rafmacalaba/data-use-mentions-extended huggingface dataset:
official CONEVAL benchmark, divided by the latter. Results are reported both for in-sample (left) and out-of-sample (right) municipalities. For out-of-sample municipalities, the relative bias estimates were top-coded at 25.

Both in and out of sample, the official 2010 CONEVAL estimates exhibit markedly less relative bias than the other estimators. For in-sample estimates, the boxplot confirms that the 2010 CONEVAL estimates are the least biased, followed by the household model estimates, the subarea model estimates, and the area-level estimates. For out-of-sample municipalities, the 2010 estimates do far better than any of the geospatial estimates, for which differences are hard to discern. The out-of-sample estimates contain several large positive outliers among non-sampled municipalities, including cases where the predicted poverty rate is more than ten times above the official CONEVAL estimates. This highlights the value of incorporating survey data in an empirical best framework, rather than simply relying on synthetic predictions, and the value of designing surveys to guarantee inclusion of all target areas in the sample.

# 6. Robustness checks

## _6.1 Benchmarking_

The results presented in Table 2 and Table 3 used estimates that were benchmarked to match the estimated poverty rates for each state. Benchmarking is appealing to ensure consistency between small area estimates and published survey results, and to remove any systematic bias at the state level. It is appropriate in cases when the small areas of interest define a hierarchical structure (Pfeffermann et al., 2014). Nonetheless, it is useful to test that the main results hold when not applying benchmarking. This is in part because existing software does not allow for benchmarking within the parametric bootstrap procedure when estimating mean squared error in unit-level models.

Table 4 shows the results for the household-level model with and without benchmarking. The mean poverty rate is defined as the unweighted mean estimated poverty rate across municipalities 25
where the dependent variable i is the sovereign bond spreads for country _i_ in period _t_ . is a country effect and is a time effect. The matrix i contains information on our pull and/or push ii ΨΨ αα factors including data transparency variables while γ is its coefficient vector. Finally, captures ii the residuals. ββ ΧΧ ii εε

# **3.2 Data Description**

We gather annual data of sovereign bond spreads for 72 countries from 1995 to 2018 from Bloomberg L.P. The explanatory variables consist of pull (internal) and push (external) factors. Pull factors include the GDP per capita (in US dollars at 2010 prices), economic growth, CPI inflation (average percentage change in consumer prices), general government primary balance to GDP (in percentages), current account balance to GDP (in percentages), general government gross debt to GDP (in percentages), external debt to GDP ( in percentages) and the ratio of the sum of import and export to GDP as a proxy of trade openness from the World Bank’s World Development Indicators (WDI). Financial openness is proxied by the Chin and Ito (2006, 2008) index. The quality of institutions is proxied by the ICRG index from the PRS group, which captures the level of the quality of domestic institutions. Push factors include the VIX index (which measures the implied volatility computed from S&P 500 index options) as an indicator of global risk aversion, and the US 10-year Treasury bond yield from the Federal Reserve Bank of St. Louis’ FRED database.

Our instrumental variables are the Freedom of Information index<sup>4</sup> taken from the website of freedominfo.org, and lagged values of the ICRG index, public or external debts, the interaction terms of ICRG with the data transparency indicator and (public or external) debt with the data transparency indicator, lagged trade and financial openness variables as instrumental variables.

# _Transparency Data_

Data transparency indicators are proxied
summer crops in 2023. This suggests that, in summer 2023, the effects on area cultivated were largely confined to VCs that were directly affected by the conflict, and their profitability or output may still be affected.

As a robustness check, we report results with coefficients for all the right-hand-side variables and interactions for the outcome variables of interest in three specifications in addition to the one reported in table 4. In different specifications, the conflict indicator is (i) a dummy of whether or not the VC was affected by any conflict (column 1), (ii) the normalized number of two-week intervals during which the VC was affected by conflict (column 2), and (iii) the measure of damaged area. The results are robust in all cases. Appendix tables A4 and A5 report the results, together with the same equations estimated using ACLED data on battles, explosions, and number of fatalities as indicators of conflict incidence and intensity. Although the signs of the estimated coefficients are mostly consistent (exception for intensity measures for winter crops), using imagery-based conflict indicators is preferable for agricultural outcomes.

