rafmacalaba/datause-encoder-data huggingface dataset:
allowance. The inter-temporal cost of these benefits is impossible to evaluate at this point. Third, there are administrative costs in running a system, which may not be financed **by** contributions. **If** we ignored these issues, our comparisons across countries would be biased. Consequently, we decided that the best method of comparison would be to use the current cost of the old-age system, and calculate what payroll tax would be required to cover these current expenditures. Table **5** presents those rates.

> **Table 5. Contribution Rate Estimates**

|**(in**percent|**of**gross wage*<br>|)<br>|||
|---|---|---|---|---|
||**Kazakhstan**|**Kyrgyz**<br>**Republic**|**Latvia**|**Moldova**|
|**Pre-reformsituation**|||||
|Payrolltaxearmarkedforpensions|**_25.5_**|**32.0**|34.0|**32.0**|
|Payroll requiredtofinance old-age current<br>benefits|**33.2**|**_37.5_**|**_27.5_**|**29.7**|
|**Post-reformsituation**|||||
|Projectedpayrolltaxafter**_25_**years|**_15_**|**25**|20|**25**|

_Source:_ Country legislation and Authors' calculations.

***** after payroll tax but before income tax

**_3.12_** _Problems with the methodology._ There are many weaknesses in the methodology presented above, the most outstanding being the use of the old system as contrafactual. As discussed above, it is unlikely that the benefits promised in the old system would have been delivered to future generations, as these systems were not affordable. The analysis, however, does not focus on the absolute size of the changes but only on the differential impact of the -forms between men and women. The use of the old systems' legal benefits as a baseline clearly leads to overestimating the replacement rates and implicit financial returns in the old system, and to underestimating the changes in net wealth after the reform. It should not, however, affect the differential impact between men and women.

**3.13** Finally, the calculation above does not include any estimation of the loss of welfare from the reduction of leisure (or time at home for unpaid work). The reason is that the value of leisure is difficult to estimate. If it is valued at the individual's wage rate, the increases in welfare related to work are just offset **by** the loss of leisure. Retirement age increase would

**17**
Table 4: The Impact of HSR on Firms’ DVAR: Baseline Results

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dependent Variable|DVAR|DVAR|DVAR|DVAR|
|CI|0.008***|0.008***|0.007**|0.025***|
||(0.002)|(0.003)|(0.003)|(0.006)|
|ln(FDI)||0.242|0.309||
|||(0.189)|(0.189)||
|FIT||0.782***|0.780***|0.779***|
|||(0.023)|(0.023)|(0.023)|
|∆ln(E)||-2.803***|-1.992***|-1.850***|
|||(0.652)|(0.653)|(0.666)|
|Control|No|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|No|No|
|Industry-Year FE|No|No|Yes|No|
|City-Year FE|No|No|No|Yes|
|Observations|249,853|213,444|213,444|213,331|
|Adjusted R<sup>2</sup>|0.712|0.720|0.721|0.722|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The city-level control variables include GDP Per capita, total output of FDI firms, the number of fixed telephone users, the number of mobile phone users, the number of internet users, the total passenger ridership and freight ridership by transportation other than railway and the secondary industry share, service industry share, inner city public transportation, and population density. Firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation). All control variables are measured in logarithm except for the secondary and third industry share, firm tariffs, and exchange rate.

# **5.1 Heterogeneous Impact of HSR**

HSR’s impact on firm DVAR may vary depending on the flexibility firms have in substituting imported materials with domestic ones. This flexibility is influenced by factors such as firm ownership, trade regime, industry, the type of materials used, and the geographical proximity between firms and potential suppliers. To better understand which firms benefit the most from HSR expansion, we reconstruct the CI incorporating distance information, split the sample by ownership, trade regime, and industry, and interact CI with the characteristics of the firms’ imported products.

**Geographical Distance** The impact of HSR on DVAR likely varies with the distance between exporting firms and potential suppliers. Firms located closer together may not rely on HSR for connections, while those further apart might turn to air travel. We expect HSR to have the most significant effect over medium to long distances. Table 5 presents results using CI constructed based on the distance between the exporting firms and their potential suppliers. The findings confirm that HSR has the greatest impact on firm DVAR when suppliers are located within medium to long distances—up to 1600 km (e.g. from Beijing to Shanghai), and up to 2300 km (e.g., from Beijing to Shenzhen).

