rafmacalaba/data-use-mentions-extended huggingface dataset:
outlook to everyday living. It is concretely realized that oil is a dwindling and unreliable asset and, accordingly, a number of oil-exporting Arab countries are exerting efforts to diversify their economies in the shortest time possible. Science and technology assume or are presumed to assume a major role in the process of diversification of such economies.

With respect to higher education, some Arab countries have become captive to traditional policies and practices and are now facing new problems engendered by new realities. :.is is exemplified by both open admissions policies and very generous support to students in higher education in countries like Algeria, Moiocco, and Tunisia. Rapid growth in pre-university enrollment was bound to have its impact in terms of enrollment growtits in higher education, making tuition support and subsiste--ce assistance to students a budgetary burden.

Thus, publicly subsidized higher education has emerged as an issue in some countries and many questions are being raised about its place in overall educational fu;:;ding and in overall public expenditure priorities. Jordan faced such problems squarely and managed to absorb societal pressure for higher education through its system of community colleges, many of which are private. Furthermore, although enrollment in the state universities has mushroomed, the government of Jordan has managed to maintain a tuition fee structure generating income to cover approximately one-third of the recurrent costs of its state universities.

In the following sections, consideration is given to many aspects of higher education as they characterize universities in the Arab countries: governance, structure, admission and enrollment, financial resources, curricula and the methods of instruction, status of faculty, facilities, research and graduate studies, language of instruction, foreign exposure, and women's education.

Higher education in the Arab countries is basically a state function. Out of seventy two state universities covered in this overview, twelve are private - - se ven in Lebanon, four in the West Bank of Jordan, and one in Egypt. Based strictly on enrollment f.gures, private universities shoulder a smaller burden than may be
<!-- Start of picture text --> 15<br>10<br>5<br>0<br>learning adjusted years of schooling years of schooling<br>United States United Kingdom South Africa Singapore Sierra Leone Qatar Mexico Korea, Rep. Kenya Israel Indonesia India Ghana Finland Colombia China Chile Brazil Botswana<br><!-- End of picture text --> _Notes: Schooling data is based on UNESCO expected years of schooling and learning data is based on Harmonized Learning Outcomes (HLO)._ _Source: The Human Capital Index is described in Kraay (2019) and is based on Angrist, Djankov, Goldberg, and Patrinos (2021) learning data and UIS enrollment data._ Figure 1: Years of Schooling and Learning-Adjusted Years of Schooling (Macro-LAYS) macro-LAYS estimates. In this section, we outline the approach to producing micro-LAYS for evaluations that report effects on schooling participation, such as attendance or years of school gained, and subsequently for evaluations that report effects on learning outcomes.

# **2.1 Micro-LAYS using schooling participation estimates**

When studies report effects on schooling participation, micro-LAYS are the product of: (1) the access gains resulting from the intervention and (2) the schooling quality in the country where the intervention took place, measured relative to a global benchmark of high performance. We then multiply these gains by the duration over which the effects of the intervention are expected to persist. The construction of micro-LAYS derived from impacts on schooling participation, denoted 8
Islam N., Winkel J. (2017). Climate change and social inequality. DESA Working Paper No. 152 Key, N. and Sneeringer, S. (2014). Potential effects of climate change on the productivity of us dairies. _American Journal of Agricultural Economics_ , 96(4):1136–1156.

<mark>Nasri, K., & Belhadj, B. (2022). Household Vulnerability and Resilience in Tunisia: Evidence Using Fuzzy Sets and Multidimensional Approach.</mark> _<mark>S</mark> tudies in Microeconomic_ _<mark>s</mark>_ <mark>, 23210222221098836.</mark> Pörtner, H.-O., Roberts, D. C., Adams, H., Adler, C., Aldunce, P., Ali, E., Begum, R. A., Betts, R., Kerr, R. B., Biesbroek, R., et al. (2022). Climate change 2022: Impacts, adaptation and vulnerability. IPCC Sixth Assessment Report.

