rafmacalaba/data-use-mentions-extended huggingface dataset:
and sectoral strategies, country partnership frameworks, and training, among others. Conversely, a considerable amount of knowledge work is produced without mobilizing earmarked budget resources.

The matching process focused first on budget codes, which are at times mentioned in the documents filed in the IB database and in the publications released through the OKR database. Successful matches were indexed by the number of the corresponding budget codes in the WH database, known as P-codes in World Bank parlance. When no P-code could be identified in IB documents or OKR publications, the matching process relied on information about the titles of the entries and the names of their authors. Further confirmation that the matching was correct was provided by the regions or countries these entries covered and the sectors they were mapped to. Entries without associated P-codes were indexed with numbers allocated sequentially; these numbers are identified in what follows as Z-codes.

13
(1984). A class of decomposable poverty measures.</mark> _<mark>Econometrica</mark>_ <mark>, 52(3): 761-766.</mark>

- <mark>---. (2010). The Foster–Greer–Thorbecke (FGT) poverty measures: 25 years later.</mark> _<mark>The Journal of Economic Inequality</mark>_ <mark>,</mark> _<mark>8</mark>_ <mark>, 491-524.</mark>

- Jerven, Morten. (2019). "The Problems of Economic Data in Africa." In _Oxford Research Encyclopedia of Politics_ .

- Kilic, T., and Sohnesen, T. (2019). “Same question but different answer: experimental evidence on questionnaire design’s impact on poverty measured by proxies.” _Review of Income and Wealth_ , 65.1, pp. 144-165.

- Lain, J. and Vishwanath, T. (2022). _A Better Future for All Nigerians: Nigeria Poverty Assessment 2022_ . Washington, D.C.: World Bank Group.

33
revenue changes, although clearly there is a good deal of variability that is not explained by the rate changes alone. In statistical terms, the slope of the line is not significantly different from zero (the confidence level is about 65 percent). (See Annex III for the regression results and Annex IV for the analysis of a single state, West Bengal.) This result suggests strongly that other factors such as shifts in underdeclaration of property values are at play.<sup>45</sup> In Figures 13 to 16, we examine the impact of tax rates changes for a few States for which we have more data. Stamp tax revenues are a function of rates, property values transacted, and the number of transactions, or R = t * PRE * N, where R is stamp duty revenues, t is the stamp duty rate, PRE is the average price of real estate transacted in the period, and N is the number of transactions in a given period. Differentiating this expression gives: where ^ indicates a proportional change. Using this relationship, we can create a predicted value of revenue changes in a given period based on changes in the determinants of revenues. Note that this is not a behavioral equation against which hypotheses would be tested, but rather it is an identity.

Now if we were could observe all of these variables directly, then we could simply ∧ substitute the right hand side variables to get a predicted _R_ . We do have information on some,

> 44 For West Bengal, a time series analysis from 1992-93-2000-2001 provides a significant and negative coefficient for the quadratic term on the tax rate, demonstrating a Laffer curve effect, in spite of very low degrees of freedom due to data limitations. Annex III shows the results of this analysis.

> 45  These findings are consistent with those of Das Gupta (2002) on Karnataka. He found that a 1 percent increase in the effective tax rate led to a 0.6 percent decrease in revenues for the period 1980-1981 to
productive establishments to the most productive ones.

Finally, in our benchmark analysis we include all establishments regardless of their size. As explained before our census covers all establishments employing 10 workers and a representative sample of firms were taken from establishments with less than 10 workers in Ethiopia and Ghana while the Kenyan census covers all manufacturing establishments in the country. One may argue that Kenyan census may not be comparable to the Ivorian, Ethiopian or Ghanaian censuses in covering the smallest establishments. To examine the extent to which this finding is an artifact of the Kenyan dataset that has sparse coverage of small firms, as a final robustness check, we redo the analysis by excluding establishments employing less than 10 workers. The result shows that the potential gains from reallocation decreases in all countries (29.4% vs. 31.4%) in Cˆote d’Ivoire, (60.9% vs. 66.6%) in Ethiopia, (66.9% vs. 75.7%) in Ghana and (141.5% vs. 162.6% ) in Kenya, implying that part of the gains from reallocation comes from small establishments but the qualitative result remains unchanged.

