rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8097

# **Abstract**

Many developing countries lack spatially disaggregated price data. Some analysts use “no-price” methods by using a food Engel curve to derive the deflator as that needed for nominally similar households to have equal food shares in all regions and time periods. This method cannot be tested in countries where it is used as a spatial deflator since they lack suitable price data. In this paper, data from Vietnam are used to test this method against benchmarks provided by multilateral price indexes calculated from repeated spatial price surveys. Deflators from a food Engel curve appear to be a poor proxy for deflators obtained from multilateral price indexes. To the extent that such price indexes reliably compare real living standards over time and space, these results suggest that estimates of the level, location, and change in poverty and inequality would be distorted if the Engel method deflator was used in their stead.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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 jkgibson@waikato.ac.nz.

_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
# **Figure 9 Total fiscal risks from early termination of public-private partnership portfolios of South Asian countries, as percentage of GDP**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Afghanistan Bangladesh Bhutan India Nepal Pakistan Sri Lanka<br>Low Medium less low High less medium<br>Percent<br><!-- End of picture text -->

## _Source:_ Authors’ calculations.

_Note:_ Total fiscal risks are the maximum expected loss at the 99 percent confidence over the entire contract period and are expressed as percentage of GDP of a single year.

Pakistan faces the most significant fiscal challenge from early termination of PPPs in South Asia, largely because of its low revenue mobilization capacity. South Asian governments are quite different in terms of their revenue mobilization capacity, which helps determine their ability to absorb the fiscal costs from early termination of PPPs in infrastructure. The Government of Bangladesh’s revenues represent only 9.6 percent of GDP, the Government of Pakistan’s revenues represent 15.6 percent of GDP, while the Government of India’s revenues represent 20.5 percent of GDP.<sup>19</sup> Pakistan’s fiscal risks represent just below 3.3 percent of the government’s annual revenues. Even though Bangladesh’s fiscal risks are low, relative to the size of its economy, they are significant compared to the annual government revenues (figure 10), posing a significant fiscal challenge to the country, if only the revenues of a single year are available to absorb the costs of early termination of PPPs.

> 19 As of 2017, from World Revenue Longitudinal Data (WoRLD), available at http://data.imf.org/?sk=77413F1D- <u>1525-450A-A23A-47AEED40FE78.</u>

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# **d. Results**

We now combine the two components together to generate the noise-signal results. To run the analysis we also need to specify the timeframe of the force measure (1-year, 2-year etc.) and the lag we expect the signal to operate over (eg. 2-years). For both the smoothing and the lag selection, we estimate the model for all combinations and choose the combination, along with the force percentile threshold, that minimizes the noise-signal ratio. For all our estimates we further split the sample into advanced and emerging economies. For our baseline estimates, the same specification resulted in the best results for advanced and emerging countries: the two-year growth in expenditure-GDP ratio; 2-year force and a 2-year lag of force. In other words, force smoothed over 2 years can best predict an extreme increase in the two-year growth rate of expenditure-GDP ratio, two years down the track.

Table 2 presents the results for advanced economies for the different categories of GFS expense data. The first column of the table shows the number of expenditure episodes for which there is corresponding GFS data available. For this specification, we have a total number of 198 episodes, with the subcomponents of expenses smaller. The second column presents the proportion of good signals (A) as a fraction of all possible good signals (A+C). Basically, this column shows what fraction of expenditure episodes was associated with a force signal.

The third column shows the proportion of bad signals (B) as a fraction of possible bad signals (B+D). This tells us what proportion of times in which there were no episodes but there was a force signal. For the total force, this estimate is 8 percent. The fourth column, the noise-signal ratio, is simply the ratio of the two previous columns. It effectively shows us the ratio of bad signals to good signals. A ratio equal to 1 would indicate that the force provides signals at roughly random times while a ratio less than 1 indicates that the signal provides some information about future expenditure episodes. The final column represents the probability of an expenditure episode occurring conditional on a signal.

