rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7414

# **Abstract**

The authors have assembled 1960–2012 infrastructure stock data from 145 countries to estimate the demand for infrastructure services in emerging markets and developing economies. This paper identifies that the required resource flows to satisfy new demand while maintaining service for existing infrastructure amounts to $836 billion or 6.1 percent of current gross domestic product per year over the period 2014–20. The annual infrastructure investment gap for emerging markets and developing economies is $452 billion per year, which implies that emerging markets and developing economies should almost double their current spending. The paper also estimates that half of the spending should be allocated to maintenance of existing assets. Acknowledging the challenges to compare infrastructure investment estimates across different methodologies, the authors recognize this result as a lower bound estimate and compare the results with others available in the literature.

This paper is a product of the Public-Private Partnerships (PPPs) Group. 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 fruiznunez@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
the problem, and it further aggravates banks‘ situation. On the one hand, the law has clearly reduced willingness to repay; on the other hand, it forces banks to keep bad debts on their balance sheets and finance them (Chart 18).

**Chart 18. 90+ delinquency ratio for certain retail product types**

<!-- Start of picture text --> Per cent Per cent<br>24 24<br>22 22<br>20 20<br>18 18<br>16 16<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>2008. II. III. IV. 2009. I. II. III. IV. 2010. I. II. III. IV.<br><!-- End of picture text --> Source: MNB.

# **_Regulatory measures_**

Drawing from the experience of the crisis, several regulatory initiatives have been announced and implemented with an impact on lending to households. In light of the problems presented above, these initiatives had two objectives. One is to ensure the formation of a sounder lending structure as lending recovers. Forint-denominated loans came into the forefront with a simultaneous, drastic cutback on foreign currency loans, while authorities strive to pressure banks to assume less risk even in the case of forint loans. The other main objective is to provide better protection to borrowers. Indeed, banks used to have an option to raise interest rates on loans at will, and as a consequence, changes in the installment amounts were not predictable to customers. On occasion, this mechanism may have contributed to the deterioration of the portfolio.

Below we examine the regulatory initiatives that have been implemented in Hungary since the onset of the crisis. In some cases we also present their short-term impacts.

- **1) Transparent pricing (step 1):** Ever since the gradual expansion in household lending, the fact that banks could unilaterally modify the terms of retail loan agreements had been an unresolved problem. After several proposals, the first step was taken in 2009. Banks were required by law to provide a cause-and-effect list in their loan agreements as to what entitles them to raise interest rates, fees, or charges. At the same time, the law entitled debtors to prepay their loans free of charge during 60 days from the date of an unfavorable, unilateral contract amendment (either by refinancing their loan or by taking out a new loan from a different provider). Market players—who denounced the legislation change and tended to stick to the regulation to the letter while they essentially evaded its spirit—inserted tediously

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of total merchandise exports. These figures demonstrate a significant move toward manufacturing exports.

Figure 3 shows income growth, real consumption, and unemployment. Export surges are associated with relatively high GDP growth, especially during the surge, real per capita consumption expenditure increases, and unemployment shows some decline in over the period.<sup>10</sup> Export surges are welfare enhancing.

# **III. The relationship between export surges, GDP surges, and export collapses**

In this section, we analyze the relationship between export surges and GDP surges in order to assess whether the episodes identified in the first part correspond to periods of pure export growth or whether the surges are a result of the overall performance of the economy. For that purpose, we define GDP surges using similar criteria as those utilized to identify export surges episodes; however, in the case of GDP surges, taking into account that 6 percent could be considered very high for GDP growth (GDP growth rates tend to be lower than export growth rates), we used 3.5 percent as the cutoff value for the condition imposed on the average rate over the 7-year period.