> 9 For the 2022 winter crops, the area in conflict-affected VCs is estimated to drop by 80.37 or 171.07 ha (= 39.66 + 0.242 * 543) without or with accounting for damaged area, respectively. For the 2023 summer crops, the figures are 1,934 and 2,254 ha, respectively (595.39 + 90281 or 470.75 + 569.64 + 0.779 * 236 + 0.967 * 563).

> 10 In principle, shapefiles for the farmstead and all parcels cultivated by a farm would allow assessing damages at the farm level as well.

11
**Efects of school shutdow**|**ns on the **|**state-mana**|**ged network**||||
||Math|||Portugues|e||
||TD|TD|TD|TD|TD|TD|
||(1)|(2)|(3)|(1)|(2)|(3)|
|H1N1|-4.25***|-8.25***|-4.12***|-2.73***|-5.71***|-1.83*|
||(1.06)|(2.10)|(1.28)|(0.90)|(1.69)|(1.11)|
|H1N1 versus||5.34**|||4.12**||
|principal managerial skills||(2.47)|||(2.01)||
|H1N1 versus|||-0.04|||-0.75|
|program to reduce dropout|||(0.61)|||(0.53)|
|N. schools|5329|5227|5239|5329|5227|5239|
|Adj. R2|0.7|0.7|0.7|0.6|0.6|0.6|

_Notes_ : The authors’ estimate is based on data from _Prova Brasil_ , Census of Education, and IBGE. Wild bootstrap p-values. 95% CI in brackets. ***, **, and * indicate significance at the 1, 5, and 10% critical levels. Standard error in parenthesis and clustered at the municipality level. All regressions are weighted by fifth-grade enrollment at the school level. Math performance on a scale from 0 to 350
’ reform agendas, especially its magnitude. As evidenced by the regression output, there is a strong and positive relationship between the number of implemented reforms on _Doing Business_ indicators and local media coverage of _Doing Business,_ applying both one and two-year lags on press releases (see Annex B, Table B1, Model 1.1a, which refers to Model 1 above). Therefore, one could argue that in countries where media is most vocal about _Doing Business,_ regulators tend to pay closer attention to their economies’ performance on the _Doing Business_ indicators, placing business regulatory reforms high on political agendas.

The results are more robust when using Factiva data as an explanatory variable as opposed to the Google data. This is most likely because Google variable includes international press in addition to local one, which dilutes the domestic focus. However, we do get significant results with Google media in specifications 1 and 3 of the model but the coefficient becomes weak and insignificant when we add a control for level of _Doing Business_ score. In the case of Factiva’s data, the relationship is significant at the 1% level in all three specifications, controlling for free press, level of _Doing Business_ score and income per capita. Direction of coefficients on the income controls also indicate that poorer countries tend to reform more than the richer ones. This message is clearly aligned with the findings of annual _Doing Business_ reports since most developed economies already follow international best practices across _Doing Business_ topics and have less need for exponential business regulatory changes.

A curious, albeit not entirely surprising, finding is that freedom of press control variable shows positive and highly significant coefficients across all the models. This finding suggests that countries with less free press reform more and that there is higher _Doing Business_ media coverage in such economies. One explanation could be that most countries with state20
**Figure 5. Public debt in select Sub-Saharan African countries, 2018** **_(% GDP)_**

