rafmacalaba/data-use-mentions-extended huggingface dataset:
POLICY RESEARCH W7ORKING PAPER 1916

# Summary findings

The explosion of informal entrepreneurial activity during Mongolia's transition to a market economy represents one of the most visible signs of change in this expansive but sparsely populated Asian country.

To deepen our understanding of Mongolia's informal sector during the transition, Anderson merges anecdotal experience from qualitative interviews with hard data from a survey of 770 informals in Ulaanbaatar, from a national household survey, and from official employment statistics.

Using varied sources, Anderson generates rudimentary estimates of the magnitude of, and trends in, informal actiity in Mongolia, estmates that are surprisingly consistent with each other.

He evaluates four types of reasons for the burst of informal activity in MIorgolia since 1990: * The crisis _oI_ tne early and mid-1990s, during which large pools of labor were released from formal employment.

- Rural to urba.n migration.

- The "market's" reallocation of resources toward areas neglected under the old system: services such as distribution and transportation.

**e** The institutional environments faced by the formal and informal sectors: hindering growth of the formal sector, facilitating entry for the informal sector.

Formal labor rrmarkets haven't absorbed the labor made available by the crisis and by migration and haven't fully responded to the demand for new services. The relative ease of entering the informal market explains that market's great expansion.

The relative difficulty of entering formal markets is not random but is driven by policy. Improving policies in the forrnal sector could afford the same ease of entry there as is currently being experienced in the informal sector.

_-_ This paper a product of the Development Research Group and the South East Asia and Mongolia Country Unit, East Asia and Pacific - is part of a larger program of research on the impact of institutional changes in Mongolia, and on the rule of law in transition economies. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Paulina Sintim-Aboagye, room
0.06|
|Child Labor|0.59|0.61|1.00|0.25|0.10|0.01|0.21|
|Relative underpayment|0.37|0.88|0.25|1.00|0.01|-0.01|0.01|
|Compensatory moonlighting|-0.09|0.08|0.10|0.01|1.00|-0.02|0.05|
|Labor stability|0.06|-0.03|0.01|-0.01|-0.02|1.00|0.05|
|Incidence of overqualifcation|-0.00|0.06|0.21|0.01|0.05|0.05|1.00|

_Notes:_ Based on data from EH 2007-2021.

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- _“Corrupt implementing firms always need corrupt consultants. And both are related to a corrupt official. Always!”_

CEO of a major Lebanese infrastructure development firm

# **1. Introduction**

Two broad observations motivate our study. First, public infrastructure procurement constitutes a major source of rents for elites, notably in countries with weak governance and control of corruption (Bosio _et al._ , 2022; Dávid-Barrett and Fazekas, 2020). As spending on procurement accounts for 12.6% of gross domestic product (GDP) in high-income countries and 13.6% in upper-middle-income countries on average (in 2015) (Djankov, Islam and Saliola, 2016), public procurement offers ample incentives for elites to interfere, even in countries with strong institutions and control of corruption (Goldman, Rocholl and So, 2013; Hessami, 2014). As such a high share of spending makes procurement a crucial part of government operations and for pursuing most development outcomes (Fazekas and Blum, 2021), even small improvements in procurement practices can have large welfare effects.

Second, an increasing body of research highlights the importance of networks, or cartels, to understand corruption in public procurement (Adam _et al._ , 2022; Fazekas, Sberna and Vannucci, 2022), including for infrastructure (Hudon and Garzón, 2016). While corruption was previously conceived mostly as a principal-agent problem (Ugur and Dasgupta, 2011), recent studies increasingly conceptualize corruption leveraging network theory (Marquette and Peiffer, 2018). Most of these studies, however, focus on the functionality of cartels, rather than their mechanisms (Fazekas, Sberna and Vannucci, 2022), even though analyzing the governance of such networks is crucial to understand their persistence and how to undermine them (Sberna, 2014).