As a result, we obtained 71 episodes of GDP surges. Table 1 shows the results from the comparison of the timing of GDP surges and export surges for those countries that experienced both episodes within a period of 8 years. We find that an export surge episode is close in time to a GDP surge episode in 33 cases. In most of them, the export surge happens before or at the same time as the GDP surge starts. Almost two-thirds of the export surge episodes happen within periods where there is no GDP surge or if there

> 10 One caveat is that the unemployment data is limited with only 14 observations in developed countries and 14 in industrial **.** In all graphs we use a balanced sample to avoid changes due to entry or exit (see Table A4).

9
**For the full GEGI,** a similar adjustment is required (expressed in terms of ratios); I equalize better

employment rates and other employment rates across genders, but the denominator needs to be reweighted.

While this adjustment does not have much effect for most countries (Appendix Figure 11), it greatly reduces the full GEGI for the seven countries with , on average by 17ppts (or 44%), and increases it by 9ppts (or 19%) for Nepal, and is the preferred measure for these countries (see Appendix Table 2). ρρ≥1.25

**Appendix Table 2: Adjusting the Basic GEGI and Full GEGI for Unequal Male-Female Working Age Populations**

|**Country Name**|**Gender Pop. Ratio ***||**Basic GEGI**|||**Full GEGI**||
|---|---|---|---|---|---|---|---|
||ρ=Pop(M)/Pop(F)|Original|Adjusted|Diff(ppts)|Original|Adjusted|Diff(ppts)|
|Bahrain|2.14|34%|19%|-14.6|34%|19%|-14.5|
|Kuwait|1.73|29%|20%|-9.2|28%|19%|-9.0|
|Maldives|2.03|29%|17%|-11||||
|Nepal|0.78|36%|42%|6.3|46%|55%|9.0|
|Oman|2.45|53%|25%|-28.1|54%|25%|-28.3|
|Qatar|3.77|24%|9%|-15.4|24%|9%|-15.4|
|Saudi Arabia|1.53|63%|44%|-19.0|63%|44%|-19.0|
|United Arab Emirates|2.62|29%|14%|-19.0|29%|14%|-15.1|
|* Workingagepopulatio|n(15-64years). Data fro|m WDI serie|s SP.POP.15|64.FE.IN an|d SP.POP.15|64.MA.IN for|2020.|

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accounted for a growing share of total water supplies. In many regions of the central Punjab as much as 70 percent of irrigation is supplied by tubewells, particularly in the _rabi_ season.

In the 1950s and 1960s, waterlogging and salinity posed major hazards for irrigated agriculture. In most areas, the threat of waterlogging has receded since the 1970s, when the Salinity Control and Reclamation Project (SCARP) tubewells were installed and drainage was improved. The problem of salinity has become more acute in recent years, however, as a result of increased tapping of brackish groundwater for irrigation, and salinity poses the greatest danger to Pakistan's most important natural resource.

**Table 3. Extent of Soil Degradation by Province, 1988 (thousands of hectares)**

||_Punjab_|_Sindh_|_NWFP_|_Balochistan_|_Northern_<br>_Areas_|_Pakistan_|
|---|---|---|---|---|---|---|
|Total area surveyed|20,625|9,222|9,139|19,141|3,685|61,812|
|Water erosion|1,904|59|4,282|2,635|2,282|11,172|
|Wind erosion|3,804|639|36|280|-|4,760|
|Salinity and sodicity|2,667|2,110|48|502|-|5,328|
|Waterlogging|696|625|92|142|-|1,554|
|Nutrient degradation|<br>1,580|246|296|96|-|2,218|
|Flooding<br>|915|763|276|598|5|2,557|
|Ponding|691|-|245|-|-|936|

_- Not available. Source:_ Mian and Mirza 1993 Salinity and sodicity (hereafter referred to as salinity) and waterlogging dominate the environmental agenda in irrigated agriculture. Other problems include soil productivity losses and excessive use of agricultural inputs, especially chemicals.

