rafmacalaba/datause-encoder-data huggingface dataset:
well as from reciprocity. Similarly, evidence for Romania shows that incumbents gained political support through a program aimed at helping poor families purchase a computer (Pop-Eleches and Pop-Eleches, 2009).

Using administrative data at the municipality level, analysis of Mexico’s _Oportunidades_ CCT suggests that turnout and incumbent vote share increased in villages that were randomly assigned to receive the program (De La O, 2009). Previous work for Colombia’s _Familias en Acci ́on_ (Nupia 2012) point to results in the same direction. The larger beneficiary rates in regions where Uribe won by more than 50% of the vote in 2002 are associated with an increase in the proportion of votes that go to the incumbent governing coalition; there is no statistically significant effect for swing regions.<sup>3</sup> Conditional and unconditional correlation analysis for Brazil suggests that the coverage of _Bolsa Familia_ , a large scale CCT program, is associated with Lula’s vote share when he sought a second term (Zucco, 2008).

Addressing causality between social policy and political participation and preferences is empirically challenging as it requires an exogenous source of variation in social transfer receipt and rich data on voting behavior. This paper exploits discontinuities in program eligibility and variation in participation across voting booths to estimate the effect of enrollment in _Familias en Acci ́on_ (FA), a Colombian CCT program, on the intent to vote, turn out and on electoral choice during the 2010 Colombian presidential election. The use of detailed data at the individual and voting booth levels together with the identification strategies (discussed in more detailed below) allow us to control for possible confounders such as policy endogeneity, individual selection in the take-up and reverse causality. Additionally, unlike other studies we use actual voting outcome data, as opposed to self-reported data which could suffer from reporting bias. We provide evidence that relative to non-participants, FA beneficiaries of voting age are 1.6-2.5 percentage

> 3This paper relies on variation at the municipality level. However, there are concerns that identification strategy (concentration of swing voters and loyal voters) may violate the necessary exclusion restriction. For instance, loyal voters’ municipalities could have been strategically targeted by politicians when the program was expanded.

4
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have few, they are less likely to obtain a loan. Another argument is that they tend to run business in sectors with fewer market growth opportunities so it may be optimal for both lenders and entrepreneurs (Oser et al., 2006).

If women entrepreneurs are less growth oriented or run firms at lower capacity, this might also explain the lower supply of finance from the bank perspective. Several studies using US and UK data have found that women entrepreneurs are more likely than men to be working part-time. This is shown with data on both self-employed individuals (Devine, 1999a, 1999b) and firms (Loscocco and Robinson, 1991). For example, Census data collected in 1982 show that while 60% of women owners worked fewer than 29 hours per week, only 41 % of their male counterparts did so. Loscocco and Robinson (1991) report that only 27% of the women in their sample of small business devoted over 40 hours per week to business compared to 48% of the men.

However, is the lower access to loans explained by demand side behavior, such as lower applications to banks by women because they are more risk averse or for other reasons? Some papers show that women tend to have higher risk aversion (Jianakopolos and Bernasek 1998, Barber and Oden 2001, Dohmen et al 2005). These differences could have important implications for business performance if higher risk aversion leads women to restrict investment in their business ventures. The Global Entrepreneurship Report 2005 found that fear of failure is significantly higher for women than men in middle income countries (Minniti et al 2005). In addition, Kepler and Shane (2007) claim that male nascent entrepreneurs examine more ideas and gather more information while pursuing a new start-up than female nascent entrepreneurs. Do women use less external finance and depend more on own reinvesting own profits? There is some evidence to suggest that men re-invest a larger share of profits generated back into their business (Grasmuck and Espinal, 2000). See also Kim (2006) for US; Carter and Rosa (1998) for similar evidence in the UK.

