylized categorical targeting schemes now attain similar or somewhat lower post-transfer poverty rates. On

> 46 <mark>The survey years are as follows: Ethiopia 2011/12 and 2013/14; Malawi 2010/11 and 2013; Nigeria 2010/11 and 2012/13; Tanzania 2010/11 and 2012/13; Uganda 2010/11 and 2011/12.</mark>

> 47 For example, recall that the mean lag between the survey year and the release date of the Progress out of Poverty Index is 3.9 years (Introduction).

> 48 Switching to the panel samples changes the targeting measures somewhat but the following observations still hold.

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the other market.

Khandker et al (2009) find that the overall effect of road improvement on household per capita annual consumption was about 8 –10 percent across affected project areas in Bangladesh. In addition, road improvements have led to a poverty reduction of 3 – 6 percent on average over the period of about 5 years. This implies that extreme poverty fell by about 1 percent each year, solely due to rural road improvements. Road improvement also has a significant positive impact on aggregate crop output and price indices. In addition, school enrollment of boys and girls increased significantly in both project samples.

Jacoby (1999) investigates the benefits from road projects at the household level using the relationship between the value of farmland and its distance to agricultural markets in Nepal based on the 1995 – 96 Living Standards Survey. Providing extensive road access to markets would confer substantial benefits on average, many of these going to poor households. However, the benefits would not be large enough or targeted efficiently enough to appreciably reduce income inequality in the population. The results highlight that rural road construction is not always the magic bullet for poverty alleviation.

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|Direct|0.471|0.623|0.363|0.324|
|---|---|---|---|---|
|AGEB-level model|0.541|0.750|0.497|0.264|
|Transformed Area-level model|0.444|0.652|0.294|0.373|
|**Estimated Pearson correlation**|||||
|Direct|0.493|0.659|0.418|0.385|
|AGEB-level model|0.553|0.770|0.415|0.614|
|Transformed Area-level model|0.405|0.619|0.209|0.384|

Table A4 in the appendix also presents a comparison of an area-level model using the transformed target variable and an area-level model using a linear (untransformed) model. The transformed model generally performs better than the linear model, with the possible exception of some female LFP statistics (coverage rates, absolute deviation, and correlations). However, the AGEB-level model remains the best choice in this context.

# **5. Conclusion**

This paper considers the extent to which combining simulated sample data with publicly available geospatial data improves state and municipal estimates of male and female labor force participation and unemployment rates. Results are compared against the full 2020 census. The small area estimation procedure greatly improves the accuracy and precision of state level estimates for all four indicators, as well as municipal estimates of male and female labor force participation. This method can therefore be used to obtain more granular information on municipalities with low LFP, which can lead to a better understanding of the causes of low female LFP and potential policies to address it.

The method does not work nearly as well for estimating urban unemployment, because the values for unemployment rates are very low and zero for many clusters in the sample. In this setting, linear
Table 2: Matched sample balance: Socio-economic characteristics (survey data)