20
procurement.

The evidence on the border effect and on the trade effects of TAs and their provisions is confirmed in three sets of robustness checks, whose results are reported in the Appendix. Tables A2 and A3 show that the estimates of the gravity models are similar when we use an alternative “narrow” definition of government procurement, which excludes purchases recorded in the “Health” and “Education” columns in the TiVA input-output tables. Results are confirmed also if we estimate the gravity models using the full yearly panel (Tables A4 and A5).<sup>24</sup> Finally, we confirm our baseline findings when the dependent variable is further disaggregated by the components of the goods and services sectors listed in Table A1. In Tables A6 and A7, the regressions control for country-industry-year fixed effects, consistent with an industry-level gravity model, while we maintain country-pair fixed effects, which reflect the assumption that trade costs vary across services and goods but not within each of the two aggregates. The estimates are close to the baseline ones in Tables 1 and 2. One – difference is that the results for goods are even weaker than in the baseline e.g., WTO GPA membership is now negatively associated with cross-border procurement.

Table 1: PPML Gravity estimates, Goods

||(1)<br>Gov.|(2)<br>Priv.|(3)<br>Gov.|(4)<br>Priv.|(5)<br>Gov.|(6)<br>Priv.|(7)<br>Gov.|(8)<br>Priv.|(9)<br>Gov.|(10)<br>Priv.|
|---|---|---|---|---|---|---|---|---|---|---|
|Same country|3.439***<br>(0.176)|3.147***<br>(0.187)|||||||||
|Contiguity|0.239**|0.280**|||||||||
||(0.121)|(0.126)|||||||||
|Common language|0.352***|0.277***|||||||||
||(0.108)|(0.101)|||||||||
|Colony|0.290*|0.385|||||||||
||(0.159)|(0.240)|||||||||
|Ln Distance|**-0.651*****<br>(0.049)|**-0.812*****<br>(0.051)|||||||||
|Common legal origin|-0.002|0.041|||||||||
||(0.083)|(0.082)|||||||||
|TA|-0.086|0.038|0.214***|0.294***|0.384**|0.318***|0.385**|0.318***|0.381**|0.314***|
||(0.120)|(0.135)|(0.042)|(0.037)|(0.152)|(0.098)|(0.152)|(0.098)|(0.152)|(0.098)|
|TA w/ procurement|0.168|0.269**|||0.191***|0.288***|0.212***|0.274***|0.163***|0.218***|
||(0.106)|(0.121)|||(0.043)|(0.038)|(0.045)|(0.041)|(0.048)|(0.039)|
|WTO GPA|0.596***|0.362***|||||-0.071|0.044|-0.171***|-0.088|
||(0.119)|(0.124)|||||(0.055)|(0.054)|(0.052)|(0.063)|
|EU|0.064|0.126|||||||0.499***|0.575***|
||(0.118)|(0.105)|||||||(0.090)|(0.069)|
|Obs|20,886|20,886|20,886|20,886|20,886|20,886|20,886|20,886|20,886|20,886|

> 24Egger et al. (2020) argue for the use of annual data for the estimation of the dynamic trade effects of TAs. Their results nonetheless confirm that using annual or time-interval data does not affect substantially the contemporaneous effects, which are the focus of our paper. Future work might investigate the anticipated and lagged effects of trade agreements on cross-border government procurement.

23
Policy Research Working Paper 8191

# **Abstract**

The combination of conflict, food insecurity, and displacement generates competing claims for financial resources that stretch the donors’ ability to provide funding and the humanitarian organizations’ capacity to provide social assistance. The paper uses Receiver Operating Characteristic curves and related indexes to determine the optimal targeting strategy of a food voucher program for refugees. The estimations focus on the 2014 food vouchers administered by the World Food Programme to Syrian refugees in Jordan. The analysis uses data collected by the United Nations High Commissioner for Refugees. Based on a poverty model, Receiver Operating Characteristic curves are used to optimize coverage and leakage rates under budget constraints. The paper shows how policy makers can use these instruments to fine-tune targeting using coverage rates, budgets, or poverty lines as guiding principles to increase the overall efficiency of a program. As humanitarian organizations operate under increasing budget constraints and increasing demands for efficiency, the proposed approach addresses both concerns.