# 3. **Data**

In this paper we use the 2005 _Business Environment and Enterprise Performance Survey_ (BEEPS) data, produced by the World Bank and the European Bank for Reconstruction and Development (EBRD). This dataset provides detailed firm level data for 26 post-socialist economies: 15 from Central and Eastern Europe (CEE) and 11 from 8
Policy Research Working Paper 11007

# **Abstract**

Public procurement in North Macedonia amounted to 16 percent of total government expenditure in 2018, or 5 percent of gross domestic product. The country’s public procurement is also vulnerable to corruption risks, which typically push prices up, leading to overspending. To support better budget policies, this paper maps corruption and state capture risks using administrative data on public procurement and examines their impact on prices. The analysis finds that overall corruption risk decreased slightly in 2011–22, with, for example, the single bidding rate remaining around 30 percent. Estimated overpricing at the contracting stage due to corruption risks ranged between 5 and 6 percent of spending throughout the period. The highest savings potentials are presented by lowering single bidding, increasing the use of a simplified open procedure, and advertising tendering opportunities longer. Network analysis of contracting relationships reveals that the 2017 change in government weakened the centrality of high corruption risk groups of organizations, leading to an overall higher integrity core.

This paper is a product of the Economic Policy Global Department—Trade. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at FazekasM@ceu.edu.

_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
scenario is conducted along with the South Asian regional trade facilitation scenario<sup>20</sup> . The GTAP results suggest that SAFTA with MFN scenario would lead to higher welfare gains for Pakistan and India than a scenario of SAFTA without MFN. However, when a South Asian trade facilitation scenario is added, such gains become much larger. The SAFTA scenario (with or without MFN) would however lead to some welfare loss for Bangladesh because of larger trade diversion effect than trade creation effect (Raihan, 2012).

**The MFN scenario would lead to larger positive effects on Pakistan’s imports and exports under SAFTA** (Table 8). Without MFN status SAFTA would lead to rise in imports of Pakistan from India by 53 percent, whereas with MFN status the import from India under SAFTA would increase by 75 percent. When trade facilitation is added to the second scenario such rise in imports from India would be 256 percent. Under the first scenario, Pakistan’s exports to India would rise by 523 percent, and the SAFTA plus MFN scenario would increase such exports by 525 percent, indicating that the MFN would not have much impact on rise in exports from Pakistan to India. However, under the SAFTA with MFN plus trade facilitation scenario such exports would rise by a staggering 1112 percent. Pakistan’s total exports would rise by 11.33 percent, which would be much higher than those under the other two scenarios.

**Table 8: Impact on Pakistan’s Import and Export under SAFTA Scenarios** <u>(% change from base)</u>

||**SAFTAwi**|**thout MFN**|**SAFTAw**|**ith MFN**|**SAFTA+**|**MFN+STF**|
|---|---|---|---|---|---|---|
||**% change in**<br>**import from**|**% change in**<br>**export to**|**% change in**<br>**import from**|**% change in**<br>**export to**|**% change in**<br>**import from**|**% change in**<br>**export to**|
|Bangladesh|37.05|130.46|36.81|130.97|181.32|403.29|
|India|53.04|523.10|75.04|524.96|256.87|1111.89|
|Nepal|-8.33|18.75|-8.33|18.75|116.00|176.08|
|Sri Lanka|33.38|39.08|30.62|39.44|129.62|128.91|
|Rest of South Asia|44.98|19.39|44.28|19.57|191.50|107.54|
|China|-0.06|-7.90|-1.08|-7.40|-7.18|-17.77|
|USA|0.96|-7.04|0.52|-6.89|-1.71|-17.04|
|EU_25|0.91|-7.44|0.26|-7.27|-1.54|-17.98|
|Rest of the World|0.00|-7.73|-1.05|-7.50|-6.66|-17.96|
|**Total **|**3.09**|**3.97 **|**3.29**|**4.21**|**10.50**|**11.33**|

Note: MFN = Most Favored Nation; STF = Trade facilitation among SAFTA member states Source: GTAP simulation

> 20 One of the direct outcomes of trade facilitation is the rise in trade flows due to fall in costs and/or time of transportation. In this study under the regional trade facilitation scenario, we assume that the price of imported goods of the regional trade among the South Asian countries is reduced by 25 percent due to improvement of trade facilitation measures. In technical term, the “ams” - import-augmenting "technical change" in the Armington nest in the GTAP model (which can be used to lower the effective price of imported products) is shocked.

