rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5. PPP contract design and early terminations of highway PPPs in India**

PPPs in national highways in India experienced several early terminations between 2013 and 2015. Anecdotal evidence points to the incentives created by some contract designs as a potential reason for the early termination (ADB and others 2018; Pratap and Chakrabarti 2017). This section takes advantage of the existence of different contract designs for national highways PPPs in India during the early 2010s to identify contract features that can help explain the large number of early terminations. Narrowing the sample to national highways PPPs in India allows looking at contract features that are not available for the global and cross-sectoral sample used in previous sections. The analysis in this section can help improve the design of road PPPs and their contract structure as well as inform the design of PPPs in other sectors with similar incentive structures.

The analysis uses the national highway PPPs in India with financial closure or concession agreement years from 2010 to 2014 included in the PPI database. Out of the 125 projects with financial closure or concession agreement year between 2010 and 2014, 26 were canceled, and 24 of those cancellations occurred between 2013 and 2014. One cancellation occurred in 2015 as a result of a lengthy court process over the terms of termination after the project company decided to withdraw from the project in 2014. The timeframe allows comparing canceled projects with contemporaneous active projects.

Data on the investment amount, the project status, the length of the road to be constructed, and the data on financing mix were extracted from the PPI database. When the data for a project is missing in the PPI database, the data were collected from the concession agreements published on the National Highways Authority of India (NHAI) website, and in the case of financing data from online news articles or private sponsors’ annual reports.

During this period, two types of PPP contracts were mainly initiated by NHAI: toll- and annuitybased projects. Toll-based projects entitled the project company to charge tolls, and annuity-based projects entitled the project company
|Nlhli b|16,266.92||6,356.04|-9910.88***|
|---|---|---|---|---|
|on-acooc everages|(2,817.81)||(753.18)|(2841.49)|
|Alhli b|3,048.25||2,971.90|-76.35|
|cooc everages|(683.66)||(758.26)|(1013.01)|
|Numberofobservations|711|701|698||

**Note:** The standard errors are in parentheses, and the differences are estimated considering the complex survey design. * p < 0.1, ** p < 0.05, *** p < 0.01. The population weights are applied. The consumption data are expressed in per adult equivalent monthly basis and are spatially and temporarily deflated within each treatment arm.

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of regional heterogeneity across states in Mexico of productivity spillovers among local suppliers. Altomonte and Colantone (2008) report for Romania that positive productivity spillovers occur only in the three regions that incorporate the largest shares of the host economy’s manufacturing sector. Similarly, Monastiriotis (2016) finds for EU transition economies that productivity spillovers only materialize in the core regions within these countries.

Another set of studies examines whether productivity spillovers from MNC affiliates are facilitated by geographical proximity between MNC affiliates and local suppliers. Jordaan (2008b) reports for Mexico that positive backward spillovers materialize both within states and between neighboring states. For China, Madaragia and Poncet (2007) find that spillovers are transmitted between neighboring cities. Wang, Ning, and Zhang (2017) also use data for Chinese cities and identify
are common in the New Keynesian model literature applied to developing countries, including Dutch disease effects, investment inefficiencies and weak tax systems. The model is parameterized following various empirical works and data from Uganda, and is used to simulate the effects of a one standard deviation oil production shock, interpreted as a boom in the energy sector.

Our results show that: ( _i_ ) a better fiscal management is to save the resource income in a sovereign wealth fund for future generation when public capital is almost unproductive; ( _ii_ ) a gradual scaling-up of public investment ( _The Sustainable-Investing Approach_ ) yields the best outcomes as it minimizes macroeconomic volatility; e.g. the real exchange rate appreciation is 30 percent lower than in the all-investing approach, which might be viewed as an attractive fiscal policy to accelerate economic development in public capital-scarce economies; the trade balance improves substantially and impulse response functions suggest that output, non-tradable and tradable goods production, employment and wages rebound faster.