<!-- Start of picture text --> 180<br>2018 Level<br>160<br>2013-18 Change (median)<br>140<br>SSA 2018 Level (median)<br>120 SSA 2013-18 Change (median)<br>100<br>80<br>60<br>40<br>20<br>0<br>Source: IMF – World Economic Outlook Database, October 2018<br>Figure 6. Change in public debt in select Sub-Saharan African countries, 2012-17 (% GDP)<br>180<br>2013-18 Change (median)<br>160 2018 Level<br>SSA 2013-18 Change (median)<br>140<br>SSA 2018 Level (median)<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Ghana Togo Sao Tome & Principe Sierra Leone Angola Zimbabwe The Gambia Mauritania Congo, Rep. Mozambique Cabo Verde Sudan<br>Gabon Benin Zambia Angola Sudan<br>Guinea<br>Mauritania Cabo Verde Equatorial Zimbabwe Sierra Leone Congo, Rep. Mozambique<br><!-- End of picture text --> _Source: IMF – World Economic Outlook Database, October 2018_

**Figure 6. Change in public debt in select Sub-Saharan African countries, 2012-17** **_(% GDP)_**

_Source: IMF – World Economic Outlook Database, October 2018_ 7
Figure 4.3: _Markets in ECA tend to be overregulated which is likely to hamper job creation_ <!-- Start of picture text --> Market regulation index<br>9 8.2<br>8<br>6.9 7.0 7.1<br>7 5.7 5.8 5.9 5.9 5.9 6.0 6.1 6.1 6.3 6.4<br>6 5.1 5.2 5.2 5.2 5.3 5.6<br>5 4.6<br>4<br>3<br>2<br>1<br>0<br>Russia CIS Turkey Albania Slovenia Slovak Rep. Lithuania Ukraine ECA Romania Poland Bulgaria CEE Czech Rep. SEE Latvia Europe Hungary Croatia Estonia US<br><!-- End of picture text --> Source: Economic Freedom of the World 2003; Bank staff calculations.

Figure 4.4: _Corruption is high in ECA raising the costs of doing business and inhibiting_ <!-- Start of picture text --> job<br>creati<br>Perceived corruption index (TI)<br>on<br>12<br>10.010.0<br>10<br>8<br>6.3 7.0<br>6 4.94.9 5.1<br>4<br>2<br>0.0 0.20.50.70.70.91.11.21.21.62.02.12.63.03.03.03.23.53.54.0<br>0<br>Azerbaijan Moldova Kazakhstan Ukraine Georgia Albania Romania CIS Russia Uzbekistan SEE Turkey ECA Czech Rep. Slovak Rep. Latvia Croatia Poland Bulgaria CEE Belarus Lithuania Hungary Estonia Slovenia Europe US Kyrgyz Rep. Serbia and Mont. Bosnia i Herz.
Policy Research Working Paper 9792

# **Abstract**

The prices of 27 internationally traded commodities are decomposed into transitory and permanent shocks by applying an ideal band-pass filter to monthly data from 1970–2020. The two types of shocks contributed roughly equally to price variations, but with wide heterogeneity. Permanent shocks ac-counted for two-thirds of the variability in agricultural prices but less than 30 percent in energy prices. The transitory shock component revealed three medium-term cycles. The first (from the early 1970s to the mid-1980s) and third (from the early 2000s to 2020 onward) exhibit similar duration and involve almost all commodities, while the second (spanning the 1990s) is mostly applicable to metals, with the notable absence of energy. The permanent shock components differ across commodities, with an up-ward trend for most industrial commodities and downward trend for agriculture. Moreover, the permanent component of commodity prices where investment is irreversible, including energy, metals, and tree crops, exhibits a high degree of nonlinearities, which also coincide with the two post–World War II supercycles. By contrast, the permanent component of annual agricultural prices is linear, reflecting greater flexibility in investment allocation and input use of these commodities. Prices of commodities subjected to widespread policy interventions, such as international commodity agreements, exhibit persistent deviations from linear trends.

This paper is a product of the Prospects 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://www.worldbank.org/prwp. The authors may be contacted at jbaffes@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.
in the sample of high-income countries. From this observation, we conclude that, among all the real-time indicators considered in our analysis, Google mobility data and staple food prices are the most robust predictors of GDP growth and that their explanatory power is highly comparable across the two samples of countries available to us. The latter conclusion speaks to the promise of the ability of the two sets of real-time indicators in question to track different crises irrespectively of the country’s income status.

