rafmacalaba/data-use-mentions-extended huggingface dataset:
However, banking credit indicators do not tell the whole story.<sup>61</sup> While net credit growth may be negative for large segments of the population, there is diversity among banks, firms and individuals (see below). Some firms borrow directly from international capital markets, though capital market financing, particularly debt financing is generally limited to a small subset of firms in each country.<sup>62</sup> Distressed domestic markets have ended up constraining those firms that are less likely to get credit on international markets (EC, 2015).<sup>63</sup> _<u>Financial sectors still need help.</u>_ In Ireland, several years of recession took their toll on banks with nonperforming loans still high at around 25% in 2014. Ireland’s deterioration was dramatic as in 2007 it had an NPL ratio of 0.6.<sup>64</sup> Banks had yet to restructure their household and SME debt, which at end‐2013 accounted for 60% of the NPL stock. Unsurprisingly, Greece’s banks were in the worst position when considering the ratio of nonperforming loans‐ estimated at over 33% in 2014, rising from 4.6% in 2007. Gross NPLs in GIPS are shown in Figure 13b. High as they are, NPLs on the books are not the whole story. Table 8 shows Gross Nonperforming Exposure, a measure that includes both on and off balance sheet liabilities (not considering the impact of collateral, which would reduce net exposure).<sup>65</sup> Among the countries listed, Ireland and Greece are in the most vulnerable position after Cyprus. Of particular note is the fact that Greece is the only one among the four where retail (including household debt) NPLs are higher than corporate ones.<sup>66</sup> Portugal (under 11% NPLs) and Spain (under 10%) have also seen very
.004<br>(0.025)||||
|Power as major obstacle|||||-0.032<br>(0.022)|||
|Power as severe obstacle||||||-0.044<sup>_∗_</sup><br>(0.022)||
|Power as biggest obstacle|||||||-0.148<sup>_∗∗∗_</sup><br>(0.042)|
|Observations|31403|31406|31406|31406|31406|31406|30491|

Notes: Table shows the average treatment effects on the treated obtained by weighted least squares regressions (Entropy Balancing). The dependent variable is the logarithm of the firm-level revenue-based total factor productivity for all regressions (1-7). In each regression, the treatment variable represents a specific degree of power constraints for firms. Firm-level controls variables are: Firm size, Firm’s longevity, Own website, Foreign private participation. Country-level controls variables are: GDP growth (annual %), Inflation (annual %), log(Bank concentration in %), log(FD index), log(GDP per capita), log(Net ODA in % of GNI), log(Regulatory Quality). Each estimate includes Year FE, Country FE, Region FE, Income group FE and a Constant. Standard errors in parentheses are clustered at industry level.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 21
records of eligible households that were compiled from the list of farmers kept by the village extension officer, when available. This list of eligible farmers recorded the name and gender of the household head as well as the area of land under cultivation of maize and rice. The VVC checked the farmer list against the eligibility criteria to come up with the list of eligible farmers.

The baseline data collection took place in January and February of 2011 immediately after the implementation of the village-level interventions in the three regions of Arusha and Kilimanjaro in the North, and Morogoro Region in the South.

In Arusha we collected baseline data for 460 households. Since the intervention was only successfully implemented in the 46 Arusha villages, at endline we doubled the number of observations for the eligible household survey, resulting in a sample of 920 households. The criteria used to draw the additional sample were the same as in the baseline eligible survey: eligible beneficiaries were defined as those that began participating in NAIVS during the 2010/11 planting season, and eligible nonbeneficiaries were defined as those that were eligible to participate in 2010/11 but were not selected as beneficiaries. Table OA4 in the Online Appendix suggests that the baseline sample and the additional sample are comparable. Endline data were collected in July and August of 2012. The households that participated in the baseline villager survey were also revisited.

Table 1 reports the mean of village level characteristics in Panel A from the village survey, VVC member characteristics in Panel B from the VVC survey and household member characteristics in Panel C from the villager survey. Column 1 of Table 1 reports the data source, column 2 reports mean characteristics across all villages, columns 3 and 4 report the means in lottery and non-lottery villages, respectively, and columns 6 and 7 report the means in meeting and no-meeting villages. Columns 5 and 8 show balance across experimental arms by reporting the p-values associated with a t-test that the differences in characteristics are different from zero.<sup>5</sup> According to column 2 of Panel
*

||Beijing|Shanghai|National in total|
|---|---|---|---|
|Total R&D investment|21.4|5.3|62.5|
|Government funds|13.6|3.1|39.9|
|Enterprises investment|1.7|0.6|5.8|
|Foreign investment|0.68|0.01|0.74|

_Source_ : National R&D Resources Survey 2000.