# **_Waterlogging, Salinity, and Groundwater Management_**

Salinity is partly the result of naturally occurring geological processes and depends on the soil material, landform, relief, climate, and land use. In Pakistan, salinity has almost certainly increased as a result of the canal irrigation system as water containing dissolved salts from the lower parts of the soil profile seep from the canal system, and evaporate from the surface of adjoining soils. WAPDA has estimated that 2.12 million hectares of land are waterlogged, and Sandhu (1993) estimates that an additional 40,000 hectares per year are lost to waterlogging. These reports are based on outdated information; regular monitoring of waterlogging and salinity using standardized assessment criteria is needed so that more accurate estimates can be made.

The most neglected problem is that of tertiary salinity from the low-quality groundwater provided by tubewells. Surveys have shown that three-fourths of tubewells provide brackish water that is unfit or only marginally fit for agriculture. The cost of salinity in terms of reduced yields is hard to evaluate. Using farm-level data, Siddiq (1994) estimated that yield losses in wheat as a result of sodic irrigation water were 9 percent and 20 percent in two different locales in the central 14
Being a household head or a household head’s spouse was associated with being less conservative for all categories, except for employment.

Respondents who were from a household owning a house and who were poor or B40 (being in the bottom 40 percent of the income distribution) exhibited more conservative attitude across all categories, but a less conservative attitude towards employment. To the contrary, respondents from a household living in an urban area and owning land exhibited less conservative attitude across all categories, but a more conservative attitude towards employment. Other individual characteristics such as age, financial situation, employment status, and personal habits, and household characteristics such as household size and composition did not clearly define respondents’ attitudes towards gender questions.

**Figure 2: Post-Estimated Distribution of Gender Attitude Composite Indexes by Migrant Type**

<!-- Start of picture text --> Standardized Employment Index Standardized General Index Standardized Decision Making Index<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Standardized GBV Index Standardized Extreme Index<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Non-migrants Returned migrants from Gulf Countries<br>Returned migrants from Saudi Arabia<br>2.5 2.5<br>2.5<br>2 2<br>2<br>1.5 1.5<br>1.5<br>1 1<br>1<br>.5 .5 .5<br>0 0 0<br>2.5 2.5<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br><!-- End of picture text --> Data source: KMS 2003, 2013 The comparison group is the “Non-migrants” with composite index with mean centered at zero and standard deviation at 1.

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- 54 -

# Table 23: Distribution of the Poor According to Levels<sup>of Growth and Instability</sup> in Foodgrains Production

||||Percent of ne|w sown area wi|th<sup>rainfall</sup>|<br>Perc|ent share in<br>||Number of agricultural<br>|
|---|---|---|---|---|---|---|---|---|---|
||||High (1,150|Medium (750|||Poverty|Ultra|workers per 100 hectares|
||||<br>|<br>1150|L ( t|Rural<sup>ou-</sup>|oulation|poor|of net sown area|
||||mm and|mm to ,<br>|ow up o<br>|<sup>pp</sup><br>|pp,<br> <br>|<br>|,<br> <br>|
||Category|States<br>|above)<br>|mm)<br>|750 mm)<br>|lation, 1983<br>|1983<br>p<br>|opulation<br>|<br>1980-83<br>9|
||1|2|3|4|5|6|7|8||
|1.|High growth and low|Punjab. Jammu-Kashmi|r||||||<br>|
||instability||5.3|20.5|74.2|3.3|1.2|0.8|<br>85|
|it.|High growth and|Haryana, Uttar||||||<br>|<br>|
||medium instability|Pradesh|8.4|64.1|27.5|19.7|19.1|<br>18.7|<br>148|
|Ill.|Iligh<br>growth and||||||||<br>83|
||high instability|Gujarat|7.3|25.0|67.7|4.6|2.8|1.8|<br>|
|IV.|Medium growth and<br>lowinstability|Andhra Pradesh|1.0|66.0|33.0|7.9|4.9|3.7|<br>145|
|V.|Medium growth and<br>|medium instability|Karnataka|9.4|24.3|66.3|5.1|4.1|4.2|<br>95|
|{v1.|Mediumgrowth and|Maharashtra,||||||<br>|<br>87|
||high instability|Orissa, Rajasthan|25.0|24.1|50.9|17.7|17.4|<br>17.3|<br>|
|VIl.|Low growth and||||||||<br>173|
||low instability|Assam, Kerala|100.0|0.0|0.0|7.5|6.7|5.3|<br>|
|Vill.|Low growth and|Tamil Nadu,||||||<br>|<br>188|
||medium instability|West Bengal|54.2|45.8|0.0|14.0|18.0|<br>20.3|<br>|
|IX.|Low growth and|||||||<br>|<br>130|
||high instability|Bihar, M.P.|63.5|33.5|3.0|20.2|25.6|<br>28.0|<br>|
|||All India|30.7|35.8|33.5|100.0|100.0|<br>100.0|<br>120|