In this paper, we explore the mechanisms of cartels in Lebanon’s infrastructure procurement sector. We analyze a data set of all 394 infrastructure procurement contracts awarded between 2008 and 2018 by Lebanon’s Council for
**Figure 4.2:  Growth of Commercial Bank Lending Against Personal and Household Access to Loans**

**Figure 4.3:  Households that Took Out Loans 2000/01-2007**

<!-- Start of picture text --> Commercial Bank Lending to Personal 2001 - 2008 Households that took Out Loans<br>9,000.00 30.0%<br>6<br>8,000.00<br>25.0%<br>7,000.00 5<br>6,000.00 20.0%<br>4<br>Urban<br>5,000.00<br>4,000.00 15.0% 3 y = 2.1x - 1.5 Rural<br>3,000.00 10.0% 2 Tanzania<br>2,000.00 Linear (Tanzania)<br>5.0% 1<br>1,000.00<br>- 2.13 0.0% 0<br>00/01 2007<br>% Households<br>In Tshs 10 bn (2001 Prices)<br>% of Total Private Sector Lending<br>2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source: Bank of Tanzania, 2009 Source: Household Budget Survey, 2007_ 47. The ILFS 2006 data corroborate the finding that access to finance by HEs (especially those in the rural areas) are severely limited. Only 7 percent of operators with HEs as their main activity reported having received credit from anyone – bank or relative - in the last 12 months, against 13 percent of microenterprise owners. Only 9 percent of all households had access to formal credit, most of whom are urban households.

48. Of the HEs who received credit, 48 percent obtained it from relatives, compared to 22 percent of microenterprises. While borrowing
from the heterogeneous IV analysis based on estimating equation (8). The top row denotes the source of heterogeneity _Hc_ , respectively across columns: a dummy for small population size (below 40 million); the Chinn-Ito index of capital account openness (Chinn and Ito, 2006), which is a continuous variable between 0 and 1; and, a dummy indicator for the post-1995 period. Across Panels, we estimate the effects of trade on the statutory corporate income tax rate (Panel A), the effective tax rate on capital (Panel B), and the effective tax rate of labor (Panel C). At the bottom of each panel, we report the coefficient on trade (and standard error) in the heterogeneity sub-sample as the linear combination of the coefficients on _Trade_ and _Trade ∗ Hc_ . Trade is measured as the sum of export and imports divided by net domestic product. We instrument for trade using the oil-price and the gravity-instruments from Egger, Nigai, and Strecker (2019). All estimates include country and year fixed effects and observations are weighted by net domestic product in constant 2019 USD at PPP. For more details, see Section 7. * p<0.10 ** p<0.05 *** p<0.01. Standard errors in parentheses are clustered at the country level.

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In half of the schools the investment in the group account was explained by using an example relating to the education sector and in the other half it was explained by using an example relating to farming.<sup>22</sup>

# **4 A first look at the data**

# **4.1 Parent characteristics**

Table 1 summarizes the characteristics of the 1800 parents randomly selected to participate in the 2009 School Stakeholder Survey and the characteristics of the Albanians selected to participate in the 2008 Albanian Living Standards Measurement Survey.<sup>23</sup> Demographics such as years of schooling (for the LSMS data we took the years of education of the adult population) and age are strikingly similar across the two samples, with educational attainment averaging approximately 11 years and an average age of 40 years in the school-level survey and 44 in the household survey. While the LSMS provides a measure of yearly household income, the School Stakeholder Survey does not. Therefore, following Filmer and Pritchett (2001), we constructed an index of household socio-economic status based on parents‟ answers to 14 questions about ownership of consumer durables, such as washing machines, refrigerators, ovens, cars, and mobile phones. We conducted a principal factor analysis on the answers to the 14 questions;<sup>24</sup> Table 1 reports the resulting first factor, which we refer to as a household wealth indicator.

