rafmacalaba/datause-encoder-data huggingface dataset:
# **The Effects of Country Risk and Conflict on Infrastructure PPPs**

Gonzalo Araya, Jordan Schwartz, and Luis Andres<sup>1</sup>

**(1) Key Words:** risk, infrastructure, public-private partnerships (PPPs), conflict, energy, transport, water, telecommunications, foreign direct investment (FDI)

## **(2) AEA Jel Classifications:** D81, E22, F21, G32, H54, O18

## **(3) Sector Board:** Infrastructure Panel

1 Gonzalo Araya is a doctoral student at Toulouse School of Economics (TSE) and a former Junior Professional Associate in the Sustainable Development Department of the World bank’s Latin America and Caribbean Region; Jordan Schwartz is Manager of Infrastructure Policy at the World Bank, based at the Bank’s Center for Infrastructure & Urban Development in Singapore; and Luis Andres is Lead Economist in the Sustainable Development Department for the South Asia Region of the World Bank. They are grateful to Marianne Fay, World Bank Chief Economist for Sustainable Development, for her thorough review and quality assurance of this paper, to Tito Cordella for early guidance on the approach, and to Karina Izaguirre and Alex Jett for their support with the PPI Database. The findings, interpretations and conclusions expressed herein do not necessarily reflect the views of the Board of the Executive Directors of the World Bank or the governments they represent. The authors’ email addresses are: gonzaloarayaa@gmail.com, <u>jschwartz3@worldbank.org, and landres@worldbank.org.</u>
_Table 10: Losses in land resource values per capita (L) due to earthquakes, with respect to number of_ _<u>people affected.</u>_

|No.|Country|Ave. L (2005<br>constant<br>|Affected<br>People in|Average<br>Affected|Estimated<br>Elasticity<sup>a</sup>|Losses<br>con|in L (2005<br>stant US$)|
|---|---|---|---|---|---|---|---|
|||US$)|Country_i_|People||||
||||(t+t-n)|(t+t-n)||US$|Std error|
|1|Australia*|13,606|1,667|20,964|0.095|1.02|0.136|
|2|Bangladesh****|1,696|333|20,964|0.095|0.03|0.003|
|3|Chile**|2,429|9,554|20,964|0.095|1.05|0.139|
|4|China***|260|2,256,960|20,964|0.095|26.47|3.519|
|5|Colombia***|4,104|260,196|20,964|0.095|48.25|6.415|
|6|India****|2,288|339,866|20,964|0.095|35.13|4.671|
|7|Indonesia***|5,472|256,522|20,964|0.095|63.41|8.432|
|8|Japan*|1,653|108,927|20,964|0.095|8.13|1.082|
|9|Mexico**|7,816|64,078|20,964|0.095|22.63|3.009|
|10|Pakistan****|3,411|3,588|20,964|0.095|0.55|0.074|
|11|Turkey**|5,478|354,595|20,964|0.095|87.75|11.668|
|12|United States*|9,533|8,408|20,964|0.095|3.62|0.482|

_Table 11: Losses in land resource values per capita (L) due to earthquakes, with respect to number of_ _<u>people affected per capita.</u>_

|No.|Country|Ave. L (2005<br>constant<br>US$)|Affected<br>People Per<br>Capita in<br>Country_i_|Average<br>Affected<br>People Per<br>Capita|Estimated<br>Elasticity<sup>b</sup>|Losses<br>con|in L (2005<br>stant US$)|
|---|---|---|---|---|---|---|---|
||||(t + t-n)|(t + t-n)||US$|Std error|
|1|Australia*|13,606|9.33E-05|4.98E-04|0.055|1.40|0.319|
|2|Bangladesh****|1,696|2.39E-06|4.98E-04|0.055|0.00|0.001|
|3|Chile**|2,429|5.90E-04|4.98E-04|0.055|1.58|0.360|
|4|China***|260|1.80E-03|4.98E-04|0.055|0.52|0.117|
|5|Colombia***|4,104|6.36E-03|4.98E-04|0.055|28.87|6.551|
|6|India****|2,288|3.24E-04|4.98E-04|0.055|0.82|0.186|
|7|Indonesia***|5,472|1.26E-03|4.98E-04|0.055|7.64|1.730|
|8|Japan*|1,653|8.65E-04|4.98E-04|0.055|1.58|0.358|
|9|Mexico**|7,816|6.75E-04|4.98E-04|0.055|5.83|1.326|
|10|Pakistan****|3,411|2.36E-05|4.98E-04|0.055|0.09|0.020|
|11|Turkey**|5,478|5.38E-03|4.98E-04|0.055|32.59|7.389|
|12|United States*|9,533|2.98E-05|4.98E-04|0.055|0.31|0.071|