The noise-signal ratios for our force estimates across the total and subcomponents of expenditures range from a low of 0.61 to a high of around 1.5.<sup>14</sup> Overall the results provide some evidence that force provides a useful signal of future large increases in the expenditure-GDP ratio. Examining the sub-components, we find that several components also provide a useful signal. Items such as compensation of employees, social benefits, subsidies and use of goods and services also have noise-signal ratios well below one. Categories such as wages and social benefits fit in with our prior assumptions that large accelerations in rigid components can cause problems in the future due to the persistence of this spending. Overall, the

> 14 In their original paper, Kaminsky et al. were examining variables that might predict exchange rate crises. In their results, the noise to signal ratio ranges from 0.19 for the real exchange rate to 1.69 for the ratio of the lending rate to deposit rate.
ΔNRi is the change in net revenue per hectare of a given district NRit is the forecasted value of net revenues per hectare under a new climate scenario NRi, t-1 is predicted value of net revenue per hectare of the base climate scenario Tt , Pt is temperature and precipitation under the new climate scenario Tt-1, Pt-1 is temperature and precipitation for the base climate scenario Δ T, ΔP is change in temperature and precipitation.

The average of ΔNRi gives the impact of a given climate change scenario.

The results of this analysis are shown in Table 8. As can be observed from this table, increasing temperature by 2.5<sup>o</sup> C and 5<sup>o</sup> C affects net revenue per hectare marginally. Moreover, reducing precipitation by 7% and 14% also reduces net revenue per hectare, even though the level of damage is very small. These results are in line with the expectations that increasing temperature and reducing precipitation is damaging to agriculture in Africa, indicating a need for policy intervention targeting adaptation through technology such as irrigation, and the use of drought tolerant and early maturing crop varieties.

# **_6.3.2 SRES climate scenarios_**

Predicted values of temperature and rainfall from three climate change models (CGM2, HaDCM3 and PCM) were also applied to help understand the likely impact of climate change on Ethiopian agriculture. The predicted values for the scenario analysis were taken from the hydrological component of the project from Colorado University. Table 9 shows the predicted values of temperature and precipitation from the three models for the years 2050 and 2100. As can be observed from this table, all the models forecasted increasing temperature levels for the years 2050 and 2100. With respect to precipitation, while the CGM2 predicted decreasing precipitation for the years 2050 and 2100, both HaDCM3 and PCM predicted increasing precipitation over these years.

The results of the predicted impacts from the SRES models are presented in Table 10. As can been seen from this table, using climate forecasts from all the models, this study revealed that by 2050 net revenue per hectare would increase, whereas by 2100 it would decrease. Even though the effects in both cases are marginal, this analysis further shows that climate change is damaging in the long run. The fact that all models show similar results in the long run indicates a need for policy intervention before damage is done.

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**Table 5: The role of technology and financial structure in financing firm innovation**