Stevanović, M., Popp, A., Lotze-Campen, H., Dietrich, J. P., Müller, C., Bonsch, M., Schmitz, C., Bodirsky, B. L., Humpenöder, F., and Weindl, I. (2016). The impact of high-end climate change on agricultural welfare. Science advances, 2(8):e1501452.

Svoboda, M., Hayes, M., Wood, D. A., et al. (2012). Standardized precipitation index user guide.

Verner, D., Treguer, D., Redwood, J., Christensen, J., McDonnell, R., Elbert, C., & Konishi, Y. (2018). Climate variability, drought, and drought management in Tunisia’s agricultural sector. World Bank, Washington, DC, World Bank.
refers to either the Lerner index or to two measures of concentration: the share of assets held by the top three banks and the Herfindahl index. F and X represent firm-level (e.g., size, manufacturing, exporter, etc.) and country-level (e.g., inflation and financial development as proxied by private credit to GDP) control variables, respectively, described in the data section. We capture unobservable differences between countries by including country fixed effects (represented in equation 1 by ac) and we cluster errors at the

> 17 While in principle it would be interesting to distinguish between firms that have access to each of these types of products, unfortunately, the design of the Enterprise Surveys does not allow for this possibility.

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Figure 4. Generation of power, by fuel (1990-2014) <!-- Start of picture text --> 200,000 0.70<br>180,000<br>0.60<br>160,000<br>140,000 0.50<br>120,000 0.40<br>100,000<br>0.30<br>80,000<br>60,000 0.20<br>40,000<br>0.10<br>20,000<br>0 0.00<br>Hydro Geothermal Other renewables<br>Coal Oil Gas<br>Biofuels and waste Nuclear Total final consumption<br>Fuel type concentration index<br>GWH<br><!-- End of picture text --> Source: IEA, 2015

# _Institutions_

In general, planning and procurement of generation and transmission in the Egyptian power sector are conducted by the Ministry of Energy, EEHC, and EETC, in close coordination and reasonably in advance of the new assets being needed by the power system. The power sector saw several new laws and policies put into place in 2015 and the country is transitioning to this new framework currently. Since 2000, generation planning was done by the EEHC and the transmission planning was done by EETC. Once the plans were submitted to and approved by the Ministry of Electricity and Renewable Energy, they were presented to the cabinet of the ministers for approval and allocation of the necessary financial resources for the execution of the plan. However, the new electricity law in 2015, Law 87, assigned the planning task to the EETC (functioning post 2015 as a TSO) jointly with the Ministry of Electricity and Renewable Energy, allowing for three transitional years. The plan will be more or less indicative, with an annual report for adequacy of generation issued by the EETC and reviewed by the regulatory agency- EgyptERA. The regulator will then report to the
**Table 10a** . Prevalence of poverty at different thresholds with confidence intervals

|**Treatment**|Natio<br>( <br>|nal poverty line<br>TSh 1,876)<br>|( <br>|US$ 1.90<br>TSh 1,644)<br>|( <br>|US$ 2.15<br>TSh 1,860)<br>|( <br>|US$ 3.2<br>TSh 2.8<br>|5<br>12)<br>|
|---|---|---|---|---|---|---|---|---|---|
||**%**|**[95% C.I.]**|**%**|**[95% C.I.]**|**%**|**[95% C.I.]**|**%**|**[95%**|**C.I.]**|
|**T0**|**20.1**|[15.1 – 25.2]|**13.1**|[9.4 – 16.7]|**19.7**|[14.8 – 24.6]|**49.6**|[44.3|– 54.9]|
|**T1**|**23.9**|[19.2 – 28.6]|**17.6**|[13.7 – 21.6]|**23.0**|[18.4 – 27.
the revealed distance between any two products within the product space matrix. Calculating PATH gives an indication as to whether any given product is located in a particularly dense or sparsely populated part of the product space: if the path or product distance is short, factors of production, skills and technology can be more easily deployed from one product to another than if the product distance is long. The product distances for any pair of products are calculated using the minimum of two conditional probabilities: the probability that a country has an RCA in making product _a_ , given that it has an RCA with _b_ , and vice versa.