The second panel of Table 1 reports a number of survey-based measures of social capital. With the exception of membership in formal organizations and generalized trust, our measures of social capital are comparable to those obtained by the 2008 LSMS. Note that only 13% of the parents in our sample think that “most people could be trusted”, and on average, only 6% of the surveyed parents are relatives or close friends of each other, although there seems to be quite a large variation in social ties in the different schools. The third panel of Table 2 shows that
most important type of product market reform measured by its contribution to aggregate employment. The average level of reforms reducing entry barriers between 1998–2013, as measured by the OECD PMR indicators, is associated with a statistically significant reduction in the unemployment rate by 1.4 percentage points. In contrast, reducing barriers to trade and investment and lessening state control play a much smaller role. Bassanini (2015) discusses the effect of lowering entry barriers on industry employment in the short term and focuses his analysis on three network industries in 23 OECD countries. He stresses that these sectors are characterized by large incumbent firms, which are more likely to immediately engage in reorganization and reduction of overstaffing after deregulation than other industries, which are dominated by small firms. This mechanism can be thought of as incumbents’ attempt to achieve entry-deterrence, making their production more cost-efficient before the entry of new competitors. By estimating local projection models, Bassanini (2015) indeed finds non-negligible negative short-term effects on employment. A decrease in the entry barriers proxy of 1 point, a relatively large change only observed in about 5 percent of the sample, leads to a decrease in industry employment of 0.6–2.5 percent. This effect vanishes only 4 years after the reform. The initial decrease in employment is accompanied by a rapid fall in prices by 0.6 percent and an increase in labor productivity by 1.7 percent.

In what follows, we outline the implications that some explicit policies hindering firm entry have on competition and labor market outcomes. Bertrand and Kramarz (2002) exploit a quasi-random variation in the entry regulation on the French retail market, finding evidence that stronger entry deterrence reduces employment growth. Exploiting variation in approval rates of new retail business registrations by regional zoning boards, they find evidence for increased concentration and prices charged by retailers in more regulated areas. In a counterfactual exercise, they show that moving a department from the first quartile of the approval rate distribution (about 30 percent) to the third quartile (about
_Conclusion_ In this paper we have documented striking cross-country patterns in education enrollment and attainment in 35 countries.

While many others have examined the differences in enrollment rate behavior between the rich and poor, a major advantage of this analysis is that the data are comparable. The attainment data are derived consistently and, while the levels of the asset index are not directly comparable across countries, they are derived using an identical methodology.

There are two overall conclusions that emerges from these results. First, many (if not most) countries the bulk of the deficit from universal enrollment up to primary (or basic) comes from the poor. The achievement of higher levels of enrollment for this group is an exercise in social inclusion, reaching out and bringing the poorest into what is already the norm for the rich and, in many cases, the middle class. Second, the evidence suggests that, except in the very poorest settings, the key to closing wealth gaps in enrollment and attainment will require actions which raise the demand for schooling of the poor. Raising the _quality_ of schooling received at the primary level is likely to be the key ingredient to attract and retain poor children in school.

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# **4. The Organization of the Supervisory Authority and Framework**

_Independence as the Most Important Prerequisite_ The safety net is importantly determined by the regulatory framework which includes the supervisory authority. International experience, as embodied in the 25 Core principles, suggests that limiting the safety net requires a proper functioning of the supervisory authority, which in turn needs to be insulated against political pressures. Independence of the supervisory authority has in many developing countries been at the core of banking system and other financial crises. Thus, the supervisor need to reside in an agency with a high degree of independence from political interference. In most countries, supervision of credit institutions is part of the central bank, which is normally also the most independent agency. This arrangement is, however, not used everywhere. In about one-third of 70 countries reviewed, supervisory functions of credit institutions are conducted in agencies separate from the central bank (See Annex Table 3). Securities markets are generally supervised through specialized institutions. Regardless of institutional arrangement, the independence of the supervisor and its regulatory functions is key to ensure a proper functioning of the supervisory authority. And adequate information sharing between the monetary authority which has access to bank data and the supervisory authority is necessary.