> 7 However, according to a more extensive definition of high-tech industries which not limited within high-tech industrial parks, the gross output value of high-tech industries in both Beijing and Shanghai has hold a large share in total industrial output, over 20%, in both cities in 2002 (See Beijing and Shanghai Statistic Yearbook 2003).

15
1980– 2019. I then present the historical decompositions to assess the impact of U.S. energy efficiency and oil supply shocks in the evolution of global economic activity during 2010–2019, a period that overlaps with the shale revolution.

**_Impulse Responses._** Figure 4 presents the IRFs of endogenous variables to U.S. energy market shocks. The left and right columns plot impulse responses for non-U.S. oil production, the global GDP factor, the real price of oil, U.S. oil production, and U.S. oil consumption to a one standard deviation increase in U.S. oil supply and U.S. energy efficiency, respectively.<sup>22</sup> The results are based on the combined restrictions discussed in Section IV.2.2. The results for the global GDP factor, oil

> 20The Kilian index is a popular measure of worldwide economic activity and it is used in many studies (see, for example, Baumeister & Kilian, 2012; Kilian & Murphy, 2014; Lütkepohl & Netšunajev, 2014; Anzuini, Pagano, & Pisani, 2015; Antolín-Díaz & Rubio-Ramírez, 2018). The Kilian’s index is constructed using cost of international shipping in commodity markets. The shipbuilding cycle moves more slowly relative to the business cycle due to the time involved in scrapping and launching ships (see Kilian & Zhou, 2018). Thus, the Kilian index captures longer swings in economic activity, which are important for accounting for cycles in commodity prices. However, because the objective of the analysis is to assess the impact of U.S. energy shocks on short-run fluctuations, I use the estimated global GDP factor in the structural VAR model instead of the Kilian index. Moreover, I multiply the estimated global GDP factor with the average factor loading in the subsequent analysis. The resulting measure is a moderated world business cycle relative to the world industrial production index. Consequently, the estimates of the impact of U
at<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

The variety and amounts of food subsidies are much smaller than energy subsidies (Figure 2). They are below $40-PPP for flour and oil and below $20-PPP for sugar. The only significant subsidies are for bread in Libya and the Islamic Republic of Iran where the amounts can reach $900-PPP and $200-PPP, respectively, for the richest quintile, and the pattern is regressive. In general, larger subsidies accrue to richer quintiles with monotonic increases across quintiles. This pattern holds for sugar, bread, and oil for all countries and for flour in Libya and Djibouti, but not for Morocco and the Islamic Republic of Iran, where for flour subsidies are larger for poorer quintiles. Therefore, exceptions to the pro-richness of subsidies may exist also for food products.

10
.401|0.451|0.459|

_Note:_ OLS regressions with sampling weights. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Only the top five export destinations are used for the estimation. The regression control for sector, exporting country, and importing country fixed effects. Distance, common border and common language between trading partners are based on BACI data.

In our analysis, firms in Enterprise Survey are matched to BACI data at the industry level, which provides disaggregated data on bilateral trade flows for more than 5,000 products (that were 15
POLICY RESEARCH WORKING PAPER 2369

# Summary findings

Using historical data on sovereign and individual borrowers, Ferri, Liu, and Majnoni assess the potential impact on non-high-income countries of linking capital asset requirements for banks to private sector ratings, as the Basel Committee has proposed.

They show that linking banks' capital asset requirements to external ratings would have undesirable effects for developing countries.

First, ratings of banks and corporations in developing countries are less common, so capital asset requirements would be practically insensitive to improvements in the - quality of assets widening the gap between banks of equal financial strength in higher- and lower-income countries.

Second, bank and corporate ratings in developing countries (unlike their counterparts in high-income countries) are strongly linked to the sovereign ratings for - the country and appear to be strongly related (asymmetrically) to changes in the sovereign ratings. A sovereign downgrading would bring greater changes in capital allocations than an upgrading, and would call for larger capital requirements at the very time access to capital markets was more difficult.

Under the new guidelines, capital requirements in developing countries would thus be exposed to the cyclical swings associated with the revision of sovereign ratings in recent crises.

