rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4855

# **Abstract**

Limited competition has been a serious concern in infrastructure procurement. Importantly, however, there are normally a number of potential bidders initially showing interest in proposed projects. This paper focuses on tackling the question why these initially interested bidders fade out. An empirical problem is that no bids of fading-out firms are observable. They could decide not to enter the process at the beginning of the tendering or may be technically disqualified at any point in the selection process. This paper applies the double selection model to procurement data from road development projects in developing countries and examines why competition ends up restricted. It shows that bidders are self-selective and auctioneers also tend to limit participation depending on the size of contracts. Therefore, limited competition would likely lead to high infrastructure procurement costs.

This paper—a product of the Economics Team, Finance, Economics and Urban Development Department—is part of a larger effort in the department to understand and enhance efficiency and effectiveness in public procurement for infrastructure. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at aiimi@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
Table 1 depicts descriptive statistics for selected variables at baseline using data from the balanced panel of 2,382 households, including tests for covariate balancing between the three treatment groups to verify the success of the randomization process.<sup>12</sup> Overall, households in the three groups seem largely balanced on a series of socio-demographic and economic indicators. Yet, they exhibit a few differences regarding agricultural production-related characteristics, which need to be considered in the outcome estimation framework. Moreover, ISFM practices are not necessarily new to farmers, since some were used prior to the interventions, though mostly to a modest extent.

## _Table 1 about here_

# Key outcome variables

Since the key concern of this study is the increase in ISFM adoption, we measure how many ISFM practices households adopted in the preceding harvest cycle (2017), and therefore assess the _number of ISFM technologies adopted_ , ranging from zero to five. We use this variable despite the fact that lime is not relevant in one of the regions (and thus, no farmer in Tigray reaches a value of five), but provide robustness checks verifying that implications do not change, if we exclude lime and employ a zero to four measure instead. Since the complementary use of the practices is pivotal to ISFM, the second main outcome is the _integrated adoption of the full ISFM package_ , assessed with a binary variable that measures whether a farmer has used all four quickwin practices in combination on at least one plot. This variable excludes the use of lime, since adoption would otherwise always be zero in Tigray.<sup>13, 14</sup> Furthermore, the adoption of the individual ISFM quickwin components is of interest, which are _compost_ , _blended fertilizer_ ,

> 12 Table 1 shows those variables that are used as additional covariates in the adoption and knowledge regressions, Table S4.1 in Online Appendix S4 presents further balance checks on selected household, farming and community characteristics 13 To check sensitivity of the results with respect to this definition, we also use several alternative measures. Firstly, a variable that equals one when at least four out of five practices (including lime) are adopted jointly. Secondly, a measure for the joint adoption of compost, blended fertilizer and line seeding, that is, excluding improved seeds, which might possibly be concentrated around a certain crop type. And lastly, a region-specific measure that requires all five practices to be adopted in Amhara and Oromia, but only four in Tigray.

> 14 These analyses are restricted to plots planted with main crops, i.e. wheat, maize, teff, barley and sorghum, which are the main focus of the interventions.

12
# **_Introduction_**

Informality is the collection of firms, workers, and activities that operate outside the legal and regulatory frameworks.<sup>1</sup> It entails avoiding the burden of taxation and regulation but, at the same time, not fully enjoying the protection and services that the law and the state can provide. Informality is sometimes the result of agents “exiting” the formal sector as consequence of cost-benefit considerations; other times, it is the outcome of agents being “excluded” from formality as this becomes restrictive and the economy segmented.

In all cases, informality is a fundamental characteristic of underdevelopment and is best understood as a complex, multi-faceted phenomenon. It is determined both by the modes of socio-economic organization inherent to economies in the transition to modernity and by the relationship that the state establishes with private agents through regulation, monitoring, and the provision of public services. Informality is not only a reflection of underdevelopment, but may also be the source of further economic retardation. It implies misallocation of resources and entails losing the advantages of legality, such as police and judicial protection, access to formal credit institutions, and participation in international markets.

According to the estimates presented below, there is large heterogeneity in the extent of informality across countries in Latin America. In all of them, however, informality is much more widespread than in the USA, and some countries in the region are among the most informal economies in the world. The typical country in Latin America produces about 40% of GDP and employs 70% of the labor force informally. These are astounding statistics, which indicate that informality is a substantive and pervasive phenomenon that must be explained and grappled with, particularly in the design of development policies.