# _The Institutional Structure of Supervision ofDifferent Financial Services_

The preferred institutional structure for regulation and supervision of different types of financial services involves an evaluation of many issues. These include the accountability of regulatory agencies, the direct costs of agencies, the merit of a degree of competition in regulation, costs imposed on regulated firms by multiple versus one regulator, possible impairment of innovation, regulatory capture, etc.

Countries have responded differently to the associated tradeoffs, and are using a variety of institutional structures. In terms of division of responsibility, for example, out of 70 countries surveyed by the Institute of International Bankers in 1998, 44 use specialist agencies for banking, securities markets and insurance, 19 combine two areas and 7 combine all three areas. Two criteria seem particularly important in determining institutional
At the time of closing the program (February 1997), only four firms had "graduated": two were privatized in 1996, while another two were liquidated. Another 13 enterprises from the program were closed down by the new government in August, 1997. This closure was not related to the isolation program, as some other companies (not on the isolation list) were also closed. The program did not have a significant impact on managerial turnover: during the implementation of the program only seven (of 147) CEOs were fired. Although these outcomes were hardly what the architects of the isolation program had in mind, perhaps the program was effective in operational turn-around and eliminating the dependency of isolated firms on the state budget. We consider the empirical evidence in the following sections.

# **IV. The Data**

I have firm-level data (balance sheet and profit and loss statements) for 1992-96 obtained from the Romanian Statistical Office. The data are annual observations and cover all firms which were registered as state-owned enterprises in 1992. If some plants are owned by the same parent company, this relationship is accounted for in the data. Overall, more than 8,000 enterprises are included. From those, I select two groups of firms. The first set contains 146 firms from the isolation program, identified by firm numbers - one utility company was excluded from the study for lack of accounting data. The second set contains a control group of 146 firms, matched one for one with enterprises in the isolation group. Within each sector, I chose firms in the control group which had the most similar size (measured by number of employees), profitability, and subsidies (as measured by their share in total revenues) in 1992 to corresponding firms in the isolation program. Firms come from nine sectors. This algorithm resulted in a distribution of firms across sectors (Table 1) which is identical between the focus and control groups. The large share of agricultural firms is due to the inclusion of pig and
etc.). However, the model assumption of proportional hazard is not satisfied. The main issue at this stage is that CDT data population presents discrete variability patterns that are hardly modeled by parametric or semi-parametric methods.

Stratification can be used for a better fit of parametric or semi-parametric models. Moreover, other treatments (e.g. using time dependent covariate (Collett 2003)) can be applied.

# **Parametric fit using discrete models: Logistic regression**

Continuous models being unsuccessful in modeling CDT data, discrete analysis of CDT is attempted. The objective is to identify which are the most significant determinants of very long and abnormal CDT in the list of variables identified in earlier sections. We transform first the CDT into three discrete levels (categories); (1) CDT less than or equal to 30 days, (2) CDT between 31 and 90 days, and (3) CDT greater than 90 days. An (Ordinal/Multinomial) Logistic regression model is then fitted with the CDT as categorical dependent variable (see Annex for details).

# **Interpretation of modeling results**

Container type is significant in both sub-models (level 2 vs. level 1 and level 3 vs. level 1). This demonstrates that “last-trip” containers, i.e. those containers that are purchased with cargo at a negotiated rate with shipping line (about $2,000 for a twenty feet container) are expected to stay longer in the port with a significant confidence level.

Last ports of call are also significant in both models. Cargos originating from Dubai for example are likely to stay longer as compared with other ports with a justification that needs to be further investigated.

Fiscal regime is also significant: Containers with finished goods and semi-finished goods are expected to have longer dwell time as compared to other categories of goods, which is probably to be linked to the high cost of customs duties that need to be paid as compared to lower fiscal pressure for raw materials for example.