# 5. Conclusion

In this paper, we examined the capability of a set of real-time indicators to track the economic impacts of the COVID-19 crisis around the world. Of five different categories of real-time indicators, Google mobility, Google search trends, and food prices seem to have the strongest potential to track the onset of the pandemic. Google mobility data showed a steep reduction in mobility trends across all six categories of places, with a steady recovery that followed a gradual relief of stringency measures worldwide. The ability of Google search terms to track the COVID19 crisis appeared to be income- and region-specific. Except for high-income countries, religious search terms lined up with the onset of the pandemic, returning to the pre-crisis level in the second half of 2020. However, the generalizability of these terms to other crises remains uncertain. Furthermore, searches for food banks, assistance programs, and online education are highly dependent on the location and level of income. Food prices also painted an informative picture of the COVID-19 crisis state throughout 2020. The data showed that prices for basic food categories dropped sharply in most countries, rising to their pre-pandemic values only at the end of 2020. While NO2 and nighttime lights do not appear to follow the beginning of the COVID-19 crisis everywhere, both indicators show recovery starting from the third quarter of 2020.

A more rigorous investigation would require longer time series of real-time indicators and a higher geographic resolution of the analysis. For example, utilizing an extended temporal coverage, future 13
>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: This figure shows the trends in the value of international investment positions and share of active links by block for foreign direct investment (FDI). The value of positions distinguishes old and new links. Old links are those that have a positive value in 2001. New links are those that have a zero or missing value in 2001. By definition, there are no new links in 2001. Values are expressed in trillions of 2011 U.S. dollars (USD). The share of active links is the number of country pairs that have a positive value in each year over the number of country pairs with a positive or zero value in the same year. Country pairs with a missing value are not considered. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.
%) Estimated difference<br>C1.3. SOSD: Social Sustainability Index C1.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.98*** 0.92*** 0.92***<br>H<br>Kendall tau-b 0.94*** 0.78*** 0.78***<br>Spearman 0.98*** 0.73** 0.77**<br>A<br>Kendall tau-b 0.94*** 0.56** 0.61**<br>Spearman 0.97*** 0.9*** 0.92***<br>M0<br>Kendall tau-b 0.89*** 0.72*** 0.78***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.3<br>1<br>.8 .2<br>.6<br>.1<br>CCDF<br>.4 Difference<br>.2 0<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C2. Change in the indicators included for
# **6 Concluding remarks**

Agnostic about the pros and cons of urbanization per se, this paper starts from the observation that the next wave of urban expansion is predicted to be concentrated in large cities (1 million plus) (UN, 2011) and explores whether the nature of the occupational and spatial transformation matters for poverty reduction (as opposed to growth alone). In so doing, the study differentiates itself from most of the literature which usually only applies a sectoral (agriculture versus non-agriculture) or spatial (rural versus urban) lens and draws attention to that the fact that the urbanization pattern may be more important for poverty reduction than urbanization itself.

The study starts from a rather unique panel of individuals from Kagera, Tanzania who have been tracked over more than 15 years (1991/4 – 2010). Poverty among these individuals almost halved (from 58 percent to 30 percent) and almost half of the poverty decline could be attributed to rural diversification and migration to small towns (i.e. the transition out of agriculture into the middle). Only 1 in 7 of the people who exited poverty, did so through migration to the larger cities. The remainder did so while remaining in farming, underscoring the continuing importance of increasing agricultural productivity in poor settings such as Kagera. But, the case study findings point especially to the importance of rural diversification and the better ability of poor rural households to connect with the rural economy and smaller towns in exiting poverty (the size effect), even though average consumption growth was substantially higher among those moving to the big cities, albeit at the expense of higher joblessness.