This paper is a product of the Fragility, Conflict and Violence Cross Cutting Solution Area. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pverme@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 4.2 Indicators of Pro-Poorness, 1996-2007

||Re<br>|lative Measu<br>|re<br>|Ab<br>|solute Meas<br>|ure<br>|
|---|---|---|---|---|---|---|
||1996-2001|2001-2007|1996-2007|1996-2001|2001-2007|1996-2007|
|Headcount|1.04|2.40|1.08|0.68|1.54|0.69|
|Poverty Gap|1.02|7.47|1.09|0.47|3.13|0.48|
|Squared Poverty Gap|0.99|15.23|1.13|0.39|5.55|0.43|
|Watts|0.99|13.37|1.12|0.38|5.22|0.45|

Source: Authors’ Calculations Table 4.2 presents indicators of pro-poorness for the period under consideration for the headcount ratio and poverty measures from the additively decomposable class. The relative measure of pro-poorness is equal to the overall poverty elasticity divided by its scale component. A pattern of growth is judged pro-poor if this measure is greater than one and the growth rate is positive. On the basis of the values for this indicator presented in table 4.2, we conclude that the amount of poverty reduction obtained between 1996 and 2001 barely matches what would have been achieved under distributional neutrality. Therefore economic growth in Cameroon was not pro-poor for that period relatively speaking. The same indicator shows that growth was pro-poor between 2001 and 2007. However, this conclusion should provide no comfort to policymakers given that the mean per adult equivalent expenditure grew only 0.5 percent on average in real terms. Sustaining a real growth rate of about 2 percent per year between 1996 and 2007 would have produced a relatively pro-poor growth path.

The absolute measure reported in table 4.2 is defined by Kakwani and Son (2008) as the ratio of the overall poverty elasticity to the _neutral absolute growth elasticity of poverty_ . The latter is the elasticity of poverty with respect to growth subject to the benefits of growth being shared equally by every member of society. The growth process is pro-poor in the absolute sense if this ratio is greater than one. Our results show that economic growth in Cameroon was not pro-poor in the absolute sense between 1996 and 2001. It was pro-poor between 2001 and 2007, though the rate of growth was very weak over that period.

38
<!-- Start of picture text --> 4.00<br>3.80 i<br>—— United Kingdom<br>3.00 i =H Germany-West<br>2.80 i. =i Germany-East<br>C —— Poland<br>2.40 — = HungarBary<br>2.20<br>2.00<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br><!-- End of picture text -->
the U.K. and Japan, with some evidence from other European markets as well.<sup>1</sup> To a large extent, the international evidence confirms the hypothesis that other factors in addition to a are important in explaining asset returns.

In this paper we expand the empirical evidence on the nature of asset returns by examining the cross-sectional pattern of returns in a number of previously unexplored markets: the Emerging Markets. Using data compiled by the International Finance Corporation (IFC) for 18 developing country markets, we examine the effect of a number of risk factors, in addition to fl, on asset returns. Our work finds that, in addition to 3, two factors, size and trading volume have significant explanatory power in a number of these markets; dividend yield and earnings/price ratios are also important, but in slightly fewer markets. Importantly, for a number of the markets studied here, the relationships between all four of these variables and returns is contrary to the relationships documented for the U.S. and Japanese markets. Finally, we also document exchange rate risk to be a significant factor in explaining stock returns in several countries.

These findings are important because, by introducing independent and new empirical evidence into the asset-pricing debate, future research will now be forced to cope with the idea that any theory hoping to explain asset pricing in all markets will have to explain how factors can be priced differently simply by crossing an international border. Is it market microstructure that causes these substantial differences? Or, perhaps more likely, the regulatory and tax regimes that force investors to behave differently in various countries? As a final hypothesis, l Hawawini (1988) reviews the evidence from the non-U.S. markets. Chan, Hamao and Lakonishok (1991) analyze the cross-sectional behavior of the Japanese market.