Table 7: Sensitivity Analysis: Potential Gains from Equalizing TFPR

||Baseline|_σ_=5|Trim 2%|L _>_ 10|WL=L|_αs_ = 1/3|Country _α_<sup>_′_</sup><br>_s_<sup>_s_</sup>|
|---|---|---|---|---|---|---|---|
|Cˆote d’Ivoire|31.4|44.7|25.8|29.4|33.2|21.8|44.9|
|Ethiopia|66.6|82.0|66.0|60.9|77.97|56.41|
0145|0.0277|
||(0.0163)|(0.0254)|(0.00685)|(0.0220)|(0.00315)|(0.00237)|(0.0165)|(0.0309)|
|Trade Openness|-0.0220|0.223|-0.00299|-0.106|0.0279|0.00685|-0.0469|0.261|
|_(% of GDP, logs)_|(0.203)|(0.241)|(0.0852)|(0.214)|(0.0392)|(0.0278)|(0.205)|(0.254)|
|Financial Openness|0.0113|0.0933|-0.00599|-0.0752|0.00213|0.000200|0.0151|0.137|
|_(Chinn‐Ito Index)_|(0.0210)|(0.134)|(0.00883)|(0.0948)|(0.00406)|(0.0126)|(0.0213)|(0.137)|
|_External Factors_|||||||||
|Foreign Growth|0.0205|0.00924|0.00211|0.00888|-0.00569|-0.000607|0.0240|0.00673|
||(0.0275)|(0.0191)|(0.0116)|(0.0184)|(0.00532)|(0.00252)|(0.0279)|(0.0196)|
|VIX Index|-0.0608|-0.276|-0.000433|0.120|-0.00684|-0.0152|
urban areas (Hallegatte et al. 2020; Gandhi et al. 2022). In contrast, in _rural_ ever-flooded locations, the relationship between the number of flood events during 2000-18 and RWI is weak and statistically not significant.

# **3.3 Firms and climate shocks in India**

**Exposure to heat.** Table 4 present summary statistics of our merged firms and weather dataset for India. Firms in urban areas are generally larger, with 2.3 workers as compared to 1.8 in rural areas. There is also larger variation in firm size in urban areas. The average temperature experienced by firms in India is about 30 degrees Celsius in both urban and rural India. The average number of

> 4 Among ever flooded locations, when the urban and rural samples are pooled, places with more flooding have lower wealth. This suggests that the exposure gradient within urban areas on wealth is strong enough to offset the urbanrural wealth gradient.

10
+ ττ yy= 1. In terms of the private and market pp ii ee signals, an individual chooses to start trading actively if EE[vv� |ss̃ , ss̃ , ss̃ ] ≥cc . When we bring the pp pp KK ii ee ee model to the data, we observe peer outcomes from transaction records, but we do not observe the private ττ ss̃ + ττ ss̃ ≥−[ττ (ss̃ −cc)] information of potential investors. In turn, we calculate the probability of entry unconditional on the private and market signals by integrating over all possible and . Consequently, the probability of market ii pp participation given the peer signal and the skill level is: ss̃ ss̃ (1) where Φ() is the cumulative distribution of the standard normal, Pr(yy= 1 | ss̃ee, KK) = Φ[ββKK(ss̃ee −cc)] and = KK ee KK KK 1 2 . Since is always positive, this implies the following: HYPOTHESIS 1: ββ = ττ /TT _Under_ TT ~~�~~ ττvv �ττpp<sup>2</sup> + ττKK�+ ττpp + ττKK ββKK _accurate communication, stock market entry is monotonically increasing in peer outcomes : >0._ ee ee ss̃ ∂∂Pr/∂∂ss̃

11
exposure to one or more ecosystem services, at the risk of underestimating the exposure of firms dependent on ecosystem services with lower materiality ratings. In addition, we break down this exposure by source of funding (earmarked vs. non-earmarked), banks’ size,<sup>13</sup> borrowers’ size, and loan quality.

Overall, our physical risk assessment should be considered as a lower bound as it only considers the first-order dependencies of an economic sector on ecosystem services. As noted by DNB (2020), in the ENCORE database a production process such as the cultivation of crops is directly dependent on animal pollination. Nonetheless, the processing of foods, a secondary industry, depends indirectly on animal pollination but this is not captured by the database.