Notes for Tables 10 and 11: *,**,***, and *** refer to high income, upper middle income, lower middle income and low income, respectively. _Ave. L_ refers to the average value of L in country _i_ during three years - 1995, 2000 and 2005. “a” and “b” mean that the elasticity estimates are obtained from Table 6 and Table 7, respectively.

As in the case of human and intangible capital, the results with respect to other disasters will also be a function of the relative elasticities of land capital, and the relative numbers of people affected. Figure 2 shows a comparison of the elasticities of natural disasters in terms of _people affected per capita_ . This is done for the two-year lag specification (again we have chosen the per capita specification because in general it performs better in the econometric equations). The figure shows that the impact of an earthquake is very similar to that of a flood. No impact is 16
# **Conclusions**

Over the last decade, finance has been recognized as an important driver of economic growth. More recently, access to financial services has been recognized as an important aspect of development and more emphasis is given to extending financial services to lowincome household segments. Although still early in the analysis, there is some evidence that access is improving. On the household side, there are some data on the use of microfinance that suggest there has been an expansion of access for households. Data here have to be interpreted carefully as increases may represent a better coverage over time, rather than an expansion. There is also evidence in terms of more main-streaming of access by commercial banks as competition forces and technology allow them to go to the lower segments. Examples in developing countries are ICICI bank, SHG Bank Linkage and South African banks that have made it a priority to reach out to lower segments.

For firms, the evidence on access to credit is more mixed. It appears to be increasing in some countries, but mostly in consumer finance forms, less so on the SME credit side. Some have argued that recent trends in banking systems may have adverse consequences. Consolidation of the banking system in many countries increases the distance between borrower and lender, making lending more based on hard information and reducing the role of relationship lending which can be especially useful for new and small firms. Yet, part of this increased consolidation is the consequence of increased competition, which in general helps to increase access. Indeed, while there is cross-country evidence that more concentrated banking systems could increase financing obstacles, this is more so if the system is not competitive and dominated by public banks.

But more definite interpretation on factors affecting access will have to wait for better data on access. This will require some national and international actions to develop more comparable data on access. Data on access will have to come from different sources: providers of financial services provision (using national statistics and financial institutions), users of financial services (on the basis of surveys), and from experts (to identify constraints). Each of these data sources has its tradeoffs, so simultaneous actions 28
Poverty would be expected to increase by 1.6 percentage points – from 13 percent in 2013 to 14.6 percent after subsidy removal – accompanied by increases in the poverty gap and in inequality. The overwhelming increase in poverty comes from LPG, which is not surprising given its high share in the budget of poorest households and the large increase in its price (figure 2 and table 8). Poverty gap, measuring how far poor are from the poverty line on average or depth of poverty, would have increased as well, with LPG being the main contributor. Finally, inequality is expected to increase modestly, as reflected by a slightly higher Gini coefficient.