Ultimately, linking banks' capital asset requirements to private sector ratings would reduce the credit available to non-high-income countries and make it more costly, limiting economic activity. Bank capital needs in developing countries would be more volatile than those in high-income countries.

These findings suggest that the Basel Committee should reassess the role it proposes assigning to external ratings, to minimize the detrimental impact of the regulatory use of such ratings on developing countries.

- - This paper a product of the Financial Sector Strategy and Policy Department is part of a larger effort in the department to study the impact of financial regulation on economic development. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Elena Mekhova, room MC9-622, telephone 202-4585984, fax 202-522-2031, email address emekhova(%worldbank.org. Policy Research Working Papers
Figure 11: Testing with alternate cutoffs <!-- Start of picture text --> Working Age Population<br>one two three four five six seven eight<br>2017q1 2017q2 2017q3 2017q4 2018q1 2018q2 2018q3 2018q4<br>15<br>10<br>5<br>0<br>−5<br>−10<br><!-- End of picture text --> first quarter of 2018 is in fact a result of the influx of Venezuelans. In absence of perfect control state for Roraima, this paper estimates placebo effect on the working age population of all the states in the north of Brazil, as Roraima is in the north and the northern region lags behind many socio-economic indicators in Brazil. Table 11 shows that the only two states that experienced a significant increase in working age population are Roraima and Amazonas, the two states bordering Venezuela at the north. While Roraima sees a 1.7%-6.7% increase working age population, Amazonas sees a mere 0.3% or 11550 people increase in the working age population in the first quarter of 2018, providing evidence that it is indeed the influx of Venezuelans which led to the population shock that may affect the labor market and the fiscal outcomes of Roraima.

Table 11: Testing for placebo outcome

||(1)<br>AC|(2)<br>AP|(3)<br>AM|(4)<br>PA|(5)<br>RO|(6)<br>RR|(7)<br>TO|
|---|---|---|---|---|---|---|---|
|RD Estimate|4.028|-3.022|11.55<sup>_∗∗_</sup>|-31.88<sup>_∗∗_
December 2013, continued through May 2015, and payouts were made to SSP clients in June 2015.<sup>9</sup>

## **2.3 Data**

Our analysis combines administrative data from NVRB with six waves of survey data. The administrative data contain information on all transactions posted to respondents’ NVRB accounts from the time of account opening to one year after the end of the intervention (32 months after baseline).

The baseline took place before SSP was introduced. The first three follow-ups took place – while SSP was ongoing during program months 6, 10, and 14. The objective of these rounds was to collect data on savings and debt during the commitment period, and assess how commitment impacted expenditure. The last two follow-ups took place 3 and 5 months after the SSP payout. These surveys were designed to assess how respondents’ financial lives had changed after the commitment amount was released.<sup>10</sup> In total, 91 percent of study participants participated in all survey rounds and 98 percent of study participants participated in at least one follow-up survey round. Appendix Table A2 that attrition is uncorrelated with treatment status.

# **3 Main Results**

## **3.1 Take Up**

Seventy-two percent of individuals in the treatment group signed up for SSP. The median monthly contribution was GHS 30, while the average contribution was GHS 42. Completion rates were also high, with just 13 percent of respondents who signed up for SSP dropping out before the savings cycle was over. Appendix Table A3 studies correlates of take-up (in the treatment group) and dropout (among those in the treatment group who signed up). The first two columns study pairwise correlations between take-up/dropout and baseline characteristics, while the last two columns present results of regressions where all baseline

> 9All study participants – in both the treatment and the control group – who did not have a savings account with NVRB were given the opportunity to open one in May
hybrids, after which expansion of hybrid maize area slowed. Figures for seed production and trade support these estimates. Hybrids have replaced open-pollinated varieties on irrigated land in Cukurova near the Mediterranean, along the Aegean, and in Marmara. In the north along the Black Sea, where maize is planted in small upland plots, farmers continue to grow OPVs.

With a simple yield response function, we estimate the impact of post-reform hybrids on yields. Since Turkey is a small producer and regularly imports maize, we assume that changes in maize productivity and production have no impact on price. With some assumptions about input costs, we then calculate the impact of higher yields on farmer net incomes.