# _Biodiversity loss and banks’ loan quality_

Loss of ecosystem services potentially impacts banks’ balance sheets. To provide an illustrative exercise of these effects, we leverage the existing literature. We first obtain trend estimates in ecosystem services and economic indicators through 2030 under a scenario of ecosystem collapse, that is, a scenario where pressure on ecosystems pushes them to tipping points, resulting in a collapse in the provision of ecosystem services. Next, the variation on banks’ NPLs associated with the macroeconomic conditions following such a collapse in ecosystem services is determined.<sup>14</sup> By incorporating select ecosystem services into a computable general equilibrium model, a novel World Bank study attempts to quantify the nexus between economies and nature (World Bank, <u>2021). The study provides country-specific estimates of the decline in GDP growth through 2030</u> (using 2021 as baseline) due to a collapse in a selection of four ecosystem services: pollination of crops by wild pollinators, climate regulation from carbon storage and sequestration, provision of food from marine fisheries, and provision of timber. The ecosystem services collapse is benchmarked to a business-as-usual scenario where no ecological tipping points are reached.<sup>15</sup> Limitations, including
mark>

- <mark>UNICEF. (2021). The state of food security and nutrition in the world 2021</mark> . <u>https://data.unicef.org/resources/sofi-2021/</u>

- <mark>Zezza, A., Carletto, C., Fiedler, J. L., Gennari, P., & Jolliffe, D. (2017). Food counts. Measuring food consumption and expenditures in household consumption and expenditure surveys (HCES). Introduction to the special issue.</mark> _<mark>Food Policy</mark>_ <mark>, 72, 1-6.</mark>

26
delays, etc. in the IEG database. This will enrich the IEG data set and make it possible to examine how these variables affect project outcomes and risks for different regions and GPs. It could possibly help to devise early warning flags for project supervision, implementation and mid‐course correction.

- IV. **Focus on strengthening assumptions and data collection at appraisal in order to reduce the difference between ERR at appraisal and at completion** .  Some aspects of the divergence are due to risk, but there is also a part due to the quality of assumptions and data.  To accommodate the element of risk, Herrera (2005) has recommended that the sensitivity or risk analysis at appraisal should be used to define an upper and lower bound of the ERR to be used as a benchmark at completion.  In this context, the systematic collecting of data on ERRs and possible correlates (such as cost overruns, restructuring, project delays, etc.) could greatly facilitate gaining a deeper understanding of the factors that affect the ERR.

- V. **Develop GP specific guidelines to doing EA (with an emphasis on how it informs project design and risk mitigation) to ensure standards and consistency across projects** .

24
# **2. Data**

We use the SARS-National Treasury panel (herein referred to as SARS-NT panel), which is an unbalanced panel data set of administrative tax data from 2010-2014.<sup>7</sup> The panel consists of four merged sources of administrative tax data: (i) company income tax (CIT) records from registered firms who submit tax forms; (ii) employee records from employee income tax certificates submitted by employers; (iii) value-added tax records from registered firms; and (iv) customs records from traders (Pieterse et al., 2018). The SARS-NT Panel constitutes a unique source for the study of firm-level behavior in South Africa, as it is at the level of individual firms and individuals, and provides a comprehensive, disaggregated picture of the economy in recent years.<sup>8</sup> The CIT records contain firm characteristics, such as the sector in which a firm operates, financial information from their income statements and balance sheets, and tax information. The employee records contain all employee related incomes, deductions, taxes, and payments made by the firm (such as skills development levy and unemployment insurance fund payments).

**Table 1: Key summary statistics**

||**2010**|**2011**|**2012**|**2013**|**2014**|
|---|---|---|---|---|---|
|**Total number of workers**|4 060 897|5 104 117|5 378 704|5 516 065|5 133 552|
|**Total number of firms**|122 516|130 387|129 860|126 484|108 150|
|**Mean number of workers per firm**|33.15|39.15|41.42|43.61|47.47|
24

