rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5451

# **Abstract**

The paper analyzes the impact of the recent global crisis in the context of the previous two decades’ growth and capital flows. Growth decomposition exercises show that Egyptian growth is driven mostly by capital accumulation. To estimate the share of labor in national income, the analysis adjusts the national accounts statistics to include the compensation of self-employed and non-paid family workers. Still, the share of labor, about 30 percent, is significantly lower than previously estimated. The authors estimate the output costs of the current crisis by comparing the output trajectory that would have prevailed without the crisis with the observed and revised gross domestic product projections for the medium term. The fall in private investment was the main driver of the output cost. Even if private investment recovers its pre-crisis levels, there is a permanent loss in gross domestic product per capita of about 2 percent with respect to the scenario without the crisis. The paper shows how the shock to investment is magnified due to the capital-intensive nature of the Egyptian economy: if the economy had the traditionally-used share of labor in income (40 percent), the output loss would have been reduced by half.

This paper—a product of the Poverty Reduction and Economic Management, Middle East and North Africa Region —is part of a larger effort in the department to analyze the drivers of growth and examine the growth prospects of the region’s countries in the medium term. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at sherrera@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
by increasing the borrowing cost in other loans to the same client.<sup>8</sup> In other words, lenders with market power would offer borrowers subsidized loans together with other more expensive loans that partially offset some of the discounts on the subsidized credit. In this context, the transmission of the subsidy to the final borrower has to be studied in terms of its effects on the total cost of credit (i.e., interest rates on both earmarked and non-earmarked loans). We argue that such crossproduct pricing strategies should be present whenever the bank cannot fully adjust the interest rate of the earmarked loan, that is, when the interest rate ceiling is binding as is the case for riskier borrowers. Furthermore, these price adjustments should be more pronounced if banks have more market power, for example, in less competitive lending markets or for firms that are less likely to switch between lenders.

We use administrative loan-level data from the Brazilian Central Bank credit registry, which includes the universe of (free-market) working capital loans issued to close to nine hundred thousand firms by 127 private banks between 2005 and 2016. Financing of working capital accounts for 44% of the total outstanding volume of private credit to businesses in the free market. We further augment our data by including information (e.g., originating bank, date, and credit type) on all earmarked loans received during the period for each firm in the sample. In all, this unique data set allows us to analyze how banks allocate earmarked loans across firms and how the government program may impact credit terms on their new free-market loans.

We document a novel cross-selling strategy whereby banks “bundle” governmentsponsored loans with privately funded short-term credit. We show that when a bank issues a government loan, it increases the interest rate of non-earmarked loans to the same borrower –on average a 0.34 percentage points (pp) increase in the interest rate of working capital loans disbursed together or after the earmarked loan (i.e.
Table A.7. Quantile Decompositions of Log Wage Gaps – Georgia Table A.8. Quantile Decompositions of Log Wage Gaps – Kenya Table A.9. Quantile Decompositions of Log Wage Gaps – Ukraine Table A.10. Quantile Decompositions of Log Wage Gaps – Vietnam

# **_Figures_**

Figure 1. Gender Gap in Hourly Earnings in Three ECA Countries Figure 2. Gender Gap in Hourly Earnings in Bolivia and Colombia Figure 3. Gender Gap in Hourly Earnings in Kenya and Vietnam

# **_Appendix Figures_**

_Figure A.1_ . Log Hourly Earnings Distribution for Men and Women, by Country

# **Common Acronyms**

ECA Europe and Central Asia IALS International Adult Literacy Survey ICT information and communication technology OECD Organisation for Economic Co-operation and Development PIAAC Program for International Assessment of Adult Competencies STEM science, technology, engineering, and math 3
From the perspective of international competitiveness, real unit labor costs in a relevant foreign currency is the most useful indicator. As can be seen from equations (4) and (5), this indicator comprises two components: a real unit labor cost in domestic currency and a real exchange rate <u>�</u><sup>PP</sup> �P <u>�</u> ~~.~~ Hence a rise (fall) in real unit labor costs denominated in a foreign currency can be<sup>ff</sup> brought about by a rise (fall) in real unit labor costs in domestic currency and/or an appreciation ee (depreciation) of the real exchange rate.

Unit labor costs have been used in three principal ways. First, as an input into real effective exchange rate (REER) indices, where they are an alternative to price indices such as the consumer price index (CPI) (see Turner and Golub, 1997; Lewney et al, 2012)). The latter are much more commonly used to compute the REER in part because CPI data are more readily available and more up to date than ULC data. Second, to compare levels of ULC across countries (usually countries which are competitors on international markets) at a point in time (see Ark, et al 2005; Golub et al 2017). Third, to track the evolution of unit labor costs in a specific country or countries across time, which usually involves converting the RULC data into indices (see Havlik, 2005; Landesmann and Hanzl-Weiss, 2013; Ordóñez et al, 2015).