||.<br>**All**|**N**|**Control**|**N**|**Treatment**|**N**|
|---|---|---|---|---|---|---|
|Muslim|0.13<br>(0.34)|959|0.13<br>(0.038)|551|0.12<br>(0.035)|408|
|Hindu|0.73<br>(0.45)|959|0.73<br>(0.041)|551|0.73<br>(0.040)|408|
|Buddhist|0.14|959|0.13|551|0.15|408|
||(0.35)||(0.032)||(0.032)||
|HH size|5.11<br>(1.98)|959|5.09<br>(0.10)|551|5.13<br>(0.11)|408|
|Female headed household|0.078<br>(0.27)|959|0.076<br>(0.012)|551|0.081<br>(0.014)|408|
|Age HH head|46.6<br>(10.3)|959|46.5<br>(0.57)|551|46.7<br>(0.57)|408|
|Head able to read|0.68<br>(0.47)|959|0.68<br>
or cost-effective for the foreseeable future, so that identifying short-term mitigation strategies remains essential. The consensus view is that household-level water treatment, dug wells, and rain-water harvesting are not viable alternatives for lowering arsenic exposure because of the cost and logistics of maintaining such systems in rural South Asia (Ahmed et al. 2006; Howard et al. 2006; Johnston et al. 2014; Sanchez et al. 2016). In contrast, despite being the main source of arsenic exposure, tube wells remain the most effective way of providing safe drinking water to the rural population of Bangladesh in the short to medium term (Krupoff et al. 2020). With the exception of the most severely affected areas of the country, the spatial distribution of high- and low-arsenic wells is highly mixed, even over small distances. At the same time, whether a well is contaminated with arsenic or not rarely changes over time (van Geen et al. 2007; McArthur et al. 2010). Therefore, exposure among users of arsenic-contaminated wells can often be avoided by switching to a nearby safe well, be it a shallow private well or a deeper—which usually means safer—community well (van Geen et al. 2002; van Geen et al. 2003). Using data from Araihazar, a sub-district bordering the location of this study, Jamil et al. (2019) estimate that blanket testing campaigns that inform households about the arsenic contamination of all private wells were significantly more cost-effective at reducing arsenic exposure than the provision of piped water, or the construction of deep wells by the government.

Previous campaigns aimed at testing tube well water for arsenic have only partially succeeded at promoting risk-avoiding behavior, highlighting the need to devise novel strategies to achieve this goal. Between 1999 and 2005, the Bangladesh Department of Public Health Engineering (DPHE), with support from the World Bank, DANIDA, UNICEF and a number of non-governmental organizations (NGOs), coordinated the Bangladesh Arsenic Mitigation and Water Supply Program (BAMWSP) testing campaign.
# **3.1 Trade Policy and Price Changes**

In the empirical application that follows, good _i_ represents one of the product classifications from the expenditure, income and home-consumption templates modules of the household surveys. Each of these classifications includes many finer product groups from the HS classification. We compute weighted average tariff rates _τi_ for each of our survey categories: where _n_ is an HS-category that belongs to survey-category _i_ and _mc,n_ are imports of good _n_ from country _c_ . The results are shown in Table 1. We report the average tariff for our 2-digit classification, Staple Agriculture, Non-Staple Agriculture, and Manufactures. Average tariffs are highest for non-staple agricultural goods (14.4 percent). They are lower for staple agricultural goods (10.8 percent) and manufactures (10.9 percent). These averages mask substantial variation in trade barriers across countries. Average tariffs on non-staple agricultural goods range from as high as 46.1 percent in Bhutan to as low as 1.9 percent in Indonesia. Countries with higher tariffs in agriculture (staple and non-staple) tend to have higher tariffs on manufactures as well.

Using the full price transmission assumption (equation 6), we calculate the price changes induced by the elimination of tariffs as follows:

# **3.2 Expenditure and Income Shares**

To measure the first order welfare effects we retrieve the expenditure shares _s_<sup>_h_</sup> _i_<sup>andthe</sup> income shares _φ_<sup>_h_</sup> _i_<sup>and</sup><sup>_φh_</sup> _w_<sup>from the household surveys.Appendix Table A1 lists the countries</sup> included in the analysis, together with the corresponding household survey, the year of the survey and the sample size. Our analysis covers all low income countries for which appropriate household survey data were available, as well as the majority of lower middle 11
capita in Purchasing Power Parity (PPP - which adjusts for differences in the cost of living across countries), income inequality as measured by the Gini index, income growth as measured by the GNP per capita growth rate, and public spending on primary education per student. All these variables are from the World Bank's World Development Indicators (World Bank, 1999) and are averaged over the period since 1990 (Annex Figure 3a shows the same figures for the male/female ratio).'<sup>8</sup> The story that emerges from these graphs is not one of a systematic relationship between the variables and the magnitude of the gender gap. The only correlate with a significant relationship at the ten percent level is a country's income inequality as measured by the Gini index (correlation coefficient equal to -.38, pvalue=.07, N=23). Other than the Gini index, income level is negatively but insignificantly related to the male female gap, and GNP per capita growth and public spending per student on primary education have close to zero and insignificant correlations. Of course this exploration is limited by its very narrow bivariate approach. As an indication though, the results do suggest that the few variables analyzed do not give a strong lead on this and more work needs to be done to explore the international correlates and determinants of gender gaps in education (for more discussion and a further exploration of this see Dollar and Gatti, 1999, Filmer, King, and Pritchett 1998).