Cross-country panel data analysis for developing countries (including Sub-Saharan Africa) further suggested a broader empirical regularity to these phenomena observed in the micro-data. Transitioning out of agriculture into the middle was associated with faster poverty reduction, while agglomeration in large cities was not. The results were robust to controls for political regimes as well as a series of other measurement issues and other competing hypotheses.
.000|
|Arellano-Bond AR(2)test(p-value)|0.812|0.790|0.847|0.749|0.769|

Notes:<sup>[A]</sup> GDP data are adjusted for international differences in purchasing power of the dollar.<sup>[B]</sup> When the p-value of the Hansen test is a borderline case, the instruments are validated by the supplementary evidence from the Sargan test of the over-identifying restrictions. Robust standard errors in parentheses; asterisks indicate statistical significance at the ***1, **5, and *10 percent level. Data for the transition economies refer only from 1997 onwards. All equations include time period and group-specific dummies.

Although the stock approach to global imbalances has mostly been applied when explaining the international adjustment mechanism (e.g., Milesi-Ferretti and Razin, 1998 and 2000), recent empirical studies have also listed the net foreign assets as a determinant of economic growth (e.g., Prasad et al., 2007; and Abiad et al., 2009). On theoretical grounds, the impact of cross-border holdings of assets and liabilities on economic growth is ambiguous due to the variety of channels at work. An improvement of the NFA position reduces the aggregate capital domiciled in the home economy. Hence, an improvement in the NFA position could be negatively correlated with economic growth. On the other hand, larger cross-border holdings of foreign assets improve the NFA position and increase the net inflow of factor incomes in the creditor economy. As Prasad et al. (2007) conclude, this implies that the countries that have accumulated more assets are expected to grow faster. But are emerging European countries also different? Assessing the evidence requires an empirical resolution.

13
, which are the largest in terms of the AUM benchmarked against these indexes.<sup>6</sup> Starting from the MSCI quarterly reports for the Standard Indexes, we identify 18 episodes of additions and deletions of Colombian stocks in the 2006-2017 period.<sup>7</sup> We retrieve from the reports the an-

> 5This behavior is consistent with benchmark indexes being the preferred habitat for many different investors. See for instance Pavlova and Sikorskaya (2020) and Kashyap et al. (2021).

> 6MSCI also provides other indexes, such as Small Caps, Value, and Growth indexes, that might differ in their composition from the Standard Indexes.

> 7Colombia is part of the MSCI All Country World Index, the MSCI Emerging Markets Index, and the MSCI EM Latin America, among other indexes. When a Colombian stock is announced to be included into or excluded from the Standard Indexes, this recomposition affects all of these indexes. Our sample includes 10 additions and 8 deletions. Stock ticker

4
of Statistics, and the National Audit Office.

In order to assess the distributional effects of fiscal interventions, the core building block of the fiscal incidence analysis is the definition and construction of the income concepts using the previous data sources (Figure 1). The allocation of fiscal interventions to individuals in the CASEN, depending on the income concept, is based on the following methods: _direct identification_ , when the survey contains information on who receives (pays) benefits (taxes), as well as the amount received (paid); _imputation_ , when the survey informs who receives (pays) benefits (taxes), but the amount received (paid) is retrieved from administrative records or program (tax) rules; _simulation_ , when neither direct identification nor imputation can be used, so that the beneficiaries (taxpayers) and the amount received (paid) is simulated based on the program (tax) rules; and imputation based on _secondary sources_ .<sup>13</sup> The income reported in the CASEN is the income after direct taxes and social contributions, which is equivalent to the _net market income_ concept —composed of wages and salaries from the formal and informal sectors, income from capital, private transfers such as remittances and alimonies, pensions, and imputed rent— and therefore the baseline for constructing the other income concepts. In order to construct _market income_ , a “reverse engineering” process from net market income is implemented by simulating and adding direct taxes and social contributions based on fiscal rules.

> 12 The official methodology for the measurement of poverty also changed. The new method incorporated new poverty lines based on updated values of both basic-food and basic non-food baskets, equated the value of the poverty lines between urban and rural areas, and adjusted the poverty lines based on equivalence scales.

> 13 For a detailed description of these and other allocation methods, see Higgins and Lustig (2017).

7