We model maize yields in tons per hectare as a function of hybrids, fertilizer, irrigation, and rainfall. Hybrid use is shown as percent of maize area sown to private hybrids, all of which came in after and because of reform; we do not include pre-reform public hybrids, which covered only I percent of maize area in 1992 (CIMMYT 1994). We also include a trend variable to capture impact of transport improvements, extension, and other activities which could increase yields. Annual fertilizer use on maize is calculated from 1980 share of fertilizer applied to maize times annual total fertilizer use (FAO reports that share of fertilizer going to maize drops slightly from 1980 to 1988; we use 1980s share throughout; see Ange 1994, p 10). Maize irrigated area is not available, so percent of total area under irrigation is used. For rainfall, we use national annual rainfall.

-22-
- Stewart CP, Fernald LCH, Weber AM, Arnold C, Galasso E. 2020 Lipid-Based Nutrient Supplementation Reduces Child Anemia and Increases Micronutrient Status in Madagascar: A Multiarm Cluster-Randomized Controlled Trial. J Nutr. 2020 Apr 1;150(4):958-966. doi: 10.1093/jn/nxz320. PMID: 32006028; PMCID: PMC7138674.

- <mark>Tzavidis, N., Zhang, L. C., Luna, A., Schmid, T., & Rojas-Perilla, N. (2018). From start to finish: a framework for the production of small area official statistics.</mark> _<mark>Journal of the Royal Statistical Society Series A: Statistics in Society</mark>_ <mark>,</mark> _<mark>181</mark>_ <mark>(4), 927-979.</mark>

- <mark>UNICEF, WHO, World Bank .</mark> _<mark>Levels and trends in child malnutrition: Key findings of the 2020 Edition of the Joint Child malnutrition estimates</mark>_ <mark>. Geneva: WHO, 2020; vol. 24, no. 2, p. 1–16</mark>

- <mark>World Bank. 2023. Madagascar Poverty Assessment: N</mark> avigating Two Decades of High Poverty and Charting a Course for Change in Madagascar. World Bank

18 | P a g e
# **7. Appendix**

_Table A 1_ : Loss-to-harvest ratio using only planted area losses.

|||Lo<br>|ss-to-harvest Ratio<br>|<br>||
|---|---|---|---|---|---|
|**_Ethiopia_**|**Crop**|**Loss /**<br>**Production**|**Loss p. ha /**<br>**Yield**|**Loss / Production**<br>**(any loss)**|**Loss p. ha / Yield**<br>**(any loss)**|
|ESS13|Maize|0.06|0.06|1.00|1.01|
||Sorghum|0.21|0.14|1.69|0.99|
|ESS15|Maize|0.14|0.11|1.31|0.91|
||Sorghum|0.33|0.36|1.58|1.80|
|ESS18|Maize|0.10|0.09|0.91|1.02|
||Sorghum|0.22|0.16|1.53|0.96|
|**_Malawi_**||||||
|IHPS19|Maize|0.79|0.74|0.71|0.70|
||Harvest Value|1.57|1.43|1.36|1.26|

_Table A 2_ : Yield regressions with different loss proxies.

## **MALAWI: Yield regressions with planted area losses proxies -- excl. full losses**

|VARIABLES|(1)<br>logValue/Ha|(2)<br>logMaize Yield|(3
We cannot reject that DVA is exogenous in a Durbin-Wu-Hausman endogeneity test. Given our prior results concerning the role of DVA – both in Panel A and in previous IV-specifications, we see no reason to change our views on the sign of the DVA effect based on these results. Turning to FVA, the coefficient on FVA becomes negative and significant when we instrument here. This brings the FVA results in this sub-sample more in line with the full sample, including RTAs. Further, we can easily reject exogeneity of the FVA-Ratio here.

Together with our previous IV results, these results corroborate our interpretation of the OLS estimates as indicative of causal relationships. In particular, the new concern in this specification concerns the role of FVA. Recalling that the principal endogeneity concern is that tariffs raise FVA and thus bias the the FVA coefficient upward (toward zero/positive values), we argued in the text that our OLS estimates likely understate FVA effects. The IV results are broadly consistent with this interpretation. That said, we are reluctant to take the magnitude of the FVA estimate too seriously here due to the wide confidence interval.

One final point to note is that we report two-stage least squares standard errors (clustered by importer-exporter pair) in the table. The appropriateness 2SLS standard errors is not obvious: the high correlations among endogenous variables and therefore the instruments we use for them could give rise to weak instrument problems. Therefore, in the table, we report various weak-IV statistics to gauge the reasonableness of the 2SLS standard errors.