||**O**<br>|**LS**<br>|**Logit - mar**<br>|**ginal effects**<br>|
|---|---|---|---|---|
|**Variables**|**low tech**|**med-high tech**|**low tech**|**med-high tech**|
|**% Fixed assets funded by banks**|0.0924***|0.0346|0.0949**|0.0286|
||(0.0352)|(0.0399)|(0.0408)|(0.0413)|
|**% Fixed assets funded by NBFI**|0.3384**|0.0129|0.3589*|0.0176|
||(0.1672)|(0.1416)|(0.1872)|(0.1106)|
|**% Fixed assets funded by other**|0.1264***|-0.0333|0.1340***|-0.0333|
||(0.0391)|(0.0432)|(0.0477)|(0.0385)|
||**_Adop_**<br>|**_tion_**<br>|||
|**% Fixed assets funded by banks**|0.1428***|0.0290|0.1591***|0.0265|
||(0.0369)|(0.0435)|(0.0454)|(0.0446)|
|**% Fixed assets funded by NBFI**|0.3818**|0.0301|0.4070**|0.0372|
||(0.1594)|(0.1381)|(0.1804)|(0.1191)|
|**% Fixed assets funded by other**|0.1379***|-0.0235|0.1455***|-0.0231|
||(0.0441)|(0.0450)|(0.0542)|(0.0413)|
||**_Inven_**|**_tion_**|||
|**% Fixed assets funded by banks**|||0.0909*|0.2347***|
||||(0.0479)|(0.0526)|
|**% Fixed assets funded by NBFI**|||0.1707**|-0.0011|
||||(0.0758)|(0.1608)|
|**% Fixed assets funded by other**|||0.1557***|0.1187**|
||||(0.0473)|(0.0549)|
|**Additional controls**|Asper Eq (1)|Asper Eq (1)|Asper Eq (2)|Asper Eq (2)|
|**Country FE**|Yes|Yes|Yes|Yes|
|**Year FE**|Yes|Yes|Yes|Yes|
|**Observations**|8,972|8,119|8,972|8,119|
|**Number of countries**|104|104|104|104|

Note: This table reports the results of the estimation of Equation (1) and (2). The dependent variable is the innovation score, as described in Table 1. *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Robust standard errors in parentheses, clustered at strata level.

We run several robustness checks to test the general validity of the results. To mitigate reverse causality issues,<sup>7</sup> we follow Caggese (2019) and restrict the analysis to firms that are not likely to be financially distressed. Because data in the Enterprise Survey is limited, we can consider just firms that are above the median value of real annual sales scaled by the total number of employees. Results in Table 6 shows that funding by NBFI remains weakly related to both the extent of firm innovation and a firm decision to innovate, allaying concerns that the overall strength of the relationship is related to the success of firms to get funding in the first place. Results for firm innovation adoption are also in line with the estimates in Tables 3 and 4.

7 Debt usage may correlate with future investment in innovation because firms may borrow in anticipation of investment in innovation, rather than borrowing to invest in current innovative projects.

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**Table 4: Peer analysis, DEA-VRS 2005**

|**Country**|**Airport**|**TE-VRS**|**As peer**<br>**for other**|||**Peers**|||
|---|---|---|---|---|---|---|---|---|
|||**2005**|**airports**|**1**|**2**|**3**|**4**|**5**|
||**AEP**|1.000|9|AEP|||||
|**Argentina**|**EZE**|0.404|0|CGH|(CGH<br>)|(XMN)|(ICN)|(SDF)|
||**FTE**|1.000|7|FTE|||||
||**BSB**|0.552|0|CGH|LAX|MEM|SNA||
||**CGH**|1.000|28|CGH|||||
|**Brazil**|**GIG**|0.316|0|(CGH<br>)|(XMN)|(ICN)|ATL||
||**GRU**|0.680|0|(CGH<br>)|(XMN)|(ICN)|ATL||
||**MAO**|0.680|0|SJO|(XMN)|MFM|SNA||
||**VCP**|1.000|0|VCP|||||
|**Chile**|**SCL**|1.000|10|SCL|||||
|**Colombia**|**BAQ**|0.507|0|FTE|SJO|(XMN)|SNA||
||**CLO**|0.747|0|(FTE<br>)|SCL|S NA|||
|**Costa Rica**|**SJO**|1.000|6|SJO|||||
|**Ecuador**|**GYE**|0.814|0|(FTE<br>)|SJO|(XMN)|SNA||
|**El Salvador**|**SAL**|0.131|0|(CGH<br>)|LAX|MEM|SNA||
||**CUN**|1.000|11|CUN|||||
|**Mexico**|**GDL**|0.615|0|CGH|FTE|(XMN)|(SDF)||
||**MEX**|0.947|0|CGH|ICN|(XMN)|ATL|SNA|
||**MTY**|0.424|0|CGH|(FTE<br>)|(ATL)|MEM|SNA|
|**Panama**|**PTY**|0.188|0|CGH|ICN|(XMN)|(SDF)|SNA|
|**Peru**|**LIM**|0.922|0|AEP|(LIM<br>)|(SCL<br>)|(XMN)|SNA|
|**Dominican Rep.**|**SDQ**|0.386|0|AEP|(LIM<br>)|(SCL)|SNA|(XMN)|