**Table 8. The impact of petroleum subsidies removal on poverty, poverty gap and** **<u>inequality</u>**

||**Poverty hea**<br>Level|**dcount, %**<br>The<br>change|**Pover**<br>Level|**ty gap**<br>The<br>change|**Gi**<br>Level|**ni**<br>The<br>change|
|---|---|---|---|---|---|---|
|Pre reform|13.0|.|2.44|.|33.66|.|
|kerosene|13.0|0.0|2.47|0.02|33.68|0.03|
|LPG|14.3|1.3|2.83|0.39|34.00|0.35|
|gasoline|13.1|0.1|2.47|0.02|33.61|-0.04|
|Diesel|13.0|0.0|2.44|0.00|33.61|-0.05|
|Post reform|14.6|1.6|2.89|0.45|33.94|0.28|
|Source: Authors’ c|alculations bas|ed on extrapol|ated HEIS|2010 data.|||

Scenario 2: Subsidy cuts with cash transfers In the second scenario we simulate the impact of petroleum price increases on well-being followed by compensatory cash transfer program to Jordanian households with annual incomes below JD10,000.

**-** **<u>Table 9. The impact of petroleum subsidy reform and cash transfer on per capita well being</u>**

||Pre-reform||Post-reform<br>Imact on er|Chane in er|
|---|---|---|---|---|
||Total expenditures<br>per capita|Total expenditures per<br>capita|p p<br>capita wellbeing,<br>JD|g p<br>capita<br>consumption, %|
|Quintile 1|843|857|14|1.6|
|Quintile 2|1,240|1244|3|0.3|
|Quintile 3|1,624|1611|‐13|‐0.8|
|Quintile 4|2,198|2166|‐32|‐1.5|
|Quintile 5|4,336|4253|‐83|‐1.9|
|Total|2,048|2026|‐22|‐1.1|

Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Notes:</u> Population quintiles <u>based on consumption per capita before the reform.</u> As can be seen in tables 9 and 10, if perfectly targeted, the cash transfer offsets the negative impact of higher prices of subsidized products for the bottom 40 percent of the population. Consumption per capita would in fact grow by 1.6 percent for the bottom quintile, though on average consumption per capita would decline by 1.1 percent. Poverty would be expected to fall by 0.6 percentage points from 13 to 12.4 percent. The depth of poverty would decline as well by an impressive 0.2 percentage points. Finally, inequality, as measured by Gini coefficient, would fall as well by 1.7 percent.

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inflation targeting. This has been done in some cases by testing whether the output gap enters significantly in an empirically-estimated interest rate rule. Leiderman and Bar-Or (2000), for instance, estimated the policy reaction function of the Central Bank of Israel using monthly data for the period 1994-99 and found that interest rates are positively related to current inflation, lagged changes in output, and the previous period's interest rate. It is important to note, however, that the fact that there is a significant coefficient attached to output in an estimated interest rate reaction function does not necessarily imply that in practice monetary policy is guided in part by an output stabilization objective. To the extent that current inflation is only an imperfect proxy for future inflation, and to the extent that the latter does depend on the level of economic activity, it is possible for output to appear in the reaction function with a significant coefficient-without, by itself being indicative of a departure from "strict" inflation targeting. Indeed, in the derivation of the optimal interest rate rule in Svensson's model given above, it was shown that even when the output gap does not enter the policy loss function (A = 0), it will (together with current inflation) affect the value of the instrument; the reason is that both variables help predict future inflation.

The second issue relates to the role of forward-looking components in actual policy reaction functions. This is important because simple forecastbased rules have some clear similarities with the type of optimal flexible inflation-forecast targeting rules derived earlier; monetary policy under both types of rules seeks to offset deviations between expected inflation and the inflation target at some horizon.<sup>40</sup> The Reserve Bank of New Zealand's policy projections, for instance, are based on an explicit, forward-looking policy reaction function that takes a relatively simple form (Archer (2000)). Some econometric studies (see Clarida, Gall and Gertler (1998a, 1998b)) have shown that a reaction function that includes a forward-looking measure of inflation, the contemporaneous output gap, and lagged interest rates (to account for smoothing considerations) characterizes monetary policy fairly well for the United States, Japan and Germany. The kind of policy rule that underlies this specification is what Clarida, Galf and Gertler call "softhearted" inflation targeting: in response to a rise in expected inflation relative to target, the central bank raises nominal interest rates sufficiently to 40Batini and Haldane (1999), Isard and Laxton (2000), Parrado (1999), and Rudebush and Svensson (1999), have analyzed the dynamic properties of both simple and optimal forecast-based rules using simulation techniques.