The second use (sometimes referred to as relative unit labor costs) is potentially problematic for several reasons. In particular, the data on labor costs may not be fully consistent across all countries, especially in EMDEs. For example, wage data may be derived from surveys (such as establishment surveys) which comprise only part of the employed workforce and the scale
children in urban areas compared to the second quintile. Secondary education spending does not benefit the poorest children in rural areas (representing only 0.1% of market income) and is relatively constant across the other quintiles at around 2% of market income. In contrast, in urban areas, transfers on secondary education represent 5.2% of market income for the poorest quintile and 4.3% in the richest quintile, but this is most pronounced for children in the middle quintile (7.8% of market income). These progressivity and pro-poorness results are also confirmed by Kakwani indices and concentration coefficients (see Tables B1 and B2 in Annex B).

# **4.2.  The effect of the fiscal system on poverty and inequality**

A thorough understanding of how fiscal policies affect poverty and inequality requires an analysis of the distributional effects of the full fiscal system, i.e., combining revenue-raising activities with public spending and transfers. In line with the CEQ methodology, this can be achieved by determining poverty and inequality at different income categories (Lustig 2018) and following individuals through the various steps of the fiscal system (see also Figure 1). While this provides crucial insights into the distinct role of different fiscal policies to reduce monetary poverty, this approach unfortunately does not allow for a similar analysis of multidimensional poverty. Table 6 and Table 7 show key statistics on monetary poverty and inequality for all four income concepts.

First, market income (pre-fiscal income) includes private market or non-market earnings, e.g., what families earn through employment (before tax), any pensions, or other income they may receive (remittances, interests on savings etc.). At this stage, we find 33% of children live in monetary poverty, with poverty rates a little higher for girls than boys (36% versus 31%). Children in rural areas are significantly more likely to be poor than those in urban areas, with poverty headcounts of 39% and
exports per firm is highly skewed - as is the case in most countries as shown by Freund and Pierola (2012) - since the median exporter in Turkey exports just 50,000 USD in 2002, 100,000 USD in 2007, and 125,000 USD in 2011. The figures for the share of the top 1% or the top 5% of exporters are indicative of a high degree of concentration across exporters - in any given year between 2002 and 2011, just the top 1% of exporters (i.e., the largest 1% of exporters according to their export values) account for close to 60% of total exports while just the top 5% account for close to 80% of total exports. However this degree of concentration of the export sector is not particularly high from a cross-country perspective: data from the Exporter Dynamics Database described in Cebeci, Fernandes, Freund, and Pierola (2012) for year 2007 shows that concentration is much higher in Mexico, New Zealand, South Africa, or Spain.<sup>14</sup> Regarding diversification at the firm-level, the average number of HS 12-digit products exported per firm increased strongly and steadily from 9.2 in 2002 to 11.8 in 2011 while the average number of HS 6-digit products exported per firm increased strongly and steadily from 7.9 in 2002 to 10.4 in 2011. The average number of export destinations served per firm also increased continuously from 3.4 in 2002 to 4.4 in 2011.<sup>15</sup> Figure 1 shows Turkey’s aggregate exports in nominal terms and in real terms based on the exporter dataset. Turkey experienced very strong export growth, with aggregate exports in nominal terms quadrupling over the 2002-2008 period. Aggregate exports declined importantly with the global financial crisis in 2009, but then recovered strongly in 2010-2011. The average annual growth rate of aggregate nominal exports during the 2002-2011 period was 16%. Focusing on growth in aggregate real exports -
# **Additional Tables**

Table A.1: Countries included in the database<sup>_†_</sup>

||**Emerging economies**<br><br>|
|---|---|
|Algeria<br>|Ghana<br>Peru<br><br>|
|Argentina|Guatemala<br>Philippines|
|Azerbaijan|Hungary<br>Poland|
|Bahamas, The|India<br>Qatar|
|Bahrain|Indonesia<br>Romania|
|Barbados|Jamaica<br>Russian Federation|
|Belarus|Jordan<br>Saudi Arabia|
|Brazil|Kazakhstan<br>Singapore<br>|
|Bulgaria|Kenya<br>South Africa|
|Chile|Kuwait<br>Korea, Rep. of|
|China|Latvia<br>Sri Lanka|
|Colombia|Lebanon<br>Thailand|
|Costa Rica|Lithuania<br>Trinidad and Tobago|
|Croatia|Malaysia<br>Turkey|
|Czech Republic|Mexico<br>Ukraine|
|Dominican Republic|Mongolia<br>UAE|
|Ecuador|Morocco<br>Uruguay|
|Egypt, Arab Rep.|Nigeria<br>Venezuela, RB|
|El Salvador|Oman<br>Vietnam|
|Estonia<br>|Pakistan|
|Georgia|Panama|

> _†_ The definition of emerging economies used in the paper were chosen on the basis of markets traditionally classified as emerging by investment banks, and to illustrate the economies that were distinct from the historically advanced economies of North America, Western Europe, Japan, and Oceania. China data aggregate the mainland and the special administrative regions of Hong Kong and Macau.