We also define criteria to determine the optimal value of the oil share to invest in public infrastructures by minimizing a social loss function; we use four loss functions that include a households’ welfare indicator and a macroeconomic/fiscal stabilization indicator. We find that the loss functions have convex shapes with optimal values of _φ_<sup>_oil_</sup> varying between 0.55 and 0.85, depending on the policy maker’s preference for macroeconomic stability. Furthermore, our results show that the optimal share of oil revenues to be used for public investment is very robust to the various parameter calibrations; only parameters that directly affect these revenues (such as interest rates on savings and the persistence of oil price shocks) play an important role. In comparison with the recent literature, our optimal share to invest domestically in public infrastructures is slightly higher than those estimated in Ag ́enor (2014), which ranges from 30 percent to 60 percent using oil price shocks.

A number of extensions could usefully be considered. First, the paper assumes an exogenous
- United Nations High Commissioner for Refugees (UNHCR). (2017). Projected Global Resettlement Needs 2017. Geneva: United Nations High Commissioner for Refugees.

- ---. (2021). _2021 Mid-Year Trends Report_ . Geneva: United Nations High Commissioner for Refugees.

- Verme, P., & Schuettler, K. (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. _Journal of Development Economics_ , 102606.

Watson, N., & Wooden, M. (2010). Data survey: the HILDA survey: progress and future developments. _Australian Economic Review_ , _43_ (3), 326-336.

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# Figure 6

A further confirmation that fast-moving countries did better than the more gradualist ones is provided by Figure 7 which shows the profile of GDP around stabilization year for two subgroups of countries: those that stabilized early in 1990-91 (Bulgaria, the Czech Republic, Hungary, Poland, and the Slovak Republic) and those that only implemented a stabilization program in 1994-95 (Macedonia, Romania, Russia, Ukraine).'° The early movers suffered from a significantly smaller decline before stabilization. The late movers are slower to recover and, by 1998, have not yet achieved positive growth. GDP could be more underestimated in slowly reforming countries, but it is doubtful that the conclusion would be reversed with more accurate data.

# Figure 7

The econometric studies quoted above use of indicators of liberalization developed by de Melo, Denizer, and Gelb 1996. For comparison, Figure 8 uses the indicator prepared by the EBRD. This indicator relies on eight criteria concerning privatization and enterprise restructuring, markets and financial institutions. The upper graph plots the 1998 GDP relative to its 1989 level (previously shown in Figure 2) against the liberalization index. There is a relatively significant positive relationship (R2 = 0.45). At the very least, this graph disproves the view that liberalization is a source of slow growth a decade after the change in regime. But was it earlier on? The lower chart repeats the same exercise using the first EBRD indicator published in 1994 and plots = it against 1994 GDPs relative to their 1989 levels. The association is weaker (R2 0.34) but certainly not negative.

# Figure 8

# **Macroeconomic Stabilization**

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Mexico, but argues that this premium is the result of pre-migration differences in ability and not the result of human capital gains derived from migration. Reinhold and Thom (2013) also find a wage premium for Mexican returnees who worked in the United States. The return to a year of occupation-specific migration experience is estimated to be as high as 9% for some occupations. De Vreyer, Gubert, and Robilliard (2010) examine the labor market outcomes of returnees in seven capital cities in West Africa and find that experience abroad results in a substantial wage premium for migrants returning from an OECD country but not for other return migrants. Controlling for the emigration and return migration selections, Wahba (2015) finds that return migrants experience a wage premium of 16% relative to non-migrants. However, none of these studies has examined the differential effects of return migration on wages with respect to migrants’ legal status.

Our paper contributes to the literature in at least two ways. Firstly, while the literature on undocumented migration has focused on the impact of irregularity on destination countries, this paper is the first to investigate the impact of undocumented migration on migrants after they return to their origin country. This is particularly important since many unauthorized migrants return to their country of origin. Secondly, by disentangling the effects of migrants’ legal status, we contribute to the literature on the impacts of migration on origin countries, which has so far overlooked the question of undocumented migration.