> 1 This definition, introduced by De Soto (1989) in his classic study of informality, has gained remarkable popularity due to its conceptual strength, which allows it to focus on the root causes of informality rather than merely its symptoms.  For an excellent review of the causes and consequences of the informal sector, see Schneider and Enste (2000).  Drawing from a public-choice approach, Gerxhani (2004) provides an interesting discussion of the differences of the informal sector in developed and developing countries.  The World Bank report by Perry et al. (2007) is the most comprehensive and in-depth study on informality in the Latin America region.

2
# **1. Introduction**

A sound business environment (BE) is increasingly recognized as a key driver for economic development (Stern 2002; World Bank 2005; Xu 2011). Governments, international organizations and think tanks have invested heavily in collecting data and finding ways to measure critical elements of the BE. The World Bank has implemented the _Doing Business (DB)_ surveys, an annual survey at the country level, and the _Enterprise Survey (ES)_ , which cover around 130 countries at the firm level. The World Economic Forum (WEF) has its own _Global Competitiveness Indices (GCI)_ , based on the Executive Opinion Survey conducted by WEF in around 140 countries, in cooperation with its partner institutes in each country (and drawing on other external data sources). These three sources of data have been used in various capacity in the literature on the effects of the business environment on firm performance and other development outcomes.

The business environment literature based on _Enterprise Survey_ data is relatively large (see, for example, Dollar, Hallward-Driemeier, and Mengistae 2005, 2006; Hallward-Driemeier, Wallsten, and Xu 2006; Cai, Fang, and Xu 2011; Xu 2011; Clarke, Qiang, and Xu 2015; Reyes, Roberts, and Xu 2017; Knack and Xu 2017). This literature has shown that firm performance depends critically on some basic elements in the business environment, such as limiting government expropriation, the provision of basic and modern infrastructure, access to finance, and agglomeration of sufficiently sized firms together _._ The literature based on the _Doing Business_ data tends to examine specific elements of the business environment one at a time, or to include the aggregate DB index (Botero et al. 2004; Djankov et al. 2002, 2003; Djankov, McLiesh, and Shleifer 2007; Djankov, Freund, and Pham 2010). There are relatively few studies based on Global Competitiveness indicators in the business environment literature, suggesting a research opportunity to exploit to yield further insights.

Each data source has its own merits and weaknesses. The _Enterprise Survey_ indicators— often measured as the average of firms’ responses in a locality (Xu 2011)—are collected every 4-5 years in a particular country and tend to capture more local variations and _de facto_ (i.e., the actual) business environment. However, they could be correlated with other local omitted variables. The _Doing Business_ indicators use a similar methodology for firms of comparable size, and are available on an annual basis. But they are largely _de jure_ (i.e., based on official rules and regulations on the books) and do not capture differences in enforcement, which have been shown to vary widely across countries (Hallward-Dreimeier and Pritchett 2015), or within a country (Cai, Fang, and Xu 2011; Goldstein and Udry 2008). The _Global Competitiveness_ 2
26 Despite these problems, there has been some success in establishing that better health contributes to higher productivity at the individual level, as the increasing availability of data sets that include information on time use of household members, as well as income and own-production and measures of health, has permitted more definitive work on the issue. In their recent review, Behrman and Deolalikar (1988) put particular weight on recent studies by Strauss (1986) using data from Sierra Leone and Deolalikar (1988) using data from south India. These two authors report positive effects of "effective family labor' (itself a function of family labor hours and measures of food availability or anthropometry) on agricultural productivity. These studies are convincing because the authors take into account the likelihood that worker health status is itself an endogenous result of prior individual choices -- and they still find separate effects of health status on labor productivity. Other recent studies, by Moock and Leslie (1986) and Jamison (1986), suggest that nutritional status of children (in particular, heightfor-age, a cumulative measure of nutritional status over time) affects school performance,31 adding to the evidence that in general better health does speed development.

Better documentation of any effects of health status on productivity and income gains could, obviously, increase the interest of policymakers concerned with economic growth, in health. It is, however, only a first step. Ideally it should be possible to compare the benefits of investments in health (as a first step at least in terms of private income gains) to the costs of the 31 Behrman and Deolalikar note that these studies may be misleading, if certain children are both healthier and do better in school, with no causal impact of the former on the latter.
in Almarj with respect to surrounding provinces might indicate that this area acts as a buffer zone separating provinces with a high concentration of migrants from areas less involved in migrant passages. By contrast, that there are more migrants in Wadi Ashshati than in all other provinces on the Libyan western border suggests that this area was a preferred point of access for migrants arriving from the western side of the country in 2017 and 2018. Likewise, the emergence of hotspots in 2018 indicates a more concentrated presence of migrants in some provinces (Ejadabia and Murzuq) and a decrease in adjacent provinces (Ghat and Tobruk). The fact that these provinces are located at the Libyan border suggests that they are used as new entering points in Libya, which is consistent with the hypothesis of the emergence of new migrant routes.