2
# Figure 2a: Ed. Projects Acknowledging Gender 1972-1981

<!-- Start of picture text --> 10% AfrIca<br>:IIIqIHD.miti<br>EMENA 1. 2%<br>40%<br>S. Asia<br>9%<br><!-- End of picture text -->

# Figure 2b: Ed Projects Acknowledging Gender 1982-1992

<!-- Start of picture text --> LAC<br>11%<br>EMENA~~~~~~~~~~~~~~~~~~MN<br>1 9 1ric 17%a<br>S. Asia<br>15%<br>38%<br><!-- End of picture text --> <!-- Start of picture text --> E. Asia~~~~~~~~~~~~~E.Ai<br><!-- End of picture text -->

# Total Investment in Ed Projects Acknowledging Gender 1972-1981

<!-- Start of picture text --> LAC<br>EMENA<br>~~~~~~65%E.Ai<br>8<br><!-- End of picture text --> Total Investment in Ed Projects Acknowledging Gender 1982-1992 <!-- Start of picture text --> S. Asia<br>15%<br>ca~~~~~~~~~~~~~~~~~~~~~~~~~~~Arc<br>6%<br><!-- End of picture text --> Source: Calculated from Table **1.**
34 8.2 _Ex.lainine the Premium,: Macroeconomic Factors Matter Most_ Regarding the determinants of the parallel exchange rate, the evidence presented in this paper indicates that macroeconomic fundamentals matter most. In particular, the premium reflects inconsistencies between policies affecting domestic absorption (namely monetary and fiscal policies) on the one hand, and the official exchange rate on the other. In addition, in the short run, the premium is largely affected by expectations of the official exchange rate. The studies reviewed in this project also indicate that the distinction between official and unofficial pamllel foreign exchange markets is not relevant regarding the determinants of the parallel exchange rate.

# _8.3 Unification: Make Sure it Sticks_

A successful unification is one that can be sustained without leading to significant increases in inflation or recurrent balance of payments problems. Sustainable unification requires the adopton of a suitable exchange rate system. In countries where fiscal deficits persist and infladon is bound to continue, this means the adoption of a crawling peg or a managed float. Unifying to a fixed exchange rate in these cases will fail as balance of payments crisis will loom in the near future. In general, it is more difficult to unify to a fixed exchange rate, because it needs a credible commitment to maintain extemal balance even in the event of extemal shocks. In this case the prerequisites are a strong fiscal balance and sufficient reserves (or quick access to extemal credit) to withstand extemal shocks or speculation against the currency. Unification to a fixed exchange rate without these pre-conditions sets up the stage for a reversal.

How important is unification? There is strong evidence from recent studies on long term growth that large premiurns are harmful,2 as they create numerous microeconomic distortions, and induce corruption. It is thus important to unify quickly, especiallv the foreign exchange market for current account transactions and ensure convertibility. Automatic access to foreign exchange for trade I See **World Bank** (1990), Easterly and **Levine** (1993).
inflation-adjusted income streams. This means that they will be primarily interested in mature, operating assets with proven, predictable cash flow. While some of the larger, more sophisticated institutional investors are able and willing to invest at the riskier end of the spectrum (i.e. greenfield projects, untested technologies, etc.), it is unlikely that this will constitute more than a small percentage of their portfolios.

**Figure 1: Types of Infrastructure Investment**

<!-- Start of picture text --> Infrastructure Investment<br>Economic Infrastructure – e.g. transport, Social Infrastructure - e.g.. schools,<br>hospitals, defense buildings, prisons,<br>utilities, communication, renewable energy stadiums<br>Greenfield Brownfield Investments Secondary Stage<br>Investments • Typically medium risk Investments<br>• Construction - e.g. operating toll • Post construction<br>• Design, build, road with need for • Low risk, low return<br>significant capital<br>operating risk investment for • Similar to long term<br>• Typically higher risk improvement/ bond with coupon<br>expansion • Well established<br>cash flows – e.g.<br>operating toll roads<br><!-- End of picture text --> Source: World Bank 15. However, the main source of demand from EMDEs tends to be for greenfield investment, often largescale in nature. From an economic and social development perspective, mobilizing financing for greenfield investments holds the greatest potential. This is different from the operational phase investments in already up and running projects delivering steady cash flows which institutional investors have mostly been making to date.