We use data from the Egypt Labor Market Panel Survey (ELMPS 2012), which has several major advantages. First, the Arab Republic of Egypt provides a particularly suitable case study since it is a country with substantial return migration. According to the ELMPS data, in 2012, 9% of individuals between the ages of 15 and 59 were return migrants.<sup>3</sup> This figure is in line with the World Bank report by Brodmann, Pouget, and Gatti (2010), who document that Egyptian migrants constitute 85% of temporary workers
window in a set of quarterly interviews. Secondary, non-agricultural sources of income are disproportionately impacted. The extent of the losses that we observe in our analysis are heavily influenced by whether an individual’s labor contribution is being self-reported or reported by the household’s primary respondent. Labor contributions of youth are more likely to be omitted when using longer recall windows and relying on proxy reports. Because women and youth are less likely to be household heads and therefore more likely to be proxied in household surveys, the use of long recall periods may especially undercount their labor contributions. The time pattern of results is suggestive of more underreporting the further is the time from the survey time frame, coupled with possible overestimation of hours worked.

Given these losses, use of long recall windows can fundamentally affect our understanding of time patterns in peoples’ labor contribution, even when respondents are asked to disaggregate their work over the course of that period. This is not a principal goal of many multi-topic surveys that are primarily focused on the number of months worked and with even less emphasis on which ones. However, as policy makers increasingly demand data driven insights, continuing to refine and improve our methods of data collection is becoming more important. Understanding the seasonality, intensity, and diversity of labor is central to rural development planning. As this interest in the intensity, diversity, and seasonality of work continues to grow, an accompanying understanding of how to accurately measure labor contributions is becoming increasingly important. This objective cannot be separated from a need to understand how survey design and measurement choices, such as recall windows and proxy reporting, impact the resulting data, and finding ways to reduce their influence and potential biases.
had been serving farmers for 9 months while the oldest had been irrigating for 42 months at the time of the survey (Figure 1). 41 of the 50 systems were less than two years old and only one system was older than three years.

This study therefore presents an early assessment of the functioning and the impact of JCLISs. There has not been a drought year since most of these systems started working. Nor have they been used during the Kharif season critical for the household food security of farmers, since it was a good monsoon year. Norms around water allocation, water fees, and fee collection, and resource mobilization for repair and maintenance of system water sources, pumps, panels, and distribution networks are still evolving. Only a few instances of physical or technical breakdown in the sampled irrigation systems have taken place, and the water user groups managing them therefore have not experienced stresses that would test their solidarity, discipline, initiative, and resourcefulness.

**Figure 1. The frequency distribution of months since the sampled JOHAR systems started irrigating farmers' fields (as of 1 September 2021)**

<!-- Start of picture text --> 20<br>12<br>9<br>5<br>4<br><1 year 12-18 months 18-24 months 24-30 months 30-42 months<br><!-- End of picture text --> _Source: Created by authors using primary data collected from system operators_ 47 of the 50 systems in the sample are powered by solar energy. The remaining three use diesel pumps. Each solar pump was connected to 15-16 solar panels with a total capacity of 4.7 to 5.6 kilowatt (kW). The capacity of the three diesel pumps varied between 5 and 8.5 horsepower (hp). 44 of the 50 system operators knew the design command area of their system. The average design command area of JCLIS in our sample is 13.7 acres (95 percent confidence interval: 11.
value of the dependent variable when available. β1 and β2 capture the effects of receiving a flexible contract for clients who received offers in the early and late recruitment periods respectively. We then estimate the average treatment effect by averaging the estimates for β1 and β2, proportionally to each period’s sample size.

First, we examine repayment behavior, default rates and loan renewal. Table 2 Columns 1 and 2 report outcomes from the administrative data for borrowers of the standard and flexible contract respectively. Panel A reports the raw outcomes while Panel B reports the residuals after regressing default outcomes on the 18 observable characteristics from Table 1 for the standard contract group (with first-stage R<sup>2</sup> values ranging from 0.07 to 0.10).