Source: All columns except 8, C.H.H.<sup>Rao, S.K. Ray, and K. Subbarao (1988); column 8 was computed in this paper.</sup> Nole: Growth rate refers to the<sup>average foodgrains</sup> production growth rate for the period 1961-1985:<sup>High-above 3.0; Medium-2.0 to 3.0; Low-below</sup> 2.0. Instability refers to the standard deviation<sup>in the annual output growth rates of foodgrains</sup> for the period 1961-1985; High-above 20; Medium-15 to 20; Low-below 15.
6 Denizer _et al._ (2011, 2013) use data from 1983 to 2009/2011 on investment projects and old structural adjustment and new policy-based loans. In addition to macroeconomic variables and basic project characteristics such as loan size, project length, sectoral dummies, and preparation and supervision costs, they deploy a sheaf of early warning indicators which are developed by the task team leader as the project evolves over its life (of approximately six to ten years). They find that loan size, project supervision costs, and certain early-warning indicators are significantly correlated with project outcomes. They also find that task team leader quality is significantly related to project success. Quality is measured by the average IEG rating of projects managed by the task team leader, other than the project in question. Denizer _et al._ (2012) find that investment projects in fragile and conflict-affected states show essentially the same characteristics as those in non-fragile states.

A difficulty with Kilby (1995, 2000), Wane (2004), Chauvet _et al._ (2010), and Denizer _et al._ (2011, 2012, and 2013), however, is that they do not adequately distinguish investment projects from policy-based loans. As explained earlier, policy-based loans support a set of policy and institutional reforms. They do not directly finance physical infrastructure and are not earmarked as are investment projects. Policy-based loans are shorter in time span and all prior actions/conditions are met before the presentation of the loan to the World Bank Board of Executive Directors. Due to their distinct nature, policy-based loans have a different concept and time-frame<sup>4</sup> as regards supervision and implementation, and involve different criteria for evaluation compared to investment projects. The only nod to the distinctive nature of policybased lending in the latter studies is the inclusion of a dummy. Policy-disbursing loans are indeed a small fraction of the total loans of the World Bank in terms of numbers in most samples of the literature reviewed, e.g. 10 percent in Chauvet _et al_ . (2010, p. 23) and in Denizer _et al_ . (2013, p. 293). Hence these papers can be taken as representative of investment loans but they apply less to policy-based lending.

> 4 Time-frames: A stand-alone development policy operation typically closes one year after its approval by the Board.  A programmatic series typically closes one year after the approval of the last operation (loan) in the series.   The Implementation Completion Review (ICR) is currently completed 12 months  after closure, but the loans that are part of the dataset used for this paper had to complete their ICR 6 months after closure.
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

## **Reducing operational and financial inefficiencies**

Reducing operational and financial inefficiencies could save Cameroon around $254 million per year (table 17), in particular by means of reducing distributional losses.