16
Table 10. Labor Targeting and Privatization Prices

# Dependent variable is net privatization price/sales

|Variables||||Two-Step pr|ocedure|
|---|---|---|---|---|---|
||OLS|TOBIT||OLS|TOBIT|
||(fl|(2)||(3)|(4)|
|**_1.-_**_Firm_**_andprivtiazion_**_characterisdes:_||||||
|Nettotalliabilities|-0.0891|-0.0926|i|-0.0932|-0.0952|
||(0.040)|(0.043)||(0.080)|(0.069)|
|ShareSold|-0.004|-0.0038|**b**|-0.0038|**b**<br>-0.0035|
||(0.002)|(0.002)||(0.002)|(0.002)|
|Foreignparticipation|0.1506|0.1652|'|0.142|'<br>0.1564|
||(0.032)|(0.028)||(0.031)|(0.028)|
|Public offering|0.0863|**b**<br>0.1289||0.0971|b<br>0.1407|
||(0.044)|(0.040)||(0.041)|(0.041)|
|DirectSale|-0.0029|0.0462||0.0086|0.0585|
||(0.047)|(0.045)||(0.046)|(0.045)|
|_2.-_**_Labor_**_Characteristics:_||||||
|Unions|-0.1321|-0.109||-0.1784|-0.1537|
||(0.036)|(0.038)||(0.036)|(0.037)|
|Strikes|-0.0041|-0.0128||0.009|0.0001|
||(0.027)|(0.026)||(0.054)|(0.050)|
|**_3.-LaborPolicies:_**||||||
|Age-biaseddownsizing|-0.0832|-0.0843||-0.0833|'<br>-0.0823|
||(0.026)|(0.026)||(0.029)|(0.030)|
|Skill-biased downsizing|0.0161|0.0039||0.1616|0.1532|
||(0.036)|(0.037)||(0.097)|(0.086)|
|Fetrale-biaseddownsicing|0.0082|0.0274||0.0170|0.0115|
||(0.059)|(0.056)||(0.095)|(0.097)|
|Employmentguarantee|-0.0992|-0.1025||-0.1029|'<br>-0.1064|
||(0.031)|(0.030)||(0.034)|(0.031)|
|Paycut|-0.0726|-0.0798||0.0749|0.0564|
||(0.044)|(0.044)||(0.119)|(0.122)|
|**_4.-Macroeconomic_**_Varabla:_||||||
|GrossDomesticProduct|0.0539|0.0565||0.0461|0.0483|
||(0.009)|(0.008)||(0.010)|(0.009)|
|Inflation|0.0001|0.0001||0.0001|0.0001|
||(0.000)|(0.000)||(0.000)|(0.000)|
|Constant|-0.8018|-1.1946||-0.3127|-0.7673|
||(0.291)|(0.237)||(0.425)|(0.352)|
|Observations|292|308||292|308|
|R-squared<br>|0.54<br>|||0.54<br>||
|F|20.05|||19.73||
|Prob>F|**0.000**|||**0.000**||
|PseudoR2||1.357|||1.364|
|LRchi2||321.34|||323.04|
|Prob> chi2||0.000|||**0.000**|

_Note:_ The dependent variable is net privatization price/sales, defined as the amount that accrues to the government after **all** privatization and restructuring costs are taken into account, such as government commitments at the time of sale, and other adjustments are made to the sale contract. This number is adjusted by the percentage of company shares sold and divided by the average net sales during the three years prior to privatization. The present value of the resulting number as of December 2000 is used. Columns (1) and (2) consider prior restructuring measures and the rest of variable as exogenous and provide estimates from an OLS and TOBIT regressions. Columns (3) and (4) show the second stage of the two-step procedure to take account of endogeneity. All regressions include sectoral controls and firm size controls. Robust standard errors are given in parentheses. a significant at I percent; b significant at 5 percent; c significant at 10 percent.