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In addition to this point estimate, we can also use the confidence interval for the difference in means to provide a 95 confidence for the incidence of illegal migration. Despite sample sizes of over 1,000 households in each case, these confidence intervals are reasonably wide, typically covering 20 percentage points (11 percentage points in the larger Filipino sample). This is one downside of this indirect elicitation approach – if we asked 1,000 individuals a direct question the 95 confidence interval for the proportion would have width at most 6 percent.

Despite the reluctance to ask about illegal migration status in surveys taken at destination, it is possible that reliable answers might be obtained by directly asking sending households in migrant source countries, particularly in cases where illegal migration is commonplace (and hence carries no stigma) and where no contact information for the migrant is collected (limiting the potential for this information to harm the migrant). During a different part of the survey to the list randomization questions, the Moroccan and Ethiopian surveys asked households who reported having a migrant abroad what type of documentation the person had acquired before entering the destination country, with possible options including work visa, student visa, refugee visa, tourist visa, no documents, or other. We code migrants who enter with no documentation or on a tourist visa as illegal migrants, and report the illegal migration rates as obtained via this direct elicitation in the last column of Table 2. The directly reported rates are 39.3 percent for Morocco and 32.6 percent for Ethiopia, which are reasonably close to (and well within the confidence intervals of) our list randomization estimates. This suggests that list randomization is giving us reasonably accurate estimates.

# **4.3 Measuring Illegal Migration Rates for Subgroups**

In addition to knowing the overall (unconditional) incidence of illegal migration, researchers and policymakers will often be interested in knowing more about the characteristics of which types of households are more likely to have illegal migrants. This can be done by simply comparing treatment and control means within subgroups defined by the characteristics of interest. Ideally one would stratify the randomization on these characteristics, but even without this stratification, randomization should still on average ensure balance within subgroups provided sample sizes are not too small.

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We have also calculated a variety of other NPC indicators which aggregate the NPCs of various groups of crops. These include aggregations for cereals (wheat, rice, maize and sorghum), foodgrains (cereals and pulses), oilseeds (groundnut, mustard, copra, soybean and sunflower), non foodgrains (oilseeds, cotton, sugar and rubber) and aggregate agriculture (all thirteen crops). These aggregations have been made alternately treating all the crops as import substitutes i.e. using the importable hypothesis NPCs only, and treating some of the crops as exportables. In particular the NPC series for all 13 crops, which is an estimate of the NPC for Indian agriculture as a whole, has been done in two ways. In the first aggregation, all the crops are treated as import substitutes with their NPCs based on import reference prices. In the second aggregation, the NPCs over the entire period ( 1965 -95) for rice and cotton use the exportable hypothesis, the NPCs for wheat use the importable hypothesis up to 1973 and the exportable hypothesis in 1974 and after.

The approach to calculating the aggregate NPCs can be illustrated by the aggregation for cereals. As regards each of the four cereals ( rice, wheat, maize and sorghum), we first assumed that the weighted average NPC of the estimated NPCs in the states included in the study, is equal to the all-India NPC for that crop. Secondly, we divided the gross value of the all-India output of each crop (obtained from the National Accounts or estimated from other sources) by its all-India NPC to give an estimate of the all-India value of each crop's output at reference prices. Finally, we divided the all-India aggregate gross value of the output of the four cereal crops by the aggregate value of the output of the same crops valued in reference prices. The same procedure was followed in the other aggregations. That is: NPCc = yi (GVPDi) / Xi (GVPDi/NPCi) where GVPD1 = (Gross value of production of crop i at domestic prices NPCi = Nominal protection coefficient of crop i NPCc = Nominal protection coefficient for cereals i = Rice, wheat, maize, and sorghum.