|Country,<br>L<br>economy, or<br>territory|ast ukbl<br>P<br>Date|ished<br>censu<br>opulation<br>(1000) *|s<br>Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, unless indicated)|Source for mortality<br>(1985-90, unless Indicated)|
|---|---|---|---|---|---|
|Thailand|Apr 80|44825 F|Official<br>est., National<br>Economic<br>and Social|Official<br>est., National<br>Economic<br>and Social|Official<br>est., National<br>Economic<br>and Social|
||||Development<br>Board|Development<br>Board|Development<br>Board|
|Togo|Nov 81|2705 F|Bank projection<br>from census|Bank assessment<br>of 1961<br>and 1971 censuses|U.N. 1988 revision<br>(prelim.)|
|Tokelau|Oct 82|2F|UNPVSR<br>4/87 (official<br>est.)|-|--|
|Tonga|Nov 76|<br>90 J|UNPVSR<br>4/87 (official<br>est.)|Bank est.|Bank est.|
|Trinidad<br>and<br>Tobago|May 80|<br>1080 F|U.N. 1988 revision<br>(prelim.)|Based on 1987 OHS|1980-85,<br>U.N. 1984<br>assessment;<br>1985-90,<br>Bank projection|
|Tunisia|Mar 84|6966 F|Bank projection from
0.0013%|
|+ 17 investigated countries||No duties in en 2002|-|
|Mexico’s total imports|168,679|1.512|0.8965%|

Source: World Trade Atlas and 2002 Antidumping Agreement.

One concern that usually arises out of the Mexican context is the adverse effect that antidumping duties and safeguard measures may have on the country’s manufacturing structure. Trade liberalization had been too difficult a process to risk losing its benefits as a result of an excessive use of antidumping duties. Consequently, Mexican authorities decided to resort to several mechanisms to mitigate the negative effects of antidumping duties, particularly in the 1991-1996 period. Several cases presented in this chapter illustrate how Mexican authorities were able to apply contingent trade protection laws with a flexible criterion, so as to protect manufacturing sectors deserving protection measures while taking other national interests into account.

The discretionary application of laws on contingent trade protection is a double-edged sword. It can be beneficial when it protects certain sectors at critical moments of the trade liberalization process, but it can also degenerate into an indiscriminate protection instrument. The China Package case is a typical example of how antidumping duties can be applied to support the opening of trade, while it also unveils its potential for discretionary use. In the 43
With lower travel budgets, the decline in average accessibility with fuel price increases is less marked. Once budget thresholds are below 10% of income, there is almost no variation in average accessibility with increasing fuel prices. Losses in accessibility are greatest for lower fuel price increases, after which most citizens are unable to afford any transit at such low travel budget levels.

# 3.3 Income distribution and spatial analysis

The previous section describes how fuel price increases may impact average accessibility levels in both cities and suggests measures to mitigate the loss of accessibility that carbon taxes would trigger. However, this analysis does not assess how specific neighborhoods, communities or socio-economic groups may be more severely impacted than others. Further analysis in subsequent sections investigates how accessibility losses caused by fuel price increases are distributed both spatially and between income groups.

For different fuel price increase levels (20%, 50% and 100%) we calculated the average accessibility level in each hexagon across all travel budget thresholds<sup>14</sup> and the difference between this value and the average accessibility in the baseline scenario (prior to any fuel price increase). The accessibility loss data is then combined with income data to assess the impact on specific socio-economic groups.

The losses in average accessibility for Rio de Janeiro with increasing fuel prices and a maximum travel time of 75 minutes are shown spatially on the left side of Figure 7 below. The middle box plot describes the reductions in accessibility per income decile in absolute terms (% of jobs no longer accessible), while the right-most box plot shows a similar figure with loss of accessible jobs shown in relation to baseline accessibility, i.e., the loss in percent accessible jobs is divided by each income decile’s baseline accessibility (percent of total jobs accessible). The red line indicates the population weighted median, while the upper and lower limits the 25<sup>th</sup> and 75<sup>th</sup> percentiles. The blue dots indicate the
νA_ is just the net consumption ratio, Deaton’s (1989) justly celebrated workhorse. Figure 6 compares the partial equilibrium welfare elasticity profile (dashed curve) to the general equilibrium case already discussed. In partial equilibrium, the poorest rural households in India would experience a welfare loss of around 0.3 percent for a 1 percent uniform increase in agricultural prices. But the distributional gradient is very steep, so much so that the top

> 33To be clear, while NSS66-CES provides household-level data on PDS consumption from 2009-10, these are not the same households as in the NSS61-CES of 2004-05. Thus, to keep 2004 as the base-year, I need to impute to this latter sample of households what their 2009 PDS coverage would have been.

> 34This is because _λ_ Π _/αK_ = Π _AKA/yαK_ = _PAYA/C,_ where _C_ is household consumption.