33
||||widowed<br>FHH|||
|---|---|---|---|---|---|
|||20<br>countries in|Self-<br>reported<br>FHH|Self-<br>reported<br>MHH|While the share of FHHs in the population is growing during 1990-2012, poverty<br>has been falling faster among FHHs. FHHs contributed more to the overall decline<br>in poverty despite their smaller overall population share.|
|12|Milazzo and van de<br>Walle (2017)|<br>Sub-<br>Saharan<br>Africa|Self-<br>reported<br>FHH w/o a<br>resident<br>adult male|Self-<br>reported<br>MHH|The poverty trends of the various types of FHHs followed different paths across<br>countries and periods, with no one type consistently outperforming the others.|
|13|Alazzawi (2018)|Egypt,|Self-<br>reported<br>urban FHH<br>with<br>children|Self-<br>reported<br>urban MHH<br>with<br>children|FHHs have a higher predicted poverty rate than MHHs in urban areas. The factors<br>contributing to the poverty differential between FHH and MHH households are<br>education, employment status, occupation, sector, and region of residence.|
|||Arab Rep.|Self-<br>reported<br>rural FHH<br>with<br>children|Self-<br>reported<br>rural MHH<br>with<br>children|FHHs have a higher predicted poverty rate than MHHs in rural areas. Education,<br>employment status, occupation, number of rooms per capita, and region of<br>residence are factors that contribute to the poverty differential between FHHs and<br>MHHs|
|14|Munoz Boudet_et al._<br>(2018)|71<br>developing|couple/singl<br>e females<br>w/o children|other HH|Adult couple households with children, children, and other adults (extended<br>family) are the most frequent among poor households. Poor and non‐poor women<br>concentrate in the adult couple household with children. One adult female<br>household with children is more prevalent among the poor in Latin America, the<br>Caribbean, and Sub‐Saharan Africa.|
|||countries|Male/female<br>earner with<br>and w/o<br>children||Poor women live in households with children and with children and earner<br>dependents, where the earner is a single male or a head couple. Single female-<br>earner households comprise the largest percentage of poor households in Latin<br>America, the Caribbean, and Sub‐Saharan Africa.|
||B d V d|43 Afi|Self-<br>reported<br>FHH|Self-|FHHs have lower poverty rates than MHHs when using per capita welfare<br>measures. FHHs are significantly worse than MHH when poverty is measured<br>using consumption adjusted for economies of scale.|
|15|rown an an e<br>Walle (2021)|rcan<br>countries|Self-<br>reported<br>married<br>FHH|reported<br>MHH|MHHs are poorer than married FHHs|

44
_1_

# CONTENTS

|1.|Introduction|_1_|
|---|---|---|
|PA|RT I||
|2.|Cost recovery: principles and objectives|1|
||<br>_Fairness_<br>_versus cross-subsidization_<br>|_3_<br>|
||_Loss of seigniorage_<br>_revenues_<br> <br>|_4_<br>|
||_Reasons_<br>_why payment costs may not be recovered_|_4_|
|3.|The cost of making a payment|_5_|
||_Changes in payment costs: economies_<br>_of scale and scope_|_5_|
|4.|Demand for payment services|6|
||_Substitution among payment instruments_|_6_|
||_Payment price elasticities_|_7_|
|5.|Methods for pricing payment services|_8_|
||<br>_Indirect pricing methods: float and minimum balances_|_8_|
||_Marginal cost pricing_|_9_|
||_Optimal departure from marginal cost pricing_<br>|_10_<br>|
||Average cost, two-part, and_benefit-flow pricing_|_10_|
||_Other pricing methods_|_11_|
|6.|From marginal costs to prices|_12_|
|**PA**|**RT II**|_12_|
|7.|Information needed to price payments and problems in implementation|_12_|
||_Information needed_|_12_|
||_Problems in implementation_|_14_|
|8.|Cost management<br>and control|_14_|
||_Key features of an acceptable pricing/price structure_<br>|_16_<br>|
||_Indicative cost recovery framework_|_18_|
|_9._|Experience<br>in pricing payment services: banks, payees, and central banks|_23_|
||_Bank payment costs and prices_|_23_|
||_Explicit payment pricing in other countries_<br>|_24_<br>|
||_Bank pricing of consumer versus business_<br>_payments_<br>|_25_<br>|
||_Payee restrictions on payment instruments_<br>|_25_<br>|
||_Central bank payment costs and prices_|_26_|
|10.|Example of a pricing system<br>-S.W.I.F.T.|_28_|
||_Membershi_**_p_**_ricing_<br>|28<br>|
||_Cost recovery_<br>|_29_<br>|
||_Invoicing method_|_31_|
||_S.W.l.F._<br>_T. connection and supplied equipment costs_|_31_|
||_S. W. I.F. T. message_<br>_pricing_|_32_|
||<br> <br>_Other S. W.l.F. T. services pricing_|_32_|
||<br>_Conclusion_|_32_|
|11.|Summary and conclusions|_32_|
|An|nex 1 - Transaction<br>costs of paper-based<br>and electronic payments in the US and Norway|_34_|
|An|nex 2 - Empirical evidence of scope and scale economies|_38_|
|**Bi**|**bliography**|_40_|
available evidence on the incidence of transport subsidies and relies on new evidence provided by case studies of the cities Santiago de Chile, Mumbai, Mexico City, Buenos Aires and Madrid prepared for a World Bank research project and completed in 2007. The evidence seems to point in the same direction as that found in the utility sectors, namely that affordability measures are usually badly targeted. In addition, the fact that in many cases poor people are priced out of public transport creates the same “access” problem as in the utility sectors.