Notes: Underlined peers are LAC airports. Between brackets are 2006 observations. Other airports: ICN (Seoul, Korea); MFM (Macau); XMN (Xiamen, China); ATL (Atlanta International); SDF (Louisville), MEM (Memphis), LAX (Los Angeles); SNA (Costa Mesa, California).

# **4. Sources of Technical Efficiency**

In this section we estimate the effect that institutional factors, socioeconomic conditions, the demographic environment and characteristics particular to each airport have on **technical** efficiency. We do this by estimating a truncated regression model, using the airport efficiency 12
# **_Table A4.4c Relationship between country-level characteristics and IEG outcome rating, weighted by original commitment (in millions, constant 2015 $)_**

|||**De**<br>|**pendent vari**<br>|**able: IEG 1-6**<br>|**outcome rati**<br>|**ng**<br>||
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|CPIA rating at|0.160*||0.425***||||0.346***|
|approval year|(0.085)||(0.117)||||(0.116)|
|CPIA change over||0.241**|0.505***||||0.374***|
|project lifetime||(0.100)|(0.150)||||(0.147)|
|Log(GDPpc PPP$||||-0.072||0.016|-0.014|
|at approval year)||||(0.091)||(0.087)|(0.081)|
|Real GDPpc growth|||||3.715***|3.992***|3.656***|
|over project lifetime|||||(1.295)|(1.374)|(1.254)|
|Constant|3.122***|3.848***|2.340***|3.733***|3.631***|3.097***|2.326***|
||(0.474)|(0.370)|(0.458)|(0.700)|(0.355)|(0.663)|(0.721)|
|Observations|2,790|2,767|2,767|2,735|2,665|2,580|2,516|
|Adjusted R-squared|0.187|0.193|0.208|0.188|0.197|0.203|0.222|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS, weighting by original commitment amount, and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project and IEG evaluator fixed effects.

# **_Table A4.4d Relationship between country-level characteristics and IEG outcome rating, mean imputation for missingness_**

|||**De**<br>|**pendent var**<br>|**iable: IEG 1-6**<br>|**outcome rati**<br>|**ng**<br>||
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|CPIA rating at|0.157**||0.406***||||0.426***|
|approval year|(0.713)||(0.077)||||(0.080)|
|CPIA change over||0.320***|0.582***||||0.580***|
|project lifetime||(0.083)|(0.106)||||(0.108)|
|Log(GDPpc PPP$||||-0.075||-0.0694|-0.116**|
|at approval year)||||(0.053)||(0.052)|(0.048)|
|Real GDPpc growth|||||1.380*|1.331*|0.586|
|over project lifetime|||||(0.734)|(0.719)|(0.425)|
|Constant|2.701***|3.386***|2.048***|3.820***|3.272***|3.789***|2.848***|
||(0.395)|(0.270)|(0.337)|(0.499)|(0.313)|(0.490)|(0.457)|
|Observations|2,837|2,837|2,837|2,837|2,837|2,837|2,837|
|Adjusted R-squared|0.080|0.087|0.106|0.077|0.078|0.079|0.109|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS, and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project and IEG evaluator fixed effects. Mean imputation used for missing values of CPIA and GDP measures.