The product PATH is then calculated as a measure of the likelihood that countries exporting any given product are likely to export other products and thus can be seen as a notional value of the potential for future export diversification associated with any particular product. Thus, for example, we find that PATH for copper (105) is low compared to the path for footwear (149). This means that there are more opportunities to diversify into other products from footwear than from copper. Within the product space, footwear is surrounded by a much larger number of related products, while copper is located in a more sparsely populated area. As with PRODY, the values used in this paper are the 2000-2004 averages.

# **_3.2 Descriptive Statistics_**

We begin with a first look at some of the descriptive statistics emerging from our analysis of the customs data. The top section of Table 1 presents the total number of firms, products and _6_ _Exporting from a small landlocked economy_
|448|2,546|
|Dushanbe–Khorog|524|2,606|

_Source:_ International Finance Corporation and World Bank 2009; Ministry of Transport, Tajikistan. aData from _Doing Business_ 2009.

The Tajik government is considering upgrading or rehabilitating a couple of roads. The market accessibility index can provide a quick assessment of which of these projects may provide better market accessibility to domestic producers. Indeed, a road crossing settlements with better domestic market accessibility is more likely to induce more demand for transport services by local producers trying to connect to markets, thus unleashing the potential for cargo consolidation along the way. Given established increasing return to scale in transport services, such freight consolidation opportunity is vital for regional and global competitiveness of Tajikistan. Map 2 overlays some of these road projects with the market accessibility level of Tajik settlements.

As highlighted by the low market accessibility of the settlements crossed in Map 2, the following projects appear to be less urgent: the reconstruction of the Highway Aini-Panjakent-Uzbek border; the reconstruction of the Highway Obburdon-Auchi-Gonchi; and the reconstruction of the Highway KulyabKalai Khumb. However, the reconstruction of the highways Kofarnihon-Romit and Rudaki-Sultanabad-

> 7 The index is calculated using the gravity-type relation , where Ai is the accessibility measure of settlement i, Wj is the activity to be reached in settlement j, cij is the generalized cost of reaching market j from i, g(.) is called the activity function, and f(.) the impedance function. We use the population of settlements for Wj and the travel time for cij to compute the market accessibility index for all settlements with an identified population.

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# **S15.3 Varying Baseline Nighttime Light Groups**

In this section, we explore heterogeneity of impacts of changes in market access across two groups of baseline nighttime lights—Woredas with no positive nighttime lights at baseline, and Woredas with some positive nighttime lights at baseline. Tables S23 and S24 show results using Kebeles and Woredas respectively. Results are consistent to models that use four groups of nighttime lights; road upgrades are associated with growth in nighttime lights and urban land, particularly in areas with positive baseline nighttime lights. As with results using four groups of baseline nighttime lights, coefficients on models explaining urban land tend to be more significant when using Woredas as the unit of analysis.

**Table S23:** Association of changes in market access on changes in outcome variables using a long difference, OLS results [Kebeles]

||NT|L|Urb|an|Crop|land|NT|L|Urba|n|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._04<sup>_∗∗∗_</sup>|_−_0_._04<sup>_∗∗∗_</sup>|_−_0_._003<sup>_∗∗_</sup>|_−_0_._003<sup>_∗∗_</sup>|0_._003|0_._003|_−_0_._04<sup>_∗∗∗_<
first studies using this data set. However, only the last four rounds, from 1991/92 (GLSS-3) to 2012/12 (GLSS-6), have been based on the same questionnaire and are therefore fully comparable.

The availability of comparable and extensive information represents a success on its own. Ghana is one of the few countries in Africa that has produced comparable, high-quality household data covering over two decades. This is an important achievement because the availability of such rich and comparable information beginning in 1991, as well as the quality improvements of the surveys over the years and the fact that they collect data on both the monetary and the non-monetary dimensions of welfare, permit the establishment of an accurate picture of inequality and polarization over time, including the drivers behind these phenomena.