Our analysis also reveals a cluster of provinces around Tripoli hosting a large number of migrants. The level of spatial autocorrelation in the distribution of migrants in this area increased from 2017 to 2018 and became significant at the 5% level in 2018.

# _5.2.2 Migrant movements within Libya_

FMP data can be used to map the internal movements of migrants (between Libyan provinces). For each FMP, DTM data indicate which Libyan province is the most common origin for the migrants at the FMP. Using this information for all FMPs in our data set, we determine the internal movements of migrants across all Libyan provinces.

The results of the analysis for 2017 and 2018 are shown in figure 8. Capitals of provinces _i_ and _j_ are connected if there is movement from an FMP located in _i_ to an FMP located in _j_ . The size of the flow is proportional to the average number of migrants registered in the FMP.

For each year, the graphs show a dense net of connections; each province is connected to at least two other provinces, suggesting that, in general, for each province, there is not a unique migration route from or to any other province. In determining the migration route chosen, it is thus likely that individuallevel characteristics play an important role.

Comparing the two graphs, we also identify a clear change in internal migration routes between 2017 and 2018. In 2017, three major corridors run through Libya (figure 8a): the eastern route, connecting Alkufra, Ejdabia, and Benghazi; the central route, from Murzuq to Tripoli; and the western corridor, from Ghat to Tripoli. The figure also shows that the second route was the most used at the time and that the eastern and central routes are less clearly recognizable in 2018 (figure 8b). This might be in part because of a general reduction in the number of migrants in the provinces, as the decrease in the width of the network linkages suggests, although it also points to a reorganization that occurred along the corridors, with migrants more evenly distributed over all paths.

12
- C. Rosenzweig (eds), Handbook of Climate Change and Agroecosystems, Imperial College Press, pp. 3751.

- Chape, S., Harrison, J., Spalding, M. and Lysenko, I.: 2005, Measuring the Extent and Eectiveness of Protected Areas as an Indicator for Meeting Global Biodiversity Targets, Philosophical Transactions of the Royal Society B: Biological Sciences 360(1454), 443455.

- Costanza, R., d'Arge, R., De Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S., O'Neill, R., Paruelo, J. et al.: 1997, The Value of the World's Ecosystem Services and Natural Capital, Nature 387(6630), 253260.

- Craneld, J., Eales, J., Hertel, T. and Preckel, P.: 2003, Model Selection when Estimating and Predicting Consumer Demands using International, Cross Section Data, Empirical Economics 28(2), 353364.

- Creutzig, F., Popp, A., Plevin, R., Luderer, G., Minx, J. and Edenhofer, O.: 2012, Reconciling Top-down and Bottom-up Modelling on Future Bioenergy Deployment, Nature Climate Change 2(5), 320327.

- Cubbage, F., Koesbandana, S., Mac Donagh, P., Rubilar, R., Balmelli, G., Olmos, V., De La Torre, R., Murara, M., Hoeich, V., Kotze, H. et al.: 2010, Global Timber Investments, Wood Costs, Regulation, and Risk, Biomass and Bioenergy 34(12), 16671678.

- Dunn, J., Eason, J. and Wang, M.: 2011, Updated Sugarcane and Switchgrass Parameters in the GREET Model, Technical report, Center for Transportation Research, Argonne National Laboratory.

- Ehrlich, P. and Mooney, H.: 1983, Extinction, Substitution, and Ecosystem Services, BioScience pp. 248254.

- EIA: 2010a, Annual Energy Outlook, Publication DOE/EIA-0383, U.S. Department of Energy.

- EIA: 2010b, International Energy Outlook, Publication DOE/EIA-0484, U.S. Department of Energy.

- Evans, L. and Fischer, R.: 1999, Yield Potential: its Denition, Measurement, and Signicance, Crop Science 39(6), 15441551.

- FAO: 2010, Global Forest Resources Assessment 2010, FAO Forestry Paper 163, Food and Agriculture Organization of the United Nations.

36
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number of lagged links and the probability of exit remains significant when excluding the sales controls, as detailed in column 9 of Table 1.<sup>24</sup> This robustness check, alongside the graphical evidence presented in Figure 8, illustrates the enduring impact of linkage density on firm longevity.