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<!-- Start of picture text --> Figure 14(c): Mongolia: Return to skill by sector over time (Tertiary<br>and above)<br>1<br>0.8<br>0.6<br>Agriculture/Mining<br>0.4<br>Manufacturing<br>0.2 Trade/Services<br>0<br>1998 2002 2007<br>Year<br>Return<br><!-- End of picture text -->

**Figure 14(d): Mongolia: Proportion of skilled labor by sector over time (Tertiary and above)**

<!-- Start of picture text --> 60<br>50<br>40 Agriculture/Mining<br>Manufacturing<br>30<br>Trade/Services<br>20<br>10<br>0<br>1998 2002 2007<br>Year<br>% skilled<br><!-- End of picture text --> 58
(i.e., below 75 _μ_ g/m<sup>3</sup> ), there is no effect of PM 2.5 on hours worked. But, at higher levels (above 75 _μ_ g/m<sup>3</sup> ) the effect becomes negative and significant.

We interpret these results as evidence that, at moderate levels, the mechanism linking pollution to labor supply is caregiving. But, at higher levels the link is more direct: pollution may reduce labor supply by harming workers’ health. This interpretation is consistent with existing epidemiological evidence suggesting that higher levels of pollution are required to affect the health of non-susceptible populations.<sup>41</sup> Moreover, this finding points to the extensive margin as a potential source of non-linearity in the relationship between air pollution and labor supply: as pollution levels increase, the effects on labor supply expands beyond households with susceptible individuals to encompass the rest of the population.

# **4.4 Attenuation behavior**

An important question is whether households attenuate the negative, short-term, impact of air pollution. The previous results suggest that households are unable to fully attenuate the negative effect of pollution on hours worked. However, they could try to reduce the negative impact on income.

A possible strategy would be to reallocate caregiving duties to household members with relatively worse labor opportunities (or a comparative advantage on caregiving). This would attenuate the negative shock on labor supply of main providers, and the associated reduction on earnings.

To illustrate this argument consider this simple model. There is a unitary household with two individuals _i_ = _{_ 1 _,_ 2 _}_ . Each individual has 1 unit of time that can be sold to labor markets, _Li_ , at wage _wi_ or used as domestic work, _hi_ , to provide a household service, namely, care for dependents.<sup>42</sup> The minimum amount of care that the household must provide is _s_ . For simplicity, we assume that the technology that transforms domestic work into caregiving is defined by _f_ ( _h_ 1 _, h_ 2) = _h_<sup>_ρ_</sup> 1<sup>_h_</sup> 2<sup>1</sup><sup>_−ρ_</sup> , with _ρ ∈_ (0 _,_ 1).<sup>43</sup> Household utility, _U_ ( _c_ ), depends of total consumption, _c_ .

The household’s allocation of domestic work, and thus choice of each individuals’ labor

> 41For example, the U.S. EPA considers 24-hour levels of PM2 _._ 5 between 35-55 _μ_ g/m3 as unhealthy for sensible populations, and between 55-150 _μ_ g/m<sup>3</sup> as unhealthy for the general public. Above that concentration, PM2 _._ 5 becomes very unhealthy or even hazardous (U.S. EPA, 2012).

> 42The model does not include leisure. However, the results including a labor-leisure trade-off are identical. 43 Results are similar using other homothetic functions.

21
companies privatized in the period of spontaneous privatization and also a number of more recent cases in which ownership control has passed to private owners in ways that do not conform to the definition of privatization agreed with the World Bank, for example, through bankruptcy or through legally questionable removal of individual mines from companies (these are generally small producers). Finally, a small percentage of current production is accounted for by _de novo_ producers.