Regardless of the panel used, the flexible contract group has 3 and 2 percentage points higher proportion of the principal in default 3 and 12 months after maturity, respectively. Column 3 of Table 2 reports the p-value of equality of means and shows this increase in default is statistically significant (p-value<0.01). Despite these default results, we see no statistically significant differences in the share of borrowers who have missed a due payment (i.e., not counting skipped payments from a pass as missed) or the rate of loan renewal. We return below to this pattern of results when exploring the repayment behavior over the course of the loan.

Columns 4 and 5 report the means of the default outcomes in column 2, separating by whether the initial offer was flexible (column 4) or standard (column 5). Column 6 assesses the selection effect by reporting the p-value of the difference in means between columns 4 and 5. As with the comparison using observable characteristics in Table 1 or the use of passes in Appendix Table 5, none of the differences in either Panel A or B is statistically significant. Finally, column 7 reports the difference between borrowers of the standard contract in column 1 and borrowers of the flexible contract in
infrastructure development accompanied these reforms with the aim of closing the gap between urban and rural areas, and developing the growth potential of more remote areas.

On the social and microeconomic side, a number of programs with the objective of reducing poverty have also been launched. These include the 2000-2004 development plan, the fund for the fight against droughts and desertification, the fight against analphabetism, the work of the foundation Mohamed V for solidarity, the work of the agency for social development and the national agency for employment and skills promotion, several initiatives for microcredits promotion and the national charts for education and health. In addition, the launch of the National Initiative for Human Development (NIHD) in 2005 – an ambitious national plan to develop the poorest areas of the country – marked an important step forward in addressing the wider spectrum of deprivation.

To our knowledge, none of these measures and programs has been fully evaluated, in part because it is difficult to tease out the impact that any particular program had on poverty. Moreover, estimates of poverty for the full period are simply not available. It is believed that Morocco has responded well to the 2007-2009 global shocks thanks to the countercyclical agricultural performance, but poverty estimates are available only for 2001 and 2007 (the two years for which household consumption surveys are available). This leaves policy makers unable to assess whether their policies have been effective in sustaining economic growth and poverty reduction against the backdrop of the global crises. This motivates the use of cross-survey imputations using Labor Force Surveys (LFSs) to fill this vacuum.

# **3. Methodology**

We adopt a standard imputation approach that is commonly used in the case of missing data. If a variable in a data set is missing altogether, but is available in a second data set representative of the same population, this second data set can be used to estimate the missing variable in the first data set. A prerequisite is that the two data sets share a set of regressors that are sufficiently correlated with this missing
respondent types.<sup>18</sup> The selected proxy then reported on up to two other randomly selected household members age 10 or older. Because in actual surveys, proxy respondents are not randomly chosen, but selected on the basis of availability, our experiment did not exactly mimic the actual conditions that result in proxy responses in household surveys.<sup>19</sup> However, it will provide estimates of proxy response bias<sup>20</sup> for a representative sample of potential proxies within the household.

The benchmark against which the commonly used approaches reflected by the short and proxy treatments are compared is the detailed self‐report questionnaire, which is generally considered “best practice”. Grosh and Glewwe (2000), providing detailed guidance to household surveys in developing countries recommend including screening questions. The use of multiple questions is also recommended by ILO as several categories of workers, including casual workers, unpaid family workers, apprentices, household members engaged in non‐market production, and workers remunerated in‐kind, have difficulties to correctly interpret a one off question about “any type of work” as referring to their situation (Hussmanns, Mehran, and Verma 1990).<sup>21</sup> The aim of this paper is to investigate whether point estimates of returns to education vary depending on the survey designs, using existing methods of analysis ‐ not necessarily to provide new or more accurate estimates of returns to education for Tanzania. The following equation provides a benchmark specification using OLS to estimate the returns to education: ln _Wi_  0  1 _Si_  2 _j_  _Si_  _Ti j_   3 _Ti j_  4 _Xi_  5 _Di_  _i_ (2) 18 The design of our survey was informed by assessing Tanzanian CWIQ 2006 data which indicated that the average Tanzanian household in this area has between two to three adults who could serve as a proxy with a minimum age of 15. Our sample
<!-- Start of picture text -->
- |<br>Public Debt in IDA and IBRD<br>(percent of GDP, median)<br>— IDA eligible —IBRD —all ——Non-cmdty exporter --=Percentile 25 and 75th<br>80 .<br>70 ee<br>60 wee<br>ae<br>50 weeneee”<br>40 ae .<br>30 pete<br>oo<br>20<br>2010 2012 2014 2016 2018 2020<br><!-- End of picture text -->