26
in power following national elections (legislative or presidential) in any given<br>year. Source: Political Database of the Americas. Georgetown<br>University/Organization of AmericanStates.Center for Latin AmericanStudies.|n.r.|n.r.|
|**Continuous variables**|**Mean **|**S.D.**|
|_Duration since award_: Indicates the number of years a concession has been in<br>operation since its award.|n.r.|n.r.|
|_Corruption_: Index from Political Risk Service, International Country Risk Guide;<br>annual values from 1989 to 1995, and 1998 value after that. Range from 1 to 6.<br>Higher value means less corruption.|2.94|0.69|
|_Rule of law_: Index from Political Risk Service, International Country Risk Guide;<br>annual values from 1989 to 1995, and 1998 value after that. Range from 1 to 6.<br>Higher value means better rule of law.|2.92|0.99|
|_Bureaucratic quality_: Index from Political Risk Service, International Country<br>Risk Guide; annual values from 1989 to 1995, and 1998 value after that. Range<br>from 1 to 6. Higher value means better bureaucraticquality.|3.36|0.61|
|_Growth_: Yearly growth rate of GDP in real terms. Source: World Bank and Inter-<br>American Development Bank.|n.r.|n.r.|
|_Exchange rate_: Annual evolution of the real exchange rate (calculated as (index<br>rate of year t – index rate of year t-1)/index rate of year t). A positive value<br>indicates depreciation. Source: Inter-American Development Bank.|n.r.|n.r.|
|Note: For variables varying over
_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

We find only weak evidence of an effect of either executive constraints or fractionalization on the average rate of decline, but effects comparable to those from the duration regressions when we consider the overall depth of declines. Columns (1) and (2) in both panels of Table 3 report the results for the interacted model without and with region fixed effects. Column 24
: Restricted physical integrity Value, come from the<br>OECD Development Centre. Equals to 1 if the value of the index above sample median, otherwise to<br>0.|
|Good Rule of Law|World Bank’s Governance Indicators: rule of law index. Equals to 1 if the value of the index above sample<br>median, otherwise to 0.|
|High Literacy Rate|<br>Literacy rate, adult female (% of females ages 15 and above), come from WDI. Equals to 1 if the value of<br>the index above sample median, otherwise to 0.|
|Muslim proportion|Country-level Muslim population by percentage in 2010, come from Pew Research Center (2011).<br>Equals to 1 if the value of the index above sample median, otherwise to 0.|
|High Gender equality|CPIA gender equality rating (1=low to 6=high), yearly, come from WDI. Equals to 1 if the value of the<br>index above sample median, otherwise to 0.|
|High Individualism|<br>Individual Empowerment (version 2), come from WVS. It measures the extent to which the people in a<br>society are mentally and habitually empowered to make their own choices and to pursue them in their<br>actions. Equals to 1 if the value of the index above sample median,otherwise to 0.|

31
whereas when individuals are more responsive, the price drops by 13 percent. In terms of welfare gains, they follow the patterns of consumption observed above with richer individuals spending more on telecom services than poorer individuals. Thus, they reap more of the benefits of a decrease in prices. Under the lower elasticity scenario, an individual in a household that uses telecom services in the second quintile is estimated to experience a welfare gain of 828 DJF. In the same scenario, an individual in the top 20 percent would gain 7,347 DJF. Corresponding to the lower price change in the scenario of an elasticity of -2.5, the welfare gains are lower across the distribution.<sup>17</sup>

**Table 5. Results from WELCOM simulations**

||**Elasticity = -1.5**|**Elasticity = -2.5**|
|---|---|---|
|**Price change**|22.2 percent|13.3 percent|
||Welfaregains(D|JFperyear) per user|
|**Quintile 1**|n.a|n.a|
|**Quintile 2**|828|276|
|**Quintile 3**|1,792|597|
|**Quintile 4**|2,290|763|
|**Quintile 5**|7,347|2,449|

Source: own calculations using EDAM 4.

Note: Quintiles based on the distribution of consumption per capita. Results for the bottom quintile were 594 and 198, respectively, but not shown as there is too little information (i.e. a coefficient of variation of 18 percent) to draw reliable estimates.

The welfare gains are low with respect to the overall level of consumption in each quintile, and thus are not conducive to tangible changes in poverty or inequality. It is important to highlight that the results