Containers
***|
||(0.0327)|(0.1153)|(0.1204)|(0.0956)|(0.0812)|(0.0836)|(0.1099)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.2226***|3.0168***|1.6135***|2.3383***|4.5253***|2.5174***|2.8994***|
||(0.0626)|(0.3755)|(0.2625)|(0.3108)|(0.3011)|(0.2823)|(0.2736)|
|Observations|54,870|54,870|54,870|54,870|54,870|54,870|54,870|
|R-squared|0.352|0.118|0.081|0.155|0.246|0.132|0.098|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

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Table 5 shows that the 1,600 households in the 1988 CIISS consisted of 9,860 people, of which 5,310 (i.e. 54%) were children or adolescents. This high percentage is the result, of course, of the high population growth rate (3%) in the country. Of these children, 3,897 (73%) were children of the head of household, while the others were children of other members of the household or of nonmembers. This reflects the fact that extended households as well as the practice of child fostering are common in Cote d'Ivoire.

For our purposes, we have used an age cut-off of 7 years, at which point all children should legally be in school. This gives an effective sample for analysis of 2,828 children.<sup>2</sup> Table 5. The 1988 CILSS S___

||Urban|Rural|All|
|---|---|---|---|
|Households|624|976|1,600|
|Individuals|3,820|6,040|9,860|
|Children 0-17 years|2,093|3,217|5,310|
|<br>Children of heads of household 0-17 years|1,538|2,359|3,897|
|<br>Children 7-17 years|1,177|1,650|2,828|
|<br>Children of heads of household 7-17 years|795|1,232|2,028|

> 2 The analysis of the CILSS data requires the use of sampling weights to reflect varying sampling probabilities. All results in this paper use these weights. The construction and use of these weights is explained in Demery and Grootaert (1993).

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in each location. In 2000, the Population Census also reported other variables such as household income and job characteristics the week before the census interview. Moreover, I use the 2015 Intercensal Survey that provides information on the workplace, residence, and transportation mode of formal and informal workers at the municipality/locality level. This data allows me to observe commuting flows in Mexico City for each sector. I also use the 2017 Origin-Destination Survey collected in the commuting zone area of Mexico City. I use this data for two purposes. First, I infer trade flows across the city using trips to restaurants and other types of shops at different hours of the day. Second, I discuss some motivational findings on commuting patterns.

Finally, I complement my results with standard household survey data from the Encuesta Nacional de Ocupación y Empleo (ENOE). I calibrate some of the parameters of the model using this data.

> 17For the Economic Census, I observe data for periods before and after the transit shock, which allows me to test for parallel trends in my main specification.

9
coordinate efforts to improve security (which means coordinating with security operations) with projects to improve service delivery.<sup>3</sup>

# Data

Summary statistics for our data sources are provided in Appendix Table A1.

## Poverty Data

Throughout this report, we employ three poverty data sets to explore the relationship between poverty and conflict. The first data set is an unbalanced panel of the poverty headcount ratio at $1.90 a day (2011 PPP) at the country level (PovcalNet) from the World Bank. The second data set is the Global Subnational Atlas of Poverty (GSAP). It provides a cross-section of international poverty measures circa 2013 at the subnational level. The third data set we rely on is a short panel from Uganda in 2012 and 2016. This panel consists of around 1,500 sub-counties in the country.

## Conflict Data

Two conflict databases are used in the study. The Georeferenced Event Dataset (GED) Global version 19.1 from the Uppsala Conflict Data Programme (UCDP), henceforth called UCDP, and the Armed Conflict Location & Event Data Project (ACLED) are databases of geolocated conflict events around the world with a number of fatalities. The two data sets are different in terms of coverage and data collecting process. The UCDP has larger coverage with more countries so it delivers a broader illustration of the global situation between 1989-2018 when we implement the cross-country study. But UCDP’s different inclusion criteria, such that a conflict dyad is only coded once it crosses the 25 battle-related deaths threshold in a given year, makes it understate fatalities from conflict in many areas (see Raleigh and Kishi 2019 comparing the two data sets in detail). When we analyze the withincountry relationship between poverty and conflict, ACLED provides a very rich set of data and we therefore show results for our within-country case study and the African continent using the ACLED data.

# Empirical Relationship between Conflict and Poverty

In what follows we will take an agnostic view regarding the channel through which the