<!-- Start of picture text --> Average maturity on official external debt<br>(years)<br>2010 2015 = 2020<br>40<br>35,<br>30<br>25<br>20<br>15<br>10<br>5<br>o<br>EAP ECA Lac MNA SSA SAS<br><!-- End of picture text --> <!-- Start of picture text --> Composition of PPG external debt<br>(percent of total PPG external debt)<br>mBilateral m Multilateral m= Private mTM Bonds<br>100%<br>80%<br>60%<br>40%<br>20%<br>0% 2010 2020 2010 2020 2010 2020<br>uc umic umic<br><!-- End of picture text --> <!-- Start of picture text --> Average interest rate on official external debt<br>(rate)<br>2010 = 2015 = 2020<br>5<br>4<br>3<br>2<br>1<br>0<br>EAP ECA bac MNA SSA SAS:<br><!-- End of picture text -->
of the variation in leverage and debt maturity ratios. Booth et al. (2001) find that firms have longer duration debt in countries where the legal system provides greater investor protection. Diamond (2004) argues that the extent to which creditors are legally protected should have an impact on the optimal maturity structure of debt. Consistent with this view, Bae and Goyal (2009) report evidence of shorter debt maturity in countries where contracts are poorly enforced. Qian and Strahan (2007) and Giannetti (2003) examining bank loans and private companies respectively, show that debt maturity is longer in countries that offer greater creditor rights.

The effect of governance quality on debt maturity is documented by a number of papers using a country-specific setting. Using data from Turkey, Arslan and Karan (2006) show that <mark>both concentrated ownership structure and the presence of large shareholders are associated with the use of long-term debt.</mark> Kim (2015) finds that Korean firms with poor governance tend to have a 5
S) which can be aggregated from the constituents sectors in the International Standard Industry Classification (ISIC, revision 3). On the other hand, the GTAP reports data using a much finer classification of 57 sectors and uses a combination ISIC and Central Product Classification (CPC) sectors to aggregate its sectors. To harmonize the sectors, I map the 57 GTAP sectors to the three broad sectors used in the WDI database using the United Nations Statistics Division correspondence tables. The mapping is near perfect with the following three exceptions:

1. GTAP sector 1 ( _pdr_ ) consists of 2 sectors, of which one (husked rice) maps to Industry in the WDI classification while the other (rice, not husked) maps to Agriculture

2. GTAP sector 10 ( _oap_ ) consists of 7 sectors of which one (hides, skins and furskins, raw) maps to Industry in the WDI classification while the rest map to Agriculture 3. GTAP sector 12 ( _wol_ ) consists of 4 sectors of which one (pulled wool, greasy, including fleece-washed pulled wool) comes under Industry in the WDI classification while the rest map to Agriculture 10
with the corresponding number of years, which were standardized using the guidelines provided by the Ministry of Education. Married [=1] if the respondent indicates being married or declares a consensual union couple in the survey . Sisb ́en Enrollment [=1] indicates whether the adult is registered in the proxy mean test, used to target social programs in Colombia. Student [=1] if the respondent declares that they currently attend an educational establishment. Health Insurance [=1] if the adult declares being enrolled or registered to the Health Security System. Unemployed [=1] if the respondent was looking for work the week before the survey, self-employed [=1] if the adult works at their own business or as an independent contractor or freelancer, informal [=1] if the adult does not contribute to pensions or health insurance through her job. Work Contract [=1] if the respondent declares to hold a written contract.

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