Table 1. Privatized Russian coal companies: auction details and share in production.

|**Company**|**Date(s) of**<br>**sale**|**Method(s)**<br>**of sale**|**Federal package**<br>**(% total shares)**|**% 2000**<br>**production**|
|---|---|---|---|---|
|Yuzhny Kuzbass|12/97; 12/98|CT/IC;SpecAuc;|80.3|4.3|
|(surface)*||StanAuc|(25.5, 28.8, 26.0)||
|Kuzbassrazrezugol|12/97; 12/98|CT/IC;SpecAuc;|80.4|13.4|
|(surface)||StanAuc|(25.5, 28.5, 26.4)||
|Krasnoyarskugol|2/2000|CT/IC|75.4|14.6|
|(surface)|||||
|Mezhdurechenskugol|6/2000|CT/IC|100.0|0.7|
|Chitaugol (surface)|10/2000|StanAuc|15.75|4.3|
|Khakasugol|11/2000|SpecAuc;|81.7|0.6|
|(surface)||StanAuc|(43.0, 38.7)||
|Vostsibugol (surface)|2/2001,<br>12/2001|SpecAuc;<br>StanAuc|41.5<br>(20.0; 21.5)|7.2|
|Kuznetskugol|6/2001,<br>7/2001|Two SpecAuc|80.7<br>(40.0; 40.7)|4.0|
|Kuzbassugol|8/2001,<br>9/2001|SpecAuc|79.37<br>(39.37; 40)|6.5|
|Kiselevskugol|12/2001,<br>12/2001|Two SpecAuc|65.2<br>(32, 33.2)|0.7|
|**Total privatized at end 2**|**001 (competiti**|**ve privatization)**||**56.2**|
|**Other private producers**|**(non-competit**|**ive privatization)**||**17.4**|
|**De novo firms**||||**3.8**|
|**TOTAL private sector s**<br>_Privatization in process_:|**hare of current**|**coal production, e**|**nd 2001**|**77.4**|
|Dalvostugol|1/2002|StanAuc|40.3|0.8|

StanAuc = standard auction. SpecAuc = specialized auction. CT/IC = commercial tender with investment conditions. * “(surface)” indicates primarily surface producer.

In all cases of competitive sale, the bidders for the assets have been Russian investors, although Russian legislation on privatization does not exclude foreigners. It is possible that foreigner investors would have been attracted to bid on coal sector assets had the Government carried out such best-practice measures as the appointment of investment bank advisors; preparation of financial accounts to international accounting standards; environmental audits; debt restructuring; etc. But this is not standard practice in Russia, and it would have been unrealistic to expect the Government to make an exception in the

> SECAL 2, “privatization” means the competitive sale to the private sector of all Federal shares in a company, with the exception of the golden share.  It does not preclude an ownership interest of regional or local governments.  These, however, are generally small, below the 25% required to block a vote, and the tendency has been for regional governments to sell their shares to the private sector.

10
13

|France||304,507|0.5%|
|---|---|---|---|
|Germany||669,408|0.8%|
|Greece||46,381|0.4%|
|Italy||147,302|0.2%|
|Japan||2,512|0.0%|
|Portugal||1,129|0.0%|
|Spain||12,943|0.0%|
|Sweden||230,103|2.3%|
|United Kingdom||118,913|0.2%|
|United<br>States<br>America|of|272,898|0.1%|
|Jordan||685,178|7.2%|
|Lebanon||1,012,954|16.9%|
|Turkey||2,869,379|3.6%|

Source: Source: UNHCR, Refugees, including refugee like situations

**Table 4: Asylum Seekers Hosted As A Share Of Native Population**

|Country||**Asylum**<br>**Seekers**<br>**(2016)**|**Percentage**<br>**of**<br>**population**|
|---|---|---|---|
|Australia||29,467|0.12%|
|Austria||76,362|0.87%|
|Canada||23,856|0.07%|
|France||62,719|0.09%|
|Germany||587,283|0.71%|
|Greece||3,993|0.04%|
|Italy||99,876|0.16%|
|Japan||18,801|0.01%|
|Portugal||805|0.01%|
|Spain||20,360|0.04%|
|Sweden||83,010|0.84%|
|United Kingdom||43,436|0.07%|
|United<br>States<br>America|of|439,833|0.14%|
|Jordan||35,570|0.38%|
|Lebanon||13,709|0.23%|
|Turkey||245,896|0.31%|

Source: Source: UNHCR, Refugees, including refugee like situations