# _Brazil_

Box 3 reviewed the transport voucher program in that country (‘vale transporte’). Gomide, A., S. Leite and J. Rebelo (2004) note that about 50% of workers in Metropolitan Regions of Brazil belong to the informal sector and do not benefit from the scheme.Therefore, it seems that the error of exclusion for this subsidy is quite high. Since informal workers will most probably earn less than formal workers, it is highly likely that the poorest workers do not benefit from the scheme.

# _Sofia, Bulgaria_

The preferential fares for students, the elderly and others are an example of categorical subsidies. In the case of Sofia, ECORYS and NEA (2004) show that the distribution of preferential passes were not well related to income, as the following table shows. Although access to preferential passes is to some extent correlated with income, especially for households, the targeting properties are not satisfactory. In addition, these results do not show the impact of the funding side of the subsidy, which could be regressive if it is funded through cross subsidies.

**<u>Table 4: Share of preferential passes by income groups, Sofia</u>**

|**Income**<br>**level**|<br>**With**<br>**preferential**<br>**pass**|<br>**Without**<br>**preferential**<br>**pass**|<br>**Total**|<br>**With**<br>**preferential**<br>**pass**|<br>**Without**<br>**preferential**<br>**pass**|**Total**|
|---|---|---|---|---|---|---|
||All ho|usehold membe|rs||Workers||
|Low|28%|72%|100%|3%|97%|100%|
|Medium|14%|86%|100%|14%|86%|100%|
|High|4%|96%|100%|8%|92%|100%|

_Source_ : Table 3.5 from ECORYS and NEA (2004). _Note_ : In Sofia 18% of trips are made with preferential passes.

# _Buenos Aires, Argentina_

Foster (2005) and Bondorevsky (2007) analyzed three transport subsidies in the Greater Buenos Aires area. These included the operating subsidy for the metro (SUBTE) and suburban rail (AMBA) as well as the subsidy introduced in 2002 for buses (‘Colectivos’) reviewed in Box 4. These are all examples of supply side subsidies where scant efforts are made to target benefits to poor households.

35
communities were classified as eligible or ineligible based on the socioeconomic data collected by a census of all the households residing in the communities to be covered by the program. On average, in the evaluation sample, 78% of the households were classified as eligible for program benefits. However, the fraction of households that actually ended up receiving the PROGRESA cash transfers during the two-year interval covered by the evaluation sample is just under 65%, due to administrative errors and delays in the final registration of beneficiary households.<sup>6</sup> The 1997 baseline household census called _ENCASEH_ ( _Encuesta de Caracteristicas Socioeconomicas de los Hogares_ ) was followed by a number of socio-economic household surveys ( _Encuesta de Evaluación de los Hogares or ENCEL)_ designed to collect information for the evaluation of PROGRESA. The first evaluation survey took place in March 1998 before the initiation of benefits in May 1998. The remaining surveys were conducted after beneficiary households in treatment villages started receiving benefits from PROGRESA. One round of surveys took place in November 1998, which was well after most households received some benefits as part of their participation in the program. The next two waves took place in June 1999 and November 1999. A number of core questions about the demographic composition of households and their socioeconomic status were applied in each round of the survey.

Data used in this paper are drawn from four rounds. The first round (R1) that took place in November 1997, the second round (R2) in November 1998 survey, the third round (R3) in June 1999, and fourth round (R4) that took place in November 1999.

The November 1997 ENCASEH survey as well as the November 1998, June 1999, and November 1999 ENCEL surveys collected detailed information on income earned or received from a variety of sources for each individual in the household.<sup>7</sup> The survey instrument used to

> 6 For more details see Skoufias (2005).

> 7 The March 1998 baseline survey was not used because it did not include household income and labor supply information for adults.

7