24
Feb 2006 -<br>Feb 2009|Biannually|-Earnings only for wage<br>and salary workers|
||Philippines|-4.5|Philippines Labor force<br>Survey|2006 -<br>2009|Biannually|-Daily wage rate for<br>earnings|
||Egypt|-2.5|Egyptian Labor Force<br>Survey (ELFS)|2006-2009|Quarterly|-rotational panel|
|**MENA**|Jordan|-5.6|Jordanian Employment<br>and Unemployment<br>Survey (JEUS)|2006-2009|Quarterly||
||Bulgaria<br>Latvia|-10.7<br>-20.7||||- Cell means by gender,<br>education, and age<br>reported by Eurostat|
||Lithuania|-21.3||2006 -||- Urban/rural|
|**ECA**|Macedonia|-6.1|Eurostat|2009|Annually|breakdown only<br>available for selected|
||Poland<br>Romania|-4.2<br>-12.2||||outcomes<br>- No earnings and hours<br>information|
||Turkey|-7.4||||-Earnings not available|
|**AFR**|South<br>Africa|-8.2|Labor force Survey and<br>Quarterly labor force<br>survey|2006 -<br>2009|Biannually|-urban/rural information<br>not available<br>-sector information is not<br>available|

Note: Slowdown in GDP growth rate due to the recession is calculated as GDP growth rate in 2009 compared from the average GDP growth rates in 2007-2008. For detailed numbers, see Appendix Table A1.

26
Policy Research Working Paper 10423

# **Abstract**

Access to reliable electricity is a Sustainable Development Goal, and key for both economic growth and individual wellbeing. Yet, in the absence of sophisticated monitoring systems, policy makers in developing countries commonly rely on surveys to measure electricity reliability and prioritize investments. The accuracy of such survey-based methods is unclear. This study built a low-cost national electricity outage monitoring network, using off-the-shelf components in Tajikistan—a country with severe electricity service constraints. The system was introduced alongside a monthly household survey called Listening to Tajikistan, which allowed benchmarking the survey summary statistics against unbiased measures. The results show that although the two measures were well correlated, the survey data suffered from significant and systematic bias. Survey respondents (i) systematically underreported the incidence and severity of electricity outages on average, but (ii) systematically overreported the incidence of outages during a period of abnormally widespread service disruption of long duration. These findings suggest that bias in survey-based measures is sensitive to the salience of outages to the respondent, and that, where feasible, automated electricity monitoring can provide more accurate quality measurement. For survey settings, the results also suggest that estimates are more accurate over short (daily) reference periods.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wseitz@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
These results provide evidence that, despite recent poverty reduction in Bangladesh, children born in the southwest coastal region during peak salinity months have suffered from higher morbidity and mortality rates. For children born in peak salinity months, death probabilities in the 2018 and 2022 surveys are 25.2% and 13.3% higher, respectively. The evidence for diarrhea is mixed, with a probability 69.9% higher in 2018 but 16.5% lower in 2022. For stunting, children born in the peak salinity months have probabilities 8.8% higher in 2018 and 26.5% higher in 2022.

# **6 Summary and Conclusions**

the southwest coastal region of Bangladesh. At the same time, annual flux dynamics propel salinity changes so large that they can provide insights into future changes as high-salinity water moves steadily inland. This paper combines measures from river salinity monitors with fish sales records from neighboring wholesale markets to assess the magnitude, spatial distribution and fishing impact of salinity changes over the annual cycle in 2023. In traditional analyses, measured changes in river salinity are combined with species-level saline tolerance parameters to project changes in the spatial distribution of fish stocks. In this analysis, we incorporate two additional factors: differential responses to salinity change by fish species with similar survival limits for salinity exposure; and fishers’ search for more profitable fishing grounds as the fish respond to changing salinity. Together, these two factors determine fluctuations in fish catches as salinity changes.