> 74Because country definitions have changed over time, we concord historical countries to modern entities as best we can. For example, Germany today corresponds most closely to the former Federal Republic of Germany. Russia today corresponds to the former USSR. And so on. Further, more trade flows in the NBER-UN data are zero in 1970 than are zero today, likely due both to true changes from zeros to positive values over time and differences in reporting thresholds and/or missing data in the two data sources.
- Newhouse, David, Pablo Suárez Becerra, and Martin Evans. (2017). “New global estimates of child poverty and their sensitivity to alternative equivalence scales.” _Economics Letters,_ 157: 125-128.

- Oswald, Andrew J., and Nattavudh Powdthavee. (2008). “Does happiness adapt? A longitudinal study of disability with implications for economists and judges.” _Journal of Public Economics_ , 92(5-6): 1061-1077

- Peichl, Andreas, Nico Pestel, and Hilmar Schneider. (2012). “Does size matter? The impact of changes in household structure on income distribution in Germany.” _Review of Income and Wealth_ , 58(1): 118-141.

- Ravallion, Martin, and Michael Lokshin. (2001). “Identifying welfare effects from subjective questions.” _Economica,_ 68(271): 335-357.

- Ravallion, Martin, and Michael Lokshin. (2002). “Self-rated economic welfare in Russia." _European Economic Review_ , 46(8): 1453-1473.

- Ravallion, Martin. (2012). “Poor, or just feeling poor? On using subjective data in measuring poverty”. _World Bank Policy Research Working Papers_ , No.5968

- Riedl, Maximilian and Ingo Geishecker (2014) “Keep it simple: estimation strategies for ordered response models with fixed effects”. _Journal of Applied Statistics_ , 41(11): 2358-2374

- Riedl, Maximilian and Ingo Geishecker (2010) “Ordered response models and non-random personality traits: Monte Carlo simulations and a practical guide.” _Center for European Governance and Economic Development Research Discussion Paper_ 116 (revised edition 2012).

- Rojas, Mariano. (2007). “A subjective well-being equivalence scale for Mexico: Estimation and poverty and income-distribution implications.” _Oxford Development Studies_ 35(3): 273-293.

- Schwarze, Johannes. (2003) “Using panel data on income satisfaction to estimate equivalence scale elasticity.” _Review of Income and Wealth_ 49(3): 359-372
with respect to the sources of observed misallocation.

**Investment Moments.** The key input into our estimation procedure is a set of carefully chosen moments from data on firm-level capital and revenues, both of which are directly available in our data. Since we are interested in capital allocation across firms within an industry, we extract the firm-level idiosyncratic components of each data series by regressing the measured values on industry-by-year fixed effects and working with the residuals. Industries are defined at the four-digit SIC code level.

We report a number of moments for each of the countries in Table 1, grouped into four categories. The first group describes the stochastic process of firm-level productivity.<sup>10</sup> In our theoretical framework, which uses a standard Cobb-Douglas production function and constant

> 7The measure of labor varies depending on reporting within each country.  For Malaysia and Taiwan, China,  we have measures of the wage bill. For the remaining countries, we have number of employees. 8For a more detailed description of these data sets, see, e.g., David and Venkateswaran (2017) and the references therein.

> 9It turns out that these issues crop up even using a single cross-country database. For example, as previously noted, the coverage differs widely even within the Orbis database alone.

> 10As is well known, in standard models of misallocation/firm dynamics, measures of productivity can also be interpreted as idiosyncratic demand factors.

4
000|0,000|-0,001|0,000|0,000|0,000|0,000|0,000|
|North region|0,715|0,040|0,000|0,691|0,027|0,000|0,595|0,022|0,000|0,368|0,017|0,000|
|Northeast region|0,501|0,017|0,000|0,483|0,012|0,000|0,392|0,011|0,000|0,230|0,010|0,000|
|West-centerTegion|0,374|0,032|0,000|0,308|0,020|0,000|0,232|0,017|0,000|0,047|0,015|0,002|
|South region|0,064|0,019|0,001|0,015|0,014|0,270|-0,026|0,012|0,032|-0,004|0,011|0,677|
|R2|0,173|||0,173|||0,000|||0,167|||

Source: Based on "Pesquisa Nacional por Anmslr de Dormicilios" (PNAD) of the 1976 and 1996.