Reducing distributional loses in the power and water system could also contribute to expand the resource envelop. Around $181 million could be saved if AES Sonel and CDE reduce the amount of power and water lost in their networks (table 17). Distributional losses of power cost the utility, AES SONEL, $168 million per year (or 0.8 percent of GDP, figure 34a). The utility’s 2009 losses of 35 percent are more than three times the best-practice benchmark of 10 percent. By reducing CDE’s distributional losses of water from 34 percent (the level in 2009), to the 20 percent benchmark of a wellfunctioning utility, Cameroon could avoid economic losses of $14 million (or 0.06 percent of GDP, figure 34b).

Undercollection of bills costs Cameroon around $73 million per year. In the power sector, $21 million per year (or 0.09 percent of GDP, figure 34a) could be saved by raising bill collection efficiency from 94 to 100 percent. In the water sector, improving bill collection ratio from 66 to 100 percent would save another $19 million annually (or 0.08 percent of GDP, figure 34b).

**Figure 34. Macroeconomic burden of operational inefficiencies of Cameroon’s power and water utilities**

<!-- Start of picture text --> Uncollected bills and unaccounted losses as percentage of GDP<br>a. Power sector<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Cameroon Resource-Rich<br>Collection inefficiencies Distributional losses<br>Percenatage of GDP<br><!-- End of picture text --> <!-- Start of picture text --> b. Water sector<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Cameroon Resource-Rich<br>Collection inefficiencies Distributional losses<br>Percenatage of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009).

# Annual funding gap

Cameroon’s infrastructure funding gap amounts to $350 million per year, or about 2 percent of GDP, once efficiencies are captured. The bulk of the gap can be traced to the water, irrigation, and transport sectors (table 18). No funding gap is found in the ICT sector or, once large potential efficiency gains are captured, in the power sector.

45
tenure in Poland, Russia, and the United Kingdom in the 1990s. They develop a model that describes the process of labor mobility in a transition economy that yields testable hypotheses about the differences in tenure and wage profiles between labor markets in transition and developed economies. They report that tenure-turnover rates were higher in Russia and lower in Poland than in the United Kingdom.

<u>Orazem, Vodopivec, and Wu (2005) analyze the process of job displacement in Slovenia during</u> the 1990s. They find that the probability of job displacement and quitting decreased with job tenure.

Using longitudinal data from Poland, Jackson and Mach (2009) examine the labor transition from the state to the private sector in the late 1990s. They show that many workers went through spells of unemployment or exited the labor force before obtaining private sector jobs. <u>Newell and Socha (2007)</u> show that the returns to years of tenure rose and steepened in Poland between 1998 and 2002, increasing wage inequality and reducing relative wages for younger workers. Labor mobility declined with job tenure in Poland, as it did in Slovenia.

<u>Noorkôiv et al. (1998)</u> and <u>Lehmann, Philips, and Wadsworth (2005)</u> investigate wage and employment dynamics during Estonia’s transition to a market economy (1989–95). They document a significant redeployment of labor from traditional manufacturing and agriculture to modern private sectors. The wage-job tenure profiles did not change significantly over the period analyzed. Returns to experience increased for most cohorts; they grew rapidly for the youngest groups of workers.

Analyzing longitudinal data from Russia and Ukraine, <u>Gordnichenko and Sebirianova-Peters (2005)</u> and <u>Lehmann, Pignatti, and Wadsworth (2005)</u> find no significant increase in returns to job tenure between 1998 and 2002. Lehmann, Pignatti, and Wadsworth report the heightened risks of job displacement for workers with either low or high job tenure. Linz and Semykina (2008) find that Russian workers with very long or very short job tenure felt more insecure about their job situation than workers with average tenures did during the period of economic transition. Earle <u>and Sabirianova (2002) report a positive correlation between the probability of having wage</u> arrears and job tenure in Russia. Rutkowski (2013) shows that job tenure in informal jobs tends to be low in Kazakhstan.

5