Our econometric analysis shows that the complex interplay of species-specific relocation behavior and fishers’ search can generate very different catch patterns for fish that have similar or identical salinity tolerance ranges. For some species increasing salinity induces a sharp decline in the catch, while for others the interplay of relocation and fishers’ profit-motivated search can induce an increase in the catch. The strength of our results suggests that technically determined saline tolerance parameters have limited utility as predictors of the magnitude and spatial distribution of fish catch quantities as salinity changes. Since fish catch records of the
.000|0.489|0.000|1.000|
|No. bank branches|17,902|247.600|87.000|417.100|1.000|3649.000|
|Local banks' average asset size in<br>2007 (log)|17,902|15.220|15.670|1.614|10.250|18.450|
|Leave-one-out mean credit<br>constraints|17,902|0.238|0.182|0.203|0.000|1.000|
|Change in local average Tier 1 ratio<br>(\% points)|17,902|1.702|1.529|7.684|-35.880|44.600|
|Leave-one-out mean green<br>management practices|17,902|0.193|0.000|0.697|-1.317|6.980|
|No data on leave-one-out mean<br>credit constraints|17,902|0.007|0.000|0.081|0.000|1.000|
|No data on leave-one-out mean<br>green management|17,902|0.118|0.000|0.323|0.000|1.000|

Source: Authors’ calculations based on Enterprise Surveys and BEPS II.

# **IV. Firms’ mitigation strategies**

Scope and strategic preferences of mitigation The number and quality of green measures that a firm adopts communicates its green investment strategy. The scope of firms’ transition to a low-carbon future describes the degree of adjustment required (Smit and Wandel, 2006) or the extent to which the firm reduces its environmental impact by, for example, pursuing several climate mitigation measures simultaneously. However, the mitigation measures pursued show different strategic preferences that reflect climate objectives and the level of ambition. A firm has a
be an assembly of tradable inputs. In fact, raw material represents about 60 percent of the products value.

_A note on the public sector_ According to CAPMAS data for 2007/2008 the public sector represents 35 percent of the gross value of output, which might appear large except that petroleum products and derivatives represent 75 percent of the total public sector output. Thus the share of the public sector in the non-oil-related sector is now about 9 percent. Moreover, the net value added of the private sector – even including petroleum – is five times that of the public. The government maintains a role in industries outside of extraction and refinement of oil, which might be considered “strategic”: spinning and fiber textiles, sugar and refining, tobacco, coke, steel, and non-ferrous metals. These six sectors, plus petroleum extraction and refining, make up 91 percent of the gross output of the public sector.

The ERPs in the public sector have fallen between 1999 and 2009, except for the case of glass and glass products.<sup>10</sup> The average for those industries still public fell to 37 percent from 122 percent across public industries in 1999. And the dispersion of ERPs also fell notably: from 145 to 63 percent. The reader will note that in Table 4a one public sector activity has a high and negative ERP using the 2009 tariff data (soap, perfume and cosmetics). This is because under some conditions, value added without intervention is _negative_ – the cost of importing these goods (at social costs for tradables) would be less than the cost of domestic production. That is, using the notation of the formulas above, _VgH_ < 0 . For example if value added with interventions is positive, say 100, and without interventions is negative, say –10, then interventions would be benefiting the sector by a positive 110, But the ERP would be negative – in this example, _minus_ 11. This is a quite different situation from the case of interventions _dis_ protecting
– – In Kampala, we find some evidence of a positive albeit small effect of interactions on perceptions. The finding that the effect of refugee-host interactions is more positive in urban contexts resonates with the work of Dustmann et al. (2019) who, using data from Denmark, find that refugee presence is associated with lower vote shares for anti-immigration parties in large urban municipalities, while the opposite is true in rural municipalities. Interestingly, this suggests that some of the mechanisms and findings relating to the impact of contact on social cohesion in the ‘North’ may also be applicable in the ‘South’.

Another robust finding of our analysis is that respondents’ attitudes are closely correlated with other household members and immediate neighbours, which highlights the importance of intra-group attitude formation processes alongside inter-group contact as closely related mechanisms for attitude formation. While Hopkins (2010), for example, looks at the interrelationships between contact and national-level processes of attitude formation, our research highlights the need to examine the inter-relationship between contact and local-level (neighbourhood or household) processes of attitude formation. This suggests the need for further research to explore how exactly neighbourhood and household-level processes of intra-group attitude formation interact with members’ experiences of inter-group contact.

Our results further suggest that ethnic and linguistic ties between refugees and hosts matter for perceptions and attitudes. ‘Group threat theory’ predicts that diversity leads to opposition (Quillian 1996; Hjerm 2007; Stephan and Stephan 2017). Steele and Abdelaaty (2019) find that greater ethnic diversity is associated with decreased support for refugees; however, this relationship is not consistent across all measures of diversity. We find suggestive evidence that proximity in terms of ethnicity and language between refugees and host communities correlates with more positive mutual attitudes of hosts towards refugees, and vice versa.

Our qualitative research complements these insights, but inevitably also speaks to some of the other theoretical explanations for social cohesion within the literature such as the labour market competition and fiscal burden hypotheses. Overall, though, we interpret both the quantitative and qualitative findings