# _Prices of commodity baskets_

The aggregate nominal protection series provide an indication of the level of and trends in the nominal protection of the groups of crops to which they refer. In order to distinguish the sources of changes in nominal protection of these aggregates over time, we calculated the weighted average domestic reference and world prices of the commodity baskets included in the aggregates. The weights change from year to year and are calculated by dividing the gross value of the output of each crop by its domestic price. That is: pi i(wi~i wi (GVP i/p i) / (E; (GVP i/P i)) where, PJ = Price at j level. j = D (domestic), R (reference) and W (world) As discussed in background paper no.6, the commodity basket price indices make it possible to distinguish how the nominal protection of Indian agriculture has been affected by changes in the xxi
Our data also allows us to understand the determinants of under-reporting across debtors and creditors. To test for the role of debtor country institutions, Panel A of Figure 6 depicts the distribution of debt data revisions across country groups with different levels of institutional strength, as measured by their average score in the Polity V dataset. Figure 6 shows that reporting noise - as measured by the dispersion of the revision distribution - is substantially higher in countries with weak institutional strength, more limited checks and balances and low capacity. Notably, however, even the countries with the strongest institutions in our sample systematically under-report their debt, with a significant mean revision of 0.5 percent of GDP.

We next disaggregate debt data revisions by creditor group and find striking differences in the propensity of different instruments to be under-reported.<sup>21</sup> Panel B of Figure 6 reveals that the upward bias in revisions is largest in debt to bilateral and non-bond private creditors. However, for debt owed to the World Bank and for debt owed to private bondholders, the figure shows only very small revisions. This result is not surprising, given that data on (publicly traded) bonds is generally widely available and the World Bank can readily monitor its own lending activities and ensure that they are accurately reflected in borrower reporting. Lending by bilateral and other private creditors, on the other hand, is not traded in secondary markets and is often shielded from public scrutiny through non-disclosure clauses (Gelpern et al., 2023; Mosley and Rosendorff, 2023).

> 21Since the size of outstanding debt differs greatly across lender groups, we scale revisions by initial debt rather than GDP. See Appendix Table C3 for alternative scaling.

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correlation between firm size and informality, it is not surprising that the probability of starting or increasing the use of digital technologies is significantly higher for fully formal firms (on average, ten percentage points higher (Figure 8 panel a), even after controlling for firm-level characteristics and initial productivity. Similar results are observed for other digital outcomes such as the probability of having online sales, investing in digital technologies, and the share of employees working remotely (Figure 8 panels b-d). Parasite firms consistently lag behind in their adjustment response relative to the other two types of informal firms.

Figure 8: Digitalization <!-- Start of picture text --> (a) Started or increased use of digital (b) Has online sales<br>60.0 60.0<br>5 4.8 2<br>50.0 46.73 50.0 47.77 4 8.4 8<br>4 2.3 7<br>40.0 3 4.4 6 3 5.8 3 34.32 40.0<br>30.0 30.0<br>20.0 20.0<br>10.0 10.0<br>0.0 0.0<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>(c) Has invested in digital technologies (d) Share of employees working remotely<br>40.0<br>14.0<br>30.0 28.13 12.0 10.97<br>10.0 9.71<br>8.72<br>7 .9 4<br>8.0<br>20.0 18.25 1 7.5 2<br>1 5.1 5 6.0<br>10.0 4.0<br>2.0<br>0.0 0.0<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>Probability of having online sales<br>Probability of increasing or starting using digital<br>Share of remote workers<br>Probability of investing in digital<br><!-- End of picture text --> Notes: Controls for size, sector, age, country, gender of the owner, and severity of the crisis. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

The descriptive finding above is also confirmed in a regression setting using _PCA 5_ , as the continuous measure for informality (Table 4). Conditional on external attributes (column 1) and initial digital readiness (column 2), the probability of starting to use or increasing the use of digital is substantially lower for informal firms. Yet, there are no significant differences when analyzing heterogeneity by different views of informality (columns 3 and 4) in the use of digital technologies but for other measures of adjustment response (e.g., online sales and investment in digital technologies), parasite firms perform worse.

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