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with the specific purpose of enhancing workforce skills through `developing a comprehensive, market-driven and performance-based adult continuing education and training framework’ (Ministry of Manpower website).

While there are no official statistics for the foreign component of the workforce, the population data reveals that 19% of the 4.17 million people in Singapore are foreigners. Indeed, given the small population base in Singapore, it has always relied on foreigners to perform various jobs in the economy. Since the late 1990s, as part of its move to build up the innovative economy, it has also embarked on an aggressive “foreign talent” policy, to attract foreigners who possess the necessary skills for innovation-related, high value-added work. As the Ministry of 9
both measures of “grand” corruption – high-level corruption assessed via expert surveys – and the actual experiences of individuals and firms assessed through direct survey questions. Although evidence for a direct effect of corruption on investment is mixed (c.f. Mauro 1995; Slinko et al. 2005; Treismann 2007), an emerging body of work suggests that the predictability of corruption is critical to investment decisions (Malesky and Samphantharak 2008; Wei 1997; World Bank 1997).

Finally, several empirical studies have also connected property rights protection directly to investment, albeit mostly through the use of macro-level data (c.f. Jensen 2006; Gehlbach and Keefer 2012). Qualitative studies have shed some light on how this mechanism works within the Russian context. The practice of predatory policing and use of inconsistent property rights protections by state officials to enrich themselves was first directly analyzed by Gerber and Mendelson (2008). Subsequent work has shed light on the legal framework of such predation, as well as the ways in which private threats by firms to other firms were replaced by state threats to property rights including seizing firms' assets, illegal corporate raiding, extortion, illicit fines, and unlawful arrests of businesspeople (Firestone 2010; Gans-Morse 2012). In contrast to the purely pecuniary motives cited by traditional work on state-led expropriation, however, evidence suggests that inconsistent property rights enforcement by Russian officials can also be tied to political incentives. Analyzing 312 cases of illegal corporate raiding between 1999 and 2010 collected from Russian mass-media, Rochlitz (2014) shows that electoral victories for the ruling party are positively correlated with hostile, violent takeovers in Russia’s regions. He argues that the federal center may tolerate a certain degree of predatory activities by regional elites, as long as these elites are able to deliver a sufficiently high level of electoral support for the center. Finally, Yakovlev et al. (2014) analyzed the logic of collective action in the Russian business community to resist predatory activity by law enforcement bodies.

# **III.
# Conclusion

This paper uses comprehensive, economy-wide administrative data to assess corruption risks in North Macedonia’s public procurement and their implications for overpricing of contracts. It also looked at the networked nature of contracting risks and how some of these networks develop into state capture formations.

The analyzed data points at persistently high risks of corruption in North Macedonia, compared to EU countries. For example, the single bidding rate of North Macedonia is very similar to the Slovak Republic, Hungary and Bulgaria, but considerably higher than core Western European countries such as Germany or France. The overall corruption risk level slightly decreased throughout 2011-2022, with, for example, the single bidding rate remaining around 30%.

This paper finds a strong association between corruption risks and relative prices. The effect of the composite risk indicator, Corruption Risk Index (CRI), on relative prices is estimated to be strong, with the CRI score moving from its maximum (1) to its minimum (0) leading to 17.9% lower prices. Looking at individual risk factors points at actionable, directly influenceable levers for policy makers. Factors estimated to lead to higher prices are the type of procedure, length of advertisement period, and supplier dependence on a buyer. For instance, greater use of more competitive and open procedures or providing more time for suppliers to prepare bids are found to be associated with lower relative prices. Other factors, such as single bidding and supplier dependence on a buyer are only indirectly influenceable, but programs aimed at stimulating competition, reducing the documentation burdens on suppliers or eliminating transaction costs can create a more favorable environment for higher competition, and consequently lower the relative prices. These factors are also likely to influence prices considerably as for example single bidding is associated with a 7.2% increase in relative prices compared to multiple bidding (i.e. a tender receiving two or more bids).