Table 2: Firm Linka es and Exit Probabilit : Alternative S <u>g y pecifications</u>

|||||Ex|it||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|log owner ties (t-1)|0.011<br>(0.098)|0.042<br>(0.091)|0.042<br>(0.091)||||||
|log wgt. degree (t-1)||-0.300<sup>_∗∗∗_</sup><br>(0.100)|-0.282<sup>_∗∗∗_</sup><br>(0.106)|-0.288<sup>_∗∗∗_</sup><br>(0.093)|||||
|has owner ties to agri (t-1)||||-0.046<br>(0.303)|||||
|log unwgt. degree (t-1)|||||-0.463<sup>_∗∗∗_</sup><br>(0.154)|-0.453<sup>_∗∗∗_</sup><br>(0.156)|||
|log wgt. degree (t-2)|||||||-0.257<sup>_∗∗_</sup><br>(0.106)|-0.242<sup>_∗∗_</sup><br>(0.107)|
|log sales (t-1)|-0.172<sup>_∗∗∗_</sup><br>(0.055)|-0.171<sup>_∗∗∗_</sup><br>(0.056)|-0.172<sup>_∗∗∗_</sup><br>(0.054)|-0.129<sup>_∗∗∗_</sup><br>(0.047)|-0.110<sup>_∗∗_</sup><br>(0.044)|-0.112<sup>_∗∗∗_</sup><br>(0.042)|||
|log sales (t-2)|||||||-0.137<sup>_∗∗∗_</sup><br>(0.048)|-0.136<sup>_∗∗∗_</sup><br>(0.047)|
|Observations|16532|16532|16532|18488|18488|18488|15168|15168|
|Year FE|||✓|✓||✓||✓|
|Num exiters|52|52|52|63|63|63|50|50|

**Note:** This table illustrates the relationship between exit probability and firm linkages. Exiting firms are entities which are not observed in any subsequent year during our sample period. All columns include a control for log pre-period annual revenue, measured in constant 2020 U.S. dollars. Columns (7) to (8) use the two-period lag. Columns (3), (4), (6), and (8) include year fixed effects. Column (1)-(3) test for a relation between the number of ownership-connected firms and firm exit, including the variable for supply chain linkages from column (2). Column (4) replaces the log owner ties variable with a dummy equal to one if a firm has ownership linkages to an agribusiness. Columns (5) and (6) replace the weighted degree measure with the unweighted degree count, i.e. a simple dummy for two firms being connected in a given period. Columns (7) and (8) show the specification with two rather than one lags. Robust standard errors are applied.

Overall, our findings highlight the importance of a well-connected network in mitigating the risks associated with potential industry exit, providing essential insights for firms

> 24We observe a total of 28 extinct exiters without surviving ultimate parent. This number reduces to 26 when considering lagged links. Only three of these firms have available sales data, limiting the feasibility of statistical inference.

27
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 5 comparability is hampered by inter-country differences in defining muncipios or equivalent lower-level administrative districts. For instance, the GPW3 data for Peru is based on 1895 third-level units, Bolivia on just 100 second-level. All intercountry comparisons should be approached with caution.

||**Landscan**|**GPW 3**|**GRUMP v1**|
|---|---|---|---|
|**Produced by**|Oak<br>Ridge<br>National<br>Laboratory|CIESIN and CIAT|CIESIN,<br>IFPRI,<br>World<br>Bank,<br>and<br>CIAT|
|**Nominal year of**<br>**data**|2002|2000|2000|
|**Grid cell size**|0.5’|2.5’|1 km|
|**Typical level of**<br>**unit for which**<br>**input**<br>**census**<br>**data**<br>**was**<br>**obtained**|province/state|municipio/county,<br>varies by country|municipio/county|
|**Within-unit**<br>**allocation**<br>**of**<br>**population**|model based on road<br>proximity, slope, land<br>cover, urban lights|homogeneous|reported<br>urban<br>population allocated<br>to cells of known<br>light areas and used<br>as<br>a<br>means<br>for<br>reallocating<br>population estimates<br>of<br>administrative<br>areas|

# **Accessibility**

We used an accessibility map constructed by Andrew Nelson, based on the DCW (Digital Chart of the World). This was the only available source of pan-continental road data, and is based on old and possibly inconsistent data. We arbitrarily assigned travel speeds of 10km/hr to 60 km/hr to roads of increasing quality, and 4 km/hr to off-road travel. These speed estimates were used to compute an accessibility map depicting the minimum travel time in hours, and distance in kilometers, along the fastest path, from each point to the nearest city of 100,000 or more (according to the DCW). Although our travel speed estimates are arbitrary, they are arguably better than an assumption of constant travel speed regardless of terrain. The resultant map (figure 2) appears to underestimate current accessibility in parts of the Brazilian Amazon. We emphasize that this map should be viewed only as a crude index rather than a literal measure of travel time, and stress the need for accurate, consistent national and continental maps of transportation networks.