16. From a growth and development standpoint, facilitating institutional investment in mature, low risk infrastructure is not a public-policy objective in and of itself, unless the resources freed up can be expected to be rolled over into new projects. Hence commercial banks will continue to play an important role in infrastructure financing, particularly for greenfield projects, which require strong credit underwriting and supervision skills that many international institutional investors do not have. With European banks continuing to adapt to fallout from the global financial crisis and the implementation of tighter regulatory standards, they have moved away from infrastructure finance in EMDEs. The challenge is therefore to explore the potential for local commercial banks in emerging markets to become more involved in such deals. How to encourage debt-to-bond rollovers between banks (and other sources such as private equity infrastructure funds) and institutional investors - which have so far been disappointing - is a topic worthy of further attention.

6
|**Table 15: Dev**|**iations from five-**|**year means**<br>Percent|Deviation|
|---|---|---|---|
|City|Country|2015-19|2017-21|
|Greensboro|United States|21.78|-20.46|
|Warsaw|Poland|17.66|-37.90|
|Melbourne|Australia|-9.38|-14.70|
|Madrid|Spain|-14.72|-14.70|
|La Rochelle|France|-15.25|-14.89|
|Fayetteville|United States|-19.15|-22.01|

# **6.  Estimating Urban Emissions**

Our econometric analysis also enables estimation of emissions levels for selected regions or local areas. In our approach, emissions levels have two components. The first, predicted by our econometric model, is the expected value of emissions from an area, given its industry structure, fire occurrences, population, income per capita, climate and mass transit infrastructure. The second component is the deviation from a city’s expected value measured by its regression residual. Negative deviations identify areas whose emissions are lower than expected, while positive deviations identify areas with higherthan-expected emissions.

For both components, atmospheric concentration measures can be converted to estimated emissions with exogenous information that provides measurement benchmarks. For the computation of expected values, we employ standard global emissions estimates. For the translation of regression residuals to emissions deviations, we use the EDGAR gridded database of CO2 emissions estimated from sectoral activity data and standard emissions parameters (Crippa et al. 2020).

# **6.1 Mapping Global CO2 Emissions**

Our econometric exercise measures marginal effects on atmospheric concentration anomalies from CO2 emissions by industry, fires, and non-industrial population-related sources. Concentration anomalies are measured in parts per billion; we convert to emissions volumes using a global adjustment. With 2019 as the benchmark year, Ritchie and Roser (2020) estimate global CO2 emissions of 35.4 gigatons (Gt) from fossil fuels and industrial sources. Bailis et al. (2015) estimate global CO2 emissions of 1.1 Gt from traditional woodfuels that are still used for heating in many locations. Adding the two yields a total estimate (GCO2) of 36.5 Gt.

Using the estimated model coefficients in Table 1, we predict industry- and population-related concentration anomalies for each of the 266,884 grid cells in our global database. The industry component (ICO2) for each cell is the sum of predicted effects for industry emissions in the cell and wind-displaced industry emissions from neighboring cells. The population component (PCO2) is the sum of predicted effects for heating degree days, cooling degree days, and income per capita, adjusted by the subway impact multiplier. We sum across cells to produce the global aggregate concentration anomaly:
# **Hunger**

The risk of experiencing hunger is large in many areas of Brazil. In the poor urban neighborhoods in Fortaleza, 27 percent of the youth answered that they experienced hunger at least once in their life, while 55 percent of the adults did. Among those youth who experienced hunger, 43 percent did so in the last 12 months (see Figure 3). They cope with the feeling of hunger by sleeping and going out. Examination of general nutrition status, which should involve weighing and measuring, is not within the scope of this study.

**Figure 3: Youth and Adults that Experienced Hunger Once in Their Lives** **<u>or in the Last 12 months (percent)</u>**

<!-- Start of picture text --> 80<br>70<br>Youth Adult<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>No Hunger (Ever) Hunger (Ever) No Hunger (12) Hunger (12)<br>Source: Data collected by authors in poor urban neighborhoods in the Northeast Brazil.<br><!-- End of picture text -->

# **Sexual Activities**

In poor urban areas, the risk of engaging in sexual activities at an early age is large. Answers reveal that 19 percent of the youth suggest that ideally a sexual relationship should start at age 15 or younger. Only 9 percent of the adult population shares this opinion. Hence, the vast majority 16