Data analysis also reveals indications of state capture patterns. There are groups of suppliers and buyers with high corruption risk contracting relationships among
only information that was available to the bank at the original time of application. Because we observe the performance of loans in our data set, we can pay participants performance incentives based on their decision and the actual outcome of the loan. Our sample of loans consists of unsecured small-business working capital loans with a ticket size of less than Rs 500,000 (US$ 10,000). Sales and origination channels for this class of loans are generally distinct, so that (analogous to low-documentation loans in the United States) loans are sourced by a bank’s sales agents who collect client information, which is then forwarded to a credit officer for approval. The task faced by the bank’s credit officers is to screen and make profitable lending decisions based on the information contained in an applicant’s loan file.

The applicant information contained in the loan files is “hard” in the sense that it can be transcribed. However, borrower information differs in its degree of verifiability, and ranges from audited financials to information that requires a significant degree of interpretation, such as trade reference reports or a description of the applicant’s business (see Petersen (2004) for a discussion). Although regulators require banks to collect an applicant’s tax record and audited income statement, this information is often unreliable and difficult to verify. New applicants typically lack a credit score or established record of formal borrowing, which rules out the use of predictive credit scoring, “scorecard lending” and other more systematic loan approval technologies. Because the ticket size of a representative small business loan is small, relative to the fixed cost of underwriting, risk-management occurs primarily through ex-ante screening, rather than prohibitively costly relationship lending or ex-post monitoring.

# **4.2 Loan Database**

As a basis for the experiment, we requested a random sample of loan applications from a large commercial lender in India (hereafter “the Lender”), and received 650 loan files. The loan files contain all information available to
from 1998 data, while the program was initiated in 2004. Thus, we can consider districts to be plausibly exogenously sorted around the cutoff of 40 percent.

Identification of program effects can also be undermined if the program assignment variable and cutoff were also used for other programs. To our knowledge, no other government program, in education or in any other sector, has used district literacy rates and this program’s cutoff for assignment.

Finally, while the enrollment data in government school censuses may not be fully accurate,<sup>14</sup> we have no reason to suspect that the quality of data varies systematically between program and nonprogram observations within _WLRD_ .

# **6. Results**

_Validity of the local randomization-based RD window_ The validity of local randomization-based RD estimation requires that there be at least one covariate whose preprogram values are correlated with the program assignment variable (the district literacy rate) everywhere except within our selected window _WLRD_ around the cutoff. This test is comparable to the baseline balance test in randomized controlled trials.

We select three important school-level covariates whose preprogram values should be associated with the district literacy rate: the number of teachers, the number of classrooms, and the condition of the school building. We test the null hypothesis of no effect on the program assignment variable for each covariate. The window would be invalid if the null hypothesis is rejected for at least one covariate within it.

> 14 Reported enrollment data may not be accurate if school enrollment records are not up to date as of the reference date for the government school census or if reported enrollment data become high stakes (and thus subject to manipulation) because they are used by government education authorities for decisions affecting the interests of stakeholders in the public education sector.

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a positive effect on agricultural productivity.

> 25 Various sources of National Accounts data were used to compile longer time series, including the United Nations, World Bank, OECD, IMF, and if necessary, country-specific sources.

> 26 Data on agricultural area, agricultural labor, and fertilizers were downloaded from the FAOSTAT database.

> 27 As previously mentioned, the list of possible state variables is vast. The variables presented in this analysis were chosen based upon our priors of what are important determinants of agricultural productivity, as well as for the availability of the data. This list is not meant to be exclusive, but rather suggestive. Indeed, there must be other determinants as the state variables in our model account for about half of the country effect (Mundlak, Larson, and Butzer, 2010).

> 28 Total education data from Barro and Lee (2001) are reported for every five years up to 2000 through the World Bank website (http://go.worldbank.org/8BQASOPK40). Data for other years are obtained through linear interpolations.

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