> 18 The annex is available from the author at dfilmer(a.worldbank.org or directly from http://www.worldbank.org/research/projects/edattainl/edattain.htm

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# **Appendix III: Central Bank Governance and Financial Supervision**

Central banks often have mandates regarding financial stability and supervision, but often central bank legislation is less specific about the clear functions and responsibilities of central banks in this regard (Ortiz 2009). Nonetheless, elements of this task, such as the lender-of-last-resort function or oversight of the payment system, have long been central bank functions (Freixas et al. 2000).<sup>22</sup> Although the trend since the 1990s has been toward consolidation of financial supervisory functions, these changes have primarily focused on consolidating functions in an agency separate from the central bank. Despite those developments, according to the results of a survey of 160 countries completed in 2019, approximately 68 percent of central banks still have banking supervisory responsibilities (Anginer et al. 2019).

Empirical evidence on the link between central bank independence and financial supervisory responsibilities remains limited. Some research has shown that <mark>central bank independence exercises a positive impact on bank soundness and that central bank involvement in banking supervision mitigates the adverse effects of financial crises (Doumpos, Gaganis, and Pasiouras, 2015). H</mark> owever, this more extensive mandate can impact the central bank's autonomy, decisionmaking, and transparency. Initial findings from the research explored in this report indicate that countries with better overall governance environments and lower country risk usually have an entity different from the central bank for financial supervision, as seen in Table III.1. The variables used here are described earlier in the paper, and as a measure of whether central banks also have supervisory responsibilities, we use the World Bank's regulation and supervision survey (World Bank 2019). This comprehensive survey covers multiple subjects on banking regulation and supervision practices 160 jurisdictions. We use this survey to establish whether the central bank or a separate entity is responsible for financial supervision. This variable aims to analyze whether reserve management is different in central banks with more responsibilities and more complex governance structures
populations under review, metrics and model specifications used. Most early research examining cognitive skills in the context of labor market outcomes is based in the United States and uses data from the National Longitudinal Survey of Youth (NLSY), which includes a measure of cognitive and vocational ability—the Armed Services Vocational Aptitude Battery (ASVAB) (Cawley, et al., 1996; Cawley, Heckman, and Vytlacil, 2001). Findings from these studies show cognitive ability having modest effects on wages.

Subsequent research on the role of cognitive skills moved toward using comparable data from large-scale international reading literacy tests, such as the International Adult Literacy Survey (IALS) and the Adult Literacy and Life Skills Survey (ALLS). It is argued these reduce the heterogeneity of cognitive skills metrics and are also better measures of functional literacy and reading proficiency (Barrett, 2012; Barone and van de Werfhorst, 2011; Fasih, Patrinos, and Sakellariou, 2013; Green and Riddell, 2003; Hanushek and Zhang, 2006). Separate studies using the Canadian IALS (Green and Riddell, 2003) and the Australian ALLS (Barrett, 2012) find that cognitive skills significantly predicted higher earnings. Similarly, IALS data have been used in cross-country comparisons.

More recent research examining the effect of cognitive skills on earnings has used reading assessment data from the Program for the International Assessment of Adult Competencies (PIAAC) survey, which is sponsored by the OECD and is designed to measure key cognitive and workplace skills. The PIACC survey measures cognitive skills in three domains: literacy, numeracy, and problem solving in technology-rich environments. It addresses some of the measurement issues noted with the IALS and ALLS.<sup>6</sup> Hanushek et al. (2013) used the PIAAC data to estimate the returns to skills in 22 countries. Their findings indicate that higher cognitive skills (proxied using the numeracy and literacy skills components of the PIAAC assessment) lead to higher wages across all countries, with prime